dataesr/scientific-paragraphs-categorization huggingface dataset:
To identify transcripts encoding enzymes involved in the general phenylpropanoid pathway, its flavonoid branch as well as in the modification and decoration of its flavonoid products and to identify candidate regulatory genes, MassBlast  [70]  and the TBLASTN algorithm-based BacHBerryGEN BLAST server  [68]  were used with search parameters of 'expect score cut-off' of 1e-10, an open reading frame (ORF) length of a minimum of 100 amino acids (aa) and aa identity greater than 40% in the alignments. Key plant enzymes involved in the general phenylpropanoid biosynthetic pathway and their corresponding sequences (60) including 23 experimentally validated genes from different plant species were used in a targeted search approach to mine the different transcriptomes for homologous transcripts encoding phenylalanine ammonia lyase (PAL), cinnamate 4-hydroxylase (C4H), 4-coumarate CoA ligase (4CL), chalcone synthase (CHS), chalcone isomerase (CHI), flavanone 3-hydroxylase (F3H), flavonoid 3′-hydroxylase (F3′H), flavonoid 3′,5′-hydroxylase (F3′5′ H), flavonol synthase (FLS), dihydroflavonol 4-reductase (DFR), anthocyanidin synthase (ANS), anthocyanidin reductase (ANR), leucoanthocyanidin reductase (LAR), flavone synthase (FNS) and stilbene synthase (STS).
These BLAST searches are detailed in Additional file 5: Table  S4. Published sequences from a total of 68 regulatory proteins (45 MYB TFs, 18 bHLH TFs and five WDRs) and 120 modifying and decorating enzymes (18 acyltransferases, 31 glucosyltransferases, 29 methyltransferases, 26 hydroxylases, nine reductases, two aurone synthases, two dehydrogenases, two dehydratases and one dirigent protein) from a range of plant species were also used in BLAST searches against the transcriptome sequences of the 13 species. Detailed BLAST search results are presented in Additional file 5: Table  S4. In total, 1248 sequences homologous to regulatory genes and 5150 sequences homologous to enzymes of the general phenylpropanoid pathway and its decoration and modification were identified from the different RNA-seq datasets (Table  3  and Additional file 5: Table  S4 ).
R. genevieri, V. uliginosum, B. buxifolia and to a lesser extent L. caerulea and C. album exhibited a greater average number of homologues than the other species. This abundance of homologues is likely due to the higher ploidy levels of these accessions. Comparison of BLAST search outputs of blackberry, blueberry, Maqui berry and strawberry myrtle also showed that transcripts encoding methyltransferases were the most conserved enzymes, with half to threequarters of the sequences exhibiting high aa similarity levels, with the exception of blueberry (44. 8% of genes). Reductases were also highly conserved between these species. In contrast, acyltransferases and glucosyltransferases were rarely detected with high levels of aa similarity. Approximately a third of the hydroxylases and glucosyltransferases were detected with high levels of aa similarity.
Phylogenetic analysis showed clustering of the different Rubus bHLH TFs together with other plant bHLH homologues in two conserved clades of bHLH regulatory proteins (SGIIIf-1: PhAN1/AtTT8 clade and SGIIIf-2: AmDEL/PhJAF13 clade; Additional file 10: Figure  S3 ). When analysing WDR homologues, RgTTG1 (two isoforms named RgTTG1-1 and RgTTG1-2 that share a 99% identity at the aa level) and RiTTG1 were identified. These contained seven WD40 repeats (36-54 aa) as predicted using the WDSPdb database for WD40-repeat proteins  [76, 77]  (Additional file 11: Figure  S4 ). Among these, four WD40 repeats corresponded to the domains previously identified in WDR proteins associated with anthocyanin biosynthesis  [78]. The characteristic 'WD' dipeptide motif at the C-terminus of each WD40 repeat as well as the GH dipeptide delimiting the N terminus of several WD40 motifs were not fully conserved in many plant WDR homologues including those identified from Rubus (Additional file 11: Figure  S4 ).
benthamiana that normally do not synthesize coloured flavonoids (Fig. 2 ). The rapid formation of an ectopic MBW complex, when endogenous WDR homologues might not be accessible or are present at limiting levels might, therefore, boost the early induction of anthocyanin biosynthesis. Our data confirm a report on the induction of anthocyanin production in N. tabacum transient assays by the Myrica rubra MBW complex which was earlier and enhanced by the WDR component  [91]. The wild accession of N. benthamiana from the Australian Northern Territories (NT) has been suggested to be particularly well-suited for anthocyanin-related studies  [80]. Generally, anthocyanin pigmentation using berry fruit genes was visible in the NT accession well before (at 2 dpi) pigmentation could be observed by the naked eye in the JIC-LAB isolate (3-4 dpi).
