dataesr/scientific-paragraphs-categorization huggingface dataset:
DKP at Work: Simulating Hazard Assessment In order to illustrate the value of the platform, we describe how it could be carried out on a practical case. We ran a co-design cycle on the basis of the platform on a virtual, though believable, example that we assumed to be located in the French West Indies. For many years, habits and customs have been built in the agricultural sector in order to fit the reality of the two traditional seasons. Nevertheless, it is quite strong evidence that agricultural practices have to adapt to the new climate in the short-and midterm. In this context, it is quite appropriate to develop climate services leveraging geographic information from the DKP database to provide support in the search for local adaptation solutions in this field. Let us assume that the context for this example of DKP implementation is related to drought hazard that may occur at times when sugarcane fields could severely suffer.
The DKP gives the capacity to geographically display this information. For instance, Figure  14  simulates the display of standardized-precipitation-index (SPI) indicator projections at various times in the future. The SPI is a drought indicator that can be easily interpreted  [14] [15] [16]. At a near-zero value, the SPI expresses a level of humidity that could be qualified as normal in comparison with past records. If the value ever reaches -1, we can consider a dry period that continues until it becomes positive again. Figure  14  displays simulated data in a given area that gathers hectares of sugarcane fields. On the basis of that information, in Figure  14a , stakeholders would observe a high SPI for September 2020, an encouraging prediction for farmers. In March 2033 (Figure  14b ), however, the SPI is near 0, and thus near the observed normal.
Conclusions In this paper, we presented the INNOVA Data and Knowledge Platform (DKP) developed within the framework of European project INNOVA that was built to meet the needs of the four hubs of the project, but which is also an extensible tool that can be tailored to new local issues requiring the development of climate services. We set the scene for supporting climate-service elaboration: a local issue on an identified geographic climate-related risk; -CS providers and end users who have to work in a co-design dynamic, analyze local trends in numerical indicators and other available resources (images, videos, narratives, scientific publications), and assess the risk on the basis of visualizations and geographic representations. The originality of the DKP lies in (i) its NoSQL data structure and its visual interface that both allow to geographically upload, store, and stage a wide variety of information on weather, climate change, and related domains; and (ii) the geographic information that it is able to deliver to stakeholders: resources (raw data, documents, pictures, videos) associated to an area or GPS point that matches their queries to assess the impact of climate change by visualization.
The DKP offers, in a way, GIR functionalities by enabling the exploration of large volumes of structured and unstructured data stored in a NoSQL-type database associated to geographic areas. Thus, we presented in detail the three modules that make up the geographic platform to meet the objectives of providing the best possible support for the CS co-design process: to gather geographic and georeferenced information on a given local climate-change case study for further design of solutions towards mitigation, to explore climate data and other categories of digital data into a geographic framework, and act out climate-change scenarios related to a given activity.
Sample preparation for Cryo-EM. The DNA duplex was prepared by mixing equivalent molar amounts of primer (5′-CGCCGGGCCGAGCCGTGC-3′) and template (5′-AGGTCGTGCACGGCTCGGCCCGGCG-3′). The mix was then heated 2 min at 90 °C and slowly cooled to room temperature. PolD-PCNA-DNA complexes were associated by mixing 0. Fig. 6  Comparison of multi-subunit polymerases in Archaea and Eukarya. The binding modes of PCNA and primase are illustrated in a schematic way and the two PIP motifs (cPIP and iPIP). The schematic representations are derived from the structures of the human Polα-primase complex (PDB ID: 5EXR), the human Polδ-PCNA complex (PDB ID: 6TNY), and the P. abyssi PolD-PCNA complex (this study). No structures are currently available for the PCNA-Polε complex, but its schematic representation is supported by the literature  68.
50). Grids were frozen in liquid ethane by using a Vitrobot Mark IV (Thermo-Fischer) at 100% humidity, 22 °C temperature, blotting force 20, and blotting time of 4 s. Cryo-EM data acquisition and image processing. Movies were collected using EPU software on a Titan Krios electron microscope (Thermo Fisher Scientific) operated at 300 kV, on a GatanK2 Summit direct electron detector coupled with a Bioquantum energy filter with 20 eV slit. The defocus range was between -0. 5 and -3. 5 µm, the pixel size was 0. 83 Å/pixel and the total dose was ∼40 electrons/Å 2 , distributed into 40 frames. Image pre-processing until two-dimensional (2D) classification was performed during data acquisition using the Scipion package  56. Images were imported and movie frames were aligned using MotionCor2 (ref.
