dataesr/scientific-paragraphs-categorization huggingface dataset:
Further, bigWigAverageOverBed (https://github. com/ ENCODE-DCC/kentUtils) was used to quantify mapped reads on peaks areas. Heatmap and densitity plots were created using computeMatrix followed by plotHeatmap or plotProfile from deepTools2. 62 To permit a comparative display of data within a deeptools profile heatmap,  62  size factors produced by edgeR based on unified peak counts were used to scale samples to account for differences in sequencing depth, library composition, and ATAC-Seq efficiency. ATAC-Seq reads from three wild-type, three Isl1 -/-and three Isl1-Cre -/-/Brg1 fl/fl E8. 75 (6 somites) embryos and ESC-derived CPCs from two independent differentiations were used for further analysis. Additional bioinformatic analysis Gene ontology analysis was performed using DAVID Bioinformatics Resources 6.
8. The Isl1 regulated gene network presented in Fig. 3d  was built by submitting a curated list of Isl1 primary downstream targets to the GeneMANIA web based software. The network was subdivided into transcription factors, signaling molecules, muscle structural and contractile genes involved in heart development and visualized using Cytoscape. Intersection of RNA-Seq and ChIP-Seq data was done by gene name. Intersection of ATAC-Seq and ChIP-Seq peaks was performed with Bedtools v2. 15 (intersect -wa) to identify open chromatin regions overlapping at least 1 bp with Isl1, Brg1 or GATA4 bound sequences. Gene list in GO terms related to cardiac muscle cell differentiation, endothelial cell differentiation and smooth muscle cell development/ differentiation was obtained from (http://amigo. geneontology.
ACKNOWLEDGEMENTS We thank  Ingrid Konrad ,  Susanne Kreutzer  and  Kerstin Richter  for technical assistance. We are grateful to  Pierre Chambon  and  Daniel Metzger  for providing us with the floxed Brg1 mice and  Pier Lorenzo Puri  for the Baf60c antibody. We like to thank  Stefanie Uhlig , the  FlowCore Mannheim  and the  Institute of Transfusion Medicine and Immunology , for excellent support. G. D. was supported by the  LOEWE Center for Cell and Gene Therapy (CGT) , financed by the  Hessian Ministry of Higher Education, Research and Arts  ( III L 4-518/17. 004(2013 ), the  SFB TRR 81 , the SFB 873, the SFB 1213, the SFB 1366 funded by the  DFG  and the  AngioFormatics grant  financed by the state of Baden-Württemberg; YFS was supported by grants from the  Ministry of Science and Technology China  ( 2013CB967400 ) and  National Natural Science Foundation of China (NSFC)  ( 81570285 ); S.
recruitment Initial expected duration of patient inclusion is 2 years, starting in January 2017. 1. End of 2015: grant award; 2. 2016: approval by an independent ethics committee. 3. 2017: inclusion of patients. 4. 2019: end of inclusions, monitoring of participating centres and queries to investigators; overseeing by the steering committee at the REVA Network meetings; blind review to determine protocol violation, to define intention-to-treat and per-protocol analysis populations; new queries to investigators, cleaning and closure of the database. 5. 2020-2021: data analysis, writing of the manuscript and submission for publication. MEthods: AssIgnEMEnt of IntErvEntIon, dAtA CollECtIon, MAnAgEMEnt And AnAlysIs Allocation and sequence intervention A computer-generated randomisation is performed with stratification according to centre and the type of immunosuppression (haematological malignancy or leucopenia <1 G/L or neutropenia ≤0.
Similar data and an evaluation of dyspnoea using a 5-point Likert scale will be recorded at H1, between H6 and H12, at H24 ±6 hours, H48 ±6 hours and H72 ±6 hours after randomisation. Duration of the first NIV session and total duration of NIV within the first 24 hours, between H24 and H48 and between H48 and H72 will be collected to ensure adherence to the protocol. The type of ventilator used for NIV and the NIV interface will be noted. For intubated patients, time and reason for intubation will be documented according to the above-mentioned criteria. Invasive ventilatory settings, arterial blood gases and chest X-ray will be recorded during the first 3 days following intubation. At day 28, vital status, need for intubation, total duration of invasive ventilation and of each oxygenation technique studied will be recorded.
