dataesr/scientific-paragraphs-categorization huggingface dataset:
■ MATERIALS AND METHODS Data Sets Used for the Evaluation. Soil characteristics, element contents and ratios of NA:Aqua Regia (AR) extracted elements are summarized in Tables S1-S4 of the Supporting Information (SI). Data Set NL1. contains 49 samples (0-20 cm depth) from The Netherlands of various types including sandy, peat and clay soils with metal contents ranging from background to heavily contaminated levels. 26 Data set NL2 contains 69 soil samples from all diagnostic horizons down to 120 cm from 11 soil profiles in The Netherlands. Metal contents range from background to moderately elevated levels. 26 Element contents in both data sets were determined using NA (4 h extraction) and AR  27  for Al, Mn, Fe, V, Cr, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Sb, Ba, and P using archived soil samples.
Additionally, NL 1 includes Cd, Cr, Cu, Ni, Pb, and Zn contents extracted with 0. 01, 0. 1, 0. 43, 2 M HNO 3 , and 0. 05 M Na 2 EDTA and measured pH in these extracts. All soils were extracted with 0. 01 M CaCl 2 (1:10 weight to volume ratio), in which pH, DOC and concentrations of Cd, Cu, Ni, Pb, and Zn were measured. A subset of 70 samples from NL1 and NL2 was extracted with 0. 01 M CaCl 2 in which dissolved As, B, Ba, Be, Co, Cr, Li, Mo, Sb, Se, Sn, V, and S were determined together with PO 4, DOC, and pH. Data Set PRT. contains 136 Portuguese soils (0-15 cm depth) including noncontaminated sites as well as fields impacted by industrial activities and mining practices. 8 Soils were extracted with AR and NA (2 h extraction). A subset of 15 samples was extracted with 0. 01 M CaCl 2 (1:10 weight to volume ratio) in which pH, dissolved concentrations and DOC were determined.
26 ata Set ECN. consists of five top-soil samples from sandy, peat and river-clay soils (see Table  S3  of the SI) in The Netherlands. The soils were extracted in triplicate using 0. 1, 0. 43, 0. 5, and 1. 0 M HNO 3 (48 h extraction). The pH was measured in the filtered extracts. Nitric Acid Extraction. Reactive concentrations in the four data sets were extracted with NA according to modified versions of the extraction procedure by Houba et al. 27  A general description of the NA-extraction is given below, details per data set are given in the SI. The standard extraction time is 2 h. Because the applied extraction time varied among data sets, being 2, 4, or 48 h, we evaluated the effect of time by extracting a subset of the samples from all three data sets for each of the three applied extraction times.
General Description of the NA-Extraction. The sieved and air-dried soil sample (<2 mm) is extracted with 0. 43 M HNO 3 at room temperature. The extraction solution is obtained by dilution of 30 mL concentrated HNO 3 (65%, analytical grade) in 1000 mL ultrapure water. The soil material together with the extracting solution at a 1:10 weight to volume ratio are shaken during 2 (standard), 4 or 48 h (see description per data set). After centrifugation and filtration dissolved concentrations in the filtrates are measured using ICP-AES and/or ICP-MS. The pH after extraction is usually between 0. 5 and 1, which is required to extract the potential environmental available metals, as defined in ISO 17402. 18 For calcareous soils, the final pH may be higher and should be adjusted by adding additional nitric acid.
This is advised to be done using 0. 2 mL of 5 M HNO 3 for each % of CaCO 3 in order to affect as little as possible the solid to solution ratio. 27 Because none of the samples in the evaluated data sets included calcareous soils, no additional nitric acid was added to any of the samples. Effect of Extraction Time. A subset of 11 samples from the NL1, NL2 and PRT data sets was extracted during 2, 4, and 48 h. The samples were selected to resemble the variation in SOM, clay and Al/Fe-(hydr)oxide contents and pH of all samples. To minimize variation due to sample heterogeneity a single sample of each soil was used for the extraction and subsamples from solution were collected after each time. The change in the solid to solution ratio was minimized by extracting 10 g of soil with 100 mL 0. 43 M HNO 3 and taking only 1 mL of the extract for analysis.
Geochemical Modeling. The geochemical model adopted from Dijkstra et al. 20  implemented in the ORCHESTRA software  28, 29  combines advanced models for ion binding, that is, the NICA-Donnan model  30  for particulate (SOM) and dissolved (DOM) organic matter, the generalized two layer model (GTLM)  31  for Fe/Al(hydr)oxides and a Donnan model for clay together with selected mineral equilibria (Table  S5  of the SI). We used the default model parameters included in ORCHESTRA, for elements for which no generic parameters are available we used additional parameters from Dijkstra et al. 20  (see SI section 3).
