dataesr/scientific-paragraphs-categorization huggingface dataset:
Interphase nuclei were imaged on a LSM 880 AxioObserver (Olympus) microscope, using a x63/1. 4 objective lens (C plan-Aprochromat 63x/1. 4 Oil DIC UV-VIS-IT M27), under 2x zoom and line-averaging 4 settings. The interval between z-slices was 0. 37 µm and the x-and y-pixel size was 132 nm. Nuclei were segmented and inter-probe 3D distances were measured by custom scripts in ImageJ  76. Distance distribution differences were assessed by Wilcoxon rank sum tests.
Coverslips were washed three times in wash buffer for 30 min at 37ºC, then rinsed in 2x SSC and washed in PBS for 5 min at 23ºC. Samples were mounted in Prolong Gold with DAPI (Invitrogen) on glass slides, and left to dry in the dark for 24 h before imaging on a Zeiss AxioObserver 7 inverted wide-field fluorescence microscope with LED illumination (SpectrX, Lumencor) and sCMOS ORCA Flash 4. 0 V3 717 (Hamamatsu). A 40x oil objective lens (NA 1. 4) with 1. 6x Optovar was used. 27 z-stacks with 300 nm slices and 1x1 binning were taken with an exposure time of 500 ms for Cy3, 750 ms for Cy5 (100% LED power for both), and 25 ms for DAPI (10% LED power). Micromanager 1. 4 software was used. A custom Python script was used to detect, localize and classify the spots (https://github. com/Lenstralab/smFISH).
Transcriptional bursting measurements Using the LiveCell pipeline (github. com/Lenstralab/livecell)  79  , maximal intensity projections were made of the 3D movies, followed by background subtraction and cell segmentation using Otsu thresholding and watershedding. Transcription bursts were detected and tracked using a 2D Gaussian fit followed by hysteresis filtering using 5 and 8 standard deviations of the background as low/high thresholds, respectively. Output data were manually checked to reject erroneous tracking or dividing cells.
MSD analysis MSD analysis was performed using the msdanalyzer utility in Matlab  80. For each individual trajectory, the time-averaged MSD was computed (time steps of 0. 5-120 s for most experiments; 1-300 s for experiments with the Sox2-SCR MS2 line), using either the trajectories uncorrected or corrected by GPTool for substrate movement, as stated. Because some trajectories exhibited large deviations from the average, especially at long time, we chose to calculate the ensemble median of all individual MSD and the median absolute deviation as a measure of the variability. For testing the fit of GPTool-measured parameters to experimental data, theoretical 2D MSD curves were made from these measured parameters for each individual trajectory, giving either 𝑀𝑀𝑆𝑆𝐷𝐷 = 4𝐷𝐷 𝛼𝛼,𝑢𝑢𝑢𝑢𝑢𝑢𝑜𝑜𝑟𝑟𝑟𝑟 𝑡𝑡 𝛼𝛼,𝑢𝑢𝑢𝑢𝑢𝑢𝑜𝑜𝑟𝑟𝑟𝑟 for parameters called without substrate correction, or 𝑀𝑀𝑆𝑆𝐷𝐷 = 4𝐷𝐷 𝛼𝛼,𝑢𝑢𝑜𝑜𝑟𝑟𝑟𝑟 𝑡𝑡 𝛼𝛼,𝑢𝑢𝑜𝑜𝑟𝑟𝑟𝑟 + 4𝐷𝐷 𝛼𝛼,𝑠𝑠𝑢𝑢𝑠𝑠𝑠𝑠𝑠𝑠𝑟𝑟𝑎𝑎𝑠𝑠𝑠𝑠 𝑡𝑡 𝛼𝛼,𝑠𝑠𝑢𝑢𝑠𝑠𝑠𝑠𝑠𝑠𝑟𝑟𝑎𝑎𝑠𝑠𝑠𝑠 for the particle and substrate parameters called when substrate motion correction is applied.
