dataesr/scientific-paragraphs-categorization huggingface dataset:
PerfectO Architecture The PerfectO architecture (depicted in Figure  1 ) follows interoperability principles established with the intention to exchange data with common ontologies  [23]. The architecture comprises GUIs, APIs, and core components. Ontology designers interact with our web-based software using Graphical User Interfaces (GUIs). Application Programming Interfaces (APIs) are either employed by the GUI or by the ontology designers and are developed according to RESTful principles  [51]. The core components of the architecture are as follows: 1. Ontology improvement methodology encourages ontology designers to improve the ontology (explained in Section 4. 2). Improvement methodologies for datasets, queries or reasoning mechanisms are left for future work. Technologies used for the implementation: The RESTful web services have been implemented in Java using the Jersey web service library.
4. Documentation eases ontology understandability. Parrot and LODE have been chosen since web services for automatic documentation are provided. More and more tools are being designed to provide such criteria to ease the task of developers (e. g. , Widoco  [50] ). We designed the ontology documentation mindmap 13 to give an overview of existing ontology documentation tools. 5. Availability advocates sharing the resource on the web. Developers do not have time, resources, or administrative skills to manage the server. Ideally, an ontology catalog server hosts any ontologies and provide the right URL. Sharing the ontology code and documentation on the web encourage ontology reuse. 6. Discoverability improves the dissemination of ontologies. For instance, suggesting ontologies on ontology catalogs and semantic search engines support the dissemination.
Note here step 8 at the early process helps detecting errors also very earlier, specially for newbies in ontology engineering. PerfectO Ontology Improvement Tool Integrated with LOV4IoT. We have implemented the ontology improvement component 15  (TripleCkecker, Oops!, Parrot, Vapour, LODE, and WebVOWL) with the ontologies referenced within the LOV4IoT RDF dataset  16. Figure  3  demonstrates the implementation of the PerfectO Ontology Improvement methodology (explained within the Architecture Section 4. 1) integrated with the LOV4IoT ontology catalog. Figure  3  comprises three main parts: 1. A drop-down list is shown with all IoT applicative domains referenced within the LOV4IoT ontology catalog (more than 20 domains) introduced in Section 2. 1. Once a domain is selected, the second drop-down list is filled in.
Use Cases Semantic Interoperability for the Web of Things White paper highlights the main interoperability issues  [27]  and citing our research work as a baseline. Furthermore, in October 2019, two "Semantic Interoperability for IoT" White Papers  [28]   [29]  have been released and disseminated by ETSI, W3C, AOITI, etc. to guide IoT and standard developers (also from OneM2M and ISO) to reuse and develop semantics-based IoT applications easily. The FIESTA-IoT ontology  [17]  has been employed within the FIESTA-IoT H2020 EU project involving 14 academic and industrial partners, it enables to process data generated by sensor within smart cities. The LOV4IoT dataset is used within experiments to rank the popularity of IoT ontologies in  [52]  and consider the following metrics: availability, believability, understandability, interlinking, PageRank, consistency, and richness.
Our first step is to focus on ontology interoperability and improvement. Instead of saying that our tool is better than existing ones, we exploit their potential when they already provide web services. Our literature review shared in a structured way on the web through FAQs and mindmaps explained in Section 8. 1 is also useful for teaching, tutorials, etc. We have in mind to have a similar approach for datasets, reasoning, and querying, as explained in Section 4. 1. We are aware that researchers can consider our methodology as not innovative since we reuse and integrate tools, but we are following software engineering research methodologies  [15]. Availability Criteria. The PerfectO software is under the GNU GPLv3 license. A GitHub repository has been initiated https://github. com/perfectkb/ perfecto which stands for perfect knowledge bases.
The LOV4IoT catalog has been designed to extend the LOV ontology catalog since LOV inserts only new ontologies when best practices are followed. Due to this experience, we realized that the learning curve to improve ontologies and use the tools needs to be reduced and disseminated within other communities (e. g. , IoT community). This work is aligned with our current activities at improving ontology quality in any projects designed at the Knoesis research center until 2020. PerfectO is hosted on Google Application Engine to avoid any server and DNS maintenance related issues. Reusability Criteria. We provide a "Semantic Web Best Practices for Dummies" documentation  [WR27]  to improve ontologies. More than 200 ontology URLs have been referenced with the LOV4IoT dataset which has been integrated with PerfectO.
