dataesr/scientific-paragraphs-categorization huggingface dataset:
The same can be observed and explained when comparing the results of coresecent and corese-fed. All execution times are below 200 ms. According to  (Zhou et al. , 2012) , this is an acceptable response time for a Web application. Use cases on dataset Corpus. Figure  7  shows the query execution times of SPARQL queries on Corpus for the four chosen triplestores deployed in a local context. Their observation confirms our previous comparative analysis on Referential: graphdb and virt get the best query execution times. We also get confirmation that, in average, a federated architecture is better for queries on a single dataset. In comparison to Figure  5 , we can note that the execution times of queries on Corpus are much lower than those of queries on Referential whereas the size of the Corpus dataset is much greater than that of the Referential dataset (see Table  2 ).
We developed a base of SPARQL queries to implement the Educlever uses cases and we proposed two software architectures based on Semantic Web technologies designed for an e-Education systems. We upgraded the Educlever software architecture following these propositions and implemented these architectures with four triplestores Corese, Allegrograph, GraphDB and Virtuoso in order to benchmark them and compare them to the existing solution on real data and real queries. We presented a complete evaluation of the quality of service and response time in an industrial context with a real-world tesbed showing that the Semantic Web based solution meets the industrial requirements, both in terms of functionalities and efficiency compared to existing operational solutions. Moreover, by relying on semantic Web we can reuse, extend and align with existing vocabularies to increase interoperability.
Use cases on both datasets. Figures 9 and 10 show the execution times of the queries on both Referential and Corpus, for the four chosen triplestores deployed respectively in a local and remote context. The trends are the same and the execution times does not exceed 200 ms for all the queries on all triplestores in a local context. Figure10does not show the execution times for educ-v2 since it does not implement these use cases with a single query. Figure 11: Execution times of SPARQL queries with property paths on Referential with a local deploymentthe Educlever industrial context, the execution time of queries with a few property paths in the graph pattern, like it is the case for Q 12 , remains under 200 ms in average, which is acceptable for a Web application. But, for more complex queries, like Q 13 and Q 14 , the execution time can reach up to to 4000 ms (4s), which is not acceptable in the Educlever industrial context.
Properties Path Queries Execution Time Online corese-cent educ-v2 alleg-cent Use cases on both datasets. Figures 9 and 10 show the execution times of the queries on both Referential and Corpus, for the four chosen triplestores deployed respectively in a local and remote context. The trends are the same and the execution times does not exceed 200 ms for all the queries on all triplestores in a local context. Figure10does not show the execution times for educ-v2 since it does not implement these use cases with a single query. Figure 11: Execution times of SPARQL queries with property paths on Referential with a local deploymentthe Educlever industrial context, the execution time of queries with a few property paths in the graph pattern, like it is the case for Q 12 , remains under 200 ms in average, which is acceptable for a Web application.
Multiple sequence alignment is a prerequisite for many evolutionary analyses. Multiple Alignment of Coding Sequences (MACSE) is a multiple sequence alignment program that explicitly accounts for the underlying codon structure of protein-coding nucleotide sequences. Its unique characteristic allows building reliable codon alignments even in the presence of frameshifts. This facilitates downstream analyses such as selection pressure estimation based on the ratio of nonsynonymous to synonymous substitutions. Here, we present MACSE v2, a major update with an improved version of the initial algorithm enriched with a complete toolkit to handle multiple alignments of protein-coding sequences. A graphical interface now provides user-friendly access to the different subprograms.
2015) , genome-wide analyses of selection  (Assis et al. 2012) , metabarcoding analyses  (Leray et al. 2013) , and phylogenomic pipelines  (Bragg et al. 2016 ). Here we present a major update of MACSE with an improved version enriched by a series of subprograms aimed at facilitating the production and handling of multiple alignments of protein-coding sequences. Altogether, the subprograms implemented in the new MACSE v2 release compose a powerful toolkit now easily accessible through a graphical user interface (fig. 1 ). The core alignment subprogram (alignSequences) has been improved in performance through a faster estimation of its objective function, namely the SP-score, thanks to recently derived optimal algorithmic solutions  (Ranwez 2016). Additional parameters have also been introduced to control the speed/extensiveness ratio of the heuristic search for an alignment optimizing the SP-score.
