dataesr/scientific-paragraphs-categorization huggingface dataset:
" [35]  When searching for articles in the IENT database, which is specifically dedicated to work on neurotrophic parasitosis in the countries of interest of this study, only the free text terms "comorbidity" or "mental health comorbidity" were used. Finally, the registration and selection of articles was done through the Zotéro software. Every article included in this meta-analysis had to meet the same criteria as the previous meta-analysis, which were: " be an original article whose full text was available; be a cross-sectional or analytical study; have been conducted on adult patients, both males and females, and on all age groups (age≥15 years); be a study involving either only hospitalised subjects or only nonhospitalised subjects, but not both hospitalised and nonhospitalised subjects at the same time; specify the method of disease diagnosis.
Funding The first author (LOB) was an intern at Sanofi for 6 months during which he received a grant. Sanofi has also provided access to its electronic databases. During his internship, LOB was supervised by VL, PEB and DG who are, or have been, Sanofi employees. VL, PEB and DG have contributed to the research protocol, the interpretation of the data and the preparation of the manuscript. Ethics approval and consent to participate Not applicable.
We used a binomial Generalised Linear Model (GLM) to test for an effect of plant population on the propensity to lay eggs (a presence or absence binomial response variable). The egg count data showed high overdispersion when modelled with a Poisson error family. Given the dataset also potentially contained false zeros (females that that were unmated and could not lay eggs), we used a zero-inflated negative binomial model to test the effect of population (in the 'pscl' package in the R statistical software,  [79] ). We assumed a constant probability of false zeros across populations and retained a fixed factor of experimental round to control for variance among experimental repetitions. We also tested for a population effect on the number of eggs laid after excluding plants on which no eggs were laid.
Immediately following this preference assay, all plants were infested with eggs and larvae were allowed to hatch. Excess larvae were removed as soon as eggs hatched to leave ten L1 larvae per plant. These were allowed to feed on the plants for 7 days, and their individual mass was measured at the end of the 3rd and 7th day. By the 7th day, the larvae had consumed most of the plant leaves, so we were unable to take leaf samples for chemical defense analysis. Variation in individual larval mass was modelled with a linear mixed effects model (R package, lme4;  [80] ) using population as a fixed factor and individual plant as a random factor. Estimates of the variance explained by the fixed and random terms were calculated using the package "piecewiseSEM"  [81]. Supplementary Materials: Supplementary materials can be found at http://www.
Acknowledgments: We would particularly like to thank  Maja Frei ,  Esther Zürcher  and  Malwine Peter  at the  ETH Research station in Plant Sciences (ETH Zürich ) for looking after the A. halleri plants and assisting with plant cloning. We are grateful to several students, particularly  Lisa Eymann  and  Michael Zehnder  for their help in conducting parts of several of the experiments on A. halleri. We are grateful to  James Sims  for help setting up the LC/MS glucosinolate method in the lab and in identifying the glucosinolate compounds.
Results and discussion The objective of this part is to validate numerically the principle of thermal fouling detector presented in section 2. The system represented in section 3 (tube and shell heat exchanger) has been modeled in 3D transient and simulated (after a mesh sensitivity study) by the commercial code COMSOL  [22]. Two numerical experiments were made with two different temporal forms of the surface heat source ( ) q t (thermal perturbation). In the first experiment, a constant surface heat source was applied from 0 to final t t t = = (step function) where final t is the experiment duration. In the second experiment, a surface heat source that varies linearly with time, 0 to final t t t = = (ramp function) was applied, such as ( ) = q t K t where K is the slope and t is the time.
7 at the 1 st bout to 3. 0 at the 3 rd bout. The mean ratio of post-exercise fMLP-LBCL to post-exercise rLBCL was also stable and reached 2. 2, 1. 8 and 1. 9 after the 1 st , 2 nd and 3 rd bout of exercise, respectively. Correlations between integrity of cf n-DNA and whole blood chemiluminescence before and after three repeated bouts of exhaustive exercise. Because we studied a relatively low group of men (n=11), the direct analysis of correlations between variables obtained during one bout was inconclusive. Therefore, to overcome (at least partially) this problem, we analyzed correlations between integrity of cf n-DNA (I 206/78 ) and LBCL by using the following datasets from three bouts of exhaustive exercise: (A)-pooled individual pre-exercise data (n=33); (B)-pooled individual post-exercise data (n=33); and (C)-pooled pre-and post-exercise data (n=66) (Table  3 ).
