dataesr/scientific-paragraphs-categorization huggingface dataset:
Importantly, evaluation of H3K27ac and H3K4me3 ChIP-seq datasets by this approach revealed large differences in reproducibility performance of the peak callers. PeakRanger and BayesPeak systematically performed worst, while MeDiChISeq versus any other peak caller gave the best scores in either biological replicate. Note that the particular IDR patterns observed for H3K4me3 in an inter-replicate comparison (right panel in Figure  2A ) was not seen when the inter-peak caller performance for each replicate dataset was compared (right panels in Figure  2B ), suggesting that it results from significant divergence between the "biological replicate datasets". Overall these analyses showed that MeDiChISeq systematically identified the most reproducible events among biological replicates and peak caller annotations, thereby revealing the high sensitivity and reliability of this approach.
MeDiChISeq's specificity in the context of curated benchmark datasets In addition to identifying the highest number of true binding events (sensitivity), a good peak caller algorithm is expected to produce the lowest amounts of false positives (specificity). As indicated above, IDR studies are expected to identify a transition from signal to noise when evaluating peak callers' binding sites reproducibility. In this manner, the highest number of significant binding sites at the lowest IDR, as observed in the case of MeDiChISeq performance (Figure  2 ), reflects a high degree of sensitivity and specificity, at least in the context of reproducible binding site discovery in biological replicates or when comparing different peak callers' performance per ChIP-seq dataset. Previous studies have evaluated peak caller performance to distinguish false positives from "true" binding sites by using a manually curated collection of binding regions (and "false" enrichment sites) that cover typical Figure  2  Irreproducibility Discovery Rate (IDR) assays to compare peak calling algorithms.
(A) IDR assay comparing biological replicate datasets (see text for details). Note that for H3K4me3, MeDiChISeq continues to find significant common events in compared replicate datasets with slowly increasing IDR while the IDRs sharply increase for the three other peak callers around 5,000 significant peaks commonly identified in the replicates. (B) Similar reproducibility analysis but performed by pairwise comparison of binding site predictions by the different peak callers indicated at the left ("virtual replicates"). This approach reflects the concordance in binding site identification between two peak callers. Note that in all illustrated IDR assays, MeDiChISeq predictions have the lowest IDR levels for the highest number of significant binding sites. In (A) and (B) dashed lines indicate IDR levels of 0.
1; i. e. a reproducibility level of 90%. variation in peak size and appearance for the TFs NRSF, SRF and MAX  [14, 17]. We have evaluated MeDiChISeq in the context of this benchmark, demonstrating in all three cases a high percentage of true binding site recovery (> 80%) and low false discovery rate (Figure  3 ). It is worth mentioning that, while its overall FDR performance is similar to that of MACS, MeDiChISeq generally retrieves more true binding sites. Furthermore, while using a background control dataset affected the false discovery rate of all other evaluated peak callers, MeDiChI-Seq performed equally well in identifying true binding events in presence and absence of this control. This is most likely due to the fact that the binding site identification relies on a pre-computed kernel and is thus less affected by artifactual enrichment events.
This performance is well illustrated in the case of NRSF datasets, Figure  3  Specificity and sensitivity of MeDiChISeq peak predictions compared with other algorithms. A manually curated ChIP-seq benchmark dataset for MAX, SRF and NRSF  [17]  was used to assess the percentage of true site recovery by the indicated peak calling algorithms relative to the false discovery rate (FDR). For the two upper panels no background control sample was used during peak calling. The two lower panels show the same analysis but with considering the corresponding background control dataset in the analysis. Note that PeakRanger performance is not illustrated in the upper panels, as this algorithm does not perform IP binding site assessment without background control. In cases where the tracings overlap, an arrow indicates the point of maximal recovery.
where in the absence of background control dataset, MACS and MeDiChISeq present a maximal percent of recovery of 90% but accompanied by high FDR levels (>0. 5). Importantly, the incorporation of background control dataset in the analysis reduces the FDR levels but the percentage of true site recovery is also compromised for MACS (less than 80%), while MeDiChISeq manages to keep the percentage of recovery levels up to 90% even under these conditions (FDR < 0. 4).