43 mol•L -1. DOC was not measured in the NA-extracts and was set to the concentration measured in the CaCl 2 -extracts. 26 This concentration is likely a low estimate for DOC in the NA-extract because Dijkstra et al. 20  measured increasing DOC with decreasing pH below pH 4 in pH-static soil extractions. Ion binding to DOM and SOM was modeled with generic humic acid (HA)  32  representing the binding to both humic-and fulvic acids  20, 26  with HA set to 50% of DOM/POM. 20, 26, 33 The content of Al-and Fe-(hydr)oxides, which are partially dissolved, was calculated as the difference between oxalate and NA extracted Al and Fe. The pH was set at 0. 9 based on the average pH in data sets NL1 and PRT. Results have to be interpreted with some care because this pH is below its range in the data used for the parametrization of the NICA-Donnan (pH > 3) and GTLM models (pH > 4).
Model calculations for 248 samples of data sets NL1, NL2, and PRT give a median recovery by the NA-extraction of more than 90% for Co, Ni, Zn, Cd, Pb, Se, and Sb (Figure  1 ). Lower recoveries are predicted for Cu (69%), Cr (2%), and V(47%), elements which share a very high affinity for binding to SOM. Chromium and V were assumed to be present in their trivalent (Cr 3+ ) and tetravalent (VO 2+ ) redox states under the ambient soil conditions. 20, 35, 36 Similarly lower recovery is calculated for the oxyanions As (89%) and Mo (49%). For Cu a clear relation is found between the modeled recovery and the ratio of measured reactive Cu:SOM (Figure  S1a , SI). Consistently, the measured ratio of NA and AR extracted Cu (NA:AR) as a function of Cu(NA):SOM (molkg -1 ) declines toward low Cu:SOM ratios (Figure  S1b , SI).
Model calculations of total dissolved Cr in these CaCl 2 extracts are therefore rather insensitive to the exact value of the model parameters. 26, 37 valuation of the 0. 1, 0. 43, and 2 M HNO 3 Extraction. is limited to data set NL1 and the elements analyzed in these extracts, that is, Cd, Cu, Ni, Pb, Zn, Cr, and As. Model calculations based on 0. 1 M HNO 3 lead to a clear underestimation of dissolved Cu, Cr, and As, that is, the elements with a high affinity for binding to SOM and/or Al/Fe-(hydr)oxides (Figure  4 ) and 0. 1 M HNO 3 is therefore considered too weak to determine their reactive concentrations. Using 0. 43 M HNO 3 the model adequately calculated dissolved concentrations of all elements (-0. 3 ≤ logME ≤ 0. 3) except Pb, and also resulted in lower logRMSE compared to 0. 1 M HNO 3.
Targeted next-generation sequencing and mutational analysis 1] [12] [13]  Libraries were generated using HaloPlex (Agilent technologies, Santa Clara, CA, USA) and sequenced in a MiSeq instrument (Illumina, San Diego, CA, USA). Sequencing data have been deposited at the European Nucleotide Archive (ENA, http://www. ebi. ac. uk/ena) under accession number ERP021212. In addition, exon1α, 1β and 2 of CDKN2A and the 3′UTR region of NOTCH1 were analyzed by Sanger sequencing (Supplementary Methods). Two different bioinformatics pipelines (Dream-Genics and SureCall tools) were used for the alignment and variant calling (Supplementary Methods). Combination of the two algorithms identified 1331 variant calls (Supplementary Table  3 ). The accuracy of the calls was confirmed by verifying 99% (151/152) of the selected variants by Sanger sequencing (Supplementary Methods).
A selection of driver mutations with potential functional effect was performed based on the criteria described in Supplementary Methods and Supplementary Figure  2. Briefly, potential driver mutations included: (1) 'truncating mutations' (n = 274), (2) 'relevant mutations' manually curated based on previous reports in the literature and COSMIC database. This group included somatic and functional mutations and mutations clustering in known functional domains (n = 216), and (3) missense mutations identified as 'functional mutations' by the Mutation Assessor (MA), OncodriveCLUST and SIFT algorithms (n = 271). 18 To test the accuracy of our 'functional prediction' algorithm for missense mutations, we selected 92 variants in 32 patients who had germline DNA available. We observed that 90% of the mutations classified as functional were somatic (28/31) while 89% of the germline mutations were classified as non-functional (24/27) (Supplementary Methods and Supplementary Table  S15 ).
