dataesr/scientific-paragraphs-categorization huggingface dataset:
Testbed Description and Initial Requirements The testbed will evaluate the digitization of manufacturing processes following the Industry 4. 0 recommendations  [60]. This use case focuses on the manufacturing of parts through turning and milling processes. Turning is a machining process for manufacturing parts by the combination of two movements: a rotational movement is applied to the part while the tool is in translation to result in chip removal. Milling is a machining process in which the tool (the cutter) undergoes a rotational movement. The advance of the part by a translational movement then makes it possible to eliminate the chips. The "as-is" situation, before joining the IoTwins project, was as follows: • Simulation capabilities were missing. A machining simulation software able to easily compute forces and spindle power would be a plus.
Requirements of the IoTwins Platform The projected IoT solution aims at digitizing manufacturing activities. Figure  4  presents a component-based view of the implemented system. The latter was built with the support of the tools provided by the IoTwins platform and covers both the edge and cloud layers. At present, the IoT layer only streams data to the edge layer. Different software tools are required to fulfil the needs of the testbed application. In order to fulfil the user needs, platform components, proprietary components and commercial components have been combined and deployed. A subset of such components, also illustrated in Figure  4 , are discussed below. The platform provides the CETIM with many dockerized services. Some of them helped in building the targeted solution for the CETIM's use case.
Communication: in both edge and cloud, RabbitMQ  [62]  is used. MQTT  [63]  is the communication protocol implemented to manage the data streaming between IOT and edge, to send data to the cloud and to manage notifications; • Data Storage: several storage types are used to manage data storage. On edge, two database systems are used for the local storage, Postgres DB  [64] , an SQL database and InfluxDB  [65] , which contain the time-series data issued by the sensors; • Visualization: data visualization is performed through Grafana  [66] , which displays live sensor data fetched from the MQTT broker deployed at the edge machine. Additional dashboards, developed in the scope of the IoTwins projects, display views combining raw data and training model information. The simulation also requires specific tools to manage visualizations of the results; • Simulation: the simulation capabilities for machining processes virtually simulate the reality and provide quantitative and qualitative information about the processes and the machine parts.
After the deployment succeeds, web endpoints of the abovedescribed services are returned through the PaaS orchestrator. This last step concludes the provisioning/configuration of the piece of the DT hosted in the cloud. The following actions are then carried out to implement the provisioning of the edgehosted DT. The latter is composed of the following docker components: an InfluxDB storage, a Grafana web application, an MQTT broker and a web-based notifier application. The MQTT broker provides the data stream that feeds (among others) the InfluxDB, which, in turn, will feed the dashboards implemented in Grafana. A final configuration step will take care of wiring all deployed components. Since all software components are offered as docker services (offering a REST interface), the sequence of the deployment steps is not relevant, as long as the final configuration step sets the service pipeline up correctly.
For validation purposes, a complete industry-related use case, showcasing different computing services accessible through the IoTwins platform, has also been presented. The present work lays the foundation to build a production-type platform for hybrid digital twin models' calculation and evaluation. IoTwins claims that IoT, edge computing and industrial cloud technologies, together, are the cornerstones of the creation of distributed digital twin infrastructures that, after testbed experimentation, refinement and maturity improvements, can be easily adopted by SMEs. The experience and discussions emerging from the project activities will directly influence further developments aimed at accelerating the adoption of digital twin techniques by exploiting SMEs' industryperceived advantages in terms of increased reliability/autonomy and of improved locality preservation of critical production data that can be maintained and used directly in a plant's premises.
The platform is currently being tested on other manufacturing testbeds within the IoTwins project. Test results are used to tune the platform specifications and implement new functionalities. Specifically, activities aimed at supporting other orchestration frameworks in the edge (e. g. , Kubernetes  [68] ) have been undertaken. With the aim of delivering a ready and easy-to-use product to the manufacturing community, the following points still need to be addressed: (i) improving platform scalability to accommodate a large number of users; (ii) improving the usability of front-end tools; (iii) adding more data services to enhance compliance with other data management tools; (iv) increasing platform robustness by running massive functional tests. Some of these points (adding data services, improving scalability) will be addressed in the course of the project, others are part of long-term future work.
