dataesr/scientific-paragraphs-categorization huggingface dataset:
C. W. was a  New York Stem Cell Foundation (NYSCF) Robertson Investigator  (grant no. NYSCF-R-NI39 ). This work was supported by the  Fondation Schlumberger pour l'Education et la Recherche  ( FSER/2017 ), the  Fondation pour la Recherche M edicale  (FRM no. Equation 202003010612 ), the  ERC Starting Grant  ''Optoloco'' no. 311673 ,  ERC PoC ''ZebraZoom '' no. 825273 , and  ERC   Consolidator Grant ''Exploratome' ' no. 101002870  (to C. W. ). We acknowledge support from  ''MeninGene '' no. 281156  and the  HFSP Program  grant nos. RGP0063/2014  and  RGP0063/ 2017  and grants from the  Agence Nationale de la Recherche (ANR)   ASCENTS  no. ANR-21-CE13-0008 ,  MOTOMYO  no. ANR-21-CE14-0042 , and  ANR   LOCOCONNECT  no. ANR-22-CE37-0023  et la  Fondation Bettencourt-Schueller don 0031. D. v. d.
BM plasma cells have a distinct transcriptome based on the isotype they express As several studies suggest that PCs are a heterogeneous population  [20, 22, 23] , we performed scRNAseq on fully differentiated CD138 high Blimp1-GFP high BM PCs (Fig. 1A ). Dimension reduction analysis after exclusion of the Ig transcripts showed a rel-atively homogenous clustering of PCs that could be explained by our stringent gating strategy (Fig. 1B ). However, when we imputed back the isotype of each PC, we observed that IgA + , IgM + and IgG + PCs tend to segregate supporting previous reports  [18, 19]. We detected 61 differentially expressed genes between BM PC subsets (Fig. 1C ). The differentially expressed genes were involved in various pathways, including metabolism (Bcat2, Ttc19, Inpp4a, Scd2, Far1, Plaat3), survival/apoptosis (Pycard, Ikzf3), protein degradation (Itch, Nedd4, Slpi, Sel1l), antigen presentation and cell activation (H2-Aa, CD74, B2m, Cd69, Ms4a1) as well as cell adhesion/migration (Ccr10, Sell, Cxcr4, Cd52, Epcam, Ly6c2) (Fig.
In silico analysis reveals that plasma cells are more likely to form interactions with certain clusters of stromal cells To unravel the most likely stromal partners of BM PCs, we took advantage of our scRNAseq dataset and of a previously published dataset for BM stromal cells covering broadly bone and marrow non-haematopoietic cells  [27]  to perform in silico transinteractome analysis using CellPhoneDB  [44, 45]. The stroma scRNASeq    2A ). Our first analysis revealed a large variation in the number of potential bi-directional interactions between PCs and the different stromal clusters (ranging from 32 to 70). Very few interactions were identified between PCs and chondrocytes and their progenitors, for most of the fibroblast clusters and for Cxcl12-expressing pericytes (cluster 12). The higher numbers of potential interactions were observed between PCs and the LepR + mesenchymal cells (MSC) (cluster 1), followed by the osteo-committed cells referred to as OLC-1 in  [27]  (cluster 7), the cluster 9 of fibroblasts and the sinusoidal endothelial cells (cluster 0) (Fig.
Acknowledgements: We thank  Dr. N. Setterblad ,  C. Doliger  and  S. Duchez  ( Plateforme technologique IRSL, Paris, France ),  Dr. V. Parietti  ( Mouse facility IRSL, Paris, France ), as well as  R. Morichon  ( UMS 29, Plateforme CISA, plateforme de Microscopie, Paris, France ) for their technical assistance. The study was supported by the  Laboratory of Excellence in Research on Medication and Innovative Therapeutics (LabEx LERMIT ) (ME and KB), an  ANR   JCJC grant  ( ANR-19-CE15-0019-01 ), an  ANR @RAction  grant ( ANR-14-ACHN-0008 ) and a grant from  IdEx Université Paris Cité  ( ANR-18-IDEX-0001 ) to ME. SMF was supported by an  ERA-EDTA Short-Term Fellowship. The 'EMiLy' U1160 INSERM unit is a member of the OPALE Carnot institute, The Organization for Partnerships in Leukemia (www. opale.
