dataesr/scientific-paragraphs-categorization huggingface dataset:
Data items The data extracted will include the aim, comparator, study design (eg, RCT), the decision-making task (eg, choosing a treatment option), setting, conflicts of interest, demographic information about the population, randomisation process, effect sizes reported for the primary outcomes: decision-making measures (eg, decisional conflict/effectiveness/confidence/N of people who made a decision), and secondary outcome: knowledge of health information, any missing outcome data and the selection of the reported result. The authors will be contacted via email if sufficient detail is not reported; and if a response is not received, this will be noted. If data are only reported via figures, then WebPlotDigitizer (http:// arohatgi. info/ WebPlotDigitizer/ app/) will be used to extract the data from figures.
Synthesis methods For eligible studies, if effect sizes are not reported, they will be calculated from means and SDs, or from analysis of Open access variance or t-tests. Where sufficient data can be extracted, we will conduct random-effects meta-analyses of the SMDs, as we anticipate significant heterogeneity across interventions between studies. The studies will be weighted using the inverse variance method. Heterogeneity across studies will be examined using the I² statistic and significance tested using a χ² test. If heterogeneity is found, a prediction interval for the true intervention effects will be calculated. 14 We will also check for outliers and conduct an influencer analysis to see if removing any outliers/influential studies affects the I². If data allow, we will include a subgroup analysis of age group (eg, young adults 18-30 years and older adults 65-80 years).
This review is funded by the  Economic HH, KLR, HAA  and  SPB  provided feedback on the manuscript and all authors approved the final draft of the protocol. JES is the guarantor of the review. Funding This work was funded by the  Economic and Social Research Council (ESRC)  (grant number  ES/V000071/1 ) and Open Access funding was provided by  NTU Psychology, Nottingham Trent University, Nottingham, UK.
Pseudosections P-T and T-X pseudosections have been calculated (1) to precise metamorphic P-T conditions and more particularly conditions of partial melting, and (2) to determine the type of prograde melting reactions. Phase relations have been modeled in the Ti(Mn)NKFMASH system using Perple_X'07 software  (Connolly, 2005)  and using the internally consistent thermodynamic database of  Holland and Powell (1998). Bulk rock composition, solution models and end-member phases considered in the modeling are listed in Tables  7  and 8 , respectively. Although the TiO 2 component is a minor component, it has been included to the model in order to take into account the rutile/ilmenite transition observed along the cross-section (Fig. 2 , Table  1 ). MnO is taken into account for garnet-bearing migmatite in order to better estimate the end-member composition of garnet.
207 Pb. 208 Pb. 232 Th and 238 U masses are acquired. The occurrence of common Pb in the sample can be monitored by the evolution of the 204 (Pb + Hg) signal intensity, but no common Pb correction was applied owing to the large isobaric interference from Hg. The 235 Usignaliscalculated from 238 U on the basis of the ratio 238 U/ 235 U = 137. 88. Single analyses consisted of 30 s of background integration with the laser off followed by 1 min integration with the laser firing and a 30 s delay to wash out the previous sample (approximately 10 s for a 6 order of magnitude decrease in signal intensity) and prepare the next analysis. Data are corrected for U-Pb and Th-Pb fractionation occurring during laser sampling and for instrumental mass discrimination (mass bias) by standard bracketing with repeated measurements of the Moacir monazite standard  (Gasquet et al.
, 2010). At the beginning and at the end of every run, repeated analyses of the Manangoutry monazite  (Paquette and Tiepolo, 2007)  standard were treated as unknowns to independently control the reproducibility and accuracy of the corrections. Data reduction was carried out with the software package GLITTER® developed by the Macquarie Research Ltd. (Jackson et al. , 2004; van Achterbergh et al. , 2001). For each analysis, the time resolved signal of single isotopes and isotope ratios was monitored and carefully inspected to verify the presence of perturbations related to inclusions, fractures, mixing of different age domains or common Pb. Calculated ratios were exported and Concordia ages and diagrams were generated using Isoplot/Ex v. 2. 49 software package of  Ludwig (2003). The concentrations in U-Th-Pb were calibrated relative to the certified contents of 91500 zircon standard  (Wiedenbeck, 1995)  and Moacir monazite  (Seydoux-Guillaume et al.
