dataesr/scientific-paragraphs-categorization huggingface dataset:
FACS Analysis for H2-K b expression and recovery at the cell surface To study the kinetics of endogenous surface K b recovery cells were treated with ice-cold citric acid buffer (0. 13M citric acid, 0. 061M Na 2 HPO 4 , 0. 15M NaCl [pH 3]) at 1x10 7 cells per milliliter for 120s, washed three times with PBS, and resuspended in culture medium. At the indicated time point, an aliquot of cells (generally 1. 5x10 6 ) was removed and stained with anti-mouse H-2K b PE. All flow cytometry experiments were conducted using the BD LSRII flow cytometer (BD Biosciences) and data are analyzed with the FlowJow software (V10). certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
25 µl/min on a UHPLC Easy-nLC 1000 (Thermo Scientific) connected to a 25-cm fused-silica emitter with an inner diameter of 75 µm (New Objective, Inc. Woburn, MA, USA), packed in-house with ReproSil-Pur C18-AQ 1. 9-µm beads (Dr. Maisch Gmbh, Ammerbuch, certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Germany) using a high-pressure bomb loader (Proxeon, Odense, Denmark). MS data were acquired using a data-dependent top-15 method for HCD fragmentation. Survey full-scan MS spectra (300-1750 Th) were acquired in the Orbitrap at a resolution of 60,000, an AGC target of 1 e6 , and an IT of 120 ms. For the HCD spectra, the resolution was set to 15,000 at m/z 200, with an AGC target of 1 e5 , an IT of 120 ms, an NCE of 28% and an isolation width of 3.
Data processing and analysis For quantitative proteomic analysis, raw data were processed with MaxQuant (ver. 1. 5. 2. 8) and searched against a database containing only the sequence of the KH-52 intron SIINFEKL. No enzyme specificity was selected, and there were no differences between I and L. The mass deviation for MS-MS peaks was set at 20 ppm, and the peptide false discovery rate (FDR) was set at 0. 01; the minimal length required for a peptide identification was eight amino acids. The list of identified peptides was filtered to eliminate reverse hits. Statistical analyses were performed with Perseus (ver. 1. 5. 1. 6) considering the peptide intensity; normalization based on the Z-score and imputation was applied. Significant peptides were determined with a t-test, Benjamini Hochberg correction and FDR<0.
Acknowledgments: We thank  Xiaotao Li  ( Department of Biochemistry and Molecular Biology, Institute of Biomedical Sciences, Shanghai, China ) for the REGγ expression plasmids. The A375 and A549 cell lines were a gift from  Dr. Fabrice André ,  Gustave Roussy Institute. The WM3526 and WM3682 cell lines were a gift from  Levi Garraway. The  HT29  and  T84  colon cell lines were a gift from  Dr. Fanny Jaulin  from the  Gustave Roussy Institute. We also thank  Prof. Laurence Zitvogel  for valuable comments on the manuscript. This work was funded by  The Avenir program (INSERM) ,  La Fondation ARC , and  Fondation Gustave Roussy. BM is supported by the  Fondation Gustave Roussy , and PA is supported by the  Université Paris Sud.
The primer sequences are listed in the Table  S1 , † except for MUC2, which was purchased from Bio-Rad. Primer efficiencies were determined, and only primers with an efficiency between 1. 8 and 2. 2 were used. The relative expression values were calculated as 2 -ΔΔCq , where ΔCq is the difference between the cycle threshold (Cq) values for the target and reference genes for each condition, and ΔΔCq is the difference between the ΔCq measured for one exposure condition and that for the respective control condition (unexposed cells). CYCLOA and B, S18 and GAPDH were used as reference genes for data normalization. Their variability of expression was assessed using Bestkeeper, which is an Excel-based pairwise mRNA correlation tool. 41 Gene expression levels were determined and statistical analyses performed using the Relative Expression Software Tool (REST2009).
Funding sources This work was supported by the  French Atomic Energy and Alternative Energies Commission (CEA)  through the  'Toxicology' research program  ( IMAGINATOX grant ), the  French Environment and Energy Management Agency (ADEME) , the  French Agency for Food, Environmental and Occupational Health & Safety (ANSES)  within the framework of the  National Research Program for Environmental and Occupational Health (PNR-EST ,  EST-2013/1/024 ,  NanoGut grant ) and the  French National Research Agency (ANR ,  ANR-16-CE34-0011-01 , PAIPITO grant). It is a contribution to the  Labex Serenade  ( ANR-11-LABX-0064 ) funded by the  French Government 's  "Investissements d'Avenir" ANR program , through the A* MIDEX  project ( ANR-11-IDEX-0001-02 ).