V. SIMULATION RESULTS To validate the efficiency and superiority of the proposed control technique, the experimental setup shown in Fig. 3  is simulated using MATLAB/Simulink 2018a. The controlled part is two converter stations connected via an HVDC cable, and each converter has the same AC system interface with the numerical parameters given in Table  2 , and the controller and observer parameters design are given in Table  3. Then, the controllers (  16 ), (  19 ),(  23 ),  (24) , and the observers (50), (53) have been implemented under Digital Signal Processor (DSP) using the Embedded Coder Support Package for Texas Instruments c2000 for auto code generation of the process in the loop test (PIL). This semi-experimental setup is intended to highlight runtime problems in the embedded system.
Additionally, the whole VSC-HVDC system model is performed on the host PC, while the generated code from the Simulink blocks runs directly on the target hardware. They   Observer Ẋ1 (t) = f 1 ( X1 (t), u) -λ 1 ∆ -1 λ 1 J -1 1 K 1 C 1 ( X1 (t) -X 1 (t)) (59) Ẋ2 (t) = f 2 ( X2 (t), u) -λ 2 ∆ -1 λ 2 J -1 2 K 2 C 2 ( X2 (t) -X 2 (t)) (60) with f 1 f 2 J 1,2 and ∆ λ 1,2 are defined in (  28 ), (  28 ), (51), and (40) and K 1,2 is the gains of the proposed observers selected such that the matrices (A 1,2 -K 1,2 C 1,2 ) are hurwitz.
We also detect absorption lines from at least two foreground absorbers at z = 5. 7390 and z = 2. 8296. The total metallicity of the z ∼ 6. 3 system is [M/H] tot = -1. 72 ± 0. 13, after correcting for dust depletion and taking α-element enhancement into account, as suggested by our analysis. This is consistent with the values found for the other two GRBs at z ∼ 6 with spectroscopic data showing metal absorption lines (GRB 050904 and GRB 130606A), and it is at the higher end of the metallicity distribution of quasar damped Lyman-α systems (QSO-DLAs) extrapolated to such a high redshift. In addition, we determine the overall amount of dust and dust-to-metal mass ratio (DTM) ([Zn/Fe] fit = 0. 33 ± 0. 09 and DTM = 0. 18 ± 0. 03). We find indications of nucleosynthesis due to massive stars and, for some of the components of the gas clouds, we find evidence of peculiar nucleosynthesis, with an overabundance of aluminum (as also found for GRB 130606A).
In this paper we study the VLT/X-shooter optical and NIR afterglow spectrum of GRB 210905A at redshift z = 6. 3. In Sects. 2 and 3, we present our dataset and the fitting of the absorption lines. In Sect. 4 we present and discuss our results. The conclusions are drawn in Sect. 5. A ΛCDM cosmological model with Ω M = 0. 308, Ω Λ = 0. 692, and H 0 = 67. 8 km s -1 Mpc -1 (Planck Collaboration XIII 2016) has been assumed for calculations. All data are in observer frame and 1σ errors are reported throughout the paper, unless otherwise specified. Two companion papers  (Rossi et al. 2022; Fausey et al. , in prep. ) present and analyze the GRB and afterglow multiwavelength spectral and temporal properties, and the use of the X-shooter spectrum to infer the neutral fraction of the intergalactic medium, respectively.
g. ,  Salvadori et al. 2019 and references therein). Components II, III and IV exhibit aluminum overabundance. It is the second time that aluminum overabundance has been observed for a GRB at z ∼ 6, the other being GRB 130606A  (Hartoog et al. 2015) , the only other high-z case where a detailed analysis of the chemical abundances and relative abundances was possible. In the SAGA Database  (Suda et al. 2008) , the stars having more similar [Al/Fe] values to those we determined are O-deficient stars with peculiar properties, the so-called Na-O anticorrelation  (Fulbright et al. 2007; Alves-Brito et al. 2010) , typical of some stars found in globular clusters and some dwarf galaxies, such as the Sagittarius dwarf spheroidal galaxy  (Fulbright et al. 2007, and references therein). The peculiar pattern shown by these giant stars may be due to dredging-up material that has undergone nucleosynthetic processing in H-burning shells, or to primordial contamination by massive stars.
