dataesr/scientific-paragraphs-categorization huggingface dataset:
Flow cytometry and cell sorting After perfusion with 10 ml cold, sterile PBS, kidneys from 10-weekold Lkb1 DTub mice and littermate controls or 12-week-old iPkd1 DTub mice, iPkd1 DTub ;iKif3a DTub mice, and littermate controls were harvested, de-capsulated, minced with fine scissors, placed into a mixture of collagenase D (Worthington Biochemical) and DNase I (Roche), and shaken at 37°C for 35 min, as previously described  (Triantafyllopoulou et al, 2010). The cell suspension was centrifuged at 500 g for 6 min at 4°C. Cells were then triturated through nylon mesh to obtain single-cell suspensions and stained with primary antibody, DAPI (Molecular Probes) followed by Streptavidin V500 (BD Horizon) to label biotinylated antibodies. Flow cytometry was performed using a BD LSR Fortessa cytometer (BD Biosciences), and the results were analyzed with FlowJo software (TreeStar).
MS data analysis The MS raw data files were uploaded into the MaxQuant software version 1. 4. 1. 2 (Cox & Mann, 2008) , which performs peak detection and generates peak lists of mass error-corrected peptides and database searches. A full-length mouse database containing common contaminants, such as keratins and enzymes used for in-gel digestion (based on IPI human version 3. 68), was employed, carbamidomethylcysteine was set as fixed modification, and methionine oxidation and protein amino-terminal acetylation were set as variable modifications. Three miss cleavages were allowed, enzyme specificity was trypsin/P, and the MS/MS tolerance was set to 0. 5 Da. The average mass precision of identified peptides was in general < 1 ppm after recalibration. Peptide lists were further used by MaxQuant to identify and relatively quantify proteins using the following parameters: peptide; and protein false discovery rates (FDR) were set to 0.
Mouse microarray gene expression Total RNAs were extracted and purified from 5-week-old control and Lkb1 DTub kidneys (five mice per genotype), using RNeasy Mini Kit (Qiagen) according to the manufacturer's protocol. RNA quality was checked using a Fragment Analyzer TM (Advanced Analytical Technologies). cDNAs were generated and hybridized on Affymetrix GeneChip â Mouse Transcriptome Arrays 1. 0 according to the GeneChip â WT Plus assay protocol. Arrays were normalized using the Robust Multichip Algorithm (RMA) and mapped to transcript clusters using the R Bioconductor packages oligo  (version 1. 36. 1; Carvalho & Irizarry, 2010) and mta10sttranscriptcluster. db (version 8. 4 ). Downstream analysis considered only Entrez-annotated transcripts. If multiple transcripts mapped to the same Entrez ID, the one with the largest interquartile range between samples was chosen.
Microarray and RNAseq data analysis Principal component analysis was done using the R labdsv package (version 1. 8). Counts from the RNAseq data were log2-transformed prior to analysis, and only the 1,000 most varying transcripts were considered. Differential gene expression was calculated using limma (Ritchie et al, 2015) and sleuth  (Pimentel et al, 2017)  for the mouse microarray and dog RNAseq data, respectively, using q < 0. 05 as significance cutoff. Functional enrichment of differentially regulated genes was calculated from a conditional hypergeometric test as implemented in the R Bioconductor GOstats package  (Falcon & Gentleman, 2007)  (P < 0. 05), and semantic similarity among GO terms was measured via the GOSemSim package  (Yu et al, 2010).
early development and cancer (Fig EV3C and Dataset EV4), while the 41 downregulated genes represented mostly metabolic processes (Dataset EV5). Surprisingly, the upregulated genes revealed clusters incorporating immune response, cytokine production, and immune cell migration (Fig EV3C). The two most highly upregulated genes in the renal tubular cell-derived set that were co-regulated in vivo were the cytokine-inducible transcription factor ANKRD1 (ankyrin repeat domain 1) and the inducible cytokine CCL2 (chemokine (C-C motif) ligand 2; Fig 3B and Dataset EV3).
