dataesr/scientific-paragraphs-categorization huggingface dataset:
Results are displayed in a Treemap ranking the number of articles identified in our database by categories in the variable analyzed. The keyword analysis was performed using the "Network Analysis" tool in the CorText Platform, which is based on keywords co-occurrence frequency. Distributional metrics was calculated using the Louvain algorithm, which is based on hierarchical and iterative detection of communities in a complex network by measuring density within and between communities  (Blondel et al. , 2008). Results are presented in a network graph composed by nodes represented by triangles (e. g. , keywords) whose size indicates frequency. Linkages between keywords are represented by a gray line, and high-frequency co-occurrence of a group of keywords are displayed in a cluster (e. g. , colored circle).
Managerial Insights e proposed model can be used for products with high maintenance costs or those subject to special conditions, including chemical and hazardous products, sensitive electronic and telecommunication products, and perishable products. As there was no official database for some parts of cost, the experts' estimations were sought to help. e questions about the inventory costs for each route have been categorized and the estimated costs have been entered into the mathematical model. e model is presented as a separate run, which paves the ground for the simulation to be run several times and to report the average of the obtained results. is minimizes simulation errors as much as possible.
Analysis 2D epifluorescence images were proceeded using ZEN 3. 3 blue software (Zeiss). 3D acquired from confocal and multiphoton microscopes were proceeded using NIS-Elements software package. (5. 30. 03 Nikon Instruments Inc. , Nikon Europe B. V. , Amsterdam, Netherland). 3D Images acquired from light sheet were proceeded using Imaris software (9. 1. 2, Oxford Instruments, Abingdon, UK). Data were analyzed using the GraphPad Prism v5. 0 statistical software package (GraphPad Software, La Jolla, CA, USA). Since the data were non normally distributed (normality tested using Shapiro-Wilk test) the non-parametric Mann-Whitney test has been chosen. Significance is depicted (** p<0. 01, **** p<0. 0001).
GFAP-GFP group. Brains from sham, TBI and PHP. GFAP-IL-2-treated TBI mice were compared by flow cytometry for the frequency of T reg cells as a proportion of CD4 + T cells. Data from c-i are shown as the mean ± s. e. m. All experiments were repeated independently (≥ twice). Statistical analyses were performed using non-parametric Mann-Whitney U test (c and e), one-way ANOVA (i) against chance level (g and h) or two-way ANOVA (c, f and d). the upregulation seen in endogenous promoter activity (Fig. 3b ) was faithfully recapitulated by the exogenous GFAP promoter in the vector (Fig. 3b ). Having validated the AAV-PHP. B. GFAP system for brain-specific expression, we sought to determine whether it could be applied to IL-2 delivery. Using AAV-PHP. B. GFAP-IL-2 (PHP. GFAP-IL-2) delivery, we observed a threefold increase in brain IL-2 production (Fig.
i, Wild-type mice were given TBI, followed by control vector. Quantification of cortical area lost on day 14 (n = 8, 6). j, Wild-type mice were given TBI, followed by treatment with PHP. TetO-IL-2. GFAP-rtTA, with or without minocycline. Quantification of lost cortical area on day 14 (n = 7, 7). k, Mice were given dMCAO stroke and treated with control vector plus minocycline, or PHP. TetO-IL-2. GFAP-rtTA, without or with minocycline. Mice were examined at 15 d after stroke for TTC-aided quantification (n = 6, 12, 14). l, EAE was induced in wild-type mice, following treatment with PHP. TetO-GFP. GFAP-rtTA or PHP. TetO-IL-2. GFAP-rtTA, with or without minocycline on day 10 (n = 10 per group) after induction. Incidence, daily clinical score and cumulative mean clinical score are shown. Data are the mean ± s.
