dataesr/scientific-paragraphs-categorization huggingface dataset:
Health Data Model Health data are stored in a PostgreSQL [37] relational database. A generic and very adaptable physical EAV data model  [34, 38]  is used to integrate the data. This model structures the information in terms of objects, attributes, and relationships and thus defines an underlying entity-association modeling of the data. It enables the preservation of any original conceptual organization of the information without altering the physical data model and consequently maintains the desired vision of the data at conceptual level. A partial and simplified representation including the main entities and a limited number of relationships and attributes of the conceptual data model used for this study is shown in Figure  3. This model is used on a daily basis to satisfy the information needs of the different health professionals of RUH.
Semantic Annotator The semantic annotator ECMT  [20, 21, 36]  matches the natural language words and expressions to the domain knowledge concepts included in the HeTOP. Technically, the ECMT relies on the bag-of-word method for concept matching but also provides pattern-matching functionalities, in particular, to deal with negation and contextual information such as numerical values in CNs. Functionally, the ECMT is used in query building processes to match user inputs to accurate sets of concepts but plays a major role in CN indexing.
Not Only Structured Query Language Layer Owing to the considerable amount of health data that need to be retrieved and the well-known limitations of the RDBMSs in terms of scalability, a NoSQL layer was designed to interface access to all the data and improve data access performances. This layer is based on the IMDG Infinispan  [39, 40]. It is a Java NoSQL solution that uses key-value hash tables as storage structure, which allows efficient recovery of unitary data via the associated keys. Moreover, the hash tables are stored in memory and not on disk, which leverages access times. The NoSQL layer was conceived in a generic way to mirror the EAV data model used to structure health data (ie, Java object used as values in hash tables mimics the objects and relationships of the relational databases).
As an HDW can be used in various contexts (eg, health care, health research, and secondary use of health data), access and search capabilities of the full scope of those types of information must be provided. Technically, the SSE is a Boolean and entity-oriented search engine. It enables the retrieval and display of data at any of the previous clinical levels. As mentioned in section above, the NoSQL key-value store used to interface data does not provide proper querying solutions. The SSE consequently relies on a specific query language based on formal grammar. It enables the expression of queries targeting any of the different conceptual entities selected through constraints focusing on attribute values and other linked entities  [24]. The SSE is used through a Web application that enables the querying of clinical data using forms and string-based queries.
The Semantic Access to a Health Information Web Application: ASIS The SSE provides a powerful means to select data using textual logical queries. To bypass the complexity of the query language syntax, we designed a user-friendly Web application known as ASIS. It enables the retrieval of clinical data by means of a form that generates an SSE-processable logic-based query. The clinical data selection process is divided into 4 numbered steps clearly identifiable on the graphical interface. Step 1 comprises building a set of constraints related to any desired entity of interest as patient, diagnosis (DRG), biological tests, stays, procedures, records (CNs), drugs, and medical devices (see Figure  4 ). Constraints are built via (1) the choice of the entity of interest; (2) the choice of the targeted metadata of this entity as date of birth (patient), gender (patient), type of biological test (biological test), date (eg, procedures, biological tests, and stays), and coding (eg, diagnosis, records, and procedures); and finally, (3) the entry of the inputs corresponding to the chosen entity and metadata as male/female for the gender metadata of a patient constraint and the desired numeric value for the biological test constraint.
Not Applicable and Instruction Level This represents criteria that either does not connect to the medical domain or that corresponds more to instructions than real requirements, for example, patient participating in another clinical trial and contraception will be required during the treatment. A total of 6 types of source of information were identified: (P) Patient structured data as age and gender, (D) DRG data corresponding to structured diagnosis coded with the 10th revision of the ICD and related health problems, (S) stay data and other organizational structured data as medical units, (B) biological structured data, (N) CN unstructured data as full-text and/or automatic indexing including drug data, and (I) for information that is not within the scope of RUH IS. Finally, the different obstacles or limitations that lower the effectiveness of the search were recorded for each atomic search directive and were distributed among 6 categories: (o) for search directives that are free of any obstacles, (d) for data obstacles corresponding to inconsistently provided or insufficiently accurate data from the IS, (s) for difficulties to perform an accurate search in CN or DRG data as complex information search, (t) for technical limitations of the system as chronological querying handling or search for quantitative values in CNs (partially implemented), (c) for subjective and/or generic criteria implying the interpretation or value judgment of a health professional, and (e) when it is necessary to meet the patient to complete the criteria.
