dataesr/scientific-paragraphs-categorization huggingface dataset:
… Cette manière de travailler a été très exigeante pendant de nombreux mois, des deux côtés, et le résultat est d'autant meilleur. Notre approche a privilégié la cohérence et la qualité au sein d'une double page, plutôt que l'exhaustivité. EV. On pourrait ajouter plusieurs exemples. Notamment le choix des figures sur la transition urbaine, p. 25, mettant en regard deux cartes sur les petites villes, émanant de deux propositions différentes, l'une sur les villes de l'Allemagne, l'autre sur les villes du Ghana, séparées par une sorte de mer Manche, qui crée un effet frappant. Plus loin, p. 36, sur la verticale de l'étalement urbain, j'avais identifié la base de données qui promettait une analyse intéressante ; mais elle s'est avérée très compliquée à mettre en image et Thomas Ansart a proposé ces quatre graphiques.
Cette méthode est proche de la base de données Géopolis que François Moriconi-Ebrard (1993) avait constituée dans les années 1990 i et régulièrement mis à jour ; mais sa base n'est pas dans un format facilement accessible et les données actuellement disponibles s'arrêtent à 2010. Les données, ici, sont croisées avec des données sur les richesses, ce qui est un autre intérêt. L'autre point qu'on peut souligner, c'est que cet atlas a été l'occasion de travailler sur des données venant de l'Internet : nous avons cherché ce qu'on pouvait trouver parmi ces nouvelles big data. Nous avons pensé au début à des données de téléphonie, particulièrement en pleine période de pandémie covid, par exemple du côté d'Orange. Ce n'était pas si simple. Nous pouvions aussi nous inspirer du modèle de l'artiste élaboré à partir des données touristiques sur le site web Flickr.
SW. Je voulais justement vous interroger sur les sources que vous avez choisi de mobiliser pour les cartes, car beaucoup d'entre elles sont vraiment originales et surprenantes, cela change considérablement le regard sur les villes. EV. Après réflexion, nous nous sommes orientés, pour les statistiques de population urbaine, vers une nouvelle base de données constituée par le Centre commun de recherches de l'Union européenne 1 , qui propose une version très critique par rapport aux sources habituelles de l'ONU. Cette dernière ne prend pas bien compte de la diversité des critères de recensement. Avec cette nouvelle base, il est possible de faire une modélisation des tâches urbaines. Elle procède à une décomposition des recensements nationaux, avec un travail très fin de pixellisation des données, selon une méthode harmonisée à l'échelle mondiale qui est assez convaincante.
Le cas de la France En France, il n'existe pour l'instant pas de réserves de vagues, et le sujet ne fait l'objet d'aucune réponse juridique (Communications personnelles). Pourtant, la France a tout à gagner à légiférer sur « ses » vagues. En effet, le littoral y est long et varié : le pays possède la seconde plus grande surface marine mondiale, répartie sur plusieurs océans et mers. En outre, certaines vagues sont emblématiques, comme en témoigne la présence de plusieurs « spots » de sports de vagues de grande qualité (et plus de 400 sont répertoriés sur la base de données de référence WannaSurf. com, ce qui représente l'un des totaux les plus élevés du monde). De plus, ces vagues sont menacées par les actions anthropiques sur le littoral, les cours d'eau et la haute mer. Par exemple, l'installation imminente d'éoliennes de grande taille au large des côtes pourrait potentiellement agir sur la déformation de la houle, mais il existe un manque de connaissances à ce sujet.