Finally, a three-dimensional (3D) gradient echo sequence (FLASH) with the following parameters was acquired: TR of 30 ms, TE of 7 ms, 20° pulse, matrix 160 × 160 × 96, FOV 20 × 20 × 12 mm, resulting in an isotropic resolution of 125 µm. Operators were masked to the experimental group. All MR images were processed using the Bruker BioSpin software Paravision 6. 1. Parametric T 2 and apparent diffusion coefficient maps were calculated in Paravision 6. 1, through a pixel-wise mono-exponential fit. For quantification of lesion volumes, Paravision 6. 1 software was used. AAV vector production and purification. AAV-PHP. B production was performed by Vigene Sciences or VectorBuilder, using the classical tri-transfection method, with subsequent vector titration performed using a quantitative PCR-based methodology  21, 55.
Data were acquired on a BD FACSymphony, with panels covering (1) CD45, CD4, CD8α, CD3, CD19, NK1. 1, Foxp3, eBioscience Fixable Viability Dye eFluor 780, CD103, CD62L, GITR, CD25, neuropilin-1, ST2, PD-1, CTLA4, KLRG1, Helios, CD69, ICOS, CD44, T-bet, TCRγδ and Ki67; or (2) CCR6, CD80, TCRγδ, CD45, Foxp3, MHCII, eBioscience Fixable Viability Dye eFluor 780, pro-IL-1β, CD25, Ly6G, ST2, CX3CR1, PD-L1, TNF, CD44, Ki67, CD4, Ly6C, TrkB, CD19, CD69, CD8α, LAMP1, CD64, CD11b, CD3, TGF-β and streptavidin; or (3) Foxp3, eBioscience Fixable Viability Dye eFluor 780, IL-17, CD4, IFN-γ, CD8α, TNF, CD3, amphiregulin, IL-10, CD11b, CD19, granulocyte macrophage-colony stimulating factor (GM-CSF), TCRγδ, pro-IL-1β, TCRβ, IL-2 and NK1. 1. For brain panels, the entire brain sample was acquired. Data were compensated using AutoSpill  57.
Examples for mouse cells were always presented as concatenated biological replicates. Cell sorting was performed using a BD FACSAria III with a panel including CD4, CD11b, CD45, TCRβ, eBioscience Fixable Viability Dye eFluor 780 and CD25. Flow cytometry data collection was performed using FACSDiva version 8. 0. 2 (BD) or SpectroFlo (Cytek). Flow sorting was performed using FACSDiva version 8. 0. 1 (BD). Representative gating for brain T reg cell quantification is shown in Supplementary Fig. 16. Fluorescence immunostaining. Mice were deeply anaesthetized with intraperitoneal injection of a ketamine (87 mg per kg body weight) and xylazine (13 mg per kg body weight) mixture and transcardially perfused with PBS followed by 4% buffered formalin solution. The brain was removed and fixed in 10% buffered formalin solution overnight and stored in 30% sucrose until preservation in tissue-freezing medium (Shandon Cryomatrix embedding resin, Thermo Scientific), and stored at -80 °C.
1% Triton X-100-PBS at 20 °C for 1-1. 5 h. Counterstaining was done with Hoechst reagent (Sigma-Aldrich; 1:1,000 dilution in PBS). Confocal laser scanning microscopy was performed using a Zeiss LSM780 confocal microscope equipped with a ×40 objective (NA 1. 4). Images files were exported and further analysis was performed in ImageJ. Surface morphology imaging. To visualize potential deformations and assess the overall shapes of mouse brains, the entire organ was imaged in a Bioptonics 3001 OPT scanner. Samples were imaged using autofluorescence f-OPT  58  and reflected light  59. Four hundred images with a 0. 9° angle pitch were acquired to image a full rotation of the sample. Consequently, 3D volumes were reconstructed using NRecon software (version 1. 7. 1. 6; Bruker) and visualized with Arivis (version 2.