abyssi PCNA, which was solved in this study using X-ray crystallography at 2. 3 Å resolution (PDB IDs: 5IJL, 5IHE and 6T7X). Concerning the DNA building, an ideal B-form DNA duplex was docked in the density as a starting point. The initial model was then subjected to global real-space refinement program from the PHENIX suite 62 using secondary structure restraints. The refined model was further manually inspected and adjusted in COOT. The final model was validated with statistics from Ramachandran plots and MolProbity scores  63. All figures were prepared using UCSF Chimera  64  and UCSF Chimera X  65. Crystallization, X-ray data collection, and processing. PCNA crystallization trials were performed at 18 °C using the hanging drop vapor diffusion technique in 2 µL drops (1 : 1 reservoir to protein ratio) equilibrated against 500 µL of reservoir solution.
For the PCNA-cPIP complex, PCNA was pre-incubated 30 min with a twofold molar excess of cPIP (KKRVISLEEFFS) (Smart Bioscience) in buffer E prior crystallization trials. PCNA crystals were obtained in 20% PEG 400, 0. 2 M CaCl 2 , and 0. 1 M MES pH 5. 5 with a PCNA solution at 5 mg mL -1 , whereas PCNA-cPIP complex crystals were obtained in 30% PEG 400, 0. 2 M MgCl 2 , and 0. 1 M Bis-Tris pH 7. 1 with a PCNA-cPIP complex solution at 10 mg mL -1. The crystals were cryoprotected by soaking in a 1 : 1 paraffin : paratone oil mix. X-ray data were collected at the European Synchrotron Radiation Facility on beamlines ID23 and ID29 and at the SOLEIL synchrotron on beamlines PX1 and PX2. Data sets were indexed using XDS, scaled and merged with Aimless (from the CCP4 program suite (Collaborative Computational Project 1994)  66  , and corrected for anisotropy with the STARANISO server (staraniso.
globalphasing. org). PCNA X-ray structure was solved by molecular replacement using the structure of PCNA from P. furious (PDB ID: 5AUJ). Molecular replacements were carried out with the Phaser program from Phenix  62  and subsequent rebuilding and refinement were achieved with COOT 61 and BUSTER  67. Coordinates and structure factors of the PCNA and cPIP-bound PCNA structures from P. abyssi were deposited in the Protein Data Bank under accession codes 6T7X and 6T7Y, respectively. BLI assays. BLI experiments were performed on an Octet RED384 instrument (ForteBio). His-MBP-fused DP2 constructs were captured at a 1. 5 nm density on Ni-NTA biosensors. Binding to PCNA and PriS-PriL(1-210) proteins was monitored for 300 s at 25 °C in buffer E supplemented with 0. 2 mg mL -1 bovine serum albumin.
Seven proteins concentrations were assayed (31. 25, 62. 5, 125, 250, 500, 1000, and 2000 nM) and a buffer-only reference was subtracted from all curves. Affinities were determined by fitting the concentration dependence of the experimental steady-state signals, using the Octet RED data analysis v11 software (ForteBio). Surface plasmon resonance assays. Surface plasmon resonance experiments were performed using a Biacore T200 instrument (GE Healthcare). All measurements were performed at 25 °C in buffer E supplemented with 100 µM EDTA. A series S sensor chip NTA (GE Healthcare) was used to immobilize ~2000 RU of His-tagged PCNA and PriS-PriL(1-210) on two of the four flowcells, and Histagged MBP on a third as a reference. Ten concentrations (0, 0. 75, 1. 5, 3, 6, 12, 25, 50, 100, and 200 µM) of cPIP-peptide (KKRVISLEEFFS) (Smart Bioscience) were injected for 30 s at 30 µL min -1 over the three flowcells.
Products were resolved on 1% (w/v) alkaline agarose gel. DNA markers (2-Log DNA ladders, New England Biolabs) were loading in 45% deionized formamide and 10 mM EDTA, and run into the same gel at 4 °C for 14 h 30 min at 30 V. After electrophoresis, gels were stained with SYBR Gold (Invitrogen). Gels were first scanned with a Mode Imager Typhoon 9500 (GE Healthcare) for Cy5 to visualize the Cy5-labeled products and then scanned for SYBR Gold for detecting the ladders. Full-length 7249 bp (%) corresponds to the intensity of 7249 bp bands as a percentage of total lane intensity. In all cases, the background value was subtracted. The mean of percentage ± SD of full-length 7249 bp (%) from three independent experiments were obtained (the raw data are provided in Supplementary Table  2 ). Reporting summary.
