dataesr/scientific-paragraphs-categorization huggingface dataset:
The major fatty acids were 9-octadecenoic acid (34%) and hexadecanoic acid (33%). This strain exhibited a G+C genome content of 38. 46 mol%. The 16S rRNA gene sequence and whole-genome shotgun sequence have been deposited in EMBL-EBI under accession numbers LT576388 and FNVY00000000, respectively. The type strain Marseille-P2817 T (= CSUR P2817 = DSM 103162) was isolated from the sputum of a French adult man living in Marseille.
Mass range was set at m/z 40-500. The individual peaks were identified by comparison of mass spectra from libraries as well as the Van den Dool and Kratz indices (LRI), which were calculated for all volatile constituents using a homologous series of n-alkanes C8-C32  (van Den Dool & Dec. Kratz 1963; Zellner et al. 2008 ) and were compared with available literature data. Mass Finder 2. 3 library, NIST library (Gaithersburg, MD, USA), Wiley library (Hoboken, NJ, USA) were used for mass spectra comparison and identification. We used mainly NIST MS Search 2. 2 software, Pherobase (The Pherobase: Database of pheromones and semiochemicals) and literature data  (Pripdeevech et al. 2010; Babushok et al. 2011; Ali et al. 2013)  for retention index comparison to identify constituents of the crude extracts.
Literature search We performed a systematic literature search using the following databases: PubMed, the Cochrane library, Psy-cINFO, PEDro, EMBASE, and Medline from inception until December 2017. The search was updated until July 2018 with PubMed, the Cochrane library, EMBASE, and Medline. The search strategy is shown in Table  1. It was initially used on PubMed and adapted for the other databases. The search was restricted to randomized controlled trials, clinical trials, and clinical studies on humans. We limited the search to articles reported in English or French.
g. randomization process, blinding of study subjects and assessors) that can result in systematic errors. Therefore, risk of bias is different from technical quality that refers to technical requirements to obtain valid data (e. g. experiment performed under well-controlled laboratory conditions, appropriate use of testing equipment). Finally, we assessed (iii) the overall certainty of evidence for each outcome variable. This was done by taking into account these two quality items together with other factors (detailed below) that can affect the certainty of evidence. These three steps are described below.
Contents of the risk of bias tool We assessed the risk of bias for each study using the criteria of the Cochrane Handbook for Systematic Reviews of Interventions  [24]. The criteria included in the risk of bias tool are: random sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment (detection bias), incomplete outcome data (attrition bias), and selective reporting (reporting bias). We identified other possible sources of bias specific to the literature that were not covered elsewhere in the tool. Thus, we added 4 criteria: i) blinding of participants to apparatus display, ii) blinding of therapist to apparatus display, iii) blinding of the data extraction / cleaning process, and iv) blinding of the statistician.
The reasons for these added observations are that several outcome variables used to assess autonomic mediated physiology are captured directly on computer and can be displayed on a screen as a graph during the experiment. Thus, blinding of both therapist and subject to apparatus display is an important issue, as purposeful or subconscious data management or interpretation may affect the results. Indeed, if there were any feedback, the subjects could be disturbed or influenced by the screen with data variations and the therapist could modify his intervention to obtain higher or lower values. Further, the data extraction (selection of relevant blocks from the recording) and especially the cleaning process of the raw data should also be blinded, as the final results depend on the choice of data and a proper cleaning process (e.
Data analysis Studies were sorted according to the JMT employed and the year of publication. Between-group / intervention differences, i. e. the difference in outcome for JMT vs Sham with a statistical test, were selected as the main result for each outcome variable. We reported an 'effect' when there was a statistically significant difference between the JMT and the Sham. The results for every study and every outcome variable are reported in the result table (Additional file 4). When data were available from at least 2 studies, and if the nature of the outcome and other key aspects of studies were judged to be similar enough, we performed a meta-analysis based on the mean difference between the JMT and the sham, using the RevMan 5. 3 software distributed by Cochrane  [33]. We used the inverse variance method with a fixed-effects model or with a random-effects model if the I 2 statistic was superior to 50% and the heterogeneity could not be explained  [24].