The ensemble medians of these MSD curves were plotted with the experimental MSD curves. Radial MSD was computed in exactly the same way as single-particle MSD, but using the evolution of Euclidean distance between two particles instead of the displacement of a single particle. The diffusive parameters were fit to the first 60 s of the log-log plots of radial MSD using linear least squares with bisquare weights, implemented in Matlab. 95% confidence intervals were estimated as two standard deviations of the point estimates. Diffusive parameter differences between Sox2-SCR and Inter-Down radial MSD were assessed by ANCOVA.
Molecular dynamics simulations Molecular dynamics simulations were performed via the multipurpose EspressoMD package  81  , as in ref 56. Individual proteins are represented by "beads" interacting via phenomenological force fields and move according to the Langevin equation, and the chromatin fiber is represented as a chain of beads connected by bonds. The position of every bead in the system evolves according to the Langevin differential equation that encodes Newton's laws in the case of thermal bath with the friction γ due to an implied solvent in the presence of forces between beads encoded by energy potential functions U  82, 83. Langevin equations for all beads are simultaneously solved in EspressoMD using a standard Velocity-Verlet numerical algorithm. The potential connecting i and i+1 beads of the fiber is a finitely extensible non-linear elastic (FENE) spring that adds up to a steric repulsion potential between non-adjacent sites of the polymer, the Weeks-Chandler-Andersen (WCA) potential: 𝑈𝑈 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 = - 𝐾𝐾 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑅𝑅 0 2 2 𝑙𝑙𝑛𝑛 �1 -� 𝑟𝑟 𝑖𝑖,𝑖𝑖+1 -𝑟𝑟 0 𝑅𝑅 0 �� 𝑈𝑈 𝑊𝑊𝑊𝑊𝑊𝑊 = � 4𝜖𝜖 �� 𝜎𝜎 𝑟𝑟 � 12 -� 𝜎𝜎 𝑟𝑟 � 6 + 1 4 � 𝑖𝑖𝑖𝑖 𝑟𝑟 < 𝜎𝜎2 1 6 � 𝑖𝑖𝑖𝑖 𝑟𝑟 < 𝜎𝜎2 1 6 � 0 𝑜𝑜𝑡𝑡ℎ𝑒𝑒𝑟𝑟𝑒𝑒𝑖𝑖𝑠𝑠𝑒𝑒 where r i,i+1 is the distance between consecutive beads, and σ is where the interaction from repulsive becomes attractive and can be interpreted as the diameter of the particles.
This renders extrusion dependent on CTCF dynamics. Finally, cohesin has LJ affinity for PolII both in the bound (𝜖𝜖 = 2. 5k B T) and unbound state (𝜖𝜖 = 2k B T, higher affinity for bound RNAPII mimicking its suggested role in cohesin loading on chromatin  85  ). PolII and loop extrusion dynamics are performed using a python script driving the EspressoMD library. The polymer initializes as a random walk and its dynamics first evolves in the absence of extrusion and transcription to generate an equilibrium coil formation. Extrusion and transcription processes are then switched on, and the evolution of the bead positions are tracked. Across all simulations, standard values for the friction coefficient (γ = 0. 5) and the time step (t = 0. 01) are used. To connect in silico space-time units with real distances and times, it is assumed that the concentration of DNA in the 3D simulation space is the same as that in a mammalian nucleus, giving a rough estimation of σ = 52 nm.
ACKNOWLEDGEMENTS ANCHOR constructs were provided by NeoVirTech. The MS2v6 constructs were built by  Biljana Atanasovska. Sequencing was performed by the  Institute of Genetics and Molecular and Cellular Biology (IGBMC) GenomEast platform , a member of the  France Génomique consortium  ( ANR-10-INBS-0009 ). This study was made possible because of the  IGBMC Imaging Center  (ici. igbmc. fr), flow cytometry and molecular biology platforms. BioRender was used for generating Figs 7d and e. This study was funded from grants by  LabEx INRT  ( ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the frame program  Investissements d'Avenir   ANR-10-IDEX-0002-02 ),  ERC  ( Starting Grant   678624 -CHROMTOPOLOGY ) and  Agence Nationale de Recherche (HUBDYN).