Library quality was checked at the recommended points using a Qubit 2 Fluorometer (Thermo Fisher) and a Bioanalyzer HS DNA kit (Agilent). Libraries were sequenced on an Illumina NovaSeq 6000 or Illumina HiSeq platform using the recommended paired-end sequencing workflow (v3 read parameters, 28-8-0-91 cycles). On average, libraries were sequenced to a depth of 50,000 reads per cell. Data were preprocessed with Cell Ranger v. 3. 1 (αCamKII IL-2 dataset) or v. 6. 0 (PHP. GFAP-IL-2 dataset) from 10x Genomics. The resulting count matrices, showing the number of transcripts (unique molecular identifiers) for each gene in a given cell, were analyzed with R v. 3. 6. 3 (https://www. r-project. org/)  60 and Seurat (https://satijalab. org/seurat/; v. 3. 1. 5) 61 (αCamKII IL-2 dataset), or v. 4. 0. 1 and v.
4. 0. 5 (PHP. GFAP-IL-2 dataset), following the standard pipeline with default parameters, unless stated otherwise. Before analysis, the data were filtered based on different quality metrics calculated to only include bona fide single cells of high quality. Genes detected in less than five cells were filtered out. Low-quality cells or empty droplets (identified as those with less than 200 genes) and cells with less than 500 unique molecular identifier counts were also filtered out. Finally, libraries with extensive mitochondrial reads, indicative of dying cells, were filtered out. The feature expression measurements for each cell within the combined datasets were normalized by the total expression and log transformed. Before clustering, unwanted variation due to Unique Molecular Identifiers (UMI) number and mitochondrial gene expression was removed.
Acknowledgements The work was supported by the  VIB , an  ERC Consolidator Grant TissueTreg  (to A. L. ), an  ERC   Proof of Concept Grant TreatBrainDamage  (to A. L. ), an  ERC Starting Grant AstroFunc  (to M. G. H. ), an  ERC Proof of Concept Grant AD-VIP  (to M. G. H. ),  FWO Research Grant   1513616N  (to M. G. H. ),  Thierry Latran Foundation Grant SOD-VIP  (to M. G. H. ), an  ERNAET Chair  ( H2020-WIDESPREAD-2018-2020-6 ; NCBio: 951923;.
Mapping dsRNA precursors on the IIV-6 genome based on siRNA coverage To address the origin of the IIV-6-derived dsRNAs, we analyzed four independent small RNA-sequencing (sRNA-seq) datasets from cells and flies infected with IIV-6 (Figure  2A ). There was a remarkably consistent pattern of siRNA coverage along the viral genome (Figure  2A , top left). The striking conservation of the siRNA coverage suggests that the production of dsRNA precursors is biased toward specific regions of the viral genome. To better define regions that generate dsRNA precursors, we selected the most covered base positions in the IIV-6 genome based on sRNA read density using a 95% confidence interval (Figure  2A ). A total of 3,006 bases were shared among the four independent datasets, which we considered to be hotspots for siRNA production in the IIV-6 genome (Figure  2A , top right).
We also sequenced small RNAs from infected cells at the same time point for a direct comparison. We chose an early time point, 6 hpi, to try to analyze the biogenesis of dsRNA and siRNAs more directly and avoid secondary effects that accumulate over time. The profile of IIV-6derived small RNAs at the early time point already showed a clear symmetrical size peak of 21 nt, characteristic of the siRNA response, and coverage along the IIV-6 genome correlated well with data from other time points (Figures  S3B-S3D ). We compared the abundance of antisense and sense transcripts from the dsRIP-seq data with the abundance of siRNAs from the sRNA-seq data (Figures  3D  and 3E ). There was positive correlation between siRNA and antisense transcript abundance (Figure  3E ). Indeed, genomic coverage by siRNAs and antisense transcripts was qualitatively very similar (Figure  3F ).
IIV-6 genes from right (R) and left (L) genomic strands are shown on top. The datasets were clustered by a distance-like metric and represented as (1 À Pearson's correlation). (B) Contribution of hotspot-associated or hotspot-depleted regions to the generation of IIV-6-derived siRNAs in four independent datasets. Hotspot-associated regions were numbered and named according to the presence of ORFs, while interspacing hotspot-depleted regions were numbered 1-14. Left: density map showing the density of siRNA coverage for each defined region (red line delimits 90% of the total density). Center: the number of hotspots in each region is depicted in the bar plot. Right: observed/expected siRNA abundance within hotspot-associated and hotspot-depleted regions. dpi, days post infection; hpi, hours post infection.
