dataesr/scientific-paragraphs-categorization huggingface dataset:
More specifically, comparisons from 7 to 14 consisted in: control vs strain B25 without pathogen at 12 hpi (comparison 7), control vs strain B25 with pathogen at 12 hpi (comparison 8), control vs strain B25 without pathogen at 48 hpi (comparison 9), control vs strain B25 with pathogen at 48 hpi (comparison 10), control vs strain PsJN without pathogen at 12 hpi (comparison 11), control vs PsJN strain with pathogen at 12 hpi (comparison 12), control vs PsJN strain without pathogen at 48 hpi (comparison 13), control vs PsJN strain with pathogen 48 hpi (comparison 14). Annotations and identifications were classified in accordance with the levels of the Metabolomics Standards Initiative (MSI), as described in  Sumner et al. (2007). Compounds in class 2 were identified based on exact mass, retention time, MS2 fragmentation pattern and UV-visible spectrum by comparison with data in databases and/or literature.
Hereafter, we will present the results of a PCA to provide an overview of the data clustering in the experimental conditions and the different time-points studied. We will then report and discuss the expression data for all the genes analyzed. According to the PCA, four groups, corresponding to the different time-points are visible (Fig. 1 ). The first includes barley plants sampled at T0 (red color). This sampling time corresponds to the start of the day with a light intensity from 0 to 75%. The second group includes samples taken at 6 hpi (purple color, corresponding to midday, i. e. 75 % light intensity). The third group includes leaves sampled 12 hpi and corresponding to the end of the day with a light intensity decreasing from 75 to 0% (blue color). The last group includes barley leaves sampled 48 hpi and corresponding again to the start of the day (green color).
Untargeted metabolomics on barley leaves infected by D. teres in the absence/presence of PGPR Our objective was to conduct a combined gene expression and metabolomic analysis to gain information concerning the biological mechanism underlying the increased defense to net blotch in the presence of beneficial bacteria. An untargeted metabolomics study was performed using the same samples as for RT-qPCR analysis. Time-points 12 h and 48 h were selected based on the results obtained by RT-qPCR. Indeed, gene expression shows more differences at 12 hpi and 48 hpi and, therefore, significant differences could also be observed in the accumulation of metabolites. The goal was to enrich and complement the gene expression data and to attempt to identify metabolites spotted as differentially abundant between modalities during the tripartite interaction barley -D.
PCIP-seq identifies BLV structural variants in multiple clones Proviruses were also examined for structural variants (SVs) using a custom script and via visualization in IGV (see "Methods"). Between the sheep and bovine samples, we identified 66 deletions and 3 tandem duplications, with sizes ranging from 15 to 4152 bp, with a median of 113 bp (Additional file 1: Table  S3 ). We validated 14 of these via clone-specific PCR (Additional file 1: Fig. S5 ). As seen in Fig. 3b , SVs were found throughout the majority of the provirus, encompassing the highly expressed micro-RNAs  [30]  as well as the second exon of the constitutively expressed antisense transcript AS1  [31]. Only two small regions at the 3′ end lacked any SVs. More proviruses will need to be examined to see if this pattern holds, but these results again suggest the importance of the 3′LTR and its previously reported interactions with adjacent host genes  [7].
Illumina-based techniques require access to a number of capitalintensive instruments. In contrast, PCIP-seq libraries can be generated, sequenced, and analyzed with the basics found in most molecular biology labs; moreover, preliminary results are available just minutes after sequencing begins  [47]. As a consequence, the method may have use in a clinical context to track clonal evolutions in HTLV-1infected individuals, especially as the majority of HTLV-1-infected individuals live in regions of the world with poor biomedical infrastructure  [48]. One of the common issues raised regarding Oxford Nanopore data is read accuracy. Early versions of the MinION had read identities of less than 60%  [49] ; however, the development of new pores and base calling algorithms make read identities of > 90% achievable  [50].
