dataesr/scientific-paragraphs-categorization huggingface dataset:
2 ). The figure for a given day and hour is therefore the average of the temperatures measured each of these years at the same day and hour. With effects limited to the first days of January, we have assumed that at the beginning of the simulation (1 January at 00:00 UT) the temperature along the entire stretch is that obtained at Bugey at that moment. We have used the 1D model called COURNAT  (Gosse et al. , 2008)  which combines a proven 0D code CALNAT (renamed THER-MIC in (Open TELEMAC-MASCARET, 2017)) for air-water thermal exchange with a 1D transport-diffusion model  (Jacquet, 1983 ) to solve Eq. (  6 ) which simulates the three-hourly change in temperature T along a river stretch based on the hypothesis of thermal homogeneity for the cross section: DT Dt ¼ 1 q w :C p :H SR þ AR À WR À C v À E ð Þ ð 6Þ The five surface power terms of the right-hand expression of Eq.
, 2008). The stretch has four NPP on its banks, two with an OTC system (St Alban with two 1. 3 GW units and Tricastin with four 0. 9 GW units), one with a wet RC system (Cruas with four 0. 9 GW units) and one (Bugey) with two 0. 9 GW units (n°2 and 3) with an OTC system and two 0. 9 GW units (n°4 and 5) with a wet RC system. Fig. 3  shows electric power levels involved for the study period of 1994-2015. The production of other power plants installed along the river stretch (Bugey 1, Loire-sur-Rhone, Phénix et Aramon closed in  1994, 2004, 2010 and 2016 respectively) , which represents in total 0,6% of the production of the four NPP considered over the period studied is not taken into account. The open-source MASCARET-TRACER -THERMIC 1D code  (Goutal et al. , 2012)  available for download  (Open TELEMAC-MASCARET, 2017)  was applied to produce the hydraulic, hydrological and thermal model for the Rhône between Bugey and Arles, in order to assess at three hourly intervals, the over-evaporation from the river attributable to these four power plants.
The THERMIC sub-program, also found in the WAQTEL module of the TELEMAC 2D code  (Open TELEMAC-MASCARET, 2017) , formalises the heat exchanges between the body of water and the atmosphere identically to the CALNAT code. The Rhône model input data are as follows: Rhône and Saone daily temperatures  (average 1990 -2015)  Fig. 2. Daily measured temperatures of the Rhône at Bugey (12. 5 °C on average) and its tributary the Saone (13. 6 °C on average) compared to the monthly equilibrium temperatures calculated with CALNAT under the meteorological conditions of Lyon-Bron (average 1990 to 2015) with the two formulations f1 and f2 of the wind function f. The equilibrium temperature which corresponds to an equilibrated heat balance (SR + AR = WR + C V + E) is a theoretical temperature towards which every water body tends under the same given local meteorological conditions: it does not depend on flow depth at a monthly scale for the domain of variation considered inr the theoretical study (2 H 7 m).
The latter is the difference between the reactor energy and the gross electricity produced which are saved in EDF's OSGE/MIREOR database. The river also receives heat inputs from the auxiliary circuits taken at respectively 0. 06 GW and 0. 07 GW per pair of units at Bugey and Cruas. For the power plants with a OTC system, the daily heating power evacuated by a unit into the river (through the condenser and auxiliary circuits) is schematically given in the form of the product of a constant k, equal respectively to 2. 01 in Bugey 2&3, 1. 82 in St Alban, and 2. 00 in Tricastin by the average daily gross electric Fig. 3. Sector of the Rhône studied downstream from Lake Geneva.
The EBSD analyzes were carried out using an EDAX PEGASUS EDS/EBSD system and the OIM DC 6. 4 software (manufacturer EDAX; Mahwah, USA) at ISTO-BRGM (Orléans, France). The operating conditions involved an accelerating voltage of 20-25 kV and a working distance of 15-18 mm. Post-acquisition treatments, which include plotting equalarea lower-hemisphere pole figures of amphibole lattice preferred orientation (LPO), were performed using the opensource MTEX toolbox for MATLAB. For the definition of a grain, five adjacent indexed points were required. Texture strength is expressed through the J index and M index  (Bunge, 1982; Skemer et al. , 2005).
