dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This study was funded by the  Japan Society for the Promotion of Science KAKENHI  grants (grants  18H03062 ,  21H04856 ,  17H03589 ,  19K10671 ,  19K19439 ,  19K10446 ,  18H03107 ,  18H04071 , and  22FA1010 ), the  Japan Science and Technology Agency Research Institute of Science and Technology for Society (RISTEX)  grants (grant  JPMJRX21K6 ), the  Japan Science and Technology Agency Center of Innovation (COI)- NEXT grants  (grant  J JPMJPF2105 ), the  Research Support Program  to Apply the University's Wisdom to tackle COVID-19-related emergency problems, the  University of Tsukuba, Health Labour Sciences Research Grants  (grants  19FA1005  and  19FA1012 ), and the  Japan Agency for Medical Research and Development  (grant  2033648 ). The content of this paper is solely the responsibility of the authors and does not necessarily represent the funders' official views.
3-0. 6 cm/yr. We fit the time series using simple functional forms and classify seven of the volcano time series as 'linear', six as 'sigmoidal' and three as 'hybrid', enabling us to discriminate between steady deformation and short-term pulses of deformation. This study provides a framework for routine volcano monitoring using InSAR on a continental scale. Here, we focus on Sentinel-1 data from the EARS, but the framework could be expanded to include other satellite systems or global coverage.
Introduction Interferometric Synthetic Aperture Radar (InSAR) can routinely produce ground displacement maps over large areas. Current satellite missions, especially Sentinel-1, are well-suited for carrying out deformation surveys on the scale of entire plate boundaries, which would not be feasible with ground-based stations. Previous large-scale surveys have mostly been retrospective analyses of strain accumulation in tectonically active regions such as Tibet  [1]  and Anatolia  [2, 3] , and surveys of volcanic unrest  [4] [5] [6] [7] [8] [9]. The availability of SAR datasets has increased dramatically over the past decade. In particular, the open-access policy of the Sentinel-1 mission opened new perspectives for the near real-time monitoring of ground deformation at global and regional scales  [3, [9] [10] [11].
This has been supported by the development of automated processing platforms that now produce large databases of InSAR products: ARIA (NASA Jet Propulsion Laboratory), SARVIEWS (University of Alaska Fairbanks), Geohazards Exploitation Platform (European Space Agency), and LïCSAR (COMET)  [12] [13] [14] [15]. Satellite observations are particularly critical for sub-saharan Africa, where the resources to deploy and maintain a dense network of ground-based stations are limited. The East African Rift System (EARS) contains 78 volcanoes considered active during Holocene  [16]. Population exposure is high, particularly in Ethiopia and Kenya, where over 70 million people live within a 100 km radius of a Holocene volcano  [17]. InSAR.
Pre-Processing of Sentinel-1 Data Sentinel-1 is a C-band imaging radar mission operated by the European Satellite Agency (ESA) through the Copernicus programme. The constellation consists of two satellites: Sentinel-1A (launched 3 April 2014) and Sentinel-1B (launched 25 April 2016). However, an anomaly occurred on Sentinel-1B satellite on 23 December 2021 and the end of the Sentinel-1B mission was announced by ESA and the European Commission on August 2022. In Africa, revisit time of Sentinel-1 acquisitions is 12 days during the 2017-2021 period and 24 days otherwise. Differential Interferometric Synthetic Aperture Radar (DInSAR) consists of computing the phase difference between two SAR images of the same geographical location acquired at different times  [18]. The processing of SAR images produces (i) wrapped interferograms which contain ambiguous phase values between -π and π, (ii) unwrapped interferograms which contain continuous phase values relative to a reference pixel and (iii) coherence maps, which indicate the degree of decorrelation of ground targets.
We generate Sentinel-1 interferograms and coherence maps using the LïCSAR processing system  [15] , which is built on the GAMMA software package  [19]  (Figure  1a ). Our dataset spans a 5-year period (2015-2020) and consists of a total of 3570 interferograms covering 64 Holocene active volcanoes from the Eastern Branch of the African Rift system, including Ethiopia, Kenya and Tanzania (Table  S1 ). All InSAR products are freely available at https://comet. nerc. ac. uk/comet-lics-portal/ (accessed on 25 October 2022). The LïC-SAR system automatically generates the 3-4 shortest temporal baseline interferograms at a spatial resolution of 0. 001 • (∼111 m at the equator)  [15]. To reduce the computing time for the time series analysis, we first crop all the LïCSAR products to a geographical region 0.
