dataesr/scientific-paragraphs-categorization huggingface dataset:
Case study 1: Identification of regulatory variants in the "Platinum" Genomes haplotypes The set of high-confidence variants from the two CEU (Northern Europeans from Utah) human Platinum Genomes NA12877 and NA12878  [26]  were downloaded through the Amazon Web Service (AWS) Command Line Interface from the Illumina Platinum Genomes AWS S3 bucket ( https://github. com/Illumina/PlatinumGenomes ). The downloaded VCF files contained phasing information of each CEU individual haplotype configuration. The genome version used was GRCh37. We selected SNPs intersecting with the annotated DNAse-seq clustered peaks V3 from the ENCODE project  [27]. The VCF file with the selected SNPs was processed using convert-variations with the option phased and then the haplotype sequences were reconstructed with retrieve-variation-seq.
8) and a maximal distance of 200 bp. Annotations (chromosomal location, type of genomic region) of the resulting 612 SNPs (  48 DA + 564 LD) were collected from Ensembl BioMart  [29, 30]. We then selected SNPs in non-coding regions, resulting in a set of 572 SNPs of interest (SOIs) for the detection of regulatory variants. Using SNPs in LD we determined LD-Block regions, we then filtered the these regions overlapping them with ChIP-seq peaks, and calculated the enrichment of TF binding peaks using ReMap  [31]  , we then calculated enrichment for disease annotations using R XGR package  [32]. Finally, we used retrieve-variation-seq to retrieve the sequence variants around each SOI, and predicted the impact of the variation on TF binding for each motif of the JASPAR non-redundant RSAT motif collection  [24]  using variation-scan , with a threshold of 1e-4 on the p-value and 100 on the p-value ratio.
Results The Var-tools provide companion programs enabling the retrieval of variants ( variation-info) , their surrounding sequences ( retrieve-variation-seq ) and interconversion between file formats ( convert-variation ). The main predictive program is variation-scan , which can be used with any set of variants provided by the user (in vcf or gvf formats) or annotated in Ensembl (from a list of rsIDs or a bed file to identify overlapping variants in genome coordinates), with any set of motifs selected from the collections available in RSAT, or provided by the user.
variation-scan accurately assesses the effect of experimentally validated regulatory variants The original version of variation-scan  [44]  required approximately five hours to assess the allele effect of nine millions variants. The novel version  [4]  significantly reduces the processing time to about one hour (Supplementary Figure  3 ). To evaluate the performance of variation-scan , we used an experimentally validated regulatory variant set obtained from a Massively Parallel Reporter Assays (MPRA) experiment  [25]. For all of the assessed allele pairs, we compared the obtained weight score difference from variation-scan, to the mRNA/DNA ratio of the MPRA (see methods). As shown in Figure  2A , we are able to recover 96. 8% of the experimentally validated variants with variation-scan. Using the variants reported as positive in the MPRA data set, we were able to observe correlation between the weight difference and the MPRA mRNA/DNA ratio in positive variants, showing that variation-scan gives accurate measurements of the impact of regulatory variants (Figure  2 B ).
Acknowledgements We thank the persons contributing to the maintenance of the RSAT servers, in particular  Laboratorio Nacional de Visualización Científica Avanzada (Mexico)  specially  Luis Alberto Aguilar Bautista  and  Jair Garcia Sotelo , the  ABims platform in Roscoff, France ,  Pierre Vincens  at the  ENS, Paris and Aurora Martín Cotaina from EEAD-CSIC  for her help on managing the Plants server. We acknowledge  Salvatore Spicuglia  for useful comments during the development of the tools. We thank  Lambert Moyon  and  Swann Floc 'hlay for providing feedback testing Var-tools. We thank  Mauricio Guzman  for styling the figures.
Toporov et al. [11]  studied the detailed numerical and experimental investigation of pulverized coal swirl flames. The combustion tests were performed in a vertical pilot-scale furnace (100 kw). The comparison between measurements and simulation has shown the validity of the numerical method. The Kangwapongpan et al. [12]  have been investigated the influence of radiation model in oxy-coal combustion, using the commercial code ANSYS Fluent 12 with the RSM model applied for the prediction of turbulent flow. Non-gray and gray gas approach for the calculation of radiative properties is tested. The predictions results obtained using a non-gray gas approach have a good agreement to experimental data of axial velocity and gas temperature in several locations compared than gray gas formulation WSGG-SmithG.