We were also particularly interested in improving functional annotation using remote homologies detection and comparisons with the recently published phage-specific PHROG protein database. Finally, we searched for useful functions for phage therapy, such as systems encoded by the phage to circumvent cellular defenses with a particular focus on anti-CRISPR proteins. We, thus, were able to genetically characterize nine virulent phages and one temperate phage and identify putative functions relevant to the formulation of phage cocktails for Salmonella biocontrol.
[22]  aimed at developing epigenetic phage biosensors used to identify and select phages from different natural environments. Others belong to the personal collections of Dr. M. Ansaldi and Prof. J. Casadesús. To improve phage gene detection and annotation, we benchmarked five different gene callers for syntaxic annotation. We could ascribe a taxonomic affiliation for each phage at the genus level, thanks to genome-wide comparative analyses enabled by vContact2  [23]  (based on the gene-sharing network) and VICTOR (based on genome-wide phylogeny)  [24]. For functional annotation of the predicted ORFs, we combined complementary approaches based on remote homologies detection using Hidden Markov Model (HMM) protein profiles comparisons with PHROG  [25] , a newly published database of viral protein clusters, and the pdb70 database at the Protein Data Bank  [26].
It is noteworthy that, Salmonella phage SeF3a (Kuttervirus) is likely to code for a novel anti-Abi system derived from the bacterial Phage Shock Protein A (PspA), making this phage an interesting candidate for phage therapy with two different anti-Abi systems, one found in all our Kutterviruses previously known to allow bacteriophage T4 to resist the Rex Abi system encoded in some prophages, and the second found in Salmonella phage SeF3a hypothesized in this study. We incidentally could propose functional annotations for 24 protein families in the PHROG database currently annotated "unknown function". Altogether, this work highlights the need to design specific pipelines for phage genomic analyses to unravel the phage genomic "dark matter" and illustrates the importance of prior knowledge of functions encoded in phage genomes for an educated selection of phage candidates for therapeutic cocktails.
Phage Genome de Novo Assembly Genome de novo assembly with SPAdes led to the identification of a total of 18 meaningful contigs in the entire dataset with contig lengths between 40 and 160 kb, a range compatible with phage genome sizes (Appendix A, Table  A1 ). Of note, SPAdes assembled two distinct contigs from Salfasec_11, Salfasec_13, Se_F3, and Se_F6 purified DNA, possibly representing two different phages in the same sample. This is in accordance with the TEM images (Figure  1 ), where we can visualize two different phage morphologies in purified phage suspensions for Salfasec_11 (two siphovirus with different tail lengths, red arrows), Se_F3 (one siphovirus with a small head and a long tail, and one myovirus, red and blue arrows, respectively), and Se_F6 (one siphovirus with a small head and a long tail and one myovirus, red and blue arrows, respectively) phage suspensions.
The intergenomic similarity matrix, as well as the species and genus cluster, are shown in Table  S1. 1 We kept for Blastn best-hits only fully sequenced phage genomes (accession numbers in Appendix A, Table  A3 ). 2 Query coverage. 3 Query length. Among these, only Salfasec_13b contig exhibits some small but significant DNA sequence identity with the propagation strain ATCC 14028S genome but only on 3 kb with 89. 6% identity. This finding rules out that any of the sequences we obtained originate from resident ATCC 14028S prophages induced during phage propagation. Hereafter, these contigs will be referred to as Salmonella phage strains.
The "phage language" is yet to be completely translated and is a current focus of many groups in the field, with great hopes put into deep learning techniques. We, thus, compared the output of AMIGene, Glimmer, MetaGeneAnnotator, Phanotate, and Prodigal to identify the best algorithm (or combination of algorithms) to optimize the gene calling of our phage genomes. We used Se_AO1 as a case study before generalizing the method to the other nine phage genomes included in this study. In order to compare the different predictions, we built a table for each gene caller where an individual predicted ORF is identified by a stop codon and a coding strand (+ or -) without its predicted initiation codon. This strategy should avoid discrepancies in the predicted initiation codon between the different algorithms.
