dataesr/scientific-paragraphs-categorization huggingface dataset:
0 k-8. 9)+0. 018]•0. 2•α DLA to account for a possible remaining contribution of high-density absorbers in the quasar spectra. This form is motivated by the study led by  [76] , and α DLA is free to vary in the fit. The fit converges to a null value of α DLA. Therefore in our standard fit procedure, we fix α DLA to zero. We summarize the cosmological results in Table  7  for different combinations (BOSS, eBOSS, XQ-100 and Planck). The results for Lyα alone are shown in columns (  1 ) and (  2 ), respectively, for BOSS DR9 (power spectrum computed in PYB13), and for eBOSS (this work). The results are consistent for the two data sets. The only significant change is for the scalar spectral index, n s. The mild tension on n s between Lyα and Planck in PY15 is no longer evident, as shown in Fig.
19. We perform a detailed, step-by-step comparison of the two analyses in order to understand the shift on n s. By reducing the eBOSS data set to those in common with the DR9 quasar sample, i. e. , quasars observed before MJD=55753, we measure n s = 0. 945 ± 0. 007. We then replace the BAL and DLA automatic catalogs that we use for eBOSS by the visually identified DLA and BAL catalogs of DR9  [53]. This change further shifts the spectral index to n s = 0. 937 ± 0. 007. The latter value is fully compatible with the value published in PY15, n s = 0. 937 ± 0. 009. Therefore, the shift observed on the spectral index arises from the combination of a statistical fluctuation and our usage of more complete BAL and DLA catalogs. As visible in Fig. 18 , the correlated absorptions by Lyα and Si III are detected with high significance, and we measure f Si III = 6.
Column (1): results of PY15 with BOSS alone corresponding to PYB13. Column (2): results with eBOSS (this work). Column (3): results for the combined fit of eBOSS (this work) and Planck 2018  [77]. Column (4): results for the combined fit of eBOSS (this work), XQ-100 and Planck 2018. For columns (1-2), we used a Gaussian constraint, H 0 = 67. 3 ± 1. 0. The large quasar parent sample and optimized selection allow us to reduce the statistical uncertainty of the measurement by a factor of two compared to the previous publication of the BOSS collaboration  [40]  referred to as PYB13. The increased data set also allows the addition of a new redshift bin at z Lyα = 4. 6. Despite the lower statistical precision of this bin, it carries useful information from an earlier epoch in the history of the Universe, where the clustering is less affected by non linearities than at lower redshift.
Lyα We performed an extensive thorough investigation of the systematic uncertainties affecting the measurement. To study systematic effects related to the analysis procedure, we employed mocks tuned to match the level of the data power spectrum as well as the evolution of the spectrograph resolution with observed wavelength. We processed these mocks with the exact same pipeline as were the data. To estimate uncertainties related to the spectroscopic pipeline, we used science data themselves, as well as calibration data. The major sources of uncertainty on small scales come from the precision on the determination of the spectrograph resolution, and from the estimation of the noise power. The latter largely dominates over the cosmological power in particular at low redshift. These two issues should be improved with the next generation WEAVE-QSO  [78]  and Dark Energy Spectroscopic Instrument (DESI)  [79]  projects, which will have almost twice the spectral resolution and a higher signal-to-noise ratio for z qso > 2.
1 quasars. On large scales, the dominant source of uncertainty arises from the incompleteness of the BAL and DLA catalogs. The data quality in next generation surveys will improve their identification. Furthermore, work is on-going to improve their automated detection. We performed a detailed comparison of the results obtained in this analysis with those of PYB13. The power spectra are in excellent agreement. Using the model of  [16, 17]  where the power spectrum is described by four cosmological parameters, five parameters for a simplified model of the IGM thermal history, two for the mean transmitted flux, and nuisance parameters for astrophysical and instrumental artefacts, all free to vary in the fit, we obtain comparable cosmological results as previously published. The main change is on the scalar spectral index n s.
Acknowledgments We acknowledge  PRACE (Partnership for Advanced Computing in Europe)  for awarding us access to resources Curie thin nodes and Curie fat nodes, based in France at TGCC, under allocation numbers 2010PA2777, 2014102371 and 2012071264. This work was also granted access to the resources of CCRT under the allocation 2013-t2013047004 made by  GENCI  ( Grand Equipement National de Calcul Intensif ). We thank  Clément Besson  for helpful contributions to the understanding of the estimate of the noise power spectrum. N. P. -D. and Ch. Y. acknowledge support from grant  ANR-17-CE31-0024-01  of  Agence Nationale de la Recherche  for the  NILAC  project. Funding for the  Sloan Digital Sky Survey IV  has been provided by the  Alfred P. Sloan Foundation , the  U. S. Department of Energy Office of Science , and the Participating Institutions.
