dataesr/scientific-paragraphs-categorization huggingface dataset:
8 kg N-NH 4 + ha -1 with 65%, 25%, and 10% in the 0-60, 06-90, and > 90 cm soil layers. Initial soil water of the entire soil profile was set at 75% of total plant available water. All simulations were executed with unlimited N supply and with total N fertilizer application rates ranging from 0 to 450 kg N ha -1 in steps of 50 kg N ha -1 , split into 0 to 4 applications depending on the total rate (Supplementary Table  S7 ). For the irrigated sites, the modelers were asked to simulate with automatic irrigation or by applying sufficient irrigation to avoid any drought stress. No irrigation was applied in the rainfed sites. The main soil characteristics used for simulations are given in Supplementary Figures  S4-5. Daily weather data for each of the 34 global locations for the baseline period (1980-2010) were obtained from the AgMERRA climate dataset 53 , which combines observations, data assimilation models, and satellite data products to provide daily maximum and minimum temperature, total solar radiation, total precipitation, wind speed, vapor pressure, dew point temperature, and relative humidity corresponding to the maximum temperature time of day for each location.
These data correspond to an atmospheric carbon dioxide concentration ([CO 2 ]) of 360 ppm for the middle 1995 year. Wheat production for the 34 global locations was also simulated under a climate change scenario corresponding to a moderate (RCP4. 5, using 499 ppm [CO 2 ] in 2055) and relatively high (RCP8. 5, using 571 ppm [CO 2 ] in 2055) emissions for the middle of the 21 st century (2040-2069). Projections were taken from five global climate models (GCMs; HadGEM2-ES, MIROC5, MPI-ESM-MR, GFDL-CM3, and GISS-E2-R) drawn from the Fifth Coupled Model Intercomparison Project (CMIP5), with historical AgMERRA conditions modified to reflect projected changes in mean temperatures and precipitation along with shifts in the standard deviation of daily temperatures and the number of rainy days 17. These scenarios were created using the Enhanced Delta Method 54 , and GCMs were selected to include models with relatively large and relatively small global sensitivity to the greenhouse gases that drive climate changes to account for the uncertainty of the fifth coupled model intercomparison project (CMIP5) GCMs ensemble 55.
0 emissions scenario now commonly used as a high emissions pathway  60. Although it should be noted that the RCP2. 6 and RCP8. 5 scenarios are not dramatically different until after 2050 56. The simulated grain protein concentration was calculated from the simulated percentage of grain N by multiplying by a conversion factor of 5. 62 61. All yield data were adjusted to 0. 13 g water g -1 of moisture content. All other variables were expressed on a dry mass basis. Simulated NUE was calculated as simulated grain yield divided by the simulated amount of N available to the crop during the growing season, which was calculated as the sum of initial mineral soil N, applied N fertilizer, and simulated total soil mineralization. Simulated grain yield and N fertilizer application rate at each location were fitted with a spline function using the smooth.
Historical national grain yield and N fertilizer use by wheat National wheat production and area for the period 2016 to 2019 were obtained from the Food and Agriculture Organization (FAO) public database (https://www. fao. org/faostat). National and global grain yields were calculated by dividing national and global production by national and global wheat area, respectively. N fertilizer used by wheat in each country of the 34 global locations was obtained from reports  38, [64] [65] [66] [67]. Data were available for one to three years depending on the country, as they showed no significant difference between years when several years were available, they were averaged (Supplementary Table  S8 ). For consistency, global N fertilizer use by wheat was calculated as the sum of the values for the countries of the 34 global locations weighted by the fraction of the global wheat area each country represents.
Acknowledgements This study was a part of the  Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase  4 and was supported by the  French National Research Institute for Agriculture, Food and Environment (INRAE) , the  International Maize and Wheat Improvement Center (CIMMYT)  and the  International Wheat Yield Partnership (IWYP , grant  IWYP115 ). Experiments carried out in Valdivia (Chile) were funded by  CIMMYT (Mexico)  and the  Chilean Technical and Scientific Research Council (CONICYT)  through  FONDECYT  (grant  1141048 ). The experimental work conducted at Valdivia by  Dr. Jaime Herrera  ( UACh ) is appreciated. S. D. and P. M. acknowledge support from the metaprogram Agriculture and forestry in the face of climate change: adaptation and mitigation (CLIMAE) of INRAE.