Post-transplantation outcomes: 3-months kidney biopsies. As protocolled kidney biopsies at 3 months post-transplant were systematically analyzed according to Banff classification since 2007  25  , only data for the last 394 recipients were available. Those included 25 CAV1 AA and 369 non-AA donors. Over the tested parameters, only the proportion of globally scarred glomeruli was significantly different between AA and non-AA (4. 80% versus 7. 70%, p = 0. 034). Of note, there was no significant difference between AA and non-AA for Banff lesions scores especially as regards markers of chronic injury, such as mesangial matrix expansion (mm score), glomerular double contours (cg score), interstitial fibrosis (ci score), or tubular atrophy (ct score). There was also no significant difference for IFTA score (Table  3 ).
7-119. 1) in our single cohort. Moore et al. provided also data on long term indication biopsies for allograft dysfunction  11  and showed that the main cause of graft failure in the AA donor cohort was interstitial fibrosis (13 graft losses among 22 AA donors), mostly related to chronic cellular rejection (n = 6/13). On the contrary, we presented data on 3-month protocolled post-transplantation kidney biopsies, which may be too early to assess differences considering kidney fibrosis. Also, even if we could not find any effect of this SNP in our cohort, we cannot exclude that its effect could not be observed because of the differences mentioned above. After Moore and coworkers study, other teams studied the impact of CAV1 rs4730751 SNP on kidney transplantation outcomes. Ma et al. presented data on CAV1 rs4730751 SNP from 1233 kidney transplantations from Afro-American (n = 675) and European American donors (n = 558)  22.
Data collection. All data were collected from the CRISTAL database (French National Biomedical Agency registry) and from the recipient personal files. General demographic parameters and well-characterized risk factors of allograft failure were extracted from the database: donor age, sex, weight, height, BMI, cause of death, time of cold ischemia; and recipient age, sex, weight, height, BMI, cause of end-stage renal disease (ESRD), rank of transplantation, type of ESRD treatment (hemodialysis, peritoneal dialysis, or no treatment), number of HLA mismatches (on HLA A, B, DR), and longitudinal assessment of serum creatinine as well as eGFRs (MDRD). Immunosuppressive therapy. The immunosuppressive regimen followed our standard care procedure. All of the patients received an induction therapy consisting in basiliximab or thymoglobulin.
Finally, the selected significant variables were further analyzed in a multivariate linear mixed model to determine those acting independently (backward selection procedure, p < 0. 05). The normal distribution of random effect on intercept, random effect on slope, residuals and homoscedasticity assumption were graphically assessed. Finally, chronic allograft injury parameters assessed on the 3 months post-transplantation systematic kidney biopsies were compared between groups of AA and non-AA genotype patients. All analyses were performed using the 3. 5. 1 version of the R software with "nlme" and "survival" packages.
Acknowledgements We would like to thank  Dr. Paul Chamley  ( University of Lille, Department of Nephrology ), as an English native speaker, for his comments on the main body text. We also would like to thank  Mr. Sébastien Gomis  ( CHU Lille, Data-Manager ) for his help concerning the data-mining. This study was supported in part by a following grant from  Santelys Association. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Dinoflagellates of the family Symbiodiniaceae (Order Suessiales) are predominantly symbiotic, and many are known for their association with corals. The genetic and functional diversity among Symbiodiniaceae is well acknowledged, but the genome-wide sequence divergence among these lineages remains little known. Here, we present de novo genome assemblies of five isolates from the basal genus Symbiodinium, encompassing distinct ecological niches. Incorporating existing data from Symbiodiniaceae and other Suessiales (15 genome datasets in total), we investigated genome features that are common or unique to these Symbiodiniaceae, to genus Symbiodinium, and to the individual species S. microadriaticum and S. tridacnidorum. Our whole-genome comparisons reveal extensive sequence divergence, with no sequence regions common to all 15.
In this study, we generated de novo genome assemblies from five isolates of Symbiodinium (the basal genus of Symbiodiniaceae), encompassing distinct ecological niches (free-living, symbiotic and opportunistic), including two distinct isolates of Symbiodinium microadriaticum. Comparing these genomes against those available from other Symbiodinium, other Symbiodiniaceae and the outgroup species Polarella glacialis (15 datasets in total), we investigated genome features that are common or unique to the distinct lineages within a single species, within a single genus, and within Family Symbiodiniaceae. This is the most comprehensive comparative analysis to date of Symbiodiniaceae based on whole-genome sequence data.
