dataesr/scientific-paragraphs-categorization huggingface dataset:
NBI HD-HuB network  (# 031A537A , # 031A537C ); the  Ontario Institute for Cancer Research  to PCB and JDM through funding provided by the  Government of Ontario, Ministry of Research and Innovation ;  Genome Canada ; the  Canada Foundation for Innovation and Prostate Cancer Canada  with funding from the  Movember Foundation  (PCB). P. C. B. was also supported by a  Terry Fox Research Institute New Investigator Award , a  CIHR New Investigator Award  and a  Genome Canada Large-Scale  Applied Project Contract. The Synergie Lyon Cancer platform has received support from the  French National Institute of Cancer (INCa)  and from the  ABS4NGS   ANR  project ( ANR-11-BINF-0001-06 ). The ICGC RIKEN study was supported partially by  RIKEN President's Fund 2011 , and the supercomputing resource for the RIKEN study was provided by the  Human Genome Center, University of Tokyo.
M. D. E. , L. B. , A. G. L. and C. L. A. were supported by  Cancer Research UK , the  University of Cambridge and Hutchison-Whampoa Limited. S. D. is supported by the  Torres Quevedo subprogramme (MICINN)  under grant agreement  PTQ-12-05391. E. H. is supported by the  Research Council of Norway  under grant agreements  221580  and  218241  and by the  Norwegian Cancer Society  under grant agreement  71220-PR-2006-0433. We specially thank  Jennifer Jennings  for administrating the activity of the ICGC Verification Working Group and  Anna Borrell  for administrative support.
Statistics Data are presented as mean (SD). All analyses were based on the 'intention-to-treat' data set, that is all randomised animals that received at least the s. c. injection and from which at least one measurement post-treatment was obtained. Statistical tests were performed using SAS® Software version 8. 2 (SAS Online Doc, Version 8, Cary NC, US; SAS Institute Inc, 1999). Groups were compared at baseline using the Mann-Whitney U test. Efficacy, cortisol, clinical chemistry and haematology variables were analysed using repeated measures analysis of covariance (RMANCOVA). Variables were transformed (log or reciprocal), if appropriate, to give the best estimate of a normal distribution which was assessed using the Shapiro-Wilk test. The RMANCOVA model contained the model parameters: treatment group, baseline value, time, weight, age, investigator/owner, duration of intubation and type of surgery.
Baseline and demographic variables A total of 147 cats were recruited at 21 investigator sites from different practices in France and the UK between December 2005 and August 2007. No cases were removed from the analysis, leaving 147 cats in the analysed data set. The number of cases per centre ranged from 1 to 22 (robenacoxib 0 to 14, meloxicam plus placebo 0 to 8, P = 0. 91). There were no significant differences between the two groups in baseline, surgery or anaesthetic variables (Table  5 ). The duration of surgery was 60 minutes or less in 69% of cases. The majority (51%) of cats underwent fracture repair.
Pain and inflammation at the injection site The dispenser assessed (unblinded) the pain during the s. c. injection. The mean (SD) scores were 0. 14 (0. 43) with   The global investigator score (the primary end point) ranged from 0 to 9. All other end points (secondary end points) ranged from 0 to 3. robenacoxib and 0. 16 (0. 48) with meloxicam. Differences were not significant (P = 0. 87). At T2 (approximately 24 hours after the s. c. injection), the dispenser assessed inflammation and pain at palpation at the original site of injection. The mean (SD) scores for robenacoxib and meloxicam were, respectively, 0. 05 (0. 22) and 0. 05 (0. 21) for inflammation (P = 1. 0), and 0. 03 (0. 17) and 0. 05 (0. 21) for pain on palpation (P = 1. 0) (data not shown).
