dataesr/scientific-paragraphs-categorization huggingface dataset:
Total Western Pacific Region**** - - - - 11 11 7,854 (36) 59 59 47,446 (25) 96 91 35,472 (18) Abbreviations: CH Includes cases confirmed by laboratory testing or epidemiologic linkage, as reported in the WHO/UNICEF Joint Reporting Form or other WHO Western Pacific Regional Office databases or reports. https://www. who. int/immunization/monitoring_surveillance/data/en. = China; FR = France; NA = not applicable; NR = not reported; NZ = New Zealand; RCV1 = first RCV dose; RCV2 = second RCV dose; RI = routine immunization; UNICEF = United Nations Children's Fund; US = United States. * Based on data from WHO-UNICEF Estimates of National Immunization Coverage, WHO/UNICEF Joint Reporting Form, or WHO Western Pacific Regional Office databases. https://www. who. int/immunization/monitoring_surveillance/data/en.
For that purpose, we compared a plasmid with a single site-specific O 6 mG:C lesion to a plasmid additionally treated with MMS, an agent known to induce essentially only N-alk adducts. The MMS treatment was adjusted as to produce the same amount of N-alk adduct as generated by MNU. The single-adducted O 6 mG:C plasmid triggered MMR mediated repair synthesis centered around the O 6 mG adduct. Interestingly, the presence of randomly distributed N-alk adducts led to a 3-fold increase of the MMR repair activity in the vicinity of the O 6 mG adduct. These data raise two questions, first, how does the MMR machinery get engaged in ccc plasmid and how is MMR activity further stimulated by N-alk adducts? In current models, functional engagement of MMR involves a mismatch recognized by MutSα, subsequent recruitment of MutLα and PCNA  (Jiricny, 2006).
Mass Spectrometry. Label-free mass spectrometry analysis was performed using on-bead digestion. In solution digestion was performed on beads from plasmid pulldowns. We added 20 µl of 8 M urea, 100 mM EPPS pH 8. 5 to the beads, then 5mM TCEP and incubated the mixture for 15 min at room temperature. We then added 10 mM of iodoacetamide for 15min at room temperature in the dark. We added 15 mM DTT to consume any unreacted iodoacetamide. We added 180µl of 100 mM EPPS pH 8. 5. to reduce the urea concentration to <1 M, 1 µg of trypsin, and incubated at 37 C for 6 hrs. The solution was acidified with 2% formic acid and the digested peptides were desalted via StageTip, dried via vacuum centrifugation, and reconstituted in 5% acetonitrile, 5% formic acid for LC-MS/MS processing. All label-free mass spectrometry data were collected using a Q Exactive mass spectrometer (Thermo Fisher Scientific, San Jose, CA) coupled with a Famos Autosampler (LC Packings) and an Accela600 liquid chromatography (LC) pump (Thermo Fisher Scientific).
The total amount of signal per lane is indicated (AU). As expected, the amount of plasmid extracted from each incubation mix is relatively constant, as quantified below the ethidium bromide image. Increase in repair at the O 6 mG:C lesion due to MMS-treatment (2. 8-fold) is in good agreement with data in 3B. A. Analysis by AGE of alkylated plasmids (pEL97: 11,3kb) incubated in NPE in the presence of α 32 P-dATP. Plasmid pEL97 was treated with MMS, MNU+ and ENU as to introduce ≈ one alkylation event on average every 500 nt on average. For MNU, a plasmid with twice the level of alkylation (MNU++, 1 lesion every 250 nt) was also produced. Alkylation of these plasmids essentially not affected their migration on agarose gels (Fig. S5A ). After 2h incubation, the reaction was stopped and a known amount of pBR322 (10 ng) plasmid was added as an internal standard.
This band is also visible in the MNU+ and ENU lanes although at a weaker intensity. C. Relative abundance of proteins captured on MNU-versus MMS-treated plasmid. Proteins captured on equal amounts of MNU-or MMS-treated plasmid were analyzed by label-free MS in triplicates. The data are analyzed and plotted as described in Fig. 1B  using Xenbase data base. Proteins enriched on MNU versus MMS-treated DNA appear in the right-side top corner and essentially turn out to be MMR proteins labelled in red (Fig. 1B ). A. Analysis by WB of the NPE extracts depleted for MMR proteins. Antibodies against Mlh1, Pms2 and Pms1 as previously described  (Kato et al. , 2017; Kawasoe et al. , 2016). Extracts were depleted, with the respective antibodies, for three rounds at 4C° at a ratio beads: antibody: extract = 1 :3 :5.
