dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  P. Charneau  for the hACE2 lentivirus. This work was supported by a  Canadian Institutes for Health Research Foundation  Grant (F. P. R. ), the  Canada Excellence Research Chairs Program  (F. P. R. ), the  Thistledown Foundation  (F. P. R. ); the  LabEx Integrative Biology of Emerging Infectious Diseases  ( 10-LABX-0062 ; Y. J. , C. D. ) and  Platform for European Preparedness Against (Re-)emerging Epidemics ,  EU  (602525; Y. J. and C. D. ), the  European Union 's  Horizon 2020 Research and Innovation Programme (Project ID 101003633 , RiPCoN; P. F. -B. , C. B. , P. A. ),  HDHL-INTIMIC 'Interrelation of the Intestinal Microbiome, Diet and Health'  ( BMBF  Project  ID 01EA1803 ; P. F. -B. ), the  Free State of Bavaria's AI for Therapy (AI4T) Initiative  through the  Institute of AI for Drug Discovery (AID)  (P.
Screening space, orthogonal validation and comparison of HuSCI and IntraSCI to previous SARS-CoV-2 related datasets. a, Schematic of the experimental contactome mapping pipelines (left) and screening space for each of the two parallel Y2H HIS and Y2H GFP screens (right). https://doi. org/10. 1038/s41587-022-01475-z a a SARS-CoV-2 BioID interactome AP-MS interactome binary interactome Human Laurent, et al. 4720 St-Germain, et 2871 695 al. 28 viral 6751 6182 Samavarchi-Y2H 4356 1059 GFP 1938 1628 Tehrani, et al. baits 28 viral preys tested pairs 784 b 14,627 human preys 409,556 tested pairs 4. 78x10 -23 Stukalov, et al. Li, et al. 1. 81x10 -43 Gordon, et al. 3. 92x10 -42 HuSCI 0. 0339 Data P-value (fisher exact test) P-value (adjusted, bonferroni) 3. 82x10 -22 1. 45x10 -42 3. 13x10 -41 0. 271 Fisher's exact test results for tissue bias comparisons relative to Human Protein Atlas coding region ORFeome 9.
1 Nabeel, et al. 1. 23x10 -26 9. 83x10 -26 synthesis Nabeel, et al. 604 43 5372 612 35 3531 634 13 7797 Laurent, et al. 1. 19x10 -143 9. 52x10 -143 St-Germain, et al. 2. 41x10 -78 1. 93x10 -77 Li, et al. 290 5 642 252 43 5372 273 22 3544 254 41 7769 900 total pairs total pairs 555,090 Samavarchi-Tehrani, et al. 1. 01x10 -136 8. 07x10 -136 (64% overlap) (59% overlap) Cloning and yeast transformation 368 1063 21 Gordon, et al. Stukalov, et al. Bait-X Prey-Y 276 19 376 1074 370 285 10 1071 13 13 634 634 337 964 120 52 5363 5295 371 963 18 121 3548 3445 25 viral 229 7581 HIS3 855 Y2H 320 7741 69 baits 26 viral preys 650 tested pairs d Number of PPIs 20 40 60 80 17,299 human preys 432,475 tested pairs Shared Unique No-domain HuSCI 196 8 381 202 2 1082 199 5 290 202 2 645 189 15 5400 201 3 3563 190 14 7796 c Dataset HuSCI HPA Organotropism analysis of SARS-CoV-2 viral targets Airway Brain 4.
Comparison of HuSCI with SARS-CoV-2 association and proximity datasets. a, 17,13ap of viral-human protein pairs between HuSCI, four AP-MS and three BioID based datasets (Gordon et al. 12,13, Stukalov et al. 9, Li et al. 14, Nabeel-Shah et al. 15, Laurent et al. 16, St-Germain et al. 17, Samavarchi-Tehrani et al. 18). b, Statistical analysis of representation of host targets in common and specific expression groups from datasets in (a), compared to the Human Protein Atlas22 (HPA) (Fisher's exact test with Bonferroni correction). c,Organotropism analysis across SARS-CoV-2 infected organs from datasets in (b). The percentage of genes within each dataset with specific organotropism ('tissue-specific' expression in tissues grouped into organ systems). band c, Full analysis is shown in Supplementary Table4.
