dataesr/scientific-paragraphs-categorization huggingface dataset:
include essential information, with one entry (row) for each proxy record (time series), with a large portion of sites ('Site Name') represented by more than one record. These are based on different proxy types (e. g. , alkenone and Mg/Ca from the same marine sediment sequence), or they represent different seasons based on a single proxy type, usually pollen. This database is a subset of a larger compilation of paleoclimate datasets that are configured in the same format, including the PAGES 2k Consortium 9 database. The 1319 records that comprise this data product are identified within this larger collection by 'In Compilation' = 'Temp12k'. The final column in Suppl. Table1, 'Link to LiPDverse,' displays the URL that links from the metadata table to each dataset where the metadata and associated proxy time series and chronology data can be viewed and downloaded individually.
acknowledgements Funding for this research was provided by the  US National Science Foundation  ( AGS-1602105 ,  AGS-1602301 ,  AGS-1903548 ),  Swiss National Science Foundation  ( IZSEZO 180887 ,  SNF 200021-165494 ),  NOAA's Climate Program Office  (Cooperative Agreement # NA17OAR4320101 ), and the  Heising-Simons Foundation  ( 2016-015 ). The  Past Global Changes (PAGES)  project provided additional support for workshops leading up to this data product. We thank the original data generators who made their data available for reuse, and we acknowledge the data repositories for safeguarding these valuable data assets, enabling the community to unlock their collective power 582.
code availability Code for working with the LiPD data files, including basic functionality in three programming languages, is available on GitHub (https://github. com/nickmckay/LiPD-utilities). MATLAB code used to map site locations (Fig. 2 ) and to compute composites (Figs. 4 5 6 7 8 ) is available at https://github. com/nickmckay/Temperature12k under an MIT license  581.
Search Strategy We searched published studies indexed in MEDLINE and EMBASE databases from inception to June 2020 using the search strategy consistent with supplementation in different types of vitamins (A, B (including folic acid), C, D, E and K) and chronic IRD including RA, spondyloarthritis (SpA) and psoriatic arthritis (PsA), as described in Appendix A. A bibliography of selected retrieved articles was also manually searched and reviewed for inclusion. Conference abstracts from selected Rheumatology meetings (European League Against Rheumatism (EULAR) and the American College of Rheumatology (ACR)) and from Nutrition meetings (International Congress of Nutrition, European Nutrition Conference, American Society of Nutrition) from 2017 to 2019 were searched and manually reviewed for inclusion.
Data Extraction Data were independently collected using a standardized form by two authors (YN, CD). The data extraction form included study design, publication date, journal, location, disease, inclusion criteria, the type and length of the intervention, primary and secondary assessed outcome, number of participants, study population characteristic (including age, duration of the disease, clinical baseline characteristics and treatments), results in terms of disease activity, side effects and treatment adherence. If data were missing, authors of primary articles were contacted.
Study Selection The search process yielded 606 records (Figure  1 ). Of these, 28 were selected after review and assessed for eligibility. Of those, 13 studies were included in the qualitative synthesis. Among them, six studies (two on vitamin E and four on vitamin D) had sufficient data and were included in the meta-analysis. No unpublished relevant studies were obtained. synthesis. Among them, six studies (two on vitamin E and four on vitamin D) had sufficient data and were included in the meta-analysis. No unpublished relevant studies were obtained.
The full and updated chemical scheme that we present in this paper, hereafter called V20, contains 108 species, 948 reversible reactions and 10 irreversible reactions (i. e. 1906 reactions in total). It can be downloaded from the KInetic Database for Astrochemistry (KIDA)  (Wakelam et al. 2012)   1. To validate the inclusion of the Burke et al. (  2016 ) methanol sub-mechanism within our chemical scheme, we have compared simulation results with experimental results from various sources  (Aronowitz et al. 1979; Cathonnet et al. 1982; Norton & Dryer 1989; Held & Dryer 1994; Ren et al. 2013; Burke et al. 2016). The model performance over a wide array of experimental conditions was found to be in better agreement than that of the original mechanism V12 (see figures in Appendix C). With the updated chemical scheme V20, we revisit in the next section the 1D thermo-photochemical model results for emblematic cases published in previous papers: HD 209458b and HD 189733b for hot Jupiters, GJ 436b and GJ 1214b for warm Neptunes, and Uranus and Neptune.
