dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses and graphical output All graphs of experimental data and statistical analyses were generated with the software GraphPad Prism 7. After verifying that the data (log transformed when appropriate) were not inconsistent with a Gaussian distribution (Shapiro-Wilk normality test), one-tailed t-tests were used to determine whether experimental values (i. e. relative fitness or phage amplification) significantly differed from a theoretical value (e. g. 1), and two-tailed unpaired t-tests were used to compare groups with each other, where appropriate. Each group was composed of 6 values (i. e. 6 independent biological replicates) and 95% confidence levels were used in all statistical tests. In all cases, observed differences were considered significant when p-values were less than a Bonferroni-corrected threshold of 0.
Acknowledgments The authors acknowledge the  Centre for Genomic Research (University of Liverpool ), in particular  R. Eccles ,  A. Lucaci  and  R. Gregory  for performing amplicon sequencing. The authors thank  Dr. L. Zhang  for help setting up qPCR analyses and  Prof. A. Buckling  for critical reading of the manuscript. This work was supported by a grant from the  ERC  ( ERC-STG-2016-714478 -EVOIMMECH ), the  BBSRC  ( BB/N017412/1 ), the  Wellcome Trust  ( 109776/Z/15/Z ) and the  NERC  ( NE/M018350/1 ), which were awarded to E. R. W.
The generalised fractional viscoelastic model predicts the response to different loading conditions with no further fitting The model relies on the assumption that the material behaves linearly. To identify the linear domain, we examined the stress response at different strain amplitudes ranging from 20% to 50%. We can observe that the material parameters are almost constant until roughly 30% (see the electronic supplementary material, figure  S10 ), which provides an upper bound of the linear domain where we expect the model to be valid. Within this range, we can assess the predictive power of our rheological description of epithelial monolayers. We extracted a distribution of parameters from the stress relaxation data and used them to estimate the response of the material to different forms of mechanical stimulation.
Numerous studies have improved our understanding of the behavior of multi-configuration radar signals, through the experimental analysis of radar data acquired over land surfaces  [29, 30]  as well as the development of various backscattering models. The latter are relevant to the study of bare soils, and can be classed as physical  [31] , semi-empirical, or empirical models  [32] [33] [34]. They are sensitive to the surface soil moisture, which is directly related to the dielectric constant. In the case of soils covered by vegetation, there are also many physical and semi-empirical models. The most commonly used of these is the WCM (Water-Cloud Model)  [35, 36]. Soil moisture can be retrieved by inverting the radar data, using various different approaches. These tend to vary according to the characteristics of each specific SAR mission (ERS, ALOS, ENVISAT, RADARSAT, TERRASAR-X, etc.
More recent studies have proposed surface soil moisture applications based on the analysis of radar data produced by ESA's new Sentinel-1 constellation. Bauer-Marschallinger et al. [53]  proposed a change detection algorithm, which allows surface soil moisture to be estimated from Sentinel-1 data at a scale of 1 km using 10 m spatial resolution data. Indeed, using data provided at a 10 m spatial resolution is very useful to correct and filter a 1 km Water 2020, 12, 866 3 of 21 pixel data that may cover several land uses. This algorithm has been tested on different sites, and was found to provide accurate results over agricultural areas. Although this operational algorithm is very useful for the production of intermediate resolution surface soil moisture maps, its overall approach remains quite general and does not allow refinements or data corrections to be implemented for specific types of landscapes.
As explained in Section 3, the proposed algorithm includes various processes that have been designed to assess, and then compensate for, factors such as land cover, vegetation cover density, and the seasonal context of the surface soil moisture dynamics. Three different options are proposed, to enable the analysis of different geographical regions. This paper is organized as follows: Section 2 is dedicated to the description of the satellite database and the validation sites; Section 3 describes the proposed methodology; Section 4 presents the results, together with our discussion of the surface soil moisture estimations derived for several study sites, which are compared to those provided by ASCAT; and our conclusions are presented in Section 5.