Statistical methods Complete response (CR), progression-free survival (PFS) and overall survival (OS) definitions were the standard ones. 20 χ 2 method was used for categorical variables and Student's t-test for continuous variables. Nonparametric tests were applied when necessary. Actuarial survival analysis was performed by the Kaplan-Meier method and differences assessed by the log-rank test. Multivariate Cox regression analysis was used to assess the independent prognostic impact of different variables in terms of PFS and OS. The P-values for multiple comparisons were adjusted using the Benjamini-Hochberg correction. Statistical analyzes were carried out with SPSS v. 22 and R software v3. 1. 3.
In silico drug prescription Genomic-guided potential therapeutic opportunities for each DLBCL patient were identified in silico by using the Cancer Genome Interpreter modified from our previous described pipeline  16  (https://www. cancergen omeinterpreter. org/). The platform matches the genomic alterations of a tumor with an expert manually curated database of genomic alterations that can be used as biomarkers of drug sensitivity, resistance and severe toxicity. The biomarkers database is organized according to the level of clinical evidence supporting the genotype-phenotype association including clinical guidelines, late (phases III-IV) or early clinical trials (phases I-II), case reports and preclinical studies. 21 The biomarkers are classified as: (1) 'Biomarker and tumor match' for those alterations reported to be targets of specific drugs in DLBCL or other lymphoid neoplasms; (2) 'Biomarker match of different gene mutation' for those alterations reported to confer sensitivity to a given drug in DLBCL upon other amino acid changes and (3) 'Biomarker match and tumor repurposing' for those genomic alterations described as biomarkers of drug response in other cancers.
ACKNOWLEDGEMENTS This study was supported by the  Ministerio de Economía y Competitividad , Grant No. SAF2015-64885-R  (to EC),  Generalitat de Catalunya Suport Grups de Recerca AGAUR   2014-SGR-795  (to EC),  Instituto de Salud Carlos III ,  Spanish Ministry of Health ,  PI12/01536  (to AL-G) and  PI16/00420  (to AL-G), the  Red Temática de Investigación Cooperativa en Cáncer (RTICC)  grant  RD12/0036/0036  (to EC),  RD12/0036/0023  (to AL-G),  RD12/0036/0069  (to MA),  BIO/SA78/15  (to MA) and the  European Regional Development Fund 'Una manera de fer Europa', CERCA Programme/Generalitat de Catalunya. EC is an  Academia Researcher of the 'Institució Catalana de Recerca i Estudis Avançats' (ICREA)  of the  Generalitat de Catalunya. KK has received a research fellowship from the  Uehara Memorial Foundation (Japan).
DT is supported by the  People Programme  ( Marie Curie Actions ) of the  Seventh Framework Programme  of the  European Union  ( FP7/2007-2013)  under  REA  grant agreement number  600388  and by the  Agency of Competitiveness for Companies of the Government of Catalonia, ACCIÓ  and  SAF2015-74072-JIN. CR-P is supported by an  FPI  fellowship. ID is supported by ' Josep Font ' grant from  Hospital Clinic. This work was also supported by  La Fundació la Marató de   TV3  and  EU H2020 Programme 2014-2020  under grant agreements no. 634143  (MedBioinformatics), and the  European Research Council  (consolidator grant  682398 ) (to NL-B). This work was mainly developed at the  Centre Esther Koplowitz (CEK), Barcelona, Spain. We are indebted to the  Genomics core facility  of the  Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)  for their technical help.
= 0 and quoted as in the original publications, except for the 2010 Belle measurement, which is quoted as 1f Υ (5S) u,d with f Υ (5S) u,d from Ref. [16]. Our average of f Υ (5S) s assuming f Υ (5S) B / = 0, given on the penultimate line, does not include the most recent Belle result quoted on the last line. 4   Experiment, year, dataset Decay mode or method Value of f   [16]  Υ (5S) → BBX 0. 263 ± 0. 032 ± 0. 051 Average of all above after adjustments to inputs of Table  3 0  Model-dependent estimate  [17]  events with a pair of non-strange bottom mesons (BB, BB as the fraction of bb events without any bottom meson in the final state. Note that the excited bottom-meson states decay via B * → Bγ and B * 0 s → B 0 s γ. These fractions satisfy f Υ (5S) u,d + f Υ (5S) s + f Υ (5S) B / = 1. (21) The CLEO and Belle collaborations have published measurements of several inclusive Υ (5S) branching fractions, B(Υ (5S) → D s X), B(Υ (5S) → φX) and B(Υ (5S) → D 0 X), from which they extracted the model-dependent estimates of f Υ (5S) s reported in Table  2.
