dataesr/scientific-paragraphs-categorization huggingface dataset:
ICA served as a principal machine learning method for discovering functional subsystems involved in the response of Ewing sarcoma cells to the induction of the chimeric oncogenic transcription factor EWSR-FLI1  [62]. In this case, ICA was applied to the temporally resolved single cell RNA-Seq dataset and revealed the existence of few tens of transcriptional programs activated or inhibited after the controlled induction of the oncogene. Quite remarkably, one of the independent components was clearly associated with the functional subsystem composed of the direct targets of EWSR-FLI1, and it was distinguished from its indirect downstream effects such as cell cycle induction (see Figure  6c ). Other functional subsystems reacting to the variations of the experimental conditions such as hypoxia or regulation of glucogenesis, were recapitulated in individual ICs.
Identification of the functional subsystems from the cell line experiments were further used in order to characterize the patient-derived xenografts (PDXs) of Ewing sarcoma, at single cell level. In principle, ICA is the methodology able to exploit strong non-Gaussianity in the multidimensional distributions formed by single cells in the space of omics profiles. However, in order to optimally use this potential, one probably needs to identify the most suitable non-linearity functions, for each particular type of single cell measurements, and take into account the nature of the multivariate distribution of points in data space. Recently, a matrix factorization-based method ZINB-WaVE was adapted to the single cell RNA-Seq measurements, using the model of zero-inflated negative binomial distribution (ZINB)  [88].
Multi-Omics ICA Applications in Cancer Research The majority of published works on applying ICA in cancer research deals with transcriptomic data. This is connected in part to the relatively high abundance of such data type from collections of bulk tumors, and in part to the availability of bioinformatics tools helping to interpret the obtained components (such as Gene Set Enrichment Analysis). Yet another aspect is that transcriptomic data are better connected so far to the clinical questions such as defining molecular subtypes of tumors. However, applying ICA should not be limited to only one level of omics profiling, and there is a lot of potential in applying it to several levels of molecular description. The multi-level datasets become increasingly available in the cancer biology. The levels of molecular description can be gene copy number profiles, binary mutation profiles, measured mutational signatures, measured total expression of genes or spliced mRNA isoforms or non-coding genes such as microRNAs, DNA methylation or histone mark modification profiling, protein or protein phosphoforms relative abundances or some other less frequently used omics types.
Identification of functional subsystems can be facilitated through the use of several data types, since the adaptation process is frequently expected to span several levels. As a good example of such a multi-omics dataset, one can cite recent work on comprehensive characterization of medulloblastoma  [89]. Ideally, several levels of omics profiling should be collected for the same and sufficiently large set of samples. Independent components can be then computed for each data type separately and then the identified components can be compared by computing correlations between the corresponding a k vectors (metasamples). Such an approach was recently applied in Reference  [39]  to a set of melanoma bulk samples, profiled at the level of transcriptome and microRNA expression. Similarly, in a recent study  [90] , 77 breast and 84 ovarian cancer samples, profiled simultaneously at transcriptome and proteome level, were analyzed using stabilized ICA, followed by integrating the discovered associations with clinical data and molecular pathways.
An alternative and somewhat more powerful idea consists in stacking several matrices corresponding to the different levels of omics profiling into a tensor (multi-dimensional array), in order to apply the tensorial version of ICA. In this case, ICA will be able to learn and jointly optimize the signals which can involve variables from several levels of molecular description. This requires making at least two dimensions of the data common, while the third matrix dimension indicates the data type. Typically, all molecular measurements are mapped onto the genes through application of procedures that can be non-trivial (e. g. , in the case of Chip-Seq experiments). The resulting three-dimensional measurement tensor X ijk has dimensions "number of samples × number of genes × number of data types".