Differential gene expression during fruit development and ripening in R. genevieri and R. idaeus cv. Prestige and expression patterns of genes related to anthocyanin biosynthesis To study the gene expression levels during fruit ripening in the wild blackberry R. genevieri and the cultivated red raspberry R. idaeus cv. Prestige, the BacHBerryEXP expression browser  [69]  was developed using the RNA-seq data analysis and a visualization platform expVIP (expression Visualization and Integration Platform)  [93]. The BacHBerryEXP browser uses the transcriptome data from three stages of fruit maturation for the two Rubus species (green fruit, intermediate/immature fruit and ripe fruit; Fig. 3a ) and calculates gene expression levels by using the pseudoalignment tool Kallisto  [94]. It displays either the expression units as raw counts or transcripts per million (tpm) and their log 2 values that can be represented as heatmaps.
BacHBerryEXP also contains a BLAST tool  [95]  to identify candidate transcript homologues for differential expression analysis of the two Rubus species. To illustrate the transcriptional expression levels of key structural and regulatory genes related to the general phenylpropanoid pathway and anthocyanin biosynthesis, we conducted a homology-based search of candidate genes using the BacHBerryGEN BLAST server  [68]  and a set of characterised plant protein homologues. We identified a range of candidate transcripts encoding proteins associated with phenylpropanoid metabolism as well as the modification, decoration and transport of its flavonoid products in the two Rubus species. For most candidate genes, up to five transcripts, with the highest homology scores (but not necessarily a full-length transcript), were selected and their expression profiles were analysed during fruit maturation using BacHBerryEXP  [69]  (Fig.
3  and Additional file 15: Table  S9 ). For anthocyanin regulators such as the Rubus R2R3-type MYB, bHLH and WDR homologues cloned in this study, the expression levels of the cloned transcripts were assessed (Fig. 3  and Additional file 15: Table  S9 ). The formation of anthocyanin pigments in ripening fruits involves the coordinated expression of genes encoding a series of enzymes in the phenylpropanoid pathway. Heatmaps representing the transcriptomic profiling of key flavonoid pathway and anthocyanin biosynthetic enzymes (PAL, CHS, CHI, F3H, FLS, FNS, DFR, ANS, ANR and LAR), modifying enzymes (e. g. , UFGT) and transcription (co-) factors (MYB, bHLH and WDR) obtained using BacHBerryEXP  [69]  is shown in Fig. 3  and transcript expression levels are presented in Additional file 15: Table  S9.
Conclusions We report transcriptome sequences and analytical tools for gene identification, cloning and functional analysis from 13 berry fruit species coming from Europe, South America and Asia, spanning eight plant genera and seven families. Tools and resources are accessible and searchable online via the BacHBerryGEN database  [67, 68]  and the BacHBerryEXP gene expression browser  [69]. These resources will assist gene expression and functional genomic studies in berry fruit species as well as contributing to the understanding of the synthesis of polyphenols, the molecular mechanisms underlying phenylpropanoid, and particularly flavonoid, synthesis and the regulatory processes controlling phenylpropanoid metabolism during fruit ripening. These tools have already contributed to identifying the genes involved in the synthesis of novel biologically active compounds in berry fruits  [26].
Mining of fruit transcriptomes, BLAST searches, expression profiling and phylogenetic analysis The BacHBerryGEN database  [67]  was created to deposit the transcriptomic data of the 13 berry fruit species. A BLAST search engine  [68]  was developed to conduct homology-based searches of candidate genes. The workflow MassBlast  [70, 108]  was also used to identify homologues and orthologues of enzymes responsible for the synthesis, decoration and regulation of phenylpropanoid compounds. The BacHBerryEXP expression browser  [69]  was established based on  [93]  to facilitate the differential expression analysis of candidate genes of two Rubus species, R. genevieri and R. idaeus cv. Prestige, during three developmental stages of fruit ripening (green/immature/ ripe). Six transcriptome sequences of R.