Velocity Uncertainties We then evaluate the LOS velocity uncertainties in the Sentinel-1 time series for our EARS dataset by looking at the time series for the volcanoes that show a continuous linear trend. The standard deviation of the LOS velocity is derived using the percentile bootstrap method  [39]. The method consists of computing the distribution of the velocity using a random selection of N resampled datasets with data replacement (here, we chose N = 100). The standard deviation of the velocity is then calculated for different duration from 1 to 5 years. Regional and global analysis of geodetic time series have showed that velocity uncertainties σ v decrease with the duration of the time series ∆t, following a power-law function  [40] [41] [42]. The parameter b corresponds to the decay rate, and it usually ranges between 1 for flicker noise (correlated) and 3  2 for pure white noise (uncorrelated).
Atmospheric and Reference Frame Uncertainties The EARS volcano dataset shows a low correlation between elevation and the phase of the interferograms. We found that (i) the coefficient of determination, R 2 , has an average value of 0. 2 and (ii) only 15% of the interferograms processed show a R 2 > 0. 5. As an illustration, Figure  3a  shows one interferogram containing a strong stratified signal from Silali volcano. An empirical model based on the linear correlation between phase and elevation (R 2 = 0. 8) largely removes the atmospheric signal (Figure  3b, d ) and the residuals after correction have a standard deviation of <2 cm (Figure  3c ). Figure  3e  shows one interferogram containing low atmospheric signal from Suswa volcano. Here, the empirical model based on the phase-elevation correlation (R 2 = 0.
Previously Unreported Deformation For two of the volcanoes with a high signal-to-noise ratio, Paka and Silali, no deformation has previously been reported in the Sentinel dataset (2015-2020). However, both experienced deformation during the Envisat era (2006-2010)  [7, 47, 48]. At Paka, five distinct phases of unrest were identified with surface LOS displacement rates between 95 ± 1. 3 cm/yr (July-August 2006) and 2. 5 ± 0. 9 cm/yr (February 2008-July 2010)  [47]. Based on the modelling of ground deformation, unrest phases have been interpreted as the inflation and deflation of four simple sources located beneath the northeastern and southern flanks of the volcano at depths between 1 and 5 km. At Silali, a long-term linear subsidence signal with maximum LOS displacement rates of 1. 6 ± 0. 4 cm/yr was detected between October 2003 and September 2010, and modelled by the deflation of a source located at 4-5 km depth below the caldera  [47].
The subsidence at Alu-Dalafilla and Dallol are thought to be long-term responses to recent activity: Alu-Dalafilla last erupted in 2008  [55]  and there was a dike intrusion at Dallol in 2004  [56]. In contrast, Paka and Silali are restless calderas, with no recent activity  [7]. The signal at Gada Ale has previously been attributed to poro-elastic fluid effects  [9]. Although the spatial pattern of deformation can be used to discriminate between source processes with different geometries, many of the deformation signals are radially symmetrical. Thus, while empirical fitting can be useful for a first order categorisation of the InSAR dataset, it cannot be used to uniquely determine the magmatic processes. Additional information from other monitoring streams, e. g. ,  [57, 58]  or baseline knowledge of the long-term deformation patterns or eruptive history is needed for monitoring purposes.
Although the EARS is mostly arid and sparsely vegetated, this effect could explain some of the uncertainties in our velocity estimates, particularly for volcanoes with multiple land cover types as the impact of the phase errors varies across the scene. Most InSAR studies do not explicitly consider the choice of reference frame as a source of uncertainty. Here, we demonstrate that this choice is not trivial and can induce variations up to 1 cm/yr on the LOS velocity derived. From our study, we show that the average error is ∼0. 5 cm/yr for the EARS, which is five times larger than the errors associated with noise. Therefore, the use of an external dataset (e. g. , GNSS data) for referencing Sentinel-1, e. g. ,  [3, 45] , is crucial to be able to study strain accumulation and partitioning in the EARS, where extension rates range from ∼2 mm/yr in Malawi to ∼6 mm/yr in the Main Ethiopian Rift  [68].