Resulting libraries were sequenced on Oxford Nanopore MinION R9. 4 or R9. 5 flow cells respectively. The endogenous retrovirus libraries were base called using albacore 2. 3. 1, all other PCIP-seq libraries were base called with Guppy 3. 1. 5 (https://nanoporetech. com) using the "high accuracy" base calling model. For the endogenous retrovirus libraries, demultiplexing was carried out via porechop (https://github. com/rrwick/Porechop) using the default setting. The HIV, HTLV-1, BLV, and HPV PCIP-seq libraries were subjected to a more stringent demultiplexing with the guppy_barcoder (https://nanoporetech. com) tool using the --require_barcodes_both_ends option. The output was also passed through porechop, again barcodes were required on both ends, adapter sequence was trimmed, and reads with middle adapters were discarded.
Identification of proviral integration sites in PCIP-seq Reads were mapped with Minimap2  [61]  to the host genome with the proviral genome as a separate chromosome. In-house R-scripts were used to identify integration sites (IS). Briefly, chimeric reads that partially mapped to at least one extremity of the proviral genome were used to extract virus-host junctions and shear sites. Junctions within a 200-bp window were clustered together to form an "IS cluster," compensating for sequencing/mapping errors. The IS retained corresponded to the position supported by the highest number of virus-host junctions in each IS cluster. Clone abundance was estimated based on the number of reads supporting each IS cluster. Reads sharing the same integration site and same shear site were considered PCR duplicates.
HIV-1 proviral sequences Sequences of the two major proviruses integrated in chr2 and chrX of the U1 cell line were generated by mapping the reads from both platforms to the HIV-1 provirus, isolate NY5 (GenBank: M38431. 1), where the 5′LTR sequence is appended to the end of the sequence to produce a full-length HIV-1 proviral genome reference. The sequence was then manually curated to produce the sequence for each provirus. To check for recombination, reads of selected clones were mapped to the sequence from the chrX provirus and the patterns of SNPs examined to determine if the variants matched the chrX or chr2 proviruses. The consensus HIV-1 sequences for both patients were generated using the medaka consensus tool (https://github. com/nanoporetech/medaka), followed by manual correction guided by Illumina reads generated from the same PCIP-seq library.
Acknowledgements We thank  Wouter Coppieters ,  Latifa Karim ,  Manon Deckers  and the  GIGA Genomics Platform  for NGS services. Computational resources were provided by  GIGA  and the  Consortium des Équipements de Calcul Intensif (CÉCI). Dr. Carine Van Lint  provided DNA from the U1 HIV-1 cell line. We thank  Dr. Lionel Habran ,  Sonia Pisvin ,  Hélène Piron ,  Renée Gathy  ( Laboratory of molecular biology and immunohistology ULiége ) and  Dr. Stéphanie Gofflot  ( BUL, Biobank of the ULiège ) for their assistance with HPV sample selection.
Availability of data and materials Sequence data that support the findings of this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB34495: https://www. ebi. ac. uk/ena/browser/view/PRJEB34495  [70]. All other relevant data are available within the article, its supplementary files, or from the corresponding author upon reasonable request. The code and detailed outline of the PCIP-seq analysis workflow are available on Github: https://github. com/GIGA-AnimalGenomics-BLV/PCIP. The DOI for the source code version used in this paper is available on Zenodo: https://doi. org/10. 5281/zenodo. 4543265 [71].
Teleseismic Body-Waveform Modeling The focal mechanisms of earthquakes can place additional constraints on the timing and kinematics of deformation in the forearc around the IFS. We used the long-period teleseismic body-waveform modeling program MT5 of  Zwick et al. (1994) , which is based on the algorithm of  McCaffrey and Abers (1988) , to determine point-source focal mechanisms and centroid depths for two moderate-magnitude earthquakes with epicenters near the IFS (Event 1: M w 5. 7 August 26, 2003 and Event 2: M w 5. 5 November 20, 2006) and one earthquake that ruptured the forearc to the southwest of the IFS 20 km offshore (Event 3: M w 6. 2 May 06, 2010). The MT5 waveform-modeling method has been described extensively elsewhere (e. g. ,  Devlin et al. , 2012; Molnar & Lyon-Caent, 1989) ; therefore, we provide the details of the data processing, and the velocity models used to generate the synthetic seismograms, in Text S1.
