dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Remote sensing science is evolving rapidly. The development of new sensors and easier access to the collected data are significantly transforming both the theory and practice of remote sensing. In this regard, two trends currently dominate. On the one hand, important research efforts are focused on data-driven techniquesIn this regard, supervised classifiers such as Random Forest  (Belgiu and Drăguţ 2016)  or Deep Learning classifiers  (LeCun, Bengio, and Hinton 2015)  led to promising results in terms of the accuracy of the created maps. These systems excel at perceptual classification (e. g. identify all pixels in an image that are similar to the annotated training samples) and are increasingly used in big data land cover applications relying on pixel-based statistical analysis of temporal dynamics on huge imagery datasets  (Inglada et al.
In this context, better handling of expert knowledge used to process remote sensing images should be a priority when implementing hybrid methods. Whereas important efforts are carried out to share products derived from satellite observations including global and regional land cover maps  (Grekousis, Mountrakis, and Kavouras 2015; Congalton et al. 2014a) , to share calibration and validation data  (See, Fritz, and McCallum 2014)  or to share methods (e. g. algorithms implemented on Google Earth Engine, R and Python libraries for image processing, GEOBIA rulesets shared online) within the scientific community, little effort is dedicated to formalizing, aggregating and sharing expert knowledge in remote sensing. In this context, knowledge representation techniques like ontologies represent a great potential to address this problem and to advance remote sensing science  (Arvor et al.
2008 (Madin et al. , 2007)) , eco-informatics  (Williams, Martinez, and Golbeck 2006)  and biology  (Bard and Rhee 2004). However, their use in geography remains limited to applications in Geographical Information Systems (GIS)  (Agarwal 2005; Kavouras and Kokla 2002) , i. e. OSM ontology by  Codescu et al (2011). In this case, formal ontologies are used to mediate between heterogeneous systems and data sources  (Fonseca et al. 2002) , to add semantic annotations to geo-databases  (Lüscher, Weibel, and Burghardt 2009) , to improve spatial data discovery  (Lutz and Klien 2006)  and knowledge sharing among user communities  (Stock et al. 2013) , and to interpret spatio-temporal changes in cadastral data  (Stock et al. 2015) , administrative units  (Gantner et al. 2013)  or in historical places  (Hyvönen et al.
e. the implementation of a classification rule set: Forest = (NDVI > 0. 6)). Once the rule has been defined, experts still have to adapt their perceptual knowledge to the computational system (e. g. to implement the rule set in a remote sensing software, such as eCognition as in Figure  2 , bottom left) in order to translate their symbolic knowledge into a numerical value the application can interpret. It is worth noting that, in the "traditional" remote sensing, the symbolic knowledge is usually implicit (Figure  2 ) since the users generally implement directly the rules based on trial and error tests without formalizing their knowledge about the relationship between NDVI and NPP. Therefore, the remote sensing experts make use of an ontology when extracting information from remote sensing data.
The semantic gap The semantic gap is closely linked to the vagueness and ambiguity of the geographic concepts. The semantic gap is the lack of agreement between the information that one can extract from the visual data and the interpretation made of the same data by a user in a given situation  (Smeulders et al. 2000). It describes the gap between the low-level representation of an image (e. g. NDVI values assigned to pixels) and the high-level semantic concepts used by users to interpret the image (e. g. forest)  (Smeulders et al. 2000)  (Figure  3 , 5-Semantic gap). The main explanation for this gap is that humans trigger perceptual mechanisms (i. e. "perceptual knowledge") to draw inferences from what they perceive in remote sensing images  (Biederman 1987). These mechanisms of human observation cannot be easily translated into computational representation, i.
Working with symbolic knowledge independently from numeric knowledge enhances knowledge management. It is possible to formalize a few pieces of knowledge (e. g. concepts and relations) without affecting the entire conceptualization (whereas changing a rule in an image analysis and processing chain may require adapting all its child processes). It also facilitates collaborative and interdisciplinary research by connecting concepts from different scientific domains (in ecology, agriculture, geo-health etc. ). Further, the users can evaluate the fitness for use of the resulting remote sensing products. Improved knowledge management also enhances knowledge sharing. For example, to share classification rules implemented in an image processing chain means sharing a set of processing tasks that have proven to be efficient for a defined remote sensing dataset.