Microstructural analysis SEM backscattered electron (BSE) images were used to produce manually digitized grain maps. Grain sizes were measured from these grain maps by using the public domain software ImageJ (http://rsb. info. nih. gov/ij/, last access: 11 March 2020). By extracting the area-equivalent diameter from these maps, the grain size is defined as the diameter of equivalent circular diameter d equ = 2 × area π. From these grain maps, the grain shape preferred orientation (SPO) and the modal proportion of phases can be analyzed. In the case in which grain boundaries are indistinguishable within am-phibole aggregates, EBSD maps have been processed with MTEX to determine the grain size. In order to estimate the proportion of phases we used SEM BSE images to produce manually digitized grain maps with the illustrator software when it was possible to distinguish the grain boundaries (e.
g. , Fe-rich opx + plag assemblages). From these phase maps we could separate the phases with the ImageJ software and estimate their proportion. This is how the amount of amphibole in the Fe-rich opx + plag assemblages is estimated. When it was impossible to distinguish the grains individually, we drew areas that corresponded either to a set of grains of the same phase or to several phases that could not be separated. Some grains are too small to be separated with enough confidence from other grains. For this reason, we have included all reaction products together and have not separated the amphibole from the plagioclase2 and the pyroxene2 in the Mg-rich opx + plag assemblages.
OH content and thermodynamic modeling Fe-rich opx and plag grains from Hidaka granulite show a broad and asymmetric infrared (IR) water absorption, with a maximum amplitude at ∼ 3580-3590 cm -1 (Fig. 9 ). The average H 2 O contents (ppm H 2 O by weight), calculated between 3750 and 3000 cm -1 , are 451 ± 35 ppm for Fe-rich opx and 226 ± 24 ppm for plag. In contrast, there are no H 2 O IR absorption bands for Mg-rich opx grains from Damaping peridotite and plag grains from Sonora (Fig. 9 ). Pseudo-sections for the Mg-rich opx + plag and Fe-rich opx + plag assemblages were calculated for different H 2 O contents in the simplified but representative system of our experiments (Na 2 O-CaO-FeO-MgO-Al 2 O3-SiO 2 -H 2 O) using the Perple_X 6. 6. 8 package  (Connolly, 2009)  combined with the updated database of  Holland and Powell (1998)  and the following solution model: amphibole  (Dale et al.
Acknowledgements. This work has received funding from the  European Research Council (ERC)  under the  Seventh Framework Programme  of the  European Union  ( ERC Advanced Grant , grant agreement no. 290864 , RHEOLITH),  Labex VOLTAIRE  ( ANR-10-LABX-100-01 ), and the project  FluPrism (CNRS INSU , funding program SYSTER). We gratefully acknowledge the help provided by  Patricia Benoist  and  Ida Di Carlo  for analytical support,  Sylvain Janiec  for the preparation of thin sections, and  Kai Neufeld  for EBSD data acquisition. The authors wish to thank  Mark Zimmerman  for generously providing the Damaping peridotite and  Laurent Arbaret  for the Massif Central amphibolite material. We also appreciate thorough and constructive reviews by  Luca Menegon  and  Jolien Linckens. We would like to thank  Florian Fusseis  for editorial handling.
Financial support. This research has been supported by the  European Research Council (ERC)  under the  Seventh Framework Programme  of the  European Union  ( ERC Advanced Grant , grant agreement no. 290864 , RHEOLITH),  Labex VOLTAIRE  (grant no. ANR-10-LABX-100-01 ),  EquipEx PLANEX  (grant no. ANR-11-EQPX-0036 ), and the project  FluPrism (CNRS INSU , funding program SYSTER). Review statement. This paper was edited by  Florian Fusseis  and reviewed by  Luca Menegon  and  Jolien Linckens.
To address this issue, the International Weight Management in Pregnancy Collaborative Group (iWIP) collected data from all available trials and used meta-analysis to explore intervention effects on a range of prioritised maternal and infant outcomes  [14, 15]. The analyses now provide level 1 evidence of the significant benefits of lifestyle intervention on specific pregnancy outcomes, including gestational diabetes, pre-eclampsia and hypertensive disorders of pregnancy, and caesarean section rates. (Note that in the iWIP meta-analysis, papers classified as 'physical activity' were broadly defined, and may have included specific physical activity classes or a behavioural intervention to increase movement. ) While understanding of the effectiveness of lifestyle interventions in pregnancy has progressed, the cost effectiveness of lifestyle interventions is still unclear.