Discussions. Ontologies designed within projects (e. g. , European projects) are more impactful, since they are used by different partners and for real use cases. For instance, the FIESTA-IoT ontology has been employed within the FIESTA-IoT H2020 EU project involving 14 academic and industrial partners. However, the major issue of European projects is the lack of ontology maintenance once the project is finished. PerfectO improves ontology quality. For instance, in July 2019, best practices have been disseminated to the robotics community: IEEE P1872. 2 Standard for Autonomous Robotics (AuR) ontology.
Catalogs of Tools The PerfectO web site provides the Catalogs of tools menu (depicted in Figure  8 ) to easily give access to software URLs and publications. The Catalogs of tools references tools for (1) ontology improvement which is the focus of this paper, (2) dataset quality, (3) querying, and (4) reasoning. Figure  9  demonstrates an extensive literature survey within the table of contents classified in two different ways: -A bullet list referencing tool's URLs and scientific publications are displayed simply with clickable links (see Figure  6  as an example). -Mind maps  [56]  are recognized as a useful methodology and a powerful graphics technique used to translate what's in the mind into a visual picture. Since mind mapping works like the brain, it allows us to organize and understand information faster and better.
Acknowledgments This work has partially received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  857237  (Interconnect),  Science Foundation Ireland (SFI)  under Grant Number  SFI/12/RC/2289 ,  Insight Centre for Data Analytics  and  H2020 FIESTA-IoT-CNECT-ICT-643943. The opinions expressed are those of the authors and do not reflect those of the sponsors.
Among the various various stream reasoning engines that exist, C-SPARQL (Continuous SPARQL)  [2]  and CQELS  [23]  are two of the most known. C-SPARQL proposes continuous query capabilities with the SPARQL query language over timestamped RDF streams with a periodic execution strategy. RDF streams are an ordered sequence of pairs where each pair is constituted by a RDF triplet and its timestamp t : (<Subject,Predicate,Object>,t). C-SPARQL has the capability to deal with both RDF streams and static background knowledge, represented as RDF triples, using time windows as a way to decompose data streams. On the other hand, CQELS provides quite the same features as C-SPARQL, combining data streams with static knowledge, but uses the arrival of new triples as a trigger for its queries. Finally, cascading reasoning has been used in the context of ambient-intelligent care rooms to triggers alarms depending on data produced by IoT  [6].
The query is executed on RDF streams that correspond to the property temperature (S temp) in the time frame of 20 minutes, sliding the window by 20 minutes (line 5). The chosen time frame is arbitrary and can be changed as desired. It produces triplets of values (line 4): the sensor name (?s), the patient (?p) and the average temperature during that period of time. In order to obtain the patient to which the sensor is attached, we indicate in the query that the C-SPARQL engine must use our ontological model as background knowledge (line 6). Line 8 enables to obtain the patient to which the sensor is attached. To get the observation's values, ?o1 individual is bound with the data values ?v1 through the appropriate properties (sosa:madeObservation and sosa:hasSimpleResult) (line 9-12). Finally, the list of output triplets are filtered out to include only the ones where the observations satisfy the restrictions in the FILTER and the HAVING clauses (line  [13] [14] [15] [16] [17] [18].
Results and Discussion The process described in this paper has been used to detect particular situations on different sets of temperature values with only two sets showing relevant events (i. e. the patient developed a fever). A graphical representation of the situations detected while streaming in real-time these data may be found in figure  2  with the first example (high temperature during the night) on the left side and the second example (high fever) on the right side. The vertical lines represent the instants where a situation has been detected, with a label indicating which situation has been raised. The time-window for this detection was 20 minutes. Videos of the examples and source code may be found at: https://gitlab. insa-rouen. fr/fgiustozzi/sicopad Results observed on figure  2  show the system correctly identified situations, based on table 2, when they happen: the first example raises situation s2 during the end of the night, then situation s3 when the temperature goes above 37.
