dataesr/scientific-paragraphs-categorization huggingface dataset:
Pacific island TB programmes aim for internationally recommended targets for TB elimination by 2050 as per the WHO Regional Strategic Plan to Stop TB in the Western Pacific: 2011-2015,  7  the WHO Stop TB Partnership and the Millennium Development Goals (MDGs). 8 iney et al Tuberbulosis in the Pacific, 2000-2013   recording and reporting tools. The year 2000 also serves as a baseline for selected TB indicators in the Regional Strategic Plan. 7 e routinely collected TB data are collated and verified by Pacific national TB programme managers before being uploaded onto the WHO online TB data collection system. The reported variables cover clinical, microbiological, demographic and programmatic factors. 13 Completeness and consistency of data are verified by WHO before public release. WHO also provides estimates of TB incidence, prevalence, mortality and case detection.
Statistical analysis Data were organized in Microsoft Excel and statistical analyses were carried out in Stata version 12 (Stata Corp 2011 College Station TX: StataCorp LP). We counted cases and calculated proportions and notification rates per 100 000 using population estimates provided to WHO by the United Nations Population Division. TB case notification rates comprised those who were registered as new or relapse unless otherwise stated. A new TB patient was defined as one who has never had TB or who has received less than one month of anti-TB drugs previously. 14 A relapse TB patient was defined as one who was previously treated for TB, declared cured and has a recurrent episode of TB. 14  Treatment success was calculated by summing TB cases who were cured and who completed treatment and dividing by the total number of notified cases for that particular year.
24 In the Pacific, active TB case finding will likely be a focus for selected national TB programmes in coming years by expanding access to TB care, including screening, diagnostic testing and treatment. 24 ere are several limitations to our study. We used routinely collected surveillance data which were incomplete. Data on age, sex and TB treatment outcomes were missing for over 80% of the 2013 TB cohort, introducing a potential for selection bias into our study. These estimates were affected by data from Papua New Guinea where age and sex data were reported as unknown, and TB treatment outcome data were not reported for all patients. In addition, four Pacific island countries did not report any data to WHO in 2013. These data may all be available at the country level and represent important data for evidence-led programming and policy-making.
Event samples The data used for this analysis were collected by the CMS experiment using pp collisions provided by the CERN LHC with a centre-of-mass energy of 8 TeV, and correspond to an integrated luminosity of 19. 7 fb -1. Events are selected online by a trigger algorithm that requires H T , the scalar sum of the transverse momenta of reconstructed jets in the detector, to be greater than 750 GeV/c. The online H T is calculated from calorimeter jets with p T > 40 GeV/c. Calorimeter jets are reconstructed from the energy deposits in the calorimeter towers, clustered by the anti-k T algorithm  [26, 27]  with a size parameter of 0. 5. Simulated samples are used to determine signal selection efficiencies as well as the background contribution from tt plus jets, ttH, and hadronically decaying W/Z plus b jet production.
Scale factors The subjet b tagging efficiency has been measured in data using a sample of semileptonic tt events. Scale factors have been derived to correct the efficiency predicted by simulation to that measured in data. The "flavor-tag consistency" (FTC) method  [45]  has been used to measure these scale factors. The FTC method requires consistency between the number of b-tagged jets in data and simulation for boosted top quark events. A maximum likelihood fit is performed in which the b tagging efficiency scale factor SF b and the tt cross section are free parameters. Usually the light flavour misidentification scale factor SF light is fixed to a value obtained independently, but in this case the simultaneous fit of SF light , SF b , and the tt cross section has been performed for the first time.
