dataesr/scientific-paragraphs-categorization huggingface dataset:
The global architecture of the MFPO ontology is shown in Fig. 2. We use UML class diagram to represent concepts and properties, where boxes are classes in the MFPO ontology, and solid arrows are object properties. Data properties are indicated by class attributes. For the purpose of clarity, only a subset of the whole classes and properties are presented. The Web Ontology Language (OWL)  [15]  is chosen as the development language of the ontology. The MFPO ontology is an extended work of the ontology introduced in  [6] , and the descriptions of the common classes of the two ontologies (e. g. , RealizedPart, ManufacturingProcess, ManufacturingFacility. ) can be found in  [6]. Compared to the ontology introduced in  [6] , the key extension proposed in the MFPO ontology is the capability of handling uncertainty.
In the MFPO ontology, the nominal categories of classes are described by data properties. In this way, the classes in the MFPO ontology are associated with membership degrees which range from 0 to 1. For example, in the ontology introduced in  [6] , hasFailureCriticality is an object property whose domain is the class Failure, and range is the predefined individuals Low, Medium and High. After applying the aforementioned method, this object property is replaced by three data properties: hasFailureCriticalityLow, hasFailureCriticalityMedium, and hasFailureCriticalityHigh, and the sum of the numeric values of these three data properties is 1. By this, the Failure class in the MFPO ontology is associated with membership degrees to three nominal categories, thus enabling ontology reasoning of vague knowledge about this class.
Experimentation To evaluate the effectiveness of our approach, we conduct experimentation on a real world data set. In this work, we apply our appraoch on the SECOM data set  [8] , which contains measurements of features of semi-conductor productions within a semi-conductor manufacturing process. A software prototype is developed based on Java 10. 0. 2, Protégé 5. 5. 0, OWL API  [13]  and SWRL API  [18]. Among them, the OWL API is used to build and manipulate the MFPO ontology, and SWRL rules are proposed to reason about the criticality of failures. To enable ontology reasoning, the SWRL API, which includes a SWRL Rule Engine API, is used to create the transformed rules and then execute them. At last, the inferred knowledge is returned to the OWL API, and stored in the new ontology. The running environment of the software prototype is Microsoft Windows 10.
In this rule, hasA63V, hasA102V, hasA209V, hasA347V, hasA476V are data properties in the MFPO ontology that link individuals of the State class with XML Schema Datatype values. They correspond to the quantitative values of the attributes A 63 , A 102 , A 209 , A 347 , and A 476 in the SECOM data set. TimeInterval corresponds to the root class of all individuals of time intervals. There are two object properties that link TimeInterval with State: hasSubState and hasProState, among which hasSubState corresponds to the subsequent state of a time interval, and hasProState indicates the proceeding state of a time interval. In this case, state S 1 is the proceeding state of the time interval between S 1 and S 2 , and state S 2 is the subsequent state of this time interval. To describe the numerical intervals which are obtained by discretization, SWRL Built-Ins are used to specify the upper and lower numerical boundaries.
Entropy Entropy estimation is important when PUF responses are used for key generation. Among the different estimation proposals, the NIST recommendations stand out, including several statistical tests aimed at estimating the min-entropy of a random number generator  [18]. This suite includes 10 different tests that have been particularly designed to avoid overestimation entropy in non-independent and identically distributed sources (non-IID). The ideal value of min-entropy in perfectly independent and identically distributed entropy sources is 1. The estimated min-entropy for the PUF responses dataset is 0. 794207-this corresponds to the minimum value obtained for the 10 tests, as shown in Table  1. This value has been obtained for the collision test. This test  [21]  gives a measure of the mean number of samples to the first collision in a dataset, where a collision is any repeated value.
2D Pose estimation Multi-person pose estimation models can be categorized as either top-down or bottom-up. For instance, AlphaPose  [3]  is a top-down approach since it detects every person in the scene then extracts their pose individually. Openpose  [2]  is a bottom-up approach since it detects all the keypoints in the scene and then puts them together to form skeletons. Other bottom-up approaches, like  [8] , do not separate the detection and grouping stages, obtaining a single-stage network. We chose OpenPose over other methods because of its proven real-time performance. It also has the most active support and has been regularly updated with new features since its release. Particularly, in this work we use the hand detection module. OpenPose Figure  1  shows the general pipeline of OpenPose.
