dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability Short-read sequence data were submitted to the European Nucleotide Archive (ENA), under study accession number PRJEB38484, and the genome accession numbers are provided in Supplementary Data 1. The whole-genome alignment for the 1,324 genomes and other files that support the findings of this study have been deposited in FigShare [https://doi. org/10. 6084/m9. figshare. 5422912]. The phylogeny and associated metadata have been uploaded to Microreact for interactive viewing [https://microreact. org/project/ choleraeurope]. The GenBank accession numbers used for our analyses are shown in Supplementary Table 1.
Escherichia coli CIP 76. 24 (ATCC 25922) was used as a control. Short-read sequence data were submitted to the European Nucleotide Archive (ENA), under study accession number PRJEB38484; genome accession numbers are provided in Supplementary Data 1. Additional genomic data. Raw sequence files or assembled genomes from 1,168 7PET genomes described by Weill et al. 10, 11  or unpublished were downloaded from the ENA or GenBank and included in this study (Supplementary Data 1). We generated 100 bp overlapping simulated reads from the assembled genomes with fas-ta_to_fastq. pl (https://github. com/ekg/fasta-to-fastq/blob/master/fasta_to_fastq. pl). Genomic sequence analyses. The paired-end reads and simulated paired-end reads were mapped onto the reference genome of Vibrio cholerae O1 El Tor N16961 (also known as A19)  8  (GenBank accession numbers LT907989 and LT907990) with Snippy v4.
1. 0/BWA-MEM v0. 7. 13 (https://github. com/tseemann/ snippy). Single-nucleotide variants (SNVs) were called with Snippy v4. 1. 0/Freebayes v1. 1. 0 (https://github. com/tseemann/snippy under the following constraints: mapping quality of 60, a minimum base quality of 13, a minimum read coverage of 4, and a 75% read concordance at a locus for a variant to be reported. An alignment of core genome SNVs was produced in Snippy v4. 1. 0 for phylogenetic inference. Short reads were assembled with SPAdes (ref. 40 ) version 3. 1. 0. The various genetic markers were analyzed with BLAST 41 version 2. 2. 26 (https://blast. ncbi. nlm. nih. gov/Blast. cgi) against the reference sequences of the O1 (ref. 10  ) and O139 (ref. 42 ) rfb genes, ctxB (ref. 43 ), wbeT (ref. 10 ), VSP-II (ref. 44 ), and WASA-1 (ref.
45 ). The presence and type of acquired antibiotic resistance genes (ARGs) or ARGcontaining structures were determined with ResFinder 46 version 3. 1. 0. (https://cge. cbs. dtu. dk/services/ResFinder/), BLAST analysis against GI-15 (ref. 47 ), Tn7 (ref. 10  ), and SXT/R391 integrative and conjugative elements  48  , and PlasmidFinder 49 version 2. 0. 1. (https://cge. cbs. dtu. dk/services/PlasmidFinder/). The presence of mutations in genes encoding resistance to quinolones (gyrA, parC) 10 , resistance to nitrofurans (VC_0715, VC_A0637)  11  , or restoring susceptibility to polymyxin B (vprA)  11  was investigated by analysis of the sequences assembled de novo with BLAST. The GenBank accession numbers used to perform these analyses are shown in Supplementary Table  1. The data were entered into an Excel (Microsoft) version 15.
Cytometric Bead Array assay (CBA) TNF-α and GrB were quantified in culture supernatants of PBMC exposed for 10 days to peptide pools or 5 days to PHA or SLA, using the BD CBA Human Soluble Protein Flex Set system (BD Biosciences, San Diego, CA), according to the instructions of the manufacturer. In order to quantitate samples, the BD CBA Human Soluble Protein Flex Standard was performed for each cytokine and in each experiment. Data was acquired by flow cytometry (FACS Canto II, BD Bioscience, San Jose, CA). Flow Cytometric Analysis Program Array (FCAP Array; BD Biosciences) software was used for samples analysis.