En somme, trois biais sont à garder en tête dans l'analyse de cette base de données : un biais de popularité qui agit sur une surreprésentation des artistes les plus renommés, un biais genré qui agit sur une surreprésentation des hommes et un biais temporel qui agit sur une surreprésentation des productions récentes 9. En parallèle de ces données de réseaux, j'ai utilisé le nombre de vues sur Youtube des clips des rappeurs présents dans le réseau comme indice de grandeur commerciale et le ratio like/dislike sur ces vidéos comme Le monde professionnel des rappeurs français est cependant plus large que ces 672 rappeurs ou groupes de rap : 1 200 autres unités artistiques ont été invitées au moins une fois dans un morceau (sans avoir eux-mêmes pu inviter). La porosité de ce monde va dans deux directions : les artistes issus d'autres genres musicaux (frontières des genres musicaux) et les rappeurs qui n'ont pas sortis d'album (frontière de la professionnalité).
Primary cell extractions Six deltoid muscle biopsies were obtained from ALS patients (50. 0 ± 6. 5 years old) who attended the Motor Neuron Diseases Center (Pitié Salpétrière, Paris), and 17 muscle biopsies from healthy subjects (51. 4 ± 18. 2 years old) from the BTR (Bank of Tissues for Research, a partner in the EU network EuroBioBank) in accordance with European recommendations and French legislation. The protocol (NCT01984957) was approved by the local Ethical Committee. Written informed consent was obtained from all patients. All biopsies were isolated from the deltoid muscle.
Discussion Notre étude a permis de recenser 33 cas de bérylliose pulmonaire chronique diagnostiqués en France entre 1982 et 2014. Les critères diagnostiques étaient respectés avec un diagnostic de BPC certaine dans la majorité des cas. Nous retrouvions une prédisposition génétique chez 14 des 17 patients testés (82%), cohérente avec les résultats des principales études  (7). Les données provenant de cette cohorte confirment les difficultés du diagnostic de la BPC en France. Selon l'analyse de la base de donnée COLCHIC de l'Institut National de Recherche et de Sécurité (l'INRS) il apparait qu'en France, le nombre de salariés exposés au Be serait compris entre 9400 et 14400, avec pour environ 14% d'entre eux des expositions à des niveaux supérieurs à 2µ/m3, pendant une durée supérieure à 8h par jour, malgré les recommandations de prévention d'exposition en vigueur  (6).
A B C D E F (legend on next page) acute-to-memory transition in the two donors with large datasets spanning the acute to late (day 500+) memory phases of the HLA-A2/NS4B response (Figure  1E ; clones with n > 3 cells used for visualization purposes only). We assessed the relationship between clone size and persistence to the late memory phase (day 593 or 720) on clones where we observed at least one cell present in multiple different time points. Large clone sizes at day 15 were negatively associated with long-term survival (log odds ratio: À1. 76 donor A, À1. 83 donor D; p < 0. 05; statistical analyses were performed on all cells), while clone sizes at day 90/136 were positively correlated with persistence to day 500+ (log odds ratio: 0. 51 donor A, 0. 73 donor D; p < 0. 05) (Table  S2 ).
1) (all from BD Biosciences), CD57 BV605 (clone QA17A04), CD62L BV650 (clone DREG-56), CD94 PE (clone DX22) (all from BioLegend), CD45RA PE-Cy5. 5 (MEM-56, ThermoFisher), and Live/Dead fixable Aqua (ThermoFisher). Flow cytometry data were analyzed using FlowJo (v10, FlowJo) using regular bi-variate gating. UMAP-analysis was performed in FlowJo v. 10. 5. 3. In Figures  2A, 2B , and S2B, 8000 bulk CD8 + T cells and 2400 YFV-specific CD8 + T cells collected at day 15 and 90 after vaccination were used for the UMAP analysis based on expression of CCR7, CD16, CD27, CD45RA, CD57, CD94, CD127, CXCR4, granzyme A, granzyme B, KLRG1, LEF-1, and TCF-1. In Figure  4C , UMAP analysis was based on expression of CCR7, CD27, CD45RA, CD57, CD95, CD127, and PD-1. Index sorting and single-cell RNA-Seq Cryopreserved PBMC isolated from donors at different time points post-vaccination were stained as described above (Flow cytometry).