According to this analysis, 69% of the predicted ORFs would depend on the specificity of the gene caller algorithms, which is very improbable. Glimmer predicted n = 144 ORFs, 32. 6% of which were unique to this software; therefore, Glimmer seems ill-fitted to our purpose. Thus, in Figure  2b , we drew another Venn diagram omitting Glimmer predictions. On the n = 243 predicted ORFs, 76. 5% were common to all algorithms (n = 186), which is an expected range if all predictors ran with a similar level of accuracy. Of note, Phanotate predicted 34 ORFs overlooked by the 3 other gene callers. This is not surprising as Phanotate has been purposely designed to identify ORFs in phage genomes. Setting a cut-off limit for the gene size was not an easy task, as we did not want to keep false predictions or discard ORFs that do code for small proteins.
One has to keep in mind that small ORFs in bacteriophages can encode small proteins enabling phages to evade host anti-phage systems. Among them, we found anti-CRISPR proteins (Acr), such as ACR3112-12 (52 aa) from Pseudomonas phage D3112  [37] ; proteins preventing superinfection, such as the immunity protein Imm (83 aa) of bacteriophage T4  [38]  or the lipoprotein Llp (77 aa) of bacteriophage T5  [39] ; anti-RM proteins, such as Ocr (117 aa) of bacteriophage T7  [40] ; the newly discovered anti-CBASS Acb (about 94 aa) found in an increasing number of phages of various phylogenetic background, such as Pseudomonas phages PaMx33, 35, 41, and 43, as well as the enterophageT4  [19, 20]. As the identification of anti-CRISPR and other types of anti-host defenses proteins was one of our goals, we chose a conservative approach as a first approximation and kept all the ORFs predicted by AMIGene, MetaGeneAnnotator, Phanotate, and Prodigal for the 10 phage genomes studied here.
Functional Annotation To date, the most common and fastest method to tentatively ascribe a function to a newly predicted gene product is by comparing the ORF or gene product sequences with existing sequences deposited in various databases, such as the nr/nt databases at NCBI. This approach relies on the confidence one can have in previous annotations. It is sometimes tedious to trace back the reasons justifying current annotations. For proteins that finally end up with "unknown function" annotation, one has to keep a critical eye for several reasons: (i) the function can truly be a novel one (the phage "dark-matter"), (ii) the amino acid sequence is too divergent to detect homologies with proteins of known function using "simple" sequence alignment algorithms such as BlastP, or (iii) the gene product does not exist (an artifact due to the ORF prediction algorithms).
2. Comparison of the predicted gene products with protein structures from the pdb70 database at the Protein Data Bank. We, thus, first compared all predicted gene products for each phage genome with the PHROG database. Table  S5  recapitulates the best hit in PHROG for each predicted ORF for each genome, as well as the comparison metrics. We then used the comparison metrics and the genomic environment to perform a manual curation of the predicted ORFs. In total, 59 ORFs were thus manually curated (highlighted in red in Tables  S2  and S5 ), ranging from 1 curated ORF per genome (Salmonella phage SeF2 and SeF6b) to 10 curated ORFs per genome (Salmonella phage SeAO1). Of these, 89. 8% of curated ORFs (53 ORFs) were predicted by Phanotate, suggesting that this gene caller designed for phage genomes tends to overpredict ORFs.
For the remaining ORFs annotated "unknown function" with a weak affiliation to a phrog family, we replace the phrog number with "singleton", meaning that this ORF does not have (yet) any ortholog in the PHROG database. This can be either a false positive ORF or a truly novel function. For gene products still annotated with "unknown function" after comparison with the PHROG database, we tried to improve the annotation by comparison with the pdb70 database of the Protein Data Bank. The best hits for each gene product of each genome are listed in Table  S6. After manual inspection of the results and the comparison metrics, we could propose a functional annotation for 24 phrog families annotated "unknown function". These propositions are summarized in Table  4. The proposed annotations cover 1120 protein sequences included in the PHROG database, 352 of which are structural proteins, and 196 participate in gene regulation according to our proposed annotations.
Of note, phrog_6201 is predicted as a CI-like repressor, one of the usual markers of the temperate bacteriophages. This phrog contains 20 protein sequences from 20 distinct phages that are, thus, likely to be temperate phages. Of note, Salmonella phage Salfasec13b Gp_057 protein belongs to the phrog_6201, hinting at a temperate lifestyle for this bacteriophage. This example illustrates that one can gain valuable functional insights into an entire set of bacteriophages by improving the functional annotation of a single phrog. Table  5  summarizes the final syntaxic and functional annotation for each phage. Detailed functional annotations for each genome can be found in Table  S7. The files for submission were built using the PHROG annotation for each ORF; when pertinent, the predicted functions derived from the PDB comparisons were added together with its corresponding PDB entry.