(1992)  and  Smith et al. (1998). The spot size used during all the sessions was around 20 μm. In this study, six data collection cycles (scans) were performed per analysis and count times (per scan) were 10 s for the 204 Pb. 206 Pb and 208 Pb mass peaks and background and 30 s for the 207 Pb mass peak. Unknown analyses were referenced to multiple analyses of the BR266 standard for U/Pb calibration. Standard analyses performed throughout each analysis session gave 1σ uncertainties of 2. 0% or less for Pb * /U Data reduction was carried out with the software package SQUID v1. 10 and ISOPLOT v3. 0  (Ludwig, 2003). Corrections for common Pb content were based on measured 204 Pb and assuming contemporary values for common Pb derived from  Stacey and Kramers (1975). 5. No shading=field divariant, then light to dark shading=increasing the variance (trivariant, quadrivariant, etc.
Acknowledgements This research was funded by the  SEDIT programme (INSU ,  2008(INSU, -2009 )). We thank the  IRD-Quito  for logistical support during the 2008 and 2010 field campaigns. We thank  Francis Coeur ,  François Senebier ,  Jean-Luc Devidal  and  Gilles Montagnac , for their help in sample preparation, microprobe and Raman analysis. We thank the  Laboratoire de Géologie de Lyon (ENS-Lyon)  to give us access to the national Raman instrument supported by  INSU CNRS. We are grateful to  Michael Gazley  and  Louise Fisher  for the thorough and constructive reviews of the manuscript. We also thank  Bernado Cesare ,  Diego Villagomez  and  David Chew  for Fig. 13. Schematic proposed geodynamical model of the Ecuadorian margin at Triassic times. (a)middle Triassic period, south America is moving eastward and the margin is affected by S-type magmatism likely due to a steep to vertical slab; (b) early Late Triassic period, partial melting of the asthenosphere produces the emplacementoftheMORB-typegabbroofthePiedras unit at crustal root level, and subsequent migmatization of the La Bocana unit; (c) shortly after slab breakoff, subduction resumes and the  Arenillas-Panupalí  blueschist unit is underplated.
, 1958; Hopson and McCroskey, 1972; Prigge et al. , 1981). Considering the potential interest of PEG as a faecal marker and its rapid determination by NIRS,  Hassoun et al. (2007a)  set up a 1000-sample database with wide ranges of PEG content in faeces. In an in vivo experiment with sheep  (Hassoun et al. , 2007b) , various PEG doses were tested. Regardless of PEG level of administration, the recovery rate was higher than 98%. With external markers used to estimate total faeces excretion, two sources of errors have to be considered. The first one deals with total recovery of the marker in faeces, meaning that few or no administered marker disappeared until reaching rectal excretion. The second one deals with the daily excretion pattern of the marker that is linked to the grab sampling approach (i.
PEG preparation, administration and NIRS measurement The PEG used was PEG 6000 (molecular weight 6000 da, Panreac Qimica SA, Barcelona, Spain). It was administered in solution form with plastic syringes. The PEG solution, with a concentration of 333. 33 g/l, was prepared a few days before in the laboratory with double distilled water. In order to precisely determine the quantity of PEG administered, each syringe was weighed to the nearest 0. 1 g before and after administration. The concentration of PEG in faeces was estimated with the NIRS method. Calibration databases were built by adding known amounts of PEG in faeces samples collected before the first doses, in order to build PEG 1 faeces databases similar to collected samples. The PEG was added to faeces in solution within a range of 0 to 100 g of PEG/kg DM (by 5 or 10 g of PEG/kg DM steps).
The PEG 1 faeces mixtures were dried (508C until constant dry weight) and ground through a 1-mm sieve, similarly to the procedure applied for faeces preparation during trials. The samples were scanned on a monochromator NIR spectrophotometer (NIRS 6500, Foss NIRSystems, Silver Spring, MD, USA). Measurement was done in reflectance mode in small circular cups (diameter: 50 mm) with quartz glass. Spectral data were collected every 2 nm from 400 to 2500 nm. Samples were scanned in duplicate (two different cup fillings) and spectra were averaged. Spectra were added to our existing database  (Hassoun et al. , 2007a) , leading to a global database of 527 samples of faeces samples collected from various origins (sheep breed and diets). The NIRS calibration was carried out using partial least squares regression (MPLS procedure, WinISI software, Infrasoft Int.