The model uses the methodology of NRQCD to compute the cross section contributions for all relevant 2S+1 L n J terms. Each cross section term has a characteristic relation between the jet energy E jet and its fraction carried by the J/ψ meson: z = E J/ψ /E jet. A study of J/ψ mesons contained in jets in the rapidity region y J/ψ > 2, dominated by charm fragmentation for large z, has been reported by the LHCb Collaboration  [14]. The LHCb analysis, which measured the z distribution integrated over jet energy, does not have the sensitivity to LDME parameter sets that characterizes this analysis. The data for this analysis were collected by the CMS detector in proton-proton (pp) collisions from the CERN LHC, corresponding to an integrated luminosity of 19. 1 fb -1 at √ s = 8 TeV. It is the first experimental study of prompt J/ψ mesons contained in jets produced in the gluondominated central rapidity region, where the FJF theory for gluonic jet fragmentation applies.
Jets are reconstructed from particle-flow objects  [27] , using an anti-k T algorithm with a distance parameter of 0. 5  [28] , as implemented in the FASTJET package  [29]. The jet response has been corrected to the particle level  [17]. Although the J/ψ candidate is not a particle-flow object, its decay muons are. This does not exclude jets that consist only of a J/ψ meson. However, such jets constitute less than 10 -4 of this sample and do not affect the results presented here. The jet properties include the energy E jet , the transverse momentum magnitude p jet T , the number of constituents, and the number of included muons. Each bunch crossing in the data produces, on average, 14 reconstructed pp vertices, corresponding to 21 interactions per bunch crossing. The extra interactions produce so-called pileup distortions, which are corrected using the procedure described in Ref.
We use ∆E jet = 8 GeV. The D'Agostini unfolding method  [38]  from the ROOUNFOLD package  [39]  is used to extract the unsmeared Ξ distribution. The procedure uses an input generator-level jet energy distribution (truth distribution) derived from PYTHIA light-quark or gluon jets. Simulation shows that for measured jet energy E jet > 44 GeV, the jet reconstruction efficiency exceeds 98. 5% and is consistent with being energy independent. Thus, 44 GeV is the lowest jet energy considered in the unfolding procedure. The unfolding procedure uses the CMS jet energy resolution and jet finding efficiency  [27]  to determine the unfolded jet energy matrix and the MISS matrix. The latter is filled for events that fail the jet efficiency test or fall outside the unfolded jet energy window 44-140 GeV.
The FJF predictions of the jet energy spectrum In this analysis, we use three z 1 values: 0. 425, 0. 525, and 0. 625. These are the centers of three nonoverlapping z subregions with ∆z = 0. 05 from the measurement region 0. 3 < z < 0. 8. In these three z regions, the FJF terms have different jet energy distributions for a given LDME parameter set. The authors of Ref. [16]  supplied tables of the normalized differential cross section terms (d σi /dE jet dz)| z 1 , computed for √ s = 8 TeV and jet radius R = 0. 5. The cone algorithm used for the theoretical calculation does not introduce a systematic effect since there are no background or pileup sources in the theory. As described in Section 6, we compare the data to sum of the 1 S 8 0 , 3 S 8 1 , and 3 P 8 J FJF functions for the LDME parameter sets from Bodwin, Chung, Kim, and Lee (BCKL)  [30] , Butenschoen and Kniehl (BK)  [41] , and Chao, et al.
The uncertainty in the predicted FJF values due to the theory normalization uncertainty is almost completely correlated for the numerator and denominator of the ratio. The resulting theoretical uncertainty is negligible compared to the experimental uncertainty in the Ξ(E jet ; z 1 ) values. We therefore ignore it in computing the χ 2 values to match data and theory. The χ 2 value and the associated p-value for comparison of data to each LDME set are given in Table  1. An a priori decision was made that a model prediction is an acceptable match to the data only if the χ 2 p-value is larger than 0. 1% for seven degrees of freedom. Otherwise, we say that the model does not match the data. For all three z 1 ranges, the FJF predictions using the BCKL LDME parameters match data. Neither the BK nor the Chao LDME parameter sets describe these jet + constituent J/ψ data for any z 1 value.