Niger The studied region is located in the southwestern part of Niger, between the Niger river and the fossil valley of Dallo Bosso  [58]. The Sahelian climate in this region is semi-arid, with an average annual rainfall ranging between 300 and 750 mm, characterized by a rainy season from June to September. The landscape is mainly flat and dominated by a dissected plateau with slopes of less than 6%, which have lateritic soils and are covered with tiger bush. These are bordered by terrain with mainly strong transitional features and steep inclines, which can reach a slope of 35%. The vegetation in the valleys is dominated by both fallow and cultivated fields (mainly millet). Continuous ground measurements were carried out at a depth of 5 cm at the Banizombou site. Data can be obtained from the International Soil Moisture Network (https://ismn.
Benin The Ouémé catchment is located in Benin, West Africa, and is a part of the AMMA-CATCH observatory (African Monsoon Multidisciplinary Analysis-Coupling the Tropical Atmosphere and the Hydrological Cycle  [58] ; http://www. amma-catch. org), whose mission is to study the hydrological impact of climate and anthropogenic changes. With a surface area of 12,000 km 2 , the Ouémé catchment is covered mainly by savanna, forests, and crops. The rainy season lasts from April to October, and its annual rainfall is approximately 1250 mm. The Ouémé basin has a very dense network of instruments, which are used to monitor the water cycle and the vegetation dynamics in this sub-humid region. Surface soil moisture is measured continuously at a depth of 5 cm. Data can be obtained from the International Soil Moisture Network (https://ismn.
Sentinel-2 Sentinel-2 is a land-monitoring mission developed by the European Space Agency (ESA), which comprises a constellation of two multi-spectral imaging satellites, Sentinel-2A and Sentinel-2B. Sentinel-2A (S-2A) was launched in June 2015 and was followed by Sentinel-2B (S-2B) in March 2017. This constellation provides global coverage of the Earth's land surface with a revisit frequency of 5 days. The images are produced in 13 spectral bands, covering visible and mid-infrared wavelengths, at three different spatial resolutions  (10, 20 , and 60 m). The optical images used for the present study were obtained from the THEIA website at the French Land Data Center (https://www. theia-land. fr/), which makes the data available in the so-called "Level-2A" format. This product includes radiometric, atmospheric, and cloud corrections, based on an algorithm developed by  [59].
ASCAT The ASCAT radar is one of the instruments carried by ESA's three Metop satellites. ASCAT soil moisture products, recorded over a 25 km swath grid, can be recovered from the Earth Observation Portal (https://eoportal. eumetsat. int/). ASCAT is operated in the C-band (5. 3 GHz) and in the vertical polarization. Over land, the measured radar backscattering coefficient depends on the surface soil moisture, surface roughness, vegetation characteristics and incidence angles of the transmitted radar beam. Surface soil moisture data is retrieved from the backscattering coefficient, using a change detection method  [10]. In the framework of the soil moisture CCI (Climate Change Initiative), the European Space Agency has proposed surface soil moisture estimates based on the combined use of these different measurements.
These results are compared with the ASCAT surface soil moisture product converted in volumetric moisture by using ground soil moisture time series, represented by black points in Figures  7 8 9 10. ASCAT soil moisture products as SMES algorithm products are retrieved for one pixel around ground measurements. The results shown in these figures were generated using data recorded in the VV polarization. In addition, the values of RMSE, R, bias, and ubRMSE obtained with the optional processes are listed in Table  3. The Niger site has a semi-arid climate (maximum in situ surface soil moisture measurement around 14 vol. % with sandy soil conditions). As can be seen in Figure  7 , the results obtained at this site are very good (RMSE around 1. 6 vol. %, ubRMSE equal to 1. 6 vol. %, and bias equal to 0).
These developments are generally supported by progress in process understanding, which in turn is driven by an increasing number of observational databases  [70] [71] [72]. However, from one generation to another, the development of marine biogeochemical models is driven not only by common scientific considerations but also by the internal priorities of individual modelling groups. As a consequence, it is difficult to anticipate how far the representation of marine biogeochemistry within the current generation of Earth system models differs from-and has improved upon-the previous one. The present study maps the changes or updates in ocean biogeochemistry components that have arisen between CMIP5 and CMIP6 and assesses how far these have led to actual improvements in model skill against present-day observations.