Colleagues who wish to reuse the processing chain then will need to adapt it to their own dataset. To do so, they will need to "capture" the symbolic knowledge triggered by the person who initially implemented the processing chain (but did not make his/her symbolic knowledge explicit) in order to build new numeric knowledge (e. g. to set down new rules with thresholds) better suited for their case studies. In this regard, different users may have different interpretations of the implicit symbolic knowledge contained in the same processing chain. With formal ontologies, it is possible to limit the subjectivity of the remote sensing analysis by directly sharing the symbolic knowledge through OWL repositories handled by semantic data stores (e. g. Jena TDB or Sesame). Jena TDB allows for example users to store domain knowledge using OWL formalism.
Stored domain knowledge can be queried using the CRUD operations (Create, Retrieve, Update, Delete). These OWL repositories represent a new modular interdisciplinary contextual knowledge base about ever more diverse remote sensing applications and may turn out to be a new object of research per se. Finally, the explicit specification of the knowledge used to extract information from remotely sensed data allows the integration of remote sensing classification products from other information sources. Interoperability is a major advantage of Ontology-RSA, especially in a context in which the technical focus has moved from the accuracy of the remote sensing information extraction and inventory to fitness for the purpose of integration of various spatial sources of information in support of specific policy goals (GEO's goal 2016-2025).
How do the ontologies improve the classification results? The overall contribution of ontologies should not be measured only with the traditional techniques used in remote sensing such as overall accuracy or the Kappa index  (Congalton 1991). From an image processing perspective, all the results produced by an ontology-based approach could perhaps have been achieved with any traditional image processing software (e. g. setting classification rules in any GEOBIA software), as long as the knowledge triggered to process the images is similar to the knowledge represented in the ontology. In this regard, one could consider that ontologies are mere duplications of the state-of-the art knowledge-based image classification, except in that they use another way of representing image interpretation knowledge but without improving the results.
g. spectral indices, textural indices, etc. ) and image processing tasks. This is a necessary step to generalize the development of numerous local but integrated (i. e. connected to upper level ontologies) ontology-based applications. Thirdly, once remote sensing domain ontologies are built, the next challenge consists in "producing" the knowledge to be represented in these connected local ontologies. To this end, it is necessary to facilitate access to ontologies by remote sensing experts. Promoting ontology engineering courses in remote sensing education programs and developing ontological extensions in image processing software (especially in GEOBIA software) is essential to ensure the adoption of Ontology-RSA by remote sensing experts. In addition, implementing procedures to capture the knowledge derived from machine learning applications and automatically populate local ontologies is a promising way forward to accumulate knowledge.
Acknowledgements The authors of this paper acknowledge the anonymous reviewers for their comments that help improve its readability and content. This works was conducted as part of the  COGIRS  project funded by the  Brittany region. Dr. -Ing. Falomir  gratefully acknowledges the project " Cognitive Qualitative Descriptions and Applications" -CogQDA  funded by the  University of Bremen  and also the support of the  Bremen Spatial Cognition Centre.
MRI acquisition All scans were performed on 3T scanners (Philips Achieva, Siemens TIM Trio, and Skyra) using surface coil arrays. Three-point Dixon images were acquired using a spoiled gradient echo sequence, Newcastle and London used 2D TR/TE = 100/3. 45,4. 6,5. 75 msec, flip angle of 10 degrees, 10 slices of 10 mm slice thickness with a 5 mm gap. Paris used a 3D sequence with TR/TE = 10/2. 75,3. 95,5. 15 msec, flip angle of 3 degrees, 64 slices of 5 mm thickness; Copenhagen as per Paris, but with 36 slices per 3D acquisition, and 2-point Dixon with correction for homogeneity of B 0. 15  The data were processed off-line to produce separate fat and water images. 15, 20 Quantitative fat fraction maps were produced by expressing the fat signal as a percentage of the total signal per voxel. Phantom measurements and healthy volunteer images were acquired at each site prior to the studies.
