dataesr/scientific-paragraphs-categorization huggingface dataset:
1. 5) was performed using the following criteria: (i) a minimum coverage of 100x, (ii) a Minimum Strand-Bias > 65% (p-value ≤ 0. 0005), and (iii) a minimum variant frequency of 1%. To rule out the presence of CBSV in the analyzed samples, all samples were processed with Kraken2 (standard database from 06/2020, Version 2. 1. 1 ) on Galaxy  (51, 52) , in addition to the BLAST mentioned above approach, and for positive samples (showing CBSV reads after Kraken results), simultaneous mapping of all reads on the closest CBSV (HG965221) and UCBSV (KX753356. 1) reference genomes was performed at maximum 10 % mismatch to avoid nonspecific Mapping of UCBSV reads on the CBSV sequence. In addition, to screen our samples for novel Ampelovirus recently reported to infect cassava in Central Africa and the South-West Indian Ocean Islands, all reads were mapped to the Congolese genome sequence of MEaV-1 Ampelovirus (MT773588)  (53).
Phylogenetic analysis The percentage of identity between the consensus sequences of UCBSV and their polyproteins from 12 districts of Rwanda was conducted in MEGA X (V10. 2. 6) with the Poisson correction model  (54). The newly generated UCBSV whole genome sequences were aligned with the 23 UCBSV genomes from the NCBI GenBank nucleotide database using Clustal Omega V1. 2. 2 (55)( release 241, November 2020). Phylogenetic analysis and evolutionary divergence between nucleotides sequences were performed using the new genomes identified in the present study as well as complete or partial coat protein sequences from GenBank isolates of UCBSV using Molecular Evolutionary Genetics Analysis (MEGA-X V10. 2. 6) under the Neighbor-Joining method  (56). The evolutionary distances were computed using the p-distance method  (57).
Genetic diversity analysis of UCBSV A recently established methodology was applied for comparing UCBSV populations at both SNP and haplotype levels  (35). Two distance measures were calculated using the consensus sequences or the fixation index (F ST ). F ST methodology considers all the SNP detected in the UCBSV populations and their relative frequency (if above 1%) to compare samples. The consensus sequence considers only the dominant base (>50% frequency) at every position. For both methods, an index ranges between 0 and 1 was calculated, where differences between populations increase as the index changes from 0 to 1. The F ST measures were obtained using Popoolation2 version 1. 201  (59). First, the reads of each sample were mapped to the closest reference genome (accession number KX753357.
1) as described above. Then, the analysis was performed with a single-window size defined by the size of the reference genome (9. 097 bp), a step size of 1, a minimum covered fraction of 0. 1, minimum coverage of 50, and maximum coverage of 200,000. The principal component analyses used ade4 and factoextra libraries from the R software version 4. 0. 3 (http://www. r-project. org/). The dendrograms were calculated using the hclust function implemented in the package stats (version 4. 0. 2).
Importantly, each cluster included SNPs present along the genome, sometimes at distant locations. For example, the first cluster included the SNPs T-1103-G and T-7529-C (SNPs being named according to their position on the genome and reference and alternative alleles) located 6,000 nt apart. Despite these distances, the frequency of the SNPs varied homogenously between the samples of a cluster, suggesting that they are linked and could constitute a haplotype. Our dataset highlighted 3 major haplotypes of UCBSV across Rwanda. Haplotype 1 (H 1 ) was defined by 30 SNPs at high frequency in clusters 1 and absent or low frequency for clusters 2 and 3. Among these SNPs, eight were non-synonymous and located mainly in the sequences of the P1 protein (3 SNPs) and the coat protein (2 SNPs) (S9 Table ).