Spatial Information We compiled previous classifications (Table  3. 2) and mapped key ones, using QGIS (QGIS Development Team 2021) and R (R Core Team 2021) for georeferencing and analyzing spatial data. We retrieved several spatial features from Open Street Map (OSM) database using "osmdata" R package  (Padgham et al. 2017 ) and other freely available shapefiles (see https://github. com/gdauby/stpa_ecosystems_review for further details on codes and data sources).
Spatial analyses were conducted in R using the packages "sf" (Pebesma 2018), "cleangeo" (Blondel 2019), and "sp"  (Bivand et al. 2013 ) (see R codes here: https://github. com/gdauby/stpa_ecosystems_review). Annobón was excluded from this analysis because rainfall data was missing, but we still discuss similarities with the other islands.
Mapping Non-Forested Areas To evaluate developmental stages of terrestrial ecosystems on Príncipe and São Tomé, an important first step was to identify forested and non-forested areas. To do so, we uploaded very high-resolution satellite images (sentinel-2) (https://peps. cnes. fr/rocket/) from the long dry season to minimize cloud cover. Then, we used eCognition software (Trimble Inc. ) to segment spectrally homogeneous non-forest polygons based on thresholding normalized difference vegetation index. The resulting polygons were manually checked using Google Earth and added to a vector layer, which was combined with the land cover types retrieved from OSM, namely polygons identified as "scrub" and various built categories (residential, commercial). We also selected the "roads" tag polylines to be converted into polygons by adding a 10 m buffer.
3. 2; Table  3. 2), which were not included in the classification process, but were overlaid over the regional-scale units (Supp. Mat. ). We estimated that secondary forests and shade plantations together cover over half of São Tomé and of Príncipe (28 and 27%, and 26 and 30%, respectively), while native vegetation covers around one-third or less (27 and 35%, respectively). Similar information was not available for Annobón, but recent observations suggest that native vegetation is mostly restricted to the highest peaks. All classifications produced in this chapter and associated resources are accessible online (https://github. com/gdauby/stpa_ecosystems_review).
Discussion Defining ecosystems as discrete units is a simplification  (Boitani et al. 2015)  but can be useful to facilitate our understanding of a complex reality  (Senterre et al. 2021). While the delineation of some ecosystems is straightforward, it is often not the case because transitions are usually not abrupt  (Exell 1944; Monod 1960). The classification we propose tries to improve on existing classifications based on the best available data, to provide better baselines for management, and for testing hypotheses regarding biodiversity dynamics. This synthetic classification is thus likely to evolve as more data become available, namely regarding the distribution of ecosystems, and specifically of vegetation types on different islands. For future reference, all maps and spatial information are available on an open-access portal (https:// github.
Acknowledgments This work has been supported by the  Critical Ecosystem Partnership Funding, joint initiative of l'Agence Française de Développement ,  Conservation International , the  European Union , the  Global Environment Facility , the  Government of Japan , and the  World Bank , through the project  CEPF-104130. RFL and FCS were funded by the  Portuguese Government  through " Fundação para a Ciência e a Tecnologia " ( FCT/MCTES -UID/BIA/00329/2021  and  PD/BD/ 140832/2018 ). Data collection in Príncipe was supported by the  Global Trees Campaign  through multiple grants. Some map contains data © OpenStreetMap contributors, www. openstreetmap. org/copyright. GD thanks  Robin Pouteau  for fruitful discussion on several ideas presented in this chapter. We thank  Tania Bird  for revising the language.