Outcomes were generally measured during the intervention, often directly afterwards, but sometimes also longer after the experimental intervention. Studies would often report on more than one outcome variable, which explains why the total number of outcome variables is larger than the number of studies. Twenty-three studies dealt with healthy subjects whereas 6 used symptomatic study subjects. This information and other items are found in Additional file 1. Two studies  [57, 58]  did not test for between-group differences but available data could be included in various meta-analyses to compare the intervention to a sham.
Methodological considerations of our review A major strength of this review is that we included only randomized sham-controlled trials, which is the preferred design to study the effect of an intervention. Although it is possible that we failed to find all relevant studies, we consider this unlikely as we used a broad search strategy across six databases with no time restriction. Further, we included also French to the usual English language and all reference lists of the included studies were searched for additional studies. As another strong point, we assessed the certainty of evidence using the Cochrane GRADE approach  [24]. This was done by following the classical approach of taking into account factors such as risk of bias, inconsistency, indirectness, imprecision, but also the technical quality of the studies.
In addition to the traditional GRADE approach, we assessed the technical quality of the study, as this is an important domain to consider, when assessing evidence in this type of research, as failures in domains such as control of the experimental conditions or in the data acquisition may lead to invalid results. However, in the absence of a gold standard to assess technical quality we created a topic-specific check-list. The items included in this assessment tool were based on personal experience and knowledge of members of the research team guided by previous research on this topic  [26] [27] [28] , and an external research engineer, specialized in this type of research. We used an arbitrary, but conservative, threshold set at 50% to decide if the studies were technically acceptable or not.
Several years ago, Pinsky et al. proposed a pragmatic, easy-to-use and physiologically robust approach of dynamic arterial elastance  [7]. Schematically, arterial elastance (Ea) is defined as the ratio of changes in pressure to changes in volume. Dynamic arterial elastance (Eadyn) could therefore be assessed using the ratio between respiratory-induced changes in arterial pulse pressure and respiratory-induced changes in stroke volume during a single respiratory cycle. Several studies investigated the clinical utility of Eadyn. The most robust data concern the evolution of Eadyn as an indicator of decrease in arterial pressure following a reduction in norepinephrine dosage in septic shock patients  [8]. A randomized control trial demonstrated that a therapeutic protocol based on Eadyn may be an efficient guide to decrease norepinephrine infusion in patients following cardiac surgery  [9].
Patients This prospective, bicentre study was approved by the Institutional Review Board (Comité de Protection des Personnes Sud-Ouest et Outre Mer III, Bordeaux, France N°DC2016/125) and was registered at French National Commission for Data Protection and Liberties (CNIL N°1980317). Fifty-six non-consecutive patients were included after oral informed consent. (Written informed consent was waived by the Institutional Review Board. ) Inclusion criteria were patients scheduled for neurosurgery or elective abdominal surgery, older than 18 years, equipped with radial arterial catheter and esophageal Doppler (CardioQ ODM+, Deltex Medical, Chichester, UK) for cardiac output monitoring. Non-inclusion criteria were preoperative lung disease, intracranial hypertension, left ventricular ejection fraction below 50%, arrhythmia, suspected right ventricular dysfunction, extreme body weight (BMI > 40 or < 15 kg/m 2 ).
Hence, we estimated the fixed intercept, fixed effects, random intercept and time interaction between time and each covariate. The contribution of each covariate on temporal evolution was tested by a Wald test on the time-covariate interaction term  [27]. Statistical analysis was performed using Medcalc (software 11. 6; Mariakerke, Belgium) and R Development Core Team  ([2008]. R: A language and environment for statistical computing; R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL).
Limitations Our study presents several limitations: First, we did not randomize the order of volume expansion and neosynephrine infusion (all patients received volume expansion before vasopressor infusion) and we only recorded the first volume expansion and the first vasopressor infusion done in each patient. Second, according to local practices, we chose neosynephrine and not norepinephrine despite recent data suggesting the possible superiority of norepinephrine over neosynephrine in patients under general anesthesia in the operating room  [30] [31] [32]. Third, the two linear mixed models show very different effects of hemodynamic covariates on the time course of Eadyn, PPV and SVV. In the two models, effects remained very low due to a foreseeable lack of power related to a low sample size.