The unconfirmed mutations occurred mainly in position with a mix of two bases, one minor at low frequency (<30%) that was not observed, probably due to the confirmed lower sensitivity of Sanger sequencing for detecting SNPs at low frequency  (71). A reduction in UCSBV diversity was observed in the current dataset compared to the reported UCBSV diversity in 2014 and should be discussed technically and scientifically. First, the present study used HTS technologies that have improved inclusivity compared to targeted RT-PCR as demonstrated by many publications  (38, 72, 73) , so they could theoretically detect a broader range of isolates. Regarding the representativeness of the obtained sequences, the partial CP sequencing was carried out in 2014 using a limited number of plants per district, from one in the Gatsibo district to thirteen in the Nyanza district, thus most probably capturing the most abundant isolates.
In the district of Ruhango, a recently discovered ampelovirus was detected in the dataset with 386 reads (S3 Fig), from which two partial ampelovirus contigs of 12,711 bp and 3,390 bp (OL579727; OL579728) were constructed. They shared 98% of their identity with the MEaV-1 Congolese isolate (MT773588). The presence of the ampelovirus was confirmed by RT-PCR (S4 Fig) and subsequent Sanger sequencing of the amplicons (NCBI reference numbers: OL579729; and OL57973).
Mitotic loop extrusion is not blocked by retained CTCF sites Next, we set out to analyze CTCF-anchored loops in mitotic mESCs in order to investigate whether mitotic loop extruders condensin I and II are blocked by bound CTCF, which would lead to positioned loops between pairs of CTCF sites and/or domain boundaries at single CTCF site. As described above no compartments and TAD boundaries are detected in mitotic mESCs at individual genomic locations (figure  2 ). Assessment of the presence of CTCF-dependent loops at specific locations typically requires much deeper sequencing (Akgol  Oksuz et al. 2021 ). To observe these features using our Hi-C datasets, boundaries can be visualized by plotting the aggregate Hi-C signal at and around either single CTCF sites (figure  3a-h ), while loops can be visualized by plotting the aggregate Hi-C signal at and around pairwise interactions of CTCF sites (figure  3i-p ).
Hi-C mapping and downstream analysis Hi-C sequencing files were mapped to reference genomes hg38 (HeLa data), mm10 (C2C12, mESC and C127 data) and galGal6 (DT40 data) using publicly available distiller-nf mapping pipeline (https://github. com/mirnylab/distiller-nf) and downstream analysis tools pairtools (https://github. com/mirnylab/pairtools) and cooltools (https://github. com/mirnylab/cooltools). Briefly, reads were mapped using bwa-mem, pcr duplicates were removed and reads were filtered for mapping quality. Distance decay and derivative plots created using cooltools code by calculating contact frequency (P) as a function of genomic distance (s) using valid pairs. For further downstream analysis, interactions were binned in matrices at a range of different resolutions using cooler  (Abdennur and Mirny 2019).
Acknowledgements We thank all current and former members of the  Dekker lab  and the  Navarro lab  for helpful discussions and suggestions, in particular  Johan Gibcus ,  Bastiaan Dekker  and  Nick Owens. We thank  Eugenio Mattei  for advice on computational analyses. We thank  Dr. Christina Baer  for thoughtful discussions on confocal imaging. This work was supported by a grant from the  National Institutes of Health  ( HG003143  to J. D. ) and by the  European Research Council  ( ERC-CoG-2017 BIND  to P. N). J. D. is an investigator of the  Howard Hughes Medical Institute.
Code availability Hi-C mapping pipeline distiller-nf is available on Github: https://github. com/mirnylab/distiller-nf. Downstream analysis tools pairtools and cooltools are available through https://github. com/mirnylab/pairtools and https://github. com/mirnylab/cooltools. Code used for analysis of ATAC-seq data can be found at Github: https://github. com/dekkerlab/CTCF_in_mitosis_GR_2018.
Publicly available data used in this study In addition to the Hi-C data that was generated for this study, we use several ATAC-seq and Hi-C datasets that are publicly available on the gene expression omnibus (GEO). ATAC-seq data in mESC  (Festuccia et al. 2019 ) is available under accession number GSE122589. Hi-C data in asynchronous and mitotic DT40 cells and HeLa are available under GSE102740  (Gibcus et al. 2018 ) and Hi-C data of mouse cell line C127 under GSE149677 (Fitz-James et al. 2020).
