dataesr/scientific-paragraphs-categorization huggingface dataset:
harvard. edu/dataverse/geospatial_human_dimensions_data, accessed January 12,  2023). We then plotted a simple linear regression between the 2018 UAF data and the original RL, giving a relationship between the fraction of urban area in a grid cell and the road density of that grid cell (RL=((2. 68*10 4 )*UAF) +292; R 2 =0. 43), where 2. 68*10 4 =is the regression coefficient (aUAF, Table  1 ). The RL data were split into categories of urban fraction, whereby each grid cell was allocated to one of twelve UAF bins, corresponding to [0-1, 1-5, 5-10, 10-20, 20-30. 90-100 percent] and the equation was used to estimate the implied RD at the numerical midpoint of each bin. Thus, on the basis of the RL/UAF a road length per unit UAF was allocated to each of the UAF-based classes, multiplied by actual UAF of each grid cell given its UAF, and the resulting 'excess' road length subtracted from original RL data, to give an 'effective' road density and AED value.
This is then the area subject to increased drying and higher temperatures owing to fragmentation: 𝐸𝑃𝑅 = ((𝜋 * 𝐴𝐸𝐷 ) ) -(𝜋 * (𝐴𝐸𝐷 -10) ) ))/(𝜋 * 𝐴𝐸𝐷 ) ) (8) In ORCHIDEE-SPITFIRE, each fuel class in each grid cell is allocated a simulated fuel moisture content (𝑊𝑒𝑡 !7 ). In addition, there is a moisture threshold for each fuel class above which fuel consumption by fire no longer occurs (𝑇ℎ𝑟𝑒𝑠ℎ !7 !,# ), where the subscripts refer to the 1hr and 10hr fuel classes subjected to edge drying. The 100hr fuel class is not affected in this scheme, as we assume that the diameter of 100hr fuel is sufficiently high to preclude edge drying from affecting its sensitivity to ignition. Here, both the calculated wetness and the ignition threshold were used to proxy edge fuel drying, and are both lowered by the product of the fractional edge-to interior moisture gradient with 𝐸𝑃𝑅.
Simulation Protocol The resulting model was spun up for 40 years to allow for vegetation to reach a quasi-equilibrium biomass state. This was done by forcing the model with the vegetation, climate and atmospheric CO2 of 1901-1910, looped over that period of time, then looped again for 40 years over 1990-2000 forcing data, to bring the model to an equilibrium consistent with the near-present day. Principal and 'control' simulations were run over the period 2000-2013. Vegetation was imposed and not predicted using ORCHIDE''s dynamic global vegetation model to reduce uncertainties associated with its output. Climate forcing data for all runs came from the CRU-NCEP v8 dataset 124 , and vegetation imposed on the model from the ESA-LUH2 suite of projections with 13 plant functional types  14. A number of additional output variables were also implemented to ease assessment of the effects of fragmentation on fire behaviour.
Data UAF was obtained from ref. ( 44 ). Fire size data used in Fig. 3c  is sourced from FRYv2. 0  87  , updated from FRYv1. 0 88 with single ignition point polygons delineation from re. (  86 ), based on pixel information MCD64A1 and FireCCI51, as recently used in ref. ( 85 ). Long-term BA data for South-east Asia from 105,106 was obtained from https://climate. esa. int/es/odp/#/project/fire (accessed 05/06/2023), while deforestation and pre-and post 2000 average tree plantation grid data were obtained from refs. ( 107,108 ). Fragmentation and fire data for Brazil in Fig. 3a  were provided by ref. ( 102 ) and upscaled using CDO's conservative remapping function from 10km to 0. 5 degree grid resolution. All other datasets above were interpolated bilinearly in CDO to 0. 5 degree resolution. Postprocessing was performed using NCL, Panoply, CDO and R, with R maps created including the following packages: ncdf4, ggplot2, raster, maptools, rgdal, rgeos, maps, ggpubr, sp, geosphere, rColorBrewer, ggmap, lattice, dplyr, tidyr, plyr.
