dataesr/scientific-paragraphs-categorization huggingface dataset:
Visualization system: MaGe (Magnifying Genome) All this data is made accessible to users through the Mi-croScope Web interface (http://www. genoscope. cns. fr/agc/ microscope) via secure or anonymous connection as appropriate. Developed using the Apache/PHP server-based language, it consists of numerous dynamic web pages containing textual and graphical representations for accessing and querying data. Several useful graphical applications, such as Artemis  (25) , MeV  (26)  and IGV  (27) , are also available in the MicroScope GUI through plugged Java applications. One of the main objectives of the platform is to allow biologists to make relevant assessments of the predicted gene functions using (i) a gene annotation editor giving access to the results of each method applied, together with links to several useful public resources, (ii) functionalities for querying and browsing the available data (i.
Users are then informed by e-mail about the progress of their integration request. To keep confidentiality, data access through the MicroScope Web interface is restricted to the user that made the request, with the exception of public genomes from databanks that remain publicly available. At any moment, users may grant access to collaborators using the 'Access Right Management' functionality. To ease data integration and comparative studies, standardization of contextual data about genome sequences is essential. Thus, we try to follow as much as possible INSDC (International Nucleotide Sequence Database Collaboration) and GSC (Genomic Standards Consortium; http:// gensc. org/) guidelines in MicroScope. For metagenomes, a dedicated form which follows the MIMS specifications (Minimum Information about a Metagenome Sequence (  28 )) is available since July 2016 in the 'Delivery of Service' Web interface.
Submitting a metagenome in Microscope is as straightforward as submitting a complete/draft single genome. After giving a name and a free text description of their metagenomic sample, users are invited to select the type of environment (e. g. soil, air, water, humanassociated, plant-associated) and to complete the associated fields (e. g. collection date, environment biome, geographic location, etc). These fields might be variable depending on the origin of the selected metagenomic sample (i. e. they are dynamically loaded and displayed upon metagenome type selection). Like in IMG/M, only assembled metagenomic data can be analyzed in MicroScope running the pipelines used for isolate genomes. These developments are quite new and submitted metagenomic data is still being analyzed by their owners.
They will become publicly available as soon as the work is published. Beside, tools to add and query metadata on genomes and metagenomes will continue to be improved in MicroScope. Indeed, the PkGDB data model is flexible enough to store predefined descriptors, like MIMS or the ones defined by users. We plan, for example, to allow users to add organism phenotypes like antibiotic resistance and growth on specific media. Data integration, service continuity and data conservation (backups) are currently provided free of charge. Mi-croScope services follow the quality management system of our laboratory (ISO 9001:2008 and NF X50-900:2013 standards).
Dynamic computation of pan-genomes A new functionality has been integrated in MicroScope to dynamically compute pan-genomes according to a se-lection of organisms. This tool is based on MicroScope gene/protein families (MICFAMs) that are computed incrementally each time a new genome is integrated into the platform. MICFAMs classify proteins in homolog groups using a single-linkage clustering (SiLiX software (29)) of protein sequence pairs sharing at least 80% of amino-acid identity and 80% of alignment coverage. A second set of MICFAMs is also available with a relaxed identity parameter of 50%. Using the pan-genome Web form, users may select up to 200 genomes and, optionally, another set of genomes to exclude (i. e. protein families of the pan-, coreor variable genomes of these genomes will be removed from the final result).
According to the user request, corresponding MICFAMs (i. e. present in at least one selected organism) are extracted from the PkGDB database and encoded as vectors of presence/absence. Bitwise operations on these binary vectors are then applied to quickly compute the pangenome components of the selected organisms. The result page displays the size of each pan-genome component in number of families and genes, and provides links to download protein sequences and annotations. Family counts are also displayed on a Venn diagram when the number of input genomes is below six (Figure  2A ). Finally, box plot graphs could be displayed to visualize the evolution of the coregenome and pan-genome size according to the number of considered genomes (i. e. for more than 10 selected genomes, ∼1000 combinations are sampled among the total combination distribution to limit the combinatorial explosion) (Figure  2B ).