Patient and public involvement Patients and public were not involved in the study study status 1. Current status: the last patient was included on 4 March 2019. 2. Expected date of complete data collection: mid-September 2019 (6-month follow-up of the last patient included). 3. Expected date of the end of monitoring of participating centres: December 2019. 4. Expected starting date of data analysis: first trimester 2020.
The study was funded by the 'Programme Hospitalier de Recherche Clinique InterRégional 2015' of the French Ministry of Health. The study promoter is the University Hospital of Poitiers, Poitiers, France. disclaimer The firm Fisher & Paykel provided the high-flow oxygen therapy equipment and masks for non-invasive ventilation to all the participating centres but has no other involvement in the study. Competing interests RC reports travel expense coverage to attend scientific meetings from Fisher & Paykel and MSD. JPF reports travel expense coverage to attend scientific meetings and personal fees from Fisher & Paykel and SOS Oxygène. SE reports consulting fees from Aerogen, La diffusion technique française, Baxter, Bayer, lecture fees from Aerogen, Fisher & Paykel, unrestricted research grants / research support from from Fisher & Paykel, Hamilton medical, Aerogen, La diffusion technique française.
funding The study was funded by the 'Programme Hospitalier de Recherche Clinique InterRégional 2015' of the French Ministry of Health. The study promoter is the University Hospital of Poitiers, Poitiers, France. disclaimer The firm Fisher & Paykel provided the high-flow oxygen therapy equipment and masks for non-invasive ventilation to all the participating centres but has no other involvement in the study. Competing interests RC reports travel expense coverage to attend scientific meetings from Fisher & Paykel and MSD. JPF reports travel expense coverage to attend scientific meetings and personal fees from Fisher & Paykel and SOS Oxygène. SE reports consulting fees from Aerogen, La diffusion technique française, Baxter, Bayer, lecture fees from Aerogen, Fisher & Paykel, unrestricted research grants / research support from from Fisher & Paykel, Hamilton medical, Aerogen, La diffusion technique française.
This case study uses several methods to highlight South Carolina mountain region agritourism motivations and areas of focus or interest in the region. A targeted web-based survey was used to collect data from members of the South Carolina New and Beginning Farmer Program (SC NBFP). This group was chosen because it is a state-wide program that serves individuals who are motivated to be farmers and may be viewing farming and agritourism through a different motivational lens. Approximately 24% of the agritourism farmers who responded to the survey are beginners (farmers with four years or less of farm operation), while 76% can be regarded as professional farmers (those with five years or more of farm operation). Approximately half of the agritourism farmers indicate they had prior farming and business experience before starting their agritourism operations.
However, as the diversity of practice has grown, it appears there are a wide range of motivations for agritourism farmers. Some of these motivations include social, environmental, innovation and entrepreneurial orientation, and to promote community development. A better understanding of the breadth of these motivations is critical for states and regions to clarify, measure and even incentivize these important individual and community efforts. This case study uses several methods to highlight South Carolina mountain region agritourism motivations and areas of focus or interest in the region. A targeted web-based survey was used to collect data from members of the South Carolina New and Beginning Farmer Program (SC NBFP). This group was chosen because it is a state-wide program that serves individuals who are motivated to be farmers and may be viewing farming and agritourism through a different motivational lens.
However, identified innovative practices of the agritourism farmers in this sample include school tours (46%), farm animal activities (46%), educational seminars (41%), U-pick fruits and vegetables (32%), farmsteading (32%), on-farm processing (23%), and wedding venues (18% of the agritourism farmers have wedding venues on their farms). The South Carolina Department of Agriculture (SCDA) reports almost 400 agritourism farm operations in the state. The SCDA classifies agritourism broadly as farm-based activities that supports tourism in agricultural settings. The range of operations considered agritourism is broad and include sales of local products, pick your own operations, seasonal activities (hay rides, apple picking, etc. ), and farm to table restaurants. Based on the SCDA database, there are 90 identified agritourism farmers in the seven counties of the mountain region of the state.