SCCHN represents a heterogeneous group of tumors arising from the squamous epithelium of the oral cavity, oropharynx, larynx, and hypopharynx. Despite being grouped as a single cancer type, distinct clinical, biological features and response to treatment have been seen between tumors from different subsites  [16] [17] [18] [19]. To characterize the immune microenvironment in SCCOT, the most common SCCHN subtype, we used transcriptome data analysis to estimate immune cell fractions and evaluated cytolytic activity according to mRNA levels of GZMA and PFR1. We show altered immune infiltration and increased cytolytic activity in tumor samples and in clinically tumor-free tongue samples from patients with SCCOT compared to normal tongue from healthy individuals. Most importantly, we found that measures of cytolytic activity in tumor-free samples confer prognostic information, whereas the same analysis of tumor samples does not.
RNA isolation and gene expression profiling Biopsies were fresh-frozen in liquid nitrogen and stored at -80 C until RNA extraction. Procedures for RNA isolation and gene expression profiling for 18 tumors, 12 tumor-free samples, and 14 healthy controls have been previously reported and raw data were deposited in ArrayExpress accession number E-MTAB-4678  [20]. For the rest of the samples, RNA isolation was performed using AllPrep DNA/RNA/miRNA Universal Kit (Qiagen, Hilden, Germany). Quantity and purity of RNA was measured using a NanoDrop ND-1000 spectrophotometer (ThermoScientific, Wilmington, DE, USA). RNA quality was confirmed by Agilent RNA 6000 Nano kit (Agilent 2100 Bioanalyzer, Agilent 241 Cytolytic activity in tumor-free tongue tissue Technologies, Santa Clara, CA, USA). As reported previously, 200 ng of total RNA was processed for gene expression profiling using Illumina HumanHT-12 v4 Expression BeadChip (Illumina Inc.
Cell type estimation and cytolytic activity calculation We applied the xCell method  [10]  to study 34 immune cell types in a total of 66 samples (21 pairs of tumor/tumor-free samples, 8 tumor samples, 2 tumor-free samples, and 14 control samples). Although not described in the original publication  [10] , xCell now also reports an ImmuneScore for each sample according to estimated levels of B cells, CD4 + T cells, CD8 + T cells, dendritic cells (DC), eosinophils, macrophages, monocytes, mast cells neutrophils, and NK cells (https://github. com/dviraran/xCell/blob/master/R/ xCell. R). Granzyme A and perforin are two key cytolytic effectors that are specifically co-expressed in cytotoxic lymphocytes  [13, 14]. To measure cytolytic activity in each sample according to the method of Rooney et al  [14] , microarray probe intensity data for GZMA and PRF1 were extracted and the geometric mean intensity of GZMA and PRF1 calculated for each sample.
Confirmation of microarray data using RT-qPCR Levels of GZMA and PRF1 mRNAs were confirmed using RT-qPCR in 12 healthy controls and in 12 matched pairs of tumor/tumor-free samples. RevertAid H minus first strand cDNA synthesis kit (Fermentas, ThermoScientific, Wilmington, DE, USA) was used for cDNA synthesis and qPCR was performed using an IQ5 multicolor real-time PCR detection system with IQ SYBR Green Supermix (Bio-Rad Laboratories, Hercules, CA, USA). Primers used were GZMA (forward: ATGCTATGACCCAGCCACAC, reverse: GGTTT CACATCGTCCCCCTT), PRF1 (forward: AAGACC CACCAGGACCAGTA, reverse: TCTTGAAGT CAGGGTGCAGC), RPL13A (reference gene, forward: GTACGCTGTGAAGGCATCAA, reverse: GTTGGTG TTCATCCGCTTG). Primers for another reference gene GAPDH were ordered from Primerdesign Ltd (Southampton, UK). The primer sequences were not provided by the company.
Statistics Cell type composition and cytolytic activity were compared between different sample groups using nonparametric Mann-Whitney U test, and Spearman correlation coefficient (rho) was calculated to evaluate correlation strength. Comparisons between clinicopathological variables and cytolytic activity (low versus high) were determined by Fisher's exact test. The Kaplan-Meier method with log-rank test was used to compare survival curves between groups. Cut-off score for patient classification into high or low groups was chosen when showing the most significant difference. For multivariate Cox regression analysis, we considered patient age at diagnosis and TNM staging as covariates. All statistical tests were conducted in IBM SPSS Statistics 25 (IBM Corp. , Armonk, NY, USA). A two-sided P value <0.