Data availability The measured and simulated crop data and model inputs for the New Zealand and South America field experiment data are available at https://doi. org/10. 7910/DVN/XA4VA2 and https://doi. org/10. 7910/DVN/VKWKUP respectively. The simulation protocols, model inputs, and simulations results for the 34 global at locations are available at Martre, Pierre, 2023, "Replication Data for: The nitrogen price of improved wheat yield under climate change", https://doi. org/10. 7910/DVN/6KBBI3.
Field experiments for model testing We evaluated the performance of the 12 wheat crop models in simulating the measured crop growth, yield components, and final grain yield for three independent global datasets from New Zealand (two locations), South America (three locations) and France (one location) to determine if the MME could simulate very high yields in environments with contrasted temperature and solar radiation patterns. In all experiments, the locally adapted elite cultivars were grown with ample N supply and agronomic practices to reach potential yield for the local soil and weather conditions. At all locations, irrigation was applied as needed to ensure that crop growth and final grain yield was not limited by water deficit. All other crop protection factors including weed, disease and pest control, potassium, phosphate, and sulfur fertilizers, were applied at levels to prevent any limitation on yield.
Each experiment consisted of three to four replicated plots per treatments or cultivar. At all sites, daily weather data were provided from a weather station located < 2 km from the experimental field. For the South America experiments, the rainfall, wind speed, and relative humidity data were obtained from the NASA POWER database (https://power. larc. nasa. gov/). A brief description of the three datasets is provided below and in Supplementary Table  S4. The ability of the MME to predict crop growth, yield components, and final grain yield measured associated with the high-yield traits identified in Bacanora x Weebil population and HiBAP panel (see below) was evaluated for the South America dataset. All yield data were adjusted to 0. 13 g water g -1 of moisture content. All other variables were expressed on a dry mass basis.
New Zealand experiments -This dataset has been described in  Dueri et al. 44. The local elite winter wheat cultivar Wakanui was grown for six consecutive years in farmer's fields, first at Leeston and then at Wakanui, in the Canterbury plan of the South Island of New Zealand. The Leeston farm established the Guinness World Record for the highest wheat grain yield in 2017 and 2020. In two years, crops were sown on two different dates, the locally recommended date and one month earlier, with the aim to increase biomass production during the autumn-winter period. The 12 crop models were calibrated with detailed crop data measured during the 2014-2015 growing season, including a combination of four sowing dates and four sowing densities, for a total of 16 different treatments. Two of the sowing dates and three of the sowing rate treatments were used only for model calibration.
Acknowledgements We thank the editor  Dr. Fangli Qiao  along with three anonymous reviewers for their comments. This study is a contribution to the 'Assessing the  Role of forced and internal Variability for the Ocean and climate Response in a changing climate' (ARVOR)  project supported by the  French 'Les Enveloppes Fluides et l'Environnement ' (LEFE) program. This work was supported via  National Science Foundation (NSF)  grants  OCE-1829856 ,  OCE-2023585 ,  OCE-2123632  and the  French 'Make   Our Planet Great Again' (MOPGA) program  managed by the  Agence Nationale de la Recherche  under the  Programme d'Investissement d'Avenir , reference  ANR-18-MPGA-0002. The latter two grants served as the primary support for  Uchida  and partially for  Jamet. Balwada  acknowledges support from the  NSF  grant  OCE-1756882.
be found at github. com/bderembl/msom. It was developed as a module of Basilisk (available at basilisk. fr). High-performance computing resources on Cheyenne (doi:10. 5065/D6RX99HX) used for running the NA ensembles were provided by NCAR's Computational and Information Systems Laboratory, sponsored by NSF, under the university large allocation UFSU0011. Uchida personally thanks the Oceanography with an Alpine Flair workshop held in Grenoble 2022, which has led to fruitful exchanges. The model diagnostics were executed on the Florida State University and Université Grenoble Alpes clusters.
1 mM acetate at 40 or 100 mM aMG). Here again, the experimental profiles are qualitatively similar to the simulated profiles, albeit with quantitative differences. These results demonstrate that the acetate concentration threshold at which the acetate flux reverses depends on the glycolytic flux. It therefore follows that when the glycolytic flux is limited, acetate is utilized as at low (0 mM) and high (30 mM) acetate concentrations. These data were used to quantify the total carbon uptake flux as a function of the aMG concentration (H). Each data point represents an independent biological replicate, and lines represent the best polynomial fits. I, J Total carbon uptake (I) and growth rate (J) as a function of the glucose uptake flux, at low (0 mM) and high (30 mM) acetate concentrations.