Data analysis Both functional assessments and MRI data were anonymized and transferred to Newcastle. Regions of interest (ROIs) were defined using the imaging software 'Image J'. 21 Two observers drew ROIs independently around the selected muscle groups at a single level. Data from the left and right legs were combined by averaging, as were the results from the two analysts. Interobserver comparison was performed using a Bland Altman analysis. The ROIs were used to calculate mean fat fraction and muscle cross sectional area (CSA). Contractile cross-sectional area (c-CSA) was calculated by multiplying the CSA by one minus the fat fraction, representing the remaining muscle content of the ROI. To reflect the fact that muscles function as groups, composite group fat fractions were calculated as areaweighted averages.
Statistical analysis Statistical analysis was performed using SPSS v24, with data presented as median and range unless otherwise indicated. Statistical significance was calculated using the Wilcoxon test for nonparametric data. For nonparametric testing of the chair rise time, where a participant was unable to perform a timed functional assessment, a value greater than the longest possible result (10,000s) was used to provide correct ranking. Statistical significance was taken to be P < 0. 05. Standardized response means (SRM) were calculated for all outcome measures by taking the average of the paired difference over the 6 years divided by the standard deviation of these differences. A high SRM (>0. 8) implied that a test had a high level of responsiveness to changes in value. 22.
Over the 6 years, all 14 muscle groups demonstrated a significant increase in percentage of fat replacement (Table  2  and Fig. 2 ). The highest median percentage of fat replacement at baseline was in the biceps femoris long head muscle (BFLH) at 69. 4%, increasing to 78. 6% at 6 years. The tibialis anterior muscle (TA) was least affected at baseline, median 5. 2%, and at follow-up showed a median of 7. 1%. The TA also had the smallest median change over the 6-year period (Fig. 2 ). LGMD R9 demonstrated phenotypic variability between individuals: changes in selected functional assessments over time are shown for individuals in Figure  3. The source data for these and all other measures are available in the Dryad data repository. When considering the most complete composite muscle groups with the highest SRM, there were small numbers of participants whose fat fraction did not increase over the 6 years (Fig.
Fat replacement as an outcome measure 6] [27] [28] [29] [30] [31] [32] [33]  The fat fraction of all 14 muscles assessed significantly increased over 6 years. For every muscle group, the SRM for the c-CSA is of smaller magnitude (and the P value less significant) than for the respective fat fraction alone, indicating that the variability in the cross-sectional areas within the group outweighs the small yet progressive changes in fat fraction. LGMD R9 has been associated with a lower c-CSA compared to controls, but preserving the ratio between the c-CSA and torque 34 : it is not possible to confirm this relationship in the present data due to different physical function tests used. c-CSA has been shown to correlate to functional measures of strength in other muscular dystrophies. 34, 35 In this case, c-CSA is a less sensitive endpoint than fat fraction alone.
5 56. 6 10. 5 47. 6 12. 1 9 38M Y/N 300 cd 1. 1 48. 4 9. 8 52. 6 11. 7 5 56F Y/N 306 0. 8 34. 9 9. 3 28. 2 5. 2 13 22M N/Y 312 cd 1. 1 3. 0 7. 8 e 5. 7 9. 3 19 42F N/N 547 2. 4 6. 0 3. 8 e 6. 9 4. 4 16 29F N/N 522 3. 1 4. 3 3. 6 e 3. 9 2. 8 4 47M Y/Y 67 cd 0. 6 62. 6 3. 1 36. 7 -0. 4 22 27F Y/Y 595 2. 9 2. 7 2. 9 e 2. 9 3. 5 3 58F Y/N 457 1. 2 12. 6 2. 5 3. 9 0. 3 18 18M N/N 625 4. 4 1. 8 1. 7 e 1. 4 1. 9 14 51M Y/N 238 0. 8 70. 8 1. 1 74. 2 -1. 6 10 8M N/N 565 3. 2 4. 5 0. 0 e 4. 6 0. 2 BL, Baseline; CM, cardiomyopathy; NIV, noninvasive ventilation; SP, slow progressor; LL, lower leg; 6MWT, 6-min walk test; 10MWR, ten-meter walk or run. a Refers to participant number in the Dryad repository. b "Target muscles" is a composite group consisting of vastus lateralis, gracilis, sartorius, medial gastrocnemius and lateral gastrocnemius with progression at 1 year and high SRM.