Index test All patients underwent transvaginal and transabdominal sonography (both during the same examination), according to a standardized gray-scale and Doppler protocol, before surgery, with high-quality ultrasound equipment. When patients exhibited multiple adnexal masses, the statistical analysis included only the mass with the most complex ultrasound morphology. When all masses had similar ultrasound morphology, we included only the most easily accessible to ultrasound examination. The ultrasound protocol provided data on: the largest tumor diameter, locularity (multilocular if ≥2 locules, yes = 1, no = 0), solid areas (yes = 1, if any papillation or a solid tumor as described elsewhere  [26] , no = 0, if none), the presence of ascites (fluid outside the pouch of Douglas, yes = 1, no = 0), pelvic or abdominal metastases (any detected with ultrasound, e.
Age and laboratory data were modelled continuouslyno threshold cut-off values were used. Blood tests were performed 1-14 days prior to surgery. The DD level was tested in citrate plasma. Briefly, venous blood was drawn from peripheral vessels, centrifuged, and the supernatant was diluted with a 0. 11 M (3. 2%) buffered solution of sodium citrate, where the component ratio was 9:1, respectively. The DD level was determined with immunoturbidometry, measured with an automatic coagulation analyser (STA R Evolution and STA Compact, Diagnostica STAGO) and a special kit of agents (i. e. , latex particles coated with DD-specific murine monoclonal antibodies). We used second generation immunoradiometric assay kits for detecting CA-125 II (Roche Diagnostics). Kits included the OC125 antibody. Laboratory tests were performed as part of routine preoperative assessment.
SUA The SUA was based on interviews, clinical and ultrasound (grey-scale and color Doppler) examinations. The examiner rated the level of diagnostic confidence as: certainly malignant; probably malignant; uncertain, but likely malignant; uncertain, but likely benign; probably benign; and certainly benign. According to our institutional practice, an ultrasound scan was performed by a GO as a routine preoperative assessment. The examiner had 8-13 years of experience in ultrasound scanning; was blinded to histological reference diagnosis. All variables were recorded prospectively in a database (Excel, Microsoft Corporation), before surgery, and they were not changed thereafter. CA-125 results could be available to the examiner at the time of the ultrasound performance. All model's calculations were performed after the study concluded; thus, the models played no role in the decision-making process.
Statistical analyses Statistical analysis was performed with R software v. 3. 4. 3  [38]. Mann-Whitney U test was used to check if there were significant differences in distributions between benign and malignant groups. The model was developed with the learning group, based on the original protocol. The model was validated and compared to other predictive models with the independent testing group, based on the amended protocol. The train and test sets were derived using a chronological split. Before modelling, we selected variables in two stages. First, we reduced number of potential predictors based on subjective matter knowledge  [39]  and aims of our study (to select simple and easily reproduced parameters). Second, a univariable logistic regression model was constructed for each selected variable in the database of the learning group.
The largest tumor diameters were similar between the benign and malignant groups (Table  2 ). Among patients with malignancies, the higher the stage, the higher the plasma DD level (p < 0. 05) (Table  3 ). There were no differences in the median plasma DD level between patients with malignant epithelial primary (2. 870, range, 0. 138-28. 618 μg/ml) and metastatic (2. 828, range, 0. 509-14. 567 μg/ml) tumors. The median time interval between the ultrasound examination and surgery was 1 day (range 0-60 days) for all included patients. No patient had clinical symptoms of VTE before surgery. The learning group (N = 190, including 101 malignancies) was enrolled from April 2012 to May 2016. The testing group (N = 100, including 52 malignancies) was enrolled from June 2016 to June 2017. Data from both groups are presented in Additional file 3.