Acknowledgments We thank  Ira Rothstein ,  Matthew Baumgart , and  Prashant Shrivastava  for their help in creating the tables of the predictions from their fragmenting jet function model to compare with our data. We thank  Nora Brambilla  for editorial input and theoretical guidance. We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC IUT , and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  NKFIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR , and  NRC KI (Russia) ;  MESTD (Serbia) ;  SEIDI, CPAN, PCTI , and  FEDER (Spain) ;  MOSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ; ThEPCenter, IPST, STAR, and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Individuals have received support from the  Marie-Curie program  and the  European Research Council and Horizon 2020  Grant, contract No. 675440  ( European Union ); the  Leventis Foundation ; the A. P. Sloan Foundation; the  Alexander von Humboldt Foundation ; the  Belgian Federal Science Policy Office ; the  Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium) ; the  Agentschap voor Innovatie door Wetenschap en Technologie  ( IWT-Belgium ); the  F. R. S. -FNRS  and  FWO (Belgium)  under the " Excellence of Science -EOS "be. hproject n. 30820817; the  Ministry of Education, Youth and Sports (MEYS)  of the Czech Republic; the  Lend ület ("Momentum") Program  and the  János Bolyai Research Scholarship of the Hungarian Academy of Sciences , the  New National Excellence Program ÚNKP , the  NKFIA  research grants  123842 ,  123959 ,  124845 ,  124850 , and  125105  (Hungary); the  Council of Science and Industrial Research, India ; the  HOMING PLUS program  of the  Foundation for Polish Science , cofinanced from  European Union, Regional Development Fund , the  Mobility Plus program of the Ministry of Science and Higher Education , the  National Science Center (Poland) , contracts Harmonia  2014/14/M/ST2/00428 ,  Opus 2014/13/B/ST2/02543 ,  2014/15/B/ST2/03998 , and  2015/19/B/ST2/02861 , Sonata-bis  2012/07/E/ST2/01406 ; the  National Priorities Research Program  by  Qatar National Research Fund ; the  Programa Estatal de Fomento de la Investigaci ón Científica y Técnica de Excelencia María de Maeztu , grant  MDM-2015-0509  and the  Programa Severo Ochoa del Principado de Asturias ; the Thalis and Aristeia programs cofinanced by  EU-ESF  and the  Greek NSRF ; the  Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University  and the  Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand) ; the  Welch Foundation , contract  C-1845 ; and the  Weston Havens Foundation (USA).
Introduction The 'Imperial Spine' marker set was developed to assess spinal and lower limb movement or kinematics during functional tasks using three dimensional motion capture (3DMC). To date, spinal movement has been examined in both healthy and patient populations using regional lumbar  [1] [2] [3]  or multiple spine segments  [4] [5] [6]. Although, some consider the contribution of the spine, pelvis and lower limbs towards assessing spinal movement, few have analysed the absolute measures of measurement error and minimal detectable change (MDC). MDC describes the amount of change that is greater than the measurement error for each joint and plane of movement  [7]. This permits kinematic data to be interpreted in a clinically meaningful manner, enabling the assessment of true differences.
Anatomical frames of the pelvis, thigh and shank were referenced to the corresponding technical frames (constructed from technical clusters of markers) in the static calibration trial such that anatomical markers (ASIS, PSIS, MFC, LFC, LMAL, MMAL) (Fig. 1 ; Additional file 1: Table  S1 ) could be removed prior to dynamic trial, permitting freedom of movement. All trials were recorded at 100 Hz using a 10-camera 3DMC system (Vicon Nexus (T160), Oxford Metrics Ltd. , Oxford, UK)  [17]. The onset and cessation of each task were determined using kinematics from each gait  [18]  and STS motion cycle  [12, 19]. Each cycle was extracted (Vicon Nexus (T160), Oxford Metrics Ltd. , Oxford, UK) and filtered using a Woltring cross-validity quantic spline routine  [20]. The data was then normalised to 100% of each motion cycle (MATLAB, Mathworks, Natick, MA.