1 ). In addition, Table  1  shows that the current generation of ocean biogeochemical models includes an air-sea gas exchange for a larger number of radiatively active biogeochemical compounds such as DMS, nitrous oxide (N 2 O) and ammonia (NH x ). The inclusion of climate active gases or greenhouse gases other than CO 2 in the current generation of ocean biogeochemical models is a result of the increased recognition of the importance of these compounds in Earth system interactions with aerosols, atmospheric chemistry and, potentially, with clouds. In particular, the inclusion of ocean NH x or N 2 O emissions in ocean biogeochemical models has been driven by a better understanding of the global nitrogen cycle and its role in climate change. In particular, the development of databases such as MEMENTO (https://memento.
Indeed, all CMIP6 models exhibit smaller domains where the direction of the sea-to-air carbon flux disagrees with observations, except for MPI-ESM1-2-LR, which used the same ocean model and displays the same pattern of model-data disagreement for CMIP5 and CMIP6. Figure  2  b shows surface chlorophyll, compared with satellite-based estimates derived from ESA-CCI-OC ocean colour data  [144]. The key geographical features are relatively high concentrations in productive temperate, subpolar and upwelling regions, and extremely low concentrations in the unproductive subtropical gyres. The latter are dominated by perennially low-nutrient conditions, while the former experience frequent, or seasonal, introduction of nutrients by upwelling or deep mixing. While these general biome scale patterns are robust across satellite algorithms, we note that estimates The first top panel shows the optimal interpolation of nitrate (no3) and silicate (si) measurements as provided in the World Ocean Atlas Database 2013 (Garcia et al.
Several CMIP6 models compare more favourably with observations than their CMIP5 predecessors. All models displaying a pattern of generally negative bias in CMIP5 now exhibit large areas of both small positive and small negative biases. Models overestimating surface chlorophyll concentrations in CMIP5 now display reduced biases (< 0. 4 mg Chl m -3 ). This improvement is small for MPI-ESM1-2-LR, which still overestimates surface concentrations of chlorophyll. Some CMIP6 models, such as CESM2, GISS-E2-1-G-CC and NorESM2-LM, display on the contrary larger model-data errors than their predecessors. Given the large diversity across the models, it is difficult to determine whether changes in physical ocean models or changes in ocean biogeochemical models are behind these changes. However, it is interesting to note that three CMIP6 models (CNRM-ESM 2-1, IPSL-CM6A-LR and UKESM1-0-LL), which share a common ocean physics model, overlap in their It is notable that most of the models reviewed here overestimate surface chlorophyll estimates in the Southern Ocean.
Indeed, the model-data fit estimated with squared correlation coefficients remains < 0. 25. This fit has not progressed much from CMIP5 to CMIP6, except possibly for IPSL and CNRM models which both employed PISCESv1  [40, 41]  for CMIP5 and PISCESv2  [91]  for CMIP6. As highlighted in Aumont et al. [91] , PISCESv2 includes a more detailed representation of the ocean iron cycle compared with PISCESv1. The poor agreement between the observed and simulated distribution of dissolved iron relative to macronutrients (Fig. 3 ) partly reflects differences in the nature of the datasets. The relatively large number of nitrate measurements globally, for example, has allowed construction of robust climatological patterns  [145]  that model climatologies can be compared against. The relative paucity of dissolved iron measurements, in contrast, requires a comparison of modelled climatologies against patchy individual measurements.
Indeed, most modelling groups have spun-up their model over a longer period for CMIP6 with respect to CMIP5 in order Table  4  Comparison between observational and model estimates of biogeochemical fluxes over the modern period. For both CMIP5 and CMIP6 models, biogeochemical fluxes are calculated over the 1995-2014 period (see Methods in Supplementary materials) Observational estimates are derived from the following database: a Landschützer et al. [143]  product average over 1995-2014 and adjusted for the preindustrial ocean source of CO 2 from river input to the ocean consistently with the methodology employed in  [155]   to fulfil the drift criterion as proposed by Jones et al. [142]. In contrast, the use of tuning and calibration for marine biogeochemical models for CMIP remains a less common feature at the time of CMIP6.
