dataesr/scientific-paragraphs-categorization huggingface dataset:
The studies were conducted in a range of low and middle income countries in settings with predominantly poor sanitation with a range of prevalence of STH in children aged from 6 months to 17 years. The prevalence of A. lumbricoides infections in our base case sample (14 studies with sufficient data for multiple imputation) was 52% (range, 9-93%), hookworm 45% (range, 1-94%) and T. trichiura 52% (range, 9-96%). Less than 15% of children had an infection intensity considered moderate or heavy for A. lumbricoides, T. trichiura or hookworm, according to the WHO criteria in our sample. Our dataset included 2,448 children <5 years of age in our main models (18% of the sample). Our data suggest that there is publication bias in the deworming literature with failure to report growth data since we obtained weight and height data from eight studies which had not previously reported these  (Beasley et al.
, 1999; Beasley, 1995; Friis et al. , 2003; Ebenezer et al. , 2013; Kirwan et al. , 2009; Le Huong et al. , 2007; Rohner et al. , 2010; Solon et al. , 2003). We also report cognition data that was not previously published from one study  (Rohner et al. , 2010). Given our findings of selective outcome reporting, it is still possible that there are additional older studies with negative findings. We compared the effect sizes observed in the studies that we retrieved to those which were excluded (due to missing baseline infection intensity) or which we were unable to obtain from the trial authors (due to lost datasets, administrative hurdles or nonresponse from the authors). We found that the test for interaction for subgroup differences was not statistically significant for weight, height or haemoglobin, but the effect on weight was higher in the studies which were not obtained, which were mostly older studies.
and created five complete datasets using Proc MI in SAS9. 4/STAT (SAS Institute Inc. , Cary, NC). All model estimates and standard errors were obtained by fitting the model to each of these five imputed datasets and aggregating results across them using Rubin's Rule which incorporates uncertainty due to imputation. Proc MIANALYZE in SAS 9. 4/STAT was used to obtain the aggregation of estimates across imputed datasets. Descriptive characteristics of each study are presented, with details on the child characteristics, environment, worm species, prevalence, and intensity of infection, geographic location, interventions, comparator and outcomes and risk of bias assessment.
Since we considered that the fortified soup and unfortified soup were not randomised, we collapsed across the fortified and unfortified soup conditions, calculating an overall effect for all children randomised to anthelminthics versus placebo (n = 178). Liu et al. (2017): We used IPD data provided by the authors. Croke et al. (2016) used published mean changes. Miguel and Kremer (2004): We used data from the public use files for the 1st year of comparison, where we considered Group 1 as STH deworming and Groups 2 and 3 as control. It is not clear which data were used by Croke et al. , but they may have had access to different datasets, or used data from the 2nd year of the study. Ndibazza et al. (2012): We used data from IPD provided by the authors. Croke et al. used mean changes provided by the authors to Welch et al.
ACKNOWLEDGMENTS We would like to thank  Celia Holland  for being part of our Advisory board and providing comments on interim results;  Nilanthi de Silva  for being part of the investigator team;  Andrew Hall  for advice and attending our investigator team meetings;  Rebecca Stoltzfus  and  James Tielsch  for sharing data;  Jessica Trawin  and  Christine Mathew , who contributed to data collection on preparation and  Jordan Bernick  who contributed to the analysis. We are co-publishing this.
and updated the search to March 27, 2018. See search strategy in Appendix. We searched in the following databases: MEDLINE, CINAHL, LILACS, Embase, the Cochrane Library, Econlit, Internet Documents in Economics Access Service (IDEAS), Public Affairs Information Service (PAIS), Social Services Abstracts, Global Health CABI and CAB Abstracts. We searched the System for Information on Grey Literature in Europe (SIGLE)-ended in 2005. We searched websites of relevant organisations such as the World Bank, World Food Program and International Food Policy Research Institute, as per the prior.
