dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion In this study, a model-data approach was used to interpret the diurnal and seasonal evolution of the triple oxygen isotope composition of F. arundinacea leaf water. All parameters relevant for modelling the triple oxygen isotope composition of grass leaf water were measured, including plot-scale grass leaf temperature and the triple oxygen isotope composition of atmospheric water vapor -two parameters that are often estimated as difficult to measure with accuracy. The results show that the steady state C-G model associated with a two-pool mixing equation reliably predicts the triple oxygen isotope composition of grass leaf water during daytime. The few model-data discrepancies (up to 4 ‰, 9 ‰, 34 per meg for d 18 O, d-excess and 17 O-excess, respectively) are related to differences between Tplot and actual Tleaf, variations in the fraction of the unevaporated water pool with changes in transpiration, and/or slight differences between measured RH close to the grass plot and actual RH right around the grass leaves.
Acknowledgements We acknowledge  Martine Couapel  for her help with the field experiments. We thank  Nicholas Devert  and  Sabrina Dubois  for performing leaf water extractions at  ISPA, Bordeaux. We thank the team of the  Stable Isotope Laboratory at the University of Cologne  under the leadership of  Michael Staubwasser  for permission of and support during leaf water triple oxygen and hydrogen isotope analyses in their laboratory. Our research was conducted in the frame of the HUMI-17 project supported by the  ANR  (grant nos. ANR-17-CE01-0002-01 ),  CNRS   FR3098 ECCOREV ,  LABEX OT-Med  and  OSU-Pytheas. It benefited from the  CNRS  human and technical resources allocated to the  Ecotron Research Infrastructure  from the state allocation " Investissement d'Avenir" AnaEE France   ANR-11-INBS-0001.
https://doi. org/10. 5194/bg-2022-230 Preprint. Discussion started: 9 December 2022 c Author(s) 2022. CC BY 4. 0 License. RH = 0. 22 (± 0. 01) 17 O-excessphyto + 115. 2 (± 3. 9) (r 2 = 0. 94) (6) From the same datasets, a relationship between 17 O-excessphyto and h can be obtained, assuming that Tleaf was 2 °C lower than Tair: h = 0. 25 (± 0. 02) 17 O-excessphyto + 130. 0 (± 4. 4) (r 2 = 0. 94).
5-conjugated anti-human IL-10 (clone JES3-9D7 BioLegend). Data were acquired using a LSRFortessa cell analyzer (BD Biosciences) and analyzed with FCS Express V4 software (De Novo Software). We identified Th1 cells (CD3 + CD4 + IFN-γ + ), Th17 cells (CD3 + CD4 + IL-17 + ), Th22 cells (CD3 + CD4 + IL-22 + ), Th2 cells (CD3 + CD4 + IL-4 + ), TNF-α + T cells (CD3 + CD4 + TNF-α + ), and Tr1 cells (CD3+CD4 + IL-10 + ). See Supplementary Figure  S1  for gating strategy.
Statistical analysis Statistical significance of the differences between two groups (T1D and HC) was calculated using the nonparametric Mann-Whitney U test and expressed as means ± SD. GraphPad Prism, version 8. 0 (GraphPad Software, San Diego, CA), was used for statistical analysis, and values of p ≤ 0. 05 were considered statistically significant. The power analysis was calculated based on previous data on intestinal immune cell subsets  29  and alpha diversity  30  in T1D patients vs HC. Group sample sizes of 17 (T1D) and 16 (HC) was expected to achieve 80. 00% power to reject the null hypothesis of zero effect size when the population effect size is 1. 00 and the significance level (alpha) is 0. 050 using a Mann-Whitney U test. Relative abundance profiling and differential analysis of 16S rRNA data was performed with DESeq2 upon variance-stabilizing transformation,  31  where p ≤ 0.
05 were considered statistically significant. We used the variance-stabilizing transformation algorithm. 28 This function calculates a variance stabilizing transformation from the fitted dispersion-mean relation(s) and then transforms the count data (normalized by division by the size factors or normalization factors), yielding a matrix of values that are now approximately homoskedastic (having constant variance along the range of mean values). The transformation also normalizes with respect to library size. Species alpha diversity was calculated with vegan::diversity (https://cran. rproject. org/web/packages/vegan). The Shannon and Simpson indices extrapolate diversity from the proportional abundance of species within a specific population. Visualization was performed with ggplot2  32  and Krona.
