dataesr/scientific-paragraphs-categorization huggingface dataset:
Nonetheless, our data show that serotypes other than those in PCV13 also can cause serotype replacement. Whether the higher valency vaccines will ultimately lead to further sustained reductions in overall IPD than those observed after PCV7 introduction is not yet clear and should be carefully monitored in the years ahead. Our findings among adults showed a similar trend as in children, with some notable differences. There was a lag of at least 2 years before significant decreases in VT IPD rates were observed, an expected finding as the level of herd protection will depend on the accumulated size of the vaccinated group  [47]. Moreover, the relative reduction in VT IPD, although substantial, was not of the same magnitude as in children. The variability of the changes in NVT IPD rate was greater in adults, with some sites having increases and others having decreases.
First, as mentioned, this review includes only data from programs using PCV7. PCV7 is no longer produced and so it will be important to be cautious when extrapolating to programs using the newer PCV10 and PCV13 formulations. Nonetheless, if PCV10 and PCV13 affect nasopharyngeal colonization in a manner similar to that of PCV7, IPD serotype replacement will likely occur to some degree following immunization with the higher valency formulations; the epidemiology and the policy implications of serotype replacement learned from PCV7 will continue to be relevant. Second, we may not have fully identified or controlled for temporal trends in IPD surveillance or possible outbreaks of serotypes besides 1 and 5 in some datasets. Third, these data represent the experience in highincome countries. Findings from the two indigenous populations (i.
e. , Navajo and Australian Indigenous), known to be at high risk of IPD and to share pneumococcal epidemiologic characteristics with lower-income settings, did not diverge substantially from the findings of the overall analysis. Nonetheless, the results of this analysis might differ in developing countries, where there are differences in the pressure of pneumococcal carriage, serotype distributions, prevalence of risk factors, and co-morbidities. To assess the impact of pneumococcal conjugate vaccines in such populations, longitudinal surveillance of serotype-specific disease will be important. Fourth, only five sites had data out to 6 and 7 years post-introduction, which might have limited the representativeness of the findings for those years, although these five sites showed similar results to all sites in years 1-5 post-introduction (Table  S8 ).
Fifth, we could not control for inherent differences in clinical practice across sites, such as the criteria for hospitalization and performing lumbar punctures and blood cultures, which might, in part, explain heterogeneity of findings across sites. The focus of our analysis was to describe post-PCV IPD epidemiology across many sites, rather than identify site-specific variables that might predict serotype replacement. Finally, these conclusions apply only to IPD and may not be fully representative of serotype replacement in non-bacteremic pneumococcal pneumonia, the most important cause of pneumococcal morbidity and mortality worldwide  [8, 57]. Based on our experience in reviewing many datasets for this evaluation, we have several recommendations for the collection and interpretation of IPD surveillance data (Table  7 ).
In settings where these recommendations cannot be implemented, introduction of PCV should still occur as quickly as possible. However, attempts to identify and characterize serotype replacement using surveillance systems that do not meet these criteria could lead to erroneous conclusions. With so many countries having introduced or about to introduce PCV, and with the need for multiple years of stable and complete pre-and post-IPD rate data, it may be too late to establish many new surveillance sites to monitor serotype replacement. Many countries have existing systems, however, which can be assessed and enhanced to meet the rigorous, high-quality IPD surveillance requirements to monitor the impact of PCVs. Optimizing surveillance data that allows for valid interpretations of the vaccine effect on disease is essential for sound policy decisions  [58].
However, increases in IPD caused by non-vaccine serotypes occurred in some settings, presumably because of ''serotype replacement. '' PCV7 prevents both IPD caused by the serotypes it contains and carriage of these serotypes. Consequently, after vaccination, previously less common, non-vaccine serotypes can colonize the nose and throat, some of which can cause IPD. In July 2010, a World Health Organization expert consultation on serotype replacement called for a comprehensive analysis of the magnitude and variability of pneumococcal serotype replacement following PCV7 use to help guide the introduction of PCVs in low-income countries, where most pneumococcal deaths occur. In this pooled analysis of data from multiple surveillance sites, the researchers investigate serotype-specific changes in IPD after PCV7 introduction using a standardized approach.
