dataesr/scientific-paragraphs-categorization huggingface dataset:
Similarly, online social networks might in some cases be easier to collect automatically. How much the networks of reported friendships or of online links correspond to or complement actual face-to-face contact networks is however not well known  [25, 34]. We have therefore asked the participating students to fill in surveys to report their friendships in the high school, and to give us access to the networks Facebook links of their Facebook friends. As in the case of contact diaries, the data suffered from low sampling rates, showing the lesser acceptability of these requests (most probably because of the added burden for students with a large number of courses to follow) with respect to the use of wearable sensors. The network of reported friendship was much less dense than the contact network, as could be expected: most declared friends had at least one face-to-face contact during the data collection, while many contacts also occurred between pairs of individuals who did not report a friendship link.
Interestingly, Facebook links yield a different picture than reported friendship ones. First, sampling issues prevent us from building a complete network of Facebook relationships. The probability that a contact is observed between two individuals, knowing that they are linked on Facebook, is also much smaller than the same probability conditioned on a reported friendship. In addition, the distribution of aggregate contact durations between individuals who are linked on Facebook but did not report a friendship to each other is much narrower than the one obtained for individuals who reported a friendship: overall, Facebook links seem to have a more 'casual' character than friendship links, in agreement with the intuition that Facebook links are easier to establish than real friendships. In conclusion, the collection of data with different methods has shown that, at least in the context of a high school in which students have a rather heavy work load (given they have competitive exams at the end of the year), the use of wearable sensors to gather data on contacts between individuals was more easily accepted than the burden of filling in contact diaries or surveys, and yielded therefore a better population sampling.
ACKNOWLEDGMENTS We are grateful to the SocioPatterns collaboration [20] for providing access to the SocioPatterns sensing platform that was used in collecting the contact data and to  Ciro Cattuto  for numerous discussions. We thank the students of Lycée Thiers in Marseilles, France, who accepted to participate to the data collection. This work was supported by the A* MIDEX project  (n  ANR-11-IDEX-0001-02 ) funded by the " Investissements dAvenir" French Government program , managed by the  French National Research Agency (ANR) , to A. B. and R. M. A. B. is also partially supported by the  French ANR  project  HarMS-flu  ( ANR-12-MONU-0018 ) and by the  EU FET  project  Multiplex 317532.
The hybrid visualization and interaction tool EarthScape is presented here. The software is able to display simultaneously LiDAR point clouds, draped videos with moving footprint, volume scientific data (using volume rendering, isosurface and slice plane), raster data such as still satellite images, vector data and 3D models such as buildings or vehicles. The application runs on touch screen devices such as tablets. The software is based on open source libraries, such as OpenSceneGraph, osgEarth and OpenCV, and shader programming is used to implement volume rendering of scientific data. The next goal of EarthScape is to perform data analysis using ENVI Services Engine, a cloud data analysis solution. EarthScape is also designed to be a client of Jagwire which provides multisource geo-referenced video fluxes.
In  (Angleraud 2014) , the author presents magHD, a visualization software who aims at using distributed storage to deal with massive amount of 3D data. The author emphasises the advantages of real time manipulation of data since the interpretation of moving images by the brain allows new features to be detected by the user. In the situational awareness domain, Luciad proposes a set of software that allows aircraft positions and sight of view, FMV videos draped on terrain or vector information such as buildings and routes to be displayed. We propose to gather some of these aspects (GIS, scientific visualization, situational awareness) in EarthScape, a new hybrid visualization and interaction tool. EarthScape is based on osgEarth, an open source terrain rendering SDK capable of reading traditional georeferenced data such as KML files.
Although many operations can be achieved with KML files  (Ballagh 2011) , advanced visualization features (volume rendering, point cloud) are implemented to propose an extensive visualization tool. The architecture of the software and its core libraries are presented first. Then, we describe the different supported sources of data. In Section 4, the visualization of volume scientific data using shader programming is presented. Section 5 deals with the touch screen capabilities of the software and some HMI aspects. In section 6, future connections with process and data services products of Exelis (ENVI Services Engine and Jagwire) is discussed. In the last section, conclusions and perspectives are drawn.
osgEarth osgEarth (http://osgearth. org) is an open source terrain rendering SDK developed by Pelican Mapping and based on OpenSceneGraph. It deals with tiled imagery, elevation and vector data from local disk or remote servers like OGC, WMS or TMS. As for OpenSceneGraph, osgEarth is a cross-platform library. There is almost no technical documentation of osgEarth. Fortunately the whole source code is available and questions can be asked to the developers in a user forum. Some parts of osgEarth are quite tricky. It took for instance a lot of time to be able to display multiple draped vector layers in the right order with good performances. Concerning the tile management, osgEarth completely manages this process and caches the remote data. However, the tile update of cache data is quite slow compared to some other solutions such as Google Earth.