QUANTIFICATION AND STATISTICAL ANALYSIS Sequencing TCR b chains from scMDA libraries Pooled libraries were sequenced on a MiSeq (Illumina). After demultiplexing of Illumina sample indexes, the reverse read (R2, 150bases) Fastq file was converted to Fasta format. Identical sequences were clustered using the FASTX-Toolkit (http://hannonlab. cshl. edu/fastx_toolkit/) FASTA Collapser. Then sequences were sorted by our 96-well indexes using the FASTX barcode splitter, and the first 44 bases were finally trimmed off using the FASTA trimmer to facilitate downstream sequence analysis. Accurate assignment of individual TCRb-chains to single cells was accomplished by first identifying the top sequences with counts above background levels (determined by examining count frequencies in 0 cell wells or wells in which no PCR product was observed).
Single-cell RNA-seq normalization and batch correction For Donor A we filtered poor quality libraries using an approach similar to that described in  Shalek et al. (2014). Briefly we applied the SVM-bagging algorithm to all cells comparing blank libraries (libraries generated from 0 cell wells) to all single cell libraries using the R package e1071 (https://cran. r-project. org/web/packages/e1071/index. html). Under the hypothesis that each random sub-sample should contain few cells looking like blanks, we classify each time the full dataset from the fitted SVM and record the results. We can then compute the probability of a given single cell library to look like 0 cell libraries from the number of times that it has been classified as such. Finally, we ran a last SVM classification trained on the 25% of cells with the highest and 25% of the cells with the lowest probability of looking like a blank to label the cells.
With less cells for the female donor in vivo data, and for the in vitro data, we used the SVM model fitted on the male donor to classify these cells. All cells determined to be of poor quality by this procedure were removed from downstream analysis. Normalization across wells was performed using the SCnorm procedure  (Bacher et al. , 2017). We processed each time-point independently to avoid removing any day specific effect. Counts obtained after normalization were used for downstream analysis. For visualization we used Combat to remove batch effect  (Leek et al. , 2012). Classifier approach for assigning probability of a single cell being a ''central memory  '' cell (pCM)  based on protein and transcriptome analysis Human T cells appear to undergo high rates of transcriptional bursting as previously judged by the fraction (approximately 80%) of genes which are expressed in a mono-allelic pattern, leading to a substantial fraction of zeros in single cell RNaseq datasets complicating downstream analysis  (Reinius et al.
For this we trained a logistic PLS model with a sparsity constraint on the manually annotated cells. For computational reasons we could not train our model on the full scRNA-seq dataset. To circumvent this problem, we build a first model on a reduced set of markers, then we used this model to detect differentially expressed genes between the two predicted groups by this first model and trained our final model on the differentially expressed genes (see Figure  S3B  for example of manual and PLS-based selection of CCR7 + and CCR7 À cells in each time point). We trained our first model using the following factors: d GNLY, GZMH, CCL4, KLRD1, GZMB and ZEB2 as typical effector/effector memory T cell genes. d LTB, TCF7, CCR7, GZMK and SELL as typical central memory T cell genes. d The cell surface proteins CCR7 and CD127 as central memory T cell markers.
This procedure is described in detail in  Durif et al. (2018). The sparsity constraint selected the cell surface proteins CCR7 and CD127, and the gene CCL4 as sufficient markers for this classification. We used this initial PLS model on the complete Donor A day 15, 136 and 593 datasets to produce a first classification of cells into memory or effector type. This classification was built on the full Donor A dataset, crossing different batches and time-points, to try to extract a memory signature independent of these two factors. We then performed a differential expression analysis between the two predicted cell types while accounting for batch and day effects (Table  S3 ). The following procedure describes the way we conducted differential expression analysis throughout the study: We place ourselves in the framework of the generalized linear model with over-dispersed count distributions.