Data sources ESC and NPC Hi-C data were taken from ref 60, and re-analyzed and visualized using FAN-C  86. The differential Hi-C map was derived as in ref 87. The normalized scores of the ESC Hi-C submatrix were subtracted from those of the NPC Hi-C submatrix, and the difference was expressed as a z-score: (diffmean(diff))/standard deviation(diff)). Insulation was computed using FAN-C with a binsize of 7 bins (28 kb). ESC ChIP-seq datasets for H3K27ac and CTCF were obtained from the CODEX database 88. For comparison of 2D Sox2-SCR distance distributions with analogous methods, results from oligoFISH were taken from ref 53, and results from CuO/TetO experiments were taken from ref 9.
Platania et al. Fig 1 A B 43, D1117-1123. 10. 1093/nar/gku895. 89. Sharova LV, Sharov AA, Nedorezov T, Piao Y, Shaik N and Ko MSH. (2009). Database for mRNA half-life of 19 977 genes obtained by DNA microarray analysis of pluripotent and differentiating mouse embryonic stem cells. DNA Res 16, 45-58. 10. 1093/dnares/dsn030. 0 12 CTCF 0 chr3 34,600,000 34,700,000 34,800,000 34,900,000 8000 Normalized 4C signal 0 34,600,000 34,700,000 34,800,000 34,900,000 Sox2 SCR E.
A network-guided protocol to discover susceptibility genes in genome-wide association studies using stability selection We present a network-based protocol to discover susceptibility genes in case-control genomewide association studies (GWASs). In short, this protocol looks for biomarkers that are informative of disease status and interconnected in an underlying biological network. This boosts discovery and interpretability. Moreover, the protocol tackles the instability of network methods, producing a stable set of genes most likely to replicate in external cohorts. To apply the procedure to a provided GWAS dataset, install the required software and execute our commandline tool.
SUMMARY We present a network-based protocol to discover susceptibility genes in casecontrol genome-wide association studies (GWASs). In short, this protocol looks for biomarkers that are informative of disease status and interconnected in an underlying biological network. This boosts discovery and interpretability. Moreover, the protocol tackles the instability of network methods, producing a stable set of genes most likely to replicate in external cohorts. To apply the procedure to a provided GWAS dataset, install the required software and execute our command-line tool. For complete details on the use and execution of this protocol, please refer to Climente-Gonzalez et al. 1.
Timing: 15 min Prepare the GWAS dataset on which we will find genes associated with the phenotype. Optionally, prepare also the files describing the gene-gene interaction network and the linkage disequilibrium patterns in the population. 9. Prepare the case-control GWAS dataset on which to conduct the biomarker discovery procedure. It must be in PLINK binary format (bed, fam, and bim). Note: If it is not in PLINK binary format already, you can probably make the conversion using PLINK 1. 9 3 using the -make-bed flag. (PLINK needs to be installed separately. ) For instance, if your data is in PLINK text format (ped and map), the following command will take input. ped and input. map and produce output. bed, output. fam, and output. bim: Note: The pipeline does not perform quality control steps (e.
g. , imputation, sample and SNP filtering, population structure stratification). If required, perform them a priori. Note: If you only have access to summary GWAS statistics, refer to Problem 2 in the troubleshooting section. Optional: Prepare a file describing the gene-gene interaction network. It must be a tab-separated table enumerating the edges by their terminal nodes. The two columns must be named ''gene1'' and ''gene2''. Since the methods assume that the network is undirected, the order of the genes does not matter. Note: If this file is not provided, the pipeline will query HINT 8 for all human protein-protein interactions obtained via high-throughput experiments. Optional: Prepare a GWAS dataset to be used as the reference for linkage disequilibrium patterns. It needs to be in PLINK binary format (bed, fam, and bim).
Note: If this dataset is not provided, the pipeline will use the control samples in the GWAS dataset. Note: One option are ancestry-matched genotypes from the Phase 3 of the 1000 Genome Project. 9 They can be downloaded in PLINK binary format from the VEGAS2 page (section Offline version). CRITICAL: Make sure the samples in this dataset have similar ancestry to those in the dataset from step 9 (e. g. , they both target the same population). Failure to do so might produce artifactual results. In case of doubt, do not provide this file.
