dataesr/scientific-paragraphs-categorization huggingface dataset:
Contribution of individual technical solutions to objectives attainment and associated risks Table  4  shows the scores attributed by the analysts of the European MCYFS to the technical solutions proposed for all the six system components. Overall, the analysts are confident with the use of weather data from reanalysis products in crop models, and estimate a low impact on the overall attainment of the system for crop monitoring and yield forecasting (minimum solution of meteorological component has a score of 4 for objectives 1-3). Nevertheless, one of the known main limitations of reanalysis products are the uncertainties in precipitation from convective events  (Lorenz and Kunstmann, 2012). This may actually lead to significant differences in the performances of reanalysis products in different areas of the world (e.
The technical solutions for both the crop modelling and remote sensing components are those, according to the analysts, with the highest impact on the performance of the system for crop monitoring (objectives 1 and 2) and, especially, for yield forecasting (objective 3). Only when calibrated crop models are used -the intermediate solutionthe system performance for these objectives reaches the minimum required score of 3, even if models are calibrated based on data from a literature review. For qualitative analysis -objective 1 and 2-upgrading to advanced technical solution results in a performance increase due to the improvement in the model calibration -using local experimental data, in the advanced solution-agreeing with the results of  Palosuo et al. , (2011). However, to improve crop models performance for quantitative yield forecasting (objective 3) the analysts considered necessary also local information on soil and crop management data, in line with  van Ittersum et al.
Selected cost-efficient MCYFS-like systems and expected performance The strategy to identify the cost-efficient alternatives to construct the crop monitoring and yield forecasting system presented in Chapter 3 tends to privilege similar technical solutions for the different countries covered by GLOBCAST. This is expected as the solution scores are fixed for all geographical areas, and the relative costs of the different technical solutions are -as mentioned in Section 3. 3-highly stable. Nevertheless, the country-specific risks identified constitute important differential factors that constrain the available technical solutions, and the feasible systems. According to this, the results of the cost-efficient analysis across the GLOBCAST countries can be summarized in three scenarios: A. Countries with low constraints to access information, however some data is missing or not accessible but this has no major effect on the overall system performance.
Conclusions This paper presents an evaluation framework followed to identify cost-efficient alternatives to extend the MARS Crop Yield Forecasting System (MCYFS), running in Europe since 1992, to other main producing areas of the world: Eastern European Neighbourhood, Asia, Australia, South America and North America. The extended systems follow the principles and components of the MCYFS Europe: a data rich system driven by expert knowledge (team of analysts), where indicators are produced by a meteorological data infrastructure, a remote sensing data infrastructure, and a crop modelling platform. They are analysed to report on crop growth and produce crop yield forecasts. An area estimation component -not existing in the European MCYFS-is added to produce independent crop area figures. The framework designed evaluates the performance of the possible MCYFS-like system realizations against six defined objectives and their costs.
According to an evaluation a priori of the possible risks, some necessary data and resources cannot be easily collected: experimental and agro-management data to improve the calibration and reliability of crop models; reliable sub-national crop statistics; experts on local conditions to recruit; and ground surveys for crop area estimation. These mentioned resources are may not always be accessible for an international organization like the EC, implementing a crop monitoring system outside its territory, but can be, in theory, achieved by national institutions interested in building such systems. The results of the evaluation framework presented were implemented in successive phases of the GLOBCAST project, and the MCYFS extension to South America, India, China, Turkey, Ukraine, Russia and Kazakhstan started in 2012.
In these cases the baseline system was implemented: reanalysis near-real-time products for the meteorological component; a crop modelling platform with crop models calibrated with existing data form scientific literature; global soil and arable land maps to run the model spatially; a remote sensing component based on low resolution biophysical products with a log-term archive; and a statistical component with simple methods -regression, trend analysis-applied at national/sub-national scale; and an analysis team with staff of different backgrounds on agronomy, remote sensing, cop modelling, etc. Only the crop area component has not been implemented for the time being. An overview of the implementation of the MCYFS in those countries and its results can be found in https://ec. europa. eu/agriculture/events/globcast-dissemination_en.
