dataesr/scientific-paragraphs-categorization huggingface dataset:
The results of our study show that, in skin and adventitial fibroblasts, only three of the eight isoforms referenced in databases exist: FBN1_001, FBN1_004, and FBN1_009. The main isoform is FBN1_001 and corresponds to the reference transcript. The other two isoforms are shorter. The FBN1_004 isoform comprises 30 exons (with a specific exon 21), which match the last 29 exons of isoform FBN1_001. As per the Ensembl database, no information for its promoter and regulation regions, as well as its start codon, is available, since the 5' untranslated region (UTR) sequence of the isoform is incomplete. Therefore, the precise reading frame of FBN1_004 isoform is unknown. Isoform FBN1_009 comprises four exons with a specific exon 1, and the last three exons of the FBN1 gene. It is referenced in Ensembl as a processed and non-coding transcript.
Its function remains unknown. In our control cell models (adventitial and skin fibroblasts), FBN1_009 expression was higher than that of FBN1_004, contrary to what is reported in the GTEx database. Furthermore, we observed a significant correlation only between the expressions of the two isoforms FBN1_001 and FBN1_009. A single promoter region is described for the FBN1 gene and its isoforms. This region contains three distinct exons (exons A, B, and C) that are alternatively spliced with the first coding exon of the gene  [17]. The three exons are embedded in an approximately 1. 8-kb cytosine-phosphate-guanine (CpG) island that contains multiple putative specificity protein 1 (Sp-1) binding sites  [18, 19]. The promoter that includes exon A appears to be the most commonly used  [18] [19] [20] [21]  and this may partly explain the positive expression correlation between the two isoforms FBN1_001 and FBN1_009.
Furthermore, the original study design did not take into account the existence of the three isoforms. Indeed, in  Aubart et al. (2015) , FBN1 expression was quantified as a mean of the whole FBN1 expression evaluated with two sets of primers located in exons 2 and 47. Therefore, correlation analyses were retrospectively performed, firstly, only using expression data obtained with exon 2 primers and, secondly, only using the data from exon 47 primers. This second analysis revealed a correlation with ectopia lentis in agreement with the original results, while no correlation was found with data from exon 2 primers. Therefore, our present results would indicate that the correlation observed with ectopia lentis is supported by isoform FBN1_004. Furthermore, a significant association was found in patients between the expressions of FBN1_004 and FBN1_009.
Funding: This research was funded by  DHU FIRE  (Emergence 2 project for M. A. ),  Programme Hospitalier de Recherche Clinique  ( AOM10108  and  CRC15014  for C. B. ),  Agence Nationale de la Recherche  ( NONAGES ,  ANR-14-CE15-0012-01  for G. J. ),  Fédération de Cardiologie  (for G. J. ), and  Société Française de Cardiologie  (for G. J. ).
Numerical scheme The numerical integration of the set of equations Eq. (  11 ) is done using the library scipy. integrate from Python2. 7, more precisely the method odeint 1. Since the method does not provide an event handle to detect if a lumen is empty, the numerical solving is done by restarting the integration for small time frames, checking the conditions and starting it again. However, because of the dynamics, the disappearance of lumens will change the network quickly at small times, and more slowly at larger times. Moreover, the dynamics at large times is much slower than at small times, because of the competition between larger lumens is slower. We therefore implemented an adaptive time stepping, that depends on the number of lumens N as 1.
Acknowledgements We are grateful to the imaging platform of the  Genetics and Developmental Biology unit at Institut Curie (PICT-IBiSA@BDD)  for their outstanding support. We thank the animal facility of  Institut Curie  for their invaluable help. We are grateful to the  Buchholz laboratory  for providing the Cdh1-GFP BAC construct. We thank  Yvonne Petersen  from the transgenesis platform at the  European Molecular Biology Laboratory ,  Takashi Hiiragi  and his lab for their very generous help in making Cdh1-GFP mouse line. We thank the  Maître and Turlier labs ,  Yohanns Bellaïche ,  Andrew Clark  and  Guillaume Charras  for fruitful discussions and comments on the manuscript. Research in the lab of JLM, who is supported by the  Institut Curie , the  CNRS  and the INSERM, is funded by the  ATIP-Avenir program , an  ERC-2017-StG 757557 , a  PSL "nouvelle équipe" grant  and  Labex DEEP  ( ANR-11-LBX-0044 ) which are part of the  IDEX PSL  ( ANR-10-IDEX-0001-02 PSL ).
