dataesr/scientific-paragraphs-categorization huggingface dataset:
Case definition A confirmed outbreak case was defined as a person living in Ireland with a laboratory-confirmed (HAV IgM and HAV RNA PCR-positive) infection of HAV subgenotype IA with a sequence identified by GenBank accession number KF182323 and with onset of symptoms from 1 January 2013. Cases who had travelled to Italy were excluded. A possible case was the same as a confirmed case, except the genotype and sequence type were unknown and the person had not travelled outside Europe to a country of high endemicity. A secondary case was defined as a confirmed or possible case with symptom onset two to seven weeks after close contact with a primary confirmed or possible case.
Case-control study Public health personnel undertook telephone interviews with the first 10 cases using a detailed trawling questionnaire which included questions on exposures previously associated with HAV infections. We generated hypotheses based on data obtained from the trawling questionnaires. Our resulting primary hypothesis was that illness was associated with the consumption of frozen berries. Other hypotheses were also considered, including illness being associated with the consumption of fresh berries, or specifically fresh blueberries. We conducted a matched case-control study to test these hypotheses. Only confirmed primary cases were eligible. Controls were randomly selected using random digit dialling of landline and mobile telephone numbers, matched on age (± 5 years), sex and county of residence.
Data analysis We described the distribution of cases notified on the national Computerised Infectious Diseases Reporting (CIDR) system for notifiable infectious diseases by time, place, person and case classification. We entered the questionnaire responses from the case-control study into an EpiData database (Epidata association, Denmark, version 3. 1) and analysed them using Stata 11. 2 (Stata Corporation,Texas, US). The association between illness and food items consumed was estimated by calculating crude matched odds ratios (mOR) and 95% confidence intervals (CI). Subsequently, we conducted conditional logistic regression to identify independent risk factors for the disease. We constructed initial models including variables with a p value ≤ 0. 25 in the univariate analysis. To simplify the model, variables were removed one at a time depending on the significance testing, using likelihood ratio tests.
Between January 2013 and June 2014, 1,444 cases of HAV potentially linked to the outbreak were reported by 12 Member States; Italy reported 90% of the cases  [15]. Apart from Ireland, seven other Member States reported cases with no travel history to Italy but with a sequence identical to the GenBank accession number KF182323 strain  [15] , including an outbreak in Norway where the likely vehicle was a cake containing a frozen berry mix  [22]. No single point source of contamination linking all cases and contaminated batches identified during the multinational outbreak could be determined  [15]. When the Irish outbreak was declared over in late October 2013, the advice to boil berries before consumption was reviewed. Due to ongoing outbreaks in other EU countries indicating that contaminated product was still available, the FSAI's precautionary advice to boil imported frozen berries remained in place.
ECDC has initiated in 2013 the nomination of food-borne viral infections experts (microbiologists and epidemiologists) from the EU/EEA countries to contribute in providing data and expertise through TESSy and EPIS-FWD. Strong collaboration with existing international networks such as the International HAV laboratory network, HAVNET (www. havnet. nl), managed by the Dutch National Institute for Public Health and the Environment (RIVM) should be ensured. The outbreak in travellers to Egypt also highlights the importance of vaccination in travellers to endemic areas in a time of increasing tourism to endemic destinations. More HAV outbreaks are expected to occur in the EU. The 2013 experience demonstrates the absolute necessity for extensive collaboration between countries and between the public health and food sectors to identify as quickly as possible the vehicle of infection and, ideally, to control the outbreak in an timely fashion.