Acknowledgements This study was supported by  Lion 's  Cancer Research Foundation ,  Umeå University ; The  Swedish Cancer Society  (contract number  18 0542 );  Umeå University ; Västerbottens Läns Landsting; the  Grant Agency of the Czech Republic  (project  P206/12/G151 ) and the  Ministry of Education Youth and Sports in the Czech Republic  (project  MEYS-NPSI-LO1413 ). The funding sources had no role other than financial support.
Abstract. Coupled physical-biological models usually resolve only parts of the trophic food chain; hence, they run the risk of neglecting relevant ecosystem processes. Additionally, this imposes a closure term problem at the respective "ends" of the trophic levels considered. In this study, we aim to understand how the implementation of higher trophic levels in a nutrient-phytoplankton-zooplanktondetritus (NPZD) model affects the simulated response of the ecosystem using a consistent NPZD-fish modelling approach (ECOSMO E2E) in the combined North Sea-Baltic Sea system. Utilising this approach, we addressed the abovementioned closure term problem in lower trophic ecosystem modelling at a very low computational cost; thus, we provide an efficient method that requires very little data to obtain spatially and temporally dynamic zooplankton mortality.
While one-way coupled approaches neglect feedbacks and therefore imply difficulties at the model interfaces, comprehensive food web models like Atlantis resolve food webs on the basis of species or specific groups and are difficult to parameterise, especially in complex ecosystems. One of the most commonly used food web modelling tools is the Ecopath with Ecosim (EwE) modelling software  (Christensen and Walters, 2004) , which provides an instantaneous snapshot of the trophic mass balance in marine food webs. The combination of the software's dynamical modelling capability (Ecosim) and a tool that replicates the model on a spatial grid (Ecospace) allows for 2-D estimates of the system's response to e. g. policy measures. However the approach still falls short when simulating ecosystem dynamics at high temporal and 3-D spatial resolutions.
Datasets and statistical methods for model analysis As described in  Daewel and Schrum (2013) , we used observational data on surface (depth < 10 m) nutrients (nitrate and phosphate) in the North Sea, which are made available by the ICES (International Council for the Exploration of the Seas, http://www. ices. dk/Pages/default. aspx, last access: 30 November 2011), for nutrient validation. Observations and modelled surface nutrients were averaged over the upper 10 m of the water column and co-located in space and in time, and corresponding statistics were calculated for the sub-areas specified in Fig. 1. The seasonal cycle was not removed prior to the analysis. The reason for this was the sparse data situation, which did not allow for the estimation of a reliable seasonal cycle at each location, in addition to the seasonal cycle changes from year to year.
Thus, the local explained variance η local,k (m) could provide additional information about the regional relevance of an EOF mode and the corresponding PC in percent: η k local (m) = 1 - Var χ (m, t) -p k (m)A k (t) Var (χ (m, t)) ×100, (12) where Var(X) = N t=1 X -X(t) 2 denotes the variance of the field X(t). " In  Daewel et al. (2015) , the method was applied to the potential larval survival (PLS) of Atlantic cod (Gadus morhua), while in this study it is used on estimated MB and fish biomass. Information on the North Sea fish community were collected during the North Sea international Bottom Trawl Survey (NS-IBTS) (ICES, 2012) and are freely available at the International Council for the Exploration of the Sea (http: //www. ices. dk/Pages/default. aspx, last access: May 2012). The NS-IBTS dataset contains spatially resolved, speciesspecific information on fish length (for some target species also age) and catch per unit effort (CPUE: in numbers captured per hour).
Given that our model estimated state variables on the base of carbon biomass, we converted fish length and abundance data to fish biomass (in grams cap-tured per hour) based on published length-weight relationships (LWRs) for each species sampled in the NS-IBTS between 1980 and 1989 (inclusive of these years). LWRs were derived from  Coull et al. (1989) ,  Froese et al. (2014) , and FishBase  (Froese and Pauly, 2000)  for teleost species;  McCully et al. (2012)  and  Templeman (1987)  for rays and skates;  Klaoudatos et al. (2013)  for crabs; and  Pierce et al. (1994)  and  Guerra and Rocha (1994)  for squids. Total body weight (W ) is a function of total length (L) following the relationship W = aL b , where b is a parameter indicating isometric growth in body proportions if b ∼ 3, and a is a parameter describing body shape and condition if b ∼ 3.