Acknowledgements We thank  Drs JP Aupetit ,  L Bellocq ,  B Ben Moura ,  A Bernardé ,  M Besset ,  P Bounous ,  F Bourgeois ,  L Delesalle ,  F Famose ,  JF Forgue ,  JP Garcin ,  C Malet ,  F Morin ,  I Papadopulo ,  FG Pradies  and  S Salmon in France  and  P Dennison ,  A Fagan ,  M Guilliard ,  D Hodges ,  S Langley-Hobbs ,  A Mayo ,  M Owen ,  H Radke  and  A Wallace  in the UK for managing the clinical cases;  E Tranchard ,  P Kubacki ,  S Lebard  and  P Watson  for monitoring the study;  Dr D Alexander  for managing the start of the UK study;  Dr A Bollinger  for the plasma cortisol assays and  A Tamta  for editorial support.
The acceptance matrices describe only the HADES fiducial volume and can be applied as a filter to the events generated by the models. The matrix coefficients have been determined by means of full GEANT simulations, with pions and protons processed through the detector and reconstructed by tracking, particle identification and selection algorithms with the same package as it was done for real events. The acceptance of HADES for the np → npπ + π -reaction is about 6% in comparison with the full phase-space. The resulting detection and reconstruction efficiency is typically about 90% for protons and pions. These efficiencies have a very smooth behavior as a function of the different observables shown in the following. Therefore, we only consider a global systematic uncertainty which will affect the normalization of our data, but not the shape of the spectra.
Replacing the expressions of the different metabolic fluxes in Eq 1 to 4 gives the following system: K 4 Cpyr cyt À K 4 Cpyr mt þ k 3 Cmal mt À k 1 Cmal mt Cpyr mt ¼ 0 ð11Þ K 5 Cmal cyt þ k 2 Ccit mt À ðK 5 þ k 3 ÞCmal mt À k 1 Cmal mt Cpyr mt ¼ 0 ð12Þ K 6 Ccit cyt þ k 1 Cmal mt Cpyr mt À ðk 2 þ K 6 ÞCcit mt ¼ 0 ð13Þ 2k 2 Ccit mt þ k 3 Cmal mt þ k 1 Cmal mt Cpyr mt ¼ Resp ð14Þ where Cpyr mt , Cmal mt , Ccit mt , Ccit cyt are the unknowns of the system, and K i , k i , Cpyr cyt , Cmal cyt are parameters. The system was solved by using the software Maple (Maple  (16). Maplesoft, a division of Waterloo Maple Inc. , Waterloo, Ontario). The solutions of Ccit mt and Ccit cyt were put into Eq 10 which gave the following expression for φ 6 : φ 6 ¼ À 1 4 ðk 3 þ K 5 ÞResp 3k 3 þ K 5 þ 1 4 1 k 1 ð3k 3 þ K 5 Þ ð3K 4 k 1 À k 3 þ K 5 Á Cpyr cyt þK 5 k 1 À 2K 5 þ 10k 3 Á Cmal cyt À À k 3 þ K 5 ÁÀ 18K 2 4 Cpyr cyt k 1 k 3 þ 12K 2 4 Cpyr cyt k 1 K 5 þ8K 4 K 2 5 Cmal cyt k 1 þ 9K 2 4 k 2 3 þ 4K 2 4 K 2 5 þ 9K 2 4 Cpyr 2 cyt k 2 1 þ 12K 2 4 k 3 K 5 þ 4K 2 5 Cmal 2 cyt k 2 1 À12K 4 Cpyr cyt k 2 1 K 5 Cmal cyt þ 36K 4 k 3 K 5 Cmal cyt k 1 þ À 18K 4 k 3 k 1 À 6K 4 Cpyr cyt k 2 1 þ 4K 4 K 5 k 1 þ 4K 5 Cmal cyt k 2 1 Á Resp þResp 2 k 2 1 Á 1 2 þ K 4 À 5k 3 K 5 þ 3k 2 3 þ 2K 2 5 Á Þ ð15Þ Pulp respiration.