Moreover, predicted clusters are compared to the recently published reference database MIBiG (Minimal Information about a Biosynthetic Gene cluster), which aims at developing a genomic standard for experimentally characterized BGC of all types (NRPS, PKS or RiPP) and from all phyla (bacteria, fungi or plants;  (32) ). This database provides a comprehensive and standardized specification for BGC annotations and gene cluster-associated metadata. In MicroScope, users can get a complete view of the BGCs predicted in one organism by using the 'antiSMASH' section of the Metabolism menu. Each antiSMASH cluster and its genomic context can be explored in a dedicated visu- alization window showing also a graphical representation of the gene domain composition (Figure  3 ). For NRPS/PKS cluster types, the predicted peptide monomer composition and its corresponding SMILES formula are specified.
The predicted chemical structure is displayed as well using the SMILES Depictor web service (https://cdkdepictopenchem. rhcloud. com/depict. html). Below the graphical representation of the predicted antiSMASH cluster, a summary of MIBiG Clusters similarities, BGC gene composition as well as tailoring cluster similarities are given. This last item relies on a knowledge database provided with antiSMASH about tailoring clusters already described in known BGCs and associated with publications. The results of antiSMASH are also available in the gene editor window and can be queried using the 'Search by keywords' functionality. In order to fully exploit the results of the different tools dedicated to genomic regions study (e. g. antiSMASH or RGPfinder), we are currently working on the development of a specific editor.
DATA GROWTH CONTENT AND MICROSCOPE USERS Since the last update  (21) , the number of genomes integrated into MicroScope has increased threefold. Presently, an average of 6 genomes per day are requested for integration in the platform. The resource contains data for more than 6000 microbial genomes of which ∼2600 are publicly available. Furthermore, expert annotations are contin-uously gathered in the MicroScope database. Indeed, ∼35 000 annotations are made in a year and more than 370 000 genes have been manually reviewed so far. This curation effort has an important impact on the overall annotation quality: (i) 36% of these annotations describe precise molecular functions that are supported by direct or indirect (i. e. from homology relationships) experimental evidences and linked to more than 38 000 publications (ii) 17% are genes of known function that were automatically annotated as putative or unknown function (iii) 21% are associated with proper chemical reactions (i.
e. ∼80 000 genes are involved in ∼160 000 Gene-Protein-Reaction associations in MicroCyc metabolic networks). Biologists generally focused their annotations on some proteins/functions of interest, but MicroScope contains also several genomes that were extensively curated (i. e. ∼100 genomes got more than 1000 curated genes). MicroScope annotations are submitted to INSDC databanks when the genomes get published. Since 2013, MicroScope was cited in more than 350 publications. All the data can also be easily downloaded via the Web interface ('Search/Export→Download Data' functionality). Moreover, we provide a RESTful API to access programmatically public genome data. A dynamic documentation of the API is available at http://rest-microscope. genoscope. cns. fr. At present time, MicroScope counts more than 2700 personal accounts.
Added-value of the genome re-annotation of reference species The general tendency of improvement in genomeannotation comes from our increasing knowledge of genomes and from progress in methods and bioinformatics software. Indeed, missed genes and wrong functional annotations impact different types of analyses (i. e. phylogenetic analysis, identification of core genome and strain or species-specific genes). Using a large range of MicroScope functionalities  (21) , two reference bacterial genomes have been (and are still currently) re-annotated since 2013. Bacillus subtilis 168 is the only model organism for the Firmicutes clade that has been continuously annotated in depth. Its genome has been re-sequenced and re-annotated  (33) , and in the mid-2013 we have published a novel curation work in which new genomic objects have been in-cluded (i.
