dataesr/scientific-paragraphs-categorization huggingface dataset:
The Human Brain Project (HBP) is a European flagship project with a 10-year horizon aiming to understand the human brain and to translate neuroscience knowledge into medicine and technology. To achieve such aims, the HBP explores the multilevel complexity of the brain in space and time; transfers the acquired knowledge to brain-derived applications in health, computing, and technology; and provides shared and open computing tools and data through the HBP European brain research infrastructure. We discuss how the HBP creates a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating perspectives on societal benefits.
These data, together with those on the cellular and molecular organization, functional and connectivity data, and many more, are integrated into the HBP Brain Atlases, which provides Although TEM/SEM (e. g. , Rodriguez-Moreno and colleagues  [5] ) and CLSM/TPFM (e. g. , Silvestri and colleagues  [6] ) can image subcellular structures, including synapse, with great detail, they cannot cover the whole human brain. 3D-PLI has a spatial resolution down to 1. 3 μm, which resolves most of the myelinated fibers, and has the potential to image the whole human brain (e. g. , Axer and colleagues  [7] ). Diffusion MRI is an in vivo method covering the whole brain, but with limited spatial resolution, which does not resolve single nerve fibers (e. g. , Beaujoin and colleagues  [8] ). The possibility to link these different data shows the great advantage of HBP-it facilitates the combination of approaches at scale, backed by high-performance computing and the data exchange infrastructure FENIX.
CLSM, confocal laser scanning microscopy; FENIX, Federated Exascale Network for data Integration and eXchange; HBP, Human Brain Project; PLI, polarized light imaging; TEM, transmission electron microscopy; TPFM, two-photon fluorescence microscopy. Provenance: Not commissioned; externally peer reviewed a basis for human and rodent brain research  [3]. The atlases also establish the structural frame for modeling and simulation studies, which is a unique feature of the HBP approach. For example, simulations use patient-specific brain connectivity and imaging of malformations to personalize network models for seizure prediction and surgery  [4]. Understanding the brain at multiple scales requires the integration of anatomy and physiology with cognitive and systems neuroscience to flesh out the neural mechanisms underlying cognition-e.
The High-Level Support Team (HLST) of the HBP is available for answering questions or resolving issues with the use of the tools and services and for adapting tools and workflows for specific use cases. Specifically, researchers outside HBP can make their data discoverable and accessible in the Knowledge Graph and Atlases by applying for access to curation services and data storage through the HLST. The HLST help desk and ticketing system is available at the following e-mail: support@humanbrainproject. eu. Various types of neuroscience data are being generated, with differences in format, metadata, or level of precision. Through a curation process, metadata are assigned and stored in the HBP Knowledge Graph, an online graph database allowing advanced searches (Fig  2 ). Thereby, data and models become findable and interpretable.
Data can be downloaded or used directly in workflows available through the computing infrastructure, with terms of use and licenses defined for all available data sets. Data are also being made available through the Knowledge Graph with integrated multilevel HBP Atlases, holding information about the brain in standard reference spaces. The Human Brain Atlas supports different reference spaces including the T1-weighted, single-subject template of the Montreal Neurological Institute (MNI)  [10]  and the BigBrain  [11]. The atlases are more and more populated with data from the different fields to form fully interoperable, multimodal representations of the human and rodent brain. They are compatible with other resources  [12]  and capable of integrating data not only from healthy subjects but also from patients.
Research data from experiments, models, or simulations are uploaded to data storage at the HBP high-performance computing centers. The data are tagged with metadata through a 3-tier curation process covering basic metadata, metadata connecting the data to the HBP Atlases, and additional detailed metadata depending on the data modality. The data are made accessible for users through searches for metadata ("Faceted data search" or "Spatial search") in the online HBP Knowledge Graph and HBP Atlases. The "Search results" give access to collections of data accompanied by information about the project they belong to, the experimental methods used to produce the data, the terms of use, and the citation requirements for use of the data. In the present implementation, users can (1) download the data, (2) inspect the data using an online atlas viewer or a virtual microscopy tool showing image data with atlas overlays and spatial coordinates, or  (3)  The Medical Informatics Platform (MIP) exploits new ICT in the context of clinical neurosciences  [13].
