dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This study is part of the  SILNE-R project , which is supported by the  European Union 's  Horizon 2020 research and innovation programme , under grant agreement  635056 ; and of the project ' Tackling socio-economic inequalities in smoking (SILNE) ', which is funded by the  European Commission, Directorate-General for Research and Innovation , under the  FP7-Health-2011 programme , with grant agreement number  278273. This work was also supported by the  Spanish Network on Addictive Disorders  [grant number  RD12/ 0028/0018 ]. The stay of  Marina Bosque-Prous  at the  University of Amsterdam  was partially funded by an  Exchange Award  from the  European Foundation for Alcohol Research. The funding sources had no involvement in the study design, the collection, analysis and interpretation of data the writing of the report and the decision to submit the article for publication.
The clusters were defined by the Apa1/Asc1 pulsotype patterns and clustered based on 80% similarity [unweighted pair-group method with arithmetic mean (UPGMA), with Dice's coefficient, tolerance and optimization set up at 1%;  Henri et al. , 2016]. The two reference strains, EGDe (accession number: NC_003210, ST35, CC9, serotype 1/2a and molecular serotype IIa) and EGD (accession number: HG421741, ST12, CC7, serotype 1/2a, and molecular serotype IIa) were included in the final set and used as reference for SNP calling. The complete list of the 209 genomes is available in Supplementary Table  1.
Genomic MLST The wgMLST and cgMLST were performed at the Centers for Disease Control and Prevention, the USA (US-CDC) by the Enteric Diseases Laboratory Branch. The wgMLST scheme was developed from a set of over 200 annotated closed and highquality draft genomes that represented the diversity of serotypes and lineages in L. monocytogenes. A total of 4,804 unique loci were identified to compose the wgMLST scheme, whereas 1,748 loci represent the cgMLST scheme. The cgMLST scheme was developed by the Pasteur Institute  (Moura et al. , 2016)  and is available at PubMLST website (https://pubmlst. org/databases. shtml). The wgMLST with the cgMLST schema is included in the commercial software  [BioNumerics v7. 5 (Applied Maths NV, Belgium) ]. Alleles were called for both the wgMLST and cgMLST schemes using BioNumerics v7.
5. Unless raw reads (fastq format) were available, assembly-based allele calling (fasta format) was completed. The contigs were assembled using SPAdes 3. 5. 0, plugin of the BioNumerics software v7. 5. Alleles were named if genes fulfill the following criteria: a start and stop codon were present, the DNA sequence met the 85% minimum homology cut-off, there were no ambiguous base calls in the allele sequence, and had less than 100 gaps in the sequence alignment. Dendrograms of wgMLST and cgMLST were created using the UPGMA algorithm with the allele calls considered categorical data.
Phylogenetic Tree Based on SNPs The SNP tree was built with the pipeline CSI phylogeny accessible from the Center for Genomic Epidemiology (www. genomicepidemiology. org)  (Leekitcharoenphon et al. , 2012a; Kaas et al. , 2014). The reference strains, EGD (ST35) and EGDe (ST12) have been previously subjected to thorough genomic investigation and their differences are well documented  (Bécavin et al. , 2014). Both reference genomes belong to the same lineage II and serovar 1/2a but with different STs (ST35 and ST12, respectively). The paired-end reads were mapped to the reference genomes using Burrows-Wheeler Aligner (BWA)  (Li and Durbin, 2009). Initially, a SNP analysis was performed using the reference genome: EGD-e (accession number NC_003210, length 2,944,528 bp). Subsequently, a second SNP analysis was performed using the second reference genome: EGD (accession number HG421741, length 2,907,193 bp).
, 2005; Friis et al. , 2010; Lukjancenko et al. , 2010 Lukjancenko et al. , , 2012;; Vesth et al. , 2010; Jacobsen et al. , 2011; Kaas et al. , 2012). To each genome corresponds a set of predicted proteins, some of which are also found in other genomes. The pan-genome is the union of the predicted proteins, while the core genome is the intersection of the predicted proteins for the genomes under consideration  (Tettelin et al. , 2005; Leekitcharoenphon et al. , 2012b). The size of the core-and pan genomes according to the number of genomes analyzed in our dataset is shown in Supplementary Figure  3.