Role of funders This work was supported by Research Grants from the Juvenile Diabetes Foundation (Grant 1-INO-2018-640-A-N to MF and 2-SRA-2019-680-S-B to JD) and from the Italian Ministry of Health (Grant RF19-12370721 to MF). The funding source had no involvement in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication. I was not paid to write this article by a pharmaceutical company or other agency. The authors were not precluded from accessing data in the study, and they accept responsibility to submit for publication.
The IEB integrity was evaluated by measuring serological levels of zonulin as well as mRNA expression of structural TJP in small intestinal tissue samples of T1D patients and healthy controls. Zonulin is an endogenous protein that is released by the IEC upon interaction with pathogens and commensal bacteria such as Escherichia coli and also by dietary protein such as gliadin through the CXCR3 receptor. 34 Zonulin presence in the serum is a biological marker of tight junction disassembly and "leaky gut". Our data confirmed previous findings of increased serological levels of zonulin in T1D subjects compared to healthy controls (****p ≤ 0. 001, Mann-Whitney U test) (Fig. 1b ). The IEB damage could lead to uncontrolled passage of bacterial components into the blood circulation. The latter scenario was suggested by the finding that T1D patients had increased serological levels of the myeloid differentiation protein 2 (MD2), a lipopolysaccharide-binding protein that forms an active complex with the TLR4 leading to production of proinflammatory cytokines  35  (*p ≤ 0.
05, Mann-Whitney U test) (Fig. 1b ). We assessed IEB integrity also by measuring by RT-qPCR the differential mRNA expression of a panel of TJP expressed in the small intestine and, specifically, the barrier forming TJPs CLN1, OCLN, ZO-1 and ZO-2. Our data did not reveal alterations of TJPs except for an increased mRNA expression of OCLN in T1D samples that could indicate a compensatory mechanism to restore GB integrity in T1D patients (Fig. 1c ). The mucus layer is an important component of the gut barrier that is fundamental to prevent bacterial translocation and protect the gut mucosa. In the spontaneous murine model of T1D, i. e. , the non-obese diabetic mouse (NOD), alterations of the mucus structure and composition were recently demonstrated and linked to activation of beta cell autoimmunity and T1D occurrence.
5  Here, in the intestinal tissue of T1D patients we detected a significant reduction of mRNA expression levels of several mucins including the gel forming MUC2 that is fundamental for maintenance of the mucus layer structure as well as transmembrane Fig. 4 : Alterations of the mucus layer composition in T1D subjects correlates with intestinal immune dysregulation. Single cell suspensions isolated from intestinal biopsies of T1D patients and healthy controls were FACS analyzed. Data are expressed as percentages of different T cell subsets out of total CD3 + CD4 + T cells. We compared Th1 (CD3 + CD4 + IFN-γ + ), Th17 (CD3 + CD4 + IL-17 + ), TNF-α + (CD3 + CD4 + TNF-α + ), Th22 (CD3 + CD4 + IL-22 + ), Tr1 (CD3 + CD4 + IL-10 + ), and Th2 (CD3 + CD4 + IL-4 + ) cells in the intestine of T1D subjects compared to healthy controls.
Sea level records As there is no sensor that recorded the sea level in the immediate vicinity of the dike, which would be highly sensitive to waves anyway, we had to resort to the nearest gauge that had a large record of measures, which was located in Marseilles's harbor (Fig. 1 ). The data of the gauge are maintained by the SHOM (https://data. shom. fr/, last access: 15 February 2023) in the REFMAR (Réseaux de rEFérence des observations MARégraphiques) database, which is part of the Global Sea Level Observing System (https://gloss-sealevel. org/, last access: 12 March 2023; GLOSS) and provides more than 100 years of hourly water elevation level. The acquisition is done using a permanent global navigation satellite system (GNSS) station. The place being located inside a port is protected from sea waves.