Data Availability Statement: Sentinel-1 InSAR products processed by LïCSAR are available on the Centre for Environmental Data Analysis (CEDA) archive (http://data. ceda. ac. uk/neodc/comet/ data/licsar_products, accessed on 25 October 2022). The cropped products and the time series of displacements for each volcano were archived with the NERC's National Geoscience Data Centre (NGDC) and available online (https://doi. org/10. 5285/e7c3177b-4c73-4c20-961f-03afd09ccf69, accessed on 25 October 2022).
Raw shotgun sequencing data that support the findings of this study have been deposited in the European Nucleotide Archive with accession codes PRJEB37249, PRJEB38742, PRJEB41311 and PRJEB46098. Serum NMR and urine NMR metabolome data have been uploaded to Metabolights with accession number MTBLS3429; serum GC-MS and isotopically quantified serum metabolites (UPLC-MS/MS) are available from MassIVE with accession numbers MSV000088042 and MSV000088043, respectively.
Acknowledgements We thank the subjects for their participation in the MetaCardis study and particularly patient associations ( Alliance du Coeur and CNAO ) for their input and interface, as well as  Dr Dominique Bonnefont-Rousselot  ( Department of Metabolic Biochemistry, Pitié-Salpêtrière hospital ) for the analysis of plasma lipid profiles. We thank the nurses, technicians, clinical research assistants and data managers from the Clinical investigation platform at the  Institute of Cardiometabolism and Nutrition  for patient investigations, at the  CRNH (Centre de recherche en Nutrition Humaine CRNH-Ile de France ) and, the  Clinical Investigation Center (CIC) from Pitié-Salpêtrière Hospital  for investigation of healthy controls. Quanta Medical provided regulatory oversight of the clinical study and contributed to the processing and management of electronic data.
Table. 5) affect circulating TMAO most, we trained extreme gradient-boosted decision-tree models. We used five-fold cross-validation to predict the Explained Variance (EV) of each variable group on plasma TMAO in the left-out BMIS participants after 100 iterations (Figure 1A, Suppl. Table. 6). Microbiota composition alone performed poorly (EV 2%) whilst diet, another purported major contributor to TMAO production, explained less than 5% of TMAO variance. Serum metabolomics, excluding TMAO and its precursor TMA, was the best predictor (EV 12%) with demographics second, explaining 10% of circulating significantly correlated with measured TMAO values (rho=0. 473, P<2. 2x10 -16 Other variable categories displayed negligible contribution to prediction suggesting considerable information overlap with the metabolomic, metagenomic, biological and dietary datasets (Suppl.
Model data We consider an ensemble of CMIP6 models summarized in Appendix A1. For each model, we consider historical and scenario (SSP1-2. 6, SSP2-4. 5, SSP3-7. 0,  SSP5-8. 5; O'Neill et al. , 2016)  simulations, and we compute the model mean over all available members. Data from the native model grid are then interpolated into a regular 0. 1 • grid (i. e. , about 10 km resolution) using a nearest-neighbor interpolation accounting for the sea-land mask. In practice, any land grid point from the target grid (points of interest are over land) takes the value of the closest land grid point from the source grid. A particular grid point is considered to be a land point if its land fraction is higher than 75 %. Next, we compute the spatial average over France, resulting in a univariate monthly time series.
Observational data To characterize the past warming over mainland France, we use data from the National Thermal Index (ITh). This index is obtained by averaging data from 30 measurement stations that are well distributed over the country. For each station time series, monthly measurements are homogenized following a state-of-the art pairwise method  (Mestre et al. , 2013)  that was applied to a much larger set of measurement stations. In addition to being spatially representative, the 30 stations used in ITh are selected as to provide data since 1900 at least and limited homogeneity breaks after 1947. As a result, before 1947, the ITh index is constructed as the average of the homogenized monthly data -it is available since 1899. After 1947, daily (unhomogenized)  values are used, and the ITh index is available at the daily time step.