Scale factors A semileptonic tt sample is used to study boosted hadronic top quark decays in data. This sample is then used to measure data to simulation scale factors for the t tagging efficiency using WP2. This procedure was introduced in Ref. [20]. The tt sample is defined by requiring one muon and at least one b-tagged AK5 jet. Additionally, a top quark candidate CA15 jet is required, with high transverse momentum p T > 200 GeV/c and with at least one b-tagged subjet. This semileptonic selection is very pure and background contributions are negligible. The efficiency of the HEPTOPTAGGER is determined as the fraction of top quark candidate CA15 jets that pass all of the tagging requirements. These measurements yield scale factors ranging from 0. 85 to 1. 15 depending on the p T and the η of the jet.
Background estimation The tt background is evaluated from simulated events, corrected for differences between data and simulation in b tagging and trigger efficiencies described above. The uncertainties in the normalization and shape of tt events are discussed in Section 9. Background contributions from ttH and hadronically decaying W/Z plus heavy flavour processes are found to be below 1% and are neglected. The QCD multijet background is estimated in data using a two-dimensional sideband extrapolation. In this method, two uncorrelated criteria in the event selection are inverted to obtain sideband regions that are enriched in QCD multijet events and depleted in signal events. Inverting each criterion individually, as well as both at the same time, results in three exclusive sideband regions, denoted A, B and C: • Sideband region B is obtained by inverting the selection criteria of the HEPTOP-TAGGER algorithm.
A systematic uncertainty in the scale factor is obtained by varying SF trig by ±0. 5(1 -SF trig ). This uncertainty does not affect the plateau region of the trigger, where SF trig = 1. This uncertainty is taken into account both as a shape and as a rate uncertainty. It only affects the low-H T range. The trigger efficiency is measured in a tt-enriched data sample. For 720 < H T ≤ 780 GeV/c the efficiency is 75%, with a SF trig of 80%. For 780 < H T ≤ 840 GeV/c the trigger efficiency is 93%, with a SF trig of 94%. For H T > 840 GeV/c the trigger has an efficiency always greater than 99% and a SF trig consistent with one. The overall impact of this uncertainty on the event yield is 3. 5%. • Luminosity: an uncertainty in the integrated luminosity of 2. 6% is taken into account  [50]. • Cross section of the tt background: an uncertainty of 13% is assigned to the tt cross section.
Results Figure  11  shows the comparison between data and the expected background contributions for the single and multiple H tag event categories after all event selection criteria are applied. In the multiple H tag category only the Higgs boson candidate with the highest transverse momentum is used. The QCD multijet background has been derived from data as discussed in Section 8. Signal samples at three different mass points are also shown. In these plots only signal samples in which all T quarks decay into a top quark and a Higgs boson are shown. In the ratio plot, the statistical uncertainty in the background is depicted by the inner central band, while the outer band shows the quadratic sum of all systematic and statistical uncertainties. discriminating quantity L is calculated for each event, where L = ln 1 + P sig (H T ) P back (H T ) P sig (m bb ) P back (m bb ).
The Customer Led Network Revolution (CLNR) Project and Methodology Th e core objectives of the project include understanding current and likely future energy demand and examining the potential for fostering customer flexibility within the domestic and SME sectors. In order to address these objectives, and in line with the socio-technical approach adopted, the CLNR project is designed around a number of 'test cells' each of which entails a diff erent combination of households, SMEs, low carbon technologies, tariff s, smart meters and/or monitoring equipment. Overall, the project involves the participation of over 12,600 energy customers, with the majority forming a control group that includes 8,900 domestic customers, all of which have smart meters from which half-hourly energy consumption data is recorded.
Having lived with the ASWHP for several months, many householders remained uncertain about the performance of the ASWHP: '[We] still really don't know if we're saving anything. We've got this wireless system in that sends information to [electricity retailer] but we haven't had any reports back or anything like that. ' (Male tenant, ST010) The interview data indicates that householders do not 'actively' manage electricity consumption or read their electricity meter regularly, but continue to rely on their electricity provider to provide this information through periodic, usually quarterly, billing. For most householders interviewed, consumption is evaluated based on cost, not kWh used. Energy pricing is not straightforward, with some energy suppliers exacting a standing charge (a fi xed daily charge), along with diff erent unit rates for peak and off -peak electricity depending on the tariff , so diffi cult to calculate.