What's Next? In this final section, we identify some directions where marine biogeochemical models could continue to improve or to progress. The green (red) shading flags an improvement (degradation) of the model performance to replicate the observed geographical structure for a given field. The ocean mixed-layer depth is computed similarly in all models; it is based on a density criterion of 0. 03 kg m -3. The ocean mixed-layer depth simulated by the various Earth system models is evaluated against the observational dataset of de Boyer Montégut et al. [162]  The first step change to expect in the next generation of models is the emergence of high-resolution ocean biogeochemical models fit to investigate centennial-scale simulation. This step change may be supported in a number of ways: (1) the availability of greater computational resources; (2) the use of hybrid-resolution numerical schemes to decrease the cost of biogeochemical models (e.
g. [108, 165] ). Then, this improvement is arguably a first step toward the representation of adaptation and fitness in ocean biogeochemical models  [166, 167]. This omission remains an important caveat for multi-stressors studies (e. g. [9] ) or time-of-emergence studies  [168]  as current models effectively assume no change in the underlying properties of modelled plankton. 4. The ocean mixed-layer depth is computed similarly in all models; it is based on a density criterion of 0. 03 kg m -3. The ocean mixed-layer depth simulated by the various Earth system models is evaluated against the observational dataset of de Boyer Montégut et al. [162]  Future developments should be pursued in the context of the internal cycling of micronutrients involved in phytoplankton physiology and metabolism such as iron, zinc or copper.
The work of Lefort et al. [57]  provides an example of this type of marine ecosystem model realizing a comprehensive coupling between a marine biogeochemical model (PISCES) with a marine trophic food web model (APECOSM). A third important step change is related to the couplings between Earth system components and ocean biogeochemistry. Our review highlights that models have evolved toward a more comprehensive treatment of biological boundary conditions (e. g. atmospheric deposition, riverine inputs, sediments, ice sheets, geothermal sources) but that these latter are currently largely represented using climatological data rather than dynamic connections. Progress toward more complete couplings between Earth system components such as rivers, ice sheet/iceberg calving and ice shelves or atmospheric aerosols can help to better simulate interactions between marine biogeochemistry, biogeochemical cycles and climate.
Existing constraints were adequate for detecting large skill differences between CMIP5 and CMIP6 models, but the overall improvement in models necessitates more precise comparisons to detect skill differences. Such comparisons are challenged by data sparsity and   [178]  uncertainties in algorithms designed to derive global fields from sparse data or infer properties of interest from remotely sensed variables. Continued improvement in the quality and quantity of data-based constraints is critical. That being said, our review of the available pairs of CMIP5-CMIP6 marine biogeochemical models strongly suggests that careful consideration is needed when selecting model complexity with regard to the fitness-for-purpose of models (i. e. carbon cycle feedbacks, multiple Earth system feedbacks, multi-stressors, adaptation and biodiversity).
Acknowledgements R. S. on behalf of the author team thank the two anonymous referees for their useful comments that have improved the quality of this paper. R. S. thanks the author team for their contributions to this paper that occurred during the coronavirus SARS-CoV-2 pandemic. R. S. and S. B. thank the support of the team in charge of the CNRM-CM climate model. Supercomputing time was provided by the  Météo-France/ DSI  supercomputing center.
Funding Information This work was supported by the  European Union 's  Horizon 2020 research and innovation program  with the  CRESCENDO  project under the grant agreement No  641816 , the  TRIATLAS  project under the grant agreement No  817578  and the  COMFORT  project under the grant agreement No  820989. JS and JT were supported by the  Research Council of Norway  through the projects  INES  (grant no. 270061 ) and  COLUMBIA  (grant no  275268 ). LK was supported by the  Agence Nationale de la Recherche  (grant no. ANR-18-ERC2-0001-01 ).