5 (RPAT-8) or with anti-CD3-APC-H7 (SK7), anti-CD4-PerCP-Cy5. 5 (RPA-T4) antibodies (BD biosciences, San Diego, CA), for 30 min at 4˚C. For intracellular staining, cells were washed, fixed and permeabilized using BD cytofix/ Cytoperm plus kit (BD Biosciences, San Diego, CA) according to the manufacturer's instructions and co-stained with anti-IFN-γ-Alexa Fluor 647 (B27) and anti-GrB-PE (GB11) or with anti-IFN-γ-Alexa Fluor 647 (B27), anti-TNF-α-Alexa Fluor 488 (MAb11) and anti-IL-2-PE (MQ1-17H12) antibodies (BD biosciences, San Diego, CA), for 30 min at 4˚C. Data acquisition was performed on a FACS canto II flow cytometer (BD Bioscience, San Jose, CA) and analysis was performed with FlowJo 7. 6 software (TreeStar, Ashland OR). For acquisition, CompBeads Set Anti-Mouse Ig, k (Anti-Mouse Ig, k/Negative Control (FBS) Compensation Particles Set) (BD Biosciences, San Diego, CA), were used.
Statistical analysis Statistical analyses were performed with GraphPad Prism 5. 0 (GraphPad Software, San Diego, CA). Results were expressed as medians (interquartile range, IQR as variances). Wilcoxon signed-rank test was used to compare median levels of cytokines between stimulated and unstimulated cultures or between percentages of cells producing cytokines after different stimulations. Mann-Whitney test was used for inter-groups analysis on normalized data after deducting the unstimulated value. p-values less than 0. 05 were considered significant. Correlations were estimated using the Spearman rank (rs) correlation coefficient.
Background: Thanks to recent developments in genomic sequencing technologies, the number of protein sequences in public databases is growing enormously. To enrich and exploit this immensely valuable data, it is essential to annotate these sequences with functional properties such as Enzyme Commission (EC) numbers, for example. The January 2019 release of the Uniprot Knowledge base (UniprotKB) contains around 140 million protein sequences. However, only about half of a million of these (UniprotKB/SwissProt) have been reviewed and functionally annotated by expert curators using data extracted from the literature and computational analyses. To reduce the gap between the annotated and unannotated protein sequences, it is essential to develop accurate automatic protein function annotation techniques.
Background Proteins are long sequences of amino acids that form the basis of life and plays vital role in all living organism through out the entire life-cycle. Proteins perform various functions in our body that needs to be understood to understand life, disease processes and guiding drug discovery efforts to combat the diseases. Due to the tremendous advancement in amino-acid sequencing technologies, it is now possible to sequence bulk amount of proteins in a rapid and affordable manner. Therefore, the number of protein sequences accumulating in public databases is rising at an unprecedented rate. This huge quantity of data calls for further exploitation and enrichment and it presents many challenges for biologists as well as computer scientists in annotating the functional properties of protein sequences.
The UniProt knowledge base (UniProtKB)  [1]  is one of the most comprehensible protein databases as well as the largest public sequence database currently. It is divided into two main components: (i) the UniProtKB/Swiss-Prot database which contains protein sequences with reliable functional annotation of the protein sequences that has been reviewed by expert bio-curators, and (ii) the UniPro-tKB/TrEMBL database that stores unannotated and unreviewed sequences. Thus, for all proteins in UniProtKB, we have the primary amino acid sequence as well as some further information such as InterPro domain definitions which may have been identified from families of similar sequences or 3D protein structures. The UniProt curators annotate UniProtKB/TrEMBL sequences using two complementary systems. The first, called UniRule, uses a large list of "if-then" rules.
Data preparation We have collected 262,564 proteins from the March 2018 release of Uniprot-KB/SwissProt  [1]  database using the following rules: (i) each protein must contain at least one InterPro signature and (ii) must be annotated with at least one EC annotation. After getting the protein data from each of the proteins, we have extracted InterPro domain composition and EC annotations. Then, we built the protein network as described in "EC annotation performance analysis" section. Each protein forms its own vertex. we did not preprocess training data to remove redundancy. Rather, while performing annotation, it ignores the same protein if it appears in the neighborhood. For example, for a query protein q, GrAPFI will collect the neighbors satisfying a maximum jaccard similarity score. When the maximum jaccard similarity is set to less than 1.