Background: The Caribbean Basin has complex biogeographical and cultural histories that have shaped its highly diverse botanical and cultural landscapes. As a result, the current ethnic composition of the Basin is a heterogeneous mixture including Amerindian, Afrocaribbean and a wide range of Mestizo populations. A comparison was done of the medicinal plant repertoires used by these groups to identify the proportion of native species they include and any differences between the groups. Methods: The TRAMIL program has involved application of ethnopharmacological surveys to gather data on the medicinal plants used for primary care in 55 locations the Caribbean Basin. Three Afrocaribbean, three Amerindian and three Mestizo communities were selected taking in account the Ethnic prevalence. Differences in native and exotic plant used by groups and between groups were done using contingency tables.
Data collection The data analyzed here was collected as part of TRA-MIL over a thirty-two year period (1982-2014) in 55 locations in 29 countries and territories in the Basin. Data were collected via ethnopharmacological surveys based on the health problems most affecting each surveyed community. The list of health problems was elaborated by a local multidisciplinary group (nurse, social worker, mothers, traditional healer, etc. ) in order to adapt it to the realities of the country. A random selection was made of 10% of the households in each community, and interviews held with the person responsible for household member health, usually the mother. Information was gathered on the plant species used in treatment of each disorder and preparation methods. The medicinal plants recorded in the interviews were identified by specialists from each country's national plant collection, and specimens deposited in each collection.
Specimens of each collected plant were also deposited in the Santo Domingo Botanical Garden Herbarium in the Dominican Republic  [35]. From the total sample of communities surveyed by TRAMIL, three Afrocaribbean, three Amerindian and three Mestizo communities were selected taking in account the Ethnic prevalence; besides, the communities selected are recognized themselves as Afrocaribbean, Amerindian and Mestizo (Table  2 ). Afrocaribbean communities were those in which most inhabitants were descendants of the African diaspora resulting from the slave trade between the 16 th and 19 th centuries  [36]. One group typical of these communities is the Garifuna, found along the coasts of Belize, Guatemala, Honduras and Nicaragua  [12, 30]. Amerindian communities were those in which inhabitants had preserved the language, social and cultural practices of their places of origin, despite sociocultural mixture  [37].
Database Data collected from the surveys was used to build a database (Microsoft Excel 2010 © ) divided into three sections: taxonomy; geography; and ethnobotany. Accuracy of data in the taxonomic section was confirmed by checking the main TRAMIL specimen collection in the JBSD, the herbarium at the Yucatan Center for Scientific Research (CICY), and the TROPICOS database of the Missouri Botanical Garden. Recorded health disorders were classified into fourteen categories according to the World Health Organization (WHO) international classification of diseases  [39]. Species distribution was divided into native and exotic plants. Native species were those distributed naturally throughout the Caribbean Basin, while exotics were those for which natural distribution does not include the Basin. Distribution was determined by consulting fourteen national and regional floras, including those for Guatemala, the Bahamas archipelago, Cuba, Hispaniola, Mesoamerica and Nicaragua.
Data analysis Comparison among ethnic groups was done by first counting the number of plant families and species with medicinal uses in each group and then counting the number of species in each use category. Differences in native and exotic plant used by groups were done using a 2×3 contingency table; 2×2 contingency tables were done to detect differences between groups. Identification of differences in the numbers of native and exotic plants used within each group was done with a one sample z-test for proportions. Intergroup similarities in use of a plant resource were estimated with the Sørensen Similarity Index  [41] : SSI = (2c/A + B) × 100; where c = is the sum of the number of species held in common between groups, A = is the number of species for group 1 and B = is the number of species for group 2.