It combines local components installed in every participating hospital and a webbased central node connecting the local components. Its architecture enables the extraction, processing, and analysis of medical data collected during either clinical practice or research. The MIP allows the automatic generation of brain regional volumes from T1 MRI data. The key is to enable General Data Protection Regulation (GDPR)-compliant federated analysis of distributed clinical datasets without moving the data out of the hospital, a crucial issue in the context of data privacy concerns and regulations. This unique feature opens the way for studies of unmatched scale, keeping in mind that 165 million European citizens suffer from brain diseases. A first proof-of-concept study in the field of dementia collected >6,000 datasets from three hospitals and analyzed them together with data from Alzheimer's Disease Neuroimaging Initiative (ADNI).
The future MIP will include many more hospitals and consider data from patients with epilepsy, traumatic brain injury, and behavioral disorders. It offers an ideal infrastructure to implement AI-based diagnostic and patient management tools for clinical practice in conjunction with modeling and simulation efforts in personalized medicine. Some of the research data in brain science already reach the petabyte range. The HBP storage at high-performance computing centers in Europe, the FENIX infrastructure, enables researchers to upload their data and use high-end simulation and processing of big data in an easy and straightforward way from wherever they are. To bring neuroscientists to supercomputing is one of the aims of the HBP; it opens new ways to solve neuroscientific questions requiring significant compute resources.
Such an example illustrates the synergistic relationship between neuroscience, ICT, and brain-like computing within HBP. analyze the data using the "Tools and workflows for viewing and analysis" accessible through the HBP Collaboratory, the portal to the use of HBP tools and services, or directly through the atlas for some cases. The HBP Knowledge Graph and HBP Atlases are openly available at https://www. humanbrainproject. eu/en/explore-the-brain/. Accounts for use of the HBP Collaboratory can be requested at https://services. humanbrainproject. eu/oidc/login. HBP, Human Brain Project. The brain research infrastructure will unify the individual components into one cloudbased superstructure, facilitating the exchange of knowledge, data, models, and algorithms within HBP as well as between the HBP and the "outside world.
CLSM, confocal laser scanning microscopy; FENIX, Federated Exascale Network for data Integration and eXchange; HBP, Human Brain Project; PLI, polarized light imaging; TEM, transmission electron microscopy; TPFM, two-photon fluorescence microscopy. Figure elements provided by Markus Axer. https://doi. org/10. 1371/journal. pbio. 3000344. g001.
Image credits: doi:10. 1371/journal. pone. 0118277, doi:10. 1371/journal. pbio. 1002383, doi:10. 1371/journal. pbio. 1000173 (based on a dataset available at doi:10. 12751/ g-node. f83565). https://doi. org/10. 1371/journal. pbio. 3000344. g002.
This project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement No. 7202070  ( HBP SGA1 ) and No. 785907  ( HBP SGA2 ). https://ec. europa. eu/programmes/horizon2020/en/h2020-section/fet-flagships The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The network visualization is generated using Cytoscape software  [79]. The Gene Ontology (GO)biological process enrichment of a subset list of GWAS genes for ΔRGR (-P-Fe, -Fe) were conducted using AraNet. Genes enriched in GO terms are marked with stars: "chromatin modification" (blue), "DNA replication" (red), and "regulation of cell cycle" (green). https://doi. org/10. 1371/journal. pgen. 1008392. g008 specific involvement in the response to -P-Fe combined stress. Finally, knock-out mutations of the VDAC3 gene (VOLTAGE-DEPENDENT ANION-SELECTIVE CHANNEL PROTEIN 3, AT5G15090) resulted in a longer PRG under -P-Fe compared to wild type plants (  Fig 9). Taken together, these genes (VIM1, FH6, and VDAC3) were not known to be involved in the control root growth under nutrient stress before, and their role in integrating combined -P and -Fe stress is particularly intriguing as the effect of their mutation was not visible under a single nutrient stress or control conditions.