Overview of Approach and Model We conducted a model-based cost-effectiveness analysis comparing diet and physical activity interventions (aiming to limit gestational weight gain and/or reduce cases of gestational diabetes) with usual care. The incremental cost-effectiveness ratio (ICER) was expressed as cost per case prevented. Cases included gestational diabetes and/or hypertensive disease in pregnancy (including pre-eclampsia and pregnancy-induced hypertension). A decision tree model (see Fig. 1 ) was developed for four health states: (1) develop gestational diabetes; (2) develop hypertensive disease in pregnancy; (3) develop gestational diabetes and hypertensive disease in pregnancy; or (4) do not develop gestational diabetes or hypertensive disease in pregnancy. In each health state, data were modelled for aspect of delivery: (1) caesarean section; (2) induction of labour with vaginal birth; (3) induction of labour with caesarean section; or (4) neither induction nor caesarean section.
The time horizon was between early pregnancy and the discharge of mother and infant from hospital after birth; hence, it was not necessary to use discounting. Data were analysed from the perspective of the public healthcare system in Australia as any savings from this intervention would have highest benefit for the healthcare funder. We only included health states where there were data from the Monash Health dataset and iWIP meta-analysis comparing intervention and control groups, which then enabled us to model cost effectiveness. Thus, health states such as miscarriage and maternal death were not included in the model.
Population Inputs for the usual care group in the model were derived from a de-identified Monash Health routine maternity care dataset capturing gestational diabetes and/or hypertensive disease in pregnancy and mode of delivery  [17]. Monash Health is one of Australia's largest public health services, providing universal free and accessible care for over 10,000 births per year across multiple hospital sites for a multicultural population, covering urban and regional areas  [18]. Antenatal care at Monash Health was largely provided in secondary and tertiary care hospitals. Data were collected between 2009 and 2013 for all singleton pregnancies resulting in births of > 20 weeks' gestation, and can be considered to be a representative sample of the general population for which the intervention modelled in this paper can apply.
The demographics of the region are representative of much of the Australian population, with significant ethnic diversity and variation in socioeconomic status. Healthcare is provided in a universally accessible public health system and the population is generally representative of the vast majority of women who attend public health services. Australia has a multi-payer health system and some women use private insurance for obstetric care. Those women accessing private obstetric care are generally more educated and affluent and more likely to have been Australian born. They are not captured in this dataset. In the Monash Health dataset, body mass index (BMI) data were categorised as per World Health Organization recommendations  [19]. The dataset contained 38,052 cases, of which 20,341 women (53.
5%) had a BMI < 25 kg/m 2 , 10,112 women (26. 6%) had a BMI over 25 to up to 30 kg/m 2 , and 7599 women (20. 0%) had a BMI of 30 kg/m 2 plus. The mean age of health service users was 29. 7 years (standard deviation 5. 43), and users were mainly English speaking (n = 25,948; 70. 1%), multiparous (n = 21,764; 55. 7%) and non-smoking (n = 31,818; 82. 9%). In the dataset, 7. 5% of health service users were diagnosed with gestational diabetes, 3. 8% with hypertensive disease in pregnancy, and 0. 5% with both, while 27. 8% of all health service users were delivered via caesarean section and 21. 9% were induced. Monash Health applied national guidelines for the diagnosis of gestational diabetes based on large interventional trials  [20, 21]. For mode of delivery, percentages of cases with caesarean sections, induction with vaginal birth, induction with caesarean section, or vaginal birth only were extracted for each health state (gestational diabetes, hypertensive disease in pregnancy, both, none of the above) and are presented in Table  1.
RR (risk ratio) is used here as it the ratio of two probabilities, rather than the OR, which, as implied by its name, is the ratio of two odds. We used the RR because the OR may exaggerate the risk when an outcome is common,  [23]. To derive the intervention effect, we multiplied the RRs from the iWIP meta-analysis by the proportion of cases in each health state, as derived from the Monash Health dataset (usual care). Risk ratios with 95% CIs are presented in Table  2. Risk ratios for induction were set to 1 as there was no information in the iWIP metaanalysis to attach to this variable.
The intervention was estimated to cost Australian dollars (AUD) 228 per person, at 2019 prices. As outlined above, the trial was powered to detect differences between groups in gestational weight gain, but was not powered to detect differences between groups for health states including gestational diabetes, hypertensive disease in pregnancy or birth/delivery mode. Hence, we modelled the intervention effect for gestational diabetes and hypertensive disease in pregnancy from the iWIP meta-analysis  [14]. Data from this study were included in the iWIP dataset.
