dataesr/scientific-paragraphs-categorization huggingface dataset:
19 * 10 -3 and 5. 29 * 10 -3 respectively). The strongest association was found for rs3935541 (p = 1. 15 * 10 -4 ), which is a SNP located approximately 50 kb upstream of the NES transcription start site. The rs3935541 is in strong LD with rs3748570 (r 2 = 0. 76, D′ = 0. 96) and rs11582300 (r 2 = 0. 57, D′ = 0. 90), according to the SNAP database (http://archive. broadinstitute. org/mpg/snap/index. php). We performed a haplotype analysis of these 3 SNPs on NES expression (Table  S3 , sheet 2) in the HAEC dataset. Four haplotypes were inferred from the 3 selected SNPs. Compared to the most frequent GAT haplotype, all other 3 haplotypes showed decreased NES mRNA expression. The greatest, and the only significant (p = 0. 023), decrease was observed for the AGC haplotype, the only haplotype carrying the minor allele of rs3935541.
Only high quality RNA (RNA integrity number ≥7. 5) was used for library preparation (PolyA) and sequencing. Next generation RNA sequencing was performed using Illumina Hiseq2000 and Hiseq2500 and the standard Illumina RNA-seq protocol with a paired end read length of 100 × 2 bp or 125 × 2 bp with on average 50 M reads/library (span of 13-84 M reads). Processed reads were mapped to the Human Genome (GRCh37 and GRCH38) using Tophat v2. 0. 8b  91  , allowing for two mismatches. Transcript abundance FPKM (fragments per kilobase of exon model per million mapped reads) values were calculated using Cufflinks v2. 1. 2  92 and Ensembl build 75  93  or Ensembl build 83  94  using summarised gene TPM, not accounting for different isoforms in the analysis. The number of protein coding genes mapped was 20,344.
3% hydrogen peroxide diluted in 95% ethanol. For antigen retrieval, a Decloaking chamber ® (Biocare Medical, CA) was used. Slides were boiled in Citrate buffer ® , pH6 (Lab Vision, CA). Primary antibody against NES (HPA007007), CLEC14A (HPA039468), VWF (HPA001815), CD34 (HPA036722), PECAM1 (HPA004690), PCNA (HPA030522), MKI67 (HPA001164) (all Atlas Antibodies) and CDK2 (AHZ0142, BioSource) and a dextran polymer visualization system (UltraVision LP HRP polymer ® , Lab Vision) were incubated for 30 min each at room temperature and slides were developed for 10 minutes using Diaminobenzidine (Lab Vision) as the chromogen. Slides were counterstained in Mayers hematoxylin (Histolab) and scanned using Scanscope XT (Aperio). Human cancer patient (survival) data. Cancer patient samples used for mRNA expression and survival analysis were collected from The Cancer Genome Atlas (TCGA) project from the initial release of Genomic Data Commons (GDC) on June 6, 2016, as previously described  96  and with extended information found at https:// portal.
S3B, C ). Laminar shear stress. Endothelial cells were cultured to confluence in plastic flow chamber slides (μ-slide VI 0. 4 , Ibidi GmbH). The slide was then attached to an Ibidi fluidic unit and pump system using silicone tubing, and a continuous unidirectional laminar shear stress of 10 dyne/cm 2 was applied for 24 hours. Immunofluorescence staining. EC were fixed using 4% paraformaldehyde solution in PBS, permeabilised in 0. 5% triton X-100 and then blocked using 5% BSA. Primary antibody against nestin (HPA007007, Atlas Antibodies), Vimentin (OMA1-06001, Invitrogen) or PCNA (LifeSpan Biosciences), was incubated with the cells for 20 minutes, followed by FITC-conjugated anti-rabbit antibody (F-9887, Sigma), or Alexa-Fluor 647 conjugated anti-mouse antibody (A-21235, Invitrogen). Some experiments were also actin-stained with TRITC-conjugated phalloidin (P1951, Sigma).