Monthly ITh values are calculated as the monthly average of daily values and are consistent with the homogenized series. All 30 measurement stations are located at low altitude. There is no estimate of measurement uncertainty provided with this product. In this study, we use this national index rather than more common global datasets such as CRUTEM  (Osborn et al. , 2021)  or BEST  (Rohde and Hausfather, 2020)  for two reasons. First, it is available at both the monthly and daily timescale. Second, the pairwise homogenization procedure is applied to a large sample of raw temperature data, which should make this procedure more accurate than in other datasets. However, a simple comparison with a reconstruction of the average France temperature from the CRUTEM5 dataset suggests that the two datasets agree very well.
e. , only observed time series are used in y. The length of these time series varies: 1850-2020 for GSAT and 1899-2020 for the French ITh dataset, leading to a total length of n y = 293 for y. Finally, all attribution and projection diagnoses presented below can be derived from the posterior distribution p(x|y). Accounting for GSAT is important because various recent studies have argued that the observational constraint on this variable is robust (e. g. , to the choice of the statistical method), with the high end of simulated GSAT model responses not consistent with observed GSAT changes  (Lee et al. , 2021, and references therein). As there is a clear correlation (across CMIP models) between GSAT and local warming over most regions including France, a reduced GSAT response is expected to imply a reduced regional warming.
Acknowledgements. We thank the climate modeling groups involved in CMIP6, CMIP5, and the  EURO-CORDEX  exercises for producing and making available their simulations. Financial support. This research has been supported by  Météo France ,  CNRS ,  CERFACS , the  European Union 's  Horizon 2020 Research and Innovation Program  under the  EUCP  (grant no. 776613 ), and the  CONSTRAIN  (grant no. 820829 ) projects. Review statement. This paper was edited by  Yun Liu  and reviewed by  Francis Zwiers  and two anonymous referees.
Code and data availability. Code and data to reproduce the key figures of this study are available at https://gitlab. com/ribesaurelien/ france_study (last access: 1 September 2022) and as a Zenodo archive: https://doi. org/10. 5281/zenodo. 6029159  (Ribes, 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/esd-13-1397-2022-supplement. Author contributions. AR designed the study, ran the calculations, and produced most figures. JB performed a large part of data processing and produced some figures. SQ contributed to the main code and re-ran most analyses independently. BD provided observational data and guidance on how to use them. All authors participated in the interpretation of the results and the writing of the paper.
Total initial soil mineral N (NO 3 and NH 4 ) content was assumed to be equal to 140 kg N ha -1 to ensure negligible N stress. The temperature and solar radiation data were provided from a weather station located <2 km from the experimental field and the rainfall, wind speed, and relative humidity data were obtained using the NASA POWER database (https:// power. larc. nasa. gov) [65, 66]. In step 2, a 'blind' simulation was conducted for the best-yielding DH line at each location using the same initial growing conditions from the calibration, without providing the modelers with observed data. Only the calculated trait percent changes (table  1 ) and instructions describing how to modify the calibrated cv. Bacanora traits for the high-yielding DH line were provided to simulate growth for three seasons at Valdivia, Chile  (2008-2009, 2009-2010, and 2010-2011) , one season at Buenos Aires, Argentina (2009-2010), and one season at Ciudad Obregon,  Mexico (2009 Mexico ( -2010)).
The ERCU uses total incoming solar radiation and RUE which are both standard inputs in most crop models. Total incoming solar radiation for each location and growing season was obtained from local weather stations and/or the NASA POWER database (https://power. larc. nasa. gov)  [65]. The production potential threshold was determined from the linear fit between the best pre-and post-anthesis crop response from the highyielding dataset. This was calculated using the average of the first two measurements of cv. Bacanora in Valdivia 2012 and the average of the final two measurements of the high-yielding DH line in Valdivia 2010 (figure  2 ). The biomass potential threshold was then multiplied by the harvest index of the highest yielding DH line, 0. 45, to obtain the yield potential threshold (figure  3 ): Yield potential = ERCU * Harvest index (3).
Crop model ensemble global simulations and extrapolation After confirming that the 29-model ensemble median accurately reproduced the observed effects of the high-yielding traits (figure  1 ), 26 models of the ensemble (table  S1 ) participated in simulating potential wheat production with and without the modified traits (   [26] , and the various temperature functions in wheat crop models are presented in Wang et al  [69]. Winter or spring wheat cultivars were chosen based on the available field experiment data and the dominant type of that region. Winter wheat cultivars have a higher yield potential than spring wheat cultivars because of the longer growing season caused by vernalization  [70] , so the benefits to spring wheat yield potential, especially through a driving trait such as RUE, would also likely benefit winter wheat yield potential  [6, 19].