Study design An international survey was conducted to study the complications occurring during religious fasting in patients with a history of MBS. This survey was approved by the ethical committee of Iran University of Medical Sciences (Approval ID: IR. IUMS. REC. 1401. 030). Well-known bariatric surgeons with experience offering MBS to Muslim patients were invited to participate in the survey. The data were collected from April 2022 to July 2022. We reported information about age, gender, BMI at fasting, medications, comorbidities, and type of MBS (SG-LDJB: Sleeve Gastrectomy with Loop Duodeno-Jejunal Bypass; SG: Sleeve Gastrectomy; SADI-S: Single Anastomosis Duodeno-Ileal Bypass with Sleeve gastrectomy; SASI: Single-Anastomosis Sleeve Ileal Bypass; SASJ: Single-Anastomosis Sleeve Jejunal Bypass; BPD-DS: Biliopancreatic Diversion with Duodenal Switch; OAGB: One Anastomosis Gastric Bypass; RYGB: Roux-en-Y Gastric Bypass), complications (abdominal pain/ Epigastric pain/dyspepsia, nausea/vomiting, dehydration needs IV fluid, syncope, dumping, GERD, marginal ulcer, kidney stone, GI Bleeding, perforated marginal or peptic ulcer, acute cholecystitis and obstruction) and their management (surgery, EGD, PPIs, avoid fasting, conservative, IV fluid, Tranexamic acid).
Data collection Metabolic/bariatric surgeons were asked to report data including patient demographics, BMI at the time of fasting, onset of fasting for the first time after MBS, type of MBS, complications and their management. Details from different centers were received through a classified Excel data reporting sheet. Collaborators were required to obtain their local approval for entering patient data into an international survey as per their local guidance, despite the IRB approval of survey. Finally, all data were merged by a single author for analysis. The patients with incomplete data were excluded from the survey. Primary outcome was complications occurring during fasting in patients with pervious history of MBS and secondary outcomes were type of MBS procedures, management strategies used to treat these complications.
Ideally, prospectively collected database with preoperative factors, surgical details, and postoperative information would help clarify some of these limitations within our survey. In summary, MBS is performed globally, and obesity is a global health issue and MBS may change common religious practices like fasting. For some patients, being unable to fast may make MBS untenable as an option and they need open counseling and advice. Patients should be advised about the risks of abdominal pain, dehydration, and marginal/peptic ulcer disease exacerbation while fasting, and review medications with their care team to garner advice about when and how to take medications. Work should be continued to identify how fasting can be done safely in the patient population, and in the meantime, patients should stay in close contact with their weight management team during periods of religious fasting.
Khakbaz et al. (2012)   [34]  used different calibration strategies and concluded that lumped calibration performed better than distributed calibration. Thus, the choice of a perfect model does not exist, and one must adapt to the available dataset and the objectives of the study that is being carried out. On the one hand, there are physically based models whose structures depend on the spatial distribution and variation of physical parameters, such as the MIKE SHE  [35, 36] , the HydroGeoSphere  [37, 38] , and the SWAT  [39, 40]  models. The latter requires spatially distributed data, such as the digital elevation model (DEM), the land cover and uses, soil properties, and climate data (precipitation, air temperature, solar radiation, and others). On the other hand, there are indeed conceptual and lumped models designed to approximate within their structures the general physical mechanisms which govern the hydrological cycle  [41].
Available Data on the Study Area Several climatic and hydrological data collected in the study area have been used, such as precipitation, air temperature, river flows, groundwater levels, and water withdrawals for the different uses (domestic, irrigation). The longest precipitation and air temperature datasets were recorded from 1974 to 2020 in Clermont-Ferrand (x = 3. 15, y = 45. 79, and z = 331 m asl) for temperature and in St-Etienne-de-Ludgarès (x = 3. 95, y = 44. 65, z = 1022 m asl) for precipitation. For the gauging stations of the Allier River, it is also possible to go back to 1974 in Vieille-Brioude and Limons. The more recent period of 2000-2020 is better documented, and the following data were collected (Table  1 ): (1) daily precipitation and air temperature from 23 weather stations distributed along the entire watershed, (2) daily river flows from three gauging stations installed on the Allier River watercourse in Vieille-Brioude and in Limons (Q VB and Q LIMONS ) and on the Alagnon River (Q ALGN ), (3) daily discharges of the Naussac Dam (Q NAUSS ), (4) water withdrawals for domestic and irrigation uses, and (5) groundwater levels at four locations between 2007 and 2020 (Figure  1b ).