We developed a novel method based on arrays of fluorescently tagged antisense oligonucleotides  19  for in vivo imaging of transcript accumulation and called it MOrpholino VIsualisation of Expression (MOVIE). MOVIE was used to demonstrate that the earliest gene expression in zebrafish embryos is confined to a unique transcription compartment, which forms during the Sphase of extremely short cleavage stage cell cycles, without gap phases. Nuclear organisation of transcription suggests a previously unappreciated nuclear body formation, which is seeded by transcribed loci on pairs of homologue chromosomes and persists in a cell cycle length-dependent manner. MOVIE enabled investigation of the spatio-temporal dynamics of this transcription compartment, which is a characteristic feature of pre-MBT embryos.
MOVIE detects native gene transcription in living embryos. We designed a morpholino oligonucleotide (MO) array and hypothesized that binding of morpholinos at 5′ ends of transcripts will lead to detectable fluorescence signal, due to their accumulation at endogenous gene loci, in living cells (Fig. 1a ). We chose the primary transcripts of miR430 microRNA genes as test for our approach. MiR430 genes are the earliest and highest expressed genes during the first wave of zygotic gene activation  10  and play a key role in the clearance of maternally deposited mRNA during MBT. Cap analysis of gene expression (CAGE) sequencing datasets  20  were used to map the transcription start sites of the miR430 gene cluster. CAGE data revealed at least eight transcriptional start sites on the reference zebrafish genome (GRCz10), with 6-9 miR430 genes predicted to be transcribed from each of them (Fig.
Transcription follows cleavage stage cell cycle periodicity. Zebrafish cleavage stages are characterised by metasynchronous cell cycles 3 in which synthesis and mitosis phases are cycling without gap phases  [21] [22] [23]. To understand the temporal relationship between miR430 transcription and the fast cell cycles of cleavage stages, we simultaneously monitored miR430 expression by 4D time lapse imaging with MOVIE, and cell cleavages using the nuclear marker mRuby3:H2B (Fig. 3a , Supplementary Movie 1). Quantification of the miR430 transcript detection led to three main observations. Firstly, miR430 expression was first detected at 64-cell stage, matching previous transcriptome analyses  10, 20, 24  , (Fig. 2a ) and was present until late epiboly stages in diminishing numbers of cells. Secondly, the number of cells with miR430 expression gradually increased during cleavage stages, potentially explaining the increase in genome-wide miR430 expression quantification data from whole embryos (Fig.
Using MOVIE it was found that rRNA transcripts can be detected, however only after the main wave of genome activation at MBT, with low signals at 30% epiboly and frequent activity at shield stages (Fig. 5g ), which is in line with previously described rRNA expression patterns  36, 39  ). At post-MBT stages, miR430 transcripts were still detected in a mosaic fashion (Fig. 5g ) and allowed subnuclear localisation analysis of both RNAs when both gene products were detected in the same cells. Thus dual MO labelling permitted miR430 RNA and rRNA distribution to be compared. At 30% epiboly stage their accumulation was distinctly localised (Fig. 5g  bottom row). This result demonstrates that the transcription compartment containing Pol II gene products is independent from the nucleolusforming RNA compartment despite 4 of 5 annotated rRNA genes reside on chromosome 4.
Methods Characterisation of miR430 locus. Identification of predicted multiple promoter sites at the miR430 locus were determined from CAGE-seq data  20  mapped to zebrafish assembly GRCz10 by Tophat 59 allowing up to 80 multi-mapped reads. Start sites of transcription were determined by usage of CAGEr software  60. Annotation of promoter regions (Fig. 2 ) were defined as 400 bases upstream and 100 bases downstream of the detected TSS with pre-miR430 gene annotations determined from Ensembl database with addition of 2 pre-miR430 c-subtype sequences found by BLAT search, completing the a-c-b triplet structure  61. Detection of unique SNPs in miR430 promoter sequences was achieved by multiple sequence alignment with Clustal Omega  62. Thus allowing detection of nascent RNA 10 reads aligning to two individual promoters in the miR430 cluster by unique mapping with STAR aligner  63.