For the validation dataset, GrAPFI was run by setting different minimum jaccard similarity index ranging from 0. 05 to 0. 5, and setting an upper limit of the similarity to 1 or less than 1. Figure  3a  to h show the GrAPFI performance for the reference proteome of A. thaliana for various min. Jaccard similarity indices. Similarly, Figs. 4, 5, 6, 7 and 8 show the performance of GrAPFI for the reference proteomes of Homo sapiens, S. cerevisiae, Mus musculus, Rattus norvegicus and E. Coli, respectively. In Fig. 9 , we also show the performance of GrAPFI on COFACTOR benchmark dataset for various Jaccard domain similarity index ranging from 0. 05 to 0. 5, and setting an upper limit of the similarity to 1 and less than 1. In these figures, we show the annotation accuracy (Y axis) against different Jaccard similarity thresholds (X axis) for the respective proteomes.
9  The accuracy and coverage for various minimum Jaccard similarity index for the COFACTOR benchmark proteins. a and b show the performance for upper similarity index of 1 and less than 1 respectively classification, To evaluate the method, we have used a well defined dataset of enzyme and non-enzyme proteins curated from UniprotKB  [1]. This dataset is called "NEW" and was constructed as described in  [17] : 1. The SWISS-PROT (released on  September 7, 2016)  database was separated into enzymes and non-enzymes based on their annotation. 2. To guarantee uniqueness and correctness, enzyme sequences with more than one set of EC numbers or incomplete EC number annotation were excluded. 3. To avoid fragment data, enzyme sequences annotated with 'fragment' or with less than 50 amino acids were excluded.
Enzyme sequences with more than 5000 amino acids were also excluded. 4. Redundancy bias is removed using CD-HIT  [41]  with 40% similarity threshold to sift the raw dataset, resulting in 22,168 low-homology enzyme sequences. 5. To construct the non-enzyme part, 22,168 non-enzyme protein sequences were randomly collected from the SWISS-PROT (released on September 7, 2016) non-enzyme part, which were also subject to the above (ii-iv) steps. Thus the original dataset contains 22,168 enzymes and an equal number of non-enzymes. The dataset contains the protein sequences along with their respective EC annotations. We have run Inter-ProScan5  [40]  to identify the domains contained in the sequences. Later, with the domain information, we have built the training graph. This graph contains 40040 proteins with 54% enzymes and 46% non-enzymes connected based on their domain composition.
In any case, if GrAPFI fails to find an annotation, it is possible to identify the reason behind the failure and it restricts itself from predicting any annotation. This attitude reduces the false positives. However, from the experiment, it is evident that GrAPFI performs with high annotation coverage. Unlike other hierarchical classification models like ECPred  [38]  and DEEPre  [17] , GrAPFI does not learn model for every class. Instead, it builts a giant network of proteins and apply label propagation for each query proteins. The described approach could easily be distributed in order to handle large protein databases. The method is scalable for larger dataset using big data processing frameworks like Hadoop/Spark. We therefore aim to extent GrAPFI to use a distributed processing framework for the large scale annotation of the entire UniProtKB/TrEMBL database.
Conclusion In this paper, We have extended and validated GrAPFI  [39] , a novel network based approach for automatic protein function annotation using the domain composition of the proteins. Pairs of proteins in the network are linked based on their jaccard similarity coefficient using InterPro domain composition. Our neighborhood based label propagation algorithm was applied to the network in order to propagate annotations from reviewed proteins to non-reviewed query proteins. This approach was validated using six popular reference proteomes from UniProtKB/SwissProt. We also compared GrAPFI results with those of ECPred, DEEPre, and SVMProt as examples of state of the art EC prediction approaches using the COFACTOR dataset. This comparison shows that GrAPFI achieves better accuracy and comparable or better coverage with respect to these earlier approaches.