We tested each gene for zero-inflation with the Vuong test  (Vuong, 1989)  to compare a zero-inflated Negative Binomial model to a Negative Binomial model of the count distribution. In case of zero-inflation the gene expression profile between cell types was modeled with a zero-inflated Negative Binomial distribution or a Negative Binomial distribution using the R package glmmTBM (https://github. com/glmmTMB/ glmmTMB). Then, we used a likelihood ratio test with the R package lmtest (https://CRAN. R-project. org/package=lmtest) between the model accounting for cell type, batch and day effect and the model only accounting for batch and day effect, for each gene. To account for the large number of batches, we used the generalized linear model framework with mixed effect to model the batch effect as a mixed effect  (Fournier et al.
, 2012; Skaug et al. , 2016). We then used the 322 differentially expressed genes (FDR%0:05)  (Benjamin and Hochberg, 1995) , in addition to the 11 genes used before identify a memory signature only relying on scRNA-seq data (without using CCR7 protein expression) (Table  S3 ). To achieve this goal, we fit a second logistic PLS model with a sparsity constraint on these factors and the manually annotated cells. The sparsity constraint selected the genes ABI3, CD8A, COTL1, FOXP1, HNRNPA1, LTB, NUCB2, GNLY, GZMH, CCL4, KLRD1, GZMB, ZEB2, TCF7, CCR7, GZMK and SELL as sufficient for this classification. We used this model on donor A in vivo dataset to compute our final classification. In addition of using the binary memory or effector classification, we also used the probability of being in the memory group (pCM-score) throughout the paper, which is a byproduct of modeling the two groups with a logistic distribution.
Analysis of single-cell data based on cell type For 'training plate' data on sorted CD8 + T cells based on cell type in Figure  S6  the following analysis was done. We selected 2580 genes based on differential expression analysis performed at each time-point on Donor A YFV-specific cells according to scRNaseq based on effector and central memory (CCR7 + ) differences (FDR % 0. 05). The genes were further filtered to keep only genes expressed in more than 20% of the cells in the training dataset (429 genes). We then performed a PCA on the scaled and anscomb transformed reads counts for the single-cell training dataset. We transformed the bulk training dataset in the same fashion and projected them on the single-cell PCA. For the heatmap in Figure  S6  we scored cells from the training plate using the second logistic PLS model from the donor A YFVspecific CD8 + T cells.
We then ran differential expression analysis (DEA) to compare gene expression profiles of cells originating from CCR7 + IL7R + SCM founder cells (clones H2, E4, and B4) or CD57 + EMRA founder cells (clones G8, F3, and H9). For the purpose, we used Wilcoxon's rank sum test followed by Bonferroni's correction for multiple testing and identified 154 differentially expressed genes (DEGs, adjusted p value < 0. 05). After regressing out the effect of cell cycle, we performed a second PCA, and identified relevant principal components (PC) to use for subsequent non-linear dimensionality reduction. We ran diffusion map using destiny R package (v2. 12. 0)  (Angerer et al. , 2016) , considering previously identified DEGs (n = 154) and top 14 PCs. The first dimension on our diffusion map (DM1) effectively ordered cells along a SCM to EMRA continuum and was therefore used to assign a score to each cell in the dataset.
TCRaÀ and b-chains were identified in bulk RNA-seq data using the MIXCR software package  (Bolotin et al. , 2015). To exclude TCR sequences which represent mapping artifacts, caused by inappropriate classification of alternative transcripts in Bulk RNaseq datasets as TCRs, we first removed any TCR sequence that was found in wells stimulated by different viral epitopes, as it would be unlikely that a cross reactive TCR would exist for two unrelated viral peptides. We used TCRb-chains to define individual clones although our final data includes information on both chains of the receptor. An unfiltered list of all TCR sequences identified by MIXCR is included (Table  S5 ). Relative amounts of TCRbÀchain sequences obtained for each replicate sample were determined by taking the sum of all counts for a given TCR within a single donor/virus combination and then calculating the frequency of counts observed in each sample relative to the total counts.