For instance, the accessibility to high resolution satellite data, with the open access data policy for Sentinel 2 imagery (https://sentinel. esa. int/web/sentinel/sentinel-data-access); opportunities to develop added-value NRT products that combine different data sources in new big data environments; or the contribution of recent initiatives on crowdsourcing and citizen science  (Beza et al. , 2017). This framework can be also of interest to other global or national initiatives on near real time crop monitoring and yield forecasting. The basic principles to evaluate the possible technical solutions and the inclusion of costs when identifying the feasible ones are general ideas that can be applied to other system architectures, e. g. with different objectives, components, or interactions between components.
The results of the analysis presented are, by contrast, specific to the MCYFS architecture, particularly on what regards the contribution of the different technical solutions to the overall system performances. Moreover, the scores to the different technical solutions were given by the current MCYFS analysts based on their own experience working with the European MCYFS, and that introduces some subjectivity in the evaluation. However, there is no easy way to circumvent it in an analysis a priori when planning a system implementation. Furthermore, the exercise was conducted in 2011 and some technical alternatives and their costs have evolved over time: e. g. open access high resolution imagery, decrease of data processing costs, adoption of transparency and open data access policies from local governmental bodies and research institutions to increase that availability.
2004; Georgy et al. 2013). Until now, A-MaZe lacked a proper treatment of a static balance between gravitational and pressure forces. The present paper remedies this shortcoming by appropriately augmenting (see Sect. 2. 3) the hydrodynamic solver. Key characteristics of the latter, as far as relevant for and used in this paper, are summarized in the following. Equations (  1 )-(3) are solved using a finite volume discretization on the basis of mapped grids  (Calhoun et al. 2008)  for general curvi-linear coordinates. A regular Cartesian mesh (the computational mesh) is mapped to the desired mesh in physical space (the physical mesh). Within this paper, physical meshes are truly Cartesian (in 1D, 2D, and 3D), 2D axi-symmetric, or 3D spherical shell wedges. Other mappings have not yet been implemented, although the code infrastructure to hold them is in place.
Acknowledgements. We acknowledge support from the  European Research Council  through grants  ERC-AdG  No. 320478-TOFU  and  787361-COBOM. This work was also partly supported by a  Science and Technology Facilities Council  Consolidated Grant ( ST/R000395/1 ). RW and DF acknowledge support from the  French National Program  for High Energies PNHE. RW and DF are also grateful for the hospitality and fruitful discussion with the colleagues, in particular  Professor Keh-Ming Shyue , of the  Center for Theoretical Sciences, Mathematical Division at National Taiwan University  where part of this work has been made. Dr. Jean M. Favre  from the  Swiss Supercompute Center (CSCS)  we thank for sharing his profound knowledge on visualization and data analysis. An anonymous referee we thank for most constructive comments.
Under a canopy, the light is filtered by foliar layers, which preferentially absorb the red and blue light wavebands, decreasing the red/far-red ratio (R:FR) and the radiance of blue light. Phytochrome photoreceptors (phy), cryptochrome blue light photoreceptors (cry) and PIF transcription factors play a major role in detecting changing light intensities and spectral variations, thereby leading to SAS responses  [21, 22]. Plant species are classically classified as shade tolerant or shade avoiding according to their response to shade. Inter-and intraspecific variations in shade responses have been demonstrated in several groups of plants, and particularly variation associated with the environmental conditions of the geographical origin of the populations  [23] [24] [25] [26]. When the vertical growth direction of a stem deviates toward lateral light, a change in its orientation relative to the gravitational field is produced.
Are the Differences Related to Stem Straightness? Our hypothesis was that differences in stem straightness among populations can be related to different sensitivities to external stimuli that induce curvature, in this case, light conditions. The straighter population, Gredos, exhibited a response that was very similar to that of the twisted Oña in terms of low photomorphogenetic and high phototropic responses during the elongation period. Hence, we can reject the hypothesis that a lower sensitivity to lateral light plus shade is related to improved straightness at the population level in our experimental conditions. However, new experiments with a wider range of light quality and intensity could add valuable information about intraspecific variation, such as the ability to detect changes in light intensities and spectral variations that allow plants to detect neighbors and lead to SAS responses.
