dataesr/scientific-paragraphs-categorization huggingface dataset:
Signal validation Out of the 27 original CA-medication exposure associations 14 (seven from the original and seven from the revised analysis) were not validated as independent signals: one was a duplicate signal as more than one formulation of the medication is available (the combined contraceptive levonorgestrel and ethinylestradiol); for five CA-medication exposure associations a proportion of the CA cases and/or first trimester medication exposures could not be verified so that the OR using validated data more than halved to less than 1. 5; eight CA-medication exposure associations were explained by confounding by registry. This left 13 (nine from the original and four from the revised analysis) validated unique CA-medication exposure signals related to gastrointestinal medications (n = 4), antihypertensives (n = 1), female sex hormones (n = 3), medications used in infertility treatments (n = 2), antiretrovirals (n = 2) and selective serotonin (5HT1) agonists (n = 1).
Discussion We have found 13 CA-medication exposure signals which require further confirmation. There was evidence in the literature, albeit conflicting at times, to support six of the 13 signals. These six signals have been strengthened and should be prioritized for further evaluation. Four of these signals were related to sex hormones (gastroschisis and the contraceptive levonorgestrel/ethinylestradiol; complete absence of a limb and pregnen (4) derivatives; hypospadias and pregnadien derivatives; hypospadias and synthetic ovulation stimulants). We also had as yet unvalidated data that some other anomalies might be associated with these medications. Sex hormone-based medications accounted for 24. 8% of the medication exposures in the database  [20]. The other two of these signals were congenital heart defects and pulmonary valve stenosis associated with nucleoside/nucleotide reverse transcriptase inhibitors, antivirals used for HIV and chronic hepatitis.
The only evidence that the antipropulsive antidiarrheals may be teratogenic was a single report of an association with hypospadias  [71] , rather than syndactyly as in our results. These two anomalies are usually considered aetiologically unrelated. The remaining six statistical signals did not have supporting evidence in the literature and should be confirmed in an independent dataset. For selective serotonin agonists, a number of previous studies have found no association with CA  [72] [73] [74] [75] [76] [77] [78] [79] [80] [81]  but these were too small to find an anomaly as rare as congenital constriction bands. Hydrazinophthalazine derivatives, antihypertensives which act on arteriolar smooth muscle, have one small previous negative study  [82]. Other types of antihypertensives, such as ace inhibitors, have been associated with an increased risk of CA  [83]  but the underlying maternal hypertension also appears to play a role in the development of CA  [84].
Strengths and limitations A strength of this study is the use of the EUROmediCAT central database. EUROmediCAT's international populationbased database contains detailed coding of all CAs  [29]  and includes TOPFA cases which constitute a large proportion of some CA  [98]. The diagnosis of CAs is standardized across the registries involved and will have ensured consistency in the diagnosis  [27]. There will also be much less underreporting and bias than in spontaneous reporting pharmacovigilance systems as all major CAs are recorded in EUROCAT, not just those which clinicians consider to be important enough or potentially linked to a medication exposure. While the EUROmediCAT database contains detailed information on medications taken during the first trimester of pregnancy, there is known underascertainment of some medications  [30, 99]  but while this may reduce the sensitivity of the system to detect certain teratogenic medications, it should not lead to bias due to the use of malformed controls.
The signal detection process we report in this paper is intended to be used in addition to the drug class by drug class approach. It can be used to identify the most highly significant associations in the database for drug classes not otherwise undergoing analysis. We recognize that there may be many other associations in the data that did not meet Selective serotonin agonists, also called triptans, are used to treat migraines. Cohort studies and a manufacturer maintained pregnancy registry explore the teratogenicity of these medications, sumatriptan (N02CC01) in particular. First trimester exposure to sumatriptan does not significantly increase the rate of all CAs combined  [72] [73] [74] [75] [76] [77]. Eletriptan (N02CC06) was found to significantly increase the rate of all CAs combined but this was based on 14 exposures and may have been a chance finding  [78].