Coupled with BIOLOG ® Phenotype Microarray data, this allowed us to improve considerably the outcome of a system biology approach where a model metabolism of the organism could be matched with experimental data. Our approach pinpointed a specific deficiency in our knowledge: we need to considerably improve explicit identification of transport systems. This should be a major task for the immediate future of studies with the P. putida chassis, but also for other chassis as well. In MicroScope, gene products that are enzymes or transporters are explicitly linked to the biochemical reactions of the RHEA reaction resource  (36) , while novel compound entries have been created in the ChEBI database  (37). The updated B. subtilis 168 and P. putida KT2440 genome sequences and annotations were deposited at the International Nucleotide Sequence Database Collaboration (accession numbers AL009126.
The main route for the degradation of aromatic compounds to enter central metabolism is the ß-ketoadipate pathway through catechol and protocatechuate. Quinate is an alicyclic compound that feeds into the protocatechuate branch and, in ADP1, it is catabolized by 14 genes, 11 of which encode enzymatic activities (pca and qui genes). This figure shows the search for differentially expressed genes in the two conditions (quinate versus succinate) and the results confirmed the involvement of the genes known to be involved in quinate degradation (middle frame). Using the Integrative Genomics Viewer (IGV) software (directly available from MicroScope by clicking the 'launch IGV' button), transcripts coverage over ADP1 genome is displayed, together with genome annotations (lower frame). The pca/qui operon is clearly over expressed in the two experiments (duplicates) when A.
baylyi ADP1 is grown on quinate instead of succinate. Differentially expressed genes listed in middle frame can be loaded into the Multi-experiment Viewer ('Launch MeV' button in the middle frame) software to perform either hierarchical or non-hierarchical clustering analyses. In this example, pca and qui genes belong to the same cluster, showing that they share the same transcriptional profile (lower frame). Finally up and downregulated genes may be mapped onto the AcinetoCyc metabolic network ('MicroCyc Overview' button in the middle frame). In the current use case, all but one gene belonging the pca/qui operon are highlighted in orange indicating that the whole Quinate Degradation I, Superpathway of salicylate degradation and Catechol Degradation III pathways are activated in quinate condition (lower frame).
Grools: reactive graph reasoning for genome annotation When curating gene/protein annotations, biologists try to assign precise molecular functions by evaluating various bioinformatics predictions and taking into account background knowledge on the studied organism. This laborious task may lead to inconsistencies and uncertainties in the annotations due to the lack of experimental results and/or the difficulty to find a correct trade-off between sensitivity and specificity of predictions. To help biologists in this slow expert curation process, we are developing an expert system named Grools (Genomic rule object-oriented logic system) that evaluates the whole microbial genome annotations through biological processes like metabolic pathways. These processes are represented as a graph of biological prior knowledge that could be extracted from generalist databases like UniPathway  (42)  or Genome Properties  (43).
MicroCloud: toward a Software as a Service (SaaS) To face the challenge of Big Data in genomics and continue to efficiently annotate and compare prokaryotic genomes, a technological evolution of the MicroScope platform is absolutely required. Especially, it should increase the flexibility in scale and cost for the need of computation and storage and offers an 'on-demand' microbial genome annotation service. In the context of the French Institute of Bioinformatics (IFB) infrastructure, we are currently designing a version of the MicroScope platform using Cloud technologies to progressively switch into a Software as a Service (SaaS) distribution model. The work is focusing around the choice of the best High Throughput Computing (HTC) Cloud solution and the design of virtual appliances for the three components of the platform (i.
e. the production system, the databases and the Web graphical user interface). The automatic annotation process will take advantage of a set of analysis tools and the know-how included in the MicroScope workflows. Moreover, the deployment of custom instances (i. e. with only organisms of interest) will provide faster analyses than the current single MicroScope instance in which the comparison of thousands of genomes is computationally expensive. This flexible distribution of Mi-croScope through Virtual Machines on the Cloud (i. e. distribution of pre-configured data analysis pipelines with all software dependencies for their execution) should ease the process of integrating NGS technologies and bioinformatic predictions, and thus, the diagnostics analysis.