Individual contrasts were performed for all the comparisons. The analyses were performed with Statistica '99 Edition and SAS 9. 4. The initial number of plants per provenance and treatment was 10, but due to the death of some plants during the experiment, the number of plants used for the analysis was 28 under DLT (9 from Gredos, 9 from Oña and 10 from Tiétar) and 20 under LLT (7 from Gredos, 7 from Oña and 6 from Tiétar). At the end of the experiment, 18 plants from each treatment were defoliated, marked on the south and west sides, and then harvested. The numbers of plants by provenance were 6 from Gredos, 5 from Oña and 7 from Tiétar in DLT and 7 from Gredos, 5 from Oña and 6 from Tiétar in LLT. Cross-sections were obtained at the base of the stem (section I), immediately above the cotyledons (section II), at the middle (section III) and at the end (section IV) of segment 01 of the stem (Figure  1 ).
ReMap (http://remap. univ-amu. fr) aims to provide the largest catalogs of high-quality regulatory regions resulting from a large-scale integrative analysis of hundreds of transcription factors and regulators from DNA-binding experiments in Human and Arabidopsis (Arabidopsis thaliana). In this 2020 update of ReMap we have collected, analyzed and retained after quality control 2764 new human ChIP-seq and 208 ChIP-exo datasets available from public sources. The updated human atlas totalize 5798 datasets covering a total of 1135 transcriptional regulators (TRs) with a catalog of 165 million (M) peaks. This ReMap update comes with two unique Arabidopsis regulatory catalogs. First, a catalog of 372 Arabidopsis TRs across 2. 6M peaks as a result of the integration of 509 ChIPseq and DAP-seq datasets.
Second, a catalog of 33 histone modifications and variants across 4. 5M peaks from the integration of 286 ChIP-seq datasets. All catalogs are made available through track hubs at Ensembl and UCSC Genome Browsers. Additionally, this update comes with a new web framework providing an interactive user-interface, including improved search features. Finally, full programmatically access to the underlying data is available using a RESTful API together with a new R Shiny interface for a TRs binding enrichment analysis tool.
INTRODUCTION The rapid accumulation of experiments capturing protein-DNA interactions in public databases provides a unique and valuable resource for thousands of protein occupancy maps. The development of high-throughput methods like chromatin immunoprecipitation followed by sequencing (ChIP-seq)  (1) , or ChIP-seq with DNA digestion steps (ChIP-exo) (2) as well as DNA affinity purification se-quencing (DAP-seq) (3) has allowed to experimentally obtain genome-wide maps of binding regions across many cell types for a variety of DNA-binding proteins. Integrating these thousands of large-scale experiments would allow to explore the depth of the transcriptional regulatory repertoire. Unfortunately, the heterogeneous experimental metadata annotations deposited in data-warehouse, the inconsistency in target name convention, the different cell type or tissue names, the variety of bioinformatics methods and underlying file formats challenge a global analysis process and the underlying mapping of TF binding regions.
ReMap has been the first large scale integrative initiative with dedicated curation and uniform data processing pipeline to reveal the complex architecture of the human regulatory landscape  (4). The core foundation of the ReMap project rely on the manual curation and annotation of experiments metadata. Each experiment metadata introduced in ReMap has been assessed and manually curated to ensure correct target and biotype annotation. The ReMap 2015 database (4) introduced a catalog of 13 million (M) DNA binding regions by compiling the genomic localization of 237 different transcriptional regulators (TRs) across 83 different human cell lines and tissue types based on the integration of 395 datasets from Gene Expression Omnibus  (5)  and ENCODE  (6, 7). For ReMap 2018 we updated the catalogue by processing 2829 quality controlled ChIP-seq datasets leading to a unique atlas of regulatory regions for 485 TRs across 346 cell types, for a total of 80M DNA binding regions  (8).