It also takes into account the features of coud computing (storage, computing capacity, etc. ). The CM ontology will contribute to intelligent and automated service discovery. The proposed methodology is inspired by the ASDI framework (analysis-specification-design-implementation), which has already been used in the supply chain, healthcare and manufacturing domains. The aim of the new methodology is to propose an easy method of designing a library of components for a CM architecture. An example of the application of this methodology with a simulation model, based on the CloudSim software, is presented. The result can be used to help the industrial decision-makers who want to design CM architectures.
A user can demand a design software (Saas) and a storage capacity to store the data (Iaas), all provided as Design_aas. A transaction is initiated by the client who requests a design service that includes a storage media to process the data. The cloud system then grants the requested services on the basis of other features such as: price, duration of use, etc. When allocating services, both services that are labelled CM (for the design software) and those that are labelled CC (for the storage media) are chosen.
This simulator is commercial; in other words, the user needs to buy it for its full functionality. This is the main limitation as opposed to other simulators. CloudSim  [32]  is a discrete event-based cloud simulator implemented in Java, enabling the simulation of data centers with a number of hosts. CloudSim is a famous simulator for cloud parameters developed in the CLOUDS Laboratory, at the Computer Science and Software Engineering Department of the University of Melbourne. It is the most popular tool available for simulating a cloud environment. It has become more and more popular because of its extendibility. The programming language is Java, which is a portable language. All that is required is to import the CloudSim package to the IDE (integrated development environment) and start coding an algorithm.
When this is complete, the user runs the file and the simulation begins; at the end, the results are provided. It lacks several 1 http: //www. isi. edu/nsnam/ns/ (available June, 2019) features, such as a GUI (graphical user interface). Recently, some authors used CloudSim 2  in a multiformalism modeling approach for cloud based systems  [33]  or to work on IoT-based fog computing issues  [34]. In order to make a CM simulation model, we have selected CloudSim because it is the most flexible and adaptable cloud computing simulation software.
It is a very important advantage of inference ontologies in our study: let the individual X be a design software proposed by a provider, and let the concepts "Des-ignaaS" (design as a service), "Saas" (software as a service) be in the ontology. X must belong to the two classes so the mediator returns this service as responsive if the user employs one of these terms in his request. Typesetting conventions used in this paper are: Class, individual and property. The top level of the ontology that constitutes the generic knowledge model (Fig. 4 ) is composed of Actor class, which represents the actors that interact with the Coud Manufacturing system  [23]. Deployment Model for the Coud Manufacturing mode of deployment this class contains the following instances: Community, Hybrid, Private, Public.
Model validation. Our generic knowledge model is validated by populating the ontology with a number of instances and performing an inference mechanism on them. This mechanism allows us to infer implicit information with only some explicit knowledge. This is one motivation for choosing ontologies since this mechanism allows for the reduction of the modelling commitment and therefore saves time and facilitates future model integration. In the following section, we present one of the unit tests performed on the ontology. We have defined an Enterprise type individual named EnterpriseA, a software individual: Mgsoftware that is Manu-facturing_software. We have also defined individuals service3 and User1 without specifying their types in order to verify whether the inference mechanism will figure out whether they are service or Consumer types, respectively, to validate the CMO.
Design phase. In this paper, we present an example based on CloudSim simulation software. To do that, we use the component library that can be reused in different systems within the ASDI framework. The UML language is an international reference of modelling software used by the builder of CloudSim. Moreover, the existing technical tools do not allow the correct implementation of OWL-based models. In fact, the tools of the ontology are not mature enough to allow full implementation including the reasoning part. Hence, we translate the static elements of the CMO to UML. The translated classes that form the CMO are shown in UML language in Fig. 6. This is just an extract and not all details are shown. The translation is based on the work of Atkinson and Kiko  [42]  who have made a detailed comparison of UML and OWL.
Implementation phase. Our aim is to create a library of reusable components. We present the architecture we considered in Fig. 7. In this architecture, the UML model derived by the CMO is integrated with the CloudSim tool. Initially, CloudSim was developed in order to simulate CC; we propose using it in our architecture. The CMO is the main element in the architecture because it is used to match the users with the providers. The first step is the publication of existing services. The communication process starts with action from the providers: they register and publish the services they wish to present. Processing the user's request consists of a set of steps: 1. Service request: the user sends a request to the module that holds the knowledge base. This base contains all of the information about available services and it implements the CMO as a knowledge base; 2.
