dataesr/scientific-paragraphs-categorization huggingface dataset:
1a )  [33]. Subsequently, each variant was further filtered based on additional gene and variant-level information from publicly available databases: Online Mendelian Inheritance in Man (OMIM: http:// www. omim. org); PubMed; the Human Gene Mutation Database (HGMD: http://www. hgmd. cf. ac. uk); and ClinVar (https://www. ncbi. nlm. nih. gov/clinvar/). This sequential analysis eliminates the bias towards a particular mode of inheritance, mutation type, and the prior knowledge of the role of a specific gene in human disease. Additionally, this approach also integrates the fundamentals of WES analysis based on previously described SNV workflows and is compatible with ACMG guidelines for SNV filtering and classification of variant pathogenicity  [3, 9, 27]. Analysis of interactomes, gene families, protein-protein interactions, co-expression data, and available literature further prioritized candidate variants (Fig.
1b ). The patterns of segregation of the remaining candidate variants were analyzed by Sanger sequencing in available family members. Finally, the most promising candidate genes/variants were used to interrogate local databases and GeneMatcher  [13, 14]  to find additional affected cases with damaging variants in the same gene and with similar clinical phenotypes. When the BHCMG identified suspected causative variants in either newly discovered or recently published known disease genes, it was communicated to the clinical exome laboratory. Sanger confirmation of the suspected pathogenic variant was performed for all of the identified variants (confirmation rate of 100%) under the auspices of the CAP/CLIA-certified diagnostic laboratory and a formal updated clinical report was issued. Similarly, identified variants both in novel genes and in recent reports from the clinical exome laboratory were reported back to BHCMG, creating a bidirectional channel of intercommunication between research and clinical laboratories.
We also designed a parallel computational analysis of "bulk data" in order to accelerate our WES data analyses by scanning the BHCMG database for rare homozygous/heterozygous stop-gain variants culled from WES data of ~5000 research participants including our pilot study cases. In this analysis, we targeted the rare (MAF <0. 5%) homozygous stop-gain variants from ~5000 research participants (including the 74 families) for each gene. Genes were then sorted according to the number of homozygous rare stop-gain variants existing in our database. Second, the genes were analyzed further according to SNV prioritization and filtering workflow as described above (Fig. 1 ). This approach was designed to accelerate the discovery of novel disease genes that exhibit phenotypic consequences through a loss-of-function mechanism.
In addition, other cohorts were interrogated for additional families with variants in the same candidate gene. MutationMapper (http://www. cbioportal. org/mutation_mapper. jsp), ARIC Atherosclerosis Risk in Communities Study, AR-Hom autosomal recessive-homozygous, BHCMG Baylor-Hopkins Center for Mendelian Genomics, BG Baylor Genetics laboratories, CNV copy number variation, Comp compound, db database, ExAC Exome Aggregation Consortium, Het heterozygous, HGMD Human Gene Mutation Database, MAF minor allele frequency; SNV single nucleotide variant, XLR-Hem X-linked recessive-hemizygous (https://github. com/BCM-Lupskilab/HMZDelFinder) [38]  to detect potential homozygous and hemizygous small intragenic deletions from WES data, including single exon "dropout alleles" that may be less robustly identified by current software.
Biallelic variants in novel and candidate recessive disease genes The parallel computational algorithm for detecting rare homozygous stop-gain mutations from "bulk data" (i. e. ~5000 research exomes in the BHCMG database, including this pilot study), identified a rare homozygous stopgain mutation in GNB5 (MIM 617182) in a study participant with DD, hypotonia, retinopathy, and Mobitz type I atrioventricular block (Fig. 3a ). A homozygous splice site variant (c. 249 + 3 A > G) in GNB5 was detected in two affected siblings from the clinical diagnostic laboratory exhibiting DD/ID, nystagmus, and sinus node dysfunction. GNB5 encodes the Gβ5 protein, a guanine nucleotide-binding protein and a member of the signal-transducing G protein β subunit family. Additional participants with pathogenic variants in this disease gene were subsequently identified  [21].
In our study, the pursuit of novel disease genes for rare disease benefits from a tremendous resource: the combined exome variant dataset from over 15,000 cases referred to either the clinical exome laboratory or the BHCMG research laboratory. Research studies also support reporting of novel disease genes at the time of initial discovery, an important step toward gaining sufficient evidence to meet ACMG guidelines for reporting by a diagnostic laboratory. Additionally, as certain tools are developed and validated in a research setting and are honed to better efficiency, they are frequently translated into the clinical pipeline; GeneMatcher, developed by the BHCMG, is one such example  [13, 14]. In this study, trio-WES analysis combined with de novo variant and CNV detection were invaluable for achieving an improved molecular diagnostic rate.