Trees Comparison and Statistical Analyses A phylogenic tree can be characterized with two properties: the topology and the branch lengths. The topology is the branching structure of the tree and it indicates patterns of relatedness among strains. The comparison of the tree topology and distance were performed using the R packages "ade4, " "ape, " "dendextend, " "phangorn, " and "phytools"  (Paradis et al. , 2004; Dray et al. , 2007; Schliep, 2011; Revell, 2012; Galili, 2015). "ade4" package was used for the graphical representation functions, "ape" package was used to read, plot and manipulate phylogenetic trees, "phangorn" and "dendextend" were used to compute pairwise distance between pairs of strains from phylogenetic network and "phytool" was used to visualize and analyse comparative data from species using colors.
Cophenetic and the Cor_cophenetic The cophenetic is the distance between two strains and the exact height of the dendrogram where the two branches that contain the two strains join into one single branch. The cophenetic correlation (hereafter termed: cor_cophenetic) calculates the correlation between the cophenetic distance matrices of the two trees. The cor_cophenetic value ranges between -1 (perfect negative correlation) and 1 (perfect positive correlation). A value close to 0 (nil) indicates the absence of correlation for the two trees. The cophenetic and cor_cophenetic functions of dendextend and phangorn package were used to evaluate the clustering  (Sokal and James, 1962; Cardona et al. , 2013).
The Fowlkes-Mallows Index The dendextend package calculates the Fowlkes-Mallows (FM) index which assess the similarity between two clusters  (Fowlkes and Mallows, 1983). The FM index values are comprised between 0 (nil) and 1. The closer it is to 1, the more the clusters are similar. We calculated the asymptotic values, E_FM (Expected_Flowlkes-Mallows) and V_FM (Variance_Flowlkes-Mallows), expected under the null hypothesis (H0) that assumes that the two trees have the same topology if one tree is a random shuffle of the strains of the other tree (for instance no correlation between the trees). If E_FM+1,65•V_FM 0. 5 is below the observed one we can reject H0 at α = 0. 05.
One solution to avoid these differences into the tree topologies, would be to remove transient variants from the SNP dataset. L. monocytogenes is a clonal species and conventional MLST has proved it robustness for population structure  (Ragon et al. , 2008; Maury et al. , 2016). We believe that the use of SNP analysis for global epidemiological purpose would require a global consensus on a set of CC-specific genomes that could be used as references to perform SNP-calling within ST-or CC-groups. The use of multiple reference genomes would increase the discriminatory power of the method for each CC. Furthermore, using SNP-based phylogeny specific SNP markers, could be proposed to discriminate ST or CC. This SNP-based barcode could cover all main lineages, ST and could classify strains in sub type within ST  (Coll et al.
, 2014). For greater accuracy and efficiency at an international level this should be accompany with the use of a common SNP calling pipeline  (Bertels et al. , 2014) , determining if the variants induced by recombination events must be removed, or not, from the variant dataset before phylogenetic reconstruction  (Hedge and Wilson, 2014). Our results demonstrate with a strong statistical support that the SNP and genomic MLST approaches led to similar phylogenetic reconstruction. This provides microbiologists and epidemiologists working on cluster analysis of L. monocytogenes two alternative methods with almost the same discriminatory power and precision. Remarkably, most of the discrepancies observed in the topology concerned full CC or ST. This result shows the noticeable clonality of L. monocytogenes and also the robustness of the conventional MLST for population structure since strains of the same CC or ST cluster together irrespective of the genomic methodology used.
This study did not find any difference in the discriminatory power of the SNP and the genomic MLST approaches. Despite that the two approaches give similar results, the SNP and genome MLST entail different advantages and disadvantages which should be taken into account in a global epidemiological perspective. None of the approaches require a substantial amount of time and substantial bioinformatics expertise, indeed wgMLST is commercially available from Bionumerics R (and cgMLST in public domain) and numerous open-source SNP calling pipelines are available. The main difference between the two approaches is that a database of loci and associated alleles is used to identify alleles for cg/wgMLST whereas one reference strain is used for SNP calling. An important benefit of the classification of isolates with cg/wgMLST is that it would be stable over time as new isolates are added, on the other hand it requires a careful curation of new alleles.