Expression of miR430 during development is demonstrated both by CAGE-seq  20  and RNA-seq 11 spanning 19 developmental time points, capturing expression both pre and post-MBT (Supplementary Fig. 1a ). Morpholino design for ZNF genes on chromosome 4. Targeting MOs were designed specifically to visualise transcription of zinc finger genes on chromosome 4, most highly expressed in the early zygote, as determined from CAGE-seq dataset  20. Twenty three such genes with a fully annotated transcript were identified. The 5′ UTRs of these genes with 50 bp up-and downstream flanking sequence were used for conservation assessment, via multiple sequence alignment using MUSCLE alignment algorithm  64  and subsequent visualizations of inherent similarity were achieved with JalView  65. Consensus sequences were generated and used for morpholino design, targeting conserved regions in the 5′UTRs.
Altogether 4 morpholino were designed (Supplementary Fig. 4b ). To identify all potential znf genes on chromosome 4, targeted by the designed MOs, a BLAST search against Ensembl version 92 cDNA database was performed. A transcript was considered as a potential target by a morpholino if at least a 20 bp match with 84% identity was found. Intersections were calculated from the identified potential target genes list for each morpholino by VennMaster  66  and quad venn-diagram graphic (Supplementary Fig. 4c ) was generated with VennDiagram CRAN R package  67. Synthesis of Cy5-tagged miR430 MO4 morpholino. Unless otherwise stated materials and reagent were used as supplied by Sigma-Aldrich and Fisher Scientific. Morpholino sequence (I) bearing 3′-alkyne functionality attached by propyl acetate linker were obtained from Gene Tools Ltd and used as supplied.
5-2. 5 h using a ×25 or ×40 objectives. Post-processing of the acquired images was performed using Zeiss ZEN Blue Software. Analysis of spatio-temporal dynamics of MO signal. For global temporal analyses, lightsheet microscopy (morpholino/transgenic mRuby3:H2B) datasets were transformed into maximum intensity projections (MIPs), using Zeiss Zen Black software. Each MIP was imported into Icy Bioimage (versions 1. 9. 5. 0-1. 9. 7. 0, Quantitative Image Analysis Unit, Pasteur Institute, France), where the spot detection plugin was used to detect transcription foci and nuclei, as these features are represented as regions of locally elevated intensity, in their, respective, channels. The plugin was guided to detect features as bright regions against a darker background. Transcription foci were typically detected using a pixel size parameter of 3px, with a sensitivity of between 80 and 150% depending on noise (with higher sensitivity for higher noise).
Feature size limits for detecting transcription foci were between 3 and 75 pixels in area. Nuclei were detected using a pixel size of between 13 and 25px, with similar sensitivity settings. Feature size limits for detecting nuclei were between 200 and 3000 pixels in area. Spot detection produced 2D regions of interest (ROIs) around features for each channel separately. Transcription foci were given a label which associated with their corresponding nucleus, and this was applied on a frame by frame basis to allow counting of nuclei expressing transcription foci. ROI metadata was exported to Microsoft Excel where transcription foci could be quantified. 3D analysis. 3D datasets (Airyscan morpholino/injected mRuby3:H2B protein) collected from the Zeiss 880 confocal microscope with Fast Airyscan Module were imported into Icy Bioimage, where the spot detection plugin was used to identify nuclei and transcription foci, using the same sensitivity settings as described previously.
Size features were scaled up to account for volumes: feature size settings for transcription foci were between 5 and 150 pixels, and size settings for nuclei were between 750 and 20,000 pixels. This would produce a 3D ROI around the detected feature. In the case of nuclei this ROI could be viewed in the 3D VTK viewer on Icy Bioimage, where a video of the rendered ROI could be recorded using the built-in recording function (Supplementary Fig. 5 ). ROI data were exported to Microsoft Excel where transcription foci volumes were extracted per frame and timeseries could be plotted (Fig. 6c ). In PCNA imaging data obtained on Zeiss 880 confocal with Airyscan, PCNA deficient regions were quantified using the spot detection plugin, with the setting to search for dark features against a bright background.