As the cost of WES continues to fall, we anticipate that trio-WES may ultimately be favored over proband-WES in the clinical setting of a sporadic suspected genetic disease. Trio analysis also supported the use of a newly developed tool, DNM-Finder (https://github. com/BCM-Lups kilab/DNM-Finder). In our study, 27% of the pathogenic or candidate variants were de novo variants. Trio analysis can also increase the molecular diagnostic yield over that attained by studies of the proband alone. This is due, in part, to being able to more readily detect by computational filtering de novo variants (Additional file 2: Figure  S1 ). All DNA was extracted from blood or saliva samples in this study; however, detection of mosaicism may be enhanced by including concurrent analyses of more than one lineage: ectodermal (buccal swab, hair), mesodermal, and/or endodermal (blood, saliva).
Our study highlights the limitations of different WES data variant calling pipelines. The research laboratory opts for less stringent filtering of variants, which allows identification of intronic variants located farther from the splice site (i. e. ABCA4); albeit, this increases the number of variants to be investigated and consequently time taken for analysis. For instance, clinical WES did not reveal a conclusive molecular variant in a proband who presented with cone-rod dystrophy from Indian-Asian ancestry and consanguineous parents. Our research bioinformatics pipeline detected a homozygous intronic frameshift deletion, which possibly could explain the phenotype. The inherited deletion was more common in the South Asian population (seen 14 times on ExAC filtered variants). We also identified shared limitations between both pipelines, i.
Introduction Remote sensing technology has a great potential to afford consistent and useful spatial information at different spatial and temporal scales. It can help understanding the links between land use and infrastructure change and a variety of social, economic, demographic and environmental processes  [1, 2]. This information derived from remotely sensed datasets leads to an improved observation and monitoring that can benefit applied urban planning and management  [2]. However, land cover mapping of the urban environment by means of the remote sensing remains a distinct challenge due to the strong diversity of materials and objects, leading to spectral heterogeneity and geometric complexity of the urban areas as well as the presence of the shadow scenes  [3]. In fact, several studies have showed that current satellite data provide limited information to deal with the richness, heterogeneity and complexity of the materials and forms in the urban areas.
The objective is to assess the gain provided by the HYPXIM mission compared to existing missions such as the multispectral or hyperspectral Pleiades coming from EnMAP (30 m) and Prisma (20 m). The aim of this paper is to analyze the potential of the simulated datasets provided by the airborne hyperspectral imagery in mapping urban impervious surfaces using an objectoriented classification approach compared to an analysis on per-pixel basis. The study is applied to Toulouse city (France) within the HYEP (Hyperspectral imagerY for Environmental urban Planning) research project in charge of using the hyperspectral imagery for the environmental urban planning.
Study area and data The study area is the city of Toulouse (43°36'19'' N, 1°26'34''E) which is located in the South-Western of France (Fig. 1 ). The used hyperspectral imagery is derived from the simulation of the future HYPXIM sensor using data acquired by the HySpex hyperspectral camera (www. hyspex. no) over the city centre in 2015. It records data with a high number of narrowly defined spectral bands  (192). It provides a continuous spectrum ranging from 400 nm to 2500 nm, covering both VNIR as well as SWIR domains (Table  1 ) with 4 m ground geometric resolution. The BDTOPO ® topographic database will be used as a reference map to validate the segmentation and classification results. The BDTOPO ® is a 3D topographic database that provides a vector and structured description of the spatial objects with a metric mapping accuracy designed to scales ranging from 1/5000 to 1/50000.
Method An image based supervised classification is performed using an object-oriented approach by means of the eCognition software. First, a routine image pre-processing including radiometric, atmospheric, and geometric corrections is performed. The atmospheric correction is carried out using the COCHISE (atmospheric correction code for hyperspectral images of remote-sensing sensors) algorithm  [15]. Second, the image segmentation is performed using all the spectral bands except those presenting an excessively noise. A multiresolution segmentation algorithm is performed to create meaningful image objects using only the spectral information. The scale factor is set to 0. 4, whereas the shape criterion is not taken into consideration. These segmentation parameters are derived based on several tests, which are performed to deal with the characteristics of the target objects present in the study area.
Engineers can refine this selection based on the real performances of the sensors resulting from previous experiments stored in the same repository. The scope of this paper is the description and validation of the proposed sensor ontology. To do so, the remainder of this paper is organized as follows. Section 2 provides a literature review to describe the role of a sensor and its ontology in industrial services. Then, a Failure Mode, Effects, and Criticality Analysis (FMECA)-based method is proposed for the choice and implementation of sensor, for the design of a maintenance service. Section 3 describes the global model of the proposed ontology. It also details the design methodology and the structure of the proposed ontology. Section 4 presents the application of the sensor ontology in an industrial case using Protégé software in order to validate the completeness and the utility of the proposed ontology.