Acknowledgements This article has been written on behalf of the  Outbreak Control Team  (membership outlined above). The authors thank the following for their contributions to the outbreak investigation: staff in the  Health Service Executive (HSE) Departments of Public Health and Environmental Health Service  i. e. Mary Conlon ,  Diana Kiely ,  Eleanor McArdle ,  Ruth McDermott ,  Jackie McElhinney  and  Grainne Parker  for interviewing the cases;  Sonya McGuirl  at  Millward Brown  for co-ordinating the recruitment of controls;  Paula Flanagan , HPSC for conducting control interviews; staff in the  HSE Environmental Health Service  and the  Department of Agriculture, Food  and the  Marine  for their work in the environmental/traceback investigations; staff at HPSC for supporting the outbreak investigation,  Sarah Jackson ,  Margaret McIver ,  Paul McKeown , Jolita Mereckiene  and  Niamh Murphy ;  Jonathan Dean ,  Molecular Research Unit, NVRL  for validating the Irish HAV genotyping protocol; colleagues in  Irish laboratories  who referred HAV positive IgM specimens to the NVRL for PCR and genotyping; staff at  FSAI  for supporting the outbreak investigation,  Ray Ellard ,  Eithne Fox ,  Kaiu Jaago ,  Dorothy Guina Dornan ,  Alan Reilly ,  Jane Ryder ,  Ana Canizares ,  Pippa Haughton ,  Eileen Lippert  and the  FSAI Advice Line team ;  Nadia Losio  of the  Istituto Zooprofilattico Sperimentale, Brescia, Italy  for testing the food for the presence of HAV;  Niamh Phillips  of the  Public Health Microbiology Laboratory at St Finbarr's Hospital, Cork  for facilitating the transport of food samples to Italy;  Ettore Severi  ( ECDC ) for facilitating communications between affected Member States;  Caterina Rizzo  ( Istituto Superiore di Sanità, Italy ) for sharing information on the Italian outbreak;  Jeff Connell , NVRL for reviewing the manuscript; EPIET co-ordinators  Kostas Danis  for advice on the case-control study and the statistical analysis and comments on the manuscript and  Pawel Stefanoff  for advice on the case-control study.
Acknowledgements We would like to thank the outbreak investigation teams at ECDC and in the affected countries for their collaboration: The ECDC outbreak response team:  D Coulombier ,  J Jansa ,  J Martinez Urtaza ,  J Takkinen  J. The outbreak response team for the Nordic countries outbreak:  M Edelstein ,  S Ethelberg ,  T Fischer ,  S Gillesberg Lassen ,  T Jensen ,  M Kontio ,  M Lofdahl ,  S Midgley ,  K Molbak ,  R Rimhanen-Finne ,  A Steens ,  B Soborg ,  K Stene-Johansen ,  L Sundqvist ,  H Vestergaard ,  L Vold. The outbreak response team for travellers returning from Egypt:  K Balogun ,  M Cormican ,  E Couturier ,  J Crofts ,  R de Sousa ,  M Edelstein ,  J Epstein ,  M Faber ,  I Fisher ,  C Frank ,  M Gertler ,  J Gil Cuesta ,  S Gillesberg Lassen ,  M Jost ,  M Koopmans ,  R Korotinska ,  K Krajcírová ,  A Kroneman ,  J Lawrence ,  R Liausediene ,  M Löfdahl ,  E Macdonald ,  K Markku ,  P Mckeown ,  S Midgley ,  S Ngui ,  JL Richard ,  R Rimhanen-Finne ,  J Sane ,  P Smit ,  A Steens ,  K Stene-Johansen ,  L Sundqvist ,  S Toikkanen ,  W van Pelt ,  H Vennema ,  L Verhoef ,  L Vold.
Questionnaires and measurement of clinical parameters At baseline, extensive questionnaires were filled in by trained medical staff during a face-to-face interview with the participant. Information on exposures was collected at baseline only. Data concerning socio-economic level, personal medical history, cardiovascular risk factors, lifestyle habits and drug intake were recorded. Participants were asked to bring their latest drug prescription at the inclusion visit. All drugs taken during the two weeks preceding the visit were recorded (international nonproprietary name (INN) and dosage). Hypoglycaemic drug use was assessed as the current consumption of a drug prescribed by a physician for treatment of glycaemic disturbances. Only metformin, sulfonylureas and insulin were considered as hypoglycaemic drugs.
Statistical analysis Statistical analysis was performed on STATA statistical software, release 11. 2 (STATA Corporation, College Station, TX, USA). Subjects with the following medical histories were excluded from the analysis: chronic renal failure (International Classification of Disease, 9th revision, codes 585. 0 to 585. 9), chronic respiratory insufficiency (496. 0 to 496. 9), chronic heart failure (428. 0 to 428. 9), chronic liver disease or cirrhosis (571. 0 to 571. 9) and cancer, excluding benign neoplasms and in-situ carcinoma (140. 0 to 209. 9 and 235. 0 to 239. 9). Overall, 64 participants were excluded from the analysis using these criteria. We first described baseline characteristics of participants and compared baseline characteristics by outcome occurrence, comparing subjects who did not die (i.
e. those alive on December 31, 2009) with subjects who had a fatal event during follow-up. Qualitative variables were compared between groups using the x 2test (or Fisher's exact test when necessary). Student's t-test was used to compare the distribution of quantitative data (or Mann-Whitney's test when distribution departed from normality or when homoscedasticity was rejected). Survival analysis was then conducted. Events were cases of death and exposure was defined by the treatment with hypoglycaemic drugs at inclusion. Hazard ratios (HRs) for mortality and 95% confidence intervals (CI) were assessed using a Cox model. The independent variables initially introduced in the survival model were hypoglycaemic drug treatments and all variables associated with mortality in univariate analysis with a pvalue,0.