e. species already detected in (exo)planet atmospheres. Following our update of the former full scheme, we also provide an update for the reduced scheme. We have derived the new reduced scheme by following the same methodology as in  Venot et al. (2019). We have used the ANSYS Chemkin-Pro Reaction Workbench package (2017), with the method Directed Relation Graph with Error Propagation (DRGEP), followed by a Sensitivity Analysis. After several reduction attempts, we have ended with the reduced scheme presented here. It is the best compromise between number of species, number of reactions, applicability range, and abundances accuracy. As in  Venot et al. (2019) , the scheme has been developed primarily for GJ 436b-like planets, in order to reproduce the abundances of the current observed neutral species (listed previously), as well as C 2 H 2 , but it can be applied to hot Jupiters, brown dwarfs and solar system giant planets as well.
Acknowledgements. The author thanks the anonymous referee for his/her careful review that helps improving the manuscript. O. V. and T. C. thank the  CNRS/INSU Programme National de Planétologie (PNP)  and  CNES  for funding support. P. T. acknowledges support from the  European Research Council  (grant Article number, page 9 of 18 A&A proofs: manuscript no. NewMethanol no. 757858 -ATMO ). The authors thank  B. Edwards ,  Q. Changeat ,  I. Waldmann  for useful discussions on JWST and ARIEL observations.
For this, Drosophila larvae were reared from early embryonic stage in food medium with minimal odors but nutritionally equivalent to regular diet  (Shim et al. , 2013). These animals were then infected with wasps and their lamellocyte response was analyzed. Interestingly, this condition recapitulated the lamellocyte formation defect seen upon loss of Or42a. Supplementing the minimal medium with food odors corrected the defect (Figure  1 -figure supplement 1C,D). Together, with the loss-of-function mutation and food-odor experiment, the data demonstrated the importance of food odors and their sensing in lamellocyte cell fate specification. Downstream of odor detection, activation of PN is necessary to mediate systemic release of GABA from neurosecretory cells of the larval brain. The GABA-producing neurosecretory cells are marked with Kurs6-Gal4 driver (Kurs6 + )-based expression of reporter transgenes  (Shim et al.
Blood cell density analysis To calculate circulating blood cell count/mm 2 and the proportion of lamellocytes in blood cells, individual larvae were bled per well, counter-stained with DAPI and phalloidin and imaged to obtain five images per well under constant magnification of 20X. All hemocytes (DAPI-positive cells) in these images were counted using the ImageJ software plugin Analyze particles tool and the numbers of cells from the five images were summed and a cell density per mm 2 was obtained. The respective number of lamellocytes per image was counted and plotted as proportions in blood cell count/mm 2. The circulating cellular response to infection was quantified at 48HPI and in uninfected conditions was undertaken in wandering 3rd instar larvae. In all experiments, control genotypes were analyzed in parallel to the experimental tests.
Quantification of lymph gland phenotypes All images were quantified using ImageJ software. Lymph gland area analysis was done as described  (Shim et al. , 2012). Roughly, middle three confocal Z-stacks were merged and threshold, selected and area was measured. This was done for respective zones and the area is represented in percent values. Controls were analyzed in parallel to the tests every time. A minimum of five animals were analyzed each time and the experiment was repeated at least three times. The quantifications represent the mean of the three independent experimental sets. For crystal cell quantification, total number of Hnt-positive cells per lymph gland lobe was counted and represented as crystal cells per lobe. A minimum of five animals were analyzed each time and this was repeated atleast twice.