As the PHROG database is not yet recognized by the International Nucleotide Sequence Database Collaboration (INSDC), it was not possible to use the "db_xref" flag for the PHROG annotations in the GenBank or ENA file. As a consequence, we reported the phrog number and the corresponding functional annotation for each gene as a "note" in the GenBank or ENA file. The 10 bacteriophage genomes were submitted to GenBank (Salmonella phage SeF1, SeF2, SeF3a, SeF6a, SeF6b, Salfasec13b, BioProject PRJNA767534) or ENA (SeAO1, SeEM1, SeEM2, SeEM4, BioProject PRJEB37792) under the accession numbers listed in Table  5.
Nevertheless, no ORF coding for an integrase could be predicted, and other key functions associated with temperate phages are missing. The same findings were made for the closest annotated sequence homolog found with Blastn in the nt database, i. e. , Salmonella phage S101 (Table  2 ). The PHROG database includes 22 Jerseyvirus genomes, 20 of them coding for RDF belonging to the phrog_66, making it a marker of this genus, although Jerseyvirus seem to be strictly virulent phages. Salmonella phage SeF1 and its relatives might have been temperate phages that have lost their integrase gene, or the RDF encoded in their genome has been acquired by horizontal gene transfer and may serve to induce resident prophage(s) in the host. The latter hypothesis is worth investigating, as induction of resident prophages is likely to ensure efficient lysis of the host.
Gp_027 is predicted as an immunity-to-superinfection protein (phrog_1039). In E. coli phage T4, the immunity protein Imm blocks the injection of T4 DNA, preventing superinfection by the same phage or another phage targeting the same bacterium and using the same infection mechanisms. Bacteriophage T4 Imm belongs to the same phrog as Gp_027, strengthening the proposed annotation for Gp_027. The production of an Imm-like protein in an infected cell will alter co-infection by another virion, either from the same strain or from a bacteriophage that resorts to the same DNA injection mechanism. Salmonella phage SeF1 seems to harbor an extensive arsenal for host lysis with two Rzlike spanins (Gp_009 and Gp_010, phrog_2536 and phrog_5672, respectively), one endopeptidase predicted from structural comparison with the pdb70 (Gp_034, phrog_12508 currently annotated "unknown function" in the PHROG database), two holins (Gp_063 and Gp_064, phrog_2359 and phrog_2508, respectively) and one endolysin (Gp_065, phrog_33083).
Chivirus: Salmonella Phage SeF2 and SeF6b Salmonella phage Chi and other Chi-like viruses are flagellotropic phages requiring a motile flagellum as its primary receptor; it is then very likely that Salmonella phage SeF2 and SeF6b share the same requirement. Both Salmonella phage SeF2 and SeF6b phages contain 3 gene products with predicted carbohydrate-binding domains that we predicted from structural comparison with the pdb70 (phrog_2448 currently annotated "unknown function" in the PHROG database) encoded on the same strand downstream of the tail genes module, suggesting that these proteins may be part of the tail and the host recognition apparatus. These genes encoding carbohydrate-binding domains are shared among 36 other Chivirus present in the PHROG database. Sugars are components of the LPS, and it has also been shown that S.
enterica Typhimurium flagella contain at least 16 different sugars  [52]. The flagellotropic Salmonella phage Chi and its relatives may use these domains to recognize and bind the bacterial flagellum and its sugar moieties. It has been reported that Salmonella phage Chi can infect both Salmonella spp. and E. coli hosts and thus has been proposed as a good candidate for the development of phagebased applications (pathogens detection, remediation, phage therapy)  [53]. Inclusion in a therapeutical cocktail of bacteriophages such as Chivirus that may target receptors other than the LPS would be a good option to avoid cross-resistance. Both phages encode a predicted DNA methyltransferase (phrog_111) homologous to the DNA N-6-adenine methyltransferase according to PFAM prediction (PF05869) attached to this phrog.