Genome wide association studies (GWAS) Genome-wide association mapping was performed on the regression coefficients of root growth of three-, four-and five-days-old seedlings grown under the above detailed conditions. The phenotypic data for the PRL of the seedlings as well as the estimated root growth rate (RGR) are shown in S1 Table. The normalized RGR used in the analysis are shown in S2 Table. All phenotypic data are also available at the AraPheno database  [76]. The genotypic data were based on whole genome sequencing data [The 1001 Genomes Consortium, 2016] and covered 4,932,457 SNPs for the 227 accessions. 1,739,142 of these markers had a minor Allele frequency of at least 5% in the population and where further used for GWAS. GWAS was performed with a mixed model correcting for population structure (emmaX,  [77] ) The kinship structure has been calculated under the assumption of the infinitesimal model using all genetic markers with a minor Allele Frequency of more than 5% in the whole population.
The analysis was performed in R (R Core Team (2016)). The used R scripts are available at https://github. com/arthurkorte/GWAS. The Genotype Data used for GWAS are available www. 1001genomes. org. All GWAS results are also available in the AraGWAS catalog  [76]. Heritability estimates have been extracted from the mixed model according to the formula: H 2 = V G /(V G + V E ), where V G is the among-genotype variance component and V E is the residual (error) variance.
This work was funded by the  "Institut National de la Recherche Agronomique -Montpellier -France" INRA  and by the  AgreeenSkills Plus  to H. R. , and supported by funds from the  Austrian Academy of Science through the Gregor Mendel Institute (GMI)  to W. B. , an  Austrian Science Fund (FWF)  stand-alone project ( P27163-). B22 ) to W. B. , and by the  National Institute of General Medical Sciences  of the  National Institutes of Health  (grant number  R01GM127759  to W. B. ), and supported by funds from the  Carnegie Institution for Science and Brigitte Berthelemot  to S. Y. R. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This framework embeds the different (nested) objects in a structure called a morphological tree, with no need of parameter tuning. Computation of such a stack of segmentations benefits from some recent scalable implementations that make realistic their very fast extraction from image datasets covering large areas (>1 million km 2 )  [4, 5]. In order to avoid confusion in the term "tree" in this document. We will use SWF (Small Woody Features) for vegetation, decision trees for Random Forest and trees for hierarchical representation (or tree structure). Once the tree structure has been extracted, further image analysis is conducted at a very low computational cost, relying on efficient implementation of Differential Attribute Profiles (DAP), that are state-of-the-art engineered features for land cover mapping  [6].
The texture information is extracted from each spectral band of the original multispectral image as well as from the NDVI image, thus leading to the production of several Sobel images. Furthermore, we compute single-scale textural features with efficient algorithms based on integral image representations  [25] , such as Haar-like features and local statistics (mean, standard deviation, entropy). This first step also allows to derive a binary mask ("border map") that will be useful to discard the pixels flagged as no-data in subsequent processing steps. Such values are given to non-significant pixels after standard pre-processing steps such as ortho-rectification and cloud masking. In our case, they have been assigned a value of zero in all bands. All bands are used to determine no-data areassince, as already stated, no-data pixels are identified as those having null values in all bands.
The differential representation is built by computing the difference between two successive values in the filtered tree. In our scenario, and in order to limit the computational cost, we rely on some efficient implementations of the tree construction and attribute computation steps. Such algorithms have been described in  [7]  and made available as an open-source library called Triskele (https://sourcesup. renater. fr/triskele) that can be used as a remote module in OTB. Furthermore, during a tuning stage, we can use random forest scoring to determine among the different attributes those that actually contribute to the prediction process. In that case, we limit the computation of full attributes to the subset of pixels relevant in the learning phase. And compute required attributes attributs for all pixels.