Molecular Weight Analysis The molecular weights of DSG and its derivatives were determined using GPC analysis. Briefly, the compounds (20 mg) were dissolved in deionized (DI) water (20 mL), and the compound solution (20 μL) was then analyzed using a Shimadzu LC system (LC-20A column, RID-10A detector) with a TSKgel PWH Aqueous Guard column. The mobile phase was DI water with the flow rate of 0. 5 mL min -1 and the temperature column was set at 60. 0 ± 0. 1 °C. The data were analyzed using Class VP by Shimadzu. The average molecular weights of the compounds were determined comparative to dextran standards with known molecular weights (DS-5000, DS-12000, DS-50000, DS-80000, DS-150000 and DS-270000, Sigma-Aldrich, USA).
The quality and concentration of the RNA was determined by measuring the absorbance at 260/280 nm using a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA). cDNA was converted from RNA (1 μg) using the Revert Aid First Strand cDNA Synthesis Kit (Thermo Scientific, USA) with the thermal cycle at 42 °C for 60 min, and followed at 70 °C for 5 min. Subsequently, mRNA expression was amplified by qPCR using cDNA (1 μL) with specific primer. The real-time qPCR reaction (10 μL) mixture contained the DNA polymerase (1 μL), SYBR green (5 μL), DEPC (3 μL), and each forward and reverse primer (0. 5 μL). Real-time qPCR was performed at 95 °C for 30 s, followed by 40 cycles at 95 °C for 10 s. The data were quantified using Bio-Rad CFX Maestro software (Bio-Rad, USA). All primers used in this study consisted of Ki67, FAK, vimentin, E-cadherin, and GAPDH.
Acknowledgements This work was financially supported by (Grant No. RGNS 64-044 ) the Office of the Permanent Secretary,  Ministry of Higher Education, Science, Research and Innovation (OPS MHESI) ,  Thailand Science Research and Innovation (TSRI) , (Grant No. N42A650206 ) the  National Research Council of Thailand (NRCT) , (Grant No. IN63318 ) the  KKU Faculty of Medicine Invitation Research , and the  Université de Picardie Jules Verne. The authors would like to acknowledge  Dr. Dylan Southard  for editing the manuscript via the  KKU Publication Clinic (Thailand).
| Reads quality filtering and clustering Generation of 18S V4 rDNA operational taxonomic units (OTUs) from the raw sequencing reads (deposited at the European Nucleotide Archive [ENA] under the project id PRJEB48571) and their assembly into a contingency table was obtained according to the following pipeline (https://doi. org/10. 5281/zenodo. 5791089). The paired-end fastq files were demultiplexed and PCR primers were trimmed using Cutadapt v2. 8  (Martin-Jézéquel et al. , 1992). Reads shorter than 100 nucleotides or untrimmed were filtered out. Trimmed pairedend reads were merged using the fastq mergepairs command from VSEARCH v2. 9. 1  (Rognes et al. , 2016)  with a minimum overlap of 10 base pairs. Merged reads longer than 200 nucleotides were retained and clustered into OTUs using Swarm v2.
| Statistical analysis All statistical analyses were performed using R (R version 4. 1. 0, R Development Core Team, 2011). The R package "vegan"  (Oksanen et al. , 2013)  and "data. table" (Dowle and Srinivasan, 2019)  were used to analyse frequency count data, diversity, and to compute variance partitioning. The dbMEM analyses were performed using the packages "ade4"  (Dray & Dufour, 2007) , "adespatial"  (Dray et al. , 2018) , "ape 5. 0"  (Paradis & Schliep, 2019)  and "spdep"  (Bivand & Wong, 2018). All figures were made with "ggplot2"  (Wickham, 2016)  (Figure  S1 , https://gitlab. com/Maria ritaC aracc iolo/rosco ff-astan -time-series). Unlike for cell counts, relative read abundance is considered for the metabarcoding analysis; samples were rarefied at 10. 000 reads.
| Alpha and beta diversity Standard alpha diversity metrics (Shannon Diversity Index and species richness) and beta diversity metrics (Jaccard similarity index and Bray-Curtis similarity index;  Krebs, 1999; Legendre & Legendre, 1998)  were calculated for both the morphological and metabarcoding data sets in order to analyse temporal changes in the composition and structure of the protist communities. Random subsampling (rarefaction) was used for the metabarcoding data set prior to the calculation of alpha diversity metrics (i. e. , species richness and Shannon diversity index) and for the calculation of the Jaccard similarity index in order to account for differences in sequencing depth (i. e. , total number of reads generated for a sample). Hellinger transformed data  (Legendre & Gallagher, 2001)  were used for the calculation of Bray-Curtis dissimilarities.