Collazo et al. [13]  studied numerically a domestic pellet boiler. The biomass is burned with several excess air ratios. The results show that the increasing excess air ratio decreases the emission of CO inside to the furnace and dilute the combustion gas produced. Murat et al. [14]  studied the experimental effect of excess air ratio in the emission characteristics of co-combustion of a low calorie and high sulfur-lignite coal and woodchips in a circulating fluidized bed combustor. It's shown that increasing the excess air lowered temperature but mixing in the combustor was improved. Recently Bonefacic et al. [15]  are studied the numerical analysis of co-firing pulverized coal and biomass in a vertical cylindrical laboratory furnace  (20 kw). A 3D-model of combustion was created using the CFD software FLUENT, to investigate the excess air and particles size effects on combustion characteristics of pulverized coal and biomass flames.
2 for the one swirl case and Sn = 1. 2 and 0. 9 for the double swirl case. Results showed that the flame was more stabilized and close to the air inlet section for the swirling jet cases and the temperature of the burned gases reached its maximum value of 1560 K. The aim of this is to study numerically the effects of excess air ratios on the flow behavior and flame characteristics of pulverized biomass flames. The development and application of a CFD model for pulverized olive cake combustion is presented. The Discrete Phase Model (DPM) of the commercial CFD software FLUENT 14, originally developed for the combustion of pulverized coal, was investigated regarding its capability to predict pulverized biomass combustion. The numerical approach is based on Reynolds Average Navier-Stokses (RANS) equations.
A post-Hajj questionnaire that collected clinical data and information on compliance with face masks use as well as hand washing, use of disinfectant hand gel and disposable handkerchiefs, was completed during a face-to-face interview, 2 days before the pilgrims' return to France. Pharyngeal swabs were collected upon inclusion on August 30 th (pre-Hajj specimens) and 2 days prior to the return of the pilgrims on September 30 th , 2015 (post-Hajj specimens). Influenza-like illness (ILI) was defined as the presence of cough, sore throat and subjective fever  [14]. The protocol was approved by the Aix-Marseille University institutional review board (July 23rd, 2013; reference no. 2013-A00961-44). The study was performed in accordance with the good clinical practices recommended by the Declaration of Helsinki and its amendments.
These effects have a non-trivial impact on clustering estimates. The statistics to be measured from the survey can be modified to remove the affected scales (e. g. Burden et al. 2017; Pinol et al. 2017) , but in doing so, information is lost. Bianchi & Percival (2017)  have proposed a method to correct clustering measurements by estimating, from many runs of the fibre assignment algorithm, the probability that a pair of galaxies will be targeted, and have shown that this method can provide an unbiased correction to the darktime ELG sample  (Bianchi et al. 2018). The method has also been shown to be successful when applied to data from the VIPERS survey  (Mohammad et al. 2018). Galaxies in the BGS have a variety of properties, and cover a wide range of galaxy bias. Many kinds of galaxy samples can be selected from the survey, such as volume limited samples, stellar-mass selected samples and colourselected samples.
4 we use r bright = 19. 452 and r faint = 19. 925, which in the BGS mock catalogue gives number densities of 818 deg -2 and 618 deg -2 for the bright and faint samples respectively. We also randomly promote 10% of the faint sample to have the same priority as the bright sample (see Section 2). 3 90% of 1 pass is chosen as a realistically incomplete dataset, representing what might be available one third of the way through the survey, where certain fields are missed due to observing conditions. Table  1. Percentage of the survey area covered by N overlapping tiles after 1 pass with 10% of tiles missing, and after the full 1, 2 and 3 passes. The total area covered by each pass is calculated by finding the fraction of objects in a random catalogue that can be potentially assigned a fibre.
4. To make sure the priority 1 and 2 galaxies have number densities of 818 deg -2 and 618 deg -2 , we define priority 1 and 2 galaxies using the magnitudes rDECam = 19. 452 and rDECam = 19. 925. 6 The mock is first cut to the set of galaxies which are within the patrol radius of a fibre in the full 3-pass survey (with no dither), and therefore could potentially be assigned a fibre. 7 We run 2048 random realizations of the fibre assignment algorithm (∼ 500 CPU hours), with the full 3 passes of tiles to simulate the complete survey. From the survey simulation output, it is also possible to determine which galaxies were assigned fibres in the first or second pass, allowing us to simulate a more incomplete survey without having to re-run the fibre assignment code. In addition to the full 3 passes, we also determine which galaxies are targeted in 1 pass, with a random 10% of tiles missing (which are the same tiles in each realization), to simulate a dataset that might realistically be achieved after 1/3 of the duration of the survey with a survey strategy that prioritizes area (i.