], transaminase and/or alkaline phosphatase levels twice the normal level of the laboratory" (DRGs and biological data) or men aged 18 to 70 years or women aged 18 to 70 years in menopause (patient data and CNs). From a holistic point of view, we observed that DRGs (D) and CNs (N) were the 2 major sources of information used. As stated before, both were involved in the search strategy of approximately 38% of criteria (58% if taken together). The support of the criteria by the system decreased as the exploitation of CNs (N) took precedence over DRG data (D). In this study, the search within CNs (N) were performed through both full-text and semantic searching. As far as semantic searching is concerned, the 11,928,168 CNs (N) of RUH have been indexed using ECMT over 55 terminologies available in French from the HeTOP server.
These CNs were first collected from the RUH IS in their original format (ie, as Microsoft Word files). The raw text was then extracted from these Word documents and stored in simple text files that were provided as input to the ECMT semantic annotator. After some performance optimizations of this annotator, the indexing of all the CNs could be completed in slightly less than 24 hours on a machine equipped with 1 TB of RAM and 144 cores. The insertion of the annotations in the database was done separately. Indexes allowing retrieval were also generated. The indexing process resulted in a total of 5,043,731,628 annotations. Some of the most redundant concepts were found clinically irrelevant, and a manual filtering process was applied based on the top 5000 most frequent medical concepts (eg, the 27 million annotations with "university hospital" were considered as irrelevant as the information was present elsewhere in the SHDW).
A total of 2,087,784,055 annotations were retained after the filtering process. This set of semantic annotations served as a basis for the SSE in the semantic retrieval process. The search accuracy obstacles (s) category represented 20. 2% of all obstacles (32/158), and 84% (27/32) of these obstacles were attributed to CN search (N). However, the exploitation of the semantics (ie, synonyms, hierarchical, and semantic relationships) through the automatic indexing of CNs (N) by the ECMT and the ability of the SSE to combine multiple search directives (using Boolean operators) enabled a broad search support of 25 criteria out of 95 (25/95, 26. 3%). Even when postfiltering was required, the system could be used effectively as a prescreening tool. For instance, the search for the criterion patients with severe heart failure (including New York Heart Association or NYHA Classes III and IV) was done through the search for heart failure in DRG data (D) and the search for NYHA Classes III and IV in CN data (N).
Separately, it resulted in 11,880 diagnoses and 3311 CNs. The combination of both search directives into a single search enabled the prescreening of only 36 patients. Data inconsistency (d) was also a major challenge, as 37 obstacles out of the 158 obstacles (37/158, 23. 4%) were of that type and found across different sources of information including 10 of those (10/37, 27%) for DRGs (D) and 6 of those (6/37, 16%) for stays (S). Many data are sparsely recorded in the IS even outside CNs (eg, weight of the patient as structured data for each stay or S, diet plan in CNs, and hypersensitivity to substances in DRG data or D). This lack of consistency of information tends to explain the focus on CNs (N) of inaccurately supported (Inaccurate level) and nonsupported (None level) criteria. In practice, these criteria suffer from the association of concurrent obstacles, often including a data consistency obstacle (d).
With regard to efficiency, the NoSQL layer used to access the data gave querying performances that were considered extremely satisfactory. On the basis of the data of 250,000 patients, each of the search directives used for this study took less than 2 seconds. As far as the POC integrating the entire patient dataset (1. 8 million patients) is concerned, similar performances were observed except for some specific queries targeting and returning huge amounts of biological tests which exceeded 1 min.