Virus Propagation and Titration In this study, we used the Oropouche orthobunyavirus strain BeAn19991 (prototype strain Brazilian isolated from the blood of a three-toed sloth, Bradypus tridactylus, in the Amazon region) with GenBank accession numbers KP052850, KP052852, and KP052851. 1  [35, 36]. OROV strain BeAn19991 was kindly supplied by Dr. Betânia Drumond from the Laboratório de Vírus, Universidade Federal de Minas Gerais, Brasil. OROV was propagated in Vero (African green monkey kidney) cells maintained on Dulbecco's modified Eagle's medium-high glucose (DMEM-High, Sigma Aldrich, MO, USA), supplemented with 5% FBS (fetal bovine serum), penicillin, and streptomycin. Briefly, cells were seeded to 70% confluency and infected at a multiplicity of infection (MOI) of 0. 01. Cultures were maintained for 4 days at 37 • C, when supernatant was collected, cells were lysed by repeated freezing and thawing to release virus particles, and then mixed with the supernatant.
Acknowledgments: We are deeply thankful to  Marco Antônio Silva Campos ,  Alexandre de Magalhães Vieira Machado , and their team who provided viral culture infrastructure at the  Imunologia de Doenças Virais Group (IDV-IRR/FIOCRUZ). We are grateful to  Betânia Paiva Drumond  for the kindly provided OROV strain and to  Felipe Gomes Naveca  for initial attempts to provide us OROV isolates. We thank the  Program for Technological Development in Tools for Health-PDTIS-FIOCRUZ  for use of its facilities. We are grateful to all members of the  Mosquitos Vetores Group  ( MV-IRR/FIOCRUZ ) and the  World Mosquito Program Brazil. We are grateful for support from the  Coordination for the Improvement of Higher Education Personnel (CAPES).
INTRODUCTION The banana family (Musaceae) is a monophyletic clade comprising of three genera, Musa, Ensete, and Musella  (Kress, 1990). Most of the identified species of this family belong to the Musa genus, which includes the edible fruit-bearing banana cultivars, with the latest database of Musa Germplasm Information System recording 6,548 accessions that are maintained in 29 collections around the world  (Ruas et al. , 2017). In contrast, the Ensete genus is comprised of cultivars with nonedible fruits  (Oyen & Lemmens, 2002). The corm or the basal pseudo-stem of the Ensete plants is used as a starch-rich food source, predominantly in Ethiopia, where it plays significant agricultural and economic roles. There are six known species within the Ensete genus  (Ploetz et al. , 2007) , with 387 accessions characterized by  Yemataw et al.
MATERIALS AND METHODS Genomic short reads of diverse banana accessions from two genera of Musaceae, Musa and Ensete, were collected from both publicly available data and newly generated from banana samples from Indonesian Agricultural Agency for Research and Development at the Indonesian Tropical Fruit Research Institute germplasm collection. The metadata of the reads used in the banana pangenome construction and analysis is listed in Supplemental Tables  S1  and S2. We applied the previously published iterative pangenome assembly pipeline  (Golicz et al. , 2016; Montenegro et al. , 2017; Zhao et al. , 2018). Bowtie2 v2. 3. 3. 1 (-I 0 -X 1000 -end-to-end -sensitive)  (Langmead & Salzberg, 2012)  and SAMtools v1. 2  (Li et al. , 2009)  were used for read mapping and subsequent extraction of unmapped reads.
2. 2  (Stanke et al. , 2006). Protein, expressed sequence tag, and transcriptome data were collected from the Sequence Read Archive and used as evidence for gene models (Supplemental Table  S3 ). Gene ontology (GO) terms were assigned to the identified gene models using Blast2GO  (Conesa et al. , 2005). The GO-term enrichment used Blast2GO and topGO  (Alexa & Rahnenführer, 2009). Benchmarking universal single-copy orthologs (BUSCO)  (Simão et al. , 2015)  from OrthoDB (www. orthodb. org) analysis used 1,440 orthologs from the plant database (embryophyta_odb9)  (Zdobnov et al. , 2017). Single nucleotide polymorphisms (SNPs) were called following Bowtie2 read mapping using Bcftools v1. 2-63  (Li, 2011)  and were quality processed (SAMtools mpileup -q 30 -Q 20). Functional annotation of the final SNPs was carried out using the SnpEff tool  (Cingolani et al.