Model performance and nutrient dynamics To get a more direct measure of the validity of the modelled fish functional group we used data from the NS-IBTS. In Fig. 10 , we compared the mean fish functional group biomass distribution to the fish biomass from the NS-IBTS calculated following the method described in Sect. 2. 3. We classified species within the North Sea fish community into "demersal" (Fig. 10a ) and "pelagic" groups (Fig. 10b ) based on life-history characteristics, and then summed the biomass of each group to form a "combined" (Fig. 10c ) group. In contrast to the species-specific differentiation into groups used for the observations, the model results do not provide this level of detail. Here, the differentiation was performed based on the vertical distribution of the fish biomass.
Acknowledgements. This work is a contribution to the  FP7 SEAS-ERA SEAMAN collaborative project  financed by the  Norwegian Research Council  (grant no. NRC-227779/E40 ). We would like to thank  Marie Maar  for her constructive comments on an earlier version of the paper. Furthermore, we are grateful to an anonymous reviewer and  Hagen Radtke , whose thoughtful comments helped to improve the paper. The article processing charges for this open-access publication were covered by a  Research Centre of the Helmholtz Association. Review statement. This paper was edited by  Guy Munhoven  and reviewed by  Hagen Radtke  and one anonymous referee.
Code and data availability. Model code access and data can be obtained upon request. The code is available from the Helmholtz Centre Geesthacht Git repository https://coastgit. hzg. de/udaewel/ hamsom-ecosmoe2e/ (last access: 30 April 2019), and is licensed under apache license version 2. Author contributions. UD and CS designed the study and developed the modelling approach. UD performed the simulation and data analysis. JM contributed to the study by compiling a comprehensive dataset of fish biomass from the IBTS data. All co-authors contributed to writing the paper.
Fluorescence PCR and capillary array electrophoresis To genotype the knockout cell lines, DNA extracts of clonal cells were subjected to PCR using 5′-FAM-labeled primers (supplemental Table  S1 ). The PCR amplicons were resolved using an ABI 3730 DNA analyzer. Data analysis was performed by GeneMapper software version 3. 1. The positions of the peaks indicate the sizes or lengths of PCR products by using ROX-labeled standards as described previously  (13).
Detection of cell surface CD36 and DiI-OxLDL uptake by flow cytometry Cells were collected and washed with cold PBS, and cell suspensions were incubated with Biotinylated anti-CD36 Antibody (Biolegend) for 30 min on ice, washed with cold PBS three times, and followed by fluorescent secondary antibody staining for another 30 min on ice. Unstained cells were used as a negative control. DiI-OxLDL (Yiyuan Biotechnology) was used to trace the Ox-LDL uptake. Cells were incubated with DiI-OxLDL for 1, 2, or 3 h at 37°C in 5% CO 2. RAW264. 7 macrophage cells were harvested, washed, and resuspended in FACS buffer. Samples were acquired on the FACS Canto flow cytometer (BD). Data were further analyzed by FlowJo10. 1.
Affymetrix microarray expression analysis and pathway enrichment analysis Genome-wide gene expression analysis was performed using Affymetrix GeneChip Mouse Genome 430 2. 0 Array (CapitalBio Technology). The data are deposited in the Gene Expression Omnibus database under the accession number GSE125746. Gene Ontology enrichment analysis to illustrate pathways affected by Vav deficiency were performed by using R package goseq version 1. 16. 2, and Gene Ontology terms with a corrected P value >0. 05 were excluded. For Kyoto Encyclopedia of Genes and Genomes analysis, the genes were mapped directly to the Kyoto Encyclopedia of Genes and Genomes database. The enriched pathways were then obtained using a q-value cutoff of 0. 05 with the R hypergeometric function and R q-value package  (14).
The single cells were stained with an antibody mix and acquired on the FACS Canto flow cytometer (BD). Cell sorting was performed with a FACSAria™ Fusion flow cytometer (BD). Blood monocytes from WT B6 mice were gated on CD5  , Ly6G  , and CD19  cells and CD115 + and CD11b + cells, which were further divided by CD36 surface expression for RNA sequencing. FACS data were analyzed by FlowJo10. 1.