In fact, expression of most of these genes (47/65) was slightly higher in the presence of 50 mM acetate and 100 mM aMG, whereas at maximal glycolytic flux it was lower at these acetate concentrations for a similar proportion (46/65). When the glycolytic flux was inhibited, ptsG expression, which controls glucose uptake  (Rohwer et al, 2000) , was 70% higher at 10 and 50 mM acetate. The expression of most glycolytic genes was 15-110% higher at 50 mM acetate, while the expression of TCA cycle genes remained stable. Regarding acetate metabolism, the expression of pta and ackA was slightly higher at 10 and 50 mM acetate, while alternative acetate utilization via Acs was inhibited by acetate (5-fold reduction in expression at 100 mM). Finally, expression of genes encoding ribosomal proteins (rpl, rpm, and rps operons) was increased by 57% on average at 50 mM acetate and 48% on average at 100 mM acetate (Dataset EV1).
Transcriptomics experiments Cells were grown in M9 medium with 15 mM glucose, 100 mM aMG, and 0, 10, 50, or 100 mM sodium acetate. Sample preparations and transcriptomics analyses were carried out as described previously  (Millard et al, 2021). Three independent replicates were analyzed for each condition. The transcriptomics data can be downloaded from the ArrayExpress database (www. ebi. ac. uk/ arrayexpress) under accession number E-MTAB-11717. Transcriptomics data in the absence of glycolytic inhibition (ArrayExpress database accession number E-MTAB-9086;  Millard et al, 2021)  were measured under the exact same conditions but without aMG.
Kinetic modeling We used a kinetic model of E. coli  (Millard et al, 2021)  available from the Biomodels database (https://www. ebi. ac. uk/biomodels; Chelliah et al, 2015)  under identifier MODEL2005050001. Simulations were carried out as described in the main text, using COPASI  (Hoops et al, 2006; v4. 34) with the CoRC package  (Forster et al, 2020; COPASI R Connector v0. 11 , https://github. com/jpahle/ CoRC) in R (v4. 1. 3, https://www. r-project. org). The R notebook used to perform the simulations and to generate the figures is provided at https://github. com/MetaSys-LISBP/glucose_acetate_ interplay.
Acknowledgements This work was supported by a grant to  Pierre Millard  and  Thomas Gosselin-Monplaisir  from the D epartement MICA of  INRAE  and the  R egion Occitanie  (grant  COCA-COLI ). This work was also supported by a grant to  Pierre Millard  from the  Toulouse Biotechnology Institute  (grant Master-2022). The authors thank  Lindsay Peyriga  and  MetaboHub-MetaToul (Metabolomics and Fluxomics facilities, Toulouse, France , http://www. metatoul. fr),part of the  French National Infrastructure for Metabolomics and Fluxomics  (http://www. metabohub. fr)funded by the  ANR  ( MetaboHUB - ANR-11-INBS-0010 ), for access to NMR facilities. The authors are grateful to  Edith Vidal  and  Abdulrahman Khabbaz  for help with the growth experiments, and the following INSA Toulouse students for help with the growth and transcriptomics experiments:  Lola Blayac ,  Arno Bruel ,  Alexis Charbinat ,  Romane Ducloux ,  Justine Dumas-Perdriau , Sol ene Frapard,  Sophie Germain ,  Benjamin Jung ,  Olivier Larrousse ,  Nicolas Papadopoulos ,  Marine Rodeghiero ,  Leyre Sarrias  and  Auriane Thomas.
Data availability The data used to make the figures can be found in the Source Data files. The data, model, and computer code produced in this study are available in the following databases: • Transcriptomics data: ArrayExpress E-MTAB-11717 (https://www. ebi. ac. uk/biostudies/arrayexpress/studies/E-MTAB-11717) • Model: BioModels MODEL2005050001 (https://www. ebi. ac. uk/ biomodels/MODEL2005050001) • Code: GitHub (https://github. com/MetaSys-LISBP/glucose_acetate_ interplay) Expanded View for this article is available online.