What Did the Researchers Do and Find? The researchers identified 21 databases that had data about the rate of IPD for at least 2 years before and 1 year after PCV7 introduction. They estimated whether changes in IPD rates had occurred after PCV7 introduction by calculating site-specific rate ratios-the observed IPD rate for each post-PCV7 year divided by the expected IPD rate in the absence of PCV7 extrapolated from the pre-PCV7 rate. Finally, they used a statistical approach (random effects meta-analysis) to estimate summary (pooled) rate ratios. For children under 5 years old, the overall number of observed cases of IPD in the first year after the introduction of PCV7 was about half the expected number; this reduction in IPD continued through year 7 after PCV7 introduction. Notably, the rate of IPD caused by the S.
These findings have several important limitations, however. For example, PCV7 is no longer made and extrapolation of these results to newer PCV10 and PCV13 formulations should be done cautiously. On the other hand, many of the serotypes causing serotype replacement after PCV7 are included in these higher valency vaccines. Moreover, because the data analyzed in this study mainly came from high-income countries, these findings may not be generalizable to low-income countries. Nevertheless, based on their analysis, the researchers make recommendations for the collection and analysis of IPD surveillance data that should allow valid interpretations of the effect of PCVs on IPD to be made, an important requisite for making sound policy decisions about vaccination against pneumococcal disease.
Box 1. Dataset quality checks performedReview of Case Counts by Year and Age Group Are there dramatic changes in overall case counts from year to year that might not be explained by vaccine introduction?If yes: Clarify with co-investigator. Exclude if indicates changes in surveillance or bias that would affect the analysis. Verify that VT plus NVTs plus unknowns equals the total number of cases provided. If no: Clarify case numbers with co-investigator. If yes: Exclude those cases and re-analyze the data without them. E. Calculate the percentage of all cases for which serotype is known. Exclude strata with ,50% serotyped from further analysis. F. Does the site distinguish between 6A and 6C cases?If yes: Do the numbers of cases of each seem plausible?If no: Redistribute the undistributed 6A/6C cases according to the distribution of known 6A and 6C cases, by age (probably ,5 versus.
Acknowledgments We thank the  GAVI Alliance , through  AVI-Technical Assistance Consortium , for support of the dosing landscape analysis from which many of the datasets included here were identified. An expert consultation meeting for the study held at WHO in September 2011 was supported by the  Bill and Melinda Gates Foundation. We thank the  Serotype Replacement Study Group  for providing feedback on the analysis at various points and contributing to the paper; the Group co-investigators for collecting and providing the site-specific data; and the  Technical Advisory Group (TAG)  members for their advice on the study design and data interpretation. Serotype Replacement Study Group (in alphabetic order; *Technical Advisory Group (TAG) members; { deceased): Richard A Adegbola *  GlaxoSmithKline Biologicals, Wavre, Belgium ;  Mary Agocs FCH/IVB/Expanded Programme on Immunization ,  World Health Organization, Geneva, Switzerland; Krow Ampofo University of Utah, Salt Lake City, Utah, USA; Nick Andrews Immunization, Hepatitis and Blood Safety Department, Health Protection Agency, London, United Kingdom; Theresa Barton Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas, USA; Javier Benito Pediatric Emergency Department, Cruces Universitary Hospital, Bilbao, Spain; Claire V Broome* Rollins School of Public Health, Emory University ,.