Cells were covered with 40 μl DAPI-containing mounting medium (Vectashield) before imaging. Fluorescence images were taken using a Leica TPS SP5 confocal microscope, and were processed using Fiji image processing software  98. For measurement of percentage of nestin coverage, between 10-30 cells per image were individually selected and outlines saved as separate overlays using selection tools and the Region of Interest (ROI) manager, and coverage determined using the default threshold tool. Cell area was determined using actin staining and a high contrast filter/look up table. Co-localisation was calculated for each individual cell using the Coloc2 plugin. qPCR. cDNA was prepared using TaqMan Gene Expression Cells-to-Ct Kit (Ambion), and qPCR was subsequently performed using Taqman Fast Universal PCR Master Mix and 18 s rRNA reference primer (4319413E), with target primers for nestin (Hs04187831_g1) and vimentin (Hs00958111_m1) using a StepOnePlus Real-Time PCR System (all Applied Biosystems).
Gap closing assay. Cells were seeded into 24 well plates, 24 hours after siRNA treatment. The following day, cells were washed, and a gap was created by carefully scratching a 100 μl pipette tip across the centre of the well. Cells were washed in PBS, and medium replaced with either normal (20% FCS), or serum starvation (0. 5% FCS) medium. The plate was placed onto a Nikon Ti-E phase contrast microscope stage, housed inside a 37 °C, 5% CO 2 fed chamber, and photographs of each scratch were taken once each hour for 96 hours using NIS Elements image capture software. Images were exported into Fiji for image processing, and the size of the gap measured by area every 12 hours.
Cell migration and cell division assay. Cells were seeded at 60% confluence to 12 well plates, before treatment with siRNA. After siRNA treatment, cells were washed and fed with medium, and then placed on a Nikon Ti-E phase contrast microscope stage inside a 37 °C, 5% CO 2 fed chamber. Photographs of each well were taken once each hour for a period of 96 hours using NIS Elements image capture software, and then exported into Fiji for image processing, cell counting and migration analysis. The number of cells in each field of view every 24 h was counted manually using the Cell Counter plugin, and the increase in cell number since 0 h calculated. Migration was measured using the Manual Tracking plugin. The positions of 20-30 individual cells per image were tracked over 96 h, and mean migration velocity calculated for each 24 h period.
Plaque microarray and staining. Microarray analysis and plaque staining was carried out as part of the bank of Karolinska Endarterectomy (BiKE), as previously described  53. Briefly, patients undergoing surgery for high-grade (>50% NASCET) carotid stenosis at the Department of Vascular Surgery, Karolinska University Hospital, Stockholm, Sweden were consecutively enrolled in the study, clinical data recorded on admission and carotid endarterectomies (carotid plaques) collected at surgery. For microarrays, plaques were divided transversally at the most stenotic part, the proximal half of the lesion used for RNA preparation while the distal half was processed for histology. Normal artery controls (NA) were nine macroscopically disease-free iliac arteries and one aorta, obtained from organ donors without history of cardiovascular disease.
All samples were collected with informed consent from patients or organ donors' guardians. The BiKE study is approved by the Ethical Committee of Northern Stockholm with following ethical permits: EPN DNr 95-276/277; 02-146; 02-147, 2005/83-31; 2009/512-31/2; 2009/295-31/2; 2011/950-32; 2012/619-32 and 213/2137-32. The project is performed under the Swedish biobank regulations and prospective sampling is approved with informed consent procedure (DNr 2009/512-31/2). BiKE is registered at Socialstyrelsen (The National Board of Health and Welfare) and Biobank of Karolinska and approved by the Swedish Data Inspection Agency (approval date/number 2002-09-30 DNr 916-2002). The BiKE database was merged with the Swedish Hospital Discharge Register and the Swedish Cause of Death Register for follow-up of major adverse cardiovascular, cerebrovascular and vascular events (MACCEs).