Acknowledgments The authors dedicate this work to our dear friend and colleague  Jérémie Mouginot  who recently passed away. He will always be remembered for his kindness and enthusiasm he brought to his science and those around him. This work was funded by the  French National Research Agency  Grants  ANR-19-CE01-0011-01  and  ANR-17-CE01-0008  and the  French space agency (CNES). J. K. A. acknowledges support from  DTU Space.
Data Availability Statement All data used to generate this manuscript is publicly available for download. Ice surface elevation: https://doi. org/10. 5067/H0KUYVF53Q8M, Land/Ice classification: https://doi. org/10. 5067/B8X58MQBFUPA, Bed topography: https://doi. org/10. 5067/2CIX82HUV88Y, Snowline elevation: https://doi. org/10. 18739/A2V40JZ6C. Sentinel-1 and Sentinel-2: https://scihub. copernicus. eu/, Landsat: https://earthexplorer. usgs. gov/, ArcticDEM:.
Methods This patient-level analysis included pooled data from 3 prospective, individually powered, randomized, sham-controlled, international studies of RDN in patients with HTN: A Study of the ReCor Medical Paradise System in Clinical Hypertension (RADIANCE-HTN SOLO and TRIO)  5, 6  and A Study of the ReCor Medical Paradise System in Stage II Hypertension (RADIANCE II)  7  trials (Figure  1 ). ] [10]  This study followed the Consolidated Standards of Reporting Trials (CONSORT) reporting guidelines for each of the original randomized trials. All 3 studies required patients to be between 18 and 75 years of age with an estimated glomerular filtration rate (eGFR) of at least 40 mL/min/1. 73m  2. For RADIANCE-HTN, the following categories were available: American Indian or Alaska Native; Asian; Black, African heritage; Black, Caribbean heritage; Black, other heritage, specify; Caucasian; Hispanic or Latino; multiple ethnicities, specify; Native Hawaiian or other Pacific Islander; or other ethnicity, specify.
Continuous variables are expressed as mean (SD), unless otherwise specified, and between-group differences are expressed with their 2-sided 95% CIs. We used SAS software, version 9. 4 (SAS Institute) and R (R Core Team). The R packages data. table, version 1. 14. 2 (Dowle & Srinivasan), metaphor,  13  and meta  14  were used to conduct the 2-stage meta-analyses.
Efficacy Outcomes At 2-month follow-up, mean (SD) daytime ambulatory systolic BP decreased from the prerandomization baseline by 8. 5 mm Hg to 141. 8 (13. 8) mm Hg after uRDN vs a decrease of 2. 9 mm Hg to 147. 9 (14. 6) mm Hg after sham (mean difference, -5. 9; 95% CI, -8. 1 to -3. 8 mm Hg; P <. 001 in favor of uRDN) (Figure  2 ; eTable 4 in Supplement 1). Reductions were consistent across the 3 included trials, and there was no heterogeneity when tested using a study by treatment arm interaction   2 ; eTable 4 in Supplement 1). In addition, decreases in diastolic BP were greater with uRDN compared with sham irrespective of whether measured with ambulatory BP monitoring, at home, or in the office (eTable 4 in Supplement 1). Per-protocol analyses as well as analyses conducted only using available data (without imputation) paralleled the primary intent-to-treat analyses (eTables 5 and 6 in Supplement 1).
Discussion This pooled analysis of individual patient data from the RADIANCE RDN clinical trial program sought to combine the results of 3 independently powered, randomized, shamcontrolled, clinical trials of uRDN to increase the precision of the estimate of BP lowering while examining the consistency of this effect across differing populations of patients with HTN. Notably, the primary end points of these 3 trials were ascertained at 2 months to ensure medication standardization across randomized groups. There were several key findings. First, there was a reduction in daytime ambulatory systolic BP of 5. 9 mm Hg with uRDN in comparison with sham with an overall reduction in daytime ambulatory systolic BP of 8. 5 mm Hg from baseline after uRDN (vs a reduction of 2. 9 mm Hg with sham), although both the magnitude of BP reduction from baseline as well as rates of BP control varied based on baseline BP.