There was a negative correlation of moderate strength between pre-exercise I 206/78 and pre-exercise fMLP-LBCL (ƍ = -0. 36, p<0. 05), while correlation between post-exercise I 206/78 and post-exercise rLBCL or fMLP-LBCL reached borderline significance (Table  3 ). In the case of analysis of dataset involving pre-and post-exercise data together, I 206/78 negatively correlated with rLBCL (ƍ =-0. 37, p<0. 05) and fMLP-LBCL (ƍ = -0. 40, p<0. 05). The same analyses performed between integrity of cf mt-DNA (I 218/97 ) and whole blood chemiluminescence (rLBCL, fMLP-LBCL) revealed no significant correlations (ƍ ranged between 0. 11 and 0. 38).
Individual results were obtained as a mean from two separate runs and expressed in ng/mL for cf n-DNA and as genome equivalents (GE)/ml plasma (1 GE = 6. 6 pg DNA) for cf mt-DNA  59. ) were chosen. The assay was designed in a way that the forward primer and the probe were the same for each pair of amplicons, whereas two different reverse primers were used. Both, GAPDH 78 and MTATP 97 primers and corresponding probes were described and successfully used in previous studies  59. The primers for longer sequences (GAPDH 206 primer and MTATP 218 primer) were planned using Primer3 software (Table  4 ). qPCR was carried out in 20 μL of total reaction volume containing 6. 5 μL H 2 O, 10 μL TaqMan® Universal PCR Master Mix (Applied Biosystems, Branchburg, New Jersey, USA), 0. 25 μL of each of the two matched primers (one forward and one reverse primer for longer amplicon or one forward and one reverse primer for shorter amplicon) (Sigma-Aldrich), 1 μL of a FAMlabeled MT-ATP 8-probe or 1 μL of a MVIC-labeled GAPDH-probe (both probes from Applied Biosystems, Branchburg, New Jersey, USA), and 2 μL of TE buffer containing cf DNA isolated from plasma.
Statistical analysis. Results are expressed as a mean (SD) and median (interquartile range). Analysis of variance (ANOVA) for repeated observations (parametric test) or Friedman's ANOVA (non parametric test) was applied for the assessment of changes in variables over time (before and after three consecutive exercise bouts) depending on data distribution which was tested with Shapiro-Wilk's W test. In case of statistically significant ANOVA, the post hoc analyses were done with Scheffe's test or post hoc analysis for Friedman's ANOVA (multiple comparisons at 2 different time points). Correlations between variables were determined using Spearman's ƍ. A p value<0. 05 was considered significant. Eleven average-trained men completed the study composed of four visits at days 1, 7, 10 and 13. After determination of VO 2 max at day 1, three repeated treadmill exercise tests to exhaustion at speed corresponding to 70% of personal VO 2 max were executed at days 7, 10 and 13.
Therefore, the test-retest reliability of temporal variables measured with SONAR-PNT versus SONAR-only in FRONT condition (17 subjects) was determined by calculating the interclass correlation coefficients (ICCs) and their associated 95% confidence intervals (95% CIs). The sum of the squares needed for the calculation of the ICC was obtained by using a two-way ANOVA mixedeffects model  (Koo and Li, 2016). For all comparisons, a value of p below 0. 05 was considered statistically significant. Statistical analyses were performed with GraphPad Prism 6. 0 (GraphPad Software, Inc. , La Jolla, CA, United States) and MedCalc Statistical Software version 14. 8. 1 (MedCalc Software bvba, Ostend, Belgium).
NEW AND NOTEWORTHY Non-contact vibrometry using airborne ultrasound is a valid tool to measure thoracic displacements during resting breathing in normal humans. It gives access to an accurate description of breathing pattern and of its variability and could be used in true ecological conditions (devoid of observer effect due to instrumentation or subjects' awareness). "R3S" and UMRS 1158 was supported by Investissement d'avenir ANR-10-AIHU 06 program of the French Government. B-PD was supported by a post-graduate fellowship grant from the Fondation du Centre Hospitalier de l'Université de Montréal (CHUM).
Data analysis Rice yield, radiation interception by the rice canopy, weed density, weed biomass, soil water and soil mineral N contents were subjected to analysis of variance (ANOVA) for linear mixed effects models for split plot design data. Experimental treatments (soil tillage/residue management, crop rotation, level of weed pressure), block and season, and their two-, and three-way interactions were considered as fixed effects, whilst the interaction season x block x crop rotation x soil tillage/residue management was considered as the split-plot random effect. The means of treatments were compared using the Tukey's honestly significant difference test (HSD). Statistical analyses were done with R software (R-3. 5. 1) using the packages lmerTest  (Kuznetsova et al. 2016)  for tests of linear mixed effects model fits, and agricolae (De Mendiburu 2016) for Tukey's HSD tests.