An additional significant advantage is that the cg/wgMLST can provide a genome sequence type which could lead to a common nomenclature, provided that timely update of alleles databases between servers are adopted. A common nomenclature and a stable scheme should ease data portability and sharing making communication more effective. Allelic database management requires extensive curation  (Jolley et al. , 2010)  which for the most part can be automated with little manual interference. For these reasons, the genomic MLST approaches appear to be better suited for the use in laboratory surveillance of listeriosis where direct comparability of analytical results by different laboratories is critical, e. g. for global outbreak detection and investigation. Concerning the SNP-based approaches, a higher discrimination would necessitate the use of different reference genomes for routine surveillance.
However, the SNP approach can be fully automated while a question mark remains concerning the automation of the curation process of cg/wgMLST alleles database  (Leekitcharoenphon et al. , 2012a; Moura et al. , 2016). Theoretically, SNP is also more discriminative by taking into account intergenic sequences but it is also more sensitive to parameters variations (reference, SNP calling filters, coverage) inducing divergence in topology of trees as shown in our study  (Pightling et al. , 2014). It must also be noticed that the SNP-based approaches give the opportunity to detect recombinaison evens  (Croucher et al. , 2015; Didelot and Wilson, 2015). As discussed and highlighted in this work the topology of trees is made of branching and distances between strains. These two parameters provide a precise idea of the relationship between strains.
The panel used literature searches (from 1966 to 2015, with additional searches for literature published in specific areas), existing guidelines and expert consensus publications, some literature suggested by panel members, and clinical experience as guides. Adapted guideline manuscripts are reviewed and approved by the Clinical Practice Guideline Committee. The review includes the following two parts: methodologic review and content review. The former was completed by two ASCO staff members and the latter by members of the Expert Panel convened by ASCO. The guideline recommendations were crafted, in part, using the Guidelines Into Decision Support methodology and accompanying BRIDGE-Wiz software (Yale, New Haven, CT). 19  Detailed information about the methods used to develop this guideline is available in the Data and Methodology Supplements at www.
Literature Search As part of the systematic literature review, the PubMed, SAGE, Cochrane Systematic Review, and National Guideline Clearinghouse databases were searched for literature and guidelines, systematic reviews, and meta-analyses that were about screening for, or treatment of, precursor lesions; developed by multidisciplinary content experts as part of a recognized organizational effort; and published between 1966 and 2015. Literature searches based on prespecified criteria as well as updates of literature searches with terms used by other guidelines were conducted, and a title screen was completed and abstract review begun. However, because several high-and moderatequality guidelines were found, the full text review and data extraction steps of the systematic review were not completed.
Methods and Results of the ASCO Updated Literature Search A search for new evidence was conducted by ASCO guidelines staff to identify relevant randomized clinical trials, systematic reviews, meta-analyses, and guidelines published since the Cancer Care Ontario (CCO) and WHO guidelines were completed. Following the strategies described in the WHO guidelines, PubMed and EMBASE databases were searched from 2011 to December 2014 in July 2015. The search was restricted to articles published in English, and the ASCO guideline inclusion criteria were applied to review of the literature search results. Titles were reviewed; as described earlier, evidence reviews conducted for other societies' guidelines were used as the evidence base. Sixteen guidelines were found in the search, and their currency, content, and methodology were reviewed.
35CaST]. 27)The Huh et al6guidance cites pooled analyses of European RCTs (Dillner et al,34which included seven prospective studies, and Ronco et al,35which included four of the trials in CCO that had data from two rounds of screening [ARTISTIC, POBASCAM, NTCC, and Swedescreen published between 2007 and 2012]), the Vrije Universiteit Medical Centre-Saltro (VUSA)-Screen study,36the ATHENA RCT, and a Kaiser database. Ronco et al35mentioned FOCAL, listed as ongoing by CCO, which was not included in the Huh et al 6 guidance. Selected RCT data have been extracted in the Data Supplement.