A Upper panel: NMR structure of the non-covalent dimer of AGR2 (PDB ID: 2LNS). The dimer domain is highlighted with a yellow circle (residue 54-70 of each monomer). Monomer A is colored green and monomer B in pink. The figure was generated in Chimera. Lower panel: a close-up illustration of the dimer domain, showing that the dimer is stabilized through two salt bridges between E60 and K64 of each monomer. B Molecular modeling showing root-mean-square deviation (RMSD) plot for the MD simulation of the AGR2 WT and E60A mutant. The interaction energies are defined as the sum of the short-range Coulomb interactions and the short-range Lennard-Jones potential between monomer A and B. C, D Western blot showing the expression of AGR2 dimers (D) and monomers (M) in HEK293T subjected to DSP-mediated cross-linking and that were previously transfected with either a control siRNA (siCTL) or a siRNA targeting AGR2 (siAGR2) for 24 h (C) or treated or not with tunicamycin (Tun) prior to cross-linking (D).
Immunoprecipitation was performed with mouse anti-Flag antibody to pull-down the ectopic protein tag. F Western blot analysis (left panel) and quantification (right panel) of AGR2 intracellular expression upon TMED2 overexpression. Data are representative of four independent experiments. Tubulin (TUB) was used as a loading control. The graph represents average signal normalized on reporter protein expression AESD. The Mann-Whitney statistical test was used. G HEK293T cells were transfected with control (ev) or TMED2 plasmid and then used in the ERMIT assay with AGR2 WT as described in (rMFI anti-CD5 AGR2 on secretion of A1AT by immunoblot under basal and stress conditions (Appendix Fig  S4D  and E ). Under basal conditions, AGR2 was not involved in the secretion of A1AT as the silencing of AGR2 did not affect the kinetics of A1AT secretion.
Statistical analyses Graphs and statistical analyses were done using GraphPad Prism 5. 0 software (La Jolla, California, USA). When experiment contained two unmatched groups of values, the nonparametric Mann-Whitney test was used for the comparison of means. When experiment contained three groups of values or more, regular one-way analysis of variance (ANOVA) was used for the comparison of multiple means. For each experiment, the ANOVA P value is as indicated in brackets. The ANOVA test was followed by a Bonferroni's multiplecomparison post-test, and selected pairs of data were compared. The Bonferroni P-value is indicated at the bottom of each figure legend. The nonparametric Spearman test was used to compare quantitative values of expression. Means were considered significantly different if the P < 0.
Molecular dynamic simulation of AGR2 WT and E60A mutant The E60A mutant (glutamate to alanine) was obtained through in silico mutation in Yasara  (Krieger & Vriend, 2014). The MD simulations of the AGR2 WT and E60A mutant were performed utilizing the Gromacs 2016. 1 package  (Hess et al, 2008)  and the Amber ff03 force field  (Wang et al, 2000; Duan et al, 2003). For each run, the protein was solvated in a cubic box with TIP3P water molecules and a buffer distance of 10 A ˚to the walls. The system was neutralized by addition of two Na + -ions, and then energy minimized by steepest decent until the force was < 1,000 kJ/mol/nm, with a maximum number of steps of 5,000 to relax bad contacts. By employing periodic boundary conditions, the system was subjected to a position restraint equilibration run, composed of 500 ps in the NVT ensemble followed by 500 ps in the NPT ensemble to relax the water and ions around the protein at constant temperature (300 K) and pressure (1 bar).
The neighbor list for non-bonded interactions was updated every 10 th step through the Verlet cut-off scheme. Following the equilibration run, a 200 ns unrestraint production run in the NPT ensemble was performed, utilizing the same settings described above. All MD simulations were run on the supercomputer cluster Beskow, provided by the supercomputing center PDC at KTH, Stockholm. All analyses of the provided trajectories were performed with the Gromacs software, and the provided data were plotted, using Gnuplot  (Williams & Kelley, 2015).
Modeling of AGR2/TMED2 interactions The structure of the Golgi dynamics (GOLD) domain of human TMED2 (PDB ID: 5AZW; Nagae et al, 2016) and the NMR structure of the non-covalent dimer of AGR2 (PDB ID: 2LNS; Patel et al, 2013) were downloaded from the Brookhaven protein databank  (Berman et al, 2000)  followed by protein preparation in YASARA  (Krieger & Vriend, 2014). Protein-protein docking was performed by using the web-server PatchDock  (Duhovny et al, 2002; Schneidman-Duhovny et al, 2005). The AGR2 dimer or AGR2 monomer was defined as the receptor, and TMED2 was defined as the ligand. In each docking run, the top 500 decoys were subjected for refinement and rescoring in the web-server FireDock  (Andrusier et al, 2007; Mashiach et al, 2008)  retaining the top 10 scored decoys from each run.