TO THE EDITOR: The diagnosis of hereditary angioedema (HAE) due to C1 inhibitor deficiency or dysfunction (type I or type II; C1-INH-HAE), a rare genetic disease affecting approximately 1. 5 in 100,000 individuals worldwide, can prove challenging owing to nonspecific signs and symptoms that overlap with other, more common conditions. 1, 2 Misdiagnosis and long delays to diagnosis are common, resulting in use of ineffective therapies and delayed initiation of appropriate therapy, which can negatively impact patients. 3, 4 However, there is currently limited understanding of the causes of diagnostic delays. Here, we used data from the Icatibant Outcome Survey (IOS) database to determine the physician specialties involved in the diagnosis of C1-INH-HAE, and whether the diagnosing physician type affects the length of diagnostic delay.
The IOS is an ongoing, international, multicenter, prospective, observational study designed to monitor the safety and effectiveness of icatibant (NCT01034969). Initiated in July 2009 by Shire (Zug, Switzerland), IOS is active in 12 countries (Austria, Brazil, Denmark, the Czech Republic, France, Germany, Greece, Israel, Italy, Spain, Sweden, and the United Kingdom) as of January 2017. Patient data are collected via an electronic case report form at the time of enrollment and at follow-up visits. 5 All patients provide written informed consent to participate in the registry. Each IOS site operates in accordance with its local ethics committee and/or health authority and adheres to the Declaration of Helsinki and the International Conference on Harmonisation Good Clinical Practice guidelines.
05 vs 11. 75 years), or pediatricians (0. 79 vs 7. 05 years). Although these findings may be reflective of greater disease awareness when presented with the symptomatic patient, a more rapid referral to the diagnosing specialist may also play a role. Although data from registries such as IOS provide a valuable source of real-world information, the voluntary nature of participation presents a number of limitations, including missing or incomplete data and potential selection bias. Icatibant Outcome Survey was designed to monitor the safety and effectiveness of icatibant, which, at the time of this analysis, was licensed to treat acute attacks only in adults with HAE but not in children or adolescents; therefore, patients enrolling into IOS were exclusively adults. As a result, retrospective data on patients' diagnostic pathway through childhood may be subject to recall bias.
More recently, since September 2017, icatibant has been licensed for symptomatic treatment of acute attacks in adults, adolescents, and children 2 years or older with C1-INH-HAE, so future analyses, including high-quality data on the diagnostic pathway, may be possible. Furthermore, patients included in this analysis originated from multiple countries, with different health systems and protocols for referral. Therefore, it is not clear whether these findings can be generalized between countries. Our results demonstrate that although symptoms of C1-INH-HAE present in childhood or adolescence, pediatricians currently diagnose few patients. Increasing the awareness of C1-INH-HAE symptoms among pediatricians could shorten the delay in diagnosis experienced by patients through prompt referral to disease specialists or increases in the frequency of pediatrician-led diagnoses.
Acknowledgments The IOS is supported by  Shire GmbH, Zug, Switzerland. Under direction of the authors,  Sally Hassan, PhD , and  Lindsay Napier, PhD , CMPP, employees of Excel Medical Affairs, provided writing assistance for this manuscript. Editorial assistance in formatting, proofreading, and copy editing also was provided by  Excel Medical Affairs. Shire Human Genetic Therapies  provided funding to Excel Medical Affairs for support in editing this presentation. The interpretation of the data was made independently by the authors.
SSR genotyping Identification of EST-SSR markers Microsatellite or SSRs were developed from 150,258 EST generated on 454 sequencing and downloaded from The Compositae Genome Project of UC Davis (http://compgenomics. ucdavis. edu/data/cwassy_2012/iAssSta. fa) according to the pipeline described in Additional file 1: Figure  S1. The program Sputnik (http://abajian. net/sputnik/) were used to identify EST containing SSR. For SSR identification, the minimum motif repeats were defined as 20 repeats for a mononucleotide unit, 10 repeats for a dinucleotide unit, 7 repeats for trinucleotide unit, 5 repeats for tetranucleotide unit and 4 repeats for pentanucleotide unit. Primer pairs flanking the SSRs were designed using Primer3Plus  [33]  with melting temperatures 52-57 °C, primer lengths 18-24 bp, expected fragment size 100-300 bp.