35CaST]. 27)The Huh et al6guidance cites pooled analyses of European RCTs (Dillner et al,34which included seven prospective studies, and Ronco et al,35which included four of the trials in CCO that had data from two rounds of screening [ARTISTIC, POBASCAM, NTCC, and Swedescreen published between 2007 and 2012]), the Vrije Universiteit Medical Centre-Saltro (VUSA)-Screen study,36the ATHENA RCT, and a Kaiser database. Ronco et al35mentioned FOCAL, listed as ongoing by CCO, which was not included in the Huh et al 6 guidance. Selected RCT data have been extracted in the Data Supplement. RCTs on pri- mary HPV testing. (The CCO guideline cites seven RCTs published between 2007 and 2010, includ- ing New Techniques in Cervical Cancer [NTCC], A Randomized Controlled Trial of HPV Testing in Primary Cervical Screening [ARTISTIC], Population- Based Screening Study Amsterdam [POBASCAM], Swedescreen, Finnish Public Health Trial [FPHT], Sankaranarayanan et al, and Canadian Cervical Cancer Screening Trial [.
The 11 B NMR chemical shift of -7. 7 ppm in 12 is less negative than in 9 (δ = -27. 5 ppm), consistent with a better developed N-B interaction in the latter case. The B-bound H atoms in 11 and 12 were located in the respective Fourier difference maps and were positionally and isotropically refined. The Ti(1)-H(1) distances of 1. 914 (13)  and 2. 009 (13) are significantly longer than in terminal titanium(+4) hydrides in general (av. 1. 66 Å, range 1. 55-1. 85 Å for nine compounds in the current Cambridge Crystallographic Database  21  ), and in the more closely related compounds 3 (Figure  1 , av. 1. 61 Å). The Ti(1)-H(1) bond in 12 is longer (Δ(Ti-H) = 0. 10(2) Å) than in 11, and its B(1)-H(1) is apparently shorter (Δ = 0. 05(2) Å). This is attributed to the electron-withdrawing nature of the C 6 F 5 groups, which render the boron atom more Lewis acidic, despite the greater steric crowding in 12.
Unless otherwise stated, all NMR spectra were recorded at 298 K. 1 H and 13 C{ 1 H} and spectra were referenced internally to residual protiosolvent ( 1 H) or solvent ( 13 C) resonances, and are reported relative to tetramethylsilane (δ = 0 ppm). 19 F and 11 B NMR spectra were referenced externally to CFCl 3 and Et 2 O•BF 3 respectively. Assignments were confirmed as necessary with the use of two-dimensional 1 H-1 H,  13  C-1 H, 1 H-  19  F and 19 F-  19  F correlation experiments. Chemical shifts are quoted in δ (ppm) and coupling constants in Hz. Unless otherwise stated, IR spectra were recorded on a Thermo Scientific Nicolet iS5 FTIR spectrometer and samples prepared in a drybox using NaCl plates as a Nujol mull or as a thin film. The data are quoted in wavenumbers (cm -1 ). Mass spectra were recorded by the mass spectrometry service of Oxford University's Department of Chemistry.
13   X-ray data collection and processing procedures. Crystals were mounted on glass fibers using perfluoropolyether oil and cooled rapidly in a stream of cold N 2 using an Oxford Cryosystems Cryostream unit. Diffraction data were measured using either an Enraf-Nonius KappaCCD or Agilent Technologies Supernova diffractometer using Mo Kα or Cu Kα radiation, respectively. As appropriate, absorption and decay corrections were applied to the data and equivalent reflections merged. 35 The structures were solved with SIR92  36  or Superflip,  37  and further refinements and all other crystallographic calculations were performed using the CRYSTALS program suite. 38 The CIF files contain specific details for the refinements of the individual structures. The CCDC codes for the structures in this paper are CCDC 1407872, 1407873, and 1556743-1556746.
Computational Details. Geometry optimizations were performed with the Gaussian09 package (rev. D. 01)  39  at the B3PW91 level of hybrid density functional theory in the gas phase with the titanium atom represented by the relativistic effective core potential (RECP) from the Stuttgart group and the associated basis set,  40  augmented by an f polarization function,  41  and the remaining atoms represented by a def2-svp basis set. 42 Dispersion was taken into account both at the optimization and single point stages using Grimme's d3(bj) corrections  43  as implemented in Gaussian09. In addition, the solvent (benzene) influence was taken into consideration in the single point calculations on the gas-phase optimized geometry with SCRF calculations within the SMD model and a def2-qzvpp basis set for all atoms.