Delivery and Related Costs Diagnosis-related group (DRG) data from 2015/2016 were used for delivery costs  [34]  as the DRG classifies patient data from inpatient stay from admission to discharge, by diagnosis, in order to encourage containment of hospitalrelated costs. Data were adjusted to 2019 prices using the health price index  [35]. Five vaginal birth outcomes (DRG codes: O02A, O02B, O60A, O60B, O60C) and three caesarean section birthing outcomes (DRG codes: O01A, O01B, O01C) were weighted by the number of separations per code, to derive average costs for vaginal and caesarean section deliveries. Vaginal deliveries were estimated to be AUD6015, and caesarean section deliveries were estimated to be AUD11,816. Induction costs were estimated to be a proportional increase from the cost of an uncomplicated vaginal birth, and half of induced births were estimated to also have an epidural  [36].
Subgroup Analyses Subgroup analysis was conducted by maternal BMI. BMI is derived by dividing weight in kilograms (kg) by height in metres squared. Height and weight were recorded at pregnancy booking, and BMI was calculated. Booking weight is regularly used as a proxy for pre-pregnancy weight in the absence of reliable pre-pregnancy measures. Subgroup analyses were conducted using the following BMI classifications: up to 25 kg/m 2 , 25 to up to 30 kg/m 2 , and 30 kg/m 2 plus, with a further analysis for BMI of 25 kg/m 2 or over. Percentages of gestational diabetes, hypertensive disease in pregnancy, and both gestational diabetes and hypertensive disease cases in pregnancy by BMI category from the Monash Health dataset are presented in electronic supplementary Table  S6. Higher BMI groups had higher rates of gestational diabetes and hypertensive disease in pregnancy.
Statistical Analysis Analysis of Monash Health data was conducted using STATA version 15. 0 (StataCorp LLC, College Station, TX, USA) to determine proportions of each health state (gestational diabetes, hypertensive disease in pregnancy, both, none of the above), and the proportions of delivery type for each health state. Decision tree models were developed in Microsoft Excel (Microsoft Corporation, Redmond, WA, USA). Data were entered into the models and the ICER for the base-case (full sample, all interventions) was calculated, representing expenditure per case (gestational diabetes, hypertensive disease in pregnancy, or both) prevented. ICERs were subsequently calculated for all subgroup and scenario analyses.
Study Design In France, the prehospital emergency medical system is a two-tiered system with a fire department ambulance available for providing a first response and basic life support (BLS) and a mobile emergency and resuscitation service (MERS) including a mobile medical team (MMT) for advanced cardiac life support (ACLS)  (18). Medical dispatch centers are responsible for outof-hospital emergency coordination. All MERSs use a specific ReAC recording form during OHCA interventions to enter patient data, times, care, and immediate survival status. The ReAC form meets the requirements of the French Emergency Medical System organizations and is structured according to the universal Utstein style  (3, 19). Data are reported in the ReAC secure database (www. registreac. org). A 30-day follow-up data collection after the onset of the OHCA or at the time of hospital discharge is performed and entered into the database.
Conditional Weighted Logistic Regression We performed a logistic regression for nonadjusted odds ratios (ORs) and a conditional weighted logistic regression based on matching pairs for adjusted OR  (26). We supplied 95% confidence intervals (CIs) in both cases. Statistical analysis were performed using R 3. 5. 1 software, with the ''survival'' package for developing timedependent propensity score algorithms and fitting a conditional logistic regression  (27). The ''MatchIt'' package was used for sequential matching  (28). The ''tableone'' package was used to compute the standardized differences before and after matching  (29).
Acknowledgments-The  French Society of Emergency Medicine (SFMU) ; the '' Federation Francaise de Cardiologie ''; the  ''Mutuelle de l'E ´ducation Nationale (MGEN) ;  Lille University ; and the northern region and the  European community  via  FEDER  founds supported the  French National Cardiac Arrest Registry (ReAC). The authors ackowledge all emergency medical teams who contributed to the ReAC database construction.