25 etween-group differences in age, WAIS, and BDI score were assessed using independent-sample t tests, and differences in the ratio between male and female participants were assessed using χ 2 tests. The study was carried out in accordance with the latest version of the Declaration of Helsinki and approved by the local ethics committee (approval number: C17-04-AU 1360, ClinicalTrial. gov ID: NCT03351218). All participants provided written informed consent before inclusion.
Neuroimaging Acquisition Parameters and Preprocessing Participants were asked to lie still in a 3-T MAG-NETOM Prisma scanner (Siemens, Erlangen, Germany) while fixating on a cross displayed on a screen during the MR session. Eye-tracking was employed to monitor their gaze. We acquired DWI data and one structural image in one single session and a 64-channel head coil. The structural image was acquired using a T1-weighted MP2RAGE sequence with repetition time (TR) = 5 seconds, echo time (TE) = Image preprocessing was performed as follows: T1-weighted images were background denoised,  26, 27  segmented, and normalized to the Montreal Neurological Institute space using the Computational Anatomy Toolbox (CAT12-http://www. neuro. uni-jena. de/cat/) extension for SPM12 (https://www. fil. ion. ucl.
ac. uk/ spm/software/spm12/). We extracted total intracranial volume (TIV) to be used in later analyses. DWI data were first denoised, that is, eddy current correction from FMRIB Software Library (FSL, Analysis Group, FMRIB, Oxford, UK),  28  and gibs artifact was removed using Mrtrix3. 29 The b = 0 volumes were then extracted and combined with the opposition phase direction volumes, and EPI distortion correction was performed using "topup tool" from FSL. 30  We then corrected for motion and eddy current using "eddy tool" from FSL. We then used the "noddi matlab toolbox" to fit the NODDI model to the multi-shell preprocessed diffusion data, as previously described. 31.
-------------------------------------------------------------------------------- The authors declare no conflict of interest. Funding agencies: This work was funded by the  Swiss National Science Foundation  ( P400PM_183958  and  P5R5PM_203160/1 ),  AMADYS ,  Fondation Brou de Laurière , and  Merz-Pharma. This project was also supported by " Agence Nationale de la Recherche (ANR) " under the frame of the European  Joint Programme on Rare Diseases (EJP RD ,  ANR-16-CE37-0003-03 ).
sensor combined with ber photometry and monitored the activity of PL terminals innervating the PVT while mice performed a two-way signaled active avoidance behavior task (2AA)  22  (Fig. 1 ; Extended Data Fig. 2 ) (See Methods). In this task, mice must shuttle to the adjacent compartment of a shuttle-box during the presentation of a warning signal (WS) to both terminate the WS and avoid a noxious stimulus (i. e. , footshock; FS). Following training, animals learned to avoid on most trials (Avoidance trials) (Fig. 1b ). Failure to avoid during the WS was observed in a small proportion of trials in well-trained mice. In these trials, animals shuttled at FS onset-typically within the rst second-, and the FS was immediately terminated upon shuttling (Escape trials) (Fig. 1b ). As such, we categorized trials into either 'Avoidances' or 'Escapes' and independently assessed their associated calcium responses during ber photometry recordings.
3 ; Supplementary Fig. 3 ). Collectively, our ndings suggest that the TRN might be a conduit via which strong cortical inputs arising in L5 impact threat representations within the midline thalamus. Below, we empirically test this prediction. To investigate whether the synaptic organization of the cortico-reticulo-thalamic circuit described here supports task-related neuronal dynamics in the PVT, we rst used ber photometry to assess the activity of PL cells projecting to the avTRN during the 2AA task. In trained mice, we found that like PL-PVT projections, PL-avTRN cells were bidirectionally modulated during the avoidance task (Fig. 4a-c ; Extended Data Fig. 6 ). However, unlike PL-PVT projections, threat associations in the PL-avTRN pathway appeared to be outcome dependent, with WS onset-related responses observed for avoidance trials only (Fig.