Notably, the system allows the search to be conducted in an iterative manner by visualizing the search of each entity before aggregating all of them into a comprehensive and coherent search. In addition, the underlying powerful query language used by the system makes the querying of entity-based co-occurring events more generic and more intuitive (ie, searching several events occurring in the same stay, hospitalization, and medical units)  [23, 24]. In contrast, that kind of functionality is usually proposed through user-friendly but predefined and specific forms (eg, STRIDE  [14]  and i2b2  [25, 26] ). One of the fundamental aspects of SHDW is the semantic description of the health information. This was achieved with the help of many health T&Os provided by the HeTOP. The ECMT semantic annotator notably enabled to automatically annotate the 11.
9 million CNs, and thus, provided a semantic access to these CNs despite the difficulties to access unstructured information contained within CNs. A bunch of semantic annotators have been proposed for English texts. Recently, Névéol et al  [41]  performed a literature review on NLP tools in health in languages other than English. In this study, French was the most studied language, followed by German and Chinese. Nevertheless, most of the existing semantic annotators usually extract concepts from the Unified Medical Language System (UMLS) Metathesaurus (eg, MetaMap  [12] )  [42]  or from mainly English T&Os such as the SNOMED-CT terminology (eg, Text Analysis and Knowledge Extraction System, SNOMED-CT and RxNorm  [43] , and the National Center for Biomedical Ontology Annotator  [44] ). French is little represented in the UMLS  [45].
The HeTOP includes only 17 KOSs of the 2017 edition of the UMLS. However, the UMLS only manages 11 resources providing concepts in French, and among the 978,233 concept unique identifiers of the UMLS included in the HeTOP, only 143,762 (143,762/978,233, 14. 7%) concepts in French originate from the UMLS. In contrast, the HeTOP provides access in French to 428,854 of them (428,854/978,233, 43. 8%; almost 3 times more than the UMLS). The vast majority of HDWs are based on RDBMS (eg, i2b2, STRIDE, DW4TR, SMEYEDAT, and Dr Warehouse). In contrast, the system proposed in this study relies on a NoSQL solution that overcomes the limitations of RDBMS as far as the scalability of data is concerned.
Nevertheless, since 2018, our team has engaged new research on the semantic annotator ECMT to investigate the development of a hybrid approach between bag-of-words algorithm and word embeddings. Temporal and chronological aspects are a topic of interest of many IR systems and particularly relevant to IR in clinical data  [15]. For instance, DW4TR fully integrates temporal aspects to data modeling by the mean of 3 types of temporal information (eg, static, events, and intervals). Together with clinical and biological data, temporal information is 1 of the 3 dimensions of the 3-dimensional representation of health data in DW4TR. Temporal querying (ie, querying data occurring at a definite moment in time) can be achieved by the underlying search engine of the SHDW and its associated specific query language, but the ASIS Web interface still needs to be enhanced to provide specific forms able to generate the entire set of proper string-based queries.
In contrast, the querying of chronologically co-occurring events (ie, searching events occurring before, after, at the same time, or within a definite time frame compared with another) is not well supported. Our department is currently discussing generic technical upgrades of the SSE that will enable us to overcome those limitations and also offer powerful functionalities beyond the scope of time handling. One of the major drawbacks of NoSQL layer used for data access (ie, Infinispan), and more generally, of many in-memory key-value stores, is that no comprehensive query language is provided as opposed to the SQL for RDBMS. Complex querying capabilities must be fully implemented from the basic application programing interface (ie, obtaining and removing a value of a specific key) proposed by this kind of solution.
A Modelling Language for Value Network Representation The modelling component was developed on a conceptual level as a modelling language dedicated to PSS models and, on a pragmatic level, as a computer-based modelling tool called PS3M (PSS Scenario Modeler). PS3M offers 9 complementary views to support PSS design process  [17, 18]. The so-called 'Scenario view' is dedicated to model PSS collaborative value networks, using three major modelling constructs: -PSS Offer: each alternative value network is associated to a specific Offer, including the specification of PSS components together with the characterization of PSS selling contract (The Offer view further details the precise elements of the offer). -Actor Roles: each value network gathers a set of Roles, with the Role specifying the responsibilities and activities taken in charge by some actor of the value network.