Acknowledgements This work was funded by grants from  INSERM, Institut National du Cancer  ( PLBIO INCa_5869 ),  Région Bretagne ,  Rennes Métropole ,  Cancéropôle Grand Sud-Ouest  to EC and  EU H2020 MSCA ITN-675448  (TRAINERS) and  MSCA RISE-734749 (INSPIRED)  to AS, LAE and EC; the  Swedish Research Council  through grant  2014-3914  to LAE; Association François Aupetit (AFA),  Université Diderot Paris 7 , and the  Investissements d'Avenir programme ANR-11-IDEX-.
(2013) , is that the risk to plant health remains unproven without a living sample of the pathogen. 4. If numerous potential pests and/or pathogens are detected, the limited resources available for carrying out the labour-intensive PRA process make it necessary to rank potential pests and pathogens according to their perceived risk. Ranking of potential pests that are detected in sentinel plantings need to be based on the biology and abundance of the pest, known substrates or hosts, frequency and severity of symptoms, or damage or known pathogenicity. Expertise or specialist knowledge from different fields (pathology, entomology, forestry) are essential to gain a holistic view. 5. Currently, the sharing of occurrence and disease data from existing sentinel plantings is rare, but a centralized database, as suggested by  Britton et al.
(2010) , needs be used by NPPOs to identify pests and pathogens for PRA. There are ongoing efforts as a part of the International Plant Sentinel Network (http://www. plantsen-tinel. org) to develop a database to store and share information related to sentinel plantings. This database should be updated with data from regular surveys and have some form of curation. Before data are added to the database, the records should be discussed with the NPPO of the exporting country. In some countries, it is obligatory to notify the NPPO of new findings of pest and pathogens whereas in all cases it is good practice to keep the NPPO duly informed  (Eschen 2017). Fostering good relations with the NPPO is vital to enable the establishment and maintenance of the sentinel plantings  (Roques et al. 2015). In many cases, NPPOs might also assist in pest/pathogen identification, data provision and further research.
In this system, the use of HTS is useful for screening of the microbial communities even in the absence of symptoms. One possible way to filter large datasets arising from HTS is to group the OTUs according to their functional guild, focusing the sampling and identification on what are grouped as pathogens or opportunistic pathogens. In the case of fungi, online applications, such as FUNGuild (http://www. stbates. org/guilds/app. php), can be used for this purpose as a base for downstream analysis  (Nguyen et al. 2016). Previous fungal studies in sentinel nurseries have not provided conclusive evidence of identified risks but rather provided information that must be analyzed to arrive at a selection of taxa for further study of whether these organisms pose a threat if introduced in a naïve habitat  (Vettraino et al.
2017). Information including a collection of isolates, with molecular barcoding and, eventually, taxonomic positions and a database of OTUs resulting from HTS analysis, would greatly strengthen further analyses. Large data sets can be difficult to interpret and require appropriate databases of molecular data and plant pathogens and, certainly, the scientific literature, to make full use of their potential. A limit to data interpretation is the fact that only a small percentage of global microorganism diversity is so far present in the databases. A positive aspect is that a large number of undescribed taxa are present as sequences in molecular databases, which may provide unexpected matches with OTUs from sentinel plantings and useful information on previous detection. During arthropod studies in sentinel nurseries  (Roques et al.
2015) , systematic sequencing of the "morphospecies" (defined as a group of individuals that are recognized as probably belonging to a same species based on morphological characteristics) of immature stages and adults was achieved using the "barcode" COI gene to compare potentially, newly recognized species with sequence data already present in global genetic databases. However, only a limited number of the organisms found, essentially lepidopteran larvae, could be identified to the species level. Therefore, arthropod DNA barcoding does not replace the classical approach of morphology-based species identification  (Hebert and Gregory 2005; Pires and Marinoni 2010). The combination of both techniques has proven successful in numerous cases  (Pires and Marinoni 2010; Okiwelu and Noutcha 2014; Kirichenko et al.