Peak caller performance relative to sequencing depth The rapid technological progress in the field of massive parallel sequencing provided over the years sequencing platforms with continuously increasing sequencing depths. In fact, while the first versions of the Illumina Genome analyzer had sequencing capacities in the range of several millions reads, the latest Hiseq2000/2500 versions provides more than 3 billion reads per flow cell. Following this continuous progress, the number of sequenced reads used per ChIP-seq assay has increased considerably. In fact, while early ChIP-seq assays generated <4 million total mapped reads (TMRs), current datasets comprise >20 million TMRs. Importantly, increasing the sequencing depth increased also the number of discovered binding sites  [5, 22, 23]  but only a few studies evaluated the peak caller performance for conditions with varying sequencing depths.
Obviously, increasing the sequencing depth will increase both the signal and the noise levels, which could potentially affect peak caller performance. To address this question, we created a high density ChIP-seq dataset by combining the datasets of the two biological CTCF replicates. This meta-profile comprised >36 M TMRs and was used for profile reconstruction from subsets generated by random sampling (80%; 60%; 40%; 20%; 10%; 5%) (Additional file 9A). To perform IDR evaluation, pseudo replicates were produced by two independent random samplings. As expected, the CTCF profile reconstructed from <2 M TMRs had unacceptably high IDR levels (Figure  4 ). In this condition MeDiChISeq and PeakRanger performed worst, followed by MACS and BayesPeak. This is readily explained by the fact that both MeDiChISeq and Peak-Ranger evaluate peak shapes, which are highly variable at low TMR levels (see pseudo replicates at 1.
Conclusions Here we present MeDiChISeq, a model-based deconvolution approach to assess binding events and chromatin marks from ChIP-seq datasets. We have previously used an early version of this methodology for mapping the chromatin localization of RXRα and RARγ nuclear receptors  [24] , as well as for profiling RNA polymerase II  [16]. This report describes the implementation of MeDi-ChI -originally developed by David Reiss to evaluate ChIP-chip profiles  [15]  for the analysis of datasets generated by massive parallel sequencing. From the conceptual point of view, this methodology applies a different rationale to define an enrichment event. In contrast to other peak detection algorithms, MeDiChISeq uses the binding pattern properties, inhe-rent to the ChIP-seq profile under study, to define enrichment and background characteristics.
CAD/LCA Integrated Tools and Methodologies Based on Feature Technology. Bonvoisin and Thiede describe a part, in CAD systems, as a 3D model, composed of a set of features which describe the part's geometry. It is used for the realization of a process plan which is defined by a set of operations. An operation is a treatment unit of one or many features by a machine. A feature can be realized by one or many operations  [13]. Mäntylä et al. show that features are generally expected to form a basis for linking CAD with downstream manufacturing applications and for organizing databases for design data  [14]. As described in ISO 14040, an LCA requires several iterations between goal scope definitions, inventoryanalysis, and impact analysis  [15]. Hence, Life Cycle Inventory (LCI) is strongly linked to CAD features, essentially in retrieving necessary data.
Success is obtained when data extraction and migration between two different types of software are carried out; thus for a digital tool, data exchange is necessary. Marosky  [22]  explores these works and presents a structure of an algorithm that allows mutual data transfer between CAD (SolidEdge) and LCA tool (SimaPro). This transfer is based on extracting data from a product CAD model which consists of assemblies, subassemblies, parts, and features. The objective is to evaluate the environmental impact of a component as well as the whole product. In a previous paper  [23] , we proposed a simple ecodesign tool by integrating CAD and LCA. Geometric characteristics of a CAD model were analyzed to estimate their environmental influences during all the phases of a product life cycle. The tool consists of a special geometric data base containing the impacts of all existing design technical solutions of a product; critical geometric features and forms concerning environmental impact are identified, after which designers can select the optimal solution in terms of feasibility and ecology.