5. Case study of a particular system 5. 1. Presentation. This example consists of the use of the implemented classes shown in the previous section. The goal is to represent a situation in which the user sends a request to choose the service needed among different possibilities. The services proposed are described in an XML file. In order to do this simulation, we have hosted data in local servers, including WAMP. WampServer is a Windows web development environment that allows web applications to be created with Apache2, PHP and a MySQL database. Along with this, PhpMyAdmin enables one to easily manage databases. Figure  9  shows the use case presented in this paper and its relationship with the multi-layer framework of CM already presented in the introduction. This means that the Company-X provides services S-A2 and S-A3, EntrepriseA provides service2 and EnterpriseB provides Service3 and S-A1.
Action model. Several action models can be deduced starting from the same knowledge model. Here, we used a computer numerical control (CNC) emulator developed by William HIL-TON during his thesis 3  in Drexel, Philadelphia, USA (emulation of a DYNA MYTE 2400 machine). We installed a MAMP 4  server to host the service, which represents a CNC machine. MAMP is an acronym for Mac OS X, the operating system; Apache, the web server; MySQL, the database management system; and PHP, Perl, or Python, all programming languages used for web development. The CM classes and the reasoner are developed using Java language. They parse the XML file which contains the description of services (Fig. 11 ) and for each service displayed the name, the type and the name of the provider. The next step is up to the user; here, the user can choose service 2.
In order to simulate the machine tool and to illustrate the function of service 2 we used a CNC simulator developed by William HILTON 5. Figure  12  represents the service 2 selected by the user in the example which uses the class Holonaas. The interface shown in the figure depicts the logical part of the Holon. The web page is written in HTML5 and JavaScript. 13  depicts the results after performing the simulation on our use case. This means the result model is fed by the action model. In the result model, we can analyse the performance of the action model. Here, it is made with the results of the simulation. For example, the service used to perform the case study produces a log file where each line represents the time spent with the machine tool to make the different movements for the milling cutter and the coordinates of the movements.
A study of a system for the domain is explained in this paper. An action model that represents the system studied is implemented with cloud computing simulation software: CloudSim. We modified this well-known software in order to adapt it to the cloud manufacturing paradigm. To do so, we followed the steps of the ASDI methodology. We then created a library of reusable components. In our case, this library represents the CM architecture components that will be used to implement a CM simulation system based on CloudSim. The advantages and benefits of our methodology are: • propose a reusable f ramework to build CM platforms to map CM users and providers in such a way that a user can choose between a set of services and select the ones that will meet his or her needs. This platform will help to enhance collaboration in PLM by reducing costs and development time,.
Mediation analysis aims at disentangling the effects of a treatment on an outcome through alternative causal mechanisms and has become a popular practice in biomedical and social science applications. The causal framework based on counterfactuals is currently the standard approach to mediation, with important methodological advances introduced in the literature in the last decade, especially for simple mediation, that is with one mediator at the time. Among a variety of alternative approaches, K. Imai et al. showed theoretical results and developed an R package to deal with simple mediation as well as with multiple mediation involving multiple mediators conditionally independent given the treatment and baseline covariates. This approach does not allow to consider the often encountered situation in which an unobserved common cause induces a spurious correlation between the mediators.
These conditions are interpreted as the requirement that there must be no confounding of the T -Y , T -M and M -Y relationships after adjustment on the measured pretreatment covariates (i. e. confounder that is not affected by T ) and T , and moreover that there must not be posttreatment confounding (i. e. confounder that is affected by T ) between M and Y whatsoever, measured or unmeasured. In particular,  Imai et al. (2010b,a)  proved that under Sequential Ignorability, the average indirect effect is non parametrically identified, see Theorem 2. 1 in the next section, and proposed a sensitivity analysis to assess the robustness of estimates to violations of Sequential Ignorability. Moreover they introduced estimation algorithms for the effects of interest that are implemented in the widely used mediation R package  (Tingley, Yamamoto, Hirose, Keele, and Imai (2014) ).