Significant wave height records In situ data of the significant wave height provided at an hourly rate are difficult to find reliably over a long period of time (decades). This means that we have to resort to data provided by a numerical model. We use the data extracted from the ANEMOC-2 (https://candhis. cerema. fr/, last access: 10 August 2023) database currently maintained by the CEREMA (https://www. cerema. fr/fr, last access: 1 November 2022), reproducing numerically the sea conditions over a long period of time  (from 1980 to 2010). The data are generated using the third generation of the spectral TOMAWAC (TELEMAC-based Operational Model Addressing Wave Action Computation), which is a part of the TELEMAC-MASCARET software suite. This model is used to interpolate sea states on many points calibrated using the GlobalWave in situ data.
The significant wave height is estimated by calculating the mean value of the upper third of the recorded waves every hour. Thus, one value is given hourly at each chosen location. We have selected point 3667 (Fig. 1 ), as it is located in front of the dike and where the water depth is high enough to be considered as offshore (≈ 80 m) as the continental shelf has been reached according to bathymetry maps. It should be noted that model data carry an intrinsic uncertainty compared to experimental data, which is bound to decrease the reliability of the study. C. Houdard et al. : Sensitivity analysis of erosion on the landward slope of an earthen flood defense.
Identifying storm surges and coupling the datasets The time series data themselves are not directly exploitable, as the copula that we want to generate is based on the identification of extreme events implying a locally high value of both N and H 0. We have many methods at our disposal to select extreme events, among which the peak-over-threshold is supposed to give the best results. However, selecting values over a certain peak gives a distribution bounded by the value of the threshold, which is not adapted to generate a full copula that can be used to calculate the return period of critical events (see Eqs. 12-14). Thus, we decided to use the block maxima approach, which gives good results and asymptotically converges to the generalized extreme value (GEV) distribution that we will use to fit the resulting data.
The choice of the block size is not obvious, as a small block size decreases the accuracy of the fit to the limit distribution (GEV), leading to a bias, but a large block size reduces the size of the sample, increasing the variance. The general approach is to consider the duration of the cycles between events. The seasonality of storm cycles and the requirement of independence between events would not allow us to use a value lower than 1 year, which is the block size we chose. This limited the size of our dataset significantly, meaning that more data would probably greatly improve our analysis. As a preliminary check, correlation coefficients have been calculated to determine the relationship between the datasets. Pearson's coefficient gave ≈ 0. 6, indicating a strong positive relationship.
Orcel et al. (2021)  also recommended using the Gumbel-Hougaard or Clayton copulas for coastal waves on the Atlantic shores of France. An application of the Gumbel-Hougaard copula has also been explored on UK shores, aiming to study extreme coastal waves by  Chini and Stansby (2006). This motivates us to directly use the Gumbel-Hougaard copula as the most adapted choice (Eq. 1). F (u, v | θ ) = exp -(-log(u)) θ + (-log(v)) θ 1/θ , (1) where u and v are the cumulative distribution functions of the histograms originating from the datasets. The copula parameter θ represents the interdependency of the data. The value of this copula parameter is important and can be calculated using a panel of different methods, i. e. , the error method (see Appendix B for the equation as written by  Capel, 2020)  and the maximum-likelihood method.
C h2 and C v2 , respectively, are scaling factors on the equations calculating the velocity and thickness of the flow at the beginning of the crest from the runup. The range is estimated as a variation of ±50 % from their suggested values in the EurOtop (2018). -The correlation parameter. θ determines the intensity of the correlation between the datasets. The results obtained from the maximum-likelihood estimation (MLE) suggest that a small plateau is located around the maximum value, so a 10 % variation interval is a reasonable assumption here.
Return period copula We start by compiling the selected storm surge events into a histogram, giving the univariate probability densities of both datasets. However, since we only work with about 20 years of hourly data, we need to fit the cumulative histogram in order to create a cumulative distribution function that allows us to extrapolate to rarer events. We use the generalized extreme value distribution, which is used for the estimation of tail risks and is currently applied in hydrology for rainfalls and river discharges in the context of extreme events as in  Muraleedharan et al. (2011). This means that the events can then be sorted into a histogram for us to observe their respective univariate distributions. In this case, the sample limits us to events that can happen up to once every 20 years, since we have no data covering a larger period.