Lederbergvirus: Salmonella Phage Salfasec13b As discussed above, Salmonella phage Salfasec13b is a temperate phage and a Lederbergvirus (as the archetypal Salmonella phage P22). This phage genome contains all the genes found in P22 that are important to promote lysogeny or confer a selective advantage to the host including gtrA (Gp_022, phrog_2224), gtrB (Gp_021, phrog_34335), and gtrC (Gp_019, phrog_4829). Together, the GtrABC complex modifies the host O-antigen at the host cell surface  [54]. Changing the LPS prevents infection by other phages using it as a primary receptor. In the light of phage therapy, this is not the desired outcome. 2. 7. 4. Kuttervirus: Salmonella Phage SeAO1, SeEM1, SeEM2, SeEM4, SeF3a, SeF6a We first pinpoint protein functions that seem to be a hallmark of the Kuttervirus genus as they are found both in our 6 Kuttervirus phage genomes and in almost all the 15 Kuttervirus genomes included in the PHROG database.
Four of the fifteen Kuttervirus phage genomes included in the PHROG database harbor such a gene. pspA belongs to the psp operon found in some bacterial genomes, noticeably in E. coli and Salmonella species. This system prevents the dissipation of the proton-motive force (PMF) triggered by various stresses, including the infection by filamentous phages or the mislocalization of secretins  [58]. PspA plays a role in the regulation of the phage-shock-response psp operon but also associates with the inner membrane to prevent the loss of PMF, although the molecular mechanisms still remain unclear. Several Abi systems, such as Rex in Lambda lysogens mentioned above or AbiZ in Lactococcus lactis  [13] , lead to cell death or cellular growth arrest through membrane permeabilization and dissipation of the PMF or loss of ATP.
For Chivirus genomes, Salmonella phage SeF2 and SeF6b genomes code for the same three predicted Acr. Interestingly, the transposable Pseudomonas aeruginosa Mu-like phage genomes contain an acr locus comprising two to three genes located among the genes involved in the phage head assembly  [37]. In these phages, the acr locus is bracketed by genes involved in phage head morphogenesis, usually upstream of a protease/scaffold gene. The lower panel in Figure  5  describes the genetic context of AcrIE3, a type-IE Acr protein from P. aeruginosa phage DMS3 that has been functionally investigated  [60]. In our six Kuttervirus phages, we found one small protein (56 aa) belonging to phrog_4097 (unknown function) predicted with good scores by both PaCRISPR and AcRanker as a type II-A anti-CRISPR protein.
Raw sequencing reads (FASTQ files trimmed from their Illumina adaptors) for each BioSample were submitted to the NCBI Sequence Read Archive under the BioProject accession number PRJNA767534. Raw sequencing reads quality was then improved with Trimmomatic  [63]  using the following parameters: SLIDINGWINDOW:4:25 MINLEN:75 for Se_AO1, Se_ML1, Se_EM1, Se_EM2, Se_EM3, and Se_EM4 or ILLUMINACLIP TruSe3-SE. fasta SLIDINGWINDOW:4:28 MINLEN:75 for all other phages. Only trimmed pairedreads were used for de novo genome assembly.
Taxonomic Classification For taxonomic classification, we used vContact2 0. 9. 19 (with the ProkaryoticViral-RefSeq201 database accessed on 27 September 2021) and VICTOR (Virus Classification and Tree Building Online Resource with the amino acid option selected, Available online: https://ggdc. dsmz. de/victor. php) (accessed on 15 November 2021) Network visualization was done with Cytoscape  [71].
PHROG-Based Functional Annotation For functional annotation, we used the newly published PHROG database (accessed on 27 September 2021) dedicated to viral proteins. In this database, viral proteins are distributed among families called "phrog"; each phrog represents a cluster of viral proteins orthologs built using remote homology detection by HMM profile-profile comparisons. At the date of its publication, the PHROG database contains 17,473 (pro)viruses of prokaryotes or archaea for a total of 938,864 proteins. A total of 38,880 clusters were defined, and manual inspection led to the annotation of 5108 phrog clusters, representing 50. 6% of the total protein dataset. Following the procedure available on the PHROG website (https: //phrogs. lmge. uca. fr/READMORE. php), we used HH-suite  [74]  to compare each predicted gene product from the phages studied here with the PHROG database and ascribe a phrog and its annotation whenever possible.