The calculated values of q g were found to not significantly differ among cultivars and among developmental stages (data not shown). Thus, for q g a value common to all cultivars and developmental stages was chosen as the mean value of the calculated values which was q g = 13 mmol CO 2 g -1 ±1. This value is in the range of values found for tomato (9. 3 mmol CO 2 g -1 ;  [49] ), peach (7. 0 mmol CO 2 g -1 ;  [50] ), and mango (3. 0 mmol CO 2 g -1 ;  [48] ). The maintenance respiration coefficient (q m ) of the pulp during banana growth and postharvest ripening were calculated respectively from measurements of respiration in harvested green fruits and in fruits subjected to ethylene treatment in the 2012 experiment (n = 180). Since the fruits were harvested, we assumed that the measured respiration corresponded only to maintenance respiration.
Model calibration Every parameters related to reaction rates k i (t) and membrane permeability K i (t) were estimated by fitting the predicted citrate concentrations to observed values of the 2011 dataset separately for each cultivar and developmental stage (60 data per cultivar during growth and 96 data per cultivar during post-harvest ripening) using the hydroPSO function of the R software  [52]. The hydroPSO function uses the computational method of particle swarm optimization (PSO) that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Parameters were estimated by minimizing the following criterion: X j X i ð x ij À y ij s j Þ 2 ð29Þ where x ij is the predicted value, and y ij is the observed value of the fruit of the j th banana plant at date t i , and σ j is the standard deviation of the observed values of the fruit of the j th banana plant.
Goodness-of-fit and predictive quality of the model The goodness-of-fit of the model was evaluated using two commonly used criteria, the root mean squared error (RMSE) and the relative root mean squared error (RRMSE), to compare the mean difference between simulated and observed results  [55]. The smaller the value of RMSE and RRMSE, the better the fit. RMSE ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X ðY i À X i Þ 2 n s ð32Þ where Y i is the predicted value of the fruit i, and X i is the measured value of the fruit i. n is the number of data. RRMSE ¼ RMSE X ð33Þ where x is the mean of all observed values. The predictive quality of the model, which ascertains the validity of the model in different scenarios, was quantified by the RMSE and RRMSE calculated on the 2012 data set.
Materials and Methods Isolates. A total of 145 Scottish isolates (Table  S1 ), comprising clinical isolates (74) originating from patients resident in NE Scotland, cattle (26), sheep (25) and food/environmental (20) isolates from previous studies. Isolates were selected to be independent of each other (i. e. not to be obtained from the same farm at the same time) and to be obtained over a broad time period. Isolates were phage typed as described previously  41. Sequenced isolates. Sequenced international E. coli O157 isolates from Canada (164), Netherlands (39), New Zealand (12), Scotland (145), USA (26) and other countries (45) were obtained along with varying levels of metadata (Table  S1 ). The Canadian and Dutch sequenced isolated were selected using the same principles as the Scottish isolates, the New Zealand isolates were obtained from two calendar years but variation was ensured by selection across their PFGE profiles.
The genomic sequence data of these isolates will be uploaded to an open access database on acceptance of this paper for publication (Table  S1 ). The isolates from USA and the rest of the world were downloaded from the NCBI and PATRIC databases. Whole genome sequencing of Scottish isolates. Overnight cultures of E. coli O157 were grown on Harlequin SMAC-BCIG agar plates (Hal 6, Lab M, Topley House, Lancashire) at 37 °C. A single, well isolated sorbitol negative colony was selected and tested with E. coli O157 latex (code DR0620M, Oxoid, Basingstoke) and the latex plated onto Columbia Agar at 37 °C for 24 hours (Code CM0031, Oxoid, Basingstoke). DNA was extracted with the Wizard Genomic DNA Purification Kit (Promega UK Ltd, Southampton) as per the manufacturers instructions with an additional Proteinase K step (25 μ l, lyophilized Proteinase K reconstituted at a concentration of 6 mg/260 μ l Proteinase buffer) post treatment with nucleic acid lysis solution.