, Port Matilda, PA, USA). Only wavelengths in the 1100 to 2500 nm range were used, because of the unstability of models built with visible wavelengths. Mathematical pre-processing was applied to spectra with detrending and normalization (SNV) of data, and use of the second derivative calculated on five consecutive points with a smoothing also on a gap of five points. During the calibration process, prediction outliers Hassoun, Viudes, Autran, Bastianelli and Bocquier (studentized residual T. 2. 5) were discarded. Cross-validation of NIR equations was performed during the calibration process, dividing the database into four groups of samples of which three were used for calibration and the fourth for validation. Criteria for the evaluation of the prediction models are SEC (standard error of calibration) and SECV (standard error of cross-validation), which is an estimate of precision that can be expected in routine analysis.
NIRS calibration for PEG content The calibration itself had an R 2 5 0. 99 and a residual standard error (SEC) of 3. 05 g/kg. No major bias was observed in the calibration process, and there were only 13 outlier samples, that is, 2. 5% of the calibration database. The validation of the calibration model by cross-validation resulted in a standard error (SECV) of 3. 47 g/kg, which is quite accurate for such a measurement. The RPD value was 9. 1. When applying the calibration to our experimental samples, it appeared that all samples to be predicted were inside the calibration database range. Only seven samples had an H (Mahalanobis) distance higher than 3, which was perfectly satisfactory.
Discussion The precision of NIRS calibration was high, with a low measurement error (3. 47 g/kg) and high R 2 and RPD values. The analytical precision is of extreme importance in studies   with markers because a small error in the quantification of markers in faeces has a strong effect on the calculation of feed intake. For this reason, it is essential to update the calibration database at each new experiment with some faecal samples obtained before PEG treatments and enriched in vitro with PEG in order to obtain concentrations within the range of those expected during the experiment. The fact that recovery rates of PEG were close to 100% in the first three trials confirms that there was no major bias in PEG estimation. It validates the experimental calibration protocol, based on PEG addition in faeces, which was used to build and update the calibration database.
This is also confirmed by the fact that the spectra of experimental samples were well fitted to the calibration database, that is, no major change was observed between PEG added in vitro and PEG having transited in vivo. The estimation of PEG concentration in faeces by NIRS proved to be very efficient. As no alternative reference analysis method exists for quick PEG determination, it would have been impossible to measure PEG concentration in several hundred samples, as required in this type of studies. The ease of NIRS analysis makes it extremely convenient for intake studies, making it possible to analyse a very high number of samples at a low cost in terms of time and money. Previously, PEG was used as faecal marker to estimate faecal output  (Hopson and McCroskey, 1972; Landau et al. , 2002).
Conclusions These results confirm that faecal PEG content can be estimated by NIRS with good accuracy, provided that faeces obtained from sheep fed the diet on which measurements are to be made are included in the general NIRS database for calibration. Because PEG is rapidly excreted, it is recommended that faeces be collected within 1 week after the start of administration. The PEG recovery rate in total faeces is high and comparable with other results found in the literature concerning dry feed. Using PEG as a faecal marker at levels of 20 to 40 g/day does not modify DMD and DMI when sheep are fed dry diets. Surprisingly, PEG modifies the DMI but not the DMD of a fresh forage diet. Further experiments with fresh forage must be conducted because most of the results in the literature deal with dry diets.
Background: Mobile phone text messaging (SMS) has the potential to promote adherence to tuberculosis treatment. This systematic review aims to synthesize current evidence on the effectiveness of SMS interventions in improving patients' adherence to tuberculosis treatment. Methods: We searched electronic databases (PubMed, EMBASE, Science Citation Index), reference lists of relevant articles, conference proceedings, and selected websites for eligible studies available by 15 February 2013; regardless of language or publication status. Two authors independently screened selected eligible studies, and assessed risk of bias in included studies; resolving discrepancies by discussion and consensus.