Analysis Set Data were pooled from patients receiving G/P in 9 Phase II and III clinical trials, wherein the regimen's safety, efficacy, and pharmacokinetics were assessed in patients with compensated liver disease (SURVEYOR-I and -II; MAGELLAN-1; ENDURANCE-1, -2, -3, and -4; and EXPEDITION-1 and -4)  [8, [13] [14] [15] [16] [17] [18] [19]. The integrated analysis set included all patients in the registrational program who received at least 1 dose of glecaprevir 300 mg and pibrentasvir 120 mg, either as separate tablets (Phase II formulation) or coformulated tablets dosed orally as 3 pills, for a total 300 mg/120 mg dose (Phase III formulation). Both formulations were taken once daily with food for 8, 12, or 16 weeks. Patients were assigned to treatment duration based on genotype, cirrhosis status, and treatment experience, as outlined in Supplementary Figure  1.
Role of the Funding Source AbbVie sponsored the studies (NCT02243280, NCT02243293, NCT02604017, NCT02640482, NCT02640157, NCT02636595, NCT02642432, NCT02651194, and NCT02446717), contributed to their design, the collection, and the analyses (including the current integrated analysis) and interpretation of the data, and participated in the writing, review, and approval of the manuscript. AbbVie is committed to responsible data sharing regarding the clinical trials we sponsor. This includes access to anonymized, individual, and trial-level data (analysis data sets), as well as other information (eg, protocols and Clinical Study Reports), as long as the trials are not part of an ongoing or planned regulatory submission. This includes requests for clinical trial data for unlicensed products and indications.
, 2006; Leroy et al. , 2017 Leroy et al. , , 2018)). Next generation DNA sequencing (NGS) has made it practical to test more rigorous models of introgression history in oaks using much larger numbers of loci (e. g. ,  Eaton et al. , 2015; Leroy et al. , 2017). Additionally, NGS has enabled economical development of genotyping toolkits for smaller applications. In a recent paper, we utilized a large RAD-seq dataset for white oaks  (McVay, Hipp, & Manos, 2017b; Hipp et al. , 2018)  to develop a low-cost SNP genotyping kit for eastern North American white oaks  (Fitzek et al. , 2018). We demonstrated our 80-marker SNP kit to be effective for identifying 15 species and F 1 hybrids, and validated it in a garden setting, where we found hybridization between non-native species in the collection and the native white oaks of the surrounding woodlands.
Sampling and genotyping Data were initially collected from 184 individuals of seven eastern North American white oak species, collected from a wide geographic range for each species; in this study, Quercus muehlenbergii Engelm. and Q. prinoides Willd. are separated in name only, as our RAD-seq data failed to distinguish the species  (McVay et al. , 2017b; Hipp et al. , 2018)  and SNPs were consequently not designed to separate these two  (Fitzek et al. , 2018). The species status of these two bears investigation with broader sampling. Throughout the remainder of this paper, we will refer to these two together as Q. muehlenbergii / prinoides, not because we are making a claim that they are not distinct taxonomically, but to reflect the fact that they are grouped for analysis. Samples represent unique adults with seven exceptions, for which a second extraction of each individual was genotyped as a technical replicate.
Briefly, an extensive RAD-seq dataset comprising multiple exemplars of all 15 species  (McVay et al. , 2017b)  was surveyed for SNP variation, using pairwise F ST to identify SNPs that were (1) fixed or nearly fixed between species and (2) flanked by at least 20 bp of conserved sequence, which could be used for primer design. Multiplexes of up to 40 primers for potential SNPs were designed using the Assay Design 4. 0 Suite (Agena Biosciences, San Diego), which is optimized for MassARRAY analysis  (Bradić, Costa, & Chelo, 2012). Samples were genotyped using the iPLEX Gold chemistry following  Gabriel et al (2009)  on a MassARRAY system (Agena Biosciences) at the Genomic Platform of Bordeaux with the help of Adline Delcamp. Data analysis was completed using MassARRAY Typer Analyzer 4. 0. 26.
0001. Each RAD-seq locus was identified as mapping to a single position on a chromsome, multiple positions, or not mapping. All SNPs were designed from distinct RAD-seq loci save two (CL_2457_66 and CL_2457_32), which both come from a single RAD-seq locus that maps to position 36,055,433 on Quercus robur chromosome 12. The two SNPs identified in this RAD-seq were designed to distinguish Quercus stellata from the remaining taxa and should not be considered independent of one another. They are not strongly decisive and do not figure prominently in downstream analyses in this study or in  Fitzek et al. (2018). Genomic clustering of loci was evaluated by calculating intervals between loci on each chromosomes and comparing these to a simulated null distribution. The null distribution was simulated based on 10,000 replicate datasets of 59 loci drawn at random from the 41,898 uniquely mapped PstI RAD-seq loci from the larger study from which our SNPs were developed  (Hipp et al.