The overall G+C content of all analysed Symbiodinium genomes is ~50% (Supplementary Figure  1 ), with the lowest (48. 21%) in S. pilosum CCMP2461 and the highest (51. 91%) in S. microadriaticum CassKB8. For a comprehensive comparison, we included in our analysis all available genome data from Symbiodiniaceae and the outgroup species of Polarella glacialis (Supplementary Table  1 ). These data comprise nine Symbiodinium isolates (three of the species S. microadriaticum and two of S. tridacnidorum), Breviolum minutum, two Cladocopium isolates, Fugacium kawagutii, and two Polarella glacialis isolates 8,10-14 (i. e. a total of 15 datasets of Suessiales, of which 13 are of Symbiodiniaceae); we used the revised genome assemblies from Chen et al. 15  where applicable. Of the 15 genome assemblies, four were generated using both short-and long-read data (those of S.
Despite the variable quality and completeness of the genome assemblies analysed here (Supplementary Table  1 , Supplementary Figure  4 ), we consider these results more reliable than those based largely on transcriptome data  7  , in which transcript isoforms, in addition to quality and completeness of the datasets, can result in overestimation of gene numbers and introduce noise and bias to the data. The smaller number of gene families shared among Symbiodiniaceae found here (i. e. 18,453 compared to 76,087 in the earlier study  7  ) likely reflects our more-conservative approach based on whole-genome sequenced data. Nonetheless, our observations support the notion that evolution of gene families has contributed to the diversification of Symbiodiniaceae 7.
Genome size and sequence read coverage were estimated from the trimmed read pairs based on k-mer frequency analysis (Supplementary Table  2 ) as counted with Jellyfish v2. 2. 6; proportion of the single-copy regions of the genome and heterozygosity were computed with GenomeScope v1. 0  41. De novo genome assembly was performed for all isolates with CLC Genomics Workbench v7. 5. 1 (qiagenbioinformatics. com) at default parameters, and using the filtered read pairs and single-end reads. The genome assemblies of S. microadriatricum 04-503SCI. 03, S. microadriaticum CassKB8, S. linucheae CCMP2456 and S. pilosum CCMP2461 were further scaffolded with transcriptome data (see below) using L_RNA_scaffolder  42. Short sequences (<1000 kbp) were removed from the assemblies.
Removal of putative microbial contaminants To identify putative sequences from bacteria, archaea and viruses in the genome scaffolds, we followed the approach of Chen et al. 15. In brief, we first searched the scaffolds (BLASTn) against a database of bacterial, archaeal and viral genomes from RefSeq (release 88), and identified those with significant hits (E ≤ 10 -20 and bit score ≥ 1000). We then examined the sequence cover of these regions in each scaffold, and identified the percentage (in length) contributed by these regions relative to the scaffold length. We assessed the added length of implicated genome scaffolds across different thresholds of percentage sequence cover in the alignment, and the corresponding gene models in these scaffolds as predicted from available transcripts (see below) using PASA v2.
3. 3  43  , with a modified script (github. com/chancx/dinoflag-alt-splice) that recognises an additional donor splice site (GA), and TransDecoder v5. 2. 0  43. Any scaffolds with significant bacterial, archaeal or viral hits covering ≥5% of its length was considered as a putative contaminant and removed from the assembly (Supplementary Figure  10 ). Additionally, the length of the remaining scaffolds was plotted against their G+C content; scaffolds (>100 kbp) with irregular G+C content (in this case, G+C ≤45% or ≥60%) were considered as putative contaminant sequences and removed (Supplementary Figure  11 ).
Gene prediction and function annotation We adopted the same comprehensive ab initio gene prediction approach reported in Chen et al. 15  , using available genes and transcriptomes of Symbiodiniaceae as supporting evidence. A de novo repeat library was first derived for the genome assembly using RepeatModeler v1. 0. 11 (repeatmasker. org/RepeatModeler). All repeats (including known repeats in RepeatMasker database release 20180625) were masked using RepeatMasker v4. 0. 7 (repeatmasker. org). As direct transcript evidence, we used the de novo and genome-guided transcriptome assemblies from Illumina short-read sequence data (see above). For S. necroappetens CCMP2469, we used transcriptome data of the other four Symbiodinium isolates for gene prediction, as well as other available transcriptome datasets of Symbiodinium: S.