The membranes were washed with PBST and rinsed in 1X PBS. Then, the ECL substrate (GE) was added to blots according to the manufacturer's recommendations. Chemiluminescent signals were acquired using a Fusion FX7 Image System. Data were analyzed with ImageJ software. dsRNA sizes were estimated based on the pattern of purified dsRNAs from DCV-infected cells in an 1% agarose gel. dsRNA enrichment by immunoprecipitation (dsRIP) Total RNAs were isolated by TRIzol, keeping samples at 4 C throughout the process to minimize complementary RNA annealing. TRIzol-isolated RNAs were diluted in ice-cold IP buffer ], 150 mM NaCl, 2 mM EDTA, 1% Nonidet P-40 (NP-40) (Sigma) and 100 U/mL RNaseOUT (Invitrogen)). The anti-dsRNA J2 antibody (English and Scientific Consulting Hungary, SCICONS) was combined to total RNAs derived from mock-, IIV-6-and DCV-infected S2 cells.
Quantitative PCR (qPCR) reactions were performed using Power SYBR Master Mix (Applied Biosystems) and run on CFX384 Real-Time System (Bio-Rad) or QuantStudio 7 (Applied Biosystems) with the standard cycling programs for Cycle Threshold (Ct) and melting temperature (dissociation curve) analysis. Data were normalized by the Drosophila housekeeping Ribosomal protein L 32 (RpL32) or U6 RNA (under ActD and ML-60218 treatments) in case of cDNA using the delta Ct method for relative RNA quantifications. Isolated DNA from infected cells was resuspended in 200 mL of nuclease-free water and 5 mL were subjected to qPCR reactions for S2 and IIV-6 genomic DNA (gDNA) quantifications using Ct method and IIV-6 gDNA normalized by S2 DNA levels. Primers were designed using primer 3 plus tool (https://primer3plus.
Details for sRNA-seq data from this study and public data are described in Table  S2. dsRNA immunoprecipitation (dsRIP) sequencing Prior RNA-seq, dsRNAs were enriched in samples from IIV-6-infected cells. 24 mg of TRIzol-isolated total RNAs from mock-and IIV-6infected samples were subjected to dsRIP, DNase I-treated (Ambion) and purified with Acid Phenol (Invitrogen). Then, libraries were cloned with the TruSeq Stranded Total RNA kit (Illumina), using the protocol recommended by the manufacturer. 2 3 100 nucleotides paired-end sequencing was performed on an Illumina HiSeq4000 sequencer by the IGBMC Sequencing Platform of Strasbourg/ France (Strasbourg, France). Analysis of small RNA sequencing datasets Small RNA-seq analysis was performed essentially as described in  (Aguiar et al. , 2015)  and consisted in (i) filtering reads by phred quality R20; (ii) adaptor trimming (Cutadapt v1.
12) and exclusion of sequences shorter than 15 nt or containing ambiguous bases  (Martin, 2011) ; (iii) Pre-processed read alignment to Drosophila melanogaster and IIV-6 reference genomes allowing 1 mismatch (Bowtie v1. 1. 2)  (Langmead et al. , 2009) ; (iv) exclusion of multi-aligned reads and sequences mapping to 2 S rRNA allowing 1 mismatch; (v) normalization of IIV-6 derived siRNAs (20-23 nt) by the total number of reads mapping to D. melanogaster genome (v6. 09) and posterior representation as Reads per Million (RPM). For sRNA datasets from Control and loqs-PD deficient flies, IIV-6 derived reads were normalized either by total reads mapping to D. melanogaster genome (RPM) and by viral RNA loads (average of geometric means from IIV-6 MCP and 393L RNA levels). miRNAs and endo-siRNAs were normalized by total read mapping to D.