The operations are largely spread across the four most populous, amenity rich, and developed counties, with the types of agritourism operations being extremely varied. A scan of the SCDA's agritourism database classifies each operation by whether it has the following characteristics: attractions, accommodations, amenities, direct sales, event space and food service. Only 10 of the mountain region agritourism sites had accommodations on site and only 15 had food service. Respectively, 49 and 48 of the farms had various amenities and event space. Characteristics with the most representation are attractions and direct sales with 69 and 65 agritourism farms respectively. Not every agritourism operator had a complete profile of their operations, however, at least 20 farmers engage in some type of u-pick operation, nine are focused on educational activities and tours, four are involved in museums and festivals, and at least four are involved in positive environmental activities such as tree planting, organic farming, nature preservation, and sustainable agriculture (South Carolina Department of Agriculture, https://agriculture.
Acknowledgments: The authors would like to thank the following contributors for the precious information provided concerning French agritourism:  Jean-Marie Lenfant ,  Spokesperson for Bienvenue à la ferme ;  Laurine Latrubesse ,  Le Bureau de Com ;  Marjorie Esprit ,  Chargée de Mission Promotion des Produits et Agritourisme, Chambre Régionale D'agriculture, Occitanie.
The highest average values of C18:2 were found for Morocco15 (15. 2%), whereas its highest variation was related to Spain15 (Fig. 1F , Supplementary Table  S1 ). Finally, the OLP ratio had the highest values in Italy15 and Italy16, while approximately the same ratios were observed in the other four environments. The highest variation of OLP among different environments was related to Spain15 with 42. 6% (Fig. 1G , Supplementary Table  S1 ). The genotype effect on trait plasticity in a common set of cultivars. The same dataset of 14 cultivars in six environments was used to investigate the cultivar variability (Fig. 2  and Supplementary Table  S2 ). In this sense, the highest average value of FrFW was registered for cv. 'Oblica' (Fig. 2A  and Supplementary Table  S2 ). The highest variation for FrFW, in six different environments, was registered for cvs.
Pearson correlation coefficients among the evaluated traits by cultivar showed significant negative correlation coefficients between C18:1 versus C16:0 and C18:2 (Supplementary Table  S3 ). These results were highly consistent among the different evaluated cultivars. The complex effect of genotype, environment and their interaction on trait plasticity of all studied cultivars. A full dataset of all cultivars (113) from nine environments (Supplementary Table  S4 ) was used to analyze the variance components using REML models (Table  1 ). σ² G was the main provider to total variance for FrFW (64. 6%) and C16:0 (47. 3%), σ² E for FrM (69. 9%) and OLP (43. 9%), and σ² GxE for OCFrDW (43. 6%). The remaining FAs evaluated (C18:1 and C18: 2), both σ² G and σ² E showed similarly high values (from 35.
The lowest amplitude of temperatures was observed in Lebanon15 and Lebanon16 (10 and 8. 6 °C, respectively), during the year and especially in spring and summer (Fig. 4  and Supplementary Table  S5 ). The whole dataset of 113 cultivars evaluated in nine environments, were used to calculate Pearson's correlation coefficients between ten-day temperature series and average values of OCFrDW and C18:1 (Fig. 5 ). Maximum temperature showed a positive correlation with OCFrDW in winter, early spring and autumn, being negatively correlated in late spring and summer. OCFrDW also showed a positive correlation with the minimum temperature throughout the year, being negative with the thermal amplitude. The C18:1 showed a negative correlation with maximum and minimum average temperatures during the year.
The complex effect of genotype, environment and their interaction on trait plasticity has been analyzed on 113 olive cultivars in five sites for two years, for a total of nine different environments. It represents the first concerted effort to gather information on the phenotype plasticity of a particularly large and representative sample of the entire variability of cultivated olive germplasm. Traits of high agricultural importance, related to fruit and olive oil composition, were taken under consideration. A high variance in the whole dataset for fruit fresh weight due to the genetic background  81  and G × E effect for oil content has been observed, while similar high genetic and environmental variance was observed for fatty acids composition. In accordance, G × E interaction was significant for maximum oil content and rate of accumulation, with similar or even higher variance component than the genetic variance, as reported by Navas-Lopez et al.