Dr Rader has received personal fees from ReCor Medical, Medtronic, and Bristol Myers Squibb. Dr Gosse reported receiving research grants from ReCor Medical and Ablative Solutions. Dr Sapoval reported receiving personal fees from ReCor Medical and Medtronic and grants from Recor research grant during the conduct of the study, grants from Merit, and consulting fees from Boston Scientific outside the submitted work. Dr Barman, Ms Claude, and Dr Augustin reported being employees of ReCor Medical, and Dr Barman reported having a patent for multiple renal denervation patents issued during the conduct of the study. Ms Thackeray and Mr Mullin reported being employees of NAMSA, a contractor for ReCor Medical, and receiving consulting fees from ReCor Medical outside the submitted work. Dr Azizi reported receiving research grants from the European Horizon 2020 program and ReCor Medical, grant and nonfinancial support from ReCor Medical and Idorsia, and personal fees from Alnylam Pharmaceutical, Poxel Pharma, Medtronic, and Novartis outside the submitted work.
7, 8 [11] [12] [13]  To help individualize antithrombotic decision among patients with CMB, a large-scale global pooled individual patient data analysis was performed by the Microbleeds International Collaborative Network (MICON) which included 20,322 participants from 38 cohorts with previous IS or transient ischemic attack, and baseline CMB evaluation. 14 The burden of CMB was found to have stronger associations with subsequent ICH than IS. However, as the absolute rate of IS was consistently higher than that of ICH irrespective of CMB burden and distribution, withholding antithrombotics routinely for all patients with CMB is therefore not justified. In this study, the analysis was performed irrespective of stroke subtypes involved in the index event. It remains uncertain if variation in risk-to-benefit ratio may exist among patients with different stroke etiology and antithrombotic therapies, particularly patients with atrial fibrillation (AF) on anticoagulants which may have a higher risk of ICH than antiplatelets,  15  but better efficacy in prevention of IS.
Study design The MICON cohort consists of patients from 18 countries in North America, Europe, the Middle East, Asia and Australia. Inclusion criteria of the MICON collaboration were cohorts with (i) prospectively recruited adult participants with IS or transient ischemic attack, (ii) documented number and anatomical distribution of CMB evaluated by MRI T2* or SWI, (iii) collected data on outcome events including IS, ICH, vascular and non-vascular death and (iv) a follow-up period of at least 3 months. 14 In this subanalysis, we included 37 cohorts who agreed to participate. We included patients with known or newly diagnosed AF. Patients who had unknown status for AF were excluded. The MICON project was approved by the Health Research Authority of the UK (REC reference:8/HRA/0188). Included cohorts obtained ethical and regulatory approvals according to local requirements.
Despite a higher proportion of patients on oral anticoagulant in the AF subcohort (86. 1%) than in the main study (38. 1%), the same absolute rate of ICH in these two studies suggests that factors other than antithrombotic use, e. g. blood pressure variability, might also influence the risk of ICH. Microbleeds in stroke patients with AF Few randomized controlled studies included MRI imaging substudies. 21, 22 In the NAVIGATE ESUS trial, the presence of CMB was associated with recurrent IS, ICH and death but the numbers of outcome events were too small to draw conclusions about the ICH risk depending on the antithrombotic treatment (i. e. Rivaroxaban vs Aspirin). 22 More importantly none of these trials included patients on combination therapy leading to a lack of randomized data in this high-risk group.
To evaluate the model, we compared with data from Arctic stations and from the central Arctic obtained on board the Research Vessel (RV) Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign in spring 2020  (Shupe et al. , 2022). The new mercury developments and WRF-Chem model setup are presented in Section 2, with the observations used to evaluate the model described in Section 3. In Section 4, we evaluate the model against meteorological and chemical observations from MOSAiC. In Section 5, we assess the model performance compared to other Arctic observations, as well as discuss the simulated pan-Arctic impacts. Finally, the outcomes and conclusions are presented in Section 6.
MOSAiC observations The MOSAiC expedition took place on board the Polarstern icebreaker, between October 2019 and September 2020, and is the largest scientific exploration of the Arctic to date. A comprehensive suite of measurements were made during drift through the Arctic Ocean to better understand the links between the atmosphere, sea ice, and ocean  (Nicolaus et al. , 2022; Rabe et al. , 2022; Shupe et al. , 2022). This extensive dataset provides a unique insight into the chemical behaviour of the atmosphere in the central Arctic. More details about the MOSAiC campaign can be found in  Shupe et al. (2022). For the purpose of our modelling study, we used a subset of meteorological and chemical observations made during MOSAiC which are introduced below.