Databases We searched the following electronic databases: PubMed; EMBASE; Cochrane Central Register of Controlled Trials (CENTRAL); ISI Web of Science (Science Citation Index); Africa-Wide Information; Cumulative Index of Nursing and Allied Health (CINAHL); and World Health Organization (WHO) library databases (WHOLIS). We used both text words and medical subject heading (MeSH) terms, in varying combinations. We adapted the search strategy to suit each database. Additional file 1 shows detailed information on the search strategy employed.
Searching other sources We also searched the WHO International Clinical trials Registry Platform, Clinicaltrials. gov, and the Pan African Clinical Trials Registry (PACTR) for ongoing studies. In addition, we searched the website of the mHealth Alliance and the mHealth in Low Resource Settings' resources database  [21]  for eligible studies. We also contacted The AIDS Support Organization (TASO) and African Medical and Research Foundation (AMREF) for information on eligible studies that we may have missed.
Data extraction and management References were managed using Thomson ISI Research-Soft Endnote 9. 0  [23]. Two authors independently extracted descriptive and outcome data for each included article using a standardized data collection form, resolving any discrepancies by discussion and consensus. MN entered the final data into the Cochrane Collaboration Review Manager Version 5. 2 statistical software  [24]. CW cross-checked the data entered to ensure that there were no data entry errors.
Data synthesis and investigation of heterogeneity We assessed clinical heterogeneity by examining types of participants, interventions and outcomes in each study. We also assessed methodological heterogeneity by examining differences between studies in methodological factors such as the comparability of groups. All included studies were judged to be clinically and methodological heterogeneous, and we decided to preclude meta-analysis and describe findings for each study individually. Finally, we used the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach  [25, 26]  to assess to the quality of evidence for the effectiveness of the SMS intervention. This method results in an assessment of the quality of the body of evidence as high, moderate, low, or very low.
Frequency matching was utilized to match participants from the pilot study with controls in a 1: 4 ratio. We analyzed participants according to whether or not they received SMS reminders. Rates of sputum smear conversion and TB cure were provided for two scenarios. Scenario one included all 24 participants who took part in the pilot and their 96 controls. Scenario two excluded 6 participants that took part in the pilot but who subsequently died, along with their 24 matched controls, thereby reducing the cohorts to 18 participants from the pilot and their 72 controls. We used health outcomes data from scenario one for this systematic review. The study found significantly higher sputum smear conversion rates among those who received standard DOTS in combination with SIMpill® than those who received only standard DOTS (RR 1.
Regarding completeness of outcome data, one study was judged to have an unclear risk of bias because it was unclear whether the proportion of missing data was balanced across intervention groups. Two studies were judged to have a low risk of bias because there were no missing data. One study was judged to have a high risk of bias because no data were missing in the intervention group but 47. 3% (9/19) of data were missing in the control group. This information was obtained from email communication with the study authors. Two studies had selective reporting of outcomes and were judged to have a high risk of bias. One study had inadequate information provided on selective outcome reporting and was judged to have an unclear risk of bias, while another had no selective outcome reporting and was judged to be at low risk of bias.
Effects of intervention A meta-analysis of data from included studies was not done because of significant clinical and methodological heterogeneity among the studies. Findings for each study are described individually. Bridges. org 2005  [37]  found that the SMS intervention and the clinic-based DOTS groups were similar with regard to rates of TB cure (62. 35% vs. 66. 40%) and treatment success ( 72. 94% vs. 69. 4%). However, the rate of completion of TB treatment was higher in the SMS intervention compared to the clinic-based DOTS group (10. 59% vs. 3. 00%). Broomhead  Other bias Unclear risk Given the observational nature of the study there might be confounding variables that were not accounted for in the analysis (comparisons for scheduled clinic appointment attendance which serves as a TB treatment adherence)  [38]  showed a higher TB cure rate among patients receiving standard DOTS plus SIMpill® than those receiving standard DOTS only (RR 2.