Acknowledgements. We thank the referee for her/his very useful report. This work was supported by  CNES. A. S. and S. D. V. acknowledge support from  DIM-ACAV+. D. A. K. acknowledges support from  Spanish National Research  Project  RTI2018-098104-J-I00  (GRBPhot). A. J. L. and D. B. M. are supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 725246 ). The Cosmic Dawn Center is funded by the  Danish National Research Foundation  under grant No. 140. NRT is supported by  STFC  consolidated grant  ST/W000857/1. A. R. , E. P. , G. S. and S. S. acknowledge support from  PRIN-MIUR 2017  (grant  20179ZF5KS ). G. S. acknowledges the support by the  State of Hesse  within the  Research Cluster ELEMENTS (Project ID   500/10.
The model produces a roughly 10,000-fold reduction in computational effort compared to CFD, and can simulate long channels using a simple Matlab code running on a single computer core. As with Sherwood number relations, the model must be properly fit. While Sherwood relations are often fit to experiments, the current approach must be fit to CFD, because to date, experimental measurements of the concentration fields in RO systems are not available. Once fit, the model provides surprisingly accurate predictions of not only the average and maximum concentration on the membrane, but also the local concentration along the membrane surface. This provides a testbed for exploratory studies of multicomponent mass transport and precipitation for which simulating spacers with CFD is prohibitive. For this exploratory study, we develop a reduced model using the results of experiments and two-dimensional CFD simulations of spacer filaments with square cross sections oriented tangential to the downstream flow direction, as in Fig.
8 using NaOH. The resulting feed is slightly undersaturated in gypsum, with a saturation index of log(IAP∕𝐾 𝑠𝑝 ) = -0. 03, where IAP is the ion activity product and 𝐾 𝑠𝑝 is the solubility product. The index was computed using the open-source software PHREEQC  [31]. After the source is switched to the feed solution, the initial 3 L of retentate is discarded to account for dilution by residual DI. The experiments are run with constant permeate flux, such that scale formation produces an increase in transmembrane pressure. The permeate and retentate are returned to the feed tank to maintain approximately constant feed concentration. Following each experiment, the system is rinsed with DI water for 2 min before disassembling the cells and removing the membranes, which are dried in ambient conditions.
Demonstration for long system lengths As a final demonstration, we use our model to simulate the nearmembrane arrangement in a feed channel of length 𝐿 = 40 cm and height ℎ = 2 mm (a length-to-height ratio of 200). We set 𝑅𝑒 = 50, and use the best-fit parameters 𝑏 = 1. 7 and 𝐴 = 0. 75 provided in Table  B. 1. The resulting salt concentration 𝑐 𝑚 (𝑥) along the membrane is shown in Fig. 15. We solve the advection equation using standard finite-volume methods detailed in  [17]. Following a mesh-independence analysis, we use a grid with 20,000 cells in the 𝑥-direction and 128 cells in the 𝑦-direction (a total of 2,560,000 cells). The grid is uniform in the 𝑥-direction, but concentrated near the membrane using a Gauss-Lobatto-Chebyshev distribution, as detailed in Ref. [17]. The code is implemented in Matlab and run on a single core of a 2020 MacBook Pro with a 2 GHz Quad-Core Intel Core i5 processor.