KEY RESOURCES TABLE MATERIALS AND EQUIPMENT The workflow presented below can run on most computational environments, efficiently using the available resources if Nextflow is configured appropriately (see step 5. b in install gwas-tools and its prerequisites). Since many steps are independent, they will run in parallel whenever possible. Hence, more available CPUs will result in lower computing time. Ultimately, the computational time and memory requirements will depend on the size of the GWAS dataset and the complexity of the network. For instance, the analysis of Climente-Gonza.
STEP-BY-STEP METHOD DETAILS We have encapsulated the complex pipeline from Climente-Gonzalez et al. (2021) 1 into a single command (see run the pipeline and Figure  1A ). The pipeline performs the following steps: Subsample the input GWAS dataset multiple times (five, by default) without replacement. Each equally sized subsample is analyzed independently, and only the final results are aggregated in the last step. The rationale is that the common part of the solutions obtained independently captures something true about the data. In contrast, the differing parts result from the sampling procedure and idiosyncrasies of the algorithm. Compute a chi-squared test of association between each SNP and the phenotype using PLINK 1. 9. Compute a gene-level association score based on the association at the SNP level using VEGAS2.
10 REAGENT or RESOURCE SOURCE IDENTIFIER Software and algorithms Docker https://www. docker. com https://www. docker. com gwas-tools GitHub https://github. com/hclimente/gwas-tools gwas-tools Docker image Docker Hub https://hub. docker. com/r/hclimente/gwas-tools Nextflow Di Tommaso et al. (2017) 7 https://nextflow. io Other Case-control GWAS dataset Provided by the user N/A (Optional) Gene-gene interaction network Provided by the user N/A (Optional) Ancestry-matched GWAS dataset Provided by the user N/A from the general population.
The use of neglected and underutilized species (NUS) in agrosystems is a potential solution to the challenges arising from global change. These species could contribute to the equitable diversification of agricultural systems. Providing knowledge on their genetic diversity and fostering access to data and results is essential for the development of strong collaborative future research. The study addressed these issues by assessing the diversity of the largest fonio (Digitaria exilis) collection existing to date. Associated with a user-friendly Shiny application (https://shinyapps. southgreen. fr/ app/foniodiv), our results reinforce research efficiency and broaden the prospects for all actors involved in enhancing fonio and indigenous crops as valuable resources for the future.
Genotyping data were scored automatically and verified manually using GeneMapper version 3. 7 (Applied Biosystems, Foster City, CA. ). We performed double reading of the profiles and used negative and positive controls. Individuals having more than two missing genotypes among the 14 SSR markers were removed. Clones were also removed for population genetic structure analyses. We thus focused on a total of 1539 unique and high-quality genotyped individuals spanning the entire current D. exilis distribution range. Among these 1539 individuals, 1352 were associated with geographical coordinates.
| Population genetic structure For each locus, we assessed the level of genetic diversity with the adegenet v2. 1. 5 R package  (Jombart, 2008)  by computing the number of alleles, the observed and expected heterozygosities (Ho and He), the F IS value and the proportion of missing data. We first assessed population genetic structure via principal component analysis (PCA) using the dudi. pca function implemented in the ade4 v. 1. 7-19 R package  (Dray & Dufour, 2007). Missing data were replaced by the mean allele frequencies using the scaleGen function in the adegenet v. 2. 1. 5 package  (Jombart, 2008). The population genetic structure was then described using the individual-based Bayesian genetic assignment method implemented in STRUCTURE v. 2. 3. 4  (Pritchard et al. , 2000). We ran 15 independent runs per K value, ranging from 1 to 12, using a burn-in period of 20,000 steps followed by 100,000 Markov Chain Monte Carlo iterations, with admixture and correlated allele frequency models.