Definition of genomic regions enriched for siRNAs in the IIV6 genome To define regions that presumably generate dsRNAs based on siRNA location, the density of siRNAs (the n times that a base was covered by any read) for each base of the IIV-6 reference genome was calculated and ranked in the different sRNA-seq datasets  (Bronkhorst et al. , 2012; Kemp et al. , 2013  and SOLiD platform from this study). The top 5% densest bases (95% percentile) were select in each dataset independently of genomic annotation (gene/Intergenic region). To filter out for bases located in regions associated with high density of siRNAs in all datasets, only shared positions among the top 5% densest bases were retained and defined as siRNA hotspots. Hotspots were then associated with genes (ORFs) and Intergenic regions.
Then, adjacent genes/intergenic regions containing hotspots were visually examined and sites with continuous coverage of siRNAs were merged into 14 genomic regions with high siRNA densities (hotspot-associated regions) that were separated by 14 hotspot depleted regions with discontinuous and low siRNA coverage. For each hotspot-associated or depleted region, the mean siRNA density (mean density of nucleotide coverage within the region) was calculated to generate a density map of the relative contribution of individual locations to total density (sum of the density means from hotspot-associated or depleted regions) in each dataset. To analyze enrichment of siRNAs within individual regions, the number of siRNAs arising from each region was calculated and compared to the expected number of siRNAs (mean coverage) covering the whole IIV-6 genome using Fisher's exact test.
siRNA abundancies within hotspot-associated or depleted region were compared between Control and loqs-PD deficient flies using Pearson's correlation. Density correlations between sRNA-seq and dsRIP-seq datasets For dsRNA-seq analysis, paired-end reads were pre-processed using FASTX-Toolkit. Reads scored with phred quality below 20 were discarded  (Gordon and Hannon, 2010). Filtered read pairs were merged into longer sequences using the PEAR program  (Zhang et al. , 2014). For transcription assessment, merged reads were compared against IIV-6 genome, genes (ORFs) and intergenic regions using Bowtie2 software and abundance separated by Left (L) and Right (R) genomic strands. For each position on the IIV-6 genome an R/L density value was attributed based on read coverages on each genomic strand.
Motif analysis Putative conserved binding sites for transcription factors (Motifs) were predicted using MEME Suite with the variant MEME-CHIP setting the parameters known motifs to ''Drosophila melanogaster'', using ''Combined Drosophila Databases'' and expected motif distribution to ''Any number of repetitions''  (Bailey et al. , 2015). Comparisons between the genomic position of motifs and hotspot-associated regions annotated in the IIV-6 genome, including Fisher's exact test for co-localization, were performed using flank, merge, fisher, reldist and closest programs built-in BEDTools package  (Quinlan and Hall, 2010). m 6 A analysis Data from m 6 A methylation sites in Drosophila melanogaster was obtained from previously published datasets  (Wang et al. , 2021). Genomic positions of TEs or cis-NAT with methylation sites were compared using intersect program built-in BEDTools package.
Image processing The images were first processed in Fiji  (Schindelin et al. , 2012). Briefly, raw Z-stacked images were open in ImageJ software, the separate channels were merged, and Z-Project was applied using Sum Slices or Average Intensity modes. The Scale bar was added, the images were flattened and all images from the same batch were combined in the same panel using Stack tools. Images were saved in TIFF format and further processed in Photoshop (PS) where contrast and brightness were adjusted, and the final panels were assembled. Magnified views in insets (zooms) are shown as white boxes from the original images, that were further processed in PS. Scale bars were also adjusted according to magnification boxes in PS to keep zoom ratios. To analyze BrdU foci location related to nuclear DNA and/or dsRNA distribution, the images were split into separated channels and different slices merged using Z-Project tool.