Acknowledgements We wish to thank  Dr Ravi Nookala  for helpful advices and  Jéril Degrouard  ( Laboratoire de Physique du Solide, Orsay, France ) for help with preparing the samples. The cryo-EM data were collected at the  Swedish National Cryo-EM Facility  funded by the  Knut and Alice Wallenberg Foundation ,  Family Erling Persson  and  Kempe Foundation ,  SciLifeLab ,  Stockholm University , and  Umeå University. We thank  Ahmed Haouz  and the  Pasteur Institute crystallization platform  for help with preparing the crystals, and the staff at Soleil (PX2 beamline) and  European Synchrotron Radiation Facility (ESRF)  synchrotrons (ID23-1 beamline) for data collection. We thank  Dr Eaazhisai Kandiah  and  Dr Gregory Effantin  at the CM01 cryo-EM facility at  ESRF , where initial screening and data collection were performed.
The work and post-doct fellowship of C. M. are funded by an  ANR   JCJC grant   ANR-17-CE11-0005-01. The work is also funded by  Institut Pasteur ,  Ifremer , and the  Swedish National Research Council  (grant number  2017-04641 ). The fellowship of P. R. was funded by  Sorbonnes University   ED515  and Fondation pour la recherche médicale.
Data availability Coordinates and structure factors for the PCNA and the PCNA-cPIP complex were deposited in the Protein Data Bank under the accession codes PDB 6T7X and PDB 6T7Y, respectively. The cryo-EM map of the PolD-PCNA-DNA ternary complex is deposited in the Electron Microscopy Data Bank under accession code EMD-10401. The atomic coordinate of the complex are deposited in the Protein Data Bank under accession code PDB 6T8H. The source data underlying Figs. 4c, d  and 5a-c , and Supplementary Figs. 6  and 7 , and Supplementary Table  2  are provided as a Source Data file. Other data are available from the corresponding authors.
Collection of Mammalian HERC5 and HERC6 Orthologous Sequences Full-length HERC5 and HERC6 coding sequences were analyzed in bats, rodents, primates, artiodactyls, and carnivores. HERC5 and HERC6 coding sequences from each group were obtained using the Little Brown bat (Myotis lucifigus), mouse (Mus musculus), human (Homo sapiens), cow (Bos taurus), and dog (Canis lupus familiaris) Refseq proteins as queries, respectively, through tBLASTn searches of the "Nucleotide" database in GenBank  (40, 41). The species and accession numbers are presented in Supplementary Table  1.
Characterizing the Evolution of HERC5 and HERC6 Synteny in Mammals The genomic locus of HERC5 and HERC6 genes in Little Brown bat, mouse, human, dog, and cow were obtained from Ensembl (http://www. ensembl. org/index. html), and GenBank Refseq genome database  (41). Their coding sequences were used as queries for BLASTn searches against a total of 110 whole genome assemblies from bats, rodents, primates, carnivores, and artiodactyls (Supplementary Table  1 ) available in GenBank database  (41). We analyzed eight additional mammalian genomes from Proboscidea, Lagomorpha, Scandentia, Perissodactyla, Sirenia, Soricomorpha, Eulipotyphla, and Tubulidentata orders (Orycteropus afer, Loxodonta Africana, Trichechus manatus, Tupaia chinensis, Condylura cristata, Ceratotherium simum, Equus przewalskii, and Sorex araneus, respectively) (Supplementary Table  1 ).
The synteny of HERC5 and HERC6 genes was analyzed and visualized through BLAST searches against the 110 annotated genomes in GenBank database  (41). Newly identified HERC-like paralogs (see Results) were confirmed by blasting and aligning their whole sequence (intron and exon regions) to the genomic region containing HERC5 and HERC6 genes in three bat species (Myotis lucifigus, Rhinolophus ferrumequinum, and Phyllostomus discolor), and three rodent species (Chinchilla lanigera, Mastomys coucha, Cavia porcellus), which are representative genomes with good assembling quality (based on the N50, the number of scaffolds, and sequencing coverage). HERC5 and HERC6 orthologs as well as HERC-like paralogous sequences were aligned for each mammalian order separately using the program MACSE  (42) , and the alignments were manually curated.
A phylogenetic tree was then built for each gene and mammalian order, and for a combined dataset of HERC5 and HERC6 genes (rooted with HERC3 as an outgroup, which is the most closely related gene to HERC5 and HERC6), using the maximum likelihood method implemented in the ATGC-PhyML Web server  (43). Each phylogenetic tree was based on the best substitution model (GTR+G+I), as determined by the Smart Model Selection (SMS) program in PhyML  (44)  and node statistical support was computed through 1,000 bootstrap replicates.