Outputs were processed with CLUMPP v. 1. 1. 2  (Jakobsson & Rosenberg, 2007)  in order to find the best alignment among each replicate run. The resulting matrices and barplots were displayed using the pophelper v2. 3. 1 R package  (Francis, 2017). We determined the most-likely number of genetic clusters (K) with Evanno's method implemented in the evannoMethod-Structure function in the pophelper v2. 3. 1 package, which identifies the optimal K based on the rate of change in the log likelihood between successive K values  (Evanno et al. , 2005). We then used the ggtess3Q function of the tess3r R package  (Caye et al. , 2016)  to interpolate ancestry coefficients of the 1352 georeferenced accessions.
| Interactive application We developed an open-access and web-based Shiny application to provide users an interactive way to explore and view the dataset and results. We used the open-source JavaScript library leaflet (https:// rstudio. github. io/leaflet/) associated with the Plotly R open source graphing library (https://plotly. com/r/). This application returns an interactive map and PCA plots in which points can be selected and viewed on the map. Passport information on the selected points are returned in a data table. This tool may be accessed at: https:// shinyapps. southgreen. fr/app/foniodiv.
Evanno's criteria (ΔK) of the STRUCTURE analysis indicated that our dataset could be best described with six genetic clusters (Figure  S3 ), although a structuring in three genetic groups was almost equally supported, as observed with the PCA (Figures  S3  and S4 ). Considering higher K values did not reveal any clear new cluster (Figure  S5 ). The barplots at K = 6, associated with the interpolation map of ancestry coefficients (Figure  2c, d ), showed a strong spatial pattern of genetic diversity. Accessions from Nigeria and southern Togo belonged to the same genetic cluster (purple). The majority of Ghanaian accessions clustered together with a few individuals from Burkina Faso, Benin and Togo (Figures  2c,d  and S6 ). Accessions originating from western and eastern Guinea belonged to different genetic groups (blue and yellow, respectively), with the latter more related to southern Mali and Senegal.
Funding information  WAAPP/PPAAO 2A , Grant/Award Number:  CERA58ID06 SE ;  Investissements d'Avenir programme (Labex Agro) , Grant/Award Number:  ANR-10-LABX-0001-01 ;  I-SITE MUSE , Grant/Award Number:  ANR-16-IDEX-0006 ;  AfriCrop  project, Grant/Award Number:  Social Impact Statement research and innovation programme  ( EWA-BELT ), Grant/Award Number:  862848 ;  ARCAD  project, Grant/Award Number:  0900-.
A number of ongoing clinical and preclinical studies are evaluating IL-15 superagonists in the context of ART, for their abilities to either reduce HIV reservoirs or enable durable control of viremia (Clinical-Trials. gov identifiers: NCT04505501, NCT04340596, NCT05245292). Our findings suggest that if these studies show promise, compounds that share a mechanism of action with HODHBt may form the basis of next-generation combination studies. What are the prospects for HODHBt-like therapeutic agents? Given the requirement for in vitro concentrations in the range of 50-100 μM, as well as other considerations, the lead compound HODHBt itself has minimal therapeutic potential. However, in very recent mechanistic work, we have uncovered that HODHBt exerts its impact on STAT5 by inhibiting the phosphatases PTPN1 and PTPN2  (53).
04% bovine serum albumin on ice and loaded into the 10x Genomics Chromium Controller with a target capture of about 5,000 cells per condition/donor using the single-cell immune profiling 5′ chip and reagent/gel bead kits according to the manufacturer's protocol. Barcoded sample libraries were quantified and pooled using Qubit fluorometric quantification (Thermo Fisher Scientific) and Bioanalyzer (Agilent). Libraries were sequenced on an Illumina NovaSeq in a 26 × 8 × 91 bp configuration. 5′ ScRNA-Seq data analysis. FASTQ files were processed using Cell Ranger 6. 1. 1 and mapped to a custom combined reference with HXB2 HIV reference genome added to human GRCh38 reference FASTA and GTF files. Following the workflow described by Amezquita et al. , scRNA-Seq analysis was carried out with R/Bioconductor packages  (63).