Thus, we can obtain information about more extreme events by extrapolating the data using a fitted distribution on each individual sample. Using the block maxima event selection, the Fisher-Tippett-Gnedenko Theorem indicates that extreme values selected this way asymptotically follow the generalized extreme value distribution (GEV) (Eq. 19), which we use to fit the data. The distribution is described here: F (x) = exp(t (x)) with t = 1 + ξ x-µ σ -1/ξ if ξ = 0 exp -x-µ σ if ξ = 0, (19) with (µ, σ, ξ ) representing the location, scale, and shape factor, respectively. The results are displayed in Fig. 3. The laws are fitted using the maximum-likelihood method. The fit gives a R 2 score higher than 0. 999 for both datasets. Hence, we consider the fit almost perfect, and it will not be accounted for during future calculations of uncertainty, as it is considered insignificant compared to other sources.
For practical reasons, we calculated return periods on an averaged profile  of the dike, which as stated by the global sensitivity analysis can lead to a return period different from the local profile. A location-wise study could bring reduced uncertainty and bring more relevant results. The data sources used to make our analysis are incomplete, located in far away places, or generated using numerical models. It is necessary to use in situ data extracted from experimental devices in order to improve the reliability of the study. Moreover, the study uses long-term data, but the impact of climate change implies an elevation of the water level as well as changes in the characteristics of storm surges such as intensity, duration, or frequency. Integrating these effects should be done in the future in order to improve long-term studies.
Conclusion We have been able to build a complete automated framework allowing the user to estimate the expected return periods of events leading to erosion on the rear side of the earthen dike submitted to wave overtopping, assuming the correctly assessed ranges of variation of the parameters are provided. The framework itself needs, firstly, metocean data in order to create a reliable copula from wave and water level data, then a description of wave propagation to the toe of dike, and finally reliable laws representing wave overtopping process, run-off on the crest then on the landward slope, and bottom erosion. The return period from which erosion on the Quenin dike located in Salin-de-Giraud starts is firstly estimated from reference parameters. This first estimate is equal to 6 years, which is significantly higher than the value of 2 years written in reports from the operating company.
| Statistical methods The baseline (time zero) was defined as the date of their first LSM after 1 January 2012. Each centre's database was examined from its inception until administrative censoring at the end of 2021. We employed AUROCs to calculate the optimal cut-off by Youden's index for predicting our primary and secondary outcomes of interest by LSM, LPR, LSPS and PH risk score. Prognostic performance was assessed by sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), percentage of missed clinical events, along with confidence intervals (CI). To validate the cut-offs in PLWH, a validation set of data was randomly generated for 50% of the sample using a simulation method with 1000 replications, not restricted (or) stratified on any of the outcome/exposure variables (in the random selection).
18, 44 Interestingly, we did not observe a reduced performance of non-invasive scores in overweight or obese patients, as it has been instead reported in etiologies of cACLD related unrelated to HIV. 31  Lower prevalence of obesity has been reported in PLWH, particularly in HIV/HCV coinfected patients. 45 r study has several strengths. First, this is a collaborative effort between 8 academic centres worldwide that care for PLWH, resulting in a large international cohort of 1695 patients with a long follow-up period, ensuring the external validity of our findings. Second, it compared four non-invasive scores head-to-head in their ability to predict clinical outcomes, validating aetiology-specific cut-off values. Our study also has limitations that we wish to acknowledge. First, although our cohort is built from locally-maintained prospective databases, certain clinical variables, including spleen size, have been retrospectively collected which explains why the evaluation of LSPS and PH risk score was only available in a subset of patients.
GS is supported by a Senior Salary Award from FRQS (#296306). CO N FLI C T O F I NTER E S T S TATEM ENT JAP reports having received consulting fees from Bristol-Myers Squibb, Abbvie, Gilead, Merck Sharp & Dome, and Janssen Cilag; he has received research support from Bristol-Myers Squibb, Abbvie and Gilead, and has received lecture fees from Abbvie, Bristol-Myers Squibb, Janssen Cilag, and Gilead. JM has been an investigator in clinical trials supported by Bristol-Myers Squibb, Gilead and Merck Sharp & Dome; he has received lectures fees from Gilead, Bristol-Myers Squibb, and Merck Sharp & Dome, and consulting fees from Bristol Myers-Squibb, Gilead, and Merck Sharp & Dome. ACG has received research funding and lecture fees from Gilead. GG received a research grant and speaker honoraria from Gilead, ViiV, MERCK and Jansen, and attended advisory boards of Gilead, ViiV and Merck.