All samples were collected with informed consent from patients or organ donor guardians. The BiKE microarray dataset is available from Gene Expression Omnibus (GSE21545). For immunohistochemistry, all reagents were purchased from Biocare Medical (Concord, CA). Tissues were fixed in 4% Zn-formaldehyde for 48 hours, dehydrated in 70% ethanol and embedded in paraffin blocks. A probe-polymer system containing alkaline phosphatase (AP) and horseradish peroxidase (HRP) was applied, with subsequent detection of NES (HPA007007, Atlas Antibodies) using Warp Red and VWF (#M0616, DAKO) using Vina Green chromogenes. Slides were counterstained with Hematoxylin QS (Vector Laboratories, Burlingame, CA), dehydrated and mounted in Pertex (Histolab, Gothenburg, Sweden). Images were taken using an automated SlideScanner System.
eQTL analysis. Expression quantitative locus (eQTL) mapping of EC gene expression has been described  55. Briefly, participants were genotyped using the Affymetrix Genome-Wide Human SNP Array 6. 0. Gene expression profiles were determined using the Affymetrix HT HG-U133A microarray. Single nucleotide polymorphisms with genotyping call rate <95%, minor allele frequency (MAF) < 0. 01 or showing signicant (p < 10 -6 ) deviation from Hardy-Weinberg equilibrium were filtered out. This led to 799,085 quality-control (QC) validated autosomal SNPs and 147 individuals. Genotype data were then imputed using the 1000 Genomes phase 3 reference dataset with the MACH (version 1. 0. 18) software. Associations between imputed genotypes and NES expression were computed using a linear model regression where the imputed allele dosage was used as covariate to assess SNP's effect.
Imputed SNPs with imputation quality criteria greater than 0. 3 were kept for analyses. Analyses were conducted by use of the Matrix_eQTL_main function (MatrixEQTL R package) while adjusting for the main principal components derived from genetic data. eQTL effects were considered as cis (local) when the SNP was located within a 10 6 bp distance upstream or downstream from NES probe sequence. Software. Image analysis was carried out using Fiji image processing software  98  and NDP. view2 (Hamamatsu), graphs and calculations used Graphpad Prism 7 and Microsoft Excel. Figures were assembled using Adobe Photoshop.
Acknowledgements This work was supported by  Hjärt Lungfonden  (project numbers 20140691 and 20150623) and  Vetenskapsrådet  ( 2013-42608-102305-28 ) grants to LMB. PD is supported by a postdoc grant from  Strategic Research Areas (SFO) ,  KTH  to JO and LB. We acknowledge the staff of the  Human Protein Atlas (HPA) program , the  Science for Life Laboratory  and the pathology team in Mumbai, India. We thank the  Department of Pathology at the Uppsala Akademiska Hospital, Uppsala ,  Sweden  and  Uppsala Biobank  for kindly providing specimens used in this study. The HPA was funded by  Knut & Alice Wallenberg Foundation. We also acknowledge the  Cell Profiling Facility at Royal Institute of Technology , funded by  Science for Life Laboratory  and the  National Microscopy Infrastructure, NMI  ( VR-RFI 2016-00968 ) for providing assistance in microscopy.
Therefore, it seems more realistic to rely on these data to characterize the velocity of the mucus in the bronchial region of the human lungs. As a consequence, we can calculate that the transport rate of the mucus in the bronchial region of the human lungs (δ μ v μ ) is expected, when going from the distal to the proximal generations, to increase by approximately a factor of 50 (considering δ μ 1 = 10 μm, v μ 1 = 5 mm min -1 , δ μ 16 = 2 μm and v μ 16 = 0. 5 mm min -1 ). When compared to the analysis presented in the previous section, this calculation shows clearly that the mucociliary transport cannot be the only mechanism controlling the balance of the mucus in the bronchial region of the lungs. According to the simplifications mentioned previously, the volume of mucus leaving the trachea per unit time due to cilia beating can be evaluated as follows: _ V m ¼ 2pR E 1 d m 1 v m 1.