An RZ5P ber photometry acquisition system with Synapse software (Tucker-Davis Technologies) collected and saved real-time demodulated emission signals and behavior-relevant TTL inputs. For each trial, GCaMP signals (F 473 nm ) were compared with auto uorescence signals (F 405 nm ) to control for movement and bleaching artifacts. Signal data were de-trended by rst applying a least-squares linear t to produce F tted 405 nm , and dF/F was calculated as (F 473 nm -F tted 405 nm )/F tted 405 nm. All GCaMP signal data are presented as the z-score of the dF/F from baseline (pre-WS) segments.
For each trial, we calculated the time interval between the moment of animal crossing the hurdle and the WS onset during trials where the animal avoided the footshock, named latency to avoid. WS, ITI freezing or latency to avoid were average within session and then within each group and plotted as mean ±s. e. m. For ber photometry, GCaMP data were normalized as dF/F. Next, we used the behavioral ags calculated from the video tracking to create average peri-event time histograms (PETHs) time-locked to the onset of the behavior events of interest, including WS onset, highest movement velocity during the WS (Max Velocity), escape or avoidance movement onset (Shuttle Initiate), escape or avoidance moment (Shuttle) and freezing onset during the WS (WS Freezing). All trials in each session were separated into avoidance and escape trials as described above.
703)( 48 h, at 4 °C). After washing, Alexa Fluor-conjugated secondary antibodies (1:500, goat anti-mouse, Thermos Fisher Scienti c A-11001; 1:500, goat anti-rabbit, Thermo Fisher Scienti c A-21245). Finally, sections were subsequently mounted onto glass slides for imaging (LSM 780 laser scanning confocal microscope, Carl Zeiss). Image analysis and cell counting were performed using ImageJ software (Fiji, version 1. 52p). Optical ber placements for all mice included in this study are presented in Figs 2a, 3c, 4b, 5c, 6b and 7c and Extended Data Figs. 1a, 4b,l, 5b, 6b, 8b,m and 9a. Sample preparation and ISH procedure for RNAscope. Fresh-frozen brains from adult male C57BL/6NJ mice (8-12 weeks old) were sectioned at a thickness of 16 µm using a Cryostat (Leica Biosystems). Sections were collected onto Superfrost Plus slides (Daigger Scienti c), immediately placed on dry ice and subsequently transferred to a -80 °C freezer.
After this, the slides were washed twice with washing (2 min each), then incubated with Hybridize Amp 1-FL for 30 min, Hybridize Amp 2-FL for 15 min, Hybridize Amp 3-FL for 30 min and Hybridize Amp 4-FL for 15 min. Next, the slides were washed twice with washing buffer (2 min each) and coverslips added using Diamond Prolong antifade mounting medium with DAPI (Thermo Fisher Scienti c). Signal detection and analysis for RNAscope. Dried slides were examined on an LSM 780 laser scanning confocal microscope (Carl Zeiss) using 20X objective 24 h after the ampli cation procedure. Signal was subsequently quanti ed with CellPro ler 3. 1. 8 using a freely available pipeline (macros) for RNAscope  5. A protocol with a step-by-step description of how to implement this pipeline for analyzing RNAscope data was recently published  6.
ACKNOWLEDGEMENTS We thank the members of the  Unit on the Neurobiology of Affective Memory (NIMH)  who offered scienti c feedback. In addition, we thank the  NIMH IRP Rodent Behavioral Core  for their support with the development of the custom apparatus for the 2AA task, and the  NIMH   IRP Systems Neuroscience Imaging Resource  for assistance with microscopy tools used for histological assessment. Lastly, we thank  Drs. Fabricio Do Monte ,  Michael Halassa , and  Briana Carroll  for offering scienti c and writing.