In fact, one might ask: -Can jet substructure be predicted by first-principle QCD calculations and compared to data in a meaningful way? The findings of the working group that was set up to evaluate the limitations and potential of the most popular approaches are presented in Section 2. While progress toward analytical predictions continues, searches for boosted objects that employ jet substructure rely on the predictions of mainstream Monte Carlo models. It is therefore vital to answer this question: -How accurately is jet substructure described by stateof-the-art Monte Carlo tools? The BOOST2010 report  [9]  provided a partial answer, based on pre-LHC tunes of several popular leadingorder generators. After the valuable experience gained in the first three years of operation of the LHC, it seems appropriate to revisit this question in Section 3.
Since then, the ATLAS experiment has performed a direct and systematic comparison of the performance of several grooming algorithms on inclusive jet samples, purified samples of high-p T W bosons and top quarks, and Monte Carlo simulations of boosted W and top-quark signal samples  [15]. The parameters of largeradius (R = 1. 0) trimmed  [2] , pruned  [3]  and massdrop filtered jet algorithms were optimized in the context of Standard Model measurements and new physics searches using multiple performance measures, including efficiency and jet mass resolution. For a subset of the jet algorithms tested, dedicated jet energy scale and mass scale calibrations were derived and systematic uncertainties evaluated for a wide range of jet transverse momenta. Relative systematic uncertainties were obtained by comparing ratios of trackbased quantities to calorimeter-based quantities in the data and MC simulation.
Jet Substructure Measurements by CMS The CMS experiment measured jet mass distributions with approximately 5 fb -1 of data at a center-of-mass energy of √ s = 7 TeV  [16]. The measurements were performed in several p T bins and for two processes, inclusive jet production and vector boson production in association with jets. For inclusive jet production, the measurement corresponds to the average jet mass of the highest two p T jets. In vector boson plus jet (V + jet) production the mass of the jet with the highest p T was measured. The measurements were performed primarily for jets clustered with the anti-k t algorithm with distance parameter R = 0. 7 (AK7). The mass of ungroomed, filtered, trimmed, and pruned jets are presented in bins of pt. Additional measurements were performed for anti-k t jets with smaller and larger radius parameter (R = 0.
4. 2) with CT10 PDF  [65]  is also included in comparisons. The Pythia6 generator with the Peru-gia2011 tune is taken as a reference in all comparisons. For each generator, tune and process 1 million protonproton events at √ s = 7 TeV are produced. The analysis relies on the FastJet 3. 0. 3 package  [66, 67]  and Rivet analysis framework  [68]. All analysis routines are available on the conference web page  [69]. In the boosted semileptonic tt analysis, large-radius jets were formed using the anti-k t algorithm  [70]  with a radius parameter of 1. 2 using all stable particles within pseudorapidity |η| < 4. The jets are selected if they passed the following cuts: p jet T > 350 GeV, 140 GeV < m jet < 250 GeV. Only the leading and subleading jets were selected if more than two jets passed the cuts.
Pile-up Each LHC bunch crossing gives rise to a number of proton-proton collisions and typically the hard scattering (signal) interaction is accompanied by several additional pile-up proton-proton collisions. The total proton-proton cross-section is about σ tot = 98 mb (inelastic σ inel = 72. 9 mb) at √ s = 7 TeV  [84] , and even slightly higher at √ s = 8 TeV in 2012. With a peak instantaneous luminosity of about 7. 7 × 10 33 cm -2 s -1 in 2012, the resulting average number of pile-up collisions reached µ = 20 at the highest intensities. The 2012 data set has a rather flat µ distribution extending from µ = 5 to µ = 35. In future LHC running even higher µ are expected. Pile-up manifests itself mostly in additional hadronic transverse momentum flow, which is generated by overlaid and statistically independent, predominantly soft proton-proton collisions that we refer to as "minimum bias" (MB).