Use value (UV) was calculated with the formula UV = ∑U /n proposed by Phillips and Gentry  [42]  and modified by Rossato and his collaborators  [43]  to determine the species more used therapeutically in each group. A non-parametric Kruskal-Wallis test was applied to calculate the mean of a species' use value between groups. Statistical analyses were run with the BioEstat 5. 3 © program  [44]. A principal components analysis (PCA) was run to determine the degree of group clustering based on the use value of medicinal species used by each group. This analysis was done with the MVSP 3. 1 © program  [45].
Acknowledgements The authors thank TRAMIL for allowing to access to information. We thank the  Posgrado en Ciencias Biológicas at the Scientific Research Center of Yucatan  and the  Consejo Nacional de Ciencia y Tecnología (CONACYT)  for academic and financial support of  Ms. studies of the first author,  National Botanical Garden "Dr. Rafael M. Moscoso " of  Dominican Republic  for logistical support in the revision of herbarium specimens of TRAMIL program and two anonymous reviewers for critical revision, useful comments and suggestions that improved the manuscript. Special thanks to Caribbean basin' inhabitants who kindly shared their knowledge.
Weekly IVIG Treatment in Xeno-GVHD Mice Induces NK Cell Expansion We have previously demonstrated that mice injected i. p with 10 7 huPBMCs and treated weekly with 50 mg IVIG i. p. showed a decreased incidence of xeno-GVHD and increased survival, without affecting their level of circulating T lymphocytes  [17]. To understand the mechanism of action of IVIG in our model, we assessed the presence of other cell populations in the peripheral blood of mice. We confirmed our previous result on T lymphocytes  [17]  and observed that the activation status of T cells was not affected by IVIG, as determined by HLA-DR and CD25 expression (data not shown). Importantly, we observed a significant increase in the percentage (Figure  1A ) and absolute number (Figure  1B ) of circulating NK cells (Figure  1C ) in the peripheral blood of IVIGtreated mice compared with the PBS-treated control group.
ACKNOWLEDGMENTS The authors thank  Rose-Marie Brito  for the cell sorting of CD3 negative huPBMCs. J. G. G. received scholarships from the  Fondation du CHU Sainte-Justine/Fondation des étoiles , and the  Fonds de la recherche du Québec e Santé (FRQS). E. H. is a scholar of the FRQS. Disclosure of potential conflict of interest: E. H. was a member of an ad board committee for CSL Behring and received honoraria. The other authors have no potential conflicts to report. Financial disclosure: This work was supported by a  Bayer-Talecris-Canadian Blood Services-Héma-Québec Partnership Fund to E. H. (" Mechanisms of action of intravenous immunoglobulin (IVIG ) on graft-versus-host disease and their influence on immune reconstitution after cord blood transplantation by using a new in vivo model of humanized NOD/SCID/gc-mice"), an investigator-initiated trial, funded by  Talecris Biotherapeutics to E.
If the wrong track is matched to the EM cluster, the charge may be incorrect. However, such electrons would also appear to be isolated far less frequently than electrons that do not emit hard radiation. Therefore the value of r for trident electrons is lower than for electrons that have a correctly measured charge, meaning that they also contribute to the fake/non-prompt electron estimate from the matrix method. To avoid double-counting events with trident electrons in the background estimate, the charge mismeasurement rate is measured in a data sample where the non-prompt/fake contribution, estimated using the matrix method, has been removed. Simulation was used to estimate the sources of events in the signal regions that have fake/non-prompt leptons and/or electrons with mismeasured charge, and it is found that t t events provide the dominant contribution.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  RGC, Hong Kong SAR, China ;  ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW  and  NCN , Poland; GRICES and FCT, Portugal; MNE/IFA, Romania;  MES of Russia  and  NRC KI , Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZ Š, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SER ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  NSC, Taiwan ;  TAEK, Turkey ;  STFC , the  Royal Society and Leverhulme Trust ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
Patients and treatment We selected patients with untreated, de novo IR-AML according to the MRC classification  3  and with available RNA samples at diagnosis for miRNA analysis (Table  1 ). All patients provided their written informed consent in accordance with the Declaration of Helsinki, and the Ethics Committee of Hospital Clinic of Barcelona approved the study. All 181 IR-AML patients were treated from 1994 to 2009 in any of 16 centers participating in three consecutive trials of intensive chemotherapy for fit patients of the Spanish AML cooperative group CETLAM: AML-94 (n = 9); AML-99 (NCT01716793) (n = 26); AML-03 (NCT01723657) (n = 146). Briefly, the induction regimen of AML-94 was ICE (idarubicin, standarddose cytarabine and etoposide), while in AML-99 and AML-03 it consisted of one or two courses of IDICE (idarubicin, intermediate-dose cytarabine and VP-16), with or without priming with G-CSF, respectively.