The Hoechst channel was used to analyze total DNA (cellular + viral DNA). An automatic threshold was applied to minimize noise while retaining most Hoechst signal. Hoechst areas were automatically selected in ImageJ Analyze Particles. The same procedure was applied to BrdU, Lamin0 and dsRNA channels. The resulting circularized areas were overlayed onto the multi-channel images (after applying Z-Project). When the analysis did not provide sufficiently stringent selection, a cut-off area was defined using ImageJ Analyze Particles for circularity selection. For cross-sections analysis, Multichannel Plot Profile from BAR tools (https://github. com/ tferr/Scripts) in ImageJ was used in multi-channel composites to analyze the distribution of pixel intensities along a cross-section line. Airyscan images were analyzed using the Fiji plugin AiryscanJ (https://gitlab.
QUANTIFICATION AND STATISTICAL ANALYSIS Evaluation of statistical significance was performed using R scripts and GraphPad Prism software. We applied the Student t-test, One-way ANOVA with Dunnett's test or two-way ANOVA with Tukey's (comparing all means) or Sidak's test (comparing specific groups of means) to analyze statistically differences in DNA and RNA levels for qPCR assays as indicated in figure legends. For IIV-6 transcripts and replication analysis, data were log-transformed before statistical inference and plotted using non-transformed values. For bioinformatic analysis, statistical tests were described elsewhere  (Aguiar et al. , 2015; Marques et al. , 2013). Correlation between dsRIP data and siRNAs were compared with Pearson's correlation. Fisher's exact test was used to evaluate Expected/ Observed abundance of siRNAs from hotspots-associated versus depleted regions, the presence of motifs along IIV-6 genome and m 6 A presence in host transcripts.
Figure2. IIV-6-derived siRNAs are concentrated in a few genomic regions (A) Computational strategy to identify regions on the IIV-6 genome associated with high density of siRNAs. The intersection of the top 5% most-covered bases in the viral genome shared among four independent small (s) RNA-seq datasets (3,006 bases) defined siRNA hotspots. These were used to define hotspot-associated regions with high sRNA coverage interspaced with hotspot-depleted regions. sRNAs (20-23 nt) mapping to the IIV-6 genome are represented as 8-bp bins.
ACKNOWLEDGMENTS We would like to thank all members of the  Marques and Imler laboratories  for invaluable assistance and discussions. We also thank  Phillip D. Zamore  for mutant flies, the  Sequencing Platform of Strasbourg (University of Strasbourg ) for high-throughput sequencing, and the  UFMG microscopy facility (CAPI)  for image acquisition. This work of the  Interdisciplinary Thematic Institute IMCBio , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS , and  Inserm , was supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ), by the  SFRI-STRAT ' US  project ( ANR 20-SFRI-0012 ), and by  EUR IMCBio (IMCBio   ANR-17-EURE-0023 ) under the framework of the  French Investments for the Future Program  as well as by the previous  Labex NetRNA  ( ANR-10-LABX-0036 ).
Funding This work was supported by the  Laboratoire d'Excellence (LabEx) ParaFrap  (grant  LabEx ParaFrap   ANR-11-LABX-0024 ), by the  Equipement d'Excellence (EquipEx) I2MC  (grant  ANR-11-EQPX-0022 ), by the  ERC Starting  Grant  Nº260918 , and by funding from  CNRS ,  Inserm , and the  University of Strasbourg. DK is funded by a  DFG postdoctoral fellowship  ( KL 3251/1-1 ).
Acknowledgements We thank the  UCI  and the  UCSD Transgenic Mouse Facilities  for design help and production of CRISPR modified mice;  Jennifer Santini  for microscopy technical assistance; and the  UCSD School of Medicine Microscopy Core  ( NINDS P30NS047101 ). The  UCI TMF  is a shared resource funded in part by the  Chao Family Comprehensive Cancer Center   Support Grant  ( P30CA062203 ) from the  National Cancer Institute. This work was supported by grants  HL 139947  and  HL 151433  to MHG and by the  American Heart   Association Career Development Award   18CDA34110228  (F. L. ) and  Scientist Development Grant   14SDG18440023  (HSL). Conflict-of-interest disclosure: The authors declare no competing financial interests.