Using the Integrative Genomics Viewer (IGV) software (directly available from MicroScope by clicking the 'launch IGV' button), transcripts coverage over ADP1 genome is displayed, together with genome annotations (lower frame). The pca/qui operon is clearly over expressed in the two experiments (duplicates) when A. baylyi ADP1 is grown on quinate instead of succinate. Differentially expressed genes listed in middle frame can be loaded into the Multi-experiment Viewer ('Launch MeV' button in the middle frame) software to perform either hierarchical or non-hierarchical clustering analyses. In this example, pca and qui genes belong to the same cluster, showing that they share the same transcriptional profile (lower frame). Finally up and downregulated genes may be mapped onto the AcinetoCyc metabolic network ('MicroCyc Overview' button in the middle frame).
Other thermophysical properties (solid density and solid heat capacity) are identical for solid 1 and solid 2. Convection conditions are applied at the bottom face (y = À12 mm). The convection coefficient is constant (h = 500 W m À2 K À1 ). However, the reference temperature (T ref )i s time dependent, as shown in Fig. 11. The temperature evolution is simulated over a long period of time (t max = 10,800 s). The computation is divided into 18 coupling periods of 600 s (Fig. 11 ). Solid time step is dt s = 60 s. Initial temperature in solid domain is T i = 300 K. The characteristic time scales of the numerical cases corresponding to Tables  3  and 4  are summarized in Table  5. Let us recall that two different multiphysics solutions have been adopted in this paper: -A reference solution provided by FLUENT in the framework of a monolithic approach -A partitioned approach provided by FLUENT/ANSYS through WORKBENCH multiphysics platform and driven by PYTHON scripts.
Introduction Building segmentation can be defined as the process of separating buildings from other objects such as natural and artificial ground, vegetation and urban objects. The first researches on automatic building extraction began in the 1980s. They used aerial imagery and focused on the extraction of high-level 2D and 3D primitives from stereo images. One of the main drawbacks of these methods is that some false positives need to be filtered out since some linear primitives may not correspond to meaningful structures. In computer vision, elevation images were introduced as data structures allowing direct access to 3D geometric features. First elevation images were mostly acquired from small objects and scenes, using active systems. During the 1990s, aerial laser scanning (ALS) became widely available, so elevation images of huge scenes and cities became possible.
Their method consists of selecting the appropriate parametric model that minimizes strain energy of fitted surfaces. The authors applied their methodology to indoor range images of the University of South Florida (USF) database  [8]. Several works on the parametric model fitting problem can be found in the literature  [9] [10] [11] [12] [13]. Those works can be extended in order to segment surfaces such as ground and façades on elevation images. Their main drawbacks are that they require a model estimation which can be different for different images, are time consuming due to minimization procedures and may produce under-and over-segmentation. Demantke et al. [14]  propose a method to adapt 3D spherical neighborhoods based on local features. Radius selection is carried out optimizing local entropy.
Then, dimensionality features are calculated. These features can be useful to classify 1D structures such as pole-like objects, 2D structures such as ground or façades, and 3D volumetric structures such as trees and urban objects. Hernández and Marcotegui  [2]  assume that façades on the same street are aligned, which is verified in their Paris-rue-Soufflot database. They use the Hough transform to find façade directions. Then, they analyze the height of each profile in order to detect façades and city blocks. Hammoudi  [15]  presents a similar technique based on the Progressive Probabilistic Hough Transform in order to detect walls and windows. He assumes that building façades are mainly vertical, so it is possible to generate an accumulation image to compute the number of points projected on each pixel.