Acknowledgments The authors gratefully acknowledge the financial support of  ESA  and  BELSPO  ( ESA-ESTEC-PRODEX arrangement 4000109631 ),  Fonds de la Recherche Scientifique-FNRS (Postdoctoral Researcher position of BS)  and the  French National Research Agency  ( ANR VirtualChest ,  ANR-16-CE19-0014  and  Idex UCA JEDI ,  ANR-15-IDEX-01 ).
This work was supported by  European Space Agency & Belgium Science Policy -Programme de developpement d'experiences scientifiques 4000109631 -CK AV BH  (http://www. belspo. be/belspo/index_fr. stm);  Fonds de la Recherche Scientifique -Chargede Recherche -BS  (http://www. fnrs. be/);and  Agence Nationale de la Recherche , Projet VirtualChest  ANR-16-CE19-0014  and  Idex UCA JEDI ,  ANR-15-IDEX-01 -BM  (http:// www. agence-nationale-recherche. fr). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Defining the value function with respect to those rewards will find the species protection strategy that will maximize, on a long-term basis, either biodiversity or the services provision. A strategy δ is a function determining which species to protect among the remaining ones. δ(  ) is the species to protect when extant species are described by a given ecosystem state at time t,. The "value" Vδ of strategy δ is the expected sum over time of immediate rewards R(  ) obtained at each time step, when strategy δ is implemented (see SI. 2). In the MDP framework, an optimal strategy δ*, maximizing Vδ, can be coded and computed using an MDP Matlab toolbox  (Chadès, Chapron et al. 2014 ) (see SI. 3).
e. , luminal A (n = 216), luminal B (n = 121), ERBB2 (n = 44), and triple-negative tumors (n = 67)  [36]. All subgroups of breast tumors showed an increased level of VOPP1 transcripts (p = 1. 10 -3 , 1. 10 -3 , 1. 10 -4 , and 6. 10 -3 , respectively) (Fig. 6a ). Of note, the ERBB2 tumors exhibited slightly higher amounts of VOPP1 compared to the other subgroups. To confirm these findings, we interrogated the Oncomine database for VOPP1 expression in several additional breast cancer datasets and obtained similar results (Additional file 4: Figure  S3A-B ). In light of the VOPP1 gene location within the 7p11. 2 chromosomal region, which contains EGFR, and frequent amplification in several malignancies, including basal-like breast cancers  [26, 27] , we investigated whether altered VOPP1 expression was due to gene amplification.
In this case, we expect that the impact of high VOPP1 expression could be more relevant in tumors not inactivated for WWOX expression. Since it has previously been demonstrated that WWOX expression positively correlates with expression of hormone receptors  [37] [38] [39] , we thus performed a log-rank test in the subgroups of luminal tumors (the hormone receptor-positive tumors) and the luminal B subtype that are those with a higher risk of metastasis. Interestingly, high VOPP1 mRNA expression was significantly associated with an increased risk of metastasis in the subgroups of luminal tumors (log-rank, p = 0. 003) and luminal B tumors (log-rank, p = 0. 005). In accordance with our findings, the analysis of independent datasets showed a significantly worse prognosis in luminal B tumors (Additional file 5: Figure  S4C ).
Samples were incubated with Annexin V-FITC antibody for 15 min in the dark and propidium iodide was added. Data were acquired on a Becton Dickinson LSR II flow cytometer and analyzed using FlowJO V10 software. Western blotting and co-immun oprecipitation methods were previously described  [51]. For endogenous WWOX-VOPP1 complexes, cell extracts containing 800 μg of total proteins were subjected to direct immunoprecipitation with the anti-VOPP1 antibody. Colony formation in soft agar and tumorigenicity assay 3. 10 4 NIH3T3 cells, stably expressing VOPP1 (3T3-VOPP1) or empty vector (ctrl-VOPP1), were mixed with melted 0. 35% agarose in DMEM medium and seeded on top of a 0. 7% agarose base layer containing the same medium. Culture medium was changed twice per week for 4 weeks, and cells were observed with an optical microscope.