Prototype Tools Based on Features for CAD/LCA Integration. Realizing a CAD/LCA integrated tool is an aim of researchers to help nonexpert designers to ecodesign products in terms of the environment. In the literature, we have identified prototypes of tools based on the extraction of CAD data from features in order to evaluate environmental impacts of products, such as the "DEMONSTRATOR" developed by Mathieux et al. [28]. This prototype tool binds CATIA (a CAD software) and EIME (an LCA software). CATIA is connected to a Product Life Management (PLM) system that extracts all data provided by different used features. The established system then selects necessary LCA data and transfers them to EIME to calculate and show the environmental impact. Following Jain  [27] , we deduce that the use of the integration between PLM and CAD systems is useful to develop our approach.
Jain presents a plugin (Eco-fit) designed for 3DSmax using the Eco-indicator99 (EI99), an ecoassessment methodology and database designed to evaluate CAD-based products. A screenshot of this tool appears in Figure  3. There is also "EcoCAD" presented by Cappelli et al. [29] , which is based on the analysis of the tree structure of a CAD project composed of assemblies, subassemblies, parts, and features. The lack of these prototypes is their dependence on tools used in the authentication of their proposed methodologies. Like the module implemented in SolidEge, Abad-Kelly et al. [30]  also connected Solidworks (CAD) and SimaPro (LCA) using macros. Computer aided sustainable tool "CAST Tool" presented by Morbidoni et al. [26]  tried to overcome this gap by providing a prototype with an open access database, allowing products to be evaluated regardless of the CAD or LCA systems used (cf.
screenshot Figure  3 ). This work is compared essentially to the sustainability module of the commercial tool "Solidworks Sustainability"  [1]. Benefits are essentially due to selecting more than one production process, introducing lifecycle scenarios and extracting the right amount of geometrical and nongeometrical data from the CAD data structure and PLM databases. "CAST Tool" overcomes many weaknesses such as the single scenario introduced where the evaluation of different possible scenarios is available. However, the evaluation is postmodeling that is realized on a final part where modifications are difficult to implement, especially when it is a complex part. This explains the need for a realtime evaluation tool. A real CAD-LCA integrated tool is also needed. In the following section, some of the existing commercial tools are presented.
Tools Based on Features for CAD/LCA Integration. Commercial CAD-LCA integrated tools are essentially based on data exchange between the two different systems Figure  4 : Screenshots of "solidworks sustainability" (a)  [1]  and "sustainable minds" (b)  [31] : the CAD-LCA commercial software tools. (CAD and LCA). Output data from the CAD system represents the input data for the LCA system. This data is essentially information provided from different types of features attributed by the designer. "EcologiCAD" is one CAD-LCA integrated tool which aims at the environmental evaluation of virtual product prototypes. It is developed by Leibrecht  [32]. It is based on a client-server concept, where the information model represents the server. The information model also provides functionality to further applications via an application programmable interface (API).
"SolidWorks Sustainability 2010"  [1] , developed by Dassault Systems, allows environmental assessment in real time once a feature has been attributed. It evaluates the environmental impacts associated with the material and manufacturing selections chosen by the designer and is most applicable for detailed design, as complete dimensions and tolerances are required for the simulation. Hence, to estimate the potential ecological footprint for another product with the same design requirements, it is necessary to provide a new full-scale CAD model. Sustainable minds provide a smooth Graphical User Interface (GUI). Its advantage relative to the other software is having the ability to see tradeoffs between different designs of a product. It can be used at all stages of Conceptual Design, based on the extracted information from attributed features.