The study was supported by grant ID#  OPP1020720  from the  Bill & Melinda Gates Foundation  (http://www. gatesfoundation. org/Pages/home. aspx). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The manuscript coauthors and members of the Serotype Replacement Study Group have the following conflicts: RD has, in the last five years, received grants/research support from  Berna/Crucell ,  Wyeth/Pfizer ,  MSD  and  Protea ; he has been a scientific consultant for  Berna/Crucell ,  GlaxoSmithKline ,  Norvatis ,  Wyeth/Pfizer ,  Protea ,  MSD ; he has been a speaker for  Berna/Crucell ,  GlaxoSmithKline , and  Wyeth/Pfizer ; he is a shareholder of  Protea/NASVAX. PDW has received research grants, honoraria, and travel expense reimbursements from vaccine manufacturers including  Glaxo SmithKline ,  Norvatis ,  Sanofi Pasteur ,  Merck , and  Wyeth , as well as from governmental agencies including the  Quebec Ministry of Health and Social Services, Health Canada , and the  Public Health Agency of Canada.
JE has served as a member of a data safety monitoring board (DSMB) for Novartis meningococcal and typhoid vaccines and participated in an advisory meeting of their pneumococcal protein vaccine in 2009. JE works at the  National Institute for Health and Welfare (THL), Helsinki, Finland , which has a research contract with GSK on pneumococcal vaccines. MH is a lead investigator for the  Switzerland IPD surveillance program , which is partly funded by an unrestricted grant from  Pfizer. JDK and OV are lead investigators of the  Calgary Streptococcus pneumoniae Epidemiology Research (CASPER)  study which is sponsored in part by an unrestricted grant from  Pfizer ,  Canada. NPK has received research support from  Pfizer ,  GSK ,  Merck ,  Sanofi Pasteur , and  Norvatis. ML has received consulting fees from  Pfizer.
Given this information, all detected particles in the event are reconstructed as electrons, muons, photons, neutral hadrons, or charged hadrons. For this paper, electrons and photons are required to have |η| < 2. 5, and muons, |η| < 2. 1. The leading hard-scattering vertex of the event is defined as the vertex whose tracks have the largest squared-sum of transverse momentum. Charged hadrons associated with other vertices are removed from further consideration. The remaining candidates are clustered into jets using the FASTJET 3. 0 software package  [27]. The semi-leptonic analyses use the anti-k T jet-clustering algorithm  [28]  with a size parameter of 0. 5 (AK5 jets), while the all-hadronic analysis uses the Cambridge-Aachen (CA) jet-clustering algorithm  [29, 30]  with a size parameter of 0.
8 (CA8 jets) to take advantage of the capability of the CA algorithm to distinguish jet substructure. Jets are required to satisfy |η| < 2. 4. Jets are identified as b-quark jets if they satisfy the combined secondary vertex algorithm defined in Ref. [31]. The data for the semi-leptonic resolved category were collected with triggers requiring a single isolated muon or electron with a p T threshold of 17 or 25 GeV, respectively, in combination with three jets with p T > 30 GeV. Offline, we select events containing exactly one isolated muon (with p µ T > 26 GeV), or electron (with p e T > 30 GeV), and at least four jets with p T > 70, 50, 30, 30 GeV, respectively. The non-W multijet background (NWMJ) is suppressed further by requiring the transverse missing momentum E miss T , the modulus of the vector sum of all measured particle p T , to be larger than 20 GeV.
The purpose of this classification is to separate the sample into regions dominated by different background processes. The events in the categories with fewer b-tagged jets have a higher fraction of W+jets events, whereas those with more b-tagged jets have a higher fraction of tt events. This characterization is used to constrain the various background components by imposing self-consistency among the channels. The reconstruction of semi-leptonic tt candidates relies on a χ 2 variable built by enforcing kinematic consistency (within uncertainties) with the tt hypothesis, imposing constraints on the reconstructed W and top candidates. In the semi-leptonic boosted regime we follow the techniques of Ref. [23] , and allow candidates with more than one parton merged into a single jet. For the boosted all-hadronic analysis, data were recorded with a trigger requiring the scalar sum of the transverse momenta of reconstructed AK5 jets to be greater than 750 GeV.