Acknowledgements We thank  Barbara Joch  and  Temel Kilic  for technical assistance and  Prof. David Bennett  for critical appraisal of the manuscript. We thank the histology facility (S. F. R. Necker INSERM  US24, Paris, France ) for technical assistance and the  ARC Foundation  for funding the whole slide scanner. The grant support received was as follows:  Amandine Viau :  ERA-EDTA ALTF 84-2011  and  FRM ARF20150934110 ;  Frank Bienaimé :  EMBO ALTF 927-2013 ;  E. Wolfgang Kuehn :  Else Kröner-Fresenius-Stiftung   2011_A87  and  Deutsche Forschungsgemeinschaft   KFO 201 ,  KU 1504/5-1 ,  1504/7-1  and  SFB1140 ;  Melanie Boerries  and  Hauke Busch :  Deutsche Forschungsgemeinschaft   SFB850 ;  Antigoni Triantafyllopoulou :  Deutsche Forschungsgemeinschaft   SFB1160 ;  Tobias B. Huber :  Deutsche Forschungsgemeinschaft   CRC1140 ,  CRC 992 ,  HU 1016/8-1   and Heisenberg program ,  BMBF  ( 01GM1518C ),  European Research Council (ERC)  Grant  616891 , and  H2020-IMI2  consortium BEAt-DKD;  Michael Köttgen :  SFB 1140  and  SFB 152 ;  Hauke Busch :  Deutsche Forschungsgemeinschaft   EXC306/2 ; and  Melanie Boerries :  German Federal Ministry of Education and Research (BMBF)   FKZ 01ZX1409B.
▸Figure 2. 2 Figure 2. LKB1 interacts with ANKS3, NEK7, and NPHP1. A LKB1 interactors identified by a proteomic screen in mouse kidney medulla. See also Datasets EV1 and EV2. B Immunoprecipitates (IP) from mouse kidney medulla. ANKS3 and NEK7 are enriched in the LKB1 precipitates. Representative Western blot of three independent experiments. Expected molecular weights: ANKS3 (72 kDa), LKB1 (54 kDa), and NEK7 (35 kDa). C Representative confocal microscopy images of primary cilia (acetylated tubulin expressing, Ac-Tub) and ANKS3 in MDCK cells expressing inducible shRNA against Anks3 (Anks3-i1) after tetracycline induction (Tet). Scale bar: 5 lm. See also Appendix Fig S3F. D Quantification of ANKS3-positive primary cilia in Anks3-i1 MDCK cells. Blinded quantification of five fields of view per biological replicate.
ACKNOWLEDGEMENTS The authors acknowledge  ACTRIS2  project (grant agreement No  654109 ). Figure 5: INP concentrations during the event. Dots: UAS measurements. Triangles and stars: lidar estimations based on N12 and D15 respectively. Peach dotted squares correspond to N12 and  D15 INPC  values for the air mass at the height of  UAS  flight (2. 55 km a. g. l. ). Blue dotted circles correspond to INP estimates at the actual heights of Temps -20, -25 and -30 °C during the time window 1-2  UTC.
The microbubble concentration and size were determined using particle counting in ImageJ software. 5 as-Encapsulated Amount. The amount of perfluorobutane gas encapsulated into the microbubbles was determined by densimetry, before and after microbubble destruction  21  with a DMA4500 densimeter (Anton Paar GmbH, Graz, Austria). For each formulation studied, freshly prepared microbubbles were first diluted in a 5% sucrose solution (6×), then introduced into the oscillating U-tube, and their density was measured at 20 °C( ρ microbubbles ). Microbubbles were then burst in a water bath sonicator to eliminate the encapsulated perfluorobutane, and the density of the remaining solution was measured (ρ solution ). Measures were repeated with eight different batches of the same formulation. The encapsulated perfluorobutane volume fraction percentage  21   Microbubble Acoustic Properties.