Acknowledgements We thank  U Banerjee  for Sima antibody,  M Freeman  for Gat antibody,  Shannon Olsson  for odor experiments,  N Mortimer  and  T Schlenke  for L. boulardi stock and FlyBase, VDRC (Austria), and BDSC for fly stocks,  NCBS, CCAMP  for their Fly facility, imaging, and metabolomics facilities. We thank  Apurva Sarin  and inStem colleagues for helpful discussion and comments on the manuscript. We specially acknowledge  Varadharajan Sundaramurthy  and  Neeraja Subhash  for imaging support in times with campus restrictions due to Covid-19 crisis. Due to space limitations, we apologize to our colleagues whose work is not cited. This study was supported by the  DBT-Center of Excellence  grant  BT/PR13446/COE/34/30/2015 ,  DST-ECR ECR/2015/000390 ,  000390DBT-IYBA 2017 ,  CEFIPRA  and  DBT Ramalingaswami Re-entry Fellowship  to TM and  Basic Science Research Program  through  National Research Foundation  ( NRF-2014S1A2A2028388  and  NRF-2017R1C1B2007343 ) to JS.
genesys-pgr. org/). However, some of the information in this database is outdated and incomplete. The aim of this paper is to provide an update of the main sunflower gene banks around the world, the numbers of accessions they contain and their specificities, and to provide in-depth details of accessions held in the USDA (USA), INRAE (France) and IFVC (Serbia) gene banks. The discussion will consider how genomics and modern phenotyping methods can improve our knowledge of the resources, mining of the traits of interest, and their importance to provide adequate material for research and breeding in the future.
The collection in Novi Sad now contains 21 perennial and eight annual species, represented by 332 and 185 accessions respectively. Each year, 60 to 80 accessions of annual species are grown for seed regeneration. Seed reserves are kept in cold storage at þ4 °C and vary from a few seeds to several thousand per accession (Tab. S1). Most of the perennial species are maintained as a living collection in quarantine fields; while some also have seed available (Tab. S2). Detailed information about the collected accessions including passport data can be accessed online on the IFVCNS site: https://www. ifvcns. rs/ kolekcija-divljeg-suncokreta/wild. html. For both annuals and perennials, accessions are available with an MTA when seed stocks are sufficient. Details of wild species are given in Tables  S1  and S2 , additional references in Supplementary data: Supplementary references 1.
It also includes the first CMS-based hybrids which are conserved as hybrid populations and lines (supplementary data 2, supplementary references, Tab. S3). DAI-General Toshevo (Bulgaria) maintains a collection of wild annual and perennial Helianthus species. The first wild sunflower accessions were obtained from the USDA-ARS, Bushland, Texas and planted in the 1970s. A second group of wild sunflower accessions was obtained in 1983. However, a major part of the collection of wild sunflower species was obtained in the course of collections made in collaboration with USDA in the USA in 1999 and 2004. The DAI collection includes accessions maintained in the field and in cold chambers at þ4 °C and at À14 °C (long term preservation). Perennial species are permanently grown in the field as a living collection, and annuals are planted each growing season.
The DAI collection includes 215 accessions from 30 perennial Helianthus species (Tab. S1), 175 accessions from seven annual Helianthus species (Tab. S2), 30 different CMS sources and some species from different genera of the Asteraceae family. These accessions are important initial material for research and breeding for resistance to abiotic and biotic stress factors and variation in fatty acid content (Supplementary data 1, Supplementary references 2). The INTA (Argentina) sunflower gene bank at Manfredi (Cordoba) is part of the National Network of Genetic Resources. The first accessions grown at the station were OPV incorporated in 1948. The wild sunflower collection is made up of introductions from the USA and naturalized wild collections in Argentina. The first inbred lines were incorporated in 1980 from the INTA breeding programme and other sources.
However, for a long time, sequences were too short to differentiate and compare the long and highly similar repeats. In 2015, the RSII PacBio sequencer produced sequences longer than the known repeats from the sunflower genome and made possible sequencing of the 17 chromosomes of the inbred line XRQ. The raw data did not themselves solve the complexity of the sunflower genome. A long and complex process of bioinformatic computation compared and assembled the millions of sequence fragments as chromosomes. Improvement of bioinformatic software also plays an important role in genomics. As an example, at the same time as using long-read sequences for the sunflower genome, NRGene developed a software permitting genome sequences to be complied using short-read fragments  (Lu et al. , 2015). This approach made it possible to obtain a complete sequence of the genome of the line HA412 of similar quality to that obtained for the line XRQ.