Façade Marker Extraction The height image f height is appropriate to compute façade markers since it contains information about high and vertical structures, as shown in Figure  3. Figure  3a ,b show maximal f max and minimal f min elevation images, respectively. While Figure  3c  presents the height image. From the work by Hernández and Marcotegui  [29] , we use the two following geometric constraints on f height in order to extract façade markers: • h min , defining the minimal allowed façade height. In our experiments, this variable has been set to 3. 5 m according to architectural characteristics of our databases. This threshold is illustrated in Figure  4a. Note that only the highest objects are preserved. • L min , defining the minimal allowed façade length. In our experiments, this variable has been set to 5 m according to architectural characteristics of our databases.
Results Our façade segmentation methods have been tested on TerraMobilita datasets in order to get qualitative and quantitative results  [4]. Two types of ground truth (GT) annotations are available: • 2D lines indicating the separation between sidewalks and buildings. These 2D manual annotations are usually provided by local authorities. In our case, they have been obtained from Open Data Paris  [48] , a project from Paris city hall (Mairie de Paris, in French) in order to make urban data available to the community. Evaluations using 2D lines are commonly used in the state of the art when 3D annotations are not available  [3, 49, 50]. These evaluations give an idea on the segmentation method performance. However, results should be carefully interpreted since the evaluation is only carried out on the 2D space at the ground level, the performance segmenting 3D features such as tilted façades, architectural details and balconies cannot be directly evaluated, as illustrated in Figure  19.
Results: Evaluation Using Open Data Paris We have chosen 4 MLS datasets from 6th Parisian district in France. Data have been acquired by Stereopolis II  [52] , a MLS system by the National French Mapping Agency (IGN-In French Institut National de l'Information Géographique et Forestière). Table  1  describes the datasets used in this evaluation. Table  1. Datasets used for evaluation of our façade segmentation methods. All datasets have been acquired in January 2013 by Stereopolis II system in the 6th Parisian district, France.
Filename Description site I TerMob2_LAMB93_0020. ply rue d'Assas, approx. 90 m. Short street section with straight façades. See Figure  20  site II TerMob2_LAMB93_0021. ply rue d'Assas, approx. 80 m. Short street section with two intersections and two big trees. See Figure  21  site III Cassette_idclass. ply rue Cassette, approx. 200 m. Long street with mainly straight façades and a small tree alignment. See Figure  22  site IV Z2. ply rue Bonaparte and St. Sulpice square, approx. 400 m. Long street section with non straight façades and several tree alignments. See Figure  23  GT annotations have been obtained from Open Data Paris and correspond to 2D lines indicating the junction between sidewalks and buildings at the ground level. Quantitative analyses are performed by comparison between automatic and GT lines on a 2D image.
When 3D façade points are projected to a 2D plane, they are usually wider than a single line due to façade inclination, architectural details and balconies. Therefore, buffers around GT lines and segmented façades are required in order to evaluate the results. On the one hand, a segmented façade is labeled as true positive or false positive if it is located inside or outside a GT buffer, respectively. On the other hand, a GT façade is labeled as segmented or missed if it is located inside or outside a segmented façade buffer, respectively. In our datasets, we consider a buffer width of 1. 0 m, which corresponds to the width of a typical Haussmannian balcony in Paris. This buffer is appropriate to quantify true positives without overestimating false positives, as shown in Figure  19. This buffer-based evaluation is commonly used in other works reported in the literature  [3, 49, 50].
Results: TerraMobilita/iQmulus Database TerraMobilita/iQmulus database  [51]  has been created with the aim of benchmarking semantic analysis methods working on 3D dense urban data. This database has been created in the framework of TerraMobilita project. It consists in 11 annotated 3D point clouds acquired by Stereopolis II system in the 6th Parisian district in January 2013. Annotation has been carried out in a manually assisted way by MATIS laboratory at IGN. Our evaluation consists of one of those point clouds. For this experiment, file "Cassette_idclass. ply" has been used. The manual annotated 3D point cloud is available at  [51]  and the 3D point cloud processed by our method is available at  [53]. It contains 12 million points from a street section approximately 200 m long in rue Cassette in Paris, France.