Positive Selection Analyses of HERC5 and HERC6 Coding Sequences in Mammals To determine whether HERC5 and HERC6 have been targets of natural selection during mammalian evolution, we carried out positive selection analyses on orthologous coding sequences from bats (n = 10 and 13, respectively), rodents (n = 11 and 16), primates (n = 19 and 20), carnivores (n = 20 and 23), and artiodactyls (n = 21 and 11). As combining highly divergent sequences for positive selection analyses can lead to misleading results, we performed positive selection analyses on separate dataset for each mammalian order. For Artiodactyls, three different datasets were analyzed: the first dataset included all the available species, the second was restricted to cetacean species, and the third excluded the cetaceans. Analyzing each mammalian order separately allowed us to qualitatively compare the evolutionary profile of both genes in each mammalian order.
We first tested for positive selection at the gene level using two different methods available in the Codeml program, which is implemented in the PAML package  (48). This program allows both gene-and site-specific detection of positive selection by comparing constrained models that disallow positive selection (models M1 and M7) to unconstrained models allowing for positive selection (M2 and M8). We ran the different models with the codon frequencies of F61 and F3x4 with a starting omega w (dN/dS ratio) of 0. 4. Likelihood ratio tests were computed to compare the models (M1 vs M2 and M7 vs M8), and codons evolving under significant positive selection (dN/dS >1) were identified using the Bayes Empirical Bayes (BEB) with a posterior probability ≥0. 95. The residues under positive selection were further assessed using two other methods, the Fast, Unconstrained Bayesian AppRoximation for Inferring Selection (FUBAR)  (49)  and the Mixed Effects Model of Evolution (MEME)  (50) , both implemented in the HYPHY package.
ACKNOWLEDGMENTS We are particularly grateful to  Adil El Filali  ( UMR5558 ) for his help on genomic analyses, and  Laurent Duret  ( UMR5558 ) for his comments on the study. We thank  Andrea Cimarelli , head of the  Host-Pathogen Interaction during Lentiviral Infection team at the CIRI Lyon , as well as the members of the " Intercontinental Retrovirus Zoomposium " for helpful discussion. We also thank all the contributors of the LBBE (UMR5558) bioinformatic server and the publicly available genome sequences. We would also like to.
FUNDING This work is funded by the  ANR LABEX   ECOFECT  ( ANR-11-LABX-0048  of the  Universitéde Lyon , within the  program Investissements d'Avenir  [ ANR-11-IDEX-0007 ] operated by the  French National Research Agency). LE is further supported by the  CNRS  and by grants from  amfAR  ( Mathilde Krim Phase II  Fellowship no. 109140-58-RKHF ), the  Fondation pour la Recherche Medicale  ( FRM Projet Innovant no. ING20160435028 ), the  FINOVI ("recently settled scientist" grant ), the  ANRS  (no. ECTZ19143  and  ECTZ118944 ), and a JORISS incubating grant. DP is supported by the  CNRS , the  European Regional Development Fund (ERDF)   2018-4512410/ convention P-2020-BAFE-9  and  2018-4729510/convention P-2020-BAFE-23 , and the  ANR EBOFAC.
Patients and Sample Collection We conducted a monocentric prospective study involving 53 renal transplantation procedures performed in the Department of Urology and Renal transplantation, La Conception University Hospital in Marseille, France from 2016 to 2018. For each renal transplant, the stromal vascular fraction (PRAT-SVF) was isolated from the perirenal AT collected during kidney procurement and submitted to analysis of cellular components, transcriptomic profile and vasculogenic activity. The study was approved by the National Ethics Committee of the Agence de la Biomédecine (ABM), the National Ministry of Research and adhered to the Jardé Law on human investigation. All procedures were conducted in compliance with the Declarations of Helsinki and Istanbul. Data were prospectively and anonymously collected in a dedicated database for the exclusive access of the authorized authors.
Clinical Variables The following demographic data were recorded for donors and recipients: sex, age, body mass index, blood group, serum creatinine, cardiovascular risk factors (history of smoking, hypertension, dyslipidemia, diabetes mellitus, coronary heart disease. Renal function (serum creatinine, glomerular filtration rate) were recorded at D7, M1, and M12 during renal transplantation follow-up. The CKD EPI formula was used to evaluate renal function in adults and the Schwartz formula was used in younger recipients (<18 years)  (27).