The best agreement was observed for the near-membrane arrangement, for which the model reproduced CFD to within 3% relative error. The worst-case error for the remaining arrangements was only 8%, which occurred at 𝑅𝑒 = 50 for the centerline arrangement. Appendix B provides the fitting parameters 𝐴 and 𝑏 for the three spacer arrangements for 50 ≤ 𝑅𝑒 ≤ 500. Our intent is that one could improve agreement even further by leveraging CFD to develop perturbation fields (𝐯 𝑝 ) tailored to specific spacer geometries. We demonstrated that approach by developing a field tailored to the centerline arrangement. The tailored field reproduced CFD data to within 1% relative error. Here we emphasize that the reduced model provided a roughly 10,000-fold speed-up compared to CFD, and allowed us to simulate a feed channel with a length-to-height ratio of 200 using a simple Matlab code running on a single CPU core.
Acknowledgments This work was generously funded by a  National Science Foundation   Career Award  ( 1752531 ), the  Embassy of France Thomas Jefferson Fund , and the  National Alliance for Water Innovation (NAWI) , which is funded by the  U. S. Department of Energy, Energy Efficiency and Renewable Energy Office, Advanced Manufacturing Office  under Funding Opportunity Announcement  De-FOA-0001905. The authors thank  Dr. Tzahi Cath  ( Colorado School of Mines ),  Dr. John Farnsworth  ( University of Colorado, Boulder ),  Dr. Yarom Polsky  ( Oak Ridge National Laboratory ),  Dr. Eric Hoek  ( University of California, Los Angeles ), and  Dr. Hariswaran Sitaraman  ( National Renewable Energy Laboratory ) for helpful discussions.
Statistical analysis The normality of distribution of analyzed samples was tested by the Kolmogorov-Smirnov test (STATISTICA software package, version 6. 0, StatSoft Inc;  Sokal and Rohlf, 1987). Since the variables lacked normality of distribution, nonparametric tests were applied. In order to assess the relationship between metal and trace element concentrations in muscle on one hand and anal fins and scales on the other, Spearman's nonparametric correlation test (p < 0. 01) was used.
Acknowledgments. This study was supported by the  Ministry of Education, Science, and Technological Development of the Republic of Serbia , contract numbers:  451-03-68/2022Serbia , contract numbers:  451-03-68/ -14/ 200007 ,  451-03-68/2022Serbia , contract numbers:  451-03-68/ -14/200053  and  451-03-68/2022-14/ 200178. -14/ 200178.
METHODS: We assessed the role of Axin1 in normal intestinal homeostasis, with control, epithelial-specific, Axin1knockout mice (Axin1 DIEC ) and Axin2-knockout mice. We evaluated the tumor-suppressor function of Axin1 during chemically induced colorectal tumorigenesis and dextran sulfate sodium-induced colitis, and performed comparative gene expression profiling by whole-genome RNA sequencing. The clinical relevance of the Axin1-dependent gene expression signature then was tested in a database of 2239 clinical colorectal cancer (CRC) samples.
Axin1 Controls an Immune IFNg/Th1 Inflammatory Response We investigated how Axin1 might be able to both stimulate an inflammatory response in the gut and to protect against tumorigenesis by comparing first the transcriptome profiling data for tumors developing in Axin1 DIEC and WT mice by RNA sequencing (RNA-seq) and then the transcriptome profiles of colon tissues from Axin1 DIEC and WT mice treated with DSS on day 7 and day 13 that was compared with those for untreated WT mice receiving drinking water. RNA-seq analysis of the tumors from Axin1-deficient and WT mice identified 327 differentially expressed genes (DEGs) (P <. 05; fold change, >±2), 232 of which were down-regulated in the Axin1-deficient mice (Supplementary Table  1 ). Interestingly, functional annotation analysis with IPA (Quiagen) and ClueGo software identified the IFN signaling and immune Th1 pathways as the highest-ranking enriched pathways inhibited in the tumors of Axin1 DIEC mice (Figure  10A , and Table  3  in Figure  10 ).