, 2013; Weber et al. , 2016)  but have not investigated older people's changing ability to cross the road safely. Our study extends the existing literature by investigating changes over time in both older people's walking speeds and older people's ability to cross the road in time, using longitudinal data collected over 12 years. We will estimate predicted mean walking speeds and predicted probabilities of the ability to cross the road in time and will interpret our findings with reference to the current required walking speed at pedestrian crossings.
Study sample and measures This study used data from seven waves  (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014)  of the English Longitudinal Study of Ageing (ELSA)  (Steptoe et al. , 2013) , a longitudinal study of people aged 50þ years living in England. Participants gave full informed consent to participate in the study, and ethical approval was obtained from the National Research Ethics Committee. Timed walks were measured biennially in participants aged 60 þ years. Participants walked eight feet (2. 44 m) and back at their usual walking pace, using walking aids if required. Walking speed was calculated in metres per second (m/s), and we used the mean of the two measurements as our outcome variable. The eligible sample is 12,472 people, all of whom provided at least one walking speed measurement, resulting in 42,135 walking speed measurements over a 12 year follow up period.
Our analytic sample included 10,249 individuals (54. 7% female) who provided 31,015 walking speed measurements with complete data on all covariates. Covariates, drawn from the literature  (Asher et al. , 2012; Zaninotto et al. , 2013; Brunner et al. , 2009) , which we hypothesise will influence older people's walking speeds and ability to cross the road in time were: age (years, centred on age 60), age squared (to account for the non-linear change in walking speed with age), sex, wealth quintiles, smoking (never, former, current), limiting longstanding illness (LLSI), and difficulties with activities of daily living  (Katz, 1963)  (ADL; 0, 1 or Z2 of a possible six), all of which were allowed to vary with time.
Acknowledgments This work was supported by the  UK Economic and Social Research Council  (grant number  ES/J019119/1  to EW), the  European Research Council  (grant number  ERC-StG-2012-309337  to SB and  ERC-2011-StG_20101124  to RL) and the  UK Medical Research Council/Alcohol Research UK  (grant number  MR/M006638/1  to SB). EW conceived the study, performed the analyses and drafted the manuscript. SB advised on the statistical analysis. SB, RL & JA approved the research plan and commented and contributed to drafts of the manuscipt. All authors appoved the final manuscript.
Functional analysis of pathways and metabolic processes Comprehensive analysis of samples FIGURE 2. Schematic workflow of data processing: Data analysis: Mass spectrometry results (24 files) were analyzed by Proteome Discoverer 2. 1 using 2 iterative steps: first step was identification only against all bacteria, fungi and human reference proteomes mined from UniProt KB with no filter applied in order to create a custom Fasta file for reducing search space. This custom database was used for identification and reporter quantification with quality filter criteria (Percolator FDR calculation). For statistical analysis of results, normalized and scaled reporter abundances were used to perform PCA analysis (Pareto scaling) and t-test to evaluate fold changes' statistical significance between groups.
Upscaling instantaneous evapotranspiration retrieved at any specific time-of-day (ET i ) to daily evapotranspiration (ET d ) is a key challenge in mapping regional ET using polar orbiting sensors. Various studies have unanimously cited the shortwave incoming radiation (R S ) to be the most robust reference variable explaining the ratio between ET d and ET i. This study aims to contribute in ET i upscaling for global studies using the ratio between daily and instantaneous incoming shortwave radiation (R Sd / R Si ) as a factor for converting ET i to ET d. This paper proposes an artificial neural network (ANN) machine-learning algorithm first to predict R Sd from R Si followed by using the R Sd / R Si ratio to convert ET i to ET d across different terrestrial ecosystems. Using R Si and R Sd observations from multiple sub-networks of the FLUXNET database spread across different climates and biomes (to represent inputs that would typically be obtainable from remote sensors during the overpass time) in conjunction with some astronomical variables (e.
g. solar zenith angle, day length, exoatmospheric shortwave radiation), we developed the ANN model for reproducing R Sd and further used it to upscale ET i to ET d. The efficiency of the ANN is evaluated for different morning and afternoon times of day, under varying sky conditions, and also at different geo-CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy covariance data harmonisation provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, the.