The genetic identity of the selected cultivars was verified through a comparison of each molecular profile based on a revision of all olive collections involved in the European MSCA Before project (data under publication) and by comparison with previously published data from the main olive collections and databases  24, 92, 93. A total of 816 trees were sampled, each accession being represented by two trees for each environment. Fourteen cultivars were in common between IOGC, WOGB-INRA and WOGB-IFAPA germplasm collections. Two kilograms of fruits were harvested from each of the two selected trees. The fruits were randomly chosen around the canopy and promptly transferred to the laboratory. From each sample, 25 g of fruits were weighed before and after fruit drying for the fresh weight (FrFW) and fruit moisture (FrM), all others were conserved at -20 °C.
Statistical analysis. Statistical descriptive analyses of the different traits were performed for one balanced and for the whole unbalanced datasets. The balanced dataset includes 14 common cultivars available in the three collections IOGC, WOGB-INRA and WOGB-IFAPA located in Italy, Morocco and Spain, respectively and two seasons (six environments). For these cultivars, variability for seven traits was calculated through Violin Plot Statlet (StatGraphics Centurion, www. statgraphics. com). The quantitative estimators of phenotypic plasticity  95, 96  , were calculated in two simplified approaches as follows; 1) The environmental effect on trait plasticity in a common set of cultivars was calculated through the coefficient of variation for each trait in each environment (CV trait ); 2) The genotype effect on trait plasticity in a common set of cultivars was calculated through the coefficient of variation over the environments (calculated as standard deviation of means/mean of means, CV m ).
The complex effect of genotype, environment and their interaction on trait plasticity of all studied cultivars was carried out on the unbalanced dataset, including all 113 genotypes from five collections and two seasons (nine environments). Trait variation in each environment, as well as overall environments, was calculated as previously described. Variance components were then estimated according to the statistical model: µ = + + + × + ε P G E (G E) ijk i j i j i jk where P ijk was the phenotypic value of the k tree of the i genotype in the j environment; µ the overall mean of the progeny; G i a random effect contributed by the i genotype; E j a random effect of the j environment; (G × E) ij the interaction between the i genotype and the j environment; and ε ijk was the random residual error effect.
From this model, variance components among genotypes (σ² G ), among environments (σ² E ), associated with the G × E interaction (σ² GE ), and residual error effect for the measured samples (σ² ε ), were obtained. Broad-sense heritability (H²) for all evaluated traits was estimated as the ratio between genotypic and phenotypic variances: σ² G /(σ² G + σ² GE + σ² ε ). Best linear unbiased prediction (BLUP) values for genotypes and environments were also estimated from these models. Analyses were performed using R version 3. 5. 0 software, with REML estimation method under lme4 package for developing mixed linear models (R Core Team, 2018). The effect of temperature and thermal time on trait variation. Climate data were registered in each collection site along the years 2015 and 2016 when fruits were collected.
Among climate variables, temperature data were selected to test their effect on phenotypic plasticity of the functional traits under analysis. Daily average maximum and average minimum temperatures, as well as thermal amplitude, were calculated from hourly data for each site. In this case, data on a new environment (Argentina15) has been included, being 10 the total number of environments under study. To calculate the accumulation of thermal time in each environment for the 14 common cultivars, linear or bilinear functions were fitted to the relationships between three main fatty acids (C18:1, C18:2 and C16:0), OCFrDW, FrM, FrFW and OLP with thermal time (TT °C × day, from 7 °C to 40 °C). Thermal time was registered from full flowering time to the harvesting time, using the single sine, horizontal cut-off method  88, 97.
Acknowledgements The research was supported by the  European Union 's  Horizon 2020 Research and Innovation Program Marie Sklodowska-Curie -Before Project  (Grant Agreement No 645595). Special thanks to  Ghina Jebbawi  and  Ghinwa Al Hawi  from  LARI -Lebanon ;  INTA, PIO CONICET-SECITI ,  CONICET ,  Secretaría de Ciencia ,  Tecnología e Innovación del Gobierno de San Juan  and also to the  Libero Consorzio Comunale of Enna, Sicily , for the support provided to these activities.