We were assessing whether previous literature existed but did not conduct a meta-analysis of the evidence to date, and this may lead to highlighting positive over negative evidence, although all evidence found is presented. It was necessary to search for each of the individual medications, rather than the broader medication group, as the 4th ATC level, chemical subgroup, was not always used in the literature or databases and returned little or no information for some of the signals. Without prior hypotheses about the mechanism of action, it can also be difficult to decide how broadly to look for related literaturefor example there is a large literature on sex hormones as a class, but much less related to specific sex hormones. While positive evidence in the literature regarding risk of all CAs combined could be considered supportive, negative evidence is more difficult to interpret, as few medications increase the rate of all CAs combined, instead tending to increase the rate of specific CAs  [101].
As far as possible, changes over time in the ATC codes used for particular medications were taken into account in both the signal detection analysis and the signal evaluation. It is possible, however, that some changes were missed, potentially leading to signals being missed as the exposed cases would be split across more than one ATC code in the dataset. Although all the cases were confirmed as first trimester exposures, it is not known if these exposures actually occurred during the critical period for CA development  [8]. Similarly there was no information available in terms of the doses of medications taken for the majority of cases. If it was possible to identify a dose-response relationship or show exposure during the critical period for development of the specific CAs, this would provide support for a causal relationship  [101].
Materials and methods Samples from 32 Dutch goats from different breeding lines were collected with a Genotek nasal swab kit. DNA was extracted by standard procedures from blood samples of 35 Danish, 32 Finnish, 32 Norwegian and 16 Icelandic goats from six farms and from tissue samples of five Iranian Bezoars collected in the wild (Table  S1 ). Genotyping with the FAO-recommended microsatellites (FAO, 2011) was performed as described  (Canon et al. 2006 ), but markers BM6444, DRBP and SRCRSP7 were excluded from analysis because of departure from HW equilibrium and SRCRSP3 because of a low scoring success in our samples. Harmonization of allele lengths with previous data  (Canon et al. 2006; Glowatzki-Mullis et al. 2008)  was achieved by genotyping two samples from the Econogene data set  (Canon et al.
2006) , which allowed a combination with this data set and an overlapping data set panel of Swiss goat breeds as well as the African Boer goats  (Glowatzki-Mullis et al. 2008). The Dutch Landrace goat was also genotyped for 37 microsatellite markers selected for genotyping ibex goats (Capra ibex, Biebach & Keller 2009), 11 of which were shared with the FAO panel (Table  S1 ). A separate data set of the Turkish Angora, Honamli, Kil, Kilis and Norduz breeds (A gao glu & Ertu grul 2012) shared with our data set the Angora breed and 13 of the 26 FAO microsatellites. Sharing the Angora breed allowed a tentative harmonization of allele sizes for the 13 common microsatellites (see Supporting Information). Data were analysed with the Microsatellite Tool Kit (http://animalgenomics. ucd. i. e/sdepark/ms-toolkit/), and allelic richness was calculated with the FSTAT program using a sample size of 13.
Genetic distances were calculated with the MICROSAT program  (Minch 1997 ). Reynolds' genetic distance was visualized via Neigh-bourNet graphs using SPLITSTREE version 4. 0  (Reynolds et al. 1983; Huson 1998). Distances from the domestication site  (Naderi et al. 2008)  in kilometres were calculated as acosðsinðlat1:p=180Þsinðlat2:p=180Þ þ cosðlat1:p=180Þcosðlat2:p=180Þcosðlon2:p=180 À lon1:p=180ÞÞ:ð6378:135Þ; in which acos is the arccosine, lat1 and lon1 latitude and longitude, respectively, of the breed in degrees (www. econogene. eu), and lat2 (37. 67°) and lon2 (43. 05°) the coordinates of a central point in the estimated domestication area in east Anatolia. The correlation of genetic and geographic distances was visualized by linear regression. Model-based clustering was carried out as described previously  (Canon et al.
602), but also illustrates the strong geographic differentiation of goats  (Canon et al. 2006; Groeneveld et al. 2010). H E F IS -0. 05 ( The Finnish, Norwegian and Icelandic Nordic breeds again form a separate cluster with as closest relative the northernmost continental breed represented in our data set, the Fawn German (BDE, Bunte Deutsche Edelziege). Remarkably, the extremely low diversity of the Icelandic goats did not obscure their Scandinavian origin (Figure  S1 ). On the other hand, sharing of short branches suggests a spurious affinity of the Danish Landrace with the Nordic cluster and of the Dutch Landrace with the Swiss Saanen, respectively (Figure  S1 ). As the complex phylogenetic networks as shown in Figure  S1  may obscure other phylogenetic relationships and the relationships between the breed clusters  (Lenstra et al.