) highlighted, were evaluated by qPCR. (B): The string diagram shows the predicted protein-protein interactions of the 186 downregulated differentially expressed genes using medium confidence (0. 4). Disconnected nodes in the network were hided. Colored nodes represent query proteins and first shell of interactors; white nodes represent second shell of interactors. Edges represent protein-protein associations and different colors of the edges represent different associations. The known interactions were found from curated databases (blue) or experimentally determined (purple); the predicted interactions were suggested by gene neighborhood (green), gene fusions (red), or gene co-occurrence (dark blue); and other protein-protein associations were suggested by text-mining (yellow-green), coexpression (black), or protein homology (gray); key gene (survivin) from main group of under expressed genes was evaluated by qPCR.
Similarities were found with more than 200 modified common genes. The STRING database was used, and protein-protein interaction networks for upregulated and downregulated genes were visualized for common genes and selectively with pesticide mixture exposition. At the same time, cluster of upregulated genes implicated for stress resistance and survey was found. Most of them connected to the insulin/ IGF-1 signaling pathway, which plays a key role in the coordination of growth, differentiation, and metabolism in response to modification of environmental conditions and nutrient availability  [34]. Moreover, a link was established between IGF signaling and resistance for the oxidative stress  [35]. An upregulation of genes connected to pathways IGF-1/IGF-1R can stimulate the process of physiological aging.
(D): Analyses revealed, respectively, that 394, 1,073, and 2,077 ESTs were significantly upregulated (red) from pesticide exposed, MSC-P14, and MSCs from aged donor and 218, 1,077, and 1,571 ESTs were downregulated (green). Color-coding in the heat map demonstrates that gene expression changes upon aging are also reflected by replicative senescence of MSCs in vitro. (C): Differential gene expression of the three datasets (MSCs-donor age, exposed-MSCs, and long-term culture MSC-P14) was further compared. Venn diagrams demonstrate the overlap of differentially expressed genes that are upregulated (A) or downregulated (B). Differentially expressed genes are indicated as shortcut (Hugo gene nomenclature). There was a relatively high overlap with the three conditions. ©2019 The Authors. STEM CELLS published by Wiley Periodicals, Inc.
Patient F3-IV-1 presented with stage 5 chronic kidney disease at the age of 20 and a biopsy showed focal segmental glomerulosclerosis. His brother, patient F3-IV-3, developed intermittent proteinuria with normal kidney function at the age of 20 years. Patient F5-II-1 was hospitalized at the age of 15 years with proteinuria and hematuria and renal biopsy displayed glomerulotubular nephropathy (Supplementary Fig. 2 )  8. Clinical follow-up of the other patients revealed asymptomatic proteinuria in two siblings from family 1 (patients IV-1 and IV-5). In all five families mutated alleles were inherited recessively from each of the unaffected consanguineous parents (Fig. 1a ). Identified variants were absent in 123,136 exomes and 15,496 genomes from the Genome Aggregation Database (gnomAD, accessed November 2017)  11  and 2497 individuals from the Greater Middle East (GME, accessed November 2017) Variome Project  12.
Furthermore, variant c. 2207dupT (p. I737NfsX7) found in Moroccan families 1 and 2 was absent in 400 alleles of individuals of Moroccan descent. All four variants are predicted to results in a premature truncation of the FAT1 protein at amino acids 744, 871, 1043, and 3270, respectively (Fig. 1b ). The gnomAD database contains only 46 LoF variants (expected number of LoF variants: 144) and none of them in homozygous condition, in comparison to 2601 missense variants (expected number of missense variants: 2585). We performed two-point linkage analysis for the FAT1 variants using genotype and pedigree information (Fig. 1 ). Assuming an autosomal recessive inheritance and a penetrance of 0. 99, we obtained a combined maximal LOD score of 5. 3 for the FAT1 variants. Patients F5-II:1 and F4-II-3 had been previously published, of whom the latter was diagnosed with Dubowitz syndrome (OMIM: 223370), although sequencing of NSUN2 was negative  13.
Downstream processing was performed using the Genome Analysis Toolkit (GATK)  17  , SAMtools 27 , and Picard. Variant calls were made with the GATK UnifiedGenotyper. Variant annotation was based on Ensembl release 71. Variants were filtered against publicly available SNPs plus variant data from more than 7000 in-house exomes (Institut Imagine). In family 2, the coding exons were captured using the Agilent SureSelect Human All Exon v5 and sequenced on the Hiseq2000 sequencer (Illumina) at a target mean depth of 50 reads per target base. SOAPaligner (version 2. 21) was used to align sequence reads to the human reference genome (NCBI build 37/hg19). For single nucleotide variants, functional annotation was performed with SOAPsnp. For insertion/deletion detection, we aligned sequence reads by BWA and performed annotation with GATK.