To determine the microbubble acoustic response under ultrasound, a homemade setup was designed using a 1 MHz Continuous Doppler probe as an ultrasound emission transducer (Blatek, State College PA, 10 mm diameter) connected to a 3Bop ultrasound generator (Image Guided Therapy, Pessac, France) and a 5 MHz transducer for detection (Panametrics, Olympus, Center Valley, 13 mm diameter). Microbubbles or carboxylated polystyrene reference particles (CPC1000; IZON Science) were diluted 30 times in degassed water in a 4 mL cuvette placed between the two transducers, and measurements were repeated 5 times for each formulation. The acoustic response was monitored with a TiePie HS-5 oscilloscope (Tie-Pie, Sneek, Netherlands) and processed with Matlab (Mathworks, Natick) with the following ultrasound emission parameters: an acoustic pressure of 375 kPa, with a duty cycle of 10% and 20 pulses each of 100 μs.
Indeed, the DMAPAP/Atri2000 isotherm should at least overlap the DMAPAP isotherm to justify a monolayer behavior. The qualitative interpretation of the compression isotherms presented so far can also be confirmed by some interfacial parameters that are determined from the isotherms. The corresponding data give insights into the physicochemical properties of the layer studied as a function of the surface pressure of the interfacial film. From such data gathered in Table  2  for DMAPAP and the mixed DMAPAP/Atri films, one can confirm that Atri500, Atri1000, and Atri2000 interact with DMAPAP at a hydrophilic/hydrophobic interface. Excess Gibbs energies of mixing  20, 32  (ΔG excess ) are negative for the three systems, indicating attractive interactions between the lipid and the copolymer and higher thermodynamic stability of the mixed monolayer (i.
30, 31 e latter interpretation is consistent with the fact that the mean molecular area at the film collapse is 20% lower in presence of Atri2000 compared with pure DMAPAP. All calorimetric and interfacial data converge toward the fact that nondenaturing interactions can be established between DMAPAP and Atri500 or Atri1000, stabilizing the monolayer. This is not the case with Atri2000. Such results validate the choice of DMAPAP/Atri500 or DMAPAP/Atri1000 lipid shells to stabilize gas microbubbles dispersed in aqueous solutions. Microbubble Characterization by Optical Microscopy. Monodisperse microbubbles were obtained with the cationic lipid combined with Atri500 and Atri1000. Their mean size was unchanged in comparison with the microbubbles formed with the cationic lipid alone (2. 5 ± 0.
The amount of encapsulated gas is usually not given for commercial microbubble formulations. However, the present study may allow discrimination between the different   microbubble formulations prepared. Performing densimetry experiments, the amount of gas has been quantified in each formulation composed of the cationic lipid alone or the cationic lipid and Atri500 or Atri1000 mixtures. Data presented in Figure  8  shows that results were significantly different between the three groups. The percentage of gas volume fraction was increased by a factor 2 with the addition of Atri500 and by a factor 3 with the addition of Atri1000. As the size of the microbubbles was unchanged despite the presence of the copolymers, a higher volume of gas means that the gas was more condensed within the microbubbles.
ApoE Genotyping and TREM2 Sequencing Blood samples were collected from LOAD patients and controls. Genomic DNA was extracted from blood by the salting-out method  [7]. ApoE genotyping was performed as previously described  [8]. Exon 2 of TREM2 gene was amplified by polymerase chain reaction (PCR) from genomic DNA using two primers: 5′-TGAATGAATGTCTCCTCCCCAG-3′ and 5′-CAGCCACT-GCCCACTCA-3′, under the following reaction conditions: denaturation at 95 ° C for 5 min followed by 30 cycles of 95 ° C for 30 s, 60 ° C for 30 s, and 72 ° C for 30 s, and a final cycle of 7-min extension at 72 ° C. PCR products were purified and sequenced using the BigDye Terminator Cycle Sequencing Kit v1. 1 on a 3,500xl Genetic Analyzer DNA sequencer (Applied Biosystems, Foster City, CA, USA). Sequences were analyzed using the SeqScape software (Applied Biosystems) and compared to the TREM2 GenBank reference sequence (NM_018965).