Besides, the background set has to be heterogeneous to adequately represent all classes in the scene but the sought one. While the user can provide such samples, it barely corresponds to all background classes. Thus, we allow for automatic refinement of background pixels among those that the random forest classifier assigns to the sought class with a very low confidence. Let us note that this strategy uses only the part of the reference dataset involved in training, and that final accuracy assessment will be conducted on the complementary validation set in Section 3. 2. Thanks to the low computational time of the random forest learning step, it is possible for the user to judge the quality of the prediction over the training set, and to adapt the content of the set if necessary. Once the prediction model is satisfactory, it can be applied over the whole image to get the final map.
Both the spatial arrangement of land cover and the presence of linear structures are the two most relevant elements characterizing landscape structures  [36]. Geospatial information on SWF is however still lacking and only available in the form of limited national investigations mostly with a focus on farmland features  [37]  or other thematically focused small-scale landscape inventories, e. g. , fragmentation studies such as  [38]. The only quantitative information on Pan-European level is available through ground observations from the LUCAS (Land Use/Cover Area frame Statistical survey) database  [39]. Recent studies such as  [13]  have derived density maps of the spatial distribution of linear landscape elements for Europe based on spatial interpolation of LUCAS data, but the resolution of such information (1 km) is too coarse for detailed assessments and does not provide factual quantitative information on their location and extent.
As part of the Pan-European Copernicus Land Monitoring Service, the High Resolution Layers (HRLs) provide maps of multi-temporal land cover characteristics for 5 thematic areas including SWF in a consistent manner for 39 European countries (EEA39 with more than 6 million km 2 ). The SWF Pan-European map is a completely new product as part of HRLs for the 2015 reference year, which is based on extended demonstrated expertise in the production of HRLs at Pan-European level  [40]  and with dedicated exploratory work specifically on SWF  [41]. The mapping of SWF is using Very High Resolution (VHR) data as primary input with a Pan-European coverage as well as in-situ data. The VHR_IMAGE_2015 dataset made available in the ESA Copernicus Data Warehouse (DWH) is the main data source for the detection of Small Woody Features identifiable within the given image resolution (≤1 m panchromatic, 2-4 m multi-spectral).
This dataset includes more than 38,000 VHR images which correspond to more than 120 TeraBytes of data. The main difficulty when dealing with VHR images comes from the internal variability of the information for a single land-use. For instance, woody elements are represented by a high number of heterogeneous pixel values hampering usual pixel-based techniques. Nevertheless, even though object-based image analysis (OBIA) appears to be the most suitable approach to delineate SWF with VHR images, it can potentially represent some serious drawbacks related to the heterogeneous size and shape of SWF objects and the difficulty to determine suitable segmentation parameters  [22]. In addition, for very small objects close to the resolution of the imagery, the segmentation can lead to separate SWF or non-SWF objects to be merged together due to mixed pixel values.
Experiments A dedicated processing chain has been developed and implemented in order to process large dataset of VHR images (>38,000 scenes) to produce the SWF layer. The computing time has been measured considering a dedicated server infrastructure with high computing capabilities (Bi-CPU Xeon, 24 cores). The workflow is shortly described as follows: (1) VHR image preprocessing (radiometric and geometric corrections, cloud masking, pan-sharpening), (2) reference database preparation (extraction of SWF reference from previous reference datasets and automatic verification), (3) automatic supervised classification, (4) post-processing (vector smoothing and differentiation between linear polygons and patches). As part of the SWF map production, other post processing steps are applied by photo-interprets such as thematic manual enhancement and random manual validation.
Data While the proposed strategy has been applied at the Pan-European scale over 38,000 VHR scenes, we conduct evaluation only on a small subset for which ground truth reference data is available. More precisely, we consider an evaluation dataset covering two study sites, one in Romania (Large Region 9-LR09) and one in Germany (LR61). The evaluation dataset contains 37 VHR images in total which covers respectively about 7000 and 10,200 km 2. The images were acquired by different satellites, Pleiades (https://www. satimagingcorp. com/satellite-sensors/pleiades-1/) and WorldView (https://www. satimagingcorp. com/satellite-sensors/worldview-2/), but all scenes include 4 spectral bands (Blue, Green, Red and Near-InfraRed) and have 2m spatial resolution. The area covered by the images varies from 192 to 1223 km 2 (ca.