For this, software tools such as "Eco-Audit" have been developed. This tool is mostly relevant for detailed design. The advantages of this software are that it is possible to explore alternative options relatively easily in the design stages, and it can apply to 19 material processes. Granta Design [34] and Autodesk Inventor CAD software are integrated. In the same way, Granta Design developed "Eco Materials Advisor" where it is possible to propose similar alternative materials. CES selector is also a software package developed by Granta Design and Cambridge University  [35]. It is a material and process selection software using a systemic procedure based on design constrains (constraint features). A screenshot of this tool is presented in Figure  5.
Even if proper names play a central role in natural language processing (NLP) applications they are still under-represented in lexicons, annotated corpora, and other resources dedicated to text processing. One of the main challenges is both the prevalence and the dynamicity of proper names. At the same time, large and regularly updated knowledge sources containing partially structured data, such as Wikipedia or GeoNames, are publicly available and contain large numbers of proper names. We present a method for a semi-automatic enrichment of Prolexbase, an existing multilingual ontology of proper names dedicated to natural language processing, with data extracted from these open sources in three languages: Polish, English and French. Fine-grained data extraction and integration procedures allow the user to enrich previous contents of Prolexbase with new incoming data.
The data contained in these sources are partly structured, which increases their usability in automatic text processing. In this paper our starting point is Prolexbase  (Krstev et al. 2005; Tran and Maurel 2006; Maurel 2008) , an open multilingual knowledge base dedicated to the representation of proper names for NLP applications. Prolexbase initially contained mainly French proper names, even if its model supports multilingualism. In order to extend its coverage of other languages we created ProlexFeeder, a tool meant for a semi-automatic population of Prolexbase from Wikipedia and, to a lesser extent, from GeoNames. Figure  1  shows the data flow in our Prolexbase population process. The three main data sources are: (i) Polish, English and French Wikipedia, (ii) Polish names in GeoNames, (iii) Polish inflection resources in Translatica, a machine translation software.
Automatically selected relevant classes in Wikipedia and in GeoNames are manually mapped on Prolexbase typology. The data belonging to the mapped classes are automatically extracted and their popularity (or frequency) is estimated. Inflection rules are used to automatically predict inflected forms of both simple and multi-word entries from Wikipedia. The resulting set of candidate names is fed to ProlexFeeder, which integrates them with Prolexbase in two steps. Firstly, a candidate is automatically checked to see if it represents an entity which is already present in Prolexbase. Secondly, the entry, together with its translations, variants, relations and inflected forms is manually validated by an expert lexicographer. The motivation behind Prolexbase is not to represent as many available names as possible, like in the case of other large automatically constructed ontologies such as YAGO  (Suchanek et al.
Wikipedia Prolexbase GeoNames manually validated, incremental resource dedicated to NLP. This implies: • A rather labour-intensive activity, thus a reduced scope of the result. This requires a definition of appropriate selection criteria that allow us to retain only the most relevant, popular and stable names. In this paper we exploit criteria based on: (i) the popularity of the corresponding Wikipedia articles, (ii) systematic lists of some major categories found in GeoNames. • Thorough data integration techniques allowing us to avoid duplication of data during an enrichment process (as opposed to extraction from scratch) in which previously validated data can be merged or completed with new incoming data. • NLP-targeted features, particularly with respect to highly inflected languages such as Polish, which are non-existent in traditional ontologies.
Prolexbase was designed with such languages in mind, notably Serbian  (Krstev et al. 2005) , which belongs, like Polish, to the family of Slavic languages. This allows us to account for rich word formation, variation and inflection processes within the same model. [ 191 ]  Prolexbase might correspond to the kernel NE lexicon, i. e. the common shared NE vocabulary appearing in texts of differents dates, types and subjects, as opposed to the peripheral NEs used infrequently and in domain-specific jargons. As suggested by  Saravanan et al. (2012) , handling peripheral NEs might then rely on their co-occurence with the kernel NEs. This paper is organized as follows. Section 2 summarizes the major features of Prolexbase and of the input data sources relevant to the population process. Section 3 describes data integration issues.