genevieri and R. idaeus cv. Prestige (two species x three fruit ripening stages) were uploaded in the BacHBerryEXP expression browser  [69]  and used to analyse differential transcription of genes involved in the anthocyanin biosynthetic pathway. The transcript identification ID can be retrieved either in the BacHBerryGEN database  [68]  or by using the built-in BLAST search module of the BacHBerryEXP expression browser  [69]. The phylogenetic analysis of the transcriptomes of the 13 berry fruit species together with the genome sequences of seven reference species (A. thaliana  [109] , P. trichocarpa  [110] , G. max  [111] , V. vinifera  [5] , S. lycopersicum  [112] , O. sativa  [113]  and A. trichopoda  [114] ) was carried out following the comparative genomic analysis detailed in  [115].
EvidentialGene  [116]  was used to translate and validate the ORFs. The longest translated ORFs from each gene with at least 100 aa were aligned with BLAST 2. 26  [117]  against each other and orthologue groups were identified using OrthoMCL 2. 0. 9  [118]. A multiple alignment for each of the 214 single-copy gene families was produced using MUSCLE v3. 8. 1551  [119]. The longest contiguous block of each aa sequence, defined as more than 20 aa with less than five contiguous positions with a gap in any sequence, were concatenated to produce a super alignment matrix containing 205 gene families (with nine gene families filtered out due to the lack of a single contiguous block) using a BioRuby script  [120]  (Additional file 16). ProtTest 3. 4. 2 [121]  was used to find the best fitting model to produce the phylogenetic tree which was JTT + I + G + F.
The phylogenetic tree was assembled using RAxML 8. 2. 12  [122]  with the option PROTGAMMAJTT and declaring O. sativa and A. trichopoda as outgroups. The divergence time among the 20 species was estimated using MCMCtree 4. 8a from the PAML software  [123]  (Additional file 16). The species divergence time was predicted using the calibration points of (i) A. thaliana and P. trichocarpa Accession no. GU173814. 1) were identified in R. genevieri and R. idaeus cv. Prestige by mining the fruit transcriptomic data deposited in the BacHBerryGEN database  [67]  using the TBLASTN programme of the BacHBerryGEN BLAST server  [68]  and a protein sequence query (Additional file 7: Table  S6 ). Total RNA (1 μg) extracted from ripe fruits of both Rubus species was used for first strand cDNA synthesis with oligo (dT) 18 primers (Sigma) using SuperScript III reverse transcriptase (Invitrogen) and RNaseOUT (Recombinant Ribonuclease Inhibitor, Invitrogen) following the manufacturer's instructions.
Acknowledgements We would like to thank the coordinators of the  BacHBerry  project ( BACterial Hosts for production of Bioactive phenolics  from bERRY fruits, http://www. bachberry. eu/) Dr. Jochen Förster  ( Carlsberg Research Laboratory, Denmark ) and  Dr. Alexey Dudnik  (The  Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark ). We would like to specially thank  Dr. Nicola Love  ( John Innes Centre, Norwich, UK ),  Bárbara Ávila  ( Pontificia Universidad Católica de Chile, Macul, Chile ) and  Pedro Oliveira  ( Instituto Nacional de Investigacao Agraria, Oeiras, Portugal ) for their help. The authors thank the  Earlham Institute (Norwich, UK) , particularly  Dr. Purnima Pachori ,  Dr. Helen Chapman  and  Heather Musk , for RNA-seq analyses. We are grateful to  Prof.
Funding This research was funded by the  European Union   Framework Program 7, Project BacHBerry  [ FP7-613793 ]. The authors also acknowledge support from the  Institute Strategic Programmes 'Designing Future Wheat'  ( BB/P016855/1 ), 'Understanding and Exploiting Plant and  Microbial Secondary Metabolism'  ( BB/J004596/1 ) and ' Molecules from Nature'  ( BB/P012523/1 ) from the  UK Biotechnology and Biological Sciences Research Council  to the  John Innes Centre  and the European funded  COST ACTION   FA1106  QualityFruit. VT, PV and CM have also received funding from the  European Union 's  Horizon 2020 research and innovation programme through the TomGEM project  under grant agreement No. 679796. The funding bodies had no role in the design of the study, collection, analysis and interpretation of data nor in writing the manuscript.