Genetic variation Allele frequencies and genetic diversity measures were calculated using PowerMarker 3. 25  [36]  and GenAlEx 6. 5  [37]. These measures included number of alleles (Na), major allele frequency, polymorphic information content (PIC), expected heterozygosity or Gene Diversity (He), observed heterozygosity (Ho). Inbreeding coefficient (Fis) was calculated using SPAGEDI 1. 3  [38]  and verified with GENETIX v4. 05  [39]. Private allelic richness was computed with ADZE software to adjust for sample size differences  [40]. We further explored the genetic differentiation and relationships among samples using an unweighted Neighbor-Joining tree constructed using simple matching dissimilarity indices of Jaccard's coefficient method and bootstrap values over 2000 replicates as implemented in the DARWIN software package v6.
Acknowledgements Authors thank the Director of the Herbarium and  Dr. Tom Zanoni  from The  New York Botanical Garden, Bronx ,  NY 10458-5126  ( USA ) for providing the samples from  Cuba ,  Coralie Chesseron  ( INRA, Bordeaux, France ) for plant production and  Pierre Jannot  for scientific support ( Rouages, Agen, France ). Grateful thanks to  Dr. S. Mariette  ( INRA, UMR BIOGECO, France ) for critical reading of the manuscript.
Parent trial In 2012, a phase III randomized, double-blind, placebocontrolled trial to test the efficacy of the bovine rotaviruspentavalent vaccine (BRV-PV) began in Madarounfa, Niger (ClinicalTrials. gov Identifier: NCT02145000). Details of the parent trial and study setting have been published elsewhere  [13]. In recognition of the need to identify potential boosters of immunogenicity in countries where oral vaccine efficacy is low, a cluster randomized sub-study was designed to identify complementary interventions that may increase vaccine efficacy. One potential mechanism hypothesized to underlie lower immunogenicity observed in some settings is related to the inhibitory effects of poor nutrition on immune development  [14]. The role of nutrition in immune development has been reasonably well documented in observational studies, where both macro-and micronutrient deficiencies have been shown to result in the impairment of various components of the immune system  [15].
Study design and procedures This study collected data during two calendar periods: June (Ramadan) and September (non-Ramadan) 2014. Ramadan is the Muslim holy month marked by daily fasting from sunrise to sunset. Pregnant women may be exempt from daily fasting, however household practices may influence individual food preparation and intake during this period. In Niger, where more than 98% of population is estimated to practice Islam, this design allowed for a description of Epi-E acceptability and utilization during two important periods when dietary intake and practices are very different for the majority of the population. In both time periods, a two-part, multimethods study design was used among two independent samples of participants. Part I was designed to evaluate Epi-E acceptability through repeated observation of two test meals provided at the health center.
Data analysis Participant characteristics, including socio-demographic characteristics and self-reported morbidity, as well as results from the Part 1 test meals and Part 2 home trial were summarized using means (SD) for continuous measures, and using counts and proportions for discrete measures. A one-sample t-test was used to assess the difference between the hypothesized and observed mean consumption  [23]. Hedonic scale responses rating sensory properties were presented as medians (min, max). Textual responses generated from the open-ended questions underwent an inductive review to identify thematic areas  [24]. We considered both their frequency of mention and importance relative to the study aims when deeming emergent themes to be salient. Exemplar quotations were presented in text to illustrate key findings and as corroboration of quantitative results  [25, 26].
All quantitative data were double-entered into an Epi-Data 3. 1 database (EpiData, Odense, Denmark) and analyzed using Microsoft 2010 Excel (Microsoft, Redmond, Washington). This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects/patients were approved by the National Consultative Ethics Committee of Niger, as well as the Western Institutional Review Board. Written informed consent was obtained from all participants.
When probed to explain why they liked Epi-E so much, women mentioned they "have begun to feel energy" and have gained "strength" from using it. Interviewers concurred, noting that all participants in both study periods seemed enthusiastic when discussing their experiences using Epi-E. Field notes from interviewers described the participants to be "very happy" and "satisfied", and "ready to continue" using it for a longer time period. When asked whether they would be willing to eat Epi-E again, 100% of women replied affirmatively. Willingness to pay data were collected from participants in September (non-Ramadan). Nearly all women (95. 7%) said they would purchase Epi-E if sold on the market. Women estimated that they would be willing to pay on average (SD) $0. 31 (0. 29) per sachet, compared to $0.