Database access The GDAL plugin provides a direct access to PostGreSQL/PostGIS data tables. Using PostGreSQL/PostGIS instead of shapefiles for vector data has many advantages like combining vector data through views and SQL relational queries using PostGIS vector operators and functions. Performances are even much better than using shapefiles thanks to GIST indexing and PostGreSQL binary cursors. And finally, PostGreSQL/PostGIS might be the best solution for storing dynamic data as the refresh of vector data source has been enhanced.
Full Motion Video The application can load and display Full Motion Videos (FMV). Generally acquired by UAVs, FMV files combine image frames and localisation metadata (MISB format in our example). For now, the localisation metadata is extracted from the video file using ENVI and its FMV module, then exported to ASCII readable file. The application uses OpenCV to load and browse the video frames, and OpenSceneGraph texture mechanism to drape them on the terrain. Figure  4  shows a UAV acquisition localised on the map using the MISB metadata. Figure  5  shows this same video but with a fake localisation on the Alps. Concerning the computational cost, this function does not affect the overall good framerate of the application.
Managing scientific data When a scientific data set has to be displayed, a range of scientific values is generally associated with a color ramp. For this purpose, a function F sc has to be defined for each scientific value V s to translate it to a displayed color data V c. As OpenGL and GLSL provide a fast and easy access to the texture memory of the graphic card, the data to display is stored as a 2D or 3D texture. Textures are encoded in RGBA format, and each channel can store an integer or a float value which offers a limited range of values. This range can be smaller than the range of the scientific data to display. A first solution consists in computing the color V c = F sc (V s ) of each value of the scientific data set on the CPU and then to store the new color dataset in the texture memory.
However, it means that all V c have to be computed from V s on the CPU. In case of real-time modification of F sc , this procedure could be required to be performed for each frame. As new data set has to be transferred to the GPU, this method is not optimal in term of performances. Another approach used here is to transfer the function F sc and the scientific data to the GPU and to compute V c = F sc (V s ) on it at each frame. To be stored in texture memory (which provides a limited type of data), each scientific scalar value of the scientific dataset is converted to a vector of four 8-bit integers to be stored in a RGBA texture. The maximum Max s and minimum Min s values of the scientific data set are considered and each scalar value between Min s and Max s is mapped between [0, 0, 0, 0] and  [255, 255, 255, 255].
Cell cycle analysis Cell cycle distribution of sensitive and resistant cell lines was studied by DNA content by fluorescence-activated cells sorting and analyzed using the Cell Quest Pro software (BD Biosciences, San Jose, USA). After 48h of treatment with two different concentrations of RG7388, cells were centrifuged at 1500g for 5 min and washed twice with PBS. Cells were then fixed with 70% ethanol at 4°C overnight. Ethanol was removed and cells washed twice with PBS. 300μl of a propidium iodide, ribonuclease-containing solution were added to cells and then analyzed by FACS. Data were analyzed with FlowJo v. 7. 6. 3. software and results expressed in terms of percentage of cells in a given phase of cycle.
The ligation product was amplified with 15 cycles of PCR. Each library was sequenced using TruSeq SBS Kit v3-HS, in paired end mode with the read length 2x101bp. We generated 135 million minimally-paired end reads for each sample run in one sequencing lane on HiSeq2000 (Illumina, Inc) following the manufacturer's protocol. Images analysis, base calling and quality scoring of the run were processed using the manufacturer's software Real Time Analysis (RTA 1. 13. 48) and followed by generation of FASTQ sequence files by CASAVA.
NGS RnaSeq sequence alignment and quality control pipeline Raw RnaSeq sequences where quality controlled using a set of published tools in order to produce curated reads. Briefly, the package sickle  [18]  was used to trim raw reads of the 5' and 3' low quality bases (phred cut off 20, max trim size 30nc, package SeqPrep (St John GitHub, https://github. com/jstjohn/SeqPrep) was used to remove sequencing adaptors from the raw reads. The application of these standard packages revealed a proportion of RNA fragments of smaller size, whose R1 and R2 paired end reads were overlapping. To keep exploiting these fragments without biasing the count of reads in reads overlapping regions, we developed a home-made script (awk language) that merged the overlapping R1 and R2 and split it into new non-overlapping read sequences.
Curated read sequences were then aligned using TopHat2  [19]  on both genome and transcriptome with fragment size 175 +/-70. Following, we applied strict quality control of aligned reads, by filtering the reads whose alignment score was lower than 20 (samtools) and removing the reads with identical align starting positions that were possibly caused by PCR amplification during the creation of the RnaSeq libraries (picard MarkDuplicates at http://broadinstitute. github. io/picard/). The statistics of the number of raw, curated and aligned sequences and curated aligned sequences was that created with a home-made tool (bash, awk).