We focused on data from three available piezometers located close to Culhat and at different distances from the Allier River (see P40-07, P40-08, and P40-09 in Figure  A1 ). Water withdrawal data included domestic and irrigation supplies expressed as volume per year. Domestic supply was assumed to be constant throughout the year; we then divided the annual volume by 365 and reported an equal volume of withdrawal each day. Irrigation occurred from April to September in the following proportions: 2% in April, 2% in May, 12% in June, 51% in July, 32% in August, and 1% in September. The annual volume was, thus, distributed daily over these six months and was reported each day according to the previous percentages. The Naussac Dam's water releases were naturally included in the incoming Allier River discharge in Vieille-Brioude (Figure  1b ).
As Gardenia requires a single record for each input parameter (Table  1 ), precipitation was averaged using the Thiessen polygon method for the twelve stations located on the upstream part and for the eleven stations on the downstream part (Figure  1b ). Although kriging method usually leads to increased accuracy, it has the disadvantage of mainly relying on the assumption of stationarity in the means  [59]. Moreover,  Ruelland et al. (2008)   [60]  compared different interpolation methods for precipitation in a lumped model; all approaches (Thiessen polygon, IDW, spline and kriging) led to a similar fitting between observed and simulated values of the river flow. Hence, the straightforward method of Thiessen polygon to average precipitation over 2000-2020 was chosen. The daily mean air temperature was calculated by averaging data from the twelve weather stations on the upstream part and the eleven on the downstream part.
Parameters Units Description  The daily mean air temperature was calculated by averaging data from the twelve weather stations on the upstream part and the eleven on the downstream part. Potential evapotranspiration was calculated from this averaged value using the Oudin method  [61]. Calibration is a semiautomatic process. The user gives for each optimized parameter (Table  2 ) (1) an initial value for the first run and (2) a range with a lower and an upper limit. In order to test and validate the model, the river flow in Limons was calibrated using 2000-2014 data and was validated using 2015-2020 data. Similarly, the groundwater levels in Culhat were calibrated using 2010-2016 data and were validated using 2017-2020 data. The first simulated timesteps strongly depend on the previous ones.
6, and for the "worst case", RegCM4-6 in RCP8. 5, based on collected data from DRIAS-2020. They all refer to the downstream part of the study area (Figure  1b ) compared to values obtained for the reference period "Ref": 2000-2020. The use of two couples of contrasted RCM-RCPs allowed us to give a range of potential evolution of the Allier River outlet discharge (Limons) and of the groundwater levels (Culhat). The analysis of the forecasted Q LIMONS and H CULHAT for 2030-2070 was compared to the reference (2000-2020 for Q LIMONS and 2007-2020 for H CULHAT ). tial evolution of the Allier River outlet discharge (Limons) and of th (Culhat). The analysis of the forecasted QLIMONS and HCULHAT for 2030 to the reference (2000-2020 for QLIMONS and 2007-2020 for HCULHAT). The work presented in this article was, thus, divided in four st of the data to characterize the evolution trends of hydrometeorolo water withdrawals, (2) the calibration of the hydrological model G collected data (Table  1 ) and the optimized parameters (Table  2 ), (3) the water balance components for 2000-2020 resulting from the data the model, and (4) the forecast of QLIMONS and HCULHAT for 2030-2070.
Trends in 1974-2020 To compare this study with a more global trend, we investigate climatic (precipitation and air temperature) and hydrologic (river flo dam releases) data from the long-term records of 1974-2020. The temperature (1974-2020) in Clermont-Ferrand was 11. 5 ± 0. 9 °C, wh 2000s were more often above that mean (Figure  4a ). The temperat creased by +0. 05 °C/y since 1974, which corresponded to an incre years. The interannual precipitation mean (1974-2020) was 580 ± 11 The work presented in this article was, thus, divided in four steps: (1) a preanalysis of the data to characterize the evolution trends of hydrometeorological parameters and water withdrawals, (2) the calibration of the hydrological model Gardenia based on the collected data (Table  1 ) and the optimized parameters (Table  2 ), (3) the quantification of the water balance components for 2000-2020 resulting from the data preanalysis and from the model, and (4) the forecast of Q LIMONS and H CULHAT for 2030-2070.