This is done dynamically by an event builder in the analysis software, and is thus not part of the signal or MB event production. All analysis is then performed on the merged list of stable particles to model one full collision event at the LHC. The example signal chosen for the Monte Carlo simulation based studies presented in this Section is the decay of a possible heavy Z boson with a chosen M Z = 1. 5 TeV to a (boosted) top quark pair, at √ s = 8 TeV. The top-and anti-top-quarks then decay fully hadronically (t → W b → jj b-jet) or semi-leptonically (t → W b → ν b-jet). The Pythia generator  [85, 86]  is used to generate the signal samples. The soft physics modeling parameters in both cases are from the pre-LHCdata tune 4C  [61]. The pile-up is simulated by overlaying generated minimum bias proton-proton interactions at √ s = 8 TeV using Poisson distributions with averages µ = {30, 60, 100, 200}, respectively, thus focusing on the exploration of future high intensity scenarios at LHC.
74 TeV. Searches in the "lepton+jets" channel are complemented by analyses of the fully hadronic (t t → 6 jets) and di-lepton (t t → b bl + ν l l -ν l ) decay chains. Only one fully hadronic tt resonance search was performed at the Tevatron  [103]. At the LHC, with a daunting multijet background, these searches are even more challenging. The advent of new algorithms has, however, greatly boosted their potential. The mass reach of the CMS  [110]  and ATLAS search  [97]  are compared to that of the "lepton+jets" searches in Table  2. The prospects for progress are good. Preliminary results on the 2012 data set  [109, 113, 114]  have significantly extended previous limits.
Summary & Conclusions This report of the BOOST2012 workshop provides answers to a number of important questions concerning the use of jet substructure for the study of boosted object production at the LHC. We evaluated the current limitations in the description of jet substructure, both at the analytical level and in Monte Carlo generators. Impressive progress is being made for the former and we expect a meaningful comparison to LHC data to be a reality soon. Two approaches -perturbative QCD and Soft Collinear Effective Theory -to a first-principle resummation of the jet invariant mass are producing mature results. Measurements of the jet mass in Z+jet events are proposed, both inclusively and exclusively in the number of jets. We hope that in the not-too-distant future these calculations can enhance our understanding of the internal structure in jets.
Acknowledgements We thank the  Spanish Center for Particle Physics, Astroparticle  and  Nuclear Physics (CPAN) , the regional government ( Generalitat Valenciana ),  Heidelberg University  and  IFIC (U. Valencia/CSIC)  for their generous support of the BOOST2012 conference. We furthermore thank the  Fundación Cultural Bancaja  for putting at our disposal the "Centro Cultural" that hosted the workshop, the IT teams at IFIC, UW and LPTHE for the facilities offered to host the BOOST samples, TotNou for the organization of the workshop,  Isidoro García  of  CPAN  for organizing the outreach event and coordinating the contacts with the media and  Pilar Ordaz  for the design of the poster and logotype.
d N and d S are higher in small-bodied birds We next surveyed synonymous and non-synonymous substitution rates and their relationships with life history individually. d S behaves as predicted if small birds with short generation times evolve more rapidly, correlating negatively with body mass in both data sets (ρ = -0. 5208, P = 0. 0002 for the gene-rich data set, Figure  3 ; ρ = -0. 3015, P = 6. 8 × 10 -5 for the taxon-rich data set). d N was similarly negatively correlated with body mass (ρ = -0. 5147, P = 0. 0003 for the gene-rich data set, Figure  3 ; ρ = -0. 3814, P = 3. 1 × 10 -7 for the taxon-rich data set). This indicates that high d N /d S in species with putatively large populations is not due to the denominator of the ratio being smaller in absolute terms, though there http://genomebiology.
Discussion Employing a data set comprising 1,185 orthologues from 48 recently sequenced bird genomes, we examined relationships between life history and lineage-specific http://genomebiology. com/2014/15/12/542 patterns of substitution. We found no evidence of reduced d N /d S in birds with putatively higher effective population size, in apparent contradiction to nearly neutral theory. On the contrary, we consistently saw a negative correlation between body mass and lineage-specific d N /d S , similar to what was recently reported based on analyses of avian mitochondria  [29]. This is particularly striking and not necessarily expected, given the many inherent differences between nuclear and mitochondrial sequences, as well as the fact that we were able to consider a much larger data set here.