Here, we describe ReMap 2020 which introduces two unique regulatory catalogs for Arabidopsis (Arabidopsis thaliana), and includes a major expansion of the human regulatory catalog, along with new user-interface features. The Arabidopsis regulatory catalog is the result of curation, annotation and integration of 179 quality controlled ChIP-seq and 330 DAP-seq datasets for transcription factors and general components of the transcriptional machinery. Those datasets have been mapped to the TAIR10 Arabidopsis assembly and analyzed with a uniform pipeline. This unified Nucleic Acids Research, 2020, Vol. 48, Database issue D181 integration of Arabidopsis datasets lead to a unique atlas of 342 TRs across 20 biotypes, 12 ecotypes, for a total of 2. 6M DNA binding regions. Additionally, we introduce a catalog of 33 Arabidopsis histone modifications and variants across 4.
5M peaks from the integration of 286 ChIP-seq datasets. The human atlas has been updated with the curation, annotation and integration of 2969 quality controlled ChIP-seq and 208 ChIP-exo datasets mapped to the GRCh38/hg38 assembly and analysed with the ReMap pipeline. In this update, we propose a unique atlas of regulatory regions for 1135 TRs across 602 cell types, in 5798 datasets, for a total of 165M DNA binding regions. The ReMap 2020 human update represents a 1. 7-fold increase in the number of cell lines/tissue types, and a 2-fold increase in the number of DNA-binding proteins, number of processed datasets and number of identified peaks. While the eMap 2018 human catalog covers 19% (0. 6 Gb) of the human genome with more than five peaks, the regulatory search space for ReMap 2020 covers 34% (1 Gb, 5+ peaks).
Finally, the fully redesigned ReMap web-interface gives the community richer options to navigate and search our data, to visualize and browse all catalogs with public track hubs integrated in Ensembl and UCSC Genome Browsers. Additionally we updated our ReMapEnrich tool with a new R Shiny interface, while also allowing programmatic access to the underlying data with a RESTful API. This report presents the third ReMap release, which comes with two unique catalogs for Arabidopsis and an extensive data increase and regulatory catalog expansion of the human atlas as a result of our large-scale data integration and analysis efforts. The manual metadata curation engaged in the ReMap project offers a unique and unprecedented collection of DNA-binding regions for two species. This data expansion is supported by a range of new functionalities for better community access.
Available Human and Arabidopsis datasets New DNA-binding experiments such as ChIP-seq, ChIP-exo and DAP-seq were extracted from the NCBI Gene Expression Omnibus (GEO)  (5)  and EN-CODE (6,7) databases. For GEO, the query 'Genome binding/occupancy profiling by high-throughput sequencing' AND 'homo sapiens'[organism] AND NOT 'ENCODE'[project]' was used to return a list of all potential datasets, which were then manually assessed and curated for further analyses. The same query with 'arabidopsis thaliana  '[organism]  was used to return all potential datasets. For each experiment ReMap metadata are manually curated and annotated with the official gene symbol. Materials and methods from published papers are often read when deposited metadata is insufficient. For human we used the HUGO Gene Nomenclature Committee (9) (www.
genenames. org), BRENDA Tissue Ontologies  (10)  for cell lines (www. ebi. ac. uk/ols/ontologies/bto) as well as the Cellosaurus database  (11)  to homogenize cell names (e. g. MCF-7 not MCF7, Hep-G2, not HepG2, Hepg2 etc. ). For Arabidopsis (Arabidopsis thaliana) we used the Ensembl Genome (12) gene symbols. Ecotypes and biotypes description were curated and homogenized when the information was available in the metadata or paper. Datasets involving polymerases (Pol2 and Pol3), and some mutated or fused TFs (e. g. KAP1 N/C terminal mutation, GSE27929) were filtered out. When multiple antibodies were pooled (eg: RUNX1 and RUNX3, GSE17954) we would name the target as RUNX1-3. In ReMap, we define a dataset as a DNA-binding experiment in a given GEO/AE/ENCODE series (e. g. GSE37345), for a given TR (e.