Pixel sensitivity and 3D size settings were the same as for transcription foci detection, described previously. Relevant ROI's were confirmed by visually inspecting that they correlated with signal deficient regions, and labelling the same feature with the same name per frame. The volume for each feature was extracted and plotted over time using Microsoft Excel. Cell cycle segmentation reference. Cell cycle phase segmentation was based on a visual library of cell cycle phase features generated from previously published confocal imaging of chromatin  [68] [69] [70]. This reference library was used as a base for segmenting nuclei from MIP Zeiss Lightsheet data into cell cycle phases. Once phases were assigned to an initial batch of nuclei, the imaging parameters were extracted. These parameters were used to generate a quantitative measure of chromatin consistency throughout the cell cycle and assign a measure of cell cycle phase to each nucleus.
Samples were counterstained with DAPI prior to imaging on a Zeiss LSM 880 with Fast Airyscan, with a 100 × 1. 48 numerical aperture objective lens. 50-100 optical sections (130 nm thickness) were acquired of each nucleus. Acquired images were processed using Zen Black software (Zeiss). Transgenic lines. The transgenic line Tg(Xla. Eef1a1:h2b-mRFP1) (http://zfin. org/ ZDB-TGCONSTRCT-130711-1  26  ) used in this study was kindly provided by Sarah Baxendale (University of Sheffield, UK). Protein synthesis. mRuby3:H2B and mApple:PCNA were PCR amplified form plasmids pKanCMV-mRuby3-10aa-H2B (Addgene, 74258) and mApple-PCNA-19-NLS-4 (Addgene, 54937), respectively, and sub-cloned into NdeI/BamHI linearized pET-28a + plasmid (in frame with the N-terminal 6xHis-Tag), using In-Fusion® HD Cloning Plus Kit (Clontech, 638910) following the manufacture instructions.
Flow cytometry was performed on the CyAn ADP machine (Beckman Coulter) using the Summit 4. 4 software package using the standard gating strategy. Cell cycle length and gene expression experiments. Wild type embryos were injected with mRuby3:H2B protein at the single-cell stage and incubated in E3 media supplemented with 1 µM triptolide (Sigma, T3652) or vehicle control following injection. In cell cycle elongation experiments, wild-type embryos were injected at the single-cell stage with a cocktail of 200 pg of Xenopus chk1 mRNA, 200 pg miR430 MO and mRuby3:H2B protein whereas controls were injected with 200 pg mCherry mRNA + 200 pg miR430 MO and mRuby3:H2B protein. Embryos were imaged using Zeiss Lightsheet Z. 1 microscope with the ZEN software. Images were analysed and timing of cell cycle transition from anaphase to anaphase were identified by mRuby3:H2B pattern variation using Icy Bioimage.
cDNA samples were diluted 1:30 with nuclease-free water. qPCR reactions were prepared using the ddSupermix for Probes (Bio-rad) and Taqman miRNA probes miR430a (004365_mat) and miR430b (465767_mat). Droplet generation oil was added to the qPCR mixture and droplets were generated with the QX100 droplet generator (Bio-rad). PCR protocol as per manufacturer (Taqman miRNA assay) and the samples were run in a QX100 droplet reader. Quantasoft software (Biorad) was used for data acquisition and analysis. Reporting summary. Further information on experimental design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements This work was supported by a  Wellcome Trust   Investigator award  ( 106955 ) and  BBSRC  project grant to F. M. , NEURAM (712821) and VISGEN (734862) projects of H2020 by the  European Commission  to F. M. , A. S. , H. K. Q. , H. v. N. & J. S. F. BBSRC MIBTP doctoral training programme to L. C. , and  European Research Council   Advanced grant  ( ERC-2013-340551 , Birtoaction) to L. T. We thank  Jon Moulton  and  Yongfu Li at Gene-Tools  for advice on morpholino designs and for offering morpholino reagents, the  COMPARE imaging facility  at the  University of Birmingham ,  Aditi Kanhere  and  Anja Baranasic  for advice in computational analyses,  Sarah Baxendale  for providing transgenic lines and  Phil Zegerman  for chk1 and replication factor expression vectors,  Marissa Williams  and  Glen Reid  for assistance with the ddPCR and  Piotr Balwierz  for comments on the manuscript.