ACKNOWLEDGMENTS We thank the  Sequencing Core Facility at SciLife Labs  for help with sequencing and data management, and  M. Toro  and  S. Giatrellis  ( Department of Cell and Molecular Biology ) and the flow cytometry core facility ( Department of Medicine Huddinge ) at  Karolinska Institutet  for assistance with FACS. This study was supported by grants from the  Swedish Research Council , the  Swedish Cancer Society , the  Karolinska Institute , the  Strategic Research Programme in Stem Cells  and  Regenerative Medicine at Karolinska Institutet  (StratRegen), the  Swedish Society for Strategic Research, and Knut och Alice Wallenbergs Stiftelse. J. E. M. was supported by a  Human Frontiers Science Program Long-Term Fellowship  ( LT-000231/2011-L ).
5M) BD Biosciences Thermo-Fisher B Single-cell RNA-seq normalization and batch correc-Clone SK7, RRID:AB_2870181 AM9261 Anti-Human CD3e PE-Cy5 Sodium Dodecyl sulfate 20% Anti-Human CD4 PE-Cy5 TE Buffer (pH 8. 0) RNase Free Anti-Human CD4 BUV615 Recombinant Phi29 DNA Polymerase Anti-Human CD8a APC-Cy7 Phi29 DNA Polymerase BD Biosciences Thermo-Fisher BD Biosciences Thermo-Fisher BD Biosciences Lucigen BD Biosciences Thermo-Fisher tion B Classifier approach for assigning probability of a single Clone UCHT1, RRID:AB_395741 AM9820 Clone SK3, RRID:AB_2833103 AM9849 cell being a ''central memory'' cell (pCM) based on pro-Clone SK3, RRID:AB_2870258 No Longer Produced tein and transcriptome analysis Clone SK1, RRID:AB_396892 EP0094 Anti-Human CD8a BV570 Deposited data BioLegend Clone RPA-T8, RRID:AB_2563213 Anti-Human CD14 V500 TCR Sequencing FASTQ Files BD Biosciences N/A Clone M4P9, RRID:AB_2737727 N/A Anti-Human CD16 BUV496 Single Cell RNaseq Datasets BD Biosciences N/A Clone 3G2, RRID:AB_2870224 N/A Anti-Human CD19 V500 Bulk RNaseq Datasets BD Biosciences N/A Clone HIB19, RRID:AB_10562391 N/A Anti-Human CD27 BV786 Critical commercial assays BD Biosciences Clone L128, RRID:AB_2744353 Anti-Human CD27 BV750 CD8 T cell Negative Selection Kit (Human) BioLegend Miltenyi Biotec SUPPLEMENTAL INFORMATION Clone O323, RRID:AB_2810436 130-096-495 Anti-Human CD45RA PE-CF594 KAPA Hotstart Ready Mix Anti-Human CD45RA BUV563 KAPA HiFi Polymerase BD Biosciences Roche BD Biosciences Roche Clone HI100, RRID:AB_11154413 KK2602 Supplemental information can be found online at https://doi.
Remerciements La SFGM-TC remercie les partenaires industriels pour leur soutien financier qui ont permis la réussite de cette dixième édition des ateliers d'harmonisation des pratiques : BIOTEST, CELGENE, CHUGAI, GILEAD/KITE, INCYTE, JANSSEN, JAZZ PHARMACEUTICALS, KEOCYT, MACOPHARMA, MALLINCKRODT THERAKOS, MSD FRANCE, NOVARTIS, OCTAPHARMA, SANOFI.