P40-07 was the piezometer where contrasts between high and low flows were more pronounced, which was similar to the river flow pattern reflecting the equilibrium between the river and the aquifer. The analysis of the hydrometeorological data of the last 20 years allowed us to identify the forces that the Allier hydrosystem was facing, particularly on its downstream part (Figure  5b, d, f, h   The distribution of Q LIMONS showed an important difference between the high (73 m 3 /s) and low flows (31 m 3 /s) in Figure  7a. During the summer, the reduced difference between Q VB and Q LIMONS indicated that the Allier River flow mainly originated from the upstream part of the study area. During the winter, it was the opposite, with a higher contribution of the downstream part to Q LIMONS. Thus, looking at the subannual scale, the hydrology of the Allier Watershed was characterized through a nivo-pluvial regime, with high flows in the autumn and spring due to abundant rainfall which were accentuated in the spring by the snowmelt  [67, 68].
At the subannual scale, the impact of the Naussac Dam was highlighted because its releases covered almost 100% of the river flow in Vieille-Brioude (the outflow of the upstream basin) and 50% of Q LIMONS (the outlet of the whole study area) during summer droughts. Gardenia showed the participation of each component in the water budget. Actual evapotranspiration, effective precipitation, and groundwater recharge accounted, respectively, for 68%, 32%, and 15% of the total precipitation in the downstream part of the study area. The change in water storage ∆S was tightly linked to the interannual variations of effective precipitation and was strongly affected by periods of drought (especially in 2011-2015, where ∆S was negative). In order to quantitatively forecast the evolution of both the surface and groundwater, two sets of climate projections from the DRIAS-2020 database (Aladin63/RCP2.
Acknowledgments: The authors gratefully acknowledge the support for this research that was funded by  I-SITE CAP20-25 ,  Clermont Auvergne Metropole  and by  L'Agence de l'Eau Loire-Bretagne ,  OPGC ,  SNO H+  and we would like to express our sincere acknowledgment to  Dominique Thiéry  for his technical support with  Gardenia.
Data Availability Statement: The data used in this article do not belong to us, most of the data are available on public websites and were downloaded on 1 November 2022. The river flow data are available at https://www. hydro. eaufrance. fr, the piezometric levels are available at https:// ades. eaufrance. fr, the water abstraction volumes are available at https://bnpe. eaufrance. fr and at www. eau-loire-bretagne. fr, the meteorological data were issued from Météo-France (https:// publitheque. meteo. fr) but require a license, and the Naussac Dam release data are property of Etablissement Public Loire (EPL) and are not publicly available. Regional climate projections were downloaded from the DRIAS platform at http://www. drias-climat. fr/. The hydrological model used in this article, Gardenia, can be openly accessed at https://www.
Data Availability Statement: The data used in this article do not belong to us, most of the data are available on public websites and were downloaded on 1 November 2022. The river flow data are available at https://www. hydro. eaufrance. fr, the piezometric levels are available at https://ades. eaufrance. fr, the water abstraction volumes are available at https://bnpe. eaufrance. fr and at www. eauloire-bretagne. fr, the meteorological data were issued from Météo-France (https://publitheque. meteo. fr) but require a license, and the Naussac Dam release data are property of Etablissement Public Loire (EPL) and are not publicly available. Regional climate projections were downloaded from the DRIAS platform at http://www. drias-climat. fr/. The hydrological model used in this article, Gardenia, can be openly accessed at https://www.
Participants Data from the Natural History Project of the Dystonia Coalition enrolled across 36 clinical sites (USA, Canada, Australia, Germany, France, Italy) were analyzed. Additional Dystonia Coalition investigators that contributed subjects to the study are listed in Online Resource 1. The multicenter Dystonia Coalition study includes dystonia patients aged 18 years and older, and the Natural History Project focuses on patients with dystonia onset no more than five years before study enrollment (https://www. rarediseasesnetwork. org/cms/dystonia). Participants included in this study were examined three times within two years (baseline, oneyear follow-up, two-year follow-up). At each study visit, subjects answered standardized questionnaires and were clinically examined using a standardized protocol.