The minimal detectable change (MDC), which expresses the amount of joint angle change was also calculated (MDC = 1. 96 × √ 2 × SEM)  [25]. ICC statistical analysis was conducted using SPSS software (SPSS Statistics Version 22, IBM, Chicago, IL. , U. S. A. ). A critical level p < 0. 05 was defined as significant. Mean, Mean Diff, 95% CI, SD Diff, 95% LOA, SEM and MDC calculations were computed using Microsoft Excel (Excel 2010, Microsoft Corporation, Redmond, WA. , U. S. A. ).
Sequencing was performed using the Big Dye Terminator Cycle Sequencing Kit (Perkin Elmer Applied Biosystems, Foster City, CA, USA) with an ABI automated sequencer (Applied Biosystems). The obtained sequences were analyzed with ChromasPro 1. 7. 7 software and compared with the sequences of known human and simian EV strains available in the GenBank database (http://www. ncbi. nlm. nih. gov). All sequences were aligned with MEGA7 (https://www. megasoftware. net/), and a phylogenetic tree was constructed using neighbor joining methods. Bootstrap analyses were conducted using 1000 replicates.
Bioinformatic Analysis NGS The quality of the sequence data obtained was controlled using CLC Genomics Workbench v7 (https://www. qiagenbioinformatics. com/products/clc-genomics-workbench/) and the assembly was performed by SPADES V3. 13. 0  [27]. Therefore, we mapped all sequences to the nearest reference to produce consensus sequences to ensure consistency in mapping methodology between high and low coverage samples using the bioinformatics tool CLC. Sequences were trimmed with MiSeq and Trimmomatic  [28]  software, whereas untrimmed data were processed only using MiSeq software. To decrease assembly gaps, we used GapCloser software  [29]. Scaffolds that have a nucleotide number <800 (bp) and scaffolds that have a depth value lower than 25% of the mean depths were removed. The best assembly was selected using different criteria (number of scaffolds, N50, number of N).
Finally, the genes were analyzed using a homology search engine, the local BLAST  [30, 31]. The sequences were aligned using MUSCLE with default parameters. Phylogenetic inferences were obtained using the maximum likelihood method and MEGA software (https://www. megasoftware. net). Bootstrap values obtained by repeating the analysis 1000 times to generate a majority consensus tree are indicated at the nodes, and tree visualization was performed with itol (https://itol. embl. de). The whole genomes of EV-A to D species available in GenBank with Ibou002 and Mbo044 genomes were analyzed. Phylogenetic trees were constructed with complete sequences from the VP1, VP2, VP3, VP4, 5'-UTR and 3D regions of EVs in MEGA7.
Isolation and Genomic Analysis of Enterovirus Strains Virus isolation yielded two strains only in human feces, including one individual from the Mbomo locality (Mbo044) and one eco-guard from the Iboubikro gorilla site in the GLLNR (Ibou002). Genomes were deposed in GenBank under accession numbers LR796218. 1 and LR796210. 1 for Mbo044 and Ibou002, respectively. The Mbo044 and Ibou002 genomes were almost identical to each other and shared 99. 8% identity. The total length of each genome was 7230 bps. Each strain contained all P1, P2 and P3 regions and all genes were present in the genomes of enteroviruses. Their genomic organization was identical to that of the whole genomes of known enteroviruses without any particular organization difference with the previously described enteroviruses  [21].
The P1 region consisted mainly of four structural proteins, VP4 (207 bps), VP2 (813 bps), VP3 (714 bps) and VP1 (924 bps). However, the P2 and P3 regions, which include all the non-structural proteins, consisted of genes 2A (444 bps), 2B (291 bps), 2C (922 bps), 3A (329 bps), 3B (66 bps), 3C (549 bps) and 3D (1263 bps). In addition to these three regions (P1, P2 and P3), a 5′-UTR region (708 bps) was also present in both genomes. The two strains were compared gene by gene with other reference strains available in the GenBank (Table  1 ). The degree of genomic similarity of Mbo044 and Ibou002 (closely related enteroviruses C) was estimated by OrthoANI software  [37]. Values among closely related species ranged from 70. 59% between PV3_NIE1219535 (KY941933) and EV-C99_IJC04 (KP793035) to 82.