MACSE requires additional costs for frameshifts and stop codons that are not easier to set than traditional gap-associated costs. We provide default values that have proved to be effective based on our experience. This is further discussed in the MACSE online documentation that provides guidelines for handling specific sequences such as pseudogenes or RNAseq contigs resulting from error prone long read sequencing technologies. The TrimNonHomologousFragments subprogram was developed to remove long sequence fragments that are unrelated to other sequences. Indeed, positioning long insertions in one or several sequences could drastically slow down and impede the alignment process. Moreover, long insertions may often prove finally useless since they are removed by alignment filtering tools in subsequent analyses.
The enrichAlignment subprogram can be used to sequentially add new DNA sequences to an existing alignment. Its input parameters allow defining criteria that the additional sequences should fulfil to be actually incorporated into the final alignment. For instance, sequences can be automatically discarded when, once aligned, they would contain a stop codon, too many gaps, or more than a given number of frameshifts. The original alignment can either be sequentially enriched, or kept unchanged so that all sequences are compared with the same reference alignment. This latter option is especially useful for metabarcoding projects based on markers such as the mitochondrial Cytochrome Oxidase subunit I (cox1) gene. This typically involves enriching a reference alignment containing sequences from databases such as BOLD  (Ratnasingham and Hebert 2007)  or MIDORI  (Machida et al.
2017 ) with thousands of newly generated sequences. The reportMaskAA2NT subprogram takes as input a nucleotide alignment and a filtered version of the corresponding amino acid alignment, for example, produced by HMMcleaner  (Philippe et al. 2017) , and reports this filtering at the codon level. By default, it additionally filters out small sequence fragments mostly surrounded by gaps or filtered nucleotides. Other MACSE v2 subprograms allow performing useful alignment manipulations such as translating sequences using different genetic codes in the same alignment (translateNT2AA); restricting a coding alignment to a subset of sequences and/or sites (splitAlignment, trimAlignment); or refining an existing alignment using the 2-cut strategy to improve its SP-score (refineAlignment). The command line interface is still key in most analyses that require running MACSE v2 in parallel on hundreds or   v4.
6. 4 (Gouy et al. 2010). Exclamation marks (!) emphasize the frameshifts detected by MACSE, most of which corresponding to programmed frameshift mutations  (Russell and Beckenbach 2008). MACSE v2. doi:10. 1093/molbev/msy159 MBE thousands of data sets using a computing cluster. However, as the number of subprograms and options increased significantly, we now provide a user-friendly graphical interface. This should make it easier for new users to adopt MACSE v2 and hopefully broaden its usage and application scope. MACSE v2 is Java software freely available under the CECILL license (GPL variant) at https://bioweb. supagro. inra. fr/macse/, last accessed August 22, 2018. MACSE v2 and OMM_MACSE, a pipeline strongly relying on the MACSE v2 toolkit that has been used to align the thousands of orthologous genes in the OrthoMAM database  (Douzery et al.
Funding This work was supported by the  Agence Nationale de la Recherche  " Investissements d'avenir/Bioinformatique " ( ANR-10-BINF-01-02  project  Ancestrome ),  Labex CeMEB  " Centre M editerran een de l'Environnement et de la Biodiversit e"  ( ANR-10-LABX-0004 ), and the  European Research Council  ( ERC-2015-CoG-683257  project  ConvergeAnt ). This is contribution ISEM 2018-151 of the  Institut des Sciences de l'Evolution de Montpellier.