Statistics Results are expressed as means ± 95% confidence limit. Unpaired Student's t-tests were routinely performed for statistical analyses, Chronic exposure of rat models to low-dose 137 Cs: impact on circulating biomarkers • 609 but were replaced by the Mann-Whitney rank sum test when the equal variance test failed (determined by SigmaStat software). Differences were considered statistically significant when P < 0. 05 (*P < 0. 05; **P < 0. 01; ***P < 0. 001).
General health status The food and water intake of each group was monitored on a weekly basis throughout the entire exposure period, as was body weight. Exposure was not associated with food or water intake changes or weight modifications (data not shown). Macroscopic examination of the testes, livers, kidneys, hearts, lungs, intestines and brains showed that all appeared normal (organ weight, volume, shape), with no notable differences from the control rats. Long-term effects: 9 months of exposure of three rat models Adult model Table  1  shows that in the adult model, the plasma levels of numerous markers of homeostasis did not differ from those of controls after 9 months of 137 Cs exposure. Indeed, the only differences observed between the exposed and control groups involved only three types of functions, and then only some of the indicators.
Gastric emptying scintigraphy The pigs were fasted for 24 hours and then lightly sedated using intramuscular ketamine (6 g). A laryngoscope was used to establish a nasogastric feeding tube to administer a meal consisting of one scrambled egg mixed with 68 MBq Tc99-m, 125 g of pellet food and 80 mL of water. Before the scintigraphy was started, the pigs were lightly anaesthetised with intravenous propofol at a low dose (5 mg/kg/h) to avoid an impact of the drug on gastric emptying. The pigs were then placed in a natural position in front of the camera (Axis Model, Philips Medical). Data were acquired for 90 min. The half emptying time (T½) is expressed in minutes.
7 N) as described by del Rio et al. (under review). Supernatant samples (100 µL) were then derivatized with diethyl ethoxymethylenemalonate as described by  Linares et al. (2013) , and the tyramine separated out and quantified in an H-Class Acquity UPLC TM UHPLC system (Waters, USA) running Empower 2 software (Waters), as described by  Redruello et al. (2013).
Phylogenetic Analysis A phylogenetic analysis of the E. faecalis bacteriophage sequences available in the NCBI database (Table  2 ) was performed by virtual digestion (with HindIII endonuclease) and gel separation of their genome sequences, using Vector NTI v9. 0 software (Invitrogen, UK). GeneTools software (SynGene) was used to analyze the HindIII restriction patterns obtained. An UPGMA (unweighted pair-group method with arithmetic mean) dendrogram was produced by profile comparisons based on the MW of generated bands and a position tolerance of 1%.
Genetic Characterization of Bacteriopahge Q69 Initial genetic characterization of phage Q69 involved restriction analysis with endonuclease HindIII. Based on the restriction profile (Figure  2 ), the genome size was estimated at 31,280 bp. Its genome-digested HindIII profile was compared to other phage genome sequences available in the NCBI database (Table  2 ). The phylogenetic tree revealed no clear association between the HindIII profiles and functional characteristics such as life style or origin, etc. However, it did indicate phage Q69 to be quite different from most other E. faecalis phages with a presence in the NCBI database. The most related phage was IMEEF3, a lytic phage isolated from water supplies/waste water in clinical environments  (Li et al. , 2014).
Intrahepatic cholangiocarcinoma (ICC) is the second most common type of malignant primary tumors in the liver. ICC is an aggressive cancer with a poor survival and limited therapeutic options. At the histological level, ICC is characterized by an abundant stroma (i. e. the tumor microenvironment that notably includes components of the extracellular matrix, stromal cells and soluble factors). Tumor microenvironment is known to play a key role in tumor onset and progression but it is poorly characterized at the molecular level. Thus, this study was specifically designed to identify genes that are significantly deregulated in the tumor microenvironment of human ICC. Here we provide a detailed description of the experimental design and methods used to acquire the genomic data deposited into Gene Expression Omnibus (GEO) under the accession number GSE45001.