Statistical analysis Predictors of two-year HR-QoL subdomains were obtained from eight cross-lagged path models (CLPM, one model per domain; Bonferroni corrected alpha ≤ 0. 006). Further, an independent analysis was done to categorize patients into clusters of high and low total HR-QoL within the two years period using a latent class growth analysis (LCGA). Data at baseline included 603 patients. Only patients with complete data sets at time point three (two-year follow-up) were included in the CLPM (n=155) and only patients with complete data sets across the three time points (baseline, one-year follow-up, two-year follow-up) were included in the LCGA (n=145). Both the CLPM and the LCGA were carried out in MPlus version 7. 4  [26]. Within the CLPM, the statistical effects of baseline dystonia severity, tremor (yes/no), age, sex, and symptom severity of depression (HADS-D), GAD (HADS-A) and SAD (LSAS), as well as the eight subscales of the RAND 36-Item Health Survey, were assessed as direct paths from baseline on the same variables at one-year follow-up and as indirect paths via (intermediate) direct effects on the same variables at two-year follow up (figure  1  and Online Resource 2_Fig.
92 compared to 0. 87). Annual changes of HR-QoL in dystonia were evaluated using a non-parametric ANalysis Of VAriance (ANOVA). Only patients with complete longitudinal data sets across the two time points were included in the ANOVA (n=72). Data from two subgroups of patients were analyzed: patients not treated with BoNT (neither at baseline nor at follow-up, n=37) and patients who received BoNT within one-year of follow-up but not before baseline (n=35). The analysis was corrected for oral medication intake such that only patients with unchanged or no oral medication at both time points were considered. Categorization of dystonia medication was performed according to Richardson et al. [29]. For this ANOVA, patients with local surgical treatment for dystonia (myectomy, denervation) were excluded (n=3).
The models were adjusted by age and gender. Secondary end points for this trial were global quality of life and quality of life or symptom burden for patients with HNC as well as patient satisfaction at the completion of radiotherapy. Secondary end points were compared between both trial groups; for statistical analysis, sum scores for each functional and symptom scale were calculated from the questionnaires, as defined by the EORTC scoring manual and compared using Wilcoxon rank sum tests with continuity correction. P values <. 05 determined statistically significant differences between groups. The statistical analysis was conducted using R (version 4. 2. 2; R Core Team). For the effect size calculation, the R package effectsize (version 0. 8. 3) was used.
On the other hand, lower proficiency in using mobile devices use did not correlate with item completion rates in a previous trial, suggesting that mobile treatment surveillance may also be feasible for patients with less experience with devices and older age  [23, 26]. Since patient enrollment in our trial was calculated based on the primary feasibility end point, we cannot dismiss the possibility that, in our data set, the cohort size in the experimental arm was too small to allow the detection of smaller effects regarding patient-related factors that may influence feasibility. Nevertheless, considering the high completion rates, our data suggest that app-based monitoring may provide a feasible approach for monitoring treatment-related symptoms of patients with HNC undergoing radiotherapy.
3. 2  (58). IL-33 1 nuclei and FoxP3 1 cells were counted using an automated cell detection tool, and the proportion of CD31 1 area was quantified using a pixel classifier. Statistical analyses. Data were analyzed with the GraphPad Prism v8. 0. 2 software (GraphPad, La Jolla, CA). All tests were two-tailed and a 5% a risk was applied. Differences between experimental groups were assessed using the Mann-Whitney U test (comparison between 2 groups) or the Kruskal-Wallis test with Dunn's multiple-comparison post hoc analysis (comparison between more than 2 groups). Evolution of mouse weight over time was assessed using a two-way analysis of variance (ANOVA) for repeated measures with Bonferroni's post-tests. Concentrations of cytokines in mouse sera were compared (i) within groups, to the baseline start (day 0), using the Kruskal-Wallis test with Dunn's multiple-comparison post hoc analysis and (ii) between groups using the Mann-Whitney U test.
ACKNOWLEDGMENTS We thank the whole IRSET Team 2, especially  Jacques Le Seyec  and  Claire Piquet-Pellorce , for their contributions. We thank the  SFR Biosit, UMS CNRS ,  3480-INSERM 018 , Rennes (https://biosit. univ-rennes1. fr/)H2P2 platform, especially  Pascale Bellaud , for their technical assistance with immunofluorescence stainings; the CytomeTRI platform; and the Arche animal house platform. B. L. -S. was supported by the  SNSF  Project  310030_192072  fund. B. A. : methodology, resources, investigation, data curation, formal analysis, validation, writingoriginal draft, visualization. C. M. : investigation, data curation, formal analysis. B. L. -S. : conceptualization, resources, writingreview & editing. J. -P. Gi. : resources, writing review & editing. B. G. : conceptualization, writingreview & editing.