Details of the methodology, data requirements, and underlying assumptions are contained in  [24]. IRWR is calculated as, IRWR CTRY = [R + I -(Qout -Qin)] (3) where R: surface runoff calculated as the long-term average annual flow of surface water generated by direct runoff from endogenous precipitation; I: groundwater recharge generated from precipitation within the country; (Qout -Qin): the difference between base flow or groundwater contribution to rivers and seepage from rivers into aquifers. Similarly, ERWR is calculated from  [24]  as, ERWR natural = SW IN + SW PR + SW PL + GW IN (4) where SW IN : surface water entering the country; SW PR : the amount of water entering the country through rivers measured at the border; SW PL : the portion of water in shared lakes belonging to the country; GW IN : groundwater entering the country.
For example, for most countries in the database, IRWR has been fixed at 1962 estimates. In this paper, therefore, we introduce the concept of Potential Available Water Storage (PAWS) derived by integrating the monthly Total Water Storage (TWS) from GRACE (Gravity Recovery and Climate Experiment) satellite data with Tropical Rainfall Measuring Mission (TRMM) precipitation estimates. The proposed index is used to assess "potentially available water" resources for 48 African countries. The proposed approach circumvents many of the limitations related to data unavailability and reliability in data-scarce regions, such as Africa. In fact, Africa's 2017 estimated population of 1. 2 billion is projected to double by the year 2050 to 2. 4 billion people. Such rapid population growth will exert considerable stress on the continent's available water resources, worsening the already acute water scarcity situation  [26].
In 2015, NASA Jet Propulsion Laboratory (JPL) introduced a new mascon product, JPL-M solutions, which made available by  [51]. The JPL-M solves the gravity field functions within a fixed mass block of 3 × 3-degree resolution  [51]. In 2016, the Center for Space Research (CSR) at the University of Texas at Austin introduced another mascons product, the CSR-M  [27]. The CSR-M data were estimated using the same standards as the preceding GRACE-SH  [27]. However, CSR-M data have the advantage of retaining location information that can be used in smaller areas (~100 km)  [52] , reducing residual noise and minimizing spatial leakage error. The CSR-M based TWS data can be integrated directly without applying any scaling factor. This research utilizes the CSR-M data at a 1-degree resolution to comply with the original GRACE-footprint, and TWS data were extracted for Africa at the country level.
TRMM Precipitation Estimates The Tropical Rainfall Measuring Mission (TRMM) is a joint mission of NASA and the Japan Aerospace Exploration Agency that began in January 1998. TRMM monthly precipitation observations products are computed as quasi-global grids of 0. 25 • resolution combining microwave-IR-gauge estimates of precipitation. The TRMM research product is recommended for global and regional water balance studies and hydrological model simulation. In this research, we utilized the TRMM 3B42 research product. Precipitation data were co-registered to a fixed 1-degree resolution grid similar to the aggregated CSR-M estimates. The TRMM data were sampled at the country level using individual country shapefile.
Ancillary Data Other ancillary data utilized include the four-soil moisture (SM) estimates, as well as the canopy water content (CWC) from the Global Land Data Assimilation System (GLDAS) Noah-LSM. Summing the average of SM and CWC estimates leads to calculate the Land Water Content (LWC) required for GW storage estimation according to Equation  (6). The LWC anomalies were constructed using the same GRACE baseline by subtracting the averaged grids from January 2004 to December 2009 from all monthly grids. The GLDAS-Noah datasets are available at 1 • resolution grids via (https://disc. sci. gsfc. nasa. gov/datasets?page=1&keywords=gldas%20noah). The data were co-registered similar to GRACE grids. The IRWR data for Africa were acquired from the AQUASTAT database at (http://www. fao. org/nr/water/aquastat/data/query/index.
html?lang=en). The lake level altimetry observations for four major African Lakes (Tana, Victoria, Malawi, and Tanganyika) and two reservoirs (Volta and Nasser) were obtained from the HYDROWEB portal (http://hydroweb. theia-land. fr/); noteworthy, these lake level altimetry observations have good agreement with in-situ water levels observations according to  [37, 53]. Additionally, through personal communications, we acquired the time series for the depth to groundwater for twenty observational wells in North Ghana from December 2005 to December 2012. The groundwater data were collected as part of project BRAVE (Building understanding of climate variability into the planning of groundwater supplies from low storage aquifers in Africa), headed by the British Geological Survey (BGS). Potential evapotranspiration (PET) estimates were acquired from the Climatic Research Unit (CRU), at the University of East Angelia (UEA).