The whole ontology is developed as a web ontology language (OWL) file with the Protégé tool. To understand whether the ontology is coherent with the domain knowledge, industrial partners from the project check its formal structure, such as taxonomies and relationships. This validation has been done to finalize the sensor ontology for integration into the global ontology. In order to use the ontology from the knowledge repository, query algorithms are proposed to provide engineers with useful information. To investigate whether the ontology is able to fulfill information retrieval and interoperability needs, it has been tested in one specific scenario from the project.
Building the ontology is based on an incremental process starting by the identification of main concepts from the identified product-service development process. This process is followed by the extraction of all concepts, properties, and relationships from the common meta-model to form the backbone of the ontology. The last step is expanding the extracted concepts to a set of detailed taxonomies. The whole ontology is developed as a web ontology language (OWL) file with the Protégé tool. To understand whether the ontology is coherent with the domain knowledge, industrial partners from the project check its formal structure, such as taxonomies and relationships. This validation has been done to finalize the sensor ontology for integration into the global ontology. In order to use the ontology from the knowledge repository, query algorithms are proposed to provide engineers with useful information.
Sensor Ontology Structure Based on the analysis of the main functionalities of maintenance, the proposed methodology is applied to support the information management of both the manufacturing process and maintenance from mechanical and electrical perspectives. The proposed modular ontology consists of semantic models of machines, sensors, and enabled services by these sensors. The above defined main structure is modeled in protégé software (Figure  5 ) based on a sensor system according to the Semantic Sensor Network (W3C Incubator Group Report). Protégé software is a free, open source ontology editor and a knowledge management system. It provides a graphic user interface to define ontologies and includes deductive classifiers to validate the consistency of developed models and to infer new information based on the analysis of an ontology.
Protégé manages ontology models in both Web Ontology Language (OWL) and The Resource Description Framework (RDF) files  [33, 34]. To build the generic sensor ontology, the global meta-model is extended and all concepts which have a direct effect on the sensor are extracted, connected, and classified. This allows for the identification of all ontology classes. Then, the relationships between classes are identified to form the final ontology structure. At the meta-level, as shown in Figure  4 , the main classes heading the ontology structure are: Industrial machine, maintenance activity, information, sensor, and sensor specification. The sensor specification can be theoretical as defined by the sensor provider or experimental coming from real tests in specific conditions. For the genericity issue, the concept of maintenance activity is substituted to the concept of maintenance and maintenance service to focus on the operational level.
Protégé manages ontology models in both Web Ontology Language (OWL) and The Resource Description Framework (RDF) files  [33, 34]. The class sensor takes a central place in the final ontology. It is one of the most critical tasks during the service design process to identify the best sensors for the related integration solution. To do so, a definition of the sensor is provided by a set of complementary taxonomies giving different points of view on the same sensor. The general logic behind this model is a scenario in which the requested service is analyzed then the information which is needed to fulfill the required maintenance activity is defined. This information describes the changes in the machine and its environment that the sensors detect. The sensor is described by a technological point of view and its connection with the machine's components.
Application in Case Study For validation perspective, the proposed ontology has been used to support the implementation of sensors for the condition-based maintenance of machine tools in High Speed Machining (HSM). Since the main objective is the validation of the proposal in terms of completeness and utility before its implementation in an industrial application, the manipulation of the ontology by engineers is ensured by Protégé software. The case study focuses on spindles, which are the most efficient machine tool components (for productivity), but also the most damageable  [36]. A spindle's failure requires its replacement that stops the production for at least a few days, and the repair process is expensive. Consequently, the lifetime of a spindle that is too short has a noticeable impact on the cost of machined work pieces.
It is widely used for the maintenance of rotating machines. Following the proposed ontology, a short list of accelerometers was chosen from the repository in relation to their dimensions, sensitivity, band-pass, and other specifications. Firstly, based on his expertise, the designer of the maintenance service has defined the constraints and the required specifications for the sensor, which characterize the real context of use (Table  1 ). The ranking of a limited number of accelerometers was obtained based on the mapping of predefined requirements and sensors specifications defined by the sensors' manufacturer (that had been implemented in the repository, Figure  12 ). This result is obtained from a number of implemented instances of sensors and by queries on capabilities offered by the Protégé software.
It eases the visualization of sensors' characteristics during design activities. The ranking and analysis of queries results are fulfilled manually by the engineers based on their expertise. For testing issues, a list of the most-known sensor models in the studied domain has been created in the Protégé software as individuals (instances) of sensor sub-classes. Sensor research is fulfilled using "DL Query" facilities offered by Protégé as shown in Figure  12. The creation of new a attribute is fulfilled at the class level and spread to all existing instances. As shown in Figure  12 , the query is created according to the Table  1 , which has been given by the machine's engineers. As an example, one simple query is defined as "PiezoelectricAccelerometer and hasSensitivity value "10" and hasConnectorType value "Cable Integrated" and hasResonantFrequency value "70 kHz"".