, 2012). To call presence-absence variations (PAVs), reads from all samples were mapped to the pangenome using Bowtie2, and SGSGeneLoss was used to call PAVs using standard settings  (Golicz et al. , 2015). Samples with a coverage below 15× were excluded. PVClust  (Suzuki & Shimodaira, 2006)  was used for hierarchical clustering based on the gene presence-absence matrix. Core and variable gene counts were plotted using PanGP (Y. Zhao et al. , 2014). This was also used for the modeling and prediction of the pangenome size. vcftools  (Danecek et al. , 2011)  was used to calculate Weir-Cockerham's F ST values at 100K nucleotide windows across the pangenome, which were visualized using R. Genes were classified into resistance gene analogs (RGAs) by using RGaugury  (Li et al. , 2016). The data generated for this project is available in the Sequence Read Archive under BioProject ID PRJNA612747.
, 2018). In addition, there are four genome assemblies for Ensete at the scaffold level  (Yemataw et al. , 2018)  (Supplemental Table  S4 ). Analysis of the assembly statistics suggests that the M. acuminata spp. malaccensis and M. schizocarpa genome assemblies are the most contiguous. The M. acuminata assembly showed the most complete BUSCO orthologs for both single-copy and duplicated genes (Supplemental Table  S5 ; Supplemental Figure  S1 ) and so was selected as the base reference for iterative pangenome assembly. Additional public sequence data is listed in Supplemental Table  S1. Whole-genome Illumina sequence data was collated from 15 banana accessions, 12 of which represent the genus Musa with three from Ensete. The Musa accessions include six representatives of M. acuminata (A genome), one from M.
Gene PAV of the Musaceae pangenome showed a similar trend to previous studies among Musaceae. A study of gene content for the published E. ventricosum draft genome by  Harrison et al. (2014)  found 662 gene models of M. acuminata that are absent in E. ventricosum, and 9,967 gene models in the E. ventricosum genome that were not found in the banana proteome database, though, as the authors acknowledge, the published E. ventricosum gene model content may be inflated. Prediction of the banana pangenome size using these gene models and PAV data suggest a core gene number of 18,288 (±29). The observed number of core genes was 18,359 (Figure  3A ), suggesting that we have defined the majority of the core gene content. The number of observed variable genes was 29,331 (Figure  3A ). Restricting the analysis to within the Musa genus (12 samples) we predict a larger number of core genes (27,858 ± 69), with the difference reflecting genes that appear to be conserved in Musa but absent from Ensete (Figure  3B ).
The precipitable water vapour (PWV) was typically less than 1 mm, the system temperatures less than 200 K, and the phase root mean square (rms) less than 50 •. We used the Common Astronomy Software Application (CASA;  McMullin et al. 2007)  for the data calibration. We then exported the calibrated visibility tables to the GILDAS 4  format, and performed the imaging in MAPPING. We produced continuum images by averaging line-free channels in the visibility plane. We produced the continuum images using natural weighting (via the CLEAN procedure). Phase self-calibration was performed for all sources with a signal-to-noise ratio (S/N) > 100. Only CSO3 did not fulfil the criteria. We applied three successive rounds of calibration with solution intervals in the range 300-60s. Solution intervals were adjusted from one source to another, depending on the S/N and of the number of flagged solutions.
Archival data at 333 and 32. 9 GHz Our source sample is part of the VLA/ALMA Nascent Disk and Multiplicity (VANDAM) Survey of Orion Protostars. In order to be able to construct the dust SED of the sources in the centimetre to millimetre range, we used archival data from the VANDAM Survey  (Tobin et al. 2020 ) at 333 GHz (0. 87mm) from ALMA, and we used the data at 32. 9 GHz (9mm) from the VLA observations. We used the robust=2 (equivalent to natural weighting) cleaned images, for both the ALMA and the VLA data. Both sets of archival data are taken from the Harvard Dataverse 5 (To-Table  1 : Continuum beam parameters at 246. 2 GHz, 333 GHz, and 32. 9 GHz, as well as rms and centre of observations used. Information on the beam size and rms at 333 and 32. 9 GHz are issued from the archived images used  (Tobin et al.
MMS9-d: Finally, MMS9-d is the most puzzling source. This source is cold (T d ≤ 20 K) and closely resembles CSO33-c in terms of geometry. The dust emissivity spectral index range is not constrained, which does not help us to understand the status of this source. Thus, it could be a Class II protostar with a disk seen edge-on as it is cold. However, our 246. 2 GHz ALMA data include a C 18 O line. We thus examined the C 18 O emission towards both CSO33-c and MMS9-d in order to be sure that those objects are associated with the filament. A clear C 18 O emission line is found at the position of CSO33-c, but it is not clear whether the C 18 O emission is associated with. In addition, the source does not show emission at 32. 9 GHz. In summary, the source is either evolved (> Class II) or a background object (perhaps a galaxy).