FastQC (version 0. 11. 5) was used to examine the read quality. Trimming of reads was performed using Trimmomatic (version 0. 33) on the base of an average phred quality of 20. The raw single-ends reads were then mapped against the human genome (GRCh38. 90) from the Ensembl database using STAR (version 2. 5. 3a) sequence mapper. The resulting BAM files were examined by Qualimap (version 2. 2). Duplicates reads were removed using the function MarkDuplicates of picard tools (version 2. 9. 0). Unduplicated reads were used to count reads per gene with FfeatureCounts (version 1. 5. 2). Raw counts are converted in reads per Million (RPKM) and log transformed (log base 2) in order to help with distributional assumptions, linearity and consistency with PCR based methods for calculating the Fold Change.
Genes of interest were filtered using a mean RPKM >25 and a coefficient of variation >50% (1183 genes passed out). R/Bioconductor packages including DESeq2 were used for gene expression analysis. Finally, we selected differentially expressed genes with a P-value <0. 05 and a Fold Change (FC) of a least 1. 5. Genes up or downregulated were separately subjected to functional annotation analysis using the Database for Annotation Visualization and Integrated Discovery (DAVID, david. ncifcrf. gov/) online tool to find significantly enriched genes biological functions and associated pathways. Gene Ontology: Biological Process and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis was performed with a cut-off criteria for the threshold of EASE score< 0. 05 (modified Fisher Exact P-value).
0. 2. 16120). As previously described  (30) , various parameters that reflect the relevant steps of SVF vasculogenic/angiogenic capacity in vitro, were quantified: the number of clusters, indicative of the capacity of the plated cells to self-assemble; the number of clusters with tip cells, indicative of the ability of cells to undergo specialization into cells able to migrate away from the cluster and initiate sprouting; the number of clusters with stalk cells that represent the capacity of cells to proliferate and elongate neovessels; and the number of branching points that provide information on the capacity of cells to develop as complex vascular networks. Cell clusters were automatically counted using Fiji software under cellular analysis with minimum object size set at 500 µm and a maximum object size set at 3,000 µm.
Las X software was used during all image acquisition procedures. Image processing prior to image measurements was performed with Huygens Essential deconvolution software (Scientific Volume Imaging,) using up to 40 iterations of the classical maximum likelihood estimation algorithm, with a theoretical PSF and automatic background correction. The images were then analyzed using the Sprout Analysis plug-in developed by Eglinger et al. (32)  in the Fiji distribution of ImageJ, to evaluate the different vascular parameters, such as sprout length and branch points.
Statistical Analysis Statistical analysis comparing continuous variables in 2 groups was performed using non-parametric Wilcoxon-Mann-Whitney test and categorical variables with Chi2 test using Xlstat R version 2018. 5 (Addinsoft, Paris, France) and Graphpad Prism R version 7 (GraphPad Software, California, USA). Categorical variables were presented as frequencies and continuous variables as the mean ± standard deviation (SD) or median and 10-90 or 25-75 percentile according to the test of normal distribution using the Kolmogorov-Smirnov test. Spearman rank correlation was used to evaluate the associations between quantitative parameters analyzed in PRAT-SVF and age or creatinine or CKD evaluated in the recipient at D7 and M1 post-transplant. Only variables with P < 0. 20 were considered. Significant differences were considered when the P-value was < 0.
Transcriptomic Analysis of PRAT-SVF Identified Inflammatory Profiles Specific to ECD Donors A comparative RNAseq transcriptomic analysis was performed to compare the PRAT-SVF molecular transcripts in ECD and non-ECD donors (Supplementary Table  3 ). Volcano plot distinguished a significant differential gene expression profile based on the comparison of ECD and non-ECD patients (Figure  3A ). Overall, differential expression analysis revealed 245 genes showing fold change (FC) values ≥ 1. 5 (111 genes overexpressed in ECD vs. non-ECD, Supplementary Table  4 ) and FC ≤ -1. 5 (134 genes under-expressed in ECD vs. non-ECD) with P ≤ 0. 05 (Supplementary Table  5 ). To provide a cohesive view of the biological functions associated with the changes in the ECD-SVF gene expression profile, we conducted a gene ontology analysis using the DAVID database.
Statistical analysis This was an observational study so that there was no opportunity to conduct a placebo-controlled trial. However, FCoV shedding records prior to treatment with Mutian X, where available, were used as a retrospective control period. Cats acted as their own controls, which meant that variables such as housing, breed, and age, which affect FCoV shedding, were constant. Fisher's exact test (two-tailed) and correlation coefficient were carried out using the statistics package in Excel (Microsoft Office 2007).