CGH array Tumors with high levels of VOPP1 transcripts were subjected to DNA extraction. The quality of tumor DNAs was verified using a Qubit dsDNA BR Assay Kit (Thermo Fisher Scientific). Tumor DNAs and reference DNA provided in the Agilent Kit were labeled, purified, and co-hybridized in equal quantity to the Agilent Microarrays, for 21-24 h. Arrays were washed and scanned on a SureScanMicroarray Scanner according to the manufacturer's protocols (Agilent). Images were acquired using the CytoScan Software V. 2. 7 (Thermo Fisher Scientific) and analyzed on CytoGenomics Software V. 2. 7 (Agilent).
Acknowledgements We thank the  Hybrigenics  staff for their contribution, the staff of  Curie-Hospital René Huguenin  for their assistance in specimen collection and patient care, and  Lou Cadau  for the excellent technical assistance. We are grateful to  Dr. R. I. Aqeilan  for kindly providing the Myc-WWOX and Myc-WWOX Y33R plasmids and  Dr. G. Melino  for kindly sharing the HA-p73α construct.
(2014 Melkonian et al. ( , 2016)). The goal is to test the sensitivity of the JIFwide mass balance to the ASTER DEM generation software. 3D coregistration of the AST14DEMs was performed using the same steps as the ASP DEMs. Unlike the ASP DEMs, the AST14DEMs contain no data gaps, as they are filled by interpolation. From the time series of 3-D coregistered ASTER DEMs, the rate of elevation changes (dh/dt in the following) was extracted for each pixel of our study domain in two steps  (Berthier et al. , 2016). The SRTM DEM was excluded when extracting the final dh/dt. dh/dt were calculated for the entire period (from 2000 to 2016) and also for different subperiods for the sake of comparability to published mass balance estimates. For both icefields and in each 50 m altitude interval, dh/dt lying outside of ±3 normalized median absolute deviations (NMADs) were considered as outliers.
We found uncertainties of 0. 03 m a -1 for JIF and 0. 04 m a -1 for SIF from 2000 to 2016. When data gaps occurred in the dh/dt map, we conservatively multiplied these uncertainties by a factor of 5. A ±5 % uncertainty for glacier area  (Paul et al. , 2013)  and ±60 kg m -3 for the density conversion factor  (Huss, 2013)  were used.
s. l. Above this elevation, their values are systematically more positive. The difference reaches 0. 7 m a -1 at 800 m a. s. l. and then remains more or less stable, around 0. 7-0. 9 m a -1. The  Melkonian et al. (2014)  data suggest thickening of the areas above 1350 m a. s. l. , where 62 % of the JIF area is located. For SIF, a good agreement is found between our dh/dt and that of  Melkonian et al. (2016)  below an elevation of 1300 m a. s. l. Above 1300 m the two curves diverge. Our dh/dt become less negative until 2100 m a. s. l. , where they become indistinguishable from 0 m a -1 up to the SIF's highest elevation band. Conversely, in the  Melkonian et al. (2016)  dataset, dh/dt increases rapidly, crossing 0 m a -1 at ∼ 1650 m a. s. l. , finally arriving at a thickening rate of > 0. 7 m a -1 above 2000 m a.
s. l. Thus the difference in SIFwide mass balance between the two datasets is due to difference in dh/dt above 1300 m a. s. l. , where 66 % of the SIF icefield area is found. Comparison of our dh/dt estimates to the ones derived from repeat laser altimetry data is not straightforward because the survey periods differ. For example, for the JIF, six out of nine glaciers were sampled for the first time in 2007. In most cases, it would be technically possible to use a temporal subset of the ASTER DEMs to match the time period of altimetry surveys, but, as said above, this would be at the cost of the coverage in our dh/dt maps and would lead to much more uncertain mass balance estimates. Consequently, we preferred to extract dh/dt and the individual glacier mass balance for the longest available time period in the ASTER series (from 2000 to 2016) in order to maximize coverage and thus minimize uncertainties.