In family 3, patient genomic DNA was isolated, followed by exome capture using the SureSelect Human All Exon kit V4 (Agilent technologies Inc). Sequencing of captured fragments was performed on the HiSeq2500 platform with a mean depth of coverage of 84X After alignment of sequence reads, the DNAnexus software (Palo Alto, CA, release October 2011) was used to perform variant calling with the human reference genome (NCBI build 37/hg19) as reference. Variant filtering was subsequently performed against public databases (dbSNP129, HapMap, 1000 Genomes). In family 4, exome capture for WES was performed using the NimbleGen SeqCap EZ Human Exome Library (Roche NimbleGen Inc. , Madison, WI). Sequencing of paired-end 100 bp fragment reads was performed on the Illumina HiSeq2000 with a mean depth of coverage of ×90.
The VarBank v. 2. 1 data analysis pipeline was used for mapping, alignment, and variant calling. Alignment of sequenced reads, indexing of the reference genome, variant calling, and annotation was conducted using a pipeline consisting of BWA, Samtools, Picard, and Annovar  28. In family 5, genomic DNA was isolated from blood lymphocyte or saliva samples. Exome capture for WES was performed using the SureSelect Human All Exon kit V5 (Agilent technologies Inc). Subsequently, captured fragments were sequenced on the llumina HiSeq2000 with a mean depth of coverage of ×50. The CLC Genomics Workbench™ (version 6. 5. 2) software (CLC bio, Aarhus, Denmark) was used to align sequence reads to the human reference genome (NCBI build 38/ hg19). Variant filtering was then performed against public databases (dbSNP147 and 1000 Genomes).
We excluded synonymous and intronic variants that were not located at splice sites. WES data was evaluated for potential disease-causing mutations in >50 known monogenic genes associated to nephrotic syndrome. A second round of filtering of the remaining variants was conducted using public databases (EVS server, ExAC, gnomAD and 1000 Genomes). Variants identified in families 1-5 were validated using Sanger sequencing and whenever parental DNA was available, segregation analysis was performed. A combined LOD score across the families was calculated using phenotype, genotype, and pedigree information of each family by two-point linkage analysis with the use of the Superlink-Online software package  29  with the assumption of a recessive pattern of inheritance, a disease-allele frequency of 0.
URLs. For dbSNP, see http://www. ncbi. nlm. nih. gov/SNP/; for CHOPCHOP, see http://chopchop. cbu. uib. no; for 1000 Genomes Project, see http:// www. 1000genomes. org/;for  NHLBI   ESP6500  databases, see http://evs. gs. washington. edu/EVS/; for VariantDB, see http://www. biomina. be/app/variantdb;for ANNOVAR, see http://www. openbioinformatics. org/annovar/; for  NCBI Map Viewer , see http://www. ncbi. nlm. nih. gov/mapview/; for  Exome Aggregation Consortium (ExAC), Cambridge , MA, see http://exac. broadinstitute. org;for Genome Aggregation Database (gnomAD), Cambridge, MA, see http://gnomad. broadinstitute. org.
Acknowledgements We thank the families for their participation and collaboration. We thank  Tiziana Cogliati  ( NEI/NIH ) for critical review of the manuscript;  Drs. G. Walz  and  Michelle Dawn  ( University Freiburg Medical Center ) for zebrafish fat1 constructs;  Dr. S. Miller  and  Omar Memon  ( NEI/NIH ) for RPE cells. We thank  Dr. Robert Farris  ( NEI, Biological Imaging Core ) for expert assistance with confocal laser scanning microscopy, and  Chris Ardeljan  and  Heba Mohammed  ( NEI/NIH ) for technical assistance with electron microscopy. Andrew Wegerski  and  Tyler Fayard  ( NEI/NIH ) for zebrafish breeding and maintenance. We thank  Dr. Ahron Bloch ,  Prof. Michal Elhalel-Dranitzky ,  Prof. Dvora Rubinger ,  Dr. Amir Haze ,  Dr. Samer Khateb  ( Hadassah-Hebrew University Hospital, Jerusalem, Israel ), and  Prof.
Francesco Semararo  ( Eye Clinic, Department of Neurological and Vision Sciences, University of Brescia, Italy ) for their clinical assistance. We thank  Leander Beekman  for technical assistance ( Academic Medical Center, Amsterdam). This work was supported by the  Agence Nationale de la Recherche  ( ANR-10-IAHU-01 ),  France and intramural program  of the  National Eye Institute/National Institutes of Health, USA  ( DK076683 ) to F. H. F. H. is the  William E. Harmon Professor.