Acknowledgments: This work was funded by ' Foibem-pirenena momba ny Fikarohana ampiharina amin'ny Fampandrosoana ny eny Ambanivohitra' (FOFIFA) ,  'Centre de cooperation Internationale en Recherche Agronomique pour le Développement' (CIRAD) , the  Agroecology-Based Aggradation Conservation Agriculture (ABACO)  project from the  European Union , the  Conservation Agriculture in Rice Cropping Systems in Madagascar (CARIM)  project from the  Agropolis Foundation , and the  Robert S. McNamara Fellowships Program. We would like to thank  Philippe Letourmy  for assistance with the statistical analyses, and all MSc students who contributed to data collection:  Antonio Randriatsimihory ,  Fidy Rabarison ,  Sedera Rakotonjanahary ,  Mirana Rabarivololona ,  Sandratra Rakotomalala ,  Ella Zafitsimalogny.
Materials and Methods Experimental Details. D 2 O-CH 4 clathrate samples were prepared using the well-established method described in ref. 27. For Raman measurements, samples were loaded cold and compressed in a diamond anvil cell to have enough pressure to stabilize the sample at room temperature. All spectra were measured at room temperature, upon increasing pressure in small steps. Further details on sample preparation, Raman measurements, and analysis are given in SI Appendix. Simulation Methods. Both MH-III and MH-IV phases were simulated via ab initio molecular dynamics at room temperature. Electronic structures and interatomic forces were determined by density functional theory simulations within the generalized gradient approximation  (28)  provided by the Quantum Espresso package  (29).
Nuclear quantum effects were taken into account by quantum thermal bath  (30) , for dynamical properties and path integral MD, for the quantum statistics counterpart via the i-PI package  (31). The reader can find further information in SI Appendix. ACKNOWLEDGMENTS. This work was granted access to the high performance computing resources of Centre Informatique National de l'Enseignement Sup érieur under the allocation A0030906719 made by Grand Equipement National de Calcul Intensif. The experimental work was supported by the Swiss National Science Fund under Grant 200021 149487. ORCID: 0000-0002-6526-9989 (S. S. ); 0000-0003-2026-6132 (U. R. ); 0000-0001-8810-5269 (P. D. ); 0000-0001-5181-423X (R. G. ); 0000-0003-3860-8807 (W. F. K. ); 0000-0002-0855-0491 (P. G. ); 0000-0002-7048-5029 (F. F.
| Patients Between July 2007 and December 2009, 100 patients ≥18 years with CML, were included in the French multicentric STIM trial (NCT00478985). Patients who received IFN-α prior to imatinib have been excluded from this study (N = 50) because of potential confounding effect in NK cell-mediated response. From which, we have genotyped all the samples (100%) for KIR. Between April 2011 and March 2015, 218 patients ≥18 years with CML, were included in the French multicentric STIM2 trial (NCT01343173). From which, we have genotyped 193 samples (88. 5%) for KIR. These trials included both patients with chronic or accelerated phase CML, treated only with imatinib as a single agent for at least 3 years, allowing a DMR, defined as no detectable BCR-ABL1 transcript with at least a sensitivity >4.
5 logs according to the European Leukemia Net recommendations for minimal residual disease evaluation. The DMR must be sustained for two consecutive years and on a minimum of five data points before stopping imatinib. Patients with previous allogenic stem cell transplantation were excluded. After imatinib cessation, minimal residual disease monitoring was performed in laboratories according to European Leukemia Net recommendations, monthly for the first 12 months, then every 2 months in year 2, and every 3 months thereafter. Molecular recurrence triggering remission or treatment-free remission rates. Conclusion: These results suggest that KIR2DL5B could carry a role in lymphocyte-mediated control of leukemic residual disease control in patient with CML relapse.
| Statistical analysis Statistical analysis was performed on STATA statistical software, release 14. 2 (STATA Corporation, College Station, TX, USA). Univariate survival analyses assessed hazard ratio (HR) and 95% confidence intervals (95% CI) using a standard Cox model and Kaplan-Meier survival curves. Multivariate analysis initially included KIR2DL5B together with potential confounding factors (associated with endpoint in univariate analysis with a P < 0. 20): others factors include KIR, age, gender, Sokal risk score (for TFR, time to first and second DMR) and imatinib therapy duration, time to first DMR, DMR duration before discontinuation of imatinib (for TFR and time to second DMR). A backward analysis was then applied until only variables significantly and independently associated with TFR, time to first or second DMR (P < 0.