These data are stored in INRAE public repository (see Data accessibility in the Specification Table for direct links to data). They have been structured using MILK MICROFILTRATION ontology and are replicated in @Web data warehouse providing additional querying tools ( https://www6. inrae. fr/cati-icat-atweb/ ). © Raw and analyzed Parameters for data collection Experiments have been classified in 4 process types namely HT-MF, HT-MF-DF, RB-MF and RB-MF-DF depending on the sequence of unit operations: HT-MF corresponds to process experiments in which a heat treatment (HT) precedes a milk microfiltration (MF) unit operation; HT-MF-DF corresponds to process experiments in which a heat treatment precedes a milk microfiltration which precedes a diafiltration unit operation (DF); RB-MF corresponds to process experiments in which a removal of bacteria by microfiltration (RB) precedes a milk microfiltration unit operation; RB-MF-DF corresponds to process experiments in which a removal of bacteria by microfiltration precedes a milk microfiltration which precedes a diafiltration unit operation.
TMP is the pressure that forces the fluid to pass through the membrane and is defined as the difference of pressure between the retentate and permeate sides. JP characterizes the mass flow passing through the membrane; Shear stress and crossflow velocity both characterise the erosion at the membrane surface. With crossflow filtration, the tangential motion of the fluid across the membrane causes retained particles at the membrane surface to be rubbed off. This article contains data associated with milk microfiltration process experiments and properties of the resulting dairy fractions annotated from a collection of scientific documents. Data are stored in five datasets (see Data accessibility in the Specification Table  above ). The four first ones contain process experiments classified in four process types namely HT-MF, HT-MF-DF, RB-MF and RB-MF-DF (see Fig.
1  ). Each process type is characterized by a given sequence of unit operations: HT-MF corresponds to process experiments in which a heat treatment precedes a milk microfiltration unit operation; HT-MF-DF corresponds to process experiments in which a heat treatment precedes a milk microfiltration which precedes a diafiltration unit operation; RB-MF corresponds to process experiments in which a removal of bacteria by microfiltration precedes a milk microfiltration unit operation; RB-MF-DF corresponds to process experiments in which a removal of bacteria by microfiltration precedes a milk microfiltration which precedes a diafiltration unit operation. The fifth dataset contains the list of documents that have not been annotated because they did not include the requested information. Documents have been annotated when it was possible to answer the following questions: 1.
Which are the different unit operations of a given microfiltration process experiment? 2. Which are the measured parameter values associated with a given unit operation of the process experiment? 3. Which are the measured parameter value evolutions and associated sample characteristics during the microfiltration unit operation? 4. Which are the characteristic values associated with samples extracted at the end of unit operations of a given process experiment? In each of the four datasets corresponding to a given process type, the data are gathered in five files. File Process Description XX 1  provides a precise description of milk microfiltration process experiments associated with produced milk samples. File Sample Identification XX lists the samples extracted at the end of unit operations of a given itinerary registered in File Process Description XX.
File Sample Characterisation XX contains the data associated with characteristics of all milk samples listed in File Sample Identification XX. The kinetics of measured parameter values and milk sample characteristics during the microfiltration unit operation are gathered in file Controlled Parameter Evolution XX. An additional file Reliability Assessment contains a set of 8 metadata values associated with each annotated article. Those metadata can be used for data reliability assessment  [1, 3]. In File Process Description XX, a process experiment is described by a set of unit operations and associated with a unique numerical identifier (column Experience number ). Each unit operation of a given process experiment is described on a separate line and associated with a unique numerical identifier (columns Experience number + Process step number ).