9% (Sagenista) to 2. 1% (Telonemia) of the total number of OTUs. Other less diverse taxa belonging to 53 classes (e. g. , Pseudofungi, Chrysophyceae, Picozoa, Dictyochophyceae, Bolidophyceae, Centroheliozoa, Radiolaria; see Figure  4b ) accounted for less than 2% of all OTUs (Figure  4d ). Using the metabarcoding data set, we extracted a list of 32 OTUs that we identified as dominant based on their mean monthly contribution to total reads counts (see material and methods). These OTUs contributed to 51. 5% of all reads over the study period (Figure  5a ), and included diatoms, Dinophyceae, Cryptophyta, Cercozoa, Syndiniales, as well as a Chlorophyta, a Picozoa, a MAST (uncultured marine stramenopiles) and a Fungi. Sequences of both photosynthetic armoured (Heterocapsa) and heterotrophic naked (e.
Interestingly, H. rotundata was also identified as a dominant taxon in the adjacent Penzé estuary  (Chambouvet et al. , 2008) , and as most abundant in recent microscopic counts obtained from our time-series station where nanoplanktonic dinoflagellates were targeted (data not shown). Some other dominant dinoflagellate OTUs detected in our metabarcoding data set are either heterotrophic or potentially mixotrophic (Gyrodinium, Gymnodinium, Azadinium, Warnowia etc. ), and some of them are purely parasitic (Syndiniales). The naked dinoflagellates Gyrodinium and Gymnodinium spp. were also identified as prominent members of the phytoplankton community in the stratified waters of the WEC, off Plymouth  (Widdicombe et al. , 2010)  and showed an increasing trend in abundance after 2001 F I G U R E 8 Spearman's correlation calculated between the environmental variables and the RDA axes (a,c), and variance partitioning analyses between environmental drivers and dbMEM (b,d).
, 2021). Likewise, an overestimation of the contribution to sequences reads obtained from natural samples of groups, such as the dinoflagellates, that harbour very high numbers of 18S rRNA gene copies, is a commonly reported bias  (Gong & Marchetti, 2019). However, the two data sets we used are complementary and allowed us to add important information about the dynamics of dominant protists thriving in permanently mixed waters of the Western English Channel. A deeper analysis of species dynamics in the different phyla for the metabarcoding data set will certainly provide more information in the future, especially since reference sequences databases and taxonomic frameworks (required for accurate assignations to genus or species levels) are constantly being updated and curated  (Guilloux et al.
Funding information Grant/ Award Number:  ANR-08-BDVA-0003 ,  ANR-11-BTBR-0008  and  ANR-15-CE01-0009 ;  Centre National de la Recherche Scientifique , Grant/Award Number:  INSU EC2CO CYCLOBS ;  Office Français de la Biodiversité , Grant/Award Number:  INDIGENE OFB. 20. 0552 ; Sorbonne Université ;  Horizon 2020 ;  European Union , Grant/Award Number: Assemble+, Horizon2020 , GrantN°287589 ;  Gordon and Betty Moore Foundation UniEuk , Grant/Award Number:  GBMF5275.
DATA AVA I L A B I L I T Y S TAT E M E N T Raw environmental data: https://meteofrance. com/, https:// www. somlit. fr/en/, https://www. somlit. fr/en/, https://data. shom. fr/, https://modis. gsfc. nasa. gov/data/dataprod/, and https:// coast watch. pfeg. noaa. gov/. Microscopic counts file: https://doi. org/10. 5281/zenodo. 5033180. Metabarcoding raw reads: European Nucleotide Archive (ENA): PRJEB48571. rDNA 18S V4 OTU table: https://doi. org/10. 5281/zenodo. 5032451.