Clinical endpoints and statistical methods Expression levels of miR-3151 and BAALC were correlated with patient outcome. OS was calculated from diagnosis to death or last follow-up and leukemia-free survival (LFS) from CR to relapse or death. Both OS and LFS were estimated with the Kaplan-Meier method and comparisons among subgroups of patients were performed using the log-rank test. Relapse risk was calculated from CR to relapse and estimated using the cumulative incidence of relapse (CIR) method computed with the cmprsk package for R 2. 12 software (The R Foundation for Statistical Computing, http://www. Rproject. org/). The competing event in the relapse risk analysis was death without relapse. Comparison of relapse risk between groups of patients was performed using the Gray test. 28 Characteristics between groups were compared using the χ 2 -test and Fisher's exact test, when applicable, for categorical variables, and the t-test for continuous variables.
Multivariate analyses for OS and LFS were performed using the Cox proportional hazards model including age (10-year intervals), gender, white blood cell count (WBC; 50 × 10 9 /l increments) at diagnosis, mutational status of NPM1 and FLT3-ITD, and miR-3151 and/or BAALC expression level. A multivariate analysis for CIR was performed using the subdistribution regression model of Fine and Gray  29  with the cmprsk package. The proportional hazard assumption was tested for each variable by analyzing the Schoenfeld residuals. Kaplan-Meier survival curves were then drawn for miR-3151 and BAALC expression predicted to show a survival risk either above or below average risk, using the cut-off points of miR-3151 and BAALC expression levels identified by MaxStat package of R software (The R Foundation for Statistical Computing, http:// www.
R-project. org/). All analyses were performed with SPSS v. 20 (Chicago, IL, USA) or R software version 2. 12. 2 (The R Foundation for Statistical Computing, http://www. R-project. org/). Significance was set at ⩽ 0. 05. To identify miRNAs with significant differential expression correlated with miR-3151 expression, data obtained from our previously identified miRNA profile  10  were analyzed using BRB Array Tools version 3. 5. 0 software (Richard Simon & BRB-ArrayTools Development Team, http://linus. nci. nih. gov/BRB-ArrayTools. html, National Cancer Institute, Bethesda, MD, USA) and TIGR Multiexperiment viewer version 4. 0 software (The Institute for Genomic Research, and ArrayAssist software, Stratagene, http://www. tm4. org/mev; Boston, MA, USA). A student's t-test based on multivariate permutation was performed with adjustment for multiple comparisons and with random variance model.
RESULTS miR-3151 expression and clinical and molecular characteristics miR-3151 was expressed with a variable expression level in the group of AML samples analyzed, with a median expression level of 3. 94 (0-9. 78) (Figure  1 ). The expression of miR-3151 was not significantly associated with any particular clinical feature, including age, WBC, bone marrow blast proportion, or FAB subtype, with the exception of NPM1 mutation. Thus, miR-3151 was more expressed among patients without NPM1 mutation and, using the same cut-off level as identified by the MaxStat package for prognostic purposes, patients with higher miR-3151 were more likely to harbor a wild-type NPM1 configuration (80 vs 20%, P = 0. 009). miR-3151 expression has independent prognostic value in younger IR-AML patients With a median follow-up of 8.