Data availability All the data that support the ndings presented in this study are available from the corresponding author upon reasonable request. Source Data are provided with this paper and are publicly available at the following repository: pending. Code availability R code used to analyze active avoidance behavior and photometric signal is available at the following repository: https://github. com/Penzolab/Data-analysis-of-Two-way-active-avoidance-task. git. Data Fig. 6g-k , Extended Data Fig. 8g-i , Supplementary Fig. 1 , Supplementary Fig. 4b-c  and.
Human myeloma cell lines, genotoxic agents and inhibitors XGs HMCLs cell lines have been derived in our laboratory as previously described  (12)  and are IL-6 dependent. XGs cell lines are routinely maintained in RPMI 1640 GlutaMAX medium (61870044, Gibco) supplemented with 10% fetal calf serum (CVFSVF00 01, Eurobio) and with IL-6 (2 ng/ml)  (12). AMO-1, LP1 and OPM2 were purchased from DSMZ (Braunsweig, Germany) and RPMI8226 from ATCC (Rockville, MD, USA). These cell lines are maintained in RPMI 1640 GlutaMAX medium (61870044, Gibco) supplemented with 10% fetal calf serum (CVFSVF00 01, Eurobio). All the HMCLs were authenticated according to their short tandem repeat profiling. Affymetrix U133 plus 2. 0 microarrays data have been deposited in the ArrayExpress public database under accession numbers E-TABM-937 and E-TABM-1088  (13).
Gene expression analysis and gene set enrichment analysis Normal bone marrow plasma cells (n=5) and patients' MMCs (n=206) were purified using anti-CD138 MACS microbeads (Miltenyi Biotec, Bergisch Gladbach, Germany) and their gene expression profile (GEP) obtained using Affymetrix U133 plus 2. 0 microarrays as described (Array Express public database (E-MTAB-372))  (14)  or RNA sequencing as described  (5, 15). Publicly available cohorts of newly-diagnosed MM patients treated with high dose melphalan and autologous hematopoietic stem cell transplantation (UAMS-TT2 and TT3 (GSE24080), and Hovon (GSE19784) cohorts) were also used. Gene expression data were normalized with the MAS5 algorithm and analyses processed with GenomicScape (http://www. genomicscape. com)  (16). Gene Set Expression Analysis (GSEA) was used to identify genes and pathways differentially expressed between populations.
Proliferation assays and synergy analyses For IC50 determination, HMCLs were seeded at 10000 cells/ well and cultured for 4 days in 96-well flat-bottom plates in presence of increasing concentrations of AZD7762 or MK1775. Cell proliferation was evaluated using CellTiter-Glo (CTG) Luminescent Assay (G7573, Promega) according to the manufacturer's protocol and luminescence was measured using a Centro LB 960 luminometer (Berthold Technologies, Bad Wildbad, Germany). IC50 for each HMCL was calculated using non-linear regression analysis in GraphPrism software  (5). For evaluation of AZD7762 and MK1775 synergy, increasing concentrations of AZD7762 were combined with IC20 of MK1775 (AMO1: 200nM; XG6: 300nM; XG7: 550nM). Cell growth was evaluated with CTG reagent as described above. Significant synergy have been calculated by the method of Chou and Talalay  (18).
Cell cycle, DNA damage and apoptosis analysis by flow cytometry Cells were culture in 12-wells plates for 4 days. For cell cycle analysis, BrdU (10 mg/ml) was added to the cultures for 1. 5 h at the end of the treatments. Cells were collected and processed with the "Apoptosis, DNA damage and cell proliferation kit" (BD Pharmingen), following the manufacturer's protocol. Samples were acquired with a Fortessa flow cytometer (BD biosciences) and results were analyzed with Kaluza software. Cells were assessed to the different phases of the cell cycle based on their DNA content (DAPI staining) and BrdU +/-staining. Cells with DNA damage were identified as "gH2AX positive" and apoptotic cells as "PARP cleavage positive" and expressed as percentage of the global population.