Measured parameters at different sampling times during the milk microfiltration operation for each itinerary registered in File Process Description are stored in file Controlled Parameter Evolution XX. Values associated with following characteristics are stored if available in the document: sampling time; process phase; volume reduction factor, VRF; concentration factor, CF; temperature; transmembrane pressure, TMP; Permeation flux, Jp; feed flow, Q; crossflow velocity; retentate pressure drop; shear stress at the membrane surface; transmission of serum proteins, SP transmission; removal of serum proteins, SP removal; β-Lactoglobulin transmission; α-Lactalbumin transmission. File reliability assessment XX contains a list of vectors composed of 8 metadata used for data reliability assessment, each vector being associated with an annotated document.
The list of metadata is: source type; assay type; number of repetitions for microfiltration assays; operating mode; number of automatic parameter controls during MF; initial product state; protein analysis method; number of repetitions for compositional analysis. These data are stored in INRAE dataverse and replicated in @Web data warehouse  [2]  ( https: //www6. inra. fr/cati-icat-atweb/ ) in which the data structuration and vocabulary standardization are controlled by MILK MICROFILTRATION ontology. MILK MICROFILTRATION ontology is available in INRAE dataverse ( https://doi. org/10. 15454/5MQMKG , version 1. 3) and Agroportal ( http: //agroportal. lirmm. fr/ontologies/MICROFILTRATION , version 2. 1). MILK MICROFILTRATION ontology defines a standardized vocabulary used to annotate and structure data retrieved from the documents in a homogeneous way.
A short description of Milk Microfiltration is available on @Web companion web site ( https://www6. inrae. fr/cati-icat-atweb/Ontologies/Microfiltration ) and additional explanations about the ontological model used may be found in  [1]. Two ways to browse the vocabulary are provided: (1) using @Web application (see a short description of Milk Microfiltration and explanation how to browse it on @Web companion web site -https://www6. inrae. fr/cati-icat-atweb/Ontologies/Microfiltration ; (2) using AgroPortal browser ( http://agroportal. lirmm. fr/ontologies/MICROFILTRATION , tab Classes). Tutorials associated with atWeb querying module are available at https://www6. inrae. fr/ cati-icat-atweb/Tutorials/Querying-thumbnail.
Experimental Design, Materials, and Methods The dataset annotated with the Microfiltration process ontology is composed of 56 scientific documents (articles, thesis, scientific reports) upon a total of 213 documents. The 213 documents were selected from Web of Science and other sources with the keywords "Microfiltration" + "Milk" and filtered by experts to keep only documents in the field of conventional milk microfiltration directed to the separation of casein micelles from serum proteins. Documents have been discarded from the initial set of 213 documents. We identified two main kinds of discarded documents: i) 96 documents were out of the scope of conventional milk microfiltration. For instance they use dynamic filtration, ultrasound or gas injection, or specifically study the analysis of membrane fouling without reporting microfiltration operating conditions; ii) 116 other documents did not provide enough data to answer the four questions listed in the data section of the paper.
2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons. org/licenses/by/4. 0/ ) Specifications Table Subject Milk microfiltration Specific subject area A dataset of milk microfiltration process experiments and properties of the resulting dairy fractions were annotated from a collection of scientific documents Type of data Table How data were acquired A collection of 190 experiments annotated from 56 scientific documents from 1987 (year of the first publication on milk microfiltration directed to the separation of casein micelles from serum proteins) to 2019 extracted from Web Of Science (WOS) and other sources Data format.
process experiment registered in Table Process Description. Table Sample Characterization: Characterization data of all samples registered in Table Sample Identification (pH, turbidity, lightness, dry matter …). Table Reliability Assessment: a collection of 8 metadata used for data reliability assessment associated with each annotated document. Data source location INRAE, FR-750 0 0, Paris, France https://doi. org/10. 14758/9T8G-WJ20 The list of 56 annotated documents is given in Appendix 1. Data accessibility Data are accessible in a public repository Repository name: INRAE dataverse ( https://data. inrae. fr/ ) Data identification number: 10. 15454/0ADYFY corresponding to HT-MF process experiments, 10. 15454/EFC7VP corresponding to RB-MF process experiments, 10. 15454/7NOR3F corresponding to HT-MF-DF process experiments, 10.