microadriaticum CassKB8 46 , S. microadriaticum CCMP2467 10 , S. tridacnidorum Sh18 12 , and S. tridacnidorum CCMP2592 and S. natans CCMP2548 8. We also combined the S. microadriaticum CassKB8 transcriptome data generated here with those from a previous study  46. We concatenated all the transcript datasets per sample, and vector sequences were discarded using SeqClean (sourceforge. net/projects/seqclean) based on shared similarity to sequences in the UniVec database build 10. 0. We used PASA v2. 3. 3  43  , customised to recognise dinoflagellates alternative splice donor sites (github. com/chancx/dinoflagalt-splice), and TransDecoder v5. 2. 0 43 to predict CDS. These CDS were searched (BLASTp, E ≤ 10 -20 ) against a protein database that consists of RefSeq proteins (release 88) and a collection of available and predicted proteins (using TransDecoder v5.
2. 0 43 ) of Symbiodiniaceae (total of 111,591,828 sequences; Supplementary Table  9 ). We used the analyze_blastPlus_topHit_coverage. pl script from Trinity v2. 6. 6  44  to retrieve only those CDS having an alignment >70% to a protein (i. e. nearly full-length) in the database for subsequent analyses. The near full-length gene models were checked for transposable elements (TEs) using HHblits v2. 0. 16 (probability = 80% and E-value = 10 -5 ), searching against the JAMg transposon database (sourceforge. net/projects/jamg/files/databases), and TransposonPSI (transposonpsi. sourceforge. net). Gene models containing TEs were removed from the gene set, and redundancy reduction was conducted using cd-hit v4. 6  47, 48  (ID = 75%). The remaining gene models were processed using the prepare_golden_genes_for_predictors.
pl script from the JAMg pipeline (altered to recognise GA donor splice sites; jamg. sourceforge. net). This script produces a set of "golden genes" that were used as training set for the ab initio gene-prediction tools AUGUSTUS v3. 3. 1  49  (customised to recognise the non-canonical splice sites of dinoflagellates; github. com/chancx/dinoflag-alt-splice) and SNAP v2006-07-28  50. Independently, the soft-masked genome sequences were used for gene prediction using GeneMark-ES v4. 32  51. Swiss-Prot proteins (downloaded on 27 June 2018) and the predicted proteins of Symbiodiniaceae (Supplementary Table  9 ) were used as supporting evidence for gene prediction using MAKER v2. 31. 10 52 protein2genome; the custom repeat library was used by RepeatMasker as part of MAKER prediction. A primary set of predicted genes was produced using EvidenceModeler v1.
1. 1  53  , modified to recognise GA donor splice sites. This package combined the gene predictions from PASA, SNAP, AUGUSTUS, GeneMark-ES and MAKER protein2genome into a single set of evidence-based predictions. The weightings used for the package were: PASA 10, Maker protein 8, AUGUSTUS 6, SNAP 2 and GeneMark-ES 2. Only gene models with transcript evidence (i. e. predicted by PASA) or supported by at least two ab initio prediction programs were kept. We assessed completeness by querying the predicted protein sequences in a BLASTp similarity search (E ≤ 10 -5 , ≥50% query/target sequence cover) against the 458 core eukaryotic genes from CEGMA  54. Transcript data support for the predicted genes was determined by BLASTn (E ≤ 10 -5 ), querying the transcript sequences against the predicted CDS from each genome.
Genes for which the transcripts aligned to their CDS with at least 50% of sequence cover and 90% identity were considered as supported by transcript data. Functional annotation of the predicted genes was conducted based on sequence similarity searches against known proteins following the same approach as Liu et al. 11  , in which the predicted protein sequences were first searched (BLASTp, E ≤ 10 -5 , minimum query or target cover of 50%) against the manually curated Swiss-Prot database, and those with no Swiss-Prot hits were subsequently searched against TrEMBL (both databases from UniProt, downloaded on 27 June 2018). The best UniProt hit with associated GO terms (geneontology. org) was used to annotate the query protein with those GO terms using the UniProt-GOA mapping (downloaded on 03 June 2019).
Comparison of genome sequences and analysis of conserved synteny We compared the genome data of 15 isolates in Order Suessiales (Supplementary Table  1 ): the five for which we generated genome assemblies in this study (S. microadriaticum CassKB8, S. 10. Genes were consistently predicted from all genomes using the same workflow  8, 14, 15. Whole-genome sequence alignment was carried out for all possible genome pairs (225 combinations counting each genome as both reference and query) with nucmer v4. 0. 0 57 , using anchor matches that are unique in the sequences from both reference and query sequences (--mum). Here, the similarity between two genomes was assessed based on the proportion of the total bases in the genome sequences of the query that aligned to the reference genome sequences (Q) and the average percent identity of one-to-one alignments (i.