, 2019)  to assess biotic and abiotic stresses on sunflower. This platform is based on a conveyor system in a greenhouse where plants are automatically imaged in visible, fluorescence and near-infrared spectra. Using the RGB images and the IPSO Phen software INRAE teams successfully characterized the responses of sunflower genotypic panels for drought stress at early stages (SUNRISE project) and Orobanche infection (Plant2Pro Phenor project). Exploitation of these methodological developments will certainly follow shortly. In semi-controlled conditions, still in the frame of the SUNRISE project, the Phenotoul-Heliaphen phenotyping platform was developed by  Gosseau et al. (2019)  to study the effects of different drought scenarios at vegetative and postflowering stages of sunflower. This platform allowed the authors to impose precisely different drought intensities and characterize yield sensitivity to post-flowering stress of sunflower varieties, but seed weight measurement still required time-consuming manual measurements.
The Phenomobile V2 was first used on sunflower trials on the Phenotoul-Agrophen platform. Equipped with multispectral cameras, LiDARs and RGB cameras, methodological developments are currently underway to estimate leaf area index and aboveground biomass more accurately and dynamically. The development of high-throughput phenotyping platforms allowed the acquisition of large amounts of images and 3D point clouds but, as in many cases, the main bottleneck is the development of sunflower-specific image analysis pipelines taking into account its leaf architecture and the position of the head. This will require new expertise in image analysis and also the collaboration of experts for the different traits and diseases. Soft-and hardware tools to manipulate these data must also be constructed. In particular, a sunflower crop ontology needs to be developed with international collaboration, and an open phenotype database developed.
HTPPproduced data could then follow the FAIR principle and be accessible as on the PhenoDB INRAE database (https:// sunrise. toulouse. inra. fr/phenoDB/) for further long-term usage and characterization of CWR. In addition to the development of high-throughput genotyping tools that are widely used to characterize the CWR genomic diversity, the emerging field of phenomics is starting to develop more robust and precise phenotyping methods for sunflower that will allow the characterization of entire collections and therefore both help their conservation and accelerate their exploitation for breeding and help to understand the genetic control of their tremendous phenotypic diversity.
LDAS-Monde is a global offline land data assimilation system (LDAS) that jointly assimilates satellitederived observations of surface soil moisture (SSM) and leaf area index (LAI) into the ISBA (Interaction between Soil Biosphere and Atmosphere) land surface model (LSM). This study demonstrates that LDAS-Monde is able to detect, monitor and forecast the impact of extreme weather on land surface states. Firstly, LDAS-Monde is run globally at 0. 25 • spatial resolution over 2010-2018. It is forced by the state-of-the-art ERA5 reanalysis (LDAS_ERA5) from the European Centre for Medium Range Weather Forecasts (ECMWF). The behaviour of the assimilation system is evaluated by comparing the analysis with the assimilated observations. Then the land surface variables (LSVs) are validated with independent satellite datasets of evapotranspiration, gross primary production, sun-induced fluorescence and snow cover.
, 2018). Subsequently, this can benefit weather forecasts, including temperature and precipitation. It can also indirectly benefit agricultural and vegetation productivity prediction, streamflow prediction, warning systems for floods and droughts and the representation of the carbon cycle  (Bamzai and Shukla, 1999; Schlosser and Dirmeyer, 2001; Bierkens and van Beek, 2009; Koster et al. , 2010; Bauer et al. , 2015; Massari et al. , 2018; Albergel et al. , 2018a Albergel et al. , , 2019;; Rodríguez-Fernández et al. , 2019; Muñoz-Sabater et al. , 2019). Amongst the current land-only LDAS activities, several are led by NASA (National Aeronautics and Space Administration) projects. Examples of such activities are the Global Land Data Assimilation System (GLDAS,  Rodell et al. , 2004) , the North American Land Data Assimilation System (NLDAS,  Xia et al.
The resulting 0. 25 • spatial resolution reanalysis of the LSVs is hereafter referred to as LDAS_ERA5. In this paper, it is shown that LDAS-Monde can be used to detect, monitor and forecast the impact of extreme events on LSVs. The following items are presented and discussed. -An evaluation of LDAS-Monde at a global scale is carried out. This assessment involves the assimilated observations to demonstrate that the system is working as intended. Importantly, LDAS-Monde is then validated using diverse, independent and complementary satellite-derived datasets of evapotranspiration (EVAP) from the GLEAM project  (Miralles et al. , 2011; Martens et al. , 2017) , gross primary production (GPP) from the FLUXCOM project  (Tramontana et al. , 2016 , Jung et al. , 2017) , solar-induced fluorescence (SIF) from the GOME-2 (Global Ozone Monitoring Experiment-2) scanning spectrometer  (Munro et al.