Analytical method DSS collected from used Drugwipe-5S ® tests were analyzed using a previously published reported method  [20] [21] [22]. The used Drugwipe-5S screening tests were broken down and two of the three OF collection pads (DSS) were analyzed using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) and high resolution mass spectrometry (LC-HRMS), and database containing more than 1400 compounds including 550 NPS and their metabolites (at the analysis period, end of 2017 ). These assay results in DSS consisted in a qualitative response (presence: yes or no) and concentrations of NPS in OF were not assessed. Briefly, the two pads were soaked in 300 µL of methanol for an hour at room temperature in order to extract NPS putatively present in DSS. After centrifugation, 50 µL of internal standard solution (methyl-clonazepam and β-OH-ethyltheophylline) were added to 100 µL of the obtained supernatant.
Elacridar, loperamide, quinidine, rifampicin, verapamil and vinblastine reduced by more than 20% the kout of CMFDA (p < 0. 001). No effect for other molecules were observed. Altogether, these data demonstrate that our fast and cheap system, without discriminating between efflux pumps substrates or inhibitors, might be used for screening drug interaction with efflux pumps such as P-gp, BCRP and MRPs. Concentration-dependent effects of quinidine and MK571 were also evaluated. As shown in Figures  4c  and 5c , these effects were concentration dependent. IC50 values towards P-gp/BCRP activity or MRPs activity were determined for quinidine and MK571 by non-linear regression of pump out assay data. These IC50 are 11 µM for quinidine and 3. 8 µM for MK571. For quinidine, P-gp IC50 is comparable to IC50 values based on the digoxin ER, i.
MTT ((3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) tetrazolium reduction assay) Cell Proliferation and Cytotoxicity Assay Kit was obtained from Alphabioregen (Boston, Massachusetts, USA) and Green 5-chloromethylfluorescein diacetate (CMFDA) Dye from Thermofisher scientific (Waltham, Massachusetts, USA). All the data are summarized in Table  1. Figure  6a ,b show that very good correlations are obtained when we compare the drug interaction with P-gp/BCRP (R² = 0. 9528) and MRPs (R² = 0. 7436) in the two formats. These correlations were confirmed by the value of the mean of ratio Kout obtained between the two tested culture formats. Indeed, these values are very close to the unit, i. e. , ratio kout for P-gp/BCRP and MRPs are 1. 009 ± 0. 163 and 1. 002 ± 0. 230, respectively.
Background: Pharmacogenomics (PGx) studies how genomic variations impact variations in drug response phenotypes. Knowledge in pharmacogenomics is typically composed of units that have the form of ternary relationships gene variant -drug -adverse event. Such a relationship states that an adverse event may occur for patients having the specified gene variant and being exposed to the specified drug. State-of-the-art knowledge in PGx is mainly available in reference databases such as PharmGKB and reported in scientific biomedical literature. But, PGx knowledge can also be discovered from clinical data, such as Electronic Health Records (EHRs), and in this case, may either correspond to new knowledge or confirm state-of-the-art knowledge that lacks "clinical counterpart" or validation. For this reason, there is a need for automatic comparison of knowledge units from distinct sources.
Units of PGx knowledge typically have the form of a ternary relationship gene variant -drug -adverse event stating that a patient having the gene variant and being treated with the drug will have a higher risk of developing the mentioned adverse event. For example, one relationship is G6PD:202A -chloroquine -anemia, stating that patients with the 202A version of the G6PD gene and treated with chloroquine (an anti-malarial drug) are likely to experience anemia (an abnormally low level of red blood cells in blood). An increasing volume of state-of-the-art knowledge in PGx can be found in reference databases, such as Phar-mGKB  [6] , or in the biomedical literature  [7]. But, a large part of this knowledge is suffering from a lack of validation, or "clinical counterpart"  [8] , and is not yet ready to be translated into clinical guidelines and practices.