The Protegé retrieves two related sensors and shows the complete specifications. The engineer can choose the best sensor from the retrieved list. This result is obtained from a number of implemented instances of sensors and by queries on capabilities offered by the Protégé software. It eases the visualization of sensors' characteristics during design activities. The ranking and analysis of queries results are fulfilled manually by the engineers based on their expertise. For testing issues, a list of the most-known sensor models in the studied domain has been created in the Protégé software as individuals (instances) of sensor sub-classes. Sensor research is fulfilled using "DL Query" facilities offered by Protégé as shown in Figure  12. The creation of new a attribute is fulfilled at the class level and spread to all existing instances.
Moreover, the performance of a given sensor can be largely affected by the application context and the working conditions. The second challenge is the choice of the suitable and robust sensor for the new condition-based maintenance of industrial machines. The proposed ontology supports the classification of a wide panel of sensor data and their connection to real and pragmatic contexts of use. Protegé software provides formal mechanisms to support this task. The first experimentation of the proposal received interest from the industrial experts, who emphasized the permanent needs of different data, generally spread between different documents, software, and databases. Connecting data from the sensor, machine, and service to a common repository is of big interest. Indeed, the resulting maintenance service finds satisfaction from maintenance experts who validate the technical choices taken during the design process and consequently demonstrate the efficiency of the proposed ontology.
Acknowledgments: The presented results were conducted within the project " ICP4Life " entitled " An Integrated Collaborative Platform for Managing the Product-Service Engineering Life cycle ". This project has received funding from the  European Union 's  Horizon 2020 research and innovation program. The authors would like to thank the academic and industrial partners involved in the research project and case study. Author Contributions:  Elaheh Maleki  and  Farouk Belkadi  conceived the proposed ontology. The design of maintenance service implementation process is performed by  Farouk Belkadi  and  Mathieu Ritou ;  Mathieu Ritou  performed the experiments;  Alain Bernard  analyzed and validated the resulted data; and  Elaheh Maleki ,  Farouk Belkadi , and  Mathieu Ritou  wrote the paper collaboratively.
Extracting Coleotpile Kinematics For each coleoptile, the KymoRod software  [22]  was used to extract movement kinematics from the images recorded during the experiment. From every image taken at time t, KymoRod extracted the total length of the coleoptile organ L(t), and determined each position s along the length of the organ from base to apex. For each position s, the organ's orientation from the vertical A(s, t) was computed in radians (see Figure  2 ). The curvature of the organ C(s, t) was then calculated with a spatial derivation of the organ's orientation from vertical as shown: ∂A(s, t) ∂s = C(s, t) (3) To fully describe the kinematics, the curvature variation DC(s,t) Dt was computed in order to account for organ deformation and elongation  [2, 3, 5 ]. DC(s,t) Dt was calculated using the material derivative D Dt , co-moving with each element of the organ derivative.
Data Analysis All statistical and image analyses were conducted in MATLAB (v. 9. 0 R2016a, Mathworks Inc. ). After calculating the balance number B for each coleoptile in the tilted group, we compared overshoot model predictions  [14]  to actual experimental data using Fisher's Exact test to quantify if the model was accurately able to predict coleoptile overshoot. We tested for differences between coleoptiles in the tilted and straight experimental treatments using Mann Whitney U tests and compared: the oscillatory periods, velocity of oscillatory pulses, and temporal averages, of both elongation growth rate Ė(s, t) and curvature variation DC(s, t) Dt. To uncover if oscillations in elongation growth and curvature variation were somehow related, and if this relationship depended on tilting treatment, we calculated the image correlation statistic r between these two oscillation kymographs for each coleoptile.
Discussion The process of gravitropic movement in plant aerial organs was recently formalized in a unifying model called the "AC model"  [14]. Yet, while this model often predicts that plants will "overshoot" the vertical, many plants have been observed to demonstrate a greater regulation of gravitropic movement than currently described by this model and PLOS 13/18 remain vertical during growth  [5, 14]. To discover the additional movement regulations that prevent plants from overshooting the vertical, we took two groups of coleoptiles and subjected coleoptiles in one of the groups to a gravitropic perturbation (tilted), while leaving coleoptiles in the second group unaffected (untilted) to serve as a control group. Using the kymorod software  [22] , we extracted the complete movement kinematics of each coleoptile from both groups, in particular focusing on the relative elongation growth rate (REGR) and curvature variation for each coleoptile.