In silico Tools The impact of TREM2 variations was predicted using SIFT (Sorting Intolerant From Tolerant; http://sift. jcvi. org/www/SIFT_ chr_coords_submit. html), PolyPhen-2 (Polymorphism Phenotyping v2; http://genetics. bwh. harvard. edu/pph2/) and Pre-dictSNP2 (http://loschmidt. chemi. muni. cz/predictsnp2/) [9]. Frequencies of variants were reported from exome database (1,000G; http://browser. 1000genomes. org/index. html), ExAC (http://exac. broadinstitute. org/), EVS (http://evs. gs. washington. edu/EVS/), and Great Middle East (GME) Variome Project database (http:// igm. ucsd. edu/gme/data-browser. php) including 508 samples from North Africa.
Demographic characteristics and ApoE genotypes of Tunisian LOAD patients (n = 172) and controls (n = 158) are summarized in Table  1. Both groups had similar sex ratios and similar mean ages. ApoE genotyping showed a Thr96Lys was also identified in controls. All variants were present in the heterozygous state and were detected in separate individuals (no individuals carried 2 or more variants). Among the 14 patients carrying the TREM2 variants, 11 had a positive family history of AD (Table  3 ). The frequencies of each variation in the population's exome sequencing database are detailed in Table  2. No common variants having minor allele frequency (MAF) > 5% were found, and 2 variants (p. Val126Gly and p. Asp-39Glu) were rare, with MAF < 1%. Three variants were predicted to have a probable damaging effect (p.
However, a previous case-control study of large European American descent cohorts showed that in addition to p. Arg47His  [10] , p. Arg62His is a risk factor for AD. The variant rs2234253 (p. Thr96Lys), detected in Belgian, French, and Japanese populations (Table  5 ), was not shown to enhance AD risk. In our study, p. Thr96Lys was the unique variant detected in controls and LOAD patients with equivalent MAF. According to public exome databases, this variant was more common in African populations (MAF = 14. 5%, 1,000G) than other populations, but was not present in North African populations of the GME (Table  2 ). Two rare variants predicted to be deleterious, p. Asp-39Glu and p. Val126Gly, were identified in our study. The p. Asp39Glu was previously reported in 2 AD patients at the heterozygous state  [11] , while homozygous p.
Val-126Gly was reported in 2 patients with Nasu-Hakola disease  [12]. Among the 5 variants, variant Gln109Gln was not found in any of the exome databases. However, this variant is synonymous and does not change the amino acid sequence of TREM2 protein. The previously reported rare variant rs75932628 (p. Arg47His), identified to be a risk factor for AD in Caucasian populations (Table  5 ), was not found in our cohort. Our findings are in agreement with published reports of African American, Iranian, and East Asian populations (Table  5 ) which showed that the p. Arg47His mutation could not be linked to an increased risk of AD. In our study, the lack of association of the p. Arg47His variant may be due to its very low MAF in the Tunisian population. In the GME database, this genetic polymorphism had a global MAF of 0.
Then, in a second step, we established orthologous relationships to identify genes with similar functions that display differential expression in nickel hyperaccumulators from distant families. We harvested leaves of 7 nickel hyperaccumulators from 5 genera (i. e, Noccaea, Psychotria, Geissois, Homalium, Leucocroton) corresponding to distant plant families in their natural environment in France, New Caledonia and Cuba (Figure  1 , Table  1 ). For each hyperaccumulator, we collected leaves of a closely related species or ecotype not accumulating nickel in the same geographic area. To identify genes differentially expressed between closely related nickel hyperaccumulator and non-accumulator species, we performed RNA sequencing (RNA-Seq) using Illumina paired-end technology (Supplementary Data 2).