Data and study area This study is based on daily rainfall amount recorded and simulated over Burkina Faso. The observed data over 1961-2009 period come from the national network of ten synoptic stations which are well spread over the country (Fig. 1 ). The advantage of this network is that all the stations present a daily time series of good quality over the considered period (only one year, 1978 is missed at Bogande). The simulated data are produced by five RCMs run under the A1B scenario  (Nakicenovic and Swart 2000)  for 1950-2050 period, except for RACMO which starts in 1970. Indeed, the five RCMs were chosen from a set of eleven RCMs which have been implemented by ENSEMBLE-Europe (http:// ensemblesrt3. dmi. dk/) for the AMMA program with a condition that the simulations present full daily climate data (rainfall, temperature, humidity, wind speed and radiation) from 1971 to 2050.
Acknowledgments The authors would like to thank the '' Direction Nationale de la Meteorologie '' of  Burkina Faso  for the rainfall data and the  French Ministry of Foreign Affairs  for the support to this study through the  financial fond of ''Fond de Solidarite Prioritaire ''. We thank also the two anonymous reviewers for their great suggestions and comments that help to improve the quality of the paper. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
Rheumatoid arthritis (RA) is a joint-destructive autoimmune disease. Three composite indices evaluating the same 28 joints are commonly used for the evaluation of RA activity. However, the relationship between, and the frequency of, the joint involvements are still not fully understood. Here, we obtained and analyzed 17,311 assessments for 28 joints in 1,314 patients with RA from 2005 to 2011 from electronic clinical chart templates stored in the KURAMA (Kyoto University Rheumatoid Arthritis Management Alliance) database. Affected rates for swelling and tenderness were assessed for each of the 28 joints and compared between two different sets of RA patients. Correlations of joint symptoms were analyzed for swellings and tenderness using kappa coefficient and eigen vectors by principal component analysis.
Many of the joints showed right-dominant tenderness (eleven of fourteen joints, p = 0. 057, binomial test), indicating mostly righthandedness. We found strong correlations for the affected rates of each joint between swellings and tenderness except for shoulder joints (Spearman's rank-sum coefficient, rho = 0. 70 and p = 3. 8610 25 , Figure  1 , Table  S1 ). Shoulder joints showed much higher frequencies of tenderness than those of swellings. Next, we tried to replicate the order of affected frequencies of the 28 joints and the correlation between tenderness and swellings in different RA patients. We obtained 579 patients whose joints data were not available for 2011, indicating we analyzed independent RA patients. We found that the order of the affected joint frequencies were well correlated for both swelling and tenderness among different sets of RA patients (Spearman's rank-sum coefficient, rho:0.
Next we used eigen vectors of principal component analysis to assess the correlations of the 28 joints involvement. When we analyzed correlations of tenderness, eigen vectors revealed that PIP and MCP joints can be clearly distinguished from large joints and wrist joints (Figure  3A ). PIP joints and MCP joints turned out to make independent groups after excluding large joints and wrist joints (Figure  3B ). These three groups of affected joints were found both for tenderness and swelling (Figure  3C  and 3D ). We confirmed these three correlation groups in four independent resampling analyses by randomly picking up one assessment from each of the 1,314 patients four times (data not shown). The three groups were observed in the two independent sets of RA patients which were used in the analysis of joints involvement frequency  (Figure  S3 ).
Characterizing the relative affected frequency of each joint and analysis of correlation between joint symptoms are important to analyze the basic mechanisms of synovitis and to efficiently select the joints to predict future joint destruction. However, there is no detailed analysis to address the correlations between the 28-joint symptoms. In the current study, we characterized the 28-joint symptoms using large numbers of joint assessments. While we reported the affected rates of each joint in the 28 joints for tenderness and swelling of RA patients registered in the KURAMA database in 2011 as a representative (Table  S1 ), these rates should not be generalized considering large effects of treatment especially biologics agents on joint symptoms. Thus, we focused on relative frequencies of joint involvement for the 28 joints.