Acknowledgments This study was a part of the  Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4. The study was supported by the  International Wheat Yield Partnership (IWYP, Grant   IWYP115 ) and the  International Maize and Wheat Improvement Center (CIMMYT). Experiments carried out in Valdivia (Chile) were funded by  IWYP, CIMMYT (Mexico) , and the  Chilean Technical and Scientific Research Council (CONICYT)  by  FONDECYT  Project  1141048. The experimental work conducted at Valdivia by  Dr Jaime Herrera  (UACh) is appreciated. F T was supported by the  National Natural Science Foundation of China  (Project Nos. 31761143006  and  41571493 ). K C K was supported by the  Ministry of Education, Youth and Sports of Czech Republic through SustEs  ( CZ. 02. 1. 01/0.
Data availability statement The global agricultural data that support the findings of this study are publicly available at the Food and Agriculture Organization (FAO) FAOSTAT database (www. fao. org/faostat/) and the global incoming solar radiation data are publicly available at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) Prediction of Worldwide Energy Resource (POWER) database (https://power. larc. nasa. gov). Individual crop models and their documentation are listed in table  S1. The data that support the findings of this study are openly available at the following URL/DOI: https:// doi. org/10. 7910/DVN/VKWKUP. Data will be available from 01 November 2022.
Discussion In sheep, synchronisation of oestrous to control the timing of ovulation is essential for most farmers performing AI. Here, we assessed the transcriptome of the cervix, recovered at the follicular phase of a synchronised oestrous, in ewe breeds with divergent pregnancy rates following cervical AI using frozen-thawed semen. The main findings of this study were that genes regulating muscle contraction and development were higher expressed in Fur and NWS (both with high fertility) compared to the low fertility Suffolk breed. Interestingly, the immune response pathway was lower in NWS compared to Suffolk. The differential expression of multiple informative immune genes in this dataset supports the case for a divergent immune profile between ewe breeds -particularly between the NWS and Suffolk breeds.
Differential expression analysis Quality assessment of the raw sequence data was carried out using the software FastQC (v 0. 11. 8; http:// www. bioin forma tics. babra ham. ac. uk/ proje cts/ fastqc/). Data were quality and adapter trimmed using the BBDuk java package to trim Illumina adapter sequences and any low quality bases (Phred score < 20) from the 3′ end of sequence read pairs. Reads were aligned to the ovine genome Oar_v3. 1 using the Spliced Transcripts Alignment to a Reference (STAR) aligner. A maximum of two mismatches with the reference genome were allowed and only uniquely mapped read pairs were retained for downstream analysis. Read counts overlapping all protein coding genes in the Oar_v3. 1 Ensembl (v. 95) annotation were estimated using featureCounts. To filter out lowly expressed genes, genes with less than one count per million in at least 10 samples were discarded from the analysis.
Remaining gene counts were normalized using the median of ratios method as implemented in DeSeq2 (version 1. 130. 0) [51]  to account for varying sequencing depth between samples. Transcript counts were modelled by fitting the data to a negative binomial distribution using genewise dispersion estimates and DEGs were identified with a generalized linear model likelihood ratio test. Statistical tests were corrected for multiple testing using the Benjamini-Hochberg method. Only DEGs with an adjusted P < 0. 05 and a FC threshold of 1. 5 were used for further differentially expressed gene data exploration and pathway analysis.
Functional and pathway enrichment analysis Aggregated functional profiles of genes and gene clusters in the DEGs lists were identified using the gProfiler2 (v. 0. 2. 0) package. GO terms and Reactome pathways were analysed with an enrichment threshold cut-off of P < 0. 05. The R package rrvgo (v. 1. 1. 4) was used to reduce the redundancy of significantly enriched GO terms by grouping similar terms based on their similarity within the GO hierarchy. Gene co-expression network analyses was carried out using the R package Cemitools (v1. 14. 0). For any modules identified a gene set enrichment analysis was carried out to indicate if each module was induced or repressed in the different ewe breeds. Finally, an over representation analysis was used to identify in each module enriched biological functions.