Data for 169 species To extend our taxon sampling, we also analysed 11,160 bp of sequence from 169 avian species, consisting of the coding sequences of the Hackett et al. [41]  data set and two additional widely used phylogenetic markers, RAG1 and RAG2, which were downloaded from GenBank (see Additional file 3 for accession numbers). The marker sequences were translated into amino acids, aligned using MUSCLE  [84]  and subsequently converted back to nucleotides. These data are what we refer to as the taxon-rich set.
Saturation analysis From the 830 orthologues with the highest and lowest variance in GC3, 200 genes were randomly selected  [40]. The pairwise divergence was computed from the number of observed differences between two sequences without correction for multiple substitutions. The phylogenetic distance (that is, the patristic distance) was obtained from the sum of branch lengths between two species, computed using a phylogenetic tree estimated by maximumlikelihood using PAML. We used a GTR+GAMMA model in baseml  [90]  for the third codon position data set and WAG, an empirical substitution matrix, in Codeml  [90]  for the protein data set.
Acknowledgments Computational analyses were performed using resources provided by the  Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX)  of  Uppsala University , supported by the  Swedish National Infrastructure for Computing (SNIC). The study was funded by the  European Research Council  ( AdG 249976 ),  Knut and Alice Wallenberg Foundation  ( Wallenberg Scholar Grant  and the  Swedish Research Council  ( 2010-5650 ; all to HE). We thank  Erich Jarvis  for coordinating the avian phylogenomics project and, along with  Guojie Zhang  and  Tom Gilbert , providing access to genomes. We also thank  Laurent Guéguen  for help with estimating d N and d S in mapNH,  Carina Mugal ,  Jochen Wolf ,  Nicolas Galtier ,  Julien Dutheil ,  Simon Whelan  and  Laurence Hurst  for helpful discussions, and two reviewers for their comments on the manuscript.
Sequencing Analysis The hel and GlcT genes were amplified by PCR from purified APMV and from samples after enrichment. The hel and GlcT genes were chosen since they have been used as markers in phylogenetic or in evolutionary studies  [5, 9, 11]. The primers were designed based on APMV sequences available in GenBank (Accession NC 014649). The amplicons were directly sequenced in both orientations and in triplicate (Mega-BACE sequencer, GE Healthcare, Buckinghamshire, UK). The sequences were aligned with Genbank references with ClustalW and were manually aligned using MEGA software version 4. 1 (Arizona State University, Phoenix, AZ, USA).
Enrichment Applicability Tests To test the applicability of the enrichment protocol, water samples were collected from 2 urban lakes and from a river in Southeast and North Brazil. A total of 475 samples of 5 ml each were collected from the water surface. Aliquots of the samples were enriched or directly inoculated onto A. castellanii monolayers for virus isolation. In parallel, real-time PCRs to detect APMV were performed. From the 475 samples, six giant viruses were isolated with the enrichment protocol (Table  2 ). These viruses induced cytopathic effects in amoebas, including cell rounding and lysis, and were positive in PCR assays. In contrast, only one of the six virus isolates was propagated by direct inoculation of the water samples in amoebas. Sequencing of hel gene confirmed that all virus isolates belonged to the Megavirales order (data not shown).
Discussion Virus isolation has technical limitations, and classical protocols based on filtration have delayed the detection of giant viruses  [22]. Another problem for giant virus isolation is their limited known host range, which restricts the cellular systems that can be used for in vitro culture  [22]. Thus, most information on giant viruses comes from environmental metagenomic studies and not from virus isolation  [22]. This approach must be made with extreme caution, as comparing fragmented environmental sequences with those found in databases can result in false assumptions about giant virus complexity and HGT events  [14]. Isolation of these viruses is imperative for understanding their roles in the environment and as potential vertebrate pathogens. The ubiquity of amoebae implies the probable ubiquity of Mimiviridae, which has been confirmed by indirect viral detection methods.
Acknowledgments We thank  Joa ˜o Rodrigues dos Santos ,  Gisele Cirilo dos Santos , and colleagues from  Gepvig  and the  Laboratorio de Vı ´rus  for their excellent technical support. We also thank  Fundaca ˜o de Amparo a Pesquisa de Minas Gerais (FAPEMIG)  and the  Pro-Reitoria de Pesquisa da Universidade Federal de Minas Gerais.