In absence of genomic sequence references for most of these species, we de novo assembled the short Illumina reads into the sequence of expressed genes in the 13 selected species. These newly generated transcriptomes contain between 41,843 and 87,243 contigs for a total assembly size ranging from 35 Mbp to 49 Mbp (Table  1 , Supplementary Data 2). Subsequent annotation of these transcriptomes using Blastx interrogation of Viridiplantae proteins database revealed significant homologies for 56 % of the contigs on average (E-value ≤10E-6). These high-quality reference transcriptomes constitute a unique and comprehensive resource for molecular studies on plant groups of high interest. The reads from each sample were then mapped to the nickel hyperaccumulator reference transcriptome to identify differentially expressed (DE) genes in 8 pairs of species, each containing a hyperaccumulator and a related non-nickel accumulator.
De novo transcriptome assembly and annotation. The transcriptome sequences of Ncfi, Nmon, Pgab, Psem, Pgra, Pcos, Pcle, Gpru, Grac, Hkan, Hbet, Lhav and Lmic were obtained independently by de novo assembly of paired-end reads using CLC Genomics Workbench v9 software (Qiagen). A single library per species was used to minimize genetic variability. Assembly parameters were set as default, except similarity (0. 95), length fraction (0. 75) and the word size was optimized for each sample (Supplementary Data 2). The sequences of the resulting contigs were blasted (Blastx, E-value of ≤ 10E-6) against the Viridiplantae protein database (NCBI) and putative function annotated by Gene Onthology (cut-off = 55; GO weight = -5) using Blast2GO  33. Filtered contigs for length (≥ 200 nt) and expression (TPKM > 1) were translated for the longest Open Reading Frame.
Differential gene expression analysis. For each pair of species, read count estimation was carried out using CLC Genomics Workbench v9 software by mapping sequencing reads of each sample (default parameters, except similarity: 0. 875 and length fraction: 0. 75) to the transcriptome of the nickel hyperaccumulator species used as the reference. Statistical analyses to identify Differentially Expressed Genes (DEGs) were performed using the edgeR Bioconductor package  35. To examine transcript abundance, reads per kilobase million (RPKM) were calculated. To evaluate the influence of the reference transcriptome, we additionally performed swapping analyzes using the transcriptome of the non-accumulator species as reference (Supplementary Data 2, Supplementary Figure  3 ).
Molecular cloning. Predicted full-length coding region of NcfiIREG2, GpruIREG1 and LhavIREG2 were amplified from leaf cDNAs of the corresponding species, using highfidelity Phusion polymerase (Thermo Scientific) with gene specific primers containing AttB recombination sequences (Supplementary Data 11). PCR products were first recombined into pDONOR207 (Invitrogen) and then in pDR195-GTW  36  or pMDC83  37  for expression in yeast and N. caerulescens respectively. Artificial miRNA construct targeting NcfiIREG2 was designed using the WMD3-Web microRNA Designer (http://wmd3. weigelworld. org). The amiRNA was engineered as previously described  38  by PCR using the pRS300 backbone and specific primers (Supplementary Data 11). The NcfiIREG2-amiRNA precursor was recombined in pDONOR207 and then into the vector pK7GW2D  39.
Sequence information for Ccap (sample TIUZ) was obtained from the 1KP project  41. Reference genes for Ncfi (6phosphogluconate decarboxylating 3, 6PGDC, Ncfi_contig_3009; Sister chromatid cohesion PDS5, PDS5, Ncfi_contig_518), and Gpru and Ccap species (Eukaryotic translation initiation factor 3, EIFL3, Gpru_contig_12008; Histone deacetylase 15, HDAC15, Gpru_contig_14406) were selected from our RNA-Seq analyzes. Specific primers for IREG/ferroportin and reference genes were designed to produce amplicons of 100-200 nt (Supplementary Data 11). Relative gene expression was calculated using the primer efficiency correction method  42. Two technical replicates and 3 independent biological replicates were used for each experiment. General statistics. Sample exploration and replication of sequencing data analysis were conducted using limma R package  43  , correlation analyses were conducted by linear regression using generic stats functions of R software  44  and Heat-map clustering was carried out using heatmap.