g. FOXA1), and in a particular biotype (e. g. LNCaP, K-562, Leaf, Seedling) in a given biological condition (e. g. 45 min DMSO, 21 days-wt-watered). Datasets were labeled with the concatenation of these pieces of information (e. g. GSE37345. FOXA1. LNCAP 45 min-DMSO). The core of ReMap data rely on ChIP-seq assays, but for this update we analysed a major human ChIP-exo experiment (GSE78099), and for Arabidopsis a major DAP-seq experiment (GSE60141). A total of 7908 datasets were processed (Supplementary Table  S1 ). Specifically, we analyzed 6498 human datasets deposited in public repositories from 1 July 2008 to 10 November 2018), and 1410 Arabidopsis datasets from 1 January 2009 to 2 February 2018 (full list of datasets in Supplementary Tables S5, S8, S11). For the ENCODE data in the 2020 update, we re-analyzed, starting from the raw data, all new ENCODE ChIP-seq experiments for TFs, transcriptional and chromatin regulators released since ReMap 2018 release (1 August 2016 to 5 February 2019), following the same processing pipeline.
We retrieved the list of ENCODE data as FASTQ files from the ENCODE portal (https://www. encodeproject. org/) using the following filters: Assay: 'ChIP-seq', Organism: 'Homo sapiens', Target of assay: 'TF', Available data: 'fastq' on 5 February 2019. Metadata information in JSON format and FASTQ files were retrieved using the Python requests module. We processed 964 ENCODE datasets, 894 of whom passed our quality filters. We renamed TR ENCODE aliases into official HGNC identifiers (e. g. p65 into RELA, see Supplementary Table  S6 ), and renamed cell lines to official BRENDA and Cellosaurus conventions (e. g. K562 into K-562, lost of modified names Supplementary Tables S6, S9, S12).
ChIP-seq processing All human and arabidopsis ChIP-seq datasets were uniformly curated, processed and analyzed. Bowtie 2 (version 2. 2. 9) (13) with options -end-to-end -sensitive was used to align all reads on the human genome GRCh38/hg38 assembly and on the A. thaliana TAIR10 assembly. Adapters were removed using Trim Galore (https://github. com/ FelixKrueger/TrimGalore), trimming reads up to 30 bp. Trim Galore is a wrapper around Cutadapt and FastQC to consistently apply adapter and quality trimming to FastQ files. Polymerase chain reaction duplicates were removed from the alignments with samtools rmdup  (14). TR binding regions were identified using the MACS2 (15) peakcalling tool (version 2. 1. 1. 2) to follow ENCODE ChIP-seq guidelines  (13) , with stringent thresholds (MACS2 default thresholds, Q-value: 1e-5, -g: with corresponding genome sizes).
Quality assessment Data quality differs across experiments, as the data we process comes from various sources and are generated under different experimental conditions and platforms. Since the first release of ReMap 2015, our pipeline has assessed the quality of each dataset processed, unlike similar databases (Supplementary Tables S5, S8, S11). For ReMap 2020 the same quality pipeline and cutoffs were applied as in ReMap 2018  (8). Briefly, for both species, and all ChIP-seq and DAP-seq datasets processed for this update we computed a score based on the cross-correlation and the FRiP (fraction of reads in peaks) metrics developed by the ENCODE consortium (  16 ) (Supplementary Figures  S1-S3 , ENCODE quality coefficients http://genome. ucsc. edu/ENCODE/qualityMetrics. html). Then our pipeline computes the normalized strand cross-correlation coefficient (NSC) as a ratio between the maximal fragment-length cross-correlation value and the background cross-correlation value, and the relative strand cross-correlation coefficient (RSC), as a ratio between the fragment-length cross-correlation and the read-length cross-correlation.
Datasets not passing the QC were not included in catalogs or BED files available for download. Rejected datasets are listed in (Supplementary Tables S2, S7, S10). For the human ChIP-exo data (GSE78099) we applied three post-processing steps as described in (  16 ) material and methods. In brief, we filtered out peaks that would meet any of these criteria: MACS2 score <80 (equivalent to a P = 1 × 10 -8 ); ratio of forward versus reverse strand reads >4; <20 reads over 500 bp per 15 million reads; normalized read count was less than twofold over the control. For the arabidopsis DAP-seq (  17 ) data (GSE60141), we applied our standard ChIP-seq ReMap pipeline. Our quality assessment protocol could not be applied to ChIP-exo data, as the specificity of ChIP-exo peaks (extremely narrow) would not allow the computing of FRiP/RSC/NCS scores.