Following the fragmentation reaction, 25 μ of 2 × Agilent hybridization buffer (HI-RPM) was added to the fragmentation mixture. Forty μL of the mixture were hybridized to Agilent SurePrint G3 Human GE 8x60K for 17 h at 65 °C in a rotating Agilent hybridization oven. After hybridization, microarrays were washed 1 min at room temperature with GE Wash Buffer 1 (Agilent) and 1 min with GE Wash buffer 2 at 37 °C (Agilent). Microarray were scanned immediately after washing with the Agilent DNA Microarray C Scanner (G2565) using one color scan setting for 8x60k array slides. The scanned images were analyzed with Feature Extraction Software 10. 7. 3. 1 (Agilent Technologies) using default parameters (protocol GE1_107_Sep09_ssSurrogates and Grid: 028004_D_F_20101102). Quality control showed a broad distribution of background subtracted signals, suggesting that RNA with various abundances (from low to high) could be analyzed from the genomic dataset (Fig.
2A ). The analysis also demonstrated an expected correlation between the coefficient of variation and signal intensity (Fig. 2B ). The use of Agilent spike-In RNA demonstrated a linear relation between signal intensity and the relative abundance or concentration of the RNA (Fig. 2C ). All 20 microarrays from the genomic dataset were qualified and further analyzed using GeneSpring software (Agilent Technologies). Microarray data were first normalized by using the quantile normalization algorithm. As shown in Fig. 3  this normalization method corrected putative technical variations between samples. Without initial gene filtration, Principal Component Analysis (PCA) using expression values of all genes in all 20 samples mostly separated tumor from non-tumor samples (Fig. 4 ). Then, a filtration by "flag" and "signal intensity" was applied.
Were retained only the entities in which at least 50% of the values in any of the two conditions (Non Tumoral vs. Tumoral) had a "detected flag" (i. e. a positive and significant feature as defined by GeneSpring). For the filtration by signal intensity, were retained the entities in which at least 50% of the values in any of the two conditions (non tumoral, NT vs. tumoral, T) were within the range of interest (i. e. 20-100th percentile). Differentially expressed genes were identified by a two-sample univariate t-test and a random variance model as described  [1]. Permutation P-values for significant genes were computed based on 10,000 random permutations. In conclusion, our genomic dataset is relevant to provide insights on the molecular pathways altered in the microenvironment of ICC and to identify novel ICC biomarkers  [1, 3].
Acknowledgments The authors thank  Biosit and Biogenouest for access to Plateforme Génomique Santé, Centre de Ressources Biologiques Santé , and H2P2 histopathological platform. This work was supported by Grants from  Inserm ,  Université de Rennes 1, Institut National du Cancer (Cancéropoles IDF & GO) , Ligue contre le cancer and  Novartis Oncology.
RSV RSV was highlighted at the inaugural PDVAC meeting as a pathogen for which there is significant clinical pipeline activity, and likelihood of a candidate emerging for licensure in the near term. Novavax's RSV F-protein based nanoparticle vaccine remains the most advanced candidate, with a Phase III study in pregnant women that started at the end of 2015, following the demonstration in a Phase II study [5] that palivizumab-competing antibody and RSV neutralising antibody were elicited and transferred to infants. In addition, recent preliminary data in the elderly demonstrated efficacy, and a phase III trial has been initiated in that target population (NCT02608502). GSK and J&J also have RSV F vaccines in clinical development that target pregnant women, and MedImmune is developing an RSV F vaccine for use in the elderly.
A number of groups have RSV F vaccines in preclinical development, including NIAID. An RSV subunit vaccine containing the SH protein (Immunovax) is also currently being evaluated in adults. A number of live vaccines are in clinical development for active immunisation of infants and young children, including native RSV candidates from which nonessential genes have been deleted, and an adenovirus vector containing the RSV F gene. Passive prophylaxis with a long-half life monoclonal antibody developed by Medimmune is currently being evaluated in a dose-escalation/pharmacokinetics trial in healthy preterm infants (NCT02290340). Following the 2014 PDVAC meeting, WHO convened a consultation to provide guidance on clinical endpoints and development pathways for maternal and paediatric RSV vaccine candidates, with a focus on considerations in LMICs  [4].