Started from the results of OpenNCP, the KONFIDO project aims to increase trust and security of eHealth data exchange as well as to increase awareness of security issues among the healthcare community, adopting a holistic approach. In this light, the KONFIDO solution provides first of all a reference scenario with basic context information on the eHealth data exchange platform provided by the epSOS project; then, we provide a description of the KONFIDO deployment architecture in the context of the OpenNCP platform, by highlighting how the security of OpenNCP data exchange is improved by using KONFIDO; we describe in detail the interaction among the KONFIDO components and we finally give our conclusions. Other aspects of the KONFIDO project are discussed in detail in other recent papers. In particular, the ethical framework that covers such transborder or inter-regional health data exchanges is discussed in  [6].
The important issue of user requirements is developed in  [11]. Specific physical-based techniques that can be used to generate seeds for cryptography are proposed in  [1]. The potential use of the novel technology of blockchains in this context is investigated in  [2]. The epSOS architecture is implemented as a set of interacting National Contact Points (NCPs) built on top of Web technologies (SOAP). The platform model adopted by epSOS can be viewed as a federations of services connected with service interfaces defined by specified contracts (a SOA system) (see Fig. 1 ). In epSOS, the NCP is the main module of cross-border interoperability, exploiting the role of connection the National Infrastructure (NI) to the European Level environment. The components of an NCP can be viewed as a logical wrapper of the different NI.
This means that, due to this chain of trust between the NCPs, if one NCP states that someone is authenticated, this will be accepted by the NCPs of other countries. Thus, compromising one NCP (having control of it) can potentially affect the whole infrastructure. In particular, looking at the Patient Summary response process (see Fig. 2 ), we can observe that the medical data is in plain text in almost all phases performed by the NCP. This means that the security level of these phases is the same as the NCP itself and, hence, an NCP vulnerability can be exploited to generate a data breach on the OpenNCP processes. The KONFIDO toolbox can be used to overcome the The opt rectangles highlight the actions that will be performed in a Trusted Execution Environment (TEE) and supported by the other KONFIDO technologies.
The high modularity of the KONFIDO toolbox, allows to address a wide range of possible eHealth scenarios (not only the ones related to OpenNCP) and to solve many vulnerabilities in the exchange and processing of health data. Trusted Execution Environment. The Trusted Execution Environment (TEE) is created starting from security Software Guard eXtension (SGX 5  ) of Intels CPU that allows the creation of protected areas of memory inside the address space of an application. These TEEs, known as Secure Enclaves in SGX jargon, provide strong protection of code and data residing inside through encryption and integrity checks of their memory range, performed directly by the CPU. SGX can be considered as a reverse sandbox, i. e. , it protects applications from the untrusted system outside, comprising the OS, implying that system calls cannot be performed into the enclaves.
In KONFIDO, we want to perform specific functions of OpenNCP in SGX enclaves. More precisely, we focus on the enhancement of the NCP host, which is the national gateway in charge of transforming Patient Summaries (PS) from one language to another and where most critical operations take place. As mentioned above, during the PS exchange, in fact, the patient health record is exposed to attacks (see Fig. 2 ), when it is unencrypted and re-encrypted into the NCP. That is, when the NCP-A receives from the NI-A (National Infrastructure of Country A) a encrypted PS and needs to decrypt, transcode, and re-encrypt before sending it towards another NCP or HCP, an attacker landed on the NCP host may steal or tamper the sensitive patient data by duping the memory content. Hence, the idea is to perform decryptions, transformations, and encryptions of PS into the TEE provided by SGX by integrating part of the transformation and security modules into an enclave.
g. which users initiated an attack). In particular, the KONFIDO SIEM will be integrated with a Visual Analytics Module for analysing large amounts of data, containing multiple types of information, and detecting anomalies, utilizing both automatic anomaly detection algorithms, such as Local Outlier Factor and Bayesian Robust Principal Component Analysis  [7] , and visual analytics methods, such as k-partite graphs and multi-objective visualizations. Blockchain Based Auditing System. The blockchain-based auditing mechanism developed in the framework of KONFIDO is a legally binding system that allows to prove that eHealth data have been requested by a legitimate entity and whether they have been provided or not. The main scenario includes the NCP of one country that requests eHealth data for a patient from the NCP of another country; in this case, both countries need to keep an unforgeable copy of the transaction, in order to be able to prove that the other NCP has requested and/or received the data.