Clustering Estimates Correlation functions are calculated using the publicly available parallelized correlation function code twopcf 11  , which contains an efficient implementation of the PIP weighting scheme. The code can also efficiently calculate jackknife errors in a single loop over the galaxy pairs  (Stothert 2018). To create the random catalogue, we uniformly generate random points on the sky, only keeping those that fall within the patrol region of a fibre, with no dither, so that the random catalogue covers the same footprint as the input catalogue. For illustrative purposes to compare correlation function correction techniques, we assume the parent volume limited sample is known, and assign each object in the random catalogue a redshift randomly sampled from this distribution. This ensures that the number density of objects in the random catalogue has the same evolution as the data catalogue.
Discussion We have shown in the previous section that the PIP weighting scheme, in combination with angular upweighting, is able to produce an unbiased correction to clustering measurements in the BGS, even for a highly incomplete survey. One simplifying assumption we have made is that the galaxies in the parent sample are known. The angular weighting term from Eq. 5 includes DD (p) , the angular datadata pair counts of the complete parent sample (and similarly for the DR counts, DR (p) ). For a flux-limited sample, the parent sample is known, but this is not true in the case of a volume limited sample, since every redshift would need to be measured to determine an absolute magnitude, and hence which galaxies belong in the sample. When applying ξ 4 (s) (h -2 Mpc 2 ) 10 -1 10 0 10 1 10 2 s (h -1 Mpc) 0.
However, in many of these fibre assignment realizations, the tiling would cover large areas of the sky which are outside the BGS footprint. Despite this, we expect that the total number of realizations needed to accurately estimate pair weights will be smaller, since the tail of pairs with extremely high weights are much more likely to be targeted in the realizations where they are covered by multiple tile overlaps. A similar method to this is used in  Mohammad et al. (2018) , where in order to estimate pair weights for galaxies in the VIPERS survey, the parent catalogue is rotated by angles of either 0, 90, 180 and 270 degrees, and the spectroscopic mask is moved spatially. The PIP weighting scheme is shown to work well, and this is the only published example of applying the PIP weights to a real dataset.
ACKNOWLEDGEMENTS This work was supported by the  Science and Technology Facilities Council  ( ST/M503472/1 ). AS, JH, SC, LS, PN and CB acknowledge the support of the  Science and Technology Facilities Council  ( ST/L00075X/1 ). JH is supported by the  European Research Council  ( ERC-StG-716532-PUNCA ) and the  Durham co-fund Junior Research Fellowship. LS acknowledges the support of the  Science and Technology Facilities Council  ( ST/J501013/1 ). PN acknowledges the support of the  Royal Society  through the award of a  University Research Fellowship. JEFR acknowledges support from  COL-CIENCIAS  Contract No. 287-2016 , Project  1204-712-50459. This work used the DiRAC Data Centric system at Durham University, operated by the  Institute for Computational Cosmology  on behalf of the  STFC DiRAC HPC Facility  (www.
Study design and data The popPK model was based on data from four clinical studies of pola 0. 1-2. 4 ] [9] [10] [11] [12]  Pola was administered as a mixture of drug-to-antibody ratio (DAR) species ranging from 0-8, with a mean DAR of 3. 65 (Genentech data on file). Studies were approved by the medical ethics committee and carried out in accordance with the International Conference on Harmonization guidelines for Good Clinical Practice. A relatively intensive PK sampling (Table  S1 ) was used for the single-agent DCS4968g study (>30 plasma samples/patient across multiple cycles), to help define the model structure. For the other studies, less-intensive PK sampling was performed.