Analysis of genomes. In silico PCR and probe based assays (Table  S4 ) were carried out both for backwards compatibility with previous studies and identification of known virulence markers. SNP discovery methods were also implemented for phylogenetic analysis. 1. Detection of E. coli O157 antigen encoding, intimin and enterohemolysin genes: in silico PCRs (Table  S4 ), performed using a Perl script were conducted for a portion of the rfb (O-antigen-encoding) region of E. coli serotype O157 and for the putative accessory virulence factors intimin (eaeA) and the plasmid-encoded enterohemolysin (hlyA). The Perl script blast searched for the primers in the genome sequences with tolerance of 90%. If the pair of primers were found, the size of the PCR product was determined and if matching the predicted size the PCR was assigned as positive.
Acknowledgements The authors acknowledge the support of the  Food Standards Agency, Scotland  ( FS102029 ) and the  University of Aberdeen  for funding sequencing of the Scottish E. coli O157 genomes, to  Chris Low  at the  Scottish Agricultural College, Edinburgh  for supplying a number of the Scottish sheep isolates,  Iain Ogden  for commenting on the manuscript and  Patricia Jaros  ( Massey University ) for preparing the  New Zealand isolate DNA  for sequencing.
Supernatant fraction was stored at -80°C until use. Cellular cytotoxicity was assessed by chromium release of 51 Cr-labelled beta cell lines. Complementdependent cytotoxicity was measured by flow cytometry of beta cell lines after incubation with human HLA-specific antibodies and rabbit complement. Cytokine-driven beta cell death was measured by propidium iodide staining and flow cytometry after 48 h culture in Th1 cell supernatant fraction or 50 U/ml IL-1β, 1,000 U/ml IFNγ and 1,000 U/ml TNFsupplemented medium. Cell surface antigen expression was assessed by flow cytometry. Experiments were not blinded. Experiments were excluded if positive controls did not respond or with responding negative controls. Mycoplasma infection was excluded for all cell lines at regular intervals. Data are represented as mean and SD unless stated otherwise.
0001 for intercept; n=4) without adding exogenous PPI peptide epitope, corresponding to HLA-A2 expressing cell frequency (Fig. 1h ). Pulsing of the transduced beta cell with exogenous cytomegalovirus (CMV) peptide epitope (mimicking CMV infection) resulted in killing by CMV-specific CTLs with similar efficacy (data not shown). Alloreactive CTLs can cause beta cell allograft rejection after transplantation. Thus, beta cells were tested against HLA-A*02:02-specific alloreactive CTLs. A beta cell line naturally expressing HLA-A*02:02 was killed (up to 66±5%) in a 4 h cytotoxicity assay only if HLA was upregulated by IFNγ (p=0. 005 for intercept; n=3). Hyperglycaemic (>25 mmol/l glucose) preincubation did not affect killing by alloreactive CTLs (Fig. 1g ). Specific recognition of beta cell lines by alloreactive CTLs after HLA upregulation was verified by expression of the cytolytic degranulation marker CD107a on responding CTLs (data not shown).
Acknowledgements The authors thank  J. H. W. Pahl  and  G. H. Boersma  ( Leiden University Medical Center, the Netherlands ) for their excellent technical assistance. Cell line HK-2 was kindly provided by  P. van der Pol  and  C. van Kooten  ( Department of Nephrology, Leiden University Medical Center, the Netherlands ). MSC were kindly provided by  V. L. van Zuylen  and  W. E. Fibbe  ( Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, the Netherlands ). Alloreactive antibodies and CMV-specific CTL clone 18 were kindly provided by  A. Mulder  and  F. Claas  ( Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, the Netherlands ). Some of the data were presented as an abstract at the International Pancreas and Islet Transplant Association meeting in 2013.