Upstream regulator analysis with IPA software identified IFNg/signal transducer and activator of transcription (STAT)1 among the topranking cytokine upstream regulators for which significant inhibition was predicted (Table  3  in Figure  10 ). Stat1 plays a vital role in IFNg signaling and is essential for Th1 cell differentiation. 25 Collectively, these results suggest a downregulation of the IFNg/STAT1/Th1 immune response in the tumors of Axin1 DIEC mice. Interestingly, these pathways are known to play a major role in antitumor immunity,  26  and specifically in CRC. 27 To support a decrease in the Th1mediated immunosurveillance in the tumors of Axin1 DIEC mice, we quantified by immunohistochemistry the infiltration of the lymphocytes T CD8 þ in the tumors. As expected, a significant decrease in the number of CD8 þ T cells was observed in tumors of Axin1 DIEC mice compared with tumors that developed in their WT counterparts (Figure  10F ).
The Gene Expression Signature From Axin1-Deficient Mice Identifies CRC Patients With Poor Antitumor Immune Responses and Clinical Outcomes We then investigated whether the Axin1-dependent 327-GES defined by subjecting Axin1 DIEC and WT mice to the AOM/DSS protocol (Supplementary Table  1 ) was clinically and biologically relevant in patients with CRC. Our hypothesis was that if the Axin1 pathway indeed promotes the development of an appropriate IFNg/Th1 antitumor response, Axin1-proficient tumors should be enriched in CRC patients from the consensus molecular subtype (CMS)1 (immune molecular subtype). We therefore applied the 327gene signature to a transcriptomic database including 2239 primary clinical samples of CRCs (Table  1 ). Samples were classified either in the Axin1-proficient-like group (N ¼ 1053), for those enriched with genes up-regulated in the WT mice, or in the Axin1-deficient-like group (N ¼ 1186), for those enriched with genes up-regulated in the Axin1 DIEC mice.
They then were incubated overnight at 4 C with the primary antibodies against the following proteins, diluted in blocking solution: cleaved caspase-3 (9661S, 1:200; Cell Signaling), Ki-67 (12202S, 1:1500; Cell Signaling), lysozyme (EC. 3. 2. 1. 17, 1:200; Dako), Olfm4 (CS 39141S, 1:400; Cell Signaling), p53 (NCL-p53-CM5p, 1:200; Leica), gH2AX (05-636, 1:300; Millipore), chromogranin B (HPA012602, 1:1000; Atlas Antibodies), villin (ab130751, 1:500; Abcam), glutamine synthetase (610518, 1:400; BD Biosciences), and CD8 (14-0808-80, 1:100; eBiosciences). Specific binding was detected with a biotinylated secondary antibody and ABC reagent (Vector) for immunohistochemistry, and the signal was revealed with 3,3 0diaminobenzidine tetra hydrochloride solution (Vector). Hematoxylin was used as a nuclear counterstain.
RNA-Seq Analysis RNA extracted from colon tissue (distal colon) was treated with DNAse1 according to the manufacturer's protocol (RNA Clean and Concentrator-5, R1013; Zymo Research). Three different mice per genotype were used for RNA-seq analysis. Library preparation and sequencing were performed at the GenomIC' sequencing facility using 3 RNA samples per genotype (https://www. institutcochin. fr/core_ facilities/genomesequencing-studies). The libraries were prepared following the TruSeq Stranded messenger RNA protocol from Illumina, starting from 800 ng high-quality total RNA (RNA Integrity Number, >8). Paired-end (2 Â 75 bp) sequencing was performed on an Illumina Nextseq 500 platform, 20-40 millions paired end reads were used for the analysis. After sequencing, a primary analysis was performed with AOZAN software (ENS, Paris) for demultiplexing and quality control analysis on the raw data (based on FastQC modules, version 0.
11. 5). The STAR algorithm (version 2. 7. 6a; Genomic'IC) then was used to align the Fastq files with the Ensembl Mus musculus GRCm38 reference, release 101 (Genomic'IC). Reads then were counted with RSEM (v1. 3. 1; Genomic'IC) and statistical analyses were performed on the read counts with R (version 3. 6. 3; Genomic'IC) and the DESeq2 package (DESeq2_1. 26. 0; Genomic'IC) to determine the proportion of genes differentially expressed between 2 conditions. We used the standard DESeq2 normalization method (DESeq2 median of ratios with the DESeq function), with a prefilter of reads and genes (reads uniquely mapped on the genome, or up to 10 different loci with a count adjustment, and genes with at least 10 reads in at least 3 different samples). In accordance with the recommendations for this package, we used the Wald test with the contrast function and the Benjamini-Hochberg false discovery rates control procedure to identify DEGs.