The P1 region consisted mainly of four structural proteins, VP4 (207 bps), VP2 (813 bps), VP3 (714 bps) and VP1 (924 bps). However, the P2 and P3 regions, which include all the non-structural proteins, consisted of genes 2A (444 bps), 2B (291 bps), 2C (922 bps), 3A (329 bps), 3B (66 bps), 3C (549 bps) and 3D (1263 bps). In addition to these three regions (P1, P2 and P3), a 5 -UTR region (708 bps) was also present in both genomes. The two strains were compared gene by gene with other reference strains available in the GenBank (Table  1 ). The degree of genomic similarity of Mbo044 and Ibou002 (closely related enteroviruses C) was estimated by OrthoANI software  [37]. Values among closely related species ranged from 70. 59% between PV3_NIE1219535 (KY941933) and EV-C99_IJC04 (KP793035) to 82.
Acknowledgments: We are grateful for all participants in the present study. We thank  Antoine Nougairede  and  Illich Manfred Mombo  for their valuable technical assistance in this work. We thank eco-guards and conservators of  Lésio-Louna National Park (NP) ,  Odzala-Kokoua NP  and  Nouabale-Ndoki NP ,  Agence Congolaise de la Faune et des Aires Protégées  (the  Republic of Congo) ,  Ministry of Health and Population  (the  Republic of Congo)  for their valuable assistance. We want to thank the  "Association pour la recherche en infectiologie (APRI) " for financial support.
Funding: This study was supported by the  Institut Hospitalo-Universitaire (IHU) Méditerranée Infection , the  National Research Agency  under the program «Investissements d'avenir», reference  ANR-10-IAHU-03 , the  Région Provence-Alpes-Côte d'Azur and European funding FEDER PRIMI ,  Service de Coopération et d'Action Culturelle (SCAC) de l'Ambassade de la France en République du Congo, Laboratoire National de Santé Publique de Brazzaville.
The articles were retrieved from MEDLINE and the Tufts Medical Center Cost-Effectiveness Analysis Registry, with an additional web search in Google and Google Scholar. Comparisons were performed on the cost-effectiveness ratio per quality-adjusted life-year between the interventions using a league table, and the likelihood of cost-effective interventions for breast cancer primary prevention was analyzed. Results: Six studies were selected. The median cost-effectiveness ratio (in 2018 USD) was $24,973, and 80% of the interventions had a ratio below the $50,000 threshold. The low-fat-diet program for postmenopausal women was cost-effective at a societal level, and the physical activity interventions, such as the Be Active Program in the UK, had the best cost saving results. A total of 11 of the 25 interventions ranked either as highly or very highly likely to be cost-effective for breast cancer primary preventions.
Information Sources and Search Strategy The studies were identified using electronic databases. We searched MEDLINE via PubMed from its database inception until January 2019. A second database, the Tufts Medical Center Cost-Effectiveness Analysis Registry (www. cearegistry. org), was searched from 2014 to 2017 since a systematic review performed by Winn et al. summarized evidence on the costutility analysis of cancer prevention and treatment with studies dated up to 2013  (19). That systematic review was identified in the studies retrieved from the Medline search. We handsearched reference lists from all of the studies and review articles included. Additional literature was searched using Google and Google Scholar. The search terms were developed by the research team in collaboration with a faculty librarian.
Selection of Sources of Evidence All search results were imported and de-duplicated using Covidence Software (https://www. covidence. org). The title/abstracts and the full text were screened by two reviewers (JPR and MB). One reviewer (MB) screened all of the abstracts and the full text of the relevant references. A second reviewer (JPR) double-checked 15% (200/2,944) of the abstracts and inspected all of the full text of the rejected articles (185/191) to ensure that no relevant study was excluded. Disagreements were resolved after discussion.
Critical Appraisal of Individual Sources of Evidence The quality of the selected studies was assessed (MB, JR, and JPR) using the guidelines recommended by Drummond and Jefferson for cost-effectiveness analysis studies  (30). The quality of the study was determined by analyzing three categories: (1) study design, (2) data collection methods (e. g. , model input such as outcome measures, cost components, and estimates), and (3) interpretation of results (e. g. , time horizon, discount rates, sensitivity analysis, including probabilistic sensitivity analysis, and relevance of alternatives compared). To rate the quality of the evidence, we used a three-point scale for each item, as suggested in previous studies by Gerard et al. and Zelle and Baltussen. The final percentage ranges were thus expressed, and the overall quality of the study was set as in Zelle and Baltussen  (31, 32).