Protists DNA metabarcoding (size fraction >3 μ m) (i. e. , Fragilaria/Brockmaniella or Cylindrotheca closterium/Nitzschia lon- gissima). The final morphological data set (https://doi. org/10. 5281/ zenodo. 5033180) (Rigaut-Jalabert et al. , 2021) consisted of counts of 146 taxonomical entities (taxa larger than 10 μm in size) across 185 dates from 2009 to 2016. and the Plankton Net Data Provider (http://www. plank tonnet. eu/). Taxonomic assignation was determined at lowest possible rank (e. g. , species). Raw 2. 4 | For the generation of DNA metabarcoding data, natural seawater microscopic counts were regularly stored in a local MS-Access data- from the Niskin bottle was transported to the laboratory in a 10 L base and uploaded in the RESOMAR PELAGOS (http://abims. sb-rosco Nalgene bottle and a volume of 5 L was collected onto 3 μm poly- ff.
fr/pelag os/) national database. The morphological taxa contingency carbonate membranes (47 mm, Whatman). Filters were preserved table was carefully examined to detect inconsistencies (e. g. , abrupt in 1. 5 ml of lysis buffer (sucrose 256 g/L, Tris 50 mM pH 8, EDTA changes in cell counts over the time series), and taxa for which identifi- 40 mM) and stored at -80°C until further processing. A total of 185 cation was uncertain were grouped into broader taxonomic categories samples were collected between 2009 and 2016.
Surgical procedure Nacre-based paste preparation Nacre powder (mean particle size: 42. 7±5. 1 µm), provided by Stansea (Saint-Etienne, France) and produced from the nacreous part of the shells of the pearl oyster Pinctada maxima, was sterilised at 121°C for 20 min in an autoclave. 28 29 acre powder was reconstituted as follows: 0. 25 mL of autologous blood was sampled and mixed with 1 g of powder just before implantation. The blood was added progressively (drop by drop) and mixed to the powder using a spatula until obtaining a paste to be implanted in the bone defects. The blood/powder ratio was determined during a preliminary feasibility test (data not published).
Acknowledgements We thank  Dr. Luc Malaval ,  U1059   INSERM-SAINBIOSE (Saint-Etienne ), for contributing to the present work with discussions, critical comments and relevant suggestions. We also thank  Sebastien Hupont ,  Institut Jean Lamour ,  UMR7198   CNRS UL(Nancy ), for his contribution to data analysis. We thank  Dr David Eglin  for his critical comments on the manuscript. Contributors DI: investigation, data analysis, writing-original draft preparation and review and editing; NL: investigation, writing-original draft preparation; KDN: writing-review and editing; MN: formal analysis; CM: writing-review and editing; GZ: writing-review and editing; LV: writing-review and editing; DM: conceptualisation, writing-review and editing; MR: supervision, guarantor, funding acquisition, conceptualisation, writing-original draft preparation and review and editing.
Funding We acknowledge the financial support by the  program  ' Maturation nacre Extracts ' ( CCI2007FR162P0015 ) and by the Lorraine region and the  European Regional Development Fund. DI  acknowledges the financial support by project  IDEXLYON of the University of Lyon  in the frame of the ' Programne Investissements D'Avenir'  ( ANR-16-IDEX-0005 ,  IDEX/ELAN/2019/01 ).
n=6 for the test material and n=5 for the control sites. ▲ One site was not used for the analyses, due to its localisation in the cortical bone. # Data relative to the humerus were not considered for the μ-CT analyses to increase data consistency. DocumentationUniversite de St Etienne. Protected by copyright. on September 15, 2022 at Service Commun De http://openscience. bmj. com/ BMJ Open Science: first published as 10. 1136/bmjos-2021-100231 on 15 September 2022. Downloaded from.