Multivariate, univariate and pathway enrichment analyses were conducted to identify significantly altered metabolites and pathways. Rigorous metabolite identification was carried out using authentic standards, the Mgryph-specific metabolite database and MS/MS mzCloud database. PCA and OPLS-DA showed clear separation and clustering of sample groups with cross-validation values of R 2 X ¼ 0. 76, R 2 Y ¼ 0. 99 and Q 2 ¼ 0. 98 in OPLS-DA. As a result, 50 metabolites linked to 45 metabolic pathways were found to be significantly altered in the tested conditions, including: glycine, serine and threonine; butanoate; alanine, aspartate and glutamate metabolism; aminoacyl-tRNA biosynthesis and; pyruvate and citric acid cycle (TCA) metabolisms. Our findings demonstrate the potential of LC-MS to characterise key metabolites in Mgryph and will contribute to further understanding the metabolic mechanisms that affect Mgryph growth and magnetosome formation.
For MS parameters, spray voltage was 4. 5 kV (ESI+) and 3. 5 (ESIÀ), and capillary voltage was 20 V (ESI+) and À15 V (ESIÀ). The sheath, auxiliary and sweep gas ow rates were 40, 5 and 1 (arbitrary unit), respectively, for both modes. Capillary and heater temperatures were maintained at 275 and 150 C, respectively. Data were acquired for the LC-MS proling with a resolution of 70 000 from m/z 70-1050. Top 5 ddMS/MS was performed on the QC sample (n ¼ 3) at a resolution of 17 500 and a stepped normalised collision energy (NEC) of 20, 30 and 40. The spent medium samples of the magnetosome, nonmagnetosome producing cells and blank media (FSM, control) were randomised and analysed in a single LC-MS analytical run with the mixtures of authentic standards and reagent blanks. Pooled QC (n ¼ 6) was injected at the beginning of the analysis to condition the column and aer every 3-5 samples (n ¼ 6) to check the stability, robustness, repeatability and performance of the analytical system.
Data analysis and metabolite identication The acquired raw LC-MS datasets of the samples in the study were pre-processed for untargeted metabolomics, univariate analysis and metabolite identication with Compound Discoverer 3. 1 SP1 soware (Thermo Fisher Scientic, Hemel Hempstead, UK). Principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were performed using Simca P+13 (Umetrics, Umeå, Sweden). The imported datasets were mean-centred and Pareto scaled. Crossvalidation using the leave-one-out method was used to evaluate the robustness of the generated PCA and OPLS-DA by monitoring the tness of model (R 2 X) for PCA, (R 2 Y) for OPLS-DA and predictive ability (Q 2 ) values. 24 Metabolite identication was performed using Compound Discoverer 3.
1 SP1 soware by matching accurate masses of the detected peaks with metabolites in Mgryph MSR-1 (MGRY1430440) BioCyc database, the retention times (RT) of the authentic standards (within 0. 25 min shi) and mzCloud fragmentation database. Briey, retention time range used was 3. 2-23. 5 min, mass range was m/z 70-1050, mass tolerance for peak picking and metabolite identication was # 5 ppm for precursor and fragment ions, minimum peak intensity was 1. 0 Â 10 6 , intensity tolerance was at 30%, area max was greater or equal to 3. 0 Â 10 6 , maximum retention time shi was 0. 25 min and activation energy tolerance was 5. The condence in metabolite identication was assigned as level 1-4 based on the recommendation by the Chemical Analysis Working Group, Metabolomics Standards Initiative (MSI).
Performance of LC-MS for untargeted metabolomics The performance of the LC-MS-based metabolite proling of the culture medium samples of magnetosome and nonmagnetosome producing cells, and FSM (control medium sample) was assessed using univariate and multivariate analysis of all the detected peaks in the samples. The LC-MS datasets generated 2556 peaks in which the variability based on the use of relative standard deviations (RSDs) across the average peak areas was less than 30% for 93. 3% of all peaks present in the QC. The variability in the QC was lower than the recommended threshold of RSD < 30% for at least 70% of all peaks for metabolomics analysis. 31 PCA was also used to assess the analytical performance of the method. 32, 33 The pooled QCs were closely clustered towards the centre of the PCA scores plot (Fig.
Data analysis The mass ion pairs (RT, m/z) from the dataset generated in this study were exported with their normalised abundances for multivariate analysis in which PCA and OPLS-DA were used for modelling the differences and similarities between samples. PCA was used to give an unbiased overview of any possible trends and clusters within the sample datasets. PCA-class analysis was rst performed to evaluate the similarity of samples within each class. Tight clusters with no signicant differences were observed within each class, indicated by the poor PCA-class Q 2 values (data not shown). This result demonstrates that the experimental setup, including the growth of microorganism, sample preparation and the analytical run, were carefully carried out, evidencing no outliers and adequate similarities within each class in the analysis.