, 2006 , Joiner et al. , 2016)   -The LDAS-Monde global analysis over 2010-2018 is used to detect droughts and heatwave events in 2018. This identification is performed by computing anomalies of LSVs over the 9-year period and identifying where the strongest negative anomalies are located in 2018. For the identified regions, the abilities of LDAS-Monde to forecast such events in near-real time is investigated by forcing it with high-resolution forecasts from the ECMWF. The paper is organized into five sections: Sect. 2 details the various components constituting LDAS-Monde (the ISBA LSM, the data assimilation scheme, the EOs assimilated as well as the different atmospheric forcing datasets used), followed by the experimental and evaluation setups. Section 3 describes and discusses the impact of the analysis on the representation of the LSVs.
It makes vegetation growth possible as a result of an uptake of CO 2. Contrastingly, a deficit of photosynthesis triggers higher mortality rates. Ecosystem respiration (RECO) represents the CO 2 being released by the soil-plant system and GPP by the carbon uptake via photosynthesis. Finally, the net ecosystem exchange (NEE) consists of the difference between GPP and RECO. Each ISBA grid cell is composed of up to 12 generic land surface types, namely bare soil, rocks, permanent snow and ice surfaces, as well as 9 plant functional types (needleleaf trees, evergreen broadleaf trees, deciduous broadleaf trees, C3 crops, C4 crops, C4 irrigated crops, herbaceous, tropical herbaceous and wetlands). The ECOCLIMAP-II land cover database  (Faroux et al. , 2013)  provides these parameters for each patch and each grid cell of the ISBA model.
Data assimilation The SEKF used in LDAS-Monde is a two-step sequential approach in which a prior forecast step is followed by an analysis step. The prior forecast propagates the initial states to the next time step with the ISBA LSM and the analysis step then corrects this forecast by assimilating observations. The flow dependency (dynamic link) between the prognostic variables and the observations is ensured in the SEKF through the observation operator and its Jacobians, which propagate information from the observations to the analysis via finitedifference computations  (de Rosnay et al. , 2013). The Jacobian matrix has as many rows as assimilated observation types (two in our case: SSM and LAI) and as many columns as model control variables requested (eight in our case, soil moisture from layers 2 to 8 and LAI).
They evaluated the performance of ERA5 compared to ERA-Interim and found improved results for ERA5 as well. Furthermore, they evaluated both reanalysis datasets for their ability to force the ISBA LSM, which demonstrates a clear advantage for ERA5 in terms of the performance of LSVs. Albergel et al. (2018a)  made similar comparisons of the ISBA LSM forcing over North America. They showed enhanced performances in the representation of evapotranspiration, snow depth, soil moisture and river discharge for ERA5 relative to ERA-Interim. At the time of writing, the ERA5 model and data assimilation system (cycle 41r2 of the ECMWF IFS) are very similar to that of the operational weather forecast, HRES, which has production cycles ranging from 41r2 to 45r1 during the study period (more information at https://www.
In practice, the rescaling gives similar results to CDF (cumulative distribution function) matching. The linear rescal-ing is performed on a seasonal basis (with a 3-month moving window) as suggested by  Draper et al. (2011)  and  Barbu et al. (2014). The LAI GEOV1 observations are based on data from both SPOT-VGT (up to 2014) and PROBA-V (from 2014) satellites. They span from 1999 to present, have 1 km spatial resolution and are produced according to the methodology developed by  Baret et al. (2013). LAI GEOV1 observations have a temporal frequency of 10 d at best and no observations are available during cloudy conditions. LAI data are masked in the presence of modelled snow by the ISBA LSM. As in previous studies (e. g. Barbu et al. , 2014; Albergel et al. , 2019) , observations are interpolated by an arithmetic average to the model grid points (0.