Western blot analysis Cells were treated as indicated and resuspended in Laemmli buffer 1X (400 ml/1 million cells), and boiled at 95°C for 5 min. 20 ml of lysate were loaded onto SDS-page acrylamide gels. Membranes containing proteins were incubated with primary antibodies diluted in 3% BSA-TBS-0. 1% Tween overnight at 4°C, washed 3 times with TBS-0. 1% Tween (10 min, RT), incubated with secondary antibodies anti-rabbit-HRP (7474, Cell Signaling) or anti-mouse-HRP (7076, Cell Signaling) (1:3000), washed 3 times, and developed with ECL (1705061, BioRad). Image acquisition was performed with BioRad system and processed with ImageLab software. Unless otherwise stated, all primary antibodies used in this study were used at a 1:1000 dilution. Wee1 (sc-9037, Santa Cruz, 1:500), pSer345-Chk1 (2348S, Cell Signaling), Chk1 (ab40866, Abcam), pTyr15-Cdk1 (4539S, Cell Signaling), Cdk1 (9112S, Cell Signaling), Cdc25A (3652S, Cell Signaling), gH2AX (05-636, Millipore), pSer15-p53 (9284S, Cell Signaling), p53 (9282S, Cell Signaling), Caspase 3 (9662S, Cell Signaling), Caspase 8 (9746S, Cell Signaling), Caspase 9 (9502S, Cell Signaling), Tubulin (AB_1157911, DSHB).
Gene expression profiling of bone marrow microenvironment Purified MM cells from patients and normal bone marrow environment fraction RNA sequencing was done as previously described  (15, 17). The RNA sequencing (RNA-seq) library preparation was done with 150 ng of input RNA using the Illumina TruSeq Stranded mRNA Library Prep Kit. Paired-end RNA-seq were performed with Illumina NextSeq sequencing instrument (Helixio, Clermont-Ferrand, France). RNA-seq read pairs were mapped to the reference human GRCh37 genome using the STAR aligner. All statistical analyses were performed with the statistics software R (version 3. 6. 2; https:// www. rproject. org), and R packages developed by Bioconductor project (https://www. bioconductor. org)  (19). The expression level of each gene was summarized and normalized using the DESseq R/ Bioconductor package  (20).
007 and p=0. 008 in the HM cohort (N=206; E-MTAB-372), p<0. 0001 and p=0. 01 in UAMS-TT2 cohort (N=345; GSE24080), p<0. 0001 and p<0. 0001 in Hovon cohort (N=282; GSE19784) and p=0. 0001 and p<0. 0001 in UAMS-TT3 cohort (N=186; GSE24080))(Figures  1E-H ). Same results were obtained for event-free survival (EFS) (Figures  1E, F ). MM patients from the different cohorts were ranked according to increasing CHK1 or WEE1 expression and a maximal difference in OS and EFS was obtained using the Maxstat R function  (24). Furthermore, a significant higher expression of CHK1 and WEE1 was identified in the patients with del17p compared to patients without del17p in the UAMS TT2 cohort. Furthermore, a higher expression of CHK1 was identified in patients with 4 or more copies of ch1q. A significant higher expression of WEE1 was identified in patients with 3 or more copies of ch1q (Supplementary Figure  S1 ).
Metabolism deregulation is also involved in MM pathophysiology  (41)  and drug resistance  (42, 43). A B D E C FIGURE 2 High CHK1 and WEE1 expression is associated with a poor outcome in MM. Correlation between CHK1 and WEE1 expression and overall survival was analyzed using Maxstat R package in independent cohorts of MM patients (Supplementary Figure  S1 ). We found that combination of low doses of Chk1 and Wee1 inhibitors synergize to induce major toxicity in MM cells with a significant increase of spontaneous DNA damage and apoptosis induction. Of note, the drug combination induced also a significant death of patients' MM cells without affecting the survival of nontumor bone marrow environment cells. Our data demonstrate that targeting Chk1 and Wee1 could have a therapeutic potential in Chk1 high /Wee1 high MM patients associated with a poor outcome.