The strongest association was in the BALF2 region (NC_007605. 1:162507C>T, P = 9. 17×10 -5 ) without any indication of inflation due to genetic structure (genomic control inflation factor λ GC = 1. 03; Fig. 2a , Supplementary Table  11  and Supplementary Fig. 6 ). We also investigated the association evidence for the recently reported EBER2 variants by Hui et al  18  in our discovery dataset. NC_007605. 1:7048A>C, which was a leading variant for the reported associations at EBER2 region, showed significant association in our genome-wide analysis (P = 1. 25×10  -7  ), but the significance was largely abolished by controlling for population structure (P = 1. 52×10 -2 ; Supplementary Fig. 6 ). In addition, we also performed a multi-SNP genome-wide association analysis using Bayesian variable-selection regression by piMASS  22  , which provided consistent and strong evidence for the association in the BALF2 region (posterior probability = 0.
1) using the Burrows-Wheeler Aligner (BWA, version 0. 7. 5a)  33, 34. The average sequencing depth was 1,282 (range, 32 to 6,629). High genome coverage (average, 98. 02%; range, 94. 44% to 99. 91%) was achieved (Supplementary Fig. 2 ). Following GATK's best practice (version 3. 2-2), an initial set of 8,469 variants was first called after base and variant recalibration  35. To avoid inaccurate calling, we further filtered out variants that had low coverage (depth < 10×) or were in repetitive elements or within 5 bp of an indel; 7,962 variants were retained for subsequent EBV phylogenetic, principal component, and association analyses. The functional annotation of the EBV variants was performed using the SNPEff package according to the reference genome (NC_007605. 1, NCBI annotation, Nov 2013)  36.
Phylogenetic and principal component analyses of EBV genome sequences The phylogenetic and principal component analyses were performed using EBV isolates sequenced by the current study and publicly accessible EBV genomes. For the phylogenetic analysis, we first created the fasta sequence for each resequenced isolate using the variant data extracted from the variant calling. The 230 EBV single-infection whole genomes were subsequently combined with the 97 public genomes and multiple sequence alignment was carried out using the multiple alignment program MAFFT  42. After masking the regions of repetitive sequences and poor coverage in resequencing, the maximum likelihood of the phylogenetic relationship was inferred using the Randomized Axelerated Maximum Likelihood (RAxML) assuming a General Time Reversible (GTR) model  43.
The inferred phylogeny was subsequently rooted using the Evolutionary Placement Algorithm (EPA) algorithm  44  from RAxML using a Macacine herpesvirus 4 genome sequence (NC_006146) as the outgroup. In the PCA analysis, genomic variation from the 97 public genomes was generated by global pairwise sequence alignment of published genome sequences against the B95-8 reference genome (NC_007605. 1) using the EMBOSS Stretcher  45. The variant set is then combined with the variation data extracted from the WGS. A combined set of 12,182 SNPs from the 270 newly sequenced isolates and 97 published ones were then used for the PCA analyses. During the PCA analysis, SNPs were first filtered by allele frequency (minor genotype frequency > 0. 05) and LD (pruning with a pairwise correlation R 2 value > 0. 6 within a 1000bp sliding window).
Estimation of the population attributable fraction of risk The proportion of NPC risk explained by the effect of the two high-risk haplotypes of SNPs 162476T>C and 163364C>T (C-T and C-C) was estimated in the validation sample. The attributable fraction of risk and 95% confidence interval were estimated in a logistic regression model adjusting for age and sex with the R package 'AF'  48. Because NPC is not a common disease (prevalence < 40/100,000), the risk ratio can be approximated by OR. Thus, the population attributable fraction can be approximated by     The association of three EBV SNPs with NPC risk was tested in discovery and validation samples and with a meta-analysis of the combined discovery and validation samples. Frequencies of high-risk genotypes in discovery, validation and combined analyses are indicated.
Acknowledgements We thank all the participants for their generous support for the current study. We would also thank  R. Sun ,  C. Wang ,  H. Chen ,  J. Shen  and  C. Jie  for helpful discussions on viral biology and genetic statistical, evolution and phylogenetic analyses,  W. -S. Liu  and  X. Zuo  for providing code supports,  Z. Lin  from  Tulane University  for kindly sharing EBV genome annotation files, and  J. -Y. Shao  from  Sun Yat-sen University Cancer Center  for providing MassArray iPlex platform. This work was supported by the  National Natural Science Foundation of China  ( 81430059  to Y. -X. Z. and  81872228  to M. X. ), the  National Key R&D Program of China  (No. 2016YF0902000  to Y. -X. Z. ; 2018YFC1406902 and 2018YFC0910400 to W. Z. ), the  National Cancer Institute at the US NIH  and  NIH  ( R01CA115873-01  to H.