Acknowledgements We gratefully acknowledge the pathogen specific landscape analyses that were prepared by the following individuals (in some cases the individuals acted as liaisons for a larger group within their institution):  Mark Alderson ,  David Beasley ,  Coreen M. Beaumier ,  Ashley Birkett ,  Maria Elena Bottazzi ,  Louis Bourgeois ,  Jonathan Carapetis ,  Jane Cardosa ,  Charles B. Chesson ,  James Dale ,  Sana S. Dastgheyb ,  Brian E. Dawes ,  Hansi J. Dean ,  John J. Donnelly ,  Anna P. Durbin ,  Sami L. Gottlieb ,  Luiza Guilherme ,  Jesse H. Erasmus ,  Thomas Evans ,  Patricia E. Fast ,  John Fraser ,  Lisa Gieber ,  Portia M. Gillespie ,  Michael Good ,  Tara Hayward ,  Paul Heath ,  Deborah Higgins ,  Joachim Hombach ,  Peter J. Hotez ,  Christine Johnston ,  Cheryl Keech ,  Imran Khan ,  Hester Kuipers ,  Wayne C.
In contrast, Malassezia microbiota analyzed at species level did not differ according to health status. A 2-step OTU assignment using combined databases substantially increased fungal assigned sequences, and revealed the presence of highly prevalent uncharacterized Malassezia organisms (>37% of the reads). Although clinical symptoms of dandruff manifest locally, microbial dysbiosis beyond clinically affected skin sites suggests that subjects undergo systemic alterations, which could be considered for redefining therapeutic approaches.
For fungi 18S-F/5. 8S-1R ITS1 primers were used  (Findley et al. , 2013). Sequencing was performed using MiSeq Illumina Platform with Illumina paired-end MiSeq Reagent Kit v2 (2 × 250) following Illumina's standard protocol. Only reads obtained with forward primers were considered in the analyses as they showed higher sequencing quality. Single reads were submitted to size filtering using Seqyclean software (https://bitbucket. org/izhbannikov/seqyclean). Sequences shorter than 200 bp were excluded from the analyses. Quality filtering was done using Phred quality score (≥20;  Ewing et al. , 1998). Size and quality filtering were performed using Qiime pipeline  (Caporaso et al. , 2010). The dataset has been deposited in the EBI Metagenomics database (project number PRJEB16723).
Taxonomic Assignment Bacterial taxonomic assignment was performed considering 97% identity and 95% coverage by comparing the sequences with Greengenes database v 13_8 (http://greengenes. lbl. gov/) using Uclust software  (Edgar, 2010) , implemented by Qiime. Fungal community analyses were done at genus level through BLAST  (Altschul et al. , 1990)  using QIIME against a database of ITS1 reference sequences manually curated from UNITE database v 2015-03-02 (https://unite. ut. ee/), with 70% similarity threshold. Singletons were removed from the analysis. A database of Malassezia sequences were constructed using all the sequences from Malassezia organisms in the UNITE database that were manually curated (reference sequences-  Kõljalg et al. , 2013) , combined with Genbank sequences presenting ≥97% similarity and ≥95% coverage in relation to those selected from the Unite database.
Analyses at species level for Malassezia organisms were performed considering 97% similarity threshold and ≥95% coverage. Fungal unassigned sequences were clustered using CD-HIT  (Li and Godzik, 2006 ; 97% similarity threshold), and clusters with relative abundance ≥1% in at least one sample were compared through BLAST against the complete Genbank database. This step of analysis also considered three Malassezia species described recently: M. brasiliensis, M. pscittasci  (Cabañes et al. , 2016)  and M. arunalokei  (Honnavar et al. , 2016) , which were not included in the Malassezia sequence database built with UNITE and Genbank.
Statistical Analysis Non-metrical Multidimensional Scaling (nmMDS) using Bray-Curtis similarity distances  (Bray and Curtis, 1957)  based on Log (X+1) transformed data were performed to assess the relationships between bacterial communities from different samples. Analysis of Similarities (ANOSIM;  Clarke, 1993)  was applied to verify differences based on body site and health condition. ANOSIM global R value ranges from 1 to -1 (R ∼ 0 indicates the same level of variation within and between groups). ANOSIM test was performed using α = 0. 05 for statistical significance. Average similarity within groups was calculated using SIMPER  (Clarke, 1993). Diversity was measured using Shannon-Weaver diversity index. These analyses were performed using Primer 6  (Clarke and Gorley, 2006). To estimate depth of sequence sampling, rarefaction curves were obtained for each sample using PAST 3.