D, TFR, which did not differ significantly according to haplotype assignment; CML, Chronic Myeloid Leukemia; DMR, Deep molecular response; KIR, Killer Immunoglobulin-like Receptors; TFR, Treatment-free remission. survival and was the only independent factor associated with inferior MMR in a response-directed sequential imatinib/nilotinib strategy. KIR2DS1 was not associated with outcomes in this second study. 16 The current one, based on a large cohort of two prospective trials, does not reproduce these results in front-line efficacy of TKI, neither for KIR2DS1 nor for KIR2DL5B, but the population of CML patients was not similar. Indeed, patients enrolled in the STIM and STIM2 trials were older and selected on their ability to reach a DMR for at least 2 years. Moreover, this part of the study is based on retrospective data and no overinterpretation of results in time to first DMR should be made.
ACKNOWLEDGMENTS This work was supported by a grant from the  SIRIC BRIO  ( B2BB   Prog ). P. -YD was a recipient of the  SIRIC Brio  for research fellowship for one year, then the  MD-PhD program  from the  University Hospital of Bordeaux  for two years. We thank  Fanny Robbesyn  for the data management concerning patients. We thank the  Centre de Ressources Biologiques Cancer, Bordeaux Biothèques Santé  ( BB-0033-00036 ) of the  Bordeaux University Hospital. The funders had no role in study design, data collection or analysis, decision to publish or preparation of the manuscript. We thank  Matthieu Lewis  for English editing of the manuscript.
Sentinel-2 and Landsat data products when combined open opportunities for capturing the dynamics of nearshore coastal and inland waters at rates that have never been possible before. Recognizing the differences in their spectral and spatial sampling, to generate a seamless data record for global water quality monitoring, it is critical to quantify how well the derived data products agree under various atmospheric and aquatic conditions. This study provides an extensive quantitative assessment of how Landsat-8 and Sentinel-2A/B equivalent data products compare and discusses implications on differences in downstream products generated via the SeaWiFS Data Analysis System (SeaDAS). These products include the top-of-atmosphere (TOA) reflectance (ρ t ), the remote-sensing reflectance (R rs ), as well as biogeochemical properties, such as the total suspended solids (TSS).
To create a consistent, seamless multi-sensor data record for monitoring global aquatic systems, it is vital to ensure that OLI and MSI are consistent at both Level-1, i. e. , calibrated top-of-atmosphere (TOA) reflectance (ρ t ), and Level-2 products, e. g. , remote-sensing reflectance (R rs ) defined as the ratio of upwelling water-leaving radiance to the total downwelling irradiance just above water. Such exercises have extensively been practiced for ocean colour missions over open-ocean waters  (Franz et al. , 2005; Maritorena and Siegel, 2005; Morel et al. , 2007; Müller et al. , 2015)  to gauge how differences in TOA measurements propagate to R rs products  (Gordon, 1998)  and other downstream physical/geophysical variables, including the inherent optical properties (IOPs). This manuscript evaluates and quantifies consistency in OLI, MSI-A, and MSI-B products through a) an extensive evaluation of nearsimultaneous nadir overpasses (n-SNOs) and b) analyses of time-series data records over several inland/coastal waters.
(  1 )). The estimated water-leaving radiance is replaced with recorded field measurements, which are propagated back to TOA with the estimated atmospheric parameters. The in situ data collected under satellite overpasses within the 2015-2017 period were applied for the vicarious calibration. These gains are slightly different than those provided in  Pahlevan et al. (2017b). The differences can be attributed to the availability of more matchups yielding more accurate estimations. The median of all the gains derived for the available images over the BOUSSOLE (n = 13) and MOBY (n = 4) sites were independently computed for MSI-A. The average of the two sites are reported in Table  1. For MSI-B, only four cloud-free images over the MOBY site were identified; hence, the derived gains are treated as preliminary.