Avancées actuelles des recherches en thérapie génique pour les rétinites pigmentaires, en fonction du gène causal Pour la RP liée à l'X (due à des altérations du gène RPGR [retinitis pigmentosa GTPase regulator]), deux études de phase I/II et une de phase I/II/III sont en cours. [42-45] [46] [47] [48] [49] [50] [51] [52] [53,54] [55,56] [57] [58] États-Unis rAAV2tYF SR, étude canine rrAAV2/5, SR États-Unis, Royaume-Uni shRNA scAAV2/5, SR étude canine RP autosomique dominante ZF6-DB rAAV2/8, SR étude porcine Italie Inactivation klf15 rAAV2/8, SR étude porcine Italie RP inverse « cone-rod » S. aureus Cas9 -étude chez le pri-mate non humain États-Unis Inactivation et Supplémentation RP autosomique dominante Rhodopsine (RhoNOVA) rAAV2/5, SR fondamentale États-Unis, Irlande STC-1 rAAV2-tripleYF, IV fondamentale États-Unis Protection RP RdCVF rAAV92YF, SR fondamentale France Reprogrammation RP Canaux rhodopsine chrim-rAAV2/2, IV sonR Phase I, NCT03326336 États-Unis L'optogénétique consiste à faire exprimer par des β-catenin, Otx2, Crx, et Nrl rAAVShH10, IV fondamentale États-Unis Canaux rhodopsine CATCH rAAV2.
7m8, rAA-V8BP2, SR NCT03326336 Suisse, France JAWS rAAV2. 7m8, SR, IV étude chez le pri-mate non humain France cellules rétiniennes des photopigments capables de restaurer la transduction du signal lumineux en signal électrique. Cette méthode présente l'avantage d'être indépendante de la mutation causale, et de la fonc-tionnalité des photorécepteurs et des cellules de l'EP. Elle serait utilisable à des stades plus avancés d'atrophie rétinienne que pour les stratégies conven-tionnelles, tant que les cellules bipolaires et/ou gan-glionnaires sont fonctionnelles. À ce jour, la preuve de concept a été réalisée chez l'animal et deux études de phase I/II chez l'homme sont prévues 8 [2]. 9 , dont l'altération est responsable d'une forme de RP autosomique dominante [25]. Aucune étude n'a encore exploré cette voie en ophtalmologie.
Pass-to-pass differences in the data within each set thus relate specifically to temporal variations in the magnetospheric system associated with dynamics, which we compare with inferred heliospheric conditions. The interval is also reasonably well supported by auroral imagery obtained both by Cassini and by the HST. We also examine these events for PPO-related dependencies. To this end we conclude this introduction with a brief discussion of nightside plasma/current sheet modulations associated with the PPO phenomenon.
Data Set In this study we combine examination of magnetic field, charged particle, radio emission, auroral UV, and model data to identify intervals of solar wind-related tail dynamics at Saturn, observed during the final set of Cassini orbits. In section 2. 1 we introduce the interval employed and the properties of the Cassini orbit that determine the nature of the in situ data. In section 2. 2 we then describe the instrument data sets and the purpose for which they are employed, while in section 2. 3 we similarly describe the model data sets. Section 2. 4 then discusses the concurrent heliospheric conditions inferred from the model solar wind/IMF data combined with the Cassini GCR/SEP data.
Interval of Study Data from two main intervals are studied here. The first corresponds to the Cassini F ring orbits, revolutions (Revs) 251-270, starting on 1 December 2016 (Day 336 of 2016 or Day 4,718 in doy2004) and ending on 23 April 2017 (Day 113 of 2017 or Day 4,861 in doy2004), though we also examine data from Rev 250, similar to the F ring orbits, occurring immediately prior to this interval. The second corresponds to the proximal orbits (Revs 271-293), starting on 23 April 2017, and ending on 15 September 2017 (Day 258 of 2017 or Day 5,006 in doy2004). Doy2004 is employed as a convenient time parameter for multiyear data sets and is defined such that t = 0 corresponds to 00 UT on 1 January 2004, effectively the start of the Cassini science mission. The trajectories of these Revs are plotted in Figure  2  in Krono-Graphic Stationary (KGS) coordinates, in which Z is Saturn's spin/magnetic axis positive to the north, the X-Z plane contains the Sun (toward positive X), and Y completes the right-hand triad pointing toward dusk.