Ontology Analyses The list of genes showing significant differential expression was explored by both over-representation and GSEAs. Over-representation analysis was performed by Ingenuity Pathway Analysis (Qiagen, Inc, https://www. qiagenbioinformatics. com/products/ingenuitypathway-anal ysis) version 60467501 for canonical pathways and upstream regulators, and ClueGO v2. 5. 8 (http://www. ici. upmc. fr/cluego) for WikiPathways, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome pathways. Significantly over-represented terms were identified with a right-tailed Fisher exact test, which calculated an overlap P value determining the probability of each term being associated with our lists of differential transcripts by chance alone. The IPA z-score is a statistical measurement of correlation between the direction of the relationship and experimental levels of protein.
It was calculated to assess the activation (positive z-score) or repression (negative score) of each term. The z-score was considered significant if its absolute value was greater than 2. GSEA was performed with Broad Institute GSEA software v4. 1. 0 (http://software. broadinstitute. org/gesa/index. jsp). 55 GSEA determined whether gene sets from the Molecular Signatures Database Hallmark collection (v7. 4) were distributed randomly throughout our ranked transcript lists or whether they were located toward the top or bottom of these lists. Heatmaps were generated with Genesis software (https://genome. tugraz. at). 56 Venn diagrams were produced with the FunRich functional enrichment analysis tool v3. 1. 3 (http://funrich. org). 57.
Gene Expression Analysis on Colon Cancer Clinical Samples We assessed the biological and clinical relevance of our 327-gene signature (Axin1-deficient mice vs Axin1-proficient mice) in human CRC samples by analyzing our database including clinical, pathologic, and gene expression data for primary CRC samples from 10 publicly available data sets and our own data set generated at the Institut Paoli-Calmettes (Marseille, France). Data were collected from the National Center for Biotechnology Information/GenBank Gene Expression Ommnibus, ArrayExpress, and The Cancer Genome Atlas (TCGA) databases (Table  1 ). Samples were profiled with DNA microarrays (Affymetrix) or by RNA sequencing (Illumina). The data set analyzed contained data for 2239 primary CRC samples. The characteristics of these samples are summarized in Table  2.
Acknowledgments The authors thank  S. Robine  ( Institut Curie, Paris, France ),  A. Berns  ( Netherlands Cancer Institute, Amsterdam, The Netherlands ), and  Anne-Amandine Chassot  ( University of Nice Sophia Antipolis, Nice, France ) for supplying mutant mice. The authors also thank  Trinath Jamma  ( BITS Pilani-Hyderabad Campus, Telangana, India ) for help and support for the cell sort at the  Flow Core Facility , the staff of the  Cochin animal facility ,  Isabelle Lagoutte  from the  Imagerie du Vivant facility of Cochin Institute , and  Lucie Adoux  from the  Genomic facility of Cochin Institute.
PMID: 22067406 E-MTAB-863 Affymetrix Sveen et al, GEO database: Human Exon 1. 0 ST array; 22,000 160 160 Genome Med 2011. PMID: 21619627 GSE24551 Affymetrix Laibe et al, GEO database: Array U133 Plus 2. 0; 54,000 130 130 OMICS GSE37892 Affymetrix 2012. PMID: 22917480 Marisa et al, GEO database: Array U133 Plus 2. 0; 54,000 455 455 PLoS Med GSE39582 Affymetrix 2013. PMID: 23700391 TCGA, COAD TCGA portal: RNA sequencing V2; 25,000 500 459 https://tcga-data. Illumina nci. nih. gov IPC Ongoing submission Array U133 Plus 2. 0; 54,000 50 50 to ArrayExpress Affymetrix Total 2239 COAD, Colon adenocarcinoma; IPC, Institut Paoli-Calmettes; TCGA, The Cancer Genome Atlas.