Quality control was carried out for each sample separately with functions from the scuttle package v. 1. 4. 0 (64), and cells with low gene content (below 10 × 10 -2. 5 to 10 × 10 -2. 75 , depending on the sample) and high mitochondrial gene content (>3 median absolute deviations; default scuttle setting) were removed from further analyses. Genes with zero expression across all cells were removed from the matrix. Cell cycle scores were calculated with the scuttle:cyclone() function using human cell cycle marker genes provided by scuttle. The different count matrices across all samples were then scaled for sequencing depth differences and log-transformed using multiBatchNorm from the batchelor v. 1. 10. 0 (65, 66) package. Cell types were annotated with SingleR v. 1. 8. 1 using celldex:HumanPrimaryCellAtlas-Data()  (67).
Differentially expressed genes (FDR < 0. 05) were detected using the pseudoBulkDGE function from the scran package  (68) , which uses the quasi-likelihood method implemented by edgeR  (69). GO analysis was performed using the enrichGO function from clusterProfiler v. 3. 10. 1  (70). Code and processed data can be found at https://github. com/abcwcm/CopertinoHODHbt (commit ID e4b5fbe). We have provided the least processed data that do not contain the actual genetic sequencing information from study participants (which is Protected Health Information). RNA-Seq validation. A total of 20 × 10 6 PBMCs from 2 PWH and 4 previously cryopreserved leuko-paks were thawed, washed with warm R-10 medium (RPMI 1640 [Thermo Fisher Scientific] supplemented with 10% FBS, l-glutamine, 10 mM HEPES, and penicillin/streptomycin) twice, and placed at a concentration of 4 × 10 6 cells/mL at 37°C and 5% CO 2 in 2 mL per condition with 12 conditions in a 12-well plate.
p-STAT5 (Y694) staining. To analyze p-STAT5, 100,000 cells were first stained with a viability dye (Fixable Viability Dye eFluor 450, Affymetrix, Thermo Fisher Scientific) for 10 minutes at 4°C. Cells were then fixed with 100 μL of prewarmed (37°C) Fix Buffer I (Becton Dickinson) for 10 minutes at 37°C. Cells were washed once with 1 mL of PBS + 3% FBS. Cells were then permeabilized while vortexing with 100 μL of Perm Buffer III (Becton Dickinson) and incubated for 30 minutes on ice. Cells were washed once with 1 mL of PBS + 3% FBS and stained with 2 μL of p-STAT5-Alexa Fluor 488 (clone 47/Stat5 [pY694]; Becton Dickinson, catalog 612598) in 100 μL of PBS + 3% FBS for 1 hour at room temperature in the dark. Finally, cells were washed once with 1 mL of PBS + 3% FBS, and p-STAT5 was measured by flow cytometric analysis in a FACSCanto II flow cytometer using the FACSDiva software (Becton Dickinson) and analyzed using FlowJo (Tree Star Inc).
DWI Tractography by Means of Constrained Spherical Deconvolution All DWI scans in the ETZ Tilburg center were acquired using a 3T Philips Achieva MRI scanner (b = 1500, 50 diffusion weighting directions, 6 b = 0 images, 2 mm isotropic voxel size). All DWI scans in the Paris center were acquired using a 3T Siemens Skyra MRI scanner (b = 2000, 64 diffusion weighting directions, 5 b = 0 images, 2. 3 mm isotropic voxel size). All scans were transferred to the ETZ center and tractography was performed using the MRTrix 3. 0 software package  [50]. DWI-MRI data were pre-processed using the MRTrix script dwifslpreproc. Probabilistic tractography was performed using the constrained spherical deconvolution-based iFOD2 method with tckgen. To seed and restrict tractography of the SLF, estimated regions of interest (ROIs) were identified using SLANT, a method that uses deep learning to generate patient-specific segmentations of 133 anatomical regions  [51].
Coregistration and Normalization For further data processing, all images (T1/T2/Flair, DWI, tractography ROIs, and tumor segmentations) were transferred into each patient's diffusion-weighted MRI space using NiftyReg affine coregistration  [56]. For voxel-wise lesion-symptom mapping (VLSM) and Disconets analyses, tumor segmentations were mapped into MNI space.