The cells from Vav1, Vav2, and Vav3 knockout clones with WT controls were incubated with anti-CD36 IgA that was then cross-linked using FITC-labeled goat anti-mouse IgA  chain. In Vav1, Vav2, and Vav3 knockout RAW264. 7 macrophages, CD36 staining on the cell surface showed comparable fluorescence intensity under a confocal microscope; however, the intercellular staining was dramatically reduced in knockout cells (Fig. 6G ). In further experiments we analyzed eight replicates for each genotype involved in the imaging study by flow cytometry. The FACS data of the CD36 mean fluorescence intensity (MFI) showed that deficiency in each member of the Vav family proteins resulted in a significant decrease in CD36 internalization (Fig. 6H ). Taken together, our results showed that Vav1, Vav2, and Vav3 form interactive complexes and all three Vav family members are mandatory for CD36 internalization and therefore interdependent in promoting foam cell formation and atherosclerosis.
This dataset is, to our knowledge, the largest ever reported for the investigation of NPF in tropospheric volcanic plume conditions, and it allowed for the first time a statistical approach to characterize the process and also assessment of its relevance with respect to non-plume conditions. NPF was observed on 90 % of the plume days vs. 71 % of the non-plume days during the 4 months when the eruptions occurred. The events were on average detected earlier on plume days, most likely benefiting from larger amounts of precursors available at the site prior to nucleation hours. The overall effect of the plume conditions on the particle growth rate was limited. However, with the exception of September, particle formation rates were significantly higher on plume days. The signature of the volcanic plume on the aerosol spectra up to d p = 600 nm was further investigated based on the analysis and fitting of the particle size distributions recorded under in-plume and offplume conditions.
(2012). Despite providing new and highly valuable information, the studies by  Boulon et al. (2011b)  and  Sahyoun et al. (2019) , however, had some limitations. Indeed, they were both based on short datasets, which did not allow for any statistical approach to evaluate the relevance of the process nor proper comparison with the occurrence of NPF outside of plume conditions. Also, airborne measurements conducted in the vicinity of Etna and Stromboli allowed  Sahyoun et al. (2019)  to investigate the presence of the newly formed particles soon after the emission of their precursors from the vent of the volcanoes up to few tens of kilometres. They were, however, unfortunately not able to document properly the evolution of the particle size distribution along the volcanic plumes, but analysed instead the particle concentration in relatively broad size ranges (2.
, 2012). The particle size distributions measured with the DMPS were used to detect the occurrence of NPF and to calculate the event characteristics, such as the particle formation and growth rates (see Sect. 2. 2), and to further evaluate the potential of the newly formed particles to reach CCN-relevant sizes. DMPS data were also used to calculate the condensation sink (CS), which describes the loss rate of gaseous precursors on pre-existing particles  (Kulmala et al. , 2012). In addition to DMPS, an air ion spectrometer (Airel, Estonia;  Mirme et al. , 2007)  was operated at Maïdo between May and October 2015. The AIS includes two DMAs which allow simultaneous detection of negative and positive ions and charged particles in the mobility range 0. 0013-3. 2 cm 2 V -1 s -1 , corresponding to mobility diameter 0.
8-42 nm in NTP conditions  (Mäkelä et al. , 1996). Each analyser is working with a flow rate of 90 L min -1 (sample flow rate of 30 L min -1 and sheath flow rate of 60 L min -1 ) in order to reduce diffusion losses in the instrument, and measurements were conducted through an individual short inlet (30 cm) to further limit the loss of ions in the sampling line. AIS data were collected with a time resolution of 5 min and were analysed in the present work to get further insight into the timing of the early stages of the NPF process. SO 2 mixing ratios used to monitor the occurrence of volcanic plume conditions at Maïdo were measured with a UV fluorescence analyser (Thermo Environmental Instrument TEI 43) operated by ATMO Réunion (formerly referred to as ORA, Observatoire Réunionnais de l'Air), with a time resolution of 15 min.
The detection limit of the instrument was about 0. 5 ppb, which is above the usual SO 2 mixing ratios encountered at Maïdo outside of the eruptive periods of the Piton de la Fournaise (see Fig. A1  in  Foucart et al. , 2018). Finally, meteorological parameters recorded with a time resolution of 3 s were used as ancillary data. Global radiation was measured with a sunshine pyranometer (SPN1, Delta-T Devices Ltd. , resolution 0. 6 W m -2 ), and other variables of interest including temperature, wind speed and direction and RH were recorded using a Vaisala Weather Transmitter WXT510. An overview of the data availability for all abovementioned instruments between 1 January and 31 December 2015 is provided in  Foucart et al. (2018, Fig. 2 ).