Therefore, identity theft is becoming common on web applications to commit scams and frauds  [7, 8]. Furthermore, centralized IdM has several weaknesses in ensuring data security and privacy  [9]. In a centralized IdM, the identity owner must completely trust their service provider, thus having no or limited control over their identity. Users are unable to control the type of data collected and shared during the identification and authentication process. There remains no way for a consumer to check whether the security and privacy regulations are being followed by their service provider  [10]. Moreover, centralized IdM remains more prone to hacking and data breaches, which may lead to susceptible data disclosure for eHealth applications  [11]. In centralized IdM, the data of users are stored, updated, and managed through centralized databases.
Centralized databases are high-value targets for different security attacks, thus remain more prone to data manipulation or theft. Centralized servers also introduce a single point of failure and are venerable to Denial-of-Service attacks. If the centralized system is compromised, it may make the network completely useless. Thus, a centralized approach always requires an adequate security system with protective, detective, and corrective measures to protect against different security threats. Therefore, decentralized methods are being proposed in  [12] [13] [14]  to ensure data security and privacy. For remote eHealth applications, it remains essential to provide a decentralized IdM. What is required is a facility that empowers users to manage their identities independently from their eHealth provider.
Blockchain Overview A Blockchain is a replicated database, managed by a consensus mechanism, in a peer-to-peer network of non-trusting parties. Blockchain can be simply defined as a timestamped series of data records that are managed by a cluster of nodes  [35]. Nodes are computers that are connected in a peer-to-peer network and have an identical copy of the data. A new data entry is validated and transmitted to the entire network to maintain the identical copy of the database. The blockchain data structure consists of a chain of blocks. Each block records transactions validated in a particular period and has not yet been recorded in any prior blocks. These blocks are linked to one another through a cryptographic hash such that each subsequent block contains the hash of the previous block (also called parent) header and hence constitutes a chain.
The first block, known as the genesis block, has no reference to a previous block since it has no parent block. This linkability is a cryptographic mechanism that maintains data integrity and immutability in the network. A transaction goes through multiple steps in a blockchain network before it ends up validated by the network. Firstly, a user digitally signs and submits its information as a transaction. Secondly, the transaction is broadcasted to the entire network, where each neighboring node conducts validation for the transaction before relaying it to the next node. Thirdly, the transaction is collected and validated by a validator who includes it in a new proposed block. Fourthly, the consensus mechanism determines the validator who has the right to propose his block to the network. Fifthly, once other nodes validate the block, the block will be added to the chain, and the block will be propagated to all nodes to allow these nodes to update their database.
Sixthly, after being recorded in the blockchain, the transaction is considered complete and henceforth consumed by its new owner. Blockchains can be classified into public, private, and consortium blockchain based on their settings. A public blockchain is permissionless if the platform is publicly open for users without permission from any authority. Generally, in a public blockchain, all transactions are visible to the public. Bitcoin is a well-known example of a public blockchain. On the other hand, a private blockchain is not entirely open for the public to use-a centralized authority controls and defines who has the right to join the network. Thus, a private blockchain is considered to be centralized due to the fact because a single authority maintains the network. In addition, data in a private blockchain is prone to tampering.
In addition to the distributed ledger, Ethereum provides a virtual machine, called the Ethereum Virtual Machine (EVM), which can execute scripts written in a high-level programming language (e. g. , Solidity). In Ethereum, the blockchain data structure is more complex than in its predecessor, Bitcoin. The block's header comprises metadata, and its body comprises multiple types of data, namely, transactions, receipts, and system states (account states). Each of these data types is organized into a Merkle tree or a Patricia tree (Radix tree) in the state tree. The state tree (or the account storage tree) is an essential component in the Ethereum ledger. It is used to implement the account model, whereby each account is linked to its related states (account balances, smart contract states, etc.