To solve this issue, we employ a blockchain (i. e. , a distributed data structure) that links each block to its predecessor via cryptography. The OpenNCP node generally interacts with 2 different types of counterparts: the national infrastructure (to retrieve patient data from the national healthcare system) or another OpenNCP node (to retrieve patient eHealth data from another country). Each event of this type is stored as a log file and OpenNCP provides a web-based interface to view registered events and critical logs. In order to capture, filter, timestamp and encrypt the most critical logs that refer to cross-border data exchange between two NCP nodes in different countries, we will adapt the SmartLog log management system. The encrypted log files will then be stored on the KONFIDO distributed ledger.
KONFIDO Deployment Architecture Considering the OpenNCP scenario and the relative vulnerability assessment, the deployment architecture and distribution of the KONFIDO toolbox is presented in Fig. 3. The KONFIDO toolbox is deployed in all actors of the scenario with varying functionalities depending on the actions to be taken and on the hardware available. In particular: -In each NCP, the entire KONFIDO solutions will be deployed. A TEE will be used to secure all actions needed to achieve a secure patient summary exchange; a PUF component will be deployed and integrated to achieve an unclonable key generator that can be used to generate keys, certificates and to secure the communication channels; an eIDAS service will be used to improve the actors authentication; the Auditing Services will be used to be compliant with log management/storing regulations; a HE technique will be used to allow the data processing for example of the PS without having to use the relative plain text.
This communication channel allows the data exchange between SGX enclaves using PUF technologies for the keys used during the remote attestation. Furthermore, in order to protect the OpenNCP infrastructure from distributed attacks (e. g. DDoS), a SIEM solution is needed. Considering the management/hosting issues and national regulations coming from a centralized SIEM, only a distributed solution is applicable: each NCP will have its SIEM that looks at corresponding NI and that is interconnected with other SIEMs to exchange security metrics via a publish-subscribe communication channel (Fig. 3 ). Two specialized TEE data hooks will be available for the SIEM, one providing plain data, and the other providing homomorphic encrypted data. The second one is needed to allow the data processing of sensitive data (respecting the privacy requirement) in terms of data threshold comparison, structure coherence and so on without access to the relative plain text.
The KONFIDO architecture can also be viewed as a system of interacting agents as shown schematically in Fig. 4 , and in this section we describe a generic KON-FIDO interface template, structured as a multi-agent Agent System (AS). This AS would be resident at each individual national or regional access point. Each AS can communicate with other similar ASs in the same or in different countries or regions, i. e. at the same local site or at remote sites, via a system such as OpenNCP. The ASs can also communicate directly with each other through the Internet. Each AS will be composed of several specialized agents (SA): -Within the AS, the SAs can communicate with each other; -One of these SAs is designed to communicate with the local NI; -Other SAs are specialized in communicating with other SAs at other national access points, and one can imagine that within an AS there would be a distinct SA that is designed specifically to communicate with the SA at each specific country, and the agents can learn and adapt individually to their specific environment  [9].
We presented the KONFIDO components and how these are combined in a holistic approach aiming at improving the security of OpenNCP eHealth data exchange. The main advantage of the KONFIDO solution is that it is designed and implemented as a toolbox composed by different services and tools the combination of which can be used to address a wide range of possible eHealth scenarios (not only the ones related to OpenNCP) and to solve many vulnerabilities in the exchange and processing of health data. eHealth data exchange. J. Netw. Comput. Appl. 116,  65-85 (2018). https:// doi. org/10. 1016/j. jnca. 2018. 05. 012. https://www. scopus. com/inward/record. uri? eid=2-s2. 0-85048715942&doi=10. 1016%2fj. jnca. 2018. 05. 012&partnerID=40&md5 =81c9e20e7d35684f36599f4d8163bf98 Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.
Acknowledgments. The research leading to these results has received funding from the  European Union 's  (EU) Horizon 2020 research and innovation programme  under grant agreement  N727528  (Action title:  KONFIDO -Secure and Trusted Paradigm for Interoperable eHealth Services , Acronym: KONFIDO). This paper reflects only the authors' views and the Commission is not liable for any use that may be made of the information contained therein.