Modeling methods The dose of acMMAE in the popPK data set was converted to MMAE equivalent dose in μg, which equates to: where mean DAR = 3. 65, molecular weight (Daltons) for unconjugated MMAE = 718, and molecular weight of the antibody-drug conjugate = 145,001. The popPK analysis was conducted via nonlinear mixedeffects modeling with Nonlinear Mixed-Effect Modeling (NONMEM) software, version 7. 3. 0 (ICON Development Solutions, Ellicott City, MD), using the first-order conditional estimation method with eta-epsilon interaction. Graphical and all other statistical analyses, including the evaluation of NONMEM outputs, were performed using R version 3. 3. 3. 21 he integrated acMMAE-MMAE popPK model was developed sequentially in four steps. For each step, the model that best described the observed data was determined by overall goodness-of-fit (GOF) measures, including GOF plots, prediction-corrected visual predictive check,  22  normalized prediction distribution error (NPDE) plots,  23, 24  and objective function value.
RESULTS acMMAE-MMAE integrated final model Data from 460 patients with NHL from the four pola clinical studies are reported (Table  S1 ) and include 4,215 acM-MAE and 4,194 unconjugated MMAE time-concentration pairs. All data collected with a time after the most recent dose of >6 weeks (1,008 hours) were excluded from analysis given that these data may not be clinically relevant in the context of the regimens tested in the clinical setting (i. e. , q3w and every 4 weeks). All post-first-dose belowlimit-of-quantification data including 215 (4. 5%) acMMAE and 434 (9. 2%) unconjugated MMAE data were excluded from the analysis. Among them, 93 acMMAE and 135 unconjugated MMAE values were collected within the time after the most recent dose of 1,008 hours. The M3 method  27, 28  was not applied to the final model given the low percentage of below-limit-of-quantification data.
PopPK of acMMAE Both the acMMAE alone (model development step 3) and integrated acMMAE-MMAE final model (model development step 4) described the observed acMMAE PK data well with comparable population and individual predictions, indicating that simultaneous fitting of the catabolite (MMAE) and parent (acMMAE) did not impact the fitting of the parent (Genentech, data on file). The acMMAE PK model is a two-compartmental model. Three clearance pathways, i. e. , nonspecific time-dependent linear clearance (CL NS ), linear time-dependent exponentially declining clearance (CL t ), and MM clearance (CL MM ), were identified to describe the data. All three clearance values, as plotted by a typical patient upon pola 1. 8 mg/kg q3w dosing, change over time with CL NS contributing to the majority of the totals clearance (Figure  2a ).
At the therapeutic dose of 1. 8 mg/kg, the CL MM pathway played a negligible role, eliminating only 2% of the dose at each cycle (Figure  2a ). The acMMAE central volume of distribution (V 1 ) was estimated as 3. 15 L, which approximates the plasma volume. PK exposures (assuming pola 1. 8 mg/kg q3w) based on individual empirical Bayes estimates were simulated using the final model and summarized for the 460 patients in the analysis data set (Table  3 ). Coefficients of variation (CV% = mean/standard deviation × 100) for acMMAE AUC and C max were 28. 3% and 16. 8%, respectively, for cycle 1, and 19. 9% and 16. 4%, respectively, for cycle 6. The cycle 6 half-life was the longest for the planned six cycles of dosing, which is the most conservative and was used to estimate the contraception duration of 3 months (in the US label) after the final dose for females of reproductive potential.
Acknowledgments. We thank the patients and their families who participated in the polatuzumab vedotin clinical trials, without whom this study would not have been possible. We acknowledge and thank the investigators and site staff. Medical writing support was provided by  Emma McConnell  and  Andrea Bothwell of Gardiner-Caldwell Communications  and was funded by  F. Hoffmann-La Roche Ltd. Funding. This study was funded by  F. Hoffmann-La Roche Ltd.
Metatranscriptome Analysis Raw sequence data quality was visualized with FastQC and trimmed with Trimmomatic v. 0. 27 as previously described  (Frischkorn et al. , 2014; Alexander et al. , 2015). Reads were mapped to the A. anophagefferens CCMP1984 genome 1 using Tophat v. 2. 0. 4 with default parameters  (Trapnell et al. , 2009). Reads were similarly mapped to other genomes from potentially co-occurring organisms including: Thalassiosira pseudonana, Phaeodactylum tricornutum, Ostreococcus tauri, and Ostreococcus lucimarinus (Supplementary Table  S1 ). Only reads that exclusively mapped to A. anophagefferens were included in this analysis. All project sequence reads are available at the National Center for Biotechnology Information (NCBI) under accession number SRP072764 and bioproject number PRJNA315054.