• Viral strain CHIKV strain was isolated in 2014 from the sera of a patient in Guadeloupe and confirmed by RT-PCR. The partial sequencing of CHIKV NS1 gene (accession number: LR792670. 1) showed 97. 7% identity with a strain from Suriname (accession number: KY435463. 1) isolated during the CHIKV outbreak in 2014. Viral stocks were produced using a multiplicity of infection of 0. 1, following two passages on African green monkey kidney Vero cells (ATCC, ref. The initial viral titre was estimated by cytopathic effect (CPE) examination using serial 10-fold dilutions on Vero cells and was expressed as a median tissue culture infectious dose (TCID50/mL). Viral stocks (initial titre 5. 62 × 10 7 TCID50/mL) were kept at -80 • C until use.
• Statistical analyses All statistical analyses were performed using the software R 3. 3. 2 [38]. Females negative for infection and non-responders (i. e. , that did not lay eggs) were removed from the analysis. Infection rate, dissemination rate and percentage of responding females were compared between groups by using a generalized linear mixed model (GLMM, lme4 package), with "replicate" coded as a random factor. The mean oviposition activity index (OAI)  [39] , with values ranging from +1 to -1, was calculated for each trial as: OAI = (NT -NC)/(NT + NC), where NT indicates the number of eggs laid in the treatment solution and NC indicates the number of eggs laid in the control solution. For these assays, a positive OAI value indicates a preference toward the treated solution, whereas a negative OAI value indicates aversion.
The mean number of eggs laid and the OAI were analyzed and compared between the infectious status and gonotrophic cycle by using a linear mixed-effects model (lmer function, lme4 package) with replicate and cage coded as random factors. The interaction between infectious status and gonotrophic cycle was always tested. Post hoc comparisons between the four groups of treatment at the second exposure were performed using multiple comparisons (Tukey's tests, multcomp package). Model selection was performed using AIC and analysis of the residuals (RVAide-Memoire package), with non-significant interactions removed from the model. EAG data were analyzed by comparing the response of each compound with those from the solvent Viruses 2023, 15, 1043 6 of 14 (µV) by using a linear mixed-effects model (lmer function, lme4 package, "individual" coded as random factor).
Sequence analysis Sequence data were processed using MR DNA analysis pipeline (www. mrdnalab. com, Shallowater, TX, USA) as described in Mofini et al. [51]. In summary, raw Illumina MiSeq paired-end reads were assembled, and sequences were demultiplexed and formatted for processing using a Python script (http://drive5. com/usearch/manual/ uparse_pipeline. html). Sequences were then separately quality-filtered and clustered into operational taxonomic units (OTUs) using the UPARSE algorithm  [54]. Briefly, sequences were quality-filtered allowing a maximum e-value of 0. 5. Reads were then trimmed to 240-bp length, dereplicated and sorted by abundance, and singletons were removed prior OTU determination at 97% sequence similarity threshold. Then, chimeric sequences were screened and removed using UCHIME  [55]  against the Gold database  [56].
Finally, bacterial and archaeal OTU tables were generated by mapping reads back to the representative prokaryotic databases. The taxonomic affiliation of each OTU was obtained using BLASTn against a curated database derived from GreenGenes  [57] , RDPII (http://rdp. cme. msu. edu), and NCBI (www. ncbi. nlm. nih. gov). The generated raw sequence data were deposited in figshare (https://doi. org/10. 6084/m9. figshare. 20348949).
Alpha diversity Prior alpha diversity analysis, the OTU tables of bacteria and archaea were rarefied to 14,824 and 23,889 sequences per sample respectively, using the rarefy_even_depth function in the phyloseq package  [58]. Alpha diversity was then estimated by the Shannon index at each rarefaction level for bacteria and archaea. After checking for normality (Shapiro-Wilk test) and homoscedasticity (Levene test), a linear mixed-effects (LME) model fit by restricted maximum likelihood (REML) was used to test the effect of long-term fertilization and developmental stage on alpha diversity, using the lmer function in the lme4 package  [59]. The LME model was: Shannon index ~ Fertilization x Developmental stage/ sampling time-point + (1|Block). Where x indicates an interaction term, sampling timepoint nested within developmental stage and plot block included as random factor to control for spatial variation.