It is widely available and is clearly associated with the risk of developing complications associated with diabetes, both in type 1 diabetes and type 2 diabetes  [12, 13]. However, HbA1c also has limitations since it can be affected by external factors unrelated to blood glucose  [14]. Furthermore, it does not provide information about clinically important measures, such as day-to-day glycaemic variability and the frequency of hyper-and hypoglycaemia which impact on the health and wellbeing of people with diabetes. The glucose data reported by rtCGM and isCGM systems provides a means of expressing these important measures for standardised reporting and analysis. This is reflected in international consensus recommendations that endorse a move beyond HbA1c as the most useful marker of individual glycaemia  [11, 15].
Although HbA1c is a more abstract and hard-tovisualize measure of individual glycaemic control, it remains the gold-standard for understanding population based risks for developing macrovascular and microvascular complications  [12, 13]. SMBG data from the Diabetes Control and Complications Trial (DCCT) in type 1 diabetes  [12]  has been reanalysed to calculate the %TIR 3. 9-10 mmol/L of participants with and without microvascular complications in the DCCT  [16]. 7-point SMBG testing was performed by 1440 DCCT participants on 1 day every 3 months during morethan 6 years of the DCCT, allowing the SMBG-based %TIR to be calculated. This showed a significant difference in TIR of 10-12 percentage points (2. 5-3. 0 hours/day) between participants who did and did not developed complications. For each 10% fall in TIR ( 2 hours 24 mins less each day with glucose levels in the target range), the risk of progression of retinopathy was increased by 64% and risk of developing microalbuminuria was increased by 40% [  Fig 2].
In that context, the first aim of this study is to investigate the evolution of the different (natural and anthropogenic) aerosol loads (mass surface concentration and aerosol optical depth) in future, using the CNRM-ALADIN63 regional climate model and its TACTIC aerosol scheme  (Nabat et al. , 2020). In addition, the use of the recent anthropogenic emission dataset provided for the sixth phase of the Coupled Model Intercomparison Project (CMIP6) allows to have more realistic emissions than those of previous studies. In a second time, this regional modelling tool will serve to address the evolution of direct radiative forcing and the impact of aerosol evolution on the future Euro-Mediterranean climate change. After a description of the CNRM-ALADIN63 regional model and its aerosol scheme in Sect.
Model and experiments 2. 1 The CNRM-ALADIN63 regional climate model The current work has been carried out with the CNRM-ALADIN63 regional climate model  (Nabat et al. , 2020) , that is developed at the CNRM (National Centre for Meteorological Research). It includes an interactive aerosol scheme described thereafter. It includes also the SURFEX land surface module  (Masson et al. , 2013)  with the ISBA scheme  (Noilhan and Planton, 1989 ) that models water and energy exchanges at the interface between the plant, the soil and the atmosphere. SURFEX will then provide the mean values of different data such as the ascending radiation or the surface albedo which are necessary for the CNRM-ALADIN63 model radiative scheme. Lastly, land surface hydrology and river flow are simulated by the TRIP model  (Oki and Sud, 1998)  according to the evaporation-precipitation balance provided by ISBA.
Only one bin is used for sulfate particles and another is used for its precursors, notably sulphur dioxide (SO 2 ). Recently, nitrate aerosols have been included in the TACTIC aerosol scheme  (Drugé et al. , 2019). This specific tracer is divided into two different bins (for fine and coarse modes). The last two bins are used for ammonium and ammonia tracers. It is important to note that one of the ammonium and nitrate aerosols precursors, the nitric acid HNO 3 , is implemented in the model as a constant monthly climatology based on the CAMS reanalysis  (Inness et al. , 2019). This nitrate-ammonium module is described in detail in  Drugé et al. (2019). The different primary aerosol mass concentrations can be dynamically calculated on-line as a function of surface wind and soil characteristics (dust, primary seasalt) or based on external emission datasets from anthropogenic and/or biomass burning particles (black carbon, organic matter, sulfate, nitrate and ammonium).