Using the full dataset and an improved B tag selection, BABAR measured  [560] : R(D) = 0. 440 ± 0. 058 ± 0. 042, R(D * ) = 0. 332 ± 0. 024 ± 0. 018 (219) where decays to both e ± and µ ± were summed and B 0 and B -were combined in a isospinconstrained fit. The fact that the BABAR result exceeded SM predictions by 3. 4σ, raised considerable interest. Belle published various measurements using different techniques, and LHCb also joined the effort with a measurement of R(D * ). The most important sources of systematic uncertainties correlated for the different measurement is due to the B → D * * background components that are difficult to disentangle from the signal. In the average the systematic uncertainties due to the B → D * * composition and kinematics are considered fully correlated among the measurements.
32 ± 0. 14)% y (0. 69 Since the previous version of this document  [4] , the b-hadron lifetime and mixing averages have mostly made gradual progress in precision. Notable exceptions with significant improvement are the averages for the mass difference in the B 0 -B 0 system (∆m d ) and the CP violation parameter in B 0 s -B 0 s system (|q s /p s |). In total eleven new results (of which ten from the LHC Run 1 data and one from the Tevatron data) have been incorporated in these averages. On the other hand, all results that remained unpublished and for which there is no publication plan, have been removed from the averages. The lifetime hierarchy for the most abundant weakly decaying b-hadron species is well established, with impressive precisions of 5 fs or less for the most common B 0 , B + and B 0 s mesons, and compatible with the expectations from the Heavy Quark Expansion.
Measurements of CP violation parameters in B 0 s → φφ allow a similar comparison to the value of φ ccs s ; again, results are consistent with the SM expectation (which in this case is very close to zero). Among measurements related to the Unitarity Triangle angle α ≡ φ 2 , results from the ρρ system allow constraints at the level of ≈ 6 •. These remain the strongest constraints, although results from all of BABAR, Belle and LHCb lead to good precision on the CP violation parameters in B 0 → π + π -decays. Knowledge of the third angle γ ≡ φ 3 also continues to improve, with the current world average being (74. 0 +5. 8 -6. 4 ) •. The precision is expected to improve further as more data becomes available at LHCb and Belle II. In semileptonic B meson decays, the anomalies reported in the last version of the document have remained: The discrepancy between |V cb | measured with inclusive and exclusive decays is of the order of 3σ The most important new measurements of rare b-hadron decays are coming from the LHC.
Acknowledgments We are grateful for the strong support of the ATLAS, BABAR, Belle, BESIII, CLEO(c),  CDF , CMS, D0 and LHCb collaborations, without whom this compilation of results and world averages would not have been possible. The success of these experiments in turn would not have been possible without the excellent operations of the BEPC, CESR, KEKB,  LHC ,  PEP-II , and  Tevatron  accelerators. We also recognise the interplay between theoretical and experimental communities that has provided a stimulus for many of the measurements in this document. Our averages and this compilation have benefitted greatly from contributions to the  Heavy Flavor Averaging Group  from numerous individuals. We thank  David Kirkby ,  Yoshihide Sakai ,  Simon Eidelman ,  Soeren Prell , and  Gianluca Cavoto  for their past leadership of HFLAV.
Data sources, search strategy, and selection criteria We conducted a systematic literature search using the online databases MEDLINE and EMBASE. MeSH terms were used in conjunction with keywords searched in the title and abstract. We restricted our search to studies published between January 2000 and April 2014. There was no restriction to the language of publication. The protocol for our systematic review was developed with guidance from the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement  [7]. It can be accessed at PROSPERO (http:// www. crd. york. ac. uk/PROSPERO/, registration number CRD42016036105). To manage the identified publications, we used EndNote X7 ® (Thomson Reuters) bibliographic database.
Study selection, data extraction and quality assessment A detailed protocol of the selection process for the initial review was published elsewhere  [3]. In short a twostep review process was performed with scanning titles of identified studies first independently by two reviewers (MP and CH), followed by a second screening of all abstracts of articles rated as 'include' or 'unclear'. A disagreement between the two reviewers was resolved by discussion to meet a consensus. If consensus was not reached, a third independent reviewer (TKei or MF) was asked to assess the relevance. Prior to data extraction, two reviewers (MP and MF) independently reviewed full texts of all selected publications rated as 'include' or 'unclear'. A pre-designed data extraction form was piloted with five studies selected from the pool of included studies.