Text mining has become an important research method in biology, with its original purpose to extract biological entities, such as genes, proteins and phenotypic traits, to extend knowledge from scientific papers. However, few thorough studies on text mining and application development, for plant molecular biology data, have been performed, especially for rice, resulting in a lack of datasets available to solve named-entity recognition tasks for this species. Since there are rare benchmarks available for rice, we faced various difficulties in exploiting advanced machine learning methods for accurate analysis of the rice literature. To evaluate several approaches to automatically extract information from gene/protein entities, we built a new dataset for rice as a benchmark. This dataset is composed of a set of titles and abstracts, extracted from scientific papers focusing on the rice species, and is downloaded from PubMed.
Introduction The last few decades have witnessed a massive explosion of information in the life sciences. However, an important proportion of this information, relevant to this field, is not available from databases, but is instead present in unstructured scientific documents, such as journal articles, reviews, abstracts, and reports. Agronomy is an overarching field that is comprised of diverse domains such as genetics, plant molecular biology, ecology and soil science  [1]. Despite advancements in information technology, scientific advancements in agronomy are still commonly based on text. To effectively develop applications to improve crop production through sustainable methods, however, it is important to overlap research findings from these various subdomains, as they are highly interconnected.
Objective On the large scale, we are currently building a Resource Description Framework (RDF) knowledge base termed Agronomics-Linked Data (AgroLD  [2] , http://www. agrold. org). This knowledge base is designed to integrate data from various public, plant-centric databases such as Gramene  [3] , Oryzabase  [4] , and TAIR  [5] , to name a few. The aim of the AgroLD project is to provide an integrated portal for both bioinformatics and domain experts, to exploit a homogenized data model for filling knowledge gaps. Using this landscape, we aim to extract relevant information from the literature, to enrich the content of integrated datasets. Due to the scope of the project, we exploited information from the Oryzabase database to build a dataset aimed to recognize named text entities such as rice genes and proteins.
Our main purpose was to solve NER of rice biological entities, to find the best approach. By sharing this dataset on the PubAnnotation platform and be available at the BioNLP Open Shared Tasks (BioNLP-OST, https://2019. bionlp-ost. org), we invited participants to implement their own methods to solve NER tasks for this dataset. Furthermore, to evaluate the performances, we compared their approaches, implemented during the task, with our method  [6] , implemented before the hackathon.
Contribution In this project, we used data from Oryzabase (http://pubannotation. org/projects/OryzaGP), a rice comprehensive database for Oryza sativa species published online since 2,000 by Japanese researchers. The latest version of Oryzabase contains 21,739 of rice genes, collected from 44,837 distinct scientific articles. Consequently, we used this information to create the basis of the Ory-zaGP dataset. Then we used PubMed as a resource to collect the raw data that was later preprocessed to compose the dataset, and developed a custom script implementing the BioPython library to query and retrieve the specific abstracts from PubMed. However, a number of scientific articles were not available in the PubMed database, due to some historical issues and lack of published resources. Due to the limited access of some resources, 10,400 articles were processed after filtering.
The detailed raw data is shown in Table  1. By focusing on the entities of the rice genome, we used the Oryzabase gene list as the ground truth to build up our dataset by keyword matching terms. The first step to preprocessing the data was filtering to remove special characters from the raw data. In fact, due to the number of articles used, the time range of articles was also wide, in that several articles were published in previous decades. To handle the problems of OCR-errors (which appear in the scanned text), we manually removed all the null and nonsense characters in utf-16 in the raw text. All the work was processed by our scripts and then after first step preprocessing. Moreover, we added part-ofspeech (POS) tags for each word, to define its type with the aim, to ensure the accuracy of the identification of entities.
The POS tagging process is supported by the Natural Language ToolKit (NLTK). To tokenize data, each word was considered a token, giv- en in the following lines; one token per line, and included three tabs: the word itself, the POS tag, and the entity type (Fig. 1 ). To minimize the errors of inaccurate tags assignation when running the script, preprocessed data were checked manually, based on the existing resources (Oryzabase gene list, etc. ) During the 5th Biomedical Linked Annotation Hackathon (BLAH5, http://blah5. linkedannotation. org), a portion of the dataset (29,098 annotation instances made to 6,107 abstracts) was uploaded to the PubAnnotation repository (http://pubannotation. org/projects/ OryzaGP), which uses JSON (JavaScript Object Notation) as its default format, to store annotations.