Availability of data and materials The transcriptome sequences generated during this current study are available from the BacHBerryGEN repository (http://jicbio. nbi. ac. uk/berries). BLAST and expression analyses can be performed using the BacHBerryGEN database (http://jicbio. nbi. ac. uk/berries/blast. html) and BacHBerryEXP gene expression browser (http://www. bachberryexp. com), respectively. Nucleotide sequences of Rubus flavonoid regulatory genes cloned in this study are deposited in GenBank. Additional data generated and/or analysed during this study are included either in this published article as supplementary information or can be requested from the authors (e. g. , transcriptome ORF/ peptide annotations and Trinotate annotations of secondary metabolite biosynthesis /phenylpropanoid pathway for each transcriptome).
The development of electronic health records, wearable devices, health applications and Internet of Things (IoT)-empowered smart homes is promoting various applications. It also makes health self-management much more feasible, which can partially mitigate one of the challenges that the current healthcare system is facing. Effective and convenient self-management of health requires the collaborative use of health data and home environment data from different services, devices, and even open data on the Web. Although health data interoperability standards including HL7 Fast Healthcare Interoperability Resources (FHIR) and IoT ontology including Semantic Sensor Network (SSN) have been developed and promoted, it is impossible for all the different categories of services to adopt the same standard in the near future.
This study presents a method that applies Semantic Web technologies to integrate the health data and home environment data from heterogeneously built services and devices. We propose a Web Ontology Language (OWL)-based integration ontology that models health data from HL7 FHIR standard implemented services, normal Web services and Web of Things (WoT) services and Linked Data together with home environment data from formal ontology-described WoT services. It works on the resource integration layer of the layered integration architecture. An example use case with a prototype implementation shows that the proposed method successfully integrates the health data and home environment data into a resource graph. The integrated data are annotated with semantics and ontological links, which make them machine-understandable and cross-system reusable.
The FHIR working group is also working on the FHIR Linked Data module that uses Semantic Web technologies such as Resource Description Framework (RDF) and Web Ontology Language (OWL) to enhance its semantic expression capability and to facilitate inference and data linkage across datasets  [20, 22]. Semantic Web of Things (SWoT) was also proposed to enable a wide-scale interoperability, integration, composition and even more by annotating semantics to the representation of the things on the Web  [23, 24]. Various vocabularies and ontologies have been proposed. The widely used contributions include, but are not limited to, the IoT extension of Schema. org [25] , Semantic Sensor Network (SSN) and Sensor, Observation, Sample, and Actuator (SOSA) Ontology  [26, 27] , and Web of Things (WoT) Thing Description  [28].
The work in this paper proposes a method, which follows the path of FHIR to apply Semantic Web technologies and the REST resource model, to integrate health data and home environment data from heterogeneously built services as linked resources. The integration method aims to semantically link the health services and WoT devices that adopted HL7 FHIR, described by formal ontologies, implemented RESTful Web APIs or published as Linked Data, i. e. , services with different levels of interoperability. It is to make the integration method compatible with the real-world service environment. The heterogeneous data are modeled as conceptual information resources by using the Linked Health Resources (LHR) ontology. It makes the entire method as a consolidated framework that aggregates health and home environment data from different sources and integrates them as a data resource graph, in the way of RDF, for upper-level collaborative use.
Use Case of Integration To demonstrate the LHR health data integration process, an example use case is presented in this section. It is depicted as an idealized scenario where it leaves aside some details like the authentication and invocation of services. The demonstration prototype is implemented as two parts: the aggregation layer is implemented in Python with the RDFlib package [53], and the integration layer is implemented in Java with the Jena framework  [54]. The example use case focuses on the integration layer since it is the main content of this paper. Therefore, the working process of aggregation is omitted here. The use case scenario is that a person named Alice is actively managing her own health condition. She has taken some medical examination at a healthcare center, which provides health data access via an HL7 FHIR implemented service.