The unprecedented scale of the Ebola outbreak in West Africa has, as of 29 April 2015, resulted in more than 10,884 deaths among 26,277 cases. Prior to the ongoing outbreak, Ebola virus disease (EVD) caused relatively small outbreaks (maximum outbreak size 425 in Gulu, Uganda) in isolated populations in central Africa. Here, we have compiled a comprehensive database of estimates of epidemiological parameters based on data from past outbreaks, including the incubation period distribution, case fatality rate, basic reproduction number (R 0 ), effective reproduction number (R t ) and delay distributions. We have compared these to parameter estimates from the ongoing outbreak in West Africa. The ongoing outbreak, because of its size, provides a unique opportunity to better understand transmission patterns of EVD.
The purpose of this review was to collect all published epidemiological parameter estimates (reprinted in detailed tables containing estimates, and corresponding confidence intervals) estimated from past EVD outbreaks. Our aim was not to perform a meta-analysis, but rather to compile and document the available parameter estimates based on data from EVD outbreaks over the past 40 years. In order to estimate any of the parameters referenced in our manuscript, we would need detailed case data of each of the cohorts studied in the original papers, which we do not have. We also reprint parameter estimates from past Marburg outbreaks and the ongoing outbreak in West Africa for comparison. This information is valuable for public health organizations that need to quickly evaluate the early behavior of a new outbreak and estimate the potential impact, in terms of morbidity, mortality and geographic spread.
Data collection All searches using the following search terms (Ebola, Marburg, EHF, EVD, MHF, EBOV, Ebola Zaire, Ebola Sudan, Ebola Reston, Ebola Bundibugyo, outbreak, model, parameterization, incubation period, case fatality rate, case fatality rate (CFR), risk factors, basic reproduction number, R 0 , effective reproduction number, serial interval, delay distributions, generation time) were carried out on 1 August 2014, 15 September 2014 and again in February 2015 using the following databases: ScienceDirect, ResearchGate, Google, GoogleScholar, BioOne, Web of Science and PubMed. Our searches aimed to find primary reports describing and analyzing data collected from investigations of EVD and Marburg outbreaks since the virus was identified in 1976. The criteria for inclusion were: sample size of EVD cases described in the study ≥5, studies of human outbreaks, studies which evaluated potential risk factors had to report prevalence proportion ratios, odds ratios or relative risks.
We then compiled the parameter estimate database into tables. Table  1  and Data Citation 1 list the human outbreaks of Ebola Zaire, Ebola Sudan and Ebola Bundibugyo that have occurred in Africa from 1976 to present. We have not provided detailed information on the outbreaks as these have been previously described  9. Table  2  (available online only) summarizes the literature we used in this review. Our manuscript and tables include estimates, confidence intervals and ranges obtained from the referenced publications (Table  2  (available online only) and Data Citation 2).
Risk for developing EVD Risk factors for human-to-human transmission of EVD or Marburg were evaluated from comparison of the exposures, behaviors and practices in cases compared to controls (including unaffected controls, defined to be suspected cases but negative serologic test results) and were described using a prevalence proportion ratio, an odds ratio or a relative risk (and the corresponding confidence interval). Significant risk factors associated with developing EVD are reported in Table  8  (available online only) and Data Citation 4 and include direct physical contact (sharing a bed, touching a cadaver or funeral preparations for an EVD patient, nursing care and contact with bodily fluids) and non-physical contact (sharing a meal, contact with a hospital where EVD patients were treated)  24, 39, 45, 47, 56, 71.
Recent investigations into the first cases of the ongoing outbreak found that the outbreak may have begun in Meliandou, Guinea in a village where the inhabitants frequently came in contact with fruit bats in a hollowed out tree  80. Although the current focus is on limiting human-to-human transmission and treating the infected, the challenging underlying factors that led to this large outbreak in West Africa will require long-term investments to improve both health care and surveillance for infectious diseases. Our dataset is the most complete collection of published epidemiological parameter estimates from EVD outbreaks available at the time of writing and provides an evidence-based foundation for both retrospective analyses and responses to future outbreaks.