At least two reviewers (TKel, CH, BC, TKei, AR, MP and MF) extracted data from the selected full texts independently with disagreements through referral to a third reviewer  (TKei). For data extraction we used a self-designed (MP) SoSci-Survey questionnaire (https://www. soscisurvey. de/) retrieving information on country, study design, description of the process of recruitment of participants, age of participants, sample size, residency, response rate, observation period, definition of disease and measurement, method of data collection, prevalence of allergic rhinitis only (i. e. subjects without asthma) and asthma only (i. e. subjects without allergic rhinitis) as well as coexisting allergic rhinitis and asthma stratified by sex. Prevalences for each study were calculated using the number of participants with the respective disease as numerator and the total number of participants as denominator.
Quantitative data synthesis Study populations were divided into age ranges of childhood (0-10 years of age), adolescence  (11-17 years) , or adulthood  (18-79 years). For each study, we extracted the prevalence rates of coexisting allergic rhinitis and Fröhlich et al. Clin Transl Allergy (2017) 7:44 asthma, as well as of allergic rhinitis only, and asthma only separately for male and female participants. We then calculated male-female ratios for each study, as well as pooled male-female-ratio estimates with 95% confidence intervals (95% CI) using random-effects meta-analyses with the inverse variance method (SR). Heterogeneity between the studies was measured by I 2. Statistical analyses were done using Review Manager (RevMan), Version 5. 3. Copenhagen (The Nordic Cochrane Centre, The Cochrane Collaboration, 2014) and R (R Foundation for Statistical Computing, Vienna, Austria).
Characteristics of included studies 1222 out of 6539 publications were selected by title screening. Of those, 247 studies were eligible for data extraction since they reported prevalence of allergic rhinitis stratified by sex. Finally, 10 studies reporting the prevalence of asthma alone and allergic rhinitis with coexisting asthma were included into the systematic review (Fig. 1 , Table  1 )  [8] [9] [10] [11] [12] [13] [14] [15] [16]. Six studies provided sex-specific prevalence of coexisting allergic rhinitis and asthma, allergic rhinitis only, and asthma only in children (0-10 years), two studies in adolescents  (11-17 years) , and two studies in adults (18-79 years). Studies with a broad age range were categorised as closely as possible to the targeted age groups, using the mean age of the participants for one study  [16].
Risk of bias We tried to identify all population based studies reporting prevalence of coexisting allergic rhinitis and asthma. Given that such observational studies require large samples, it seems unlikely that a study of this dimension will have been published and not identified by our search. Furthermore, in population-based prevalence studies publication bias seems to be less of a concern than e. g. in interventional studies. Thus, we believe that a bias due to unpublished data is unlikely. Our systematic review was embedded in a larger review considering the difference in prevalence for rhinitis only  [3]. Although we used broad inclusion criteria, we may have missed studies that provided information on prevalence of having allergic rhinitis and asthma but did not provide information of having allergic rhinitis only or were published in journals that are not listed in the 2 major databases of medical literature, MED-LINE and EMBASE.
Urinary creatinine concentration was measured by Diagnostic Creatinine Assay (Sigma, Poole, UK) (see ref. 51 for details). This allowed us to assess urinary protein output as mg/mg creatinine, and examine whether any differences in urinary protein concentration were due to differences in urine dilution or to a difference in protein output per se. Protein and creatinine data are provided in Supplementary Table  S2. SDS-PAGE analysis of urine was according to ref. 74, using a tris-chloride/tris glycine discontinuous buffer system under reducing conditions. Native gel electrophoresis  42  was used to resolve charge variants of MUPs. Electrospray ionization mass spectrometry. All ESI-MS  75  was undertaken on a Q-ToF Micro mass spectrometer (Waters, Manchester, U. K. ) in positive ion mode. All data sets were processed at 0.
Data analysis. General linear models examined the fixed effects of subspecies, sex and geographical origin (contact/allopatric) on total urinary protein output (mg/mg creatinine), urinary protein concentration and urinary creatinine concentration (data for n = 143 samples). These response variables were first log transformed to meet the assumptions of parametric analysis (normality of residuals confirmed by Shapiro-Wilks, p > 0. 05 after sequential Bonferonni). Intact mass profiles (n = 137 samples) were aligned for each subspecies, sex and origin (contact/allopatric) to identify the mass peaks consistently expressed across multiple samples before calculating an average profile for each group in SpecAlign. For central MUPs, the ESI/MS peak heights of each of the masses were then summed to calculate the expression of each peak in a sample profile as a proportion of total central MUP output.