To evaluate the potential influence of strain heterogeneity on read mapping, reads were aligned from June 14 (S1) against assembled transcriptomes from two strains of A. anophagefferens, the genome isolate (CCMP1984) and an additional toxic isolate (CCMP1850) from the same region. The CCMP1984 RNA was sequenced as described above for field isolates, and CCMP1850 RNA was sequenced as described elsewhere  (Frischkorn et al. , 2014). The raw reads were trimmed from both strains using Trimmomatic  (Lohse et al. , 2012)  and then normalized by k-mer coverage (max coverage 30) and assembled de novo using the Trinity software suite  (Grabherr et al. , 2011). Following assembly, the contigs were clustered at 90% identity using CD-Hit  (Fu L. et al. , 2012)  after the procedures optimized by  Frischkorn et al.
(2014). The reads were then aligned from the June 14 field sample (S1) against the clustered and unclustered contigs from strain CCMP1984 and strain CCMP1850 with Bowtie2 using default settings (0 mismatches allowed). To assign significance in differential expression, Analysis of Sequence Counts (ASC) was used  (Wu et al. , 2010) , with a fold change greater than or equal to 2 and a posterior probability (post-p) > 0. 95 as has been used in studies of similar design  (Dyhrman et al. , 2012; Thomas et al. , 2012; Konotchick et al. , 2013; Frischkorn et al. , 2014; Alexander et al. , 2015; Haley et al. , 2017; Frischkorn et al. , 2018). ASC is an empirical Bayes method that estimates the prior distribution by modeling biological variability using the data itself, rather than imposing a negative binomial distribution.
ASC has been shown to perform similarly to, though more conservatively than, other differential expression analyses implemented on data sets with and without replicates  (Wu et al. , 2010). To assess reproducibility in our data, RNA was extracted from two independent samples from June 22. The RNA pool from the first sample was sequenced at a target depth of 50 million reads (S3a). The RNA pool from the second sample was sequenced at a target depth of 100 million reads (S3). Consequently, S3a and S3 represent replicated field samples sequenced at different depths. Changes in transcript abundance between samples were examined using ASC (fold change ≥ 2; post-p > 0. 95). Only four genes had significant changes in abundance between S3a and S3 (biological replicates). For comparison, over 2000 genes had significant changes in abundance between S1 (June 14) and S3 (June 22).
These data emphasize that the sequencing and analysis are reproducible among independent replicates. Using a Kyoto Encyclopedia of Genes and Genomes (KEGG) framework, global expression patterns were generated as per  Alexander et al. (2015). In brief, KEGG annotations were obtained from the Joint Genome Institute (JGI) 2. KEGG annotations were associated into higher level functional categorizations based on KEGG-defined BRITE hierarchy ko00002  (Kanehisa et al. , 2008). Many genes are associated with multiple different metabolic pathways, thus if a gene was present in more than one pathway it was counted in all pathways. Reads mapping to a KEGG id were normalized to total library size and then normalized to the summed reads with KEGG annotation.
, 2015). The metatranscriptome data were assigned KEGG orthology (KO) to visualize the expression of KO gene families within a KEGG module from bloom peak (S1 and S2) through the initial decline (S3) (Supplementary Dataset S1). There were dramatic transcriptional shifts between all samples (Figure  2 ). The splicesome, RNA processing, carbon fixation, and central carbohydrate metabolism categories all varied between S1 and S2, despite consistent cell densities (Figure  2 ). Compared to S1 and S2, there were changes in the relative expression of the lipid processing, purine metabolism, and proteasome categories in S3 (Figure  2 ). To examine the role of nutrients in bloom dynamics, an in situ nutrient amendment experiment was performed on June 22nd that included four treatments: a no nutrient addition control, +NH 4 + (+N), +PO 4 -3 (+P), and +NH 4 + +PO 4 -3 (+N&P).
FUNDING This research was funded by  NOAA  Grant  NA15NOS4780199  (SD),  NA09NOA4780206  (SD and CG), and  NA15NOS4780183 (CG)  through the  ECOHAB Program , publication number  ECO929. Partial support was also provided by the  World Surf League  through the  Columbia Center for Climate and Life , the  Woods Hole Oceanographic Institution Coastal Ocean Institute , and the  Link Foundation. Kyle Frischkorn  was funded under a  National Science Foundation Graduate Research Fellowship.