This resource is passed through the data aggregation layer to the integration layer directly since the FHIR observation resource is an instance of the class fhir:Observation that maps to lhr:FHIRObservation. @prefix fhir: <http://hl7. org/fhir/>. @prefix loinc: <http://loinc. org/owl#>. @prefix xsd: <http://www. Listing 1. Simplified FHIR observation of blood glucose example represented in RDF/Turtle format. The observation resource contains the value, the effective date time, and the LOINC code indicating the medical terminology. Next we are going to integrate the daily steps performed data. Fitbit provides Web APIs to access the data recorded by their wearable devices. For integrating the data resource from Fitbit Web service into the LHR model, the semantic annotation method SemREST  [47]  is applied to annotate its service description and aggregate the resource representation on the data aggregation layer.
On the integration layer, the coming resources are integrated using the LHR ontology. As mentioned before, the FHIR blood glucose observation resource is an instance of lhr:FHIRObservation, which is an equivalent class to fhir:Observation. For the ease of accessing the data value of an FHIR observation resource with LHR, the property fhir:Observation. valueQuantity is mapped to the corresponding object property lhr:hasFHIRObservation. valueQuantity, which is a sub-class of lhr:hasObservationData. In addition, the fhir:Observation. effectiveDateTime is mapped to the corresponding data property lhr:hasObservationDateTime. Other objects could be accessed directly through the fhir:Observation instance, including the fhir:Observation. code, fhir:Observation. interpretation and fhir:Observation.
referenceRange. Furthermore, for a more convenient use for upper-level APIs, the object of fhir:Observation. code could be mapped to lhr:DataItem's value type to indicate the semantic meaning of the data value. Listing 2. Simplified resource graph of Fitbit steps resource generated by SemREST and represented in RDF/Turtle format. The resource contains each day's step count, date, time, and the steps term in schema. org as a tag indicating the semantic meaning. For the Fitbit daily steps performed resource, the semrest:Representation instance fitbit:representation1 in the resource graph from the aggregation layer is instantiated as a lhr:HealthResource, indicated by the mapping extension. Since the steps data has been annotated as the property semrest:dataItem's objects, so it is mapped to the objects of lhr:hasObservationData, a sub-property of lhr:hasDataItem, to this LHR health resource.
The data value, date, time, and tag are also mapped to the objects in the ontology for the upper-level APIs. The home environment temperature and humidity observation resources are made as instances of lhr:EnvSSNResource, which is an equivalent class to sosa:Observation. For the ease of accessing the data value of an SOSA observation resource with LHR, the property sosa:hasResult is mapped to the corresponding object property lhr:hasSOSAObservationResult, which is a sub-class of lhr:hasObservationData. In addition, the sosa:resultTime is mapped to the corresponding data property lhr:hasObservationDateTime. Other objects could be accessed directly through the sosa:Observation instance, including sosa:madeBySensor and sosa:observedProperty. The RDF of the integrated health and environment resources is depicted graphically in Figure  5  with relatively important objects.
Each square block represents an instance with its class and super-class. Instances are linked by the object properties and data properties. Since there exist many equivalent classes and properties, the ones considered more appropriate are placed in the graph to avoid information redundancy. As one of the methods that can use the integrated health resources, SPARQL queries is executed to demonstrate that the different data resources are integrated and can be used together. Apache Jena Fuseki is used to serve the integrated resource graph as SPARQL server. The queries are executed by using its Web-based client. The SPARQL query in Listing 5 is executed to query for Alice's health resources. The purpose of this SPARQL query is to test that if the blood glucose FHIR data resource and the daily steps performed Fitbit data resource are successfully integrated and can be retrieved in a single SPARQL query.
The query result is presented in Figure  6. @prefix semrest: <http://semrest. org/vocab#>. @prefix lhr: <http://www. semanticweb. org/pc/ontologies/2018/4/lhr#>. @prefix schema: <http://schema. org/>. @prefix owl: <http://www. w3. org/2002/07/owl#>. @prefix rdf: <http://www. w3. org/1999/02/22-rdf-syntax-ns#>. @prefix rdfs: <http://www. w3. org/2000/01/rdf-schema#>. We can see that both the FHIR observation resource and the Fitbit resource are retrieved, since both the two types of resources were modeled as lhr:HealthResource directly (the Fitbit daily steps performed data resource <:steps/response1/representation1>), or through sub-class relation (the FHIR blood glucose data resource <fhir:Observation/f001>, through the mapping between class lhr:FHIRObservation and fhir:Observation). Within the integrated resource graph, the value, date time, and value type have been mapped into the LHR ontology as well (through the class lhr:DataItem), so we can query them through the properties of lhr:DataItem.