Icefield The pattern of dh/dt with altitude for individual glaciers is in broad agreement between laser altimetry and our ASTERbased results (Fig. S1  in the Supplement). Importantly, for both datasets, no clear thickening was observed in the accumulation areas of glaciers. When individual elevation bins of 50 m are considered, averaged differences between dh/dt from laser altimetry and the ASTER DEMs are typically 0. 2 to 0. 3 m a -1 for individual glaciers. This level of error is similar to the one found previously for the ASTER method in the Mont Blanc area  (Berthier et al. , 2016). Glacier-wide mass balances for individual glaciers match well (Table  1 , Fig. S2 ). The mean mass balance of these 12 glaciers is nearly the same (-0. 73 and -0. 74 m w. e. a -1 ) using the two techniques.
Discussion We find an excellent agreement between repeat laser altimetry survey and our multi-temporal analysis of ASTER DEMs in term both of mass balances and of the pattern of dh/dt with altitude for the JIF and SIF since 2000 (Figs. S1-S2 ). This agreement suggests that an appropriate analysis of centerline data may be sufficient to measure the glacier-wide mass balance of these glaciers as previously shown for the nearby Glacier Bay area  (Johnson et al. , 2013). Our results also suggest that the limited number of glaciers sampled using laser altimetry are representative of the icefields as a whole. This is rather expected for the JIF because nine glaciers covering a large fraction of the icefield (66 %) were monitored using airborne data, but it was not straightforward for the SIF, where only three glaciers, accounting for 15 % of the total icefield area, were surveyed.
This agreement between our ASTER results and airborne laser altimetry, together with the fact that most studies point toward steady or accelerating mass losses in southeast Alaska (see Sect. 1), suggests that the mass balance is overestimated in  Melkonian et al. (2014 Melkonian et al. ( , 2016)). There are two main differences between the  Melkonian et al. (2014 Melkonian et al. ( , 2016) )  method and ours that could explain these contending mass balances: (i) they did not generate the DEM themselves but directly downloaded the AST14DEM product from the LP DAAC website, and (ii) they used the SRTM DEM as a starting elevation in their regression analysis to compute dh/dt. To test the sensitivity of our results to the ASTER DEM generation software, we applied our processing chain (in particular, excluding the SRTM DEM to infer the final dh/dt) to the AST14DEMs.
From 2000 to 2016, we found a JIF-wide mass balance of -0. 67 ± 0. 27 m w. e. a -1 , in striking agreement with the value derived from ASP DEMs (-0. 68 ± 0. 15 m w. e. a -1 ). The pattern of dh/dt with elevation is also in excellent agreement (Fig. 2a ). Uncertainties are nearly doubled when applying our method to the AST14DEMs: this is explained by larger errors of dh/dt offglacier (0. 06 m a -1 for AST14DEMs vs. 0. 03 m a -1 for ASP DEMs) and a lower coverage of the JIF with valid dh/dt data (49 % for AST14DEMs vs. 59 % for ASP DEMs). The latter may appear counterintuitive as the AST14DEMs are delivered with no data gap. The larger percentage of data gaps in the final AST14DEMs dh/dt maps results from the higher noise level of the individual AST14DEMs and demonstrates the efficiency of our filters to exclude unreliable dh/dt values.
This is also consistent with a firstorder estimate of the penetration depth inferred from the elevation difference between the SRTM DEM and laser altimetry profiles acquired in late August 1999 and May 2000 over Baird and Taku glaciers. However, the latter estimates should be considered with care given the complexity of accounting simultaneously for seasonal elevation changes and long-term elevation changes, and the difficulty of estimating the vertical offset between the two elevation datasets on ice-free terrain. The fact that the positive bias in  Melkonian et al. (2014 Melkonian et al. ( , 2016) )  mass balances was larger for the JIF than for the SIF suggests a larger SRTM penetration depth for the JIF. It indicates that this penetration is probably spatially variable (depending on the firn conditions in February 2000) such that a correction determined on a single icefield (or worse a single glacier) may not apply to neighboring glacier areas.