48 to 306 Mpixels). As introduced before, the reference dataset is automatically built from extraction and validation of SWF elements in previous reference datasets: the Copernicus HRL Tree Cover Density Layer (TCD), the Riparian Zone and Natura 2000 dataset from the Copernicus Local components and LUCAS database. The automatic classification is then applied using 70% of the reference sample dataset for the training and the remaining 30% are used for the accuracy assessment in order to derive quantitative evaluation measures.
Discussion Our approach is used for the production of the Pan-European HRL Small Woody Features map with 38,000 VHR scenes. In progress production on larger area over Europe (nearly 800,000 km 2 with 6000 scenes) gives acceptable results with a producer accuracy of 82. 8 ± 1. 51% and a user accuracy of 86. 3 ± 0. 86%. Our approach is flexible and robust as it gives nearly constant classification accuracies over Europe. Though we have produced acceptable classification accuracies, it can be further improved. To do so, one of the limiting factor is to have access to accurately labeled foreground reference samples. However, in practice we do not have access to them, because we are relying on the previous databases and existing auxiliary maps to derive the labeled reference samples. It is known that this could introduce label noise or mislabeled samples in the training set due to several factors such as misregistration, out-dated maps and databases, etc.
We present a new database, specifically devoted to ROS homeostasis regulated proteins. This database replaced our previous database, the PeroxiBase, which was focused only on various peroxidase families. The addition of 20 new protein families related with ROS homeostasis justifies the new name for this more complex and comprehensive database as RedoxiBase. Besides enlarging the focus of the database, new analysis tools and functionalities have been developed and integrated through the web interface, with which the users can now directly access to orthologous sequences and see the chromosomal localization of sequences when available. OrthoMCL tool, completed with a post-treatment process, provides precise predictions of orthologous gene groups for the sequences present in this database. In order to explore and analyse orthogroups results, taxonomic visualization of organisms containing sequence of a specific orthogroup as well as chromosomal distribution of the orthogroup with one or two organisms have been included.
Among these proteins, haem or non-haem peroxidases were already centralized in a dedicated database namely the PeroxiBase  [2]. In order to have a more integrative and phylogenomic overview on ROS-regulated proteins, new classes, families and superfamilies have been added to cover most of the proteins able to regulate ROS level. Then, the RedoxiBase, which includes all the data and the tools already present in the former PeroxiBase, was created. In the new database all living kingdoms are represented. The PeroxiBase served as a reference in the field of peroxidase families, the new enhanced version of this database should become a similar reference for all ROS regulation proteins. It is cross-referenced in UniProt  [3]  since 2006 and, more recently, in the Arabidopsis database TAIR  [4]. Several databases centralize entries of all (InterPro  [5] ) or particular protein families (PLantCAZyme  [6] , CAZy  [7] , MEROPS  [8] , ThYme  [9]  and CaspBase, a curated database dedicated to the caspase family  [10] , or specific to a species such as GFDP which includes 6551 genes of poplar from 145 families  [11].
Regarding the oxidase families, two independent databases are currently present in the web. Namely, PREX  [12]  is dedicated to only one type of non-haem peroxidases and fPOXDB  [13]  a fungal-specific database. They both bring structural and sequence information complementary to those found in our previous database PeroxiBase but they are merely devoted to subfamily assignment. Lastly, the antioxidant protein database AOD  [14] , was https://doi. org/10. 1016/j. redox. 2019. 101247 Received 22 May 2019; Accepted 5 June 2019 developed to understand the biological function of important antioxidant proteins but is was not maintained anymore. Despite these different repositories, the (updated) RedoxiBase is still unique, since it is the only specialized collection of public sequences deduced from expert annotations with manual curation leading to reannotation.