Specification Our aim is to represent and reconcile what we defined as PGx knowledge units. These are ternary relationships between one (or more) genetic factor(s), one (or more) drug treatment(s) and one (or more) phenotype(s). Such phenotypes can be the expected outcomes of the drug treatments or some adverse effects. In order to keep our ontology simple and leverage existing works representing PGx components, we restrain the scope of PGxO only to representing PGx knowledge units and not all facets of pharmacogenomics. The objective of PGxO is twofold: reconciling and tracing these PGx knowledge units. To enable this reconciliation, we need to encode metadata and provenance information about a PGx relationship.
Evaluation To evaluate our ontology, we used competency questions as proposed by Gangemi  [25]. The questions we defined are the following: 1 Does PGxO enable to represent a PGx knowledge unit from the PGx state of the art (i. e. from a reference database or extracted from the biomedical literature), along with its provenance? 2 Does PGxO enable to represent a PGx knowledge unit discovered from clinical data, along with its provenance? 3 Does PGxO, coupled with its reconciliation rules, enable to decide if two knowledge units, with distinct provenances, may refer to the same thing? We answered these questions twice, once early and once late in the iterations of the development of PGxO. For the former iteration, we manually instantiated PGxO with examples of knowledge units, associated with their provenances, from (i) PharmGKB, (ii) the literature (extracted by Semantic Medline  [26]  or FACTA+  [27] ) and (iii) hand designed facts corresponding to what we thought may be discovered in EHRs.
by activities. Activities are realized by agents that can be either human or software agents. Entities can also be directly attributed to agents. In terms of PROV-O concepts, authorities which publish sources from which we extract knowledge units are considered to be agents (e. g. the PharmGKB team, the National Library of Medicine (NLM) in charge of PubMed, an hospital in charge of a repository of EHRs). Data sources (e. g. a version of PharmGKB, of PubMed, a repository of EHRs) are attributed to agents, and then may be used to derive data. These data, in turn, are used during the execution of an activity (e. g. a mining algorithm). Such execution generates entities that in our case are PGx knowledge units. Quantitative and qualitative metadata may be associated to an activity and to the entities it generates.
Tests of equalities or inclusions can be combined using conjunctions (AND) and / or disjunctions (OR). On the right side, a link between the PGx relationships is to be added to the populated ontology if and only if the left side of the rule is true. This link can specify the two PGx relationships as identical, related or one being broader than the other. As the rules are not formalized using a particular semantic web rule language, they are kept separated from the definitions of PGxO. Therefore, we implemented them in an independent Python program interacting with the populated ontology using the SPARQL query language. This program is executed once, derives novel facts from the rules and adds them to the knowledge base. We take advantage of Semantic Web technologies, by using associated reasoning mechanisms for the comparison of PGx relationships.
With preexisting LOD We initiated PGxLOD from a preexisting set of Linked Open Data made of interconnected genes, drugs, and diseases according to 6 standard databases  [36]. Such LOD data set follows the Semantic Web standards, particularly by using the Resource Description Framework (RDF) language and Uniform Resource Identifiers (URI)  [1] , which makes it adequate to connect with Semantic Web ontologies. This preexisting data set is an aggregation of data from ClinVar  [37] , DisGeNET  [38] , DrugBank  [39] , SIDER  [40]  and MediSpan (a proprietary database). Accordingly, it includes and relates data about drugs, diseases and phenotypes, but no PGx relationships. Nevertheless, this data set groups together data related to entities involved in PGx relationships, and mappings between entities that may be present in different data sources, e.
With PharmGKB data Second, PGxO was instantiated with data from Phar-mGKB  [6] , a reference database for pharmacogenomics. PharmGKB's clinical annotations describe PGx relationships between genes (potentially their variants), drugs, and phenotypes. They are produced by PharmGKB curators after a review of the biomedical literature and of recommendations of health agencies such as the US Food and Drug Administration. In addition, PharmGKB contains cross-references, i. e. identifiers of genes, variants, drugs and phenotypes within other databases (such as NCBI Gene for genes) or ontologies (such as the Anatomical Therapeutic Chemical Classification System for drugs). Part of PharmGKB data are already available in the form of LOD as a part of the Bio2RDF project  [41]. Nonetheless, this version is outdated and provides only a small portion of PharmGKB.