NGS allelic variant detection and annotation pipeline Positions in the transcriptome showing at least one alternative allele were detected via samtools mpileup  [20]  taking care to filter out bases with less than 20 as phred quality score. Variant Calling format information was created via bcftools view (v 0. 1. 19) of the samtools suite. Positions detected as variant in several samples (sensitive and resistance cell lines) were merged using a homemade script (bash awk). Variant positions were annotated using the Annovar tool(  [21]. Briefly, Annovar determines the type of genomic location of the variant for example exonic, intergenic, and its class (e. g. missense, nonsense), its presence in population genetics databases dbSNP  [22]  and 1000g database  [23]  and somatic variants (COSMIC, http://cancer.
sanger. ac. uk/cancergenome/projects/ cosmic). For variants in coding positions the impact of the aminoacid change in the protein domains is reported via the use of Sift  [24]  and PolyPhen2  [25]  databases. We obtained on average 135M and 120M Paired End reads respectively after sequencing and post-alignment quality controls. As a starting point of our analysis we produced a table, based on post-alignment controlled reads, collecting all genetic variants (either polymorphic or not, single nucleotides or Indels) found in sequences in at least one of the sensitive or resistant samples, of any of the three cell lines. The table resumes the variants that present at least 1 alternative allele counts in any sample, each base having sequencing quality (phred score) of at least 20, without additional filters.
The table, containing 285,999 variants (herein called Merged Variant Table ) allows checking the alternative and reference allele counts at a position, in any of the sequenced samples. We extracted from the Merged Variant table the variants detected in sensitive and resistant cells. We then included in the analysis only variants whose genotype was estimated as a nonhomozygous reference in both samples, with more than 10 reads at the variant position, localized in coding regions and of non-synonymous type. We included all variants whose positions were reported in the COSMIC database (http://cancer. sanger. ac. uk/cancergenome/projects/cosmic) and whose alternative allele frequency (AF) in the Caucasian population (CEU), as reported in 1000g, was lower than 5%. If a variant was neither reported in 1000g CEU nor dbSNP, it was included only if either SIFT or Polyphen2 HDIV functional interpretations were reported as "deleterious" for the protein.
Detection of genomic ploidy and Differential Gene Expression/IPA RnaSeq was used to estimate the ploidy of the tumor samples, where the term ploidy defines the total number of parental alleles in large genomic regions (see S1 Methods). The RnaSeq Differential Gene Expression between two NGS samples was calculated using the methodology described in S1 Methods and implemented in R language. The selection criteria for detection of Differentially Expressed Genes were based on a Fold Change greater or equal to 2. 5 and an adjusted p-value of less than or equal to 1% (Benjamini Hochberg adjustment). Data were analyzed through the use of QIAGEN's Ingenuity 1 Pathway Analysis (IPA 1 , QIAGEN Redwood City,www. qiagen. com/ingenuity) in order to predict the interactions between the genes and the potential effects on cellular pathways and networks.
Gene expression analysis identifies defects in apoptosome activity and autophagy induction as a mechanism of secondary resistance to RG7388 We also analyzed the sequencing data to identify differences in gene expression between secondary resistant and parental cell lines. We found 196, 105 and 370 genes with 2. 5X or more fold changes (adjusted p-value < 0. 01) between the parental and secondary resistant cell lines in IB111, IB115 and IB128 respectively (S2 Table ). We then applied these genes to Ingenuity Pathway Analysis software (IPA 1 ,QIAGEN Redwood City, www. qiagen. com/ingenuity). The results showed that these genes were mainly enriched in proliferative, growth and movement networks (Table  3 ). We focus particularly on the genes differentially expressed between the resistant and parental IB111 cells in order to identify some genetic and transcriptomic alterations that may compensate for the lack of TP53 mutations observed in the IB111 resistant cells.
Acknowledgments The authors thank  Roche Pharma ,  Accord and Fresenius Kabi  for provision of RG-7388, doxorubicin and gemcitabine respectively,  Pippa McKelvie-Sebileau  of  Institut Bergonié  for medical editorial services and the  Centro Nacional de Análisis Genómico (CNAG) ,  Barcelona, Spain. All authors received Grant « INCa-DGOS-Inserm 6046 ».
Thus, this paper proposes and applies JPI feature model to draw features and crosscut-features along with their associations in a JPI + FOP context that represents a 1st step for a complete JPI + FOP software development process  [3]. Clafer  [9]  is a work that presents, likes a meta-model, a mix of a class diagram and feature model for a system. Thus, Clafer represents crossed levels in the software development to add dominium knowledge in a feature model context. In addition, as one of its main advantages, Clafer offers tools to create model and for models validation. In comparison to Clafer, this article preserves conceptual modeling of feature models and extend them to support concepts of JPI for a complete JPI + FOP symbiosis. Developing tools for mode validity is part of future applied research works.