When patients who were regularly classified into the first subgroup of patients characterized by no synovitis were removed, more than 60% of patients were still classified into regular subgroups (data not shown), indicating that the stable patterns were observed regardless of activity of RA. As joint destruction was influenced by disease duration, disease activity, and treatment, we analyzed the relative distribution of joint destruction between the three joint groups in a patient with RA. We found that the sixth subgroup of patients, characterized by moderate activity with dominant involvement of large and wrist joints, demonstrated dominant destruction of wrist joints. This suggests that classifying patients with RA into appropriate subgroups would lead to prediction of patterns of joint destruction.
Ethics Statement Written informed consent to enroll in the database described below was obtained from most of the patients, but for some patients the information regarding the construction of this database was disclosed instead of obtaining written informed consent. Participants who were informed regarding the construction of the database (instead of obtaining written informed consent) were allowed to withdraw from the study if desired. All data were de-identified and analyzed anonymously. This study was designed in accordance with the Helsinki Declaration. This study including the consent procedure was approved by the ethics committee of Kyoto University Graduate School and Faculty of Medicine.
The KURAMA database The KURAMA (Kyoto University Rheumatoid Arthritis Management Alliance) database was established in 2011 at Kyoto University to store detailed clinical information and specimens from patients with arthritis and arthropathy. The alliance is composed of rheumatic disease-associated departments in Kyoto University Hospital as well as its allied, integrating previous database and specimen collections in each department and allied. A template for electronic clinical charts developed at Kyoto University Hospital in 2004 to evaluate joint involvements in RA patients was used to obtain joint assessments. Rheumatologists evaluated swelling and tenderness of the 28 joints in patients with RA on each visit and filled in the template. The synovitis information of the 28 joints and data for C-reactive protein and erythrocyte sedimentation rate were extracted from electronic clinical charts  [15]  and stored in the KURAMA database.
Patients and data of joint assessment A total of 17,311 joint assessments from 1,314 patients with RA from 2005 to 2011 were obtained in a retrospective manner from the KURAMA database. All of the patients fulfilled ACR revised criteria for RA in 1987  [10]  or ACR and EULAR classification criteria for RA in 2010  [16] [17]. Analysis of affected frequencies in the 28 joints RA patients were subdivided depending on whether their data were available in 2011 or not, and the affected frequency in each of the 28 joints was calculated. We compared the order of the affected frequency in the 28 joints between the two patient sets with Spearman's rank-sum coefficient. We separately analyzed the affected rates of joints for swelling and tenderness. When multiple joint assessments in different visits were available in the same patient with RA, we randomly selected one of the assessments as representative in the patient.
Dominant destruction of joints was evaluated by paired-t test. Statistical analysis was performed by R software or SPSS (ver18). Figure  S3  Three groups of joints regardless of different sets of RA patients. Analysis using one of four resampled assessments in one of the two sets of RA patients is shown as a representative. The 1 st and 2 nd components of eigen vectors of the joint symptoms are plotted, using principal component analysis of the 28 joint involvement for tenderness (A) and swelling (C) or using that of the 20 joint involvement other than large and wrist joints for tenderness (B) and swelling (D). Green: large and wrist joints. Red: MCP joints. Blue: PIP joints. (TIF).
Acknowledgments We would like to thank to  Mr. Wataru Yamamoto  at  Kurashiki Kosai Hospital  for his excellent support to establish and maintain the KURAMA database. We also thank  Drs Hisashi Yamanaka ,  Katsunori Ikari , and  Ayako Nakajima  at  Institute of Rheumatology, Tokyo Women's Medical University  for their kind instruction and advice for management of rheumatic diseases database.
This study was supported by research grants from  Mitsubishi Tanabe Pharma Corporation  (http://www. mt-pharma. co. jp/e/), Eisai Co. , Ltd. (http://www. eisai. co. jp/index. html), Abbott Japan Co. , Ltd. (http://www. abbott. co. jp/), Chugai Pharmaceutical Co. , Ltd. (http://www. chugai-pharm. co. jp/hc/ss/english/index. html),  Pfizer Japan Inc. (http://www. /),and Bristol-Myyers K. K. (http://www. bms. co. jp/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding was received for this study.