3. 4  (Pritchard, Stephens, & Donnelly, 2000). We utilized the admixture model with correlated allele frequencies and λ fixed at 1. 0, allowing K (the number of populations) to range from 1 to 12. For each value of K, we ran 10 replicate MCMC runs of 1E06 generations following a 1E05 generation burn-in. We followed the method of  Evanno et al. (2005)  to identify the most probable value of K based on the maximum value of ΔK, but given the problematic nature of identifying K with hierarchical data, we report the structures recovered under multiple values of K. We utilized STRUCTURE HARVESTER (http://taylor0. biology. ucla. edu/structureHarvester/) (Earl & vonHoldt, 2012)  to calculate the Evanno statistics and CLUMPP v 1. 1. 2 for 64 bit Linux  (Jakobsson & Rosenberg, 2007)  to average STRUCTURE run replicates for each value of K.
The reduced set may have practical benefit for both cost and because the combinability of primer pairs plays a crucial role in multiplexing  (Fitzek et al. , 2018). Decisiveness was overlaid on the mapped SNPs to identify whether loci that are fixed or nearly fixed within species are genomically clustered (Table  3 ). All data and code required to reproduce analyses presented here are archived in https://github. com/andrew-hipp/white-oak-syngameon.
RESULTS In the full dataset of 184 individuals for 80 loci, missing data per individual averaged 2. 56% ± 4. 10 (s. d. ) loci, and missing data per locus averaged 14. 6% ± 26. 8 (s. d. ) individuals. In the dataset cleaned to 163 individuals for 75 loci, excluding individuals with >10% missing loci and loci with > 30% missing individuals, missing data dropped to 1. 19 ± 1. 13% missing loci per individual and 5. 60 ± 13. 9% missing individuals per locus. Of the 75 cleaned loci, 20 were monomorphic and 55 had two or more polymorphisms. Among 7 pairs of technical replicates, a total of 38 differences were found. Of these, 37 were differences in whether a locus amplified or not; only one difference in allele call was found (for locus CL_55087_OAKMOR340_32, G/T in Quercus stellata QUE002706 vs.
G/G in specimen QUE000137). Thus among 7 × 75 = 525 replicated sites, only one genotyping error (0. 17%) and 37 loci that failed to amplify in one of the two replicates (6. 43%) were detected. Seven loci exhibit only a single SNP for exactly one species in our dataset-one in Q. alba, two in Q. michauxii, four in Q. montana-and three exhibit a single SNP in Q. muehlenbergii / prinoides. An additional ten SNPs exhibit a summed proportion between 0. 95 and 1. 05, suggesting relatively high decisiveness for Q. stellata (2 SNPs) and Q. bicolor (3 SNPs). Based on these, we hand-picked 20 SNPs that suffice to diagnose the species in our study (Fig. 2 , red bars along left edge). Using all loci, the UPGMA (Fig. 3a ) and NMDS ordination (Fig. 4 ) both clearly separate individuals by species, except for Quercus prinoides and Q.
3b ). Bayesian admixture analysis in STRUCTURE favors a K = 4 solution using the ΔK statistic of  Evanno et al. (2005). Given the susceptibility of STRUCTURE and particularly the ΔK statistic to the highest hierarchical level of genetic structure in a dataset, we find the K = 4 solution not a useful description of genetic structure in our phylogenetically structured dataset. To the contrary, the K = 4 clustering does the best job at separating species by clade, following well supported phylogenetic relationships  (Hipp et al. , 2018) , viz. four clusters comprising Quercus macrocarpa and Q. bicolor; Q. alba, Q. michauxii, and Q. montana; and Q. stellata and Q. muehlenbergii / prinoides each on their own (Fig. 5 ). Given our phylogenetically structured sample, it is not surprising that ΔK favors a configuration that splits individuals among clades above the species level.