2 µm nitrocellulose membranes by semi-dry blotting. Nitrocellulose membranes were blocked in 5% BSA in TBST for 1 h at RT and subsequently incubated overnight with primary antibody in blocking solution at 4°C. The next day, membranes were washed three times with TBST, incubated with IR-dye-conjugated secondary antibodies (DyLight TM , Cell Signaling Technology) diluted 1:15,000 in TBST for 1 h at room temperature. Subsequently, membranes were washed three times before visualization of target proteins using an Odyssey IR imaging system (LI-COR Biosciences). Primary antibodies and concentrations were as follows: Acetylated a-tubulin (AC-TUB) (Cell Signaling Technology, 1:1,000) and ß-Actin (Cell Signaling Technology, 1:15,000). Of note: due to the lack of a reliable EML1 antibody, we were not able to include data on EML1 protein levels.
If not stated otherwise, data analysis was performed using the Seurat (v3. 2. 2) package in R  (Stuart et al, 2019). Count matrix was filtered with following parameters: Any feature that was expressed in less than three cells was removed from the analysis. For the control sample, any cell with < 2,000 expressed features, more than 10% mitochondrial genes expressed, or less than 5,000 total UMI counts was removed from further analysis. For the EML1-heKO sample, any cell with < 1,500 expressed features, more than 10% mitochondrial genes expressed, or < 3,000 total UMI counts was removed from further analysis. The data were normalized using sctransform. Dimensional reduction was performed using UMAP with dims = 1:30. Shared nearest-neighbor graph was constructed with dims = 1:30. Clusters were generated with resolution = 0.
4. Cells were defined by interpreting expression of known marker genes. One group of cells that was defined as   (Hadley, 2016)  and show normalized expression values. For GO violin plots, the genes that are collected in a GO term were retrieved from org. Hs. eg. db (org. Hs. eg. db: Genome wide annotation for Human. R package version 3. 12. 0. ). For each GO term of interest, the expression per cell was calculated as proportion of feature set. For detailed information about the data analysis pipeline in R, refer to https://github. com/ahoffrichter/Jabali_et_al_2021_scRNAseq_analysis. Software and algorithms used for analysis can be seen in Table  6.
Data were normalized to 18S rRNA levels. Primers used to validate EML1patient-specific mutations and the EML1-heKO as well as EML1 expression can be seen in Table  7. Quantitative assessment of 2D and 3D cell cultures Ectopic neural rosettes were quantified based on NCAD staining and localization in the organoid structure. More precisely, accumulation of NCAD at the basal side of VZ areas was defined as ectopic neural rosettes and quantified per VZ area using the cell counter tool in ImageJ. The percent of VZ areas with ectopic neural rosettes was then calculated for the different conditions. Organization of neurons was investigated per cortical area (composed of a VZ and CP area) based on MAPT staining and localization. If the majority of MAPT-positive neurons were found within the CP area, we called them organized.
Acknowledgements We thank  Nadia Bahi-Buisson ,  Grazia Mancini , and  Anne-Gaelle Le Moing  for their contributions of patient samples, and  Carmen Cifuentes-Diaz  for her contribution to EM studies. The work was supported by the  ERA-NET NEURON ,  JTC 2015 Neurodevelopmental Disorders, STEM-MCD  (to JL), the  German research foundation (DFG) , Project  LA 2933/2-1  (to JL), the  French ANR , under the frame of E-Rare-3, the  ERA-Net for Research on Rare Diseases  ( E-RARE18-049 , to FF), the  French Foundation for Medical Research  for a team FRM grant (Equipe FRM to FF), and the generous financial support by the  Hector Stiftung II  (to PK and JL). Open Access funding enabled and organized by Projekt DEAL.
Data availability The sc-RNA-seq data will be deposited in NCBI's Gene Expression Omnibus and will be accessible through a GEO Series accession number. For detailed information about the data analysis pipeline in R and the scRNAseq datasets generated and / or analyzed during the current study, refer to the GitHub repository https://github. com/ahoffrichter/Jabali_et_al_2021_scRNAseq_analysis. Any additional information required to reanalyze the data reported in this study is available from the lead contact upon request. Expanded View for this article is available online.