For 15 smaller samples, we measured their hyperspectral BCRF at a single viewing and illumination geometry. The characterization of the optical properties of the sand samples was completed by the determination of their mineralogical composition and grain size distribution. A database including the sand optical properties measured from these sand samples, as well as other measurements of sand spectro-directional properties available in the literature, was built and made available to the scientific community. The dataset can be downloaded from the dedicated web portal https://picsand. noveltis. fr. The next section defines the measured optical quantities. The third section deals with the material, including a description of the selected sites, the collected sand samples and the laboratory devices used for the measurements.
Materials As this work was motivated by contributing to PICS characterization most commonly used, the priority was given to sand samples from the six PICS endorsed by the Comity of Earth Observation Satellites (CEOS) (Algeria3, Algeria5, Libya1, Libya4, Mauritania1, and Mauritania2) and then to the other PICS defined by  Cosnefroy et al. in 1998   [1]  and new ones proposed by  Bacour et al. in 2019 (Arabia_PICSAND1 and Namibia_PICSAND1, PICSAND being the name of the database presented in section 5. 5)  [3]. Collecting sand samples from these sites has been a challenging task. Indeed, most N  N  N  N  N  N  N  N  N  good approximation of the BRF. As BRF (unitless) is just equal to BRDF (sr -1 ) multiplied by π, for the measurements made, we consider that: BCRF≈BRF = π • BRDF (1) Table  1.
Materials As this work was motivated by contributing to PICS characterization most commonly used, the priority was given to sand samples from the six PICS endorsed by the Comity of Earth Observation Satellites (CEOS) (Algeria3, Algeria5, Libya1, Libya4, Mauritania1, and Mauritania2) and then to the other PICS defined by  Cosnefroy et al. in 1998 [1]  and new ones proposed by Bacour et al. in 2019 (Arabia_PICSAND1 and Namibia_PICSAND1, PICSAND being the name of the database presented in section 5. 5)  [3]. Collecting sand samples from these sites has been a challenging task. Indeed, most N  N  N  N  N  N  N  N  N  good approximation of the BRF. As BRF (unitless) is just equal to BRDF (sr -1 ) multiplied by π, for the measurements made, we consider that: BCRF≈BRF = π • BRDF (1).
Materials As this work was motivated by contributing to PICS characterization most commonly used, the priority was given to sand samples from the six PICS endorsed by the Comity of Earth Observation Satellites (CEOS) (Algeria3, Algeria5, Libya1, Libya4, Mauritania1, and Mauritania2) and then to the other PICS defined by  Cosnefroy et al. in 1998 [1]  and new ones proposed by Bacour et al. in 2019 (Arabia_PICSAND1 and Namibia_PICSAND1, PICSAND being the name of the database presented in section 5. 5)  [3]. Collecting sand samples from these sites has been a challenging task. Indeed, most N  N  N  N  N  N  N  N  N  Conical consider that: BCRF≈BRF = π • BRDF (1).
Materials As this work was motivated by contributing to PICS characterization most commonly used, the priority was given to sand samples from the six PICS endorsed by the Comity of Earth Observation Satellites (CEOS) (Algeria3, Algeria5, Libya1, Libya4, Mauritania1, and Mauritania2) and then to the other PICS defined by  Cosnefroy et al. in 1998 [1]  and new ones proposed by Bacour et al. in 2019 (Arabia_PICSAND1 and Namibia_PICSAND1, PICSAND being the name of the database presented in section 5. 5)  [3]. Collecting sand samples from these sites has been a challenging task. Indeed, most N  N  N  N  N  N  N  N  N  consider that: BCRF≈BRF = π • BRDF (1).
Materials As this work was motivated by contributing to PICS characterization most commonly used, the priority was given to sand samples from the six PICS endorsed by the Comity of Earth Observation Satellites (CEOS) (Algeria3, Algeria5, Libya1, Libya4, Mauritania1, and Mauritania2) and then to the other PICS defined by  Cosnefroy et al. in 1998 [1]  and new ones proposed by Bacour et al. in 2019 (Arabia_PICSAND1 and Namibia_PICSAND1, PICSAND being the name of the database presented in section 5. 5)  [3]. Collecting sand samples from these sites has been a challenging task. Indeed, most N  N  N  N  N  N  N  N  N  Hemispherical Table 1. Illustration of various kinds of reflectance (extracted from  [8] ).