Introduction The aim of Genome-Wide Association Studies (GWAS) is to identify associations between genetic variants and a phenotype observed in a population. They have recently emerged as an important tool in the study of bacteria, given the availability of large panels of bacterial genomes combined with phenotypic data  (Farhat et al. , 2013; Sheppard et al. , 2013; Alam et al. , 2014; Chewapreecha et al. , 2014; Earle et al. , 2016; Lees et al. , 2016; Jaillard et al. , 2017b). GWAS require encoding the genomic variation as numerical factors. The most common approaches rely on single nucleotide polymorphisms (SNPs), defined by aligning all genomes in the panel against a reference genome  (Farhat et al. , 2013; Alam et al. , 2014; Chewapreecha et al. , 2014)  and on gene presence/absence, using a pre-defined collection of genes  (Earle et al.
, 2016; Rahman et al. , 2017). Nonetheless, kmer representation often loses in interpretability what it gains in flexibility, and the best way to encode the genomic variation in bacterial GWAS is not yet clearly defined  (Read and Massey, 2014; Power et al. , 2017). Our approach, coined DBGWAS, for De Bruijn Graph GWAS, bridges the gap between, on the one hand, SNP-and gene-based representations lacking the right level of flexibility to cover complete genomic variation, and, on the other hand, kmer-based representations which are flexible but not readily interpretable. We use De Bruijn graphs  (de Bruijn, 1946)   (DBGs) , which are widely used for de novo genome assembly  (Pevzner et al. , 2001; Zhang et al. , 2011)  and variant calling  (Iqbal et al. , 2012; Le Bras et al. , 2016). These graphs connect overlapping kmers (here DNA fragments), yielding a compact summary of all variations across a set of genomes.
The subgraph was mostly grey and linear with a localised blue and red fork. The most reliable annotation for this subgraph was fabG1 (also known as mabA), a core gene previously shown to be involved in ethionamide and isoniazid resistance  (Lee et al. , 2000; Farhat et al. , 2016). None of the significantly associated unitigs mapped to the fabG1 gene, but their close neighbours did (highlighted in Figure  2C  by black circles), sug-gesting that the detected variant was located in the promoter region of the gene. This was confirmed by mapping the significant unitig sequences using the Tuberculosis Mutation database of the mubii resource  (Flandrois et al. , 2014).
, 1995; Gordon et al. , 2014)  together with a replication and maintenance protein in strong linkage disequilibrium with ermC. For P. aeruginosa amikacin resistance, the third subgraph (min q = 2. 21 × 10 -6 ) represented a 10,000 bp plasmid acquisition. Using the NCBI nucleotide database  (Benson et al. , 2012) , most of the unitigs in this subgraph mapped to the predicted prophage regions of an integrative and conjugative plasmid, whose structure was recently described as the pHS87b plasmid in the amikacin resistant P. aeruginosa HS87 strain  (Bi et al. , 2016). Supplementary Figures S4 and S5  tuberculosis (TB) and P. aeruginosa (PA) panels. For each antibiotic, subgraphs were reported with their rank, number of significant unitigs over all unitigs in the subgraph (Sign. unit. ), q-value of the unitig with the lowest q-value (min q ), the corresponding estimated effect (β coefficient of the linear mixed model) and annotation of the subgraph.
For kmer-based methods, annotations were retrieved by mapping unitig/kmer sequences on the resistance and Uniprot databases. Green cells correspond to resistance determinants already described in the literature, orange cells to resistance determinants described for association with other antibiotics (annotations not found by RWAS are written in bold), and grey cells to unknown determinants. Within each category, annotations are ordered by increasing minimum p/q-values, corresponding to the lowest p/q-value found for each annotation before deduplication (p/q-values are reported only for the most significant annotations). For each method, the annotation with the lowest p/q-values is underlined. The execution time and memory load (in Gigabytes) are shown in the header (see also Supplementary Table  S2 ).