Potential biases in the review process Although this systematic review adheres to the standardized guidelines of conduct and reporting of systematic reviews  [22] , there are certain limitations. Although we did not set out to exclude non-English studies in this review, non-English studies may have been missed because they are less commonly indexed in the selected databases compared to English studies. In addition, unpublished eligible studies may have been missed. Factors which tend to influence publication are statistically significant results, size of the study, funding, prestige, type of design and study quality  [52]. We found that the majority of publications were retrieved from the grey literature, and these publications may have been systematically different from those in journal articles, which have undergone scrutiny prior to publication.
For each sample, three different LB films were taken to ensure reproducibility. For data processing, AFM data were processed using the Nanoscope (version 7. 30, Veeco) and the Gwiddion softwares. Each time, six images were recorded at the same time: trace and retrace height images (topography), trace and retrace amplitude images (error signal) and trace and retrace phase images (mechanical properties) in tapping mode; trace and retrace height images (topography), trace and retrace deflection images (error signal) and trace and retrace friction images (mechanical properties) in contact mode. For more clarity flattened images are mainly shown in the paper.
No difference was evidenced in the expression profile of PB1 and N40 (Fig. 1B ). Densitometric measurements of PB1 levels standardized to a-tubulin levels on independent experiments confirmed that mutations introduced had no effect on PB1 and N40 expressions (data not shown), allowing us to exclude the involvement of N40 in host-response differences processes. To further characterize the effect of PB1-F2 on virus growth and virulence in vivo, we first determined the MLD50 of both WT and DF2 Nig06 viruses after intranasal inoculation to C57BL/6 mice. Their MLD50 were calculated to be around 10 1. 5 and 10 0. 9 TCID50, respectively, a small difference that we considered not significant. Moreover, kinetics of weight loss, signs of either sickness or death did not notably differ in the groups infected with 200 and 40 TCID50 of either virus.
1D ). Viral RNA quantification in lungs of infected mice showed fast and efficient replication of both viruses at 2 and 4 days pi, and, remarkably, the viral loads were not reduced at later time points but still increased at 8 days pi. We also observed an increase in IFN-b mRNA levels: IFN-b transcription was induced as early as 2 days pi, peaked at 4 days pi and then slightly decreased at 8 days pi. Viral loads and IFN-b curves were statistically equivalent between the 2 viruses. Genomic stability of both viruses was confirmed by nucleotide sequencing of the segment #2 at the different time points. Collectively, these data show that Nig06 PB1-F2 has no impact on the replication of the virus in vitro and in vivo and does not modulate the induction of the antiviral mediator IFN-b in mice.
3 ). Genes specific for each virus were used to characterize the functional consequences of PB1-F2 expression. We used Ingenuity Pathway Analysis (IPA) software to identify canonical pathways associated to the differentially expressed genes. The significance of each identified pathways are represented in the histograms of Fig. 3. At day 2 pi, global gene expression patterns indicate that the DF2 virus regulated a more important number of genes than the WT virus (3007 vs. 1394; Fig. 3A ). This is an unexpected result given that in our previous mouse model, using the low pathogenic WSN H1N1 virus  [17] , the WT virus induced a much more intense response than the DF2 homologous virus. The most remarkable insight is that only DF2-regulated groups of genes reached statistical significance (i. e.
p,0. 05, dashed line), indicating that the associated canonical pathways are significantly regulated during infection by the DF2 virus but not during infection by the WT virus. Among the most significantly regulated pathways, 3 of them are tightly associated to macrophage activation: ''Acute Phase Response Signaling'', ''HMGB1 Signaling'' and ''MSP-RON Signaling''. Another major information given by the transcriptome analysis at day 2 pi is the importance of PI3K/ AKT pathway and to a lesser extent of SAPK/JNK signaling during infection with the DF2 virus. The PI3K/AKT pathway appears to be at a central position of the host response process since it is implicated in almost all the other 12 pathways represented in Fig. 3A. The data collected at day 2 pi suggest that PB1-F2 expressed from WT Nig06 IAV disturbs directly or indirectly the alveolar macrophages function and may therefore delay the mounting of an efficient antiviral defense by the host.