Conclusions Using new 246. 2 GHz ALMA data from the ORANGES project complemented with archival 333 GHz ALMA and 32. 9 GHz VLA data from the VANDAM survey  (Tobin et al. 2019a (Tobin et al. ,b, 2020)) , we have constructed the 100 au scale dust SED in the millimetre to centimetre range of 16 protostars located in the OMC-2/3 filament. We have derived several dust parameters, such as the dust temperature, the dust emissivity spectral index, the (envelope+disk) mass of the protostars, and whether freefree emission is present towards the sources. Here are our main conclusions: -From the dust continuum maps, we detected 28 sources, of which 16 could be analysed in this study. Almost all the sources are detected at 333, 246. 2, and 32. 9 GHz. -(67 ± 33)% of our fields reveal multiple systems, with average projected separations of at least 1000 au.
Acknowledgements. We deeply thank the referee for their careful reading and their precise and constructive comments that helped to significantly improve the paper. The authors wish to thank  F. Motte ,  F. -X. Désert  and  M. Benisty  for their fruitful help on the dust continuum method and useful suggestions and discussions. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme , for the Project "The  Dawn of Organic Chemistry" (DOC) , grant agreement No  741002. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2016. 1. 00376. S and ADS/JAO. ALMA# 2015. 1. 00041. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC  Article number, page 14 of 30  M.
aegypti SG proteome used uninfected mosquitoes and one-or two-dimensional gel electrophoresis (DGE) coupled with mass spectrometry (MS) to identify a few proteins  30, 31. Recently, using high-resolution MS, 1,208 proteins were detected in uninfected A. aegypti SG  32  , although these included proteins identified by only one unique peptide. To our knowledge, only two studies for DENV2, one for CHIKV and none for ZIKV reported SG proteomic response to infection with low resolution MS  [33] [34] [35]. Here, to bridge this knowledge gap in SG proteomic response to viral infections, we deployed high-resolution MS with isobaric tag for relative and absolute quantitation (iTRAQ) on DENV2-, ZIKV-and CHIKV-infected and non-infected A. aegypti SG. We identified 218 proteins using a custom protein database and described those with functions related to immunity, blood feeding, digestion, metabolism and ribosome, stress and mitochondria.
The tandem MS analysis was performed using a 5600 TripleTOF system (AB SCIEX, USA) under Information Dependent Acquisition (IDA) mode. The mass range of 400-1800 m/z and accumulation times of 250 millisec per spectrum were chosen for precursor ion selection. MS/MS analysis was performed on the 20 most abundant precursors (accumulation time: 100 millisec) per cycle with 15 s dynamic exclusion. Recording of MS/MS was acquired under high sensitivity mode with rolling collision energy and adjusted capillary electrophoresis (CE) when iTRAQ reagent use was selected. Protein identification and quantification. Peptide identification and quantification was carried out on the ProteinPilot 5. 0 software Revision 4769 (AB SCIEX, Singapore) using the Paragon database search algorithm (5. 0. 0. 0. 4767) and the integrated false discovery rate (FDR) analysis function.
Spectra were searched against a custom protein database including Aedes VectorBase (VB) (AAegL3. 3 on July 2017 and AAegL5. 1 on February 2019), Aedes UniProt (February 2019), and a translated de novo assembled A. aegypti SG transcriptome (unpublished) based on our previous work  15. Within this database, we also included DENV2, ZIKV and CHIKV proteins downloaded from the National Center for Biotechnology Information (accessed July 2017). For peptide identification the tolerance for MS was set at 0. 05 Da and for MS/MS was kept at 0. 1 Da. The MS/ MS spectra obtained were searched using the following user-defined search parameters: Sample Type: iTRAQ 8-plex (Peptide Labeled); Cysteine Alkylation: methyl methanethiosulfonate; Digestion: Trypsin; Instrument: TripleTOF5600; Special Factors: None; Species: None; ID Focus: Biological Modification; Database for Aedes VB Search Effort: Thorough; and FDR Analysis: Yes.