Phylogenomics seeks to use next-generation data to robustly infer an organism's evolutionary history. Yet, the practical caveats of phylogenomics motivates investigation of improved efficiency, particularly when quality of phylogenies are questionable. To achieve improvements, one goal is to maintain or enhance the quality of phylogenetic inference while severely reducing dataset size. We approach this goal by designing an optimized subsample of data with an experimental design whose results are determined on the basis of phylogenetic synecdoche -a comparison of phylogenies inferred from a subsample to phylogenies inferred 40 from the entire dataset. We examine locus mutation rate, saturation, evolutionary divergence, rate heterogeneity, selection, and a priori information content as traits that may determine optimality.
Our controlled experimental design is based on 265 loci for 102 blaberoidean cockroaches and 22 outgroup species. High phylogenetic utility is demonstrated by loci with high mutation rate, low saturation, low sequence distance, low rate heterogeneity, and low selection. We found that some phylogenetic information content estimators may not be meaningful for assessing information content a priori. We use these findings to design concatenated datasets with an optimized subsample of 100 loci. The tree inferred from the optimized subsample alignment was largely identical to that inferred from all 265 loci but with less evidence of long branch attraction and improved statistical support. In sum, optimized subsampling can improve tree quality while reducing data collection costs and yielding 4-6x improvements to computation time in tree 50 inference and bootstrapping.
INTRODUCTION The current "postgenomic" era of phylogenetics is characterized by large datasets and increased efforts to maximize their usage  (Bravo et al. 2019 unpublished data). Yet, while more data increase potential phylogenetic information  (Simon et al. 2018) , they can also increase data artefacts and bias  (Breinholt and Kawahara 2013; Dell'Ampio et al. 2014; Brown and Thomson 2017; Shen et al. 2017; Platt et al. 2018). In this study, we combine two approaches, data targeting and phylogenetic subsampling, aimed at improving phylogenomic inference by limiting confounding signal. Data targeting seeks to only utilize positively informative data, which requires identifying features of genetic loci that are efficient in recovering reasonable phylogenetic relationships with confidence (e.
2012; Edwards 2016; Evangelista et al. 2018; Simmons et al. 2018; Simon et al. 2018). We test six features suspected to affect the accuracy of phylogenetic inference, including: mutation rate, mutation saturation, pairwise sequence distance, among site rate heterogeneity, selective pressure, and a priori phylogenetic information content. Phylogenetic subsampling has commonly been used to assess clade support or the effect of missing data  (Edwards 2016)  but it has rarely been applied to search for the most optimal tree. Despite the trend of ever-increasing dataset sizes in phylogenetics, there are yet advantages to utilizing subsamples of data  (Bravo et al. 2019 unpublished data). An alignment subsample can contain a higher density of information content than whole alignments  (Edwards 2016).
From then on, a subsample of a dataset created for concentrating signal and excluding noise could be referred to as an optimized subsample. Such subsamples are not only improved in their data quality, but assist in improving efficiency of nearly every step of a phylogenetic pipeline. To evaluate the optimality of a subsample of empirical data, we propose the phylogenetic synecdoche criterion. We define phylogenetic synecdoche as: a phylogenetic pattern inferred from a subsample that is reproducing the pattern inferred from a whole. Phylogenetic synecdoche occurs when a loss in data abundance does not result in a loss of signal, which would only be expected to occur in optimized subsamples. Of course, any two phylogenetic inferences will rarely yield the exact same tree (even if the datasets are almost identical;  Shen et al.
2017) , particularly for a dataset with a large number of samples. Without some guideline on an arbitrarily defined topological distance below which synecdoche is demonstrated, subsamples can be experimentally compared and ones that yield trees with lower distances to the full data tree are more optimal. Once we have demonstrated that a subsample of data is optimized we can use that subsample to infer a new tree. We would logically assume the subsample data tree to be an improvement to the tree inferred from all the data (e. g. ,  Cox et al. 2014; Shen et al. 2017 ). Now we can interpret the differences between the subsample tree and the full data tree as potential improvements instead of suboptimal, as we did before establishing locus feature optimality. Figure  1  provides a conceptual visualization of the entire workflow.