GO enrichment of genes in families core to Symbiodiniaceae and Symbiodinium (defined as those common to all isolates in, and exclusive to, each group) was conducted using the topGO Bioconductor package 69 executed in R v3. 5. 1, implementing Fisher's Exact test and the 'elimination' method; the GO terms associated to the genes of all isolates surveyed here were used as background to compare against. We considered a p ≤ 0. 01 as significant.
To ensure high-quality read data for downstream analyses, the pairedend mode of Trimmomatic was run with the settings: HEADCROP:10 ILLUMINACLIP:[AdapterFile]:2:30:10 CROP:125 SLIDINGWINDOW:4:13 MINLEN:50. The surviving read pairs were further trimmed with QUADTrim v2. 0. 2 (bitbucket. org/arobinson/quadtrim) with the flags -m2 and -g to remove homopolymeric guanine repeats at the end of the reads (a systematic error of Illumina NovaSeq 6000).
Acknowledgements R. A. G. P. was supported by an  International Postgraduate Research Scholarship  and a  University of Queensland Centenary Scholarship. This work was supported by two  Australian Research Council  grants ( DP150101875  awarded to M. A. R. , C. X. C. and D. B. , and  DP190102474  awarded to C. X. C. and D. B. ), and the computational resources of the  National Computational Infrastructure (NCI) National Facility  systems through the  NCI Merit Allocation Scheme  (Project  d85 ) awarded to C. X. C. and M. A. R. We thank  Mary Alice Coffroth  for her generosity in providing access to the two S. microadriaticum cultures used in this study, and  Guillaume Bernard  for his advice and assistance in interpreting the alignment-free phylogenetic tree and network.
[41]. In this configuration, the spatial resolution achievable by ECCI is 10-20 nm  [44]. The AE signal was continuously monitored using a Euro Physical Acoustics SA (EPA) PCI-2 Mistras data acquisition system with 40-dB pre-amplification and 50 kHz-1. 2 MHz bandwidth. Two piezoelectric sensors (Nano30 EPA, 125-750 kHz bandwidth, resonance peak at 300 kHz) were glued on the sample surface using caf4® silicone glue. The raw AE signal was sampled at a frequency of 5 MHz. A representative raw AE signal is shown in Figure  2 (a) for a 1s period of the first cycle of a cyclic test on a copper sample. Individual bursts, i. e. the so-called discrete AE signature of dislocation avalanches  [7] , were automatically identified by thresholding of this raw signal above 0. 06 V (36 dB), and their characteristics (e.
Pharmacokinetic analysis and modelling Plasma pharmacokinetic parameters were evaluated in all eight patients treated in the doseescalation phase. BAY 1238097 demonstrated apparent dose linearity in the area under the curve (AUC) from 0 to 72 hours following drug administration and maximum observed plasma concentration (Cmax) (Fig. 2 ). BAY 1238097 geometric mean pharmacokinetic parameters are provided in Table  S2  (supplementary file). Predictions were based on preclinical xenograft data (MOLP-B16, with AUC(0-24) of 1. 3 mg•h/L) [personal communication] translated to clinical exposures and schedule using pharmacokinetic/pharmacodynamic modelling  [15]  and resulted in a predicted efficacious BAY 1238097 dose in humans of 500 mg/week on a twice-weekly dosing schedule (250 mg/dose event). To achieve the predicted efficacious exposure via an alternate schedule while minimising Cmax, and therefore limiting toxicity, it was estimated that dosing would need to reach at least 20 mg thrice daily (420 mg/week) [personal communication].
cancer. gov/publications/dictionaries/cancer-drug; www. clinicaltrials. gov). Despite enthusiasm surrounding the anti-proliferative effects of BET inhibitors, the BET family of proteins regulates a vast network of transcriptional pathways, raising concerns regarding the safety of systemic pan-BET inhibitors  [20, 24]. Further study into their biological properties and functions in a variety of cell types is necessary for successful development of these inhibitors. Moreover, selective inhibitors of individual BET family members might have improved efficacy as well as decreased toxicity compared to pan-BET inhibitors, as the patterns of activity are not identical among them  [25].