Also, plants typically produce methyl-and acetylesterases that are used for fine-tuning the in muro functionalities of cell wall components to suit local requirements  (15, 16). Techniques for mining genomes and metagenomes have developed rapidly in recent years, and so have medium and high throughput strategies for cloning and expressing recombinant enzymes. Furthermore, bioinformatics resources and associated depositories, such as the carbohydrate-active enzymes database (CAZy)  (17)  have expanded greatly. However, there are considerable challenges inherent in the exploitation of microbial enzyme diversity for industrial purposes, and the empirical determination of enzyme activities has now become a serious bottleneck. For example, it is estimated that, using cur-rent methods, we can safely predict the activities of no more than 4% of the proteins within the CAZy  (18).
Immobilized metal ion affinity chromatography was performed following the protocol described previously  (27)  for purification of the recombinant proteins. The concentration of purified proteins was determined using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific) and applying the Beer-Lambert law. The theoretical extinction coefficient and molecular weight values were determined using the ProtParam service. Sequence data can be found in GenBank TM under the following accession numbers: CCO21106. 1 (Pm12), CCO21108. 1 (Pm23), CCO21110. 1 (Pm25), CCO21640. 1 (Pm83), CCO21632. 1 (Pm84), and CCO21658. 1 (Pm85). Fungal broth supernatants (Fig. 13 ) were prepared as follows. Baffled Erlenmeyer flasks (500 ml) with 200 ml of Czapek Doxbased medium with apple pomace (35. 3 mM NaNO 3 , 5.
Probes were diluted 1:10, 1:1000, and 10 g/ml, respectively, in MPBS. Afterward arrays were washed thoroughly in PBS and incubated for 2 h with anti-rat, anti-mouse, or anti-His tag secondary antibodies conjugated to alkaline phosphatase (Sigma) diluted 1:5000 (anti-rat and anti-mouse) or 1:1500 (anti-His tag) in MPBS. Once washed in PBS and deionized water, microarrays were developed in a solution containing 5-bromo-4-chloro-3-indolylphosphate and nitro blue tetrazolium in alkaline phosphatase buffer (100 mM NaCl, 5 mM MgCl 2 , 100 mM diethanolamine, pH 9. 5). Data Analysis-Developed arrays were scanned at 2400 dots/ inch (CanoScan 8800F, Søborg, Denmark) and converted to TIFFs; afterward, probe signals were quantified using appropriate software (Array-Pro Analyzer 6. 3, Media Cybernetics, Rockville, MD).
Reproducibility of the technique was tested by performing a series of identical analyses and comparing the data from each replicate (Fig. 6 ). One such example was done by analyzing enzyme activities on crude fungal broths (24 samples from six different species grown at four different conditions). Each sample was incubated separately with a series of different polysaccharide substrates (Fig. 6, A-D ). These reactions were duplicated in the same microwell plate, all the reactions were then printed together as microarrays, which were probed with appropriate antibodies, and signal intensities were quantified. Signal intensities from the duplicated incubations were then plotted against each other, and R 2 values were calculated to determine correlation coefficients (Fig. 6, A-D ). For all four different permutations of substrate and antibody, the R 2 values were Ͼ0.
001-0. 000001 unit/ml enzyme, which could be optimized by adjustment of the substrate concentration. Because reactions are performed in 384-microwell plates that are loaded directly into microarray robots, we envisage that the technique would potentially be well suited to full or partial automation and would be complementary to associated technologies for colony picking of enzyme-expressing microbes and high throughput protein expression methods. Most of the work described here used commercially available enzymes with well characterized specificities with which our data were consistent. Furthermore, the robustness of our approach was confirmed by parallel analyses of enzymes using AZCL, reducing sugar assay, MALDI-TOF MS, and HPAEC. The high throughput capacity of microarrays allows multiplexed analysis involving multiple sets of enzymes, substrates, and time points.