2 function of gplots R package  45. Data availability. Raw sequence files, read count files and de novo assembled transcriptomes are available in the NCBI's Gene Expression Omnibus under the SuperSeries accession number GSE116054 or SubSeries accession numbers GSE115411 (Brassicaceae), GSE116051 (Rubiaceae New Caledonia), GSE116050 (Rubiaceae Cuba), GSE116048 (Cunoniaceae), GSE116052 (Salicaceae) and GSE116049 (Euphorbiaceae).
ACKNOWLEDGMENTS. We thank professor  Rosalina Berazaín Iturralde  ( UNAH, Cuba ) for invaluable information on Cuban flora,  Louis-Charles Brinon  ( IAC, New Caledonia ) for sample collection in New Caledonia,  Véronique Brunaud  and  Marie-Laure Martin-Magniette  ( IPS2, France ) for guidance on de novo assembly and biostatistical analysis, and  Mark G. M. Aarts  ( WUR, Netherlands ) for the Noccaea root transformation protocol. This work was supported by Grants  ANR-13-ADAP-0004  (SM, BF, VBS) and  CNRS Defi Enviromics Gene-4-Chem  to SM, a  SCAC fellowship  from the  French Embassy in Cuba  to  DAGand SM  and an  ATIGE grant from Génopole  to GJR. The I2BC and POPS platform benefit from the support of the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ). This work has benefited from the core facilities of  Imagerie-Gif , a member of  Infrastructures en Biologie Santé et Agronomie (IBiSA) , supported by  France BioImaging Grant   ANR-10INBS-04-01  and the  Saclay Plant Science Labex  Grant  ANR-11-IDEX-0003-0.
2017). For sequencing, immunoprecipitation and input samples were used as templates with a GenomePlex Complete whole-genome amplification kit (Merck) to amplify the DNA. The library was prepared to obtain a fragment size of 200 bp according to the manufacturer's protocol (Ion Torrent PGM). The quality of the samples was certified with a Bionalyzer and a high-sensitivity DNA analysis kit, and runs were performed in an Ion 316 ChIP version 2. The data generated are available under Gene Expression Omnibus accession number GSE113580. Bioinformatic analysis was performed as described (see the Supplemental Material). DRIPseq signal values were multiplied by three for visualization.
Acknowledgments We thank  A. Greenleaf  for generously providing the pET15-Yra1 plasmid for recombinant Yra1 purification, and  E. Andújar  and  M. Pérez  for technical assistance in the DRIP-seq experiments performed in the  Genomic Unit of the Andalusian Center of Molecular Biology  and Regenerative Medicine. Research was funded by grants from the  European Research Council  ( ERC2014 AdG669898  TARLOOP), the  Spanish Ministry of Economy and Competitiveness  ( BFU2016-75058-P ), the  Junta de Andalucía  ( PA12-BIO1238 ), and the  European Union Regional Funds (FEDER)  to A. A. , and the  Ligue Nationale Contre le Cancer (Équipe Labellisée)  to V. G. P. A. was the recipient of  Région Provence-Alpes-Côte d'Azur and Association pour la Recherche Contre le Cancer (ARC)  predoctoral training grants.
Thermomechanical fatigue tests and Taira model identification The elastoviscoplastic model but also a large database of thermomechanical fatigue tests are needed to identify the damage model. TMF tests are carried out up to 1050°C and post mortem samples analysis enables to better understand damage mechanisms. For ferritic grades, failure is driven by out of phase mechanisms in the intrados of V-shape specimen due to the low cyclic oxidation sensitivity and the low thermal expansion. Indeed, for austenitic grades, at high temperature, the cracks tip tends to open in extrados (tensile loading) increasing the oxidation risk. The K44X high creep resistance leads to a conservation of the V-shape of the specimen and to a high lifetime in thermomechanical loading up to 1025°C. Above 1050°C, the damage is more pronounced and grain growth can occur.