By identifying optimized subsamples under the criterion of phylogenetic synecdoche, we attempt to recover a robust phylogeny of blaberoidean cockroaches. Blaberoidea is a 260 Myr old clade  (Evangelista et al. 2019 ) comprising ~3600 of the ~7500 species of Blattodea  (Beccaloni and Eggleton 2013). The major relationships of Blaberoidea were recently investigated in a phylogenomic study  (Evangelista et al. 2019)  but taxonomic sampling was limited. The major lineages of Blaberidae and many other splits occurring in the last 100 MY were not sampled  (Evangelista et al. 2019). Studies that included denser taxon sampling recovered unstable topologies  (Legendre et al. 2015; Legendre et al. 2017; Bourguignon et al. 2018; Evangelista et al. 2018). We combine previously published phylogenomic datasets with 265 genomic loci for 90 newly sequenced species of blaberoidean cockroaches.
Quality filtered reads are available on NCBI GenBank (SRP155429). We aligned each locus in MAFFT v. 7. 3  (Katoh and Standley 2013; options:  -retree 4 -maxiterate 10 -adjustdirection) and then trimmed from the edges to eliminate leading or trailing sites missing >80% of data. A second, more thorough, alignment was conducted in MAFFT v. 7. 3 (-localpair -maxiterate 1000), which was then adjusted to maintain consistent reading-frame. Alignments were finalized with manual adjustment in AliView v. 1. 18  (Larsson 2014)  to remove poor quality reads and correct misaligned sections. Custom scripts in Mathematica 10 (Wolfram Research, 2012) available in the package Phyloinformatica v. 0. 9  (Evangelista 2019)  were used to manage sequence files, translate sequences, trim and concatenate alignments.
Designing Test Subsamples We calculated various statistics for each of the 265 individual alignments. Site-specific evolutionary rates were calculated using a reading-frame-adjusted concatenated alignment in fastTIGER v. 0. 0. 2  (Cummins and McInerney 2011; Frandsen et al. 2015). Locus mutation-saturation was calculated as the slope of a best-fit line of Hamming distances plotted against F81 corrected distances in the R package Phangorn v. 2  (Schliep 2011). In IQ-TREE v. 1. 5, mean pairwise sequence distance and non-synonymous to synonymous mutation ratio (dN/dS) were calculated under the MG model  (Muse and Gaut 1994) , which is known to yield less biased dN/dS values than other commonly used models  (Spielman and Wilke 2015). We quantified rate heterogeneity as the number of local maxima in a frequency plot of rates after applying a Gaussian filter of scale = 25.
Finally, the methods could be confounded by long branch attraction (LBA), mistaking saturated loci for highly tree-like loci and wrongly attributing homoplasy to split support. Although our final tree does not show exceedingly long branches, all the reconstructed trees exhibited features of other long branch effects [abundant short internodes (Fig. 5  and Fig. S3. 1) and a high leafiness (Table  1 )]  (Wägele and Mayer 2007; Kück et al. 2012). While high information content loci performed poorly, fast evolving and low saturation loci converged rapidly towards the expected topology. This result contradicts the idea that saturation is a consequence of high evolutionary rate, two features that were not highly correlated (R = -0. 11; Table  S1. 1, S1. 2) in our dataset. This also contrasts with the assumption that slow evolutionary rates are assumed to be superior  (Chen et al.
Increased taxon sampling and further optimization of the dataset could further improve tree quality, particularly for recent relationships in Blaberidae. Finally, applying more robust tests of topological support such as four-cluster likelihood mapping  (Strimmer and Haeseler 1997) , other quartet methods  (Pease et al. 2018 ) and better handling rogue taxa  (Aberer et al. 2013 ) could further improve our identification of robust relationships and dark nodes.