2. 1 Prolexbase Prolexbase  (Krstev et al. 2005; Tran and Maurel 2006; Maurel 2008 ) offers a fine-grained multilingual model of proper names whose specificity is both concept-oriented and lexeme-oriented. Namely, it comprises a language-independent ontology of concepts referred to by proper names, as well as detailed lexical modules for proper names in several languages (French, English, Polish and Serbian being the best covered ones). Prolexbase is structured in four levels for which a set of relations is defined. The metaconceptual level defines a two-level typology of four supertypes and 34 types, cf. (Agafonov et al. 2006 ): 1. Anthroponym is the supertype for individuals -celebrity, first name, patronymic, pseudo-anthroponym -and collectives -dynasty, ethnonym, association, ensemble, firm, institution, and organization.
Meronymy is the classical relation of inclusion between the meronym (Samuel Beckett) and the holonym (Ireland, understood as a collective anthroponym). Accessibility means that one referent is accessible through another, generally better known, referent  (Tran and Maurel 2006). The accessibility subject file with 12 values  (relative, capital, leader, founder, follower, creator, manager, tenant, heir, headquarters, rival, and companion)  informs us about how/why the two pivots are linked (The Magic Flute is accessible from Mozart as creator). The linguistic level contains prolexemes, i. e. the lexical representations of pivots in a given language. For instance, pivot 42786 is linked to the prolexeme Italy in English, Italie in French and Włochy in Polish. There is a 1:1 relation between pivots and prolexemes within a language, thus homonyms (Washington as a celebrity, a city and  [ 193 ]  Figure  2 : Extract of the intended contents of Prolexbase with four levels and three prolexemes.
Wikipedia Wikipedia is a constantly growing project grouping a set of opensource online encyclopaedia initiatives run by the MediaWiki software and filled with content by volunteer authors. Polish is the sixth largest 1 Note that Prolexbase terminology is non-standard with respect to WordNet  (Miller 1995). Notably, in Prolexbase hypernyms of entities referred to by proper names are metaconcepts, entities are concepts (represented by pivot identifiers), and the inflected forms of names are called instances. In WordNet, hypernyms of entities are concepts while the surface forms of the entities themselves are called instances. See also Section 6 for a discussion on the instance-to-concept mapping which we perform, as opposed to the concept-to-concept mapping standard in the related work. [ 195 ]  Wikipedia project with its 900,000 articles.
Pivot selection Data extracted from Wikipedia represent concepts and relations which may already be present in Prolexbase. Thus, the main challenge is to preserve the uniqueness of concepts, i. e. to select the proper (language-independent) pivot if the current concept is already present in Prolexbase, and to create a new pivot otherwise. Working on three languages simultaneously greatly increases the reliability of this process. Recall that Prolexbase originally contained mostly French data. If new Polish or English data were to be examined separately, few hints would be available as to the pre-existence of adequate pivots. For instance, if Prolexbase already contains the prolexeme Aix-la-Chapelle with pivot 45579, it is hard to guess that the incoming Polish prolexeme Akwizgran should be attached to the same pivot.
For instance, the main entry in Example (1) ('Great Britain'), is shorter than its alias ('United Kingdom of Great Britain and Northern Ireland') in Wikipedia, conversely to Prolexbase, where the most complete name is usually chosen as the prolexeme. Thirdly, a common URL is a strong evidence of concept similarity. Consider Table  1  showing a sample set of Wikipedia data resulting (except the pivot attribute) from the preprocessing described in the preceding sections. Figure  3   [ 204 ]  in Prolexbase such that, for each language l (PL, EN or FR), the data linked with p (if any) are similar to e. The similarity between e and p grows with the decreasing value of the distance function, which compares the lexemes, aliases, URLs and types of e and p in the given language. We assume that e is likely to be similar to p in any of the following cases: (i) e and p share the same lexeme in a particular language (line 15), (ii) e is an alias of p (line 16), (iii) e and p share the same URL (line 17).