Chemical text corpora As opposed to the previously introduced corpora, a number of corpora have also been described that are more focused on chemistry and chemical entities rather than on biological aspects of chemical substances. They provided important lessons for the construction of the CHEMDNER corpus. Nevertheless they also showed crucial differences in scope, used document collections, availability (both of annotation guidelines together with the resulting corpus), format and size. Early attempts to build a chemical NER systems, due to the lack of a chemical entity text corpus, explored the use of lexical resources related to chemistry derived from the UMLS Metathesaurus, which was used for training and testing various methods  [24]. Wren published a machine learning method trained on the chemical ChemID database and used it to find chemical entity mentions in PubMed abstracts.
Narayanaswamy and colleagues described a small corpus of 55 abstracts selected by a keyword search (using as query acetylates, acetylated and acetylation) that contained also a small number of chemical names  [27]. The text corpus introduced in the article describing the ChemicalTagger system consisted in 50 paragraphs from the experimental sections of full text articles selected using a keyword search related to polymer synthesis. It is concerned with the annotation of chemical phrases rather than on chemical entity mentions and the associated link to the annotation guidelines was not functional anymore (broken link)  [28]. The ChEBI Patent Gold Standard corpus was created as a joint effort between curators of the ChEBI database and the European Patent Office  [29]. It involved the annotation of chemical entities in 40 patent documents (18,061 chemical entities, 47% of them were initially linked to ChEBI records).
This corpus is publicly available but more details on the annotation criteria and process were not released together with the corpus. This corpus was generated manually without using any software to create pre-annotations. An updated version of this corpus was also published to increase the initial mapping of mentions by using an updated version of the ChEBI database (53. 7% of ChEBI mapped chemical entities)  [30]. A recent effort carried out by both academia and commercial teams resulted in a larger corpus of 200 patents annotated with chemical information  [31]. These patents were automatically pre-annotated with chemical names and human curators revised and corrected mis-identified pre-annotations and added missing chemical mentions manually. The annotation guidelines used for constructing this corpus were partially based on the annotation guidelines that we have released for the CHEMDNER corpus, as detailed later in this manuscript.
A relevant contribution to the development of chemical corpora was provided by the authors of the Sciborg corpus  [32, 33]  and the Chemistry PubMed corpus by Corbett et al. [33, 34]  Unfortunately neither of these two corpora are publicly available, but the underlying annotation criteria shared by both datasets had a deep impact on the annotation guidelines prepared for the CHEMDNER corpus. The Sciborg corpus consisted of 42 full text chemistry research papers annotated manually with chemical compounds while the chemistry PubMed corpus by Corbett et al. consisted in an handannotated corpus of 500 PubMed abstracts selected using the query 'metabolism[Mesh] AND drug AND hasabstract'. Both corpora consisted in exhaustively annotated chemical texts done by chemists according to very detailed annotation rules (31 pages long guideline containing 93 rules, together with example cases  [33] ).
Chemical names and challenges for NER To be able to implement and compare the performance of chemical NER systems the availability of large enough manually tagged text corpora is a key requisite. It is thus not surprising that a comparative evaluation effort for this topic had not been carried out prior to the release of the CHEMDNER corpus. The intrinsic difficulty in defining annotation guidelines of what actually constitutes a chemical compound that can be linked to structural information was the main difficulty in constructing the CHEMDNER corpus. Although the International Union of Pure and Applied Chemistry (IUPAC) has defined a set of rules for the chemical nomenclature, those naming standards are not sufficiently followed in practice when examining the scientific literature  [36]. Chemistry is a research discipline with a considerable degree of specialization that can explain the encountered variability of language use between its sub-disciplines.
BioCreative task on chemical entity recognition Chemical entities of practical importance are those that can be ultimately linked to chemical structure information, rather than general vague chemical concepts. Being able to associate a given chemical compound name to a chemical structure was the central annotation criteria followed for the construction of the CHEMDNER corpus. The details on the construction of the CHEMDNER corpus will be provided in the following sections. To demonstrate its utility, the CHEMDNER corpus was used as the dataset to train and evaluate chemical NER systems that participated in a task posed at the fourth BioCreative community challenge  [11]. The BioCreative challenges are an ongoing effort to promote the evaluation and development of text mining and natural language processing software for the life sciences community  [37].
Document selection and sampling An often-underestimated aspect when constructing text corpora is the initial selection of the documents that should be annotated. Using a keyword based article selection has the risk of generating a rather narrow or biased dataset, especially when the aim is named entity recognition. In order to make sure that the NER tools developed on the CHEMDNER corpus will generalize well on any chemistry-related document we used a careful selection strategy. The used CHEMDNER document set had to be representative and balanced in order to reflect the kind of documents that might mention the entity of interest. In case of chemical entities it is essential to cover articles that show sufficient diversity of the kind of mentions expected to emerge across various chemical disciplines.