Remote-sensing reflectance The median ratios of vicariously calibrated MSI-and OLI-derived R rs Fig. 4. The intercomparison of OLI and MSI-A ρ t products before and after applying vicarious corrections (Table  1 ) for the visible and the 865-nm bands. The data points represent median values of MSI-A to OLI ratios computed from 103 scene pairs at n-SNO events. The error bars represent one-standard deviation. Note that the NIR band has not been vicariously adjusted. ⁎ The analysis associated with the 497-nm band applies to the 2017-2015 timeframe. Following the change in RSRs, this channel is centered at 492 nm. Fig. 5. The intercomparison of OLI and MSI-B ρ t products before (left) and after applying vicarious corrections using preliminary gains (Table  1 ) for the visible and the 865-nm bands.
The data points represent median values of MSI-B to OLI ratios computed from 24 scene pairs collected at n-SNO events. The error bars represent one-standard deviation. Note that the NIR band has not been vicariously adjusted. products are shown in Fig. 6. Overall, the average differences in products are estimated to range from 1 to 7% indicating relatively consistent products. In particular, R rs (443) products agree to within < 1. 5%. This is partially contradictory to the results shown for MSI-B's ρ t (442) (Fig. 5 ) where MSI-B was found ~1. 5% larger in magnitude than that of OLI. In principle, this difference is anticipated to translate to 15% difference in R rs  (443)  over open oceanic waters  (Gordon, 1997). This unexpected deviation from theoretical realizations can be attributed either to uncertainties arising from differences in the spectral band adjustments  (Pahlevan and Schott, 2012)  or to the lack of rigor in the statistical analyses (n = 24).
Time-series analyses In this section, we demonstrate the utility of combined OLI-MSI products by assessing time-series of TSS and vicariously corrected R rs products over several lakes, reservoirs, and bays under various atmospheric and climatic conditions. Figs. 7, 8, and 9 illustrates the timeseries plots exhibiting valid retrievals beginning from July 2015. These sites show various seasonal variability and dynamic ranges in R rs (665) and TSS. The star symbols mark a same-day overpass, i. e. , < one-hour time difference, at these sites. The temporal data are extracted from arbitrarily select areas indicated with a star on the TSS maps provided on the left panel. The extraction was conducted by taking the median over 6 × 6-and 9 × 9-element windows (equivalent of 180 × 180 m 2 ) from OLI and MSI data, respectively.
This is to minimize random noise and/or artifacts. Pixels adjacent to land were avoided in data extraction as frequent negative retrievals were found  (Santer and Schmechtig, 2000). The analyses over various aquatic systems characterized with different TSS and R rs (665) magnitudes highlight the value of combined Landsat-8 and Sentinel-2 data products. It is clearly seen that MSI data fill the gap in OLI data products available every 16 (or 8) days. For instance, there is a major gap in OLI products during the wet season over the central basin of Lake Bangweulu (Fig. 7 ). This has been partially filled by MSI-A data products. Furthermore, Fig. 7  indicates that OLI produces slightly larger TSS products than those generated from MSI-A. This is, however, the case in 4 of the 7 same-say overpasses; the other three samples agree.
The triplet of the sensors clearly capture the seasonal variability in Lake Apopka with peaks in TSS in the month of July (Fig. 8 ). Relatively frequent OLI, MSI-A, and MSI-B products towards the end of 2017 and in 2018 further demonstrate Landsat-Sentinel-2 capability in monitoring global inland and nearshore coastal waters. In San Francisco Bay (Fig. 8 ), a Gaussian-like feature is also captured in the winter-spring of 2017-2018. Likewise, a sharp peak is observed in Lake Kyoga in July 2017 (Fig. 7 ). These two features would have not been fully captured with each individual sensor. Another gap in time-series data occurs in the winter of 2015-2016 in Upper Klamath Lake, where OLI-MSI-A data products are very scarce due to cloud coverage. This gap has partially been filled in with MSI-B products in 2017.
The difference in the red channel, however, is relatively small (i. e. , < 5%) compared to that in the 497-nm, which is ~9% and is much larger than what was reported in Fig. 6. While the MD and RMSD in the 443-nm channel are negligible (i. e. , < 2%), the differences in the green channel is relatively high in magnitude. For these mostly turbid waters with high peaks in the green ( = R sr (559) 0. 0143 [1/ ] rs ), these differences may   1 ). The data points and the error bars represent median statistics and one standard deviations for all the available matchups, i. e. , n-SNO events. The MSI products agree with those of OLI within ± 7% in all the visible bands. The differences in MSI-A and MSI-B R rs products are on the order of 2% except in the green channel where 5% difference in observed.