For example, Xi = 4, j = 5, k = 2 element in the tensor can indicate DNA methylation level of the promoter of the gene 5 in the sample 4. Xi = 4, j = 5, k = 1 could indicate expression of the same gene in the same sample. In the case of tensor factorization, the resulting components represent matrices rather than vectors having dimensions "number of genes versus number of data types" (for metagenes) and "number of data types versus number of samples" (for metasamples). The existence of correlations among different data types within the same matrix-component indicates coupling among several levels of molecular descriptions captured tensorial ICA. Tensorial ICA was recently applied in Reference  [91]  to colon cancer dataset from The Cancer Genome Atlas (TCGA) composed of a matched subset of copy-number variation (CNV), DNAm, and RNA-seq data.
A specific implementation of tensorial ICA called tWFOBI, standing for tensorial fourth-order blind identification, accompanied by a tensorial version of whitening (W), using tensor PCA, was used to compute 37 independent components. Most of these components can be associated with the differences between normal and cancer samples, while only four components capturing correlations between CNV and gene expression, and one among them was also characterized by concomitant correlation among all three data types. Of note, the tWFOBI method showed several orders of magnitude better computational performance compared to the state-of-the-art methods developed for multi-level omics data integration (such as iCluster). Applications of ICA to data types other than transcriptomic or to several data types simultaneously remain limited; however, first applications of this approach in cancer biology are rather promising  [91, 92].
Correlations and Interactions among Functional Subsystems Defined by ICA Functional subsystems identified through ICA and fixed in the form of metagenes can be studied for their statistical relationships within a dataset, among datasets of the same kind, or among datasets that are not closely related in terms of the nature of the biological samples profiled. In the latter case, one can use ICA for studying disease-disease relationships. An example of such a relation is the phenomenon of inverse comorbidity between cancer and some other diseases, in terms of the anti-correlated activation pattern of the common functional subsystems. For example, the ICA method was exploited in Reference  [95]  in order to identify inversely associated transcriptional modules common in breast cancer and Alzheimer's disease.
In a more extensive study  [44] , 17 transcriptomic datasets (11 collected for the post-mortal brain samples of patients suffering from Alzheimer's disease and six collected for the lung cancer samples) have been analyzed using ICA. The notion of reciprocal best hit (RBH, see the methodological section of this review) was used in order to match the ICA components and define their communities. In order to detect the anti-correlation patterns among the matched components, a specific method was developed to assign an orientation of the components and, hence, the weight signs in the metagene, based on the analysis of the subset of normal control samples, in the a k vectors. This analysis confirmed previously identified comorbidity patterns based on the analysis of individual gene expression profiles (related to the role of immune system and mitochondrial metabolism) and suggested new molecular mechanisms of comorbidity between lung cancer and Alzheimer's disease such as estrogen receptor signaling pathway or the involvement of cadherins.
Another possibility for exploiting the ICA-based definitions of modules is to study the phenomenon of functional subsystem integration as a result of adaptation to stress or harmful conditions  [63]. It was shown in many studies that the correlations among the activation patterns of different functional subsystems can be more informative than the patterns themselves  [96]. ICA can deconvolute even strongly correlated signals (see Figure  2A  and the Section 2. 2 of this review). Also, it computes components which are as mutually independent as possible, but the level of dependence can be different even for subsets of samples within a single dataset. For example, one can expect that in the normal subset of samples, some of the functional subsystems will be less coupled with each other than in stressful conditions caused by more aggressive stages of tumorigenesis.
In this review, we made a comprehensive effort to mention most of the recent studies in cancer research where ICA served as the essential data analysis tool. We classified them into several common topics: data preprocessing, data dimension reduction and visualization, identification of functional subsystems and their correlations or interactions, deconvolution of cell types, data integration and meta-analysis. We also reviewed the methodological works aimed at defining the best practices of applying ICA to concrete types of omics data. Compared to the early times of applying ICA to omics datasets, today there exists a variety of implementations and improved methodologies allowing us to use the valuable idea behind ICA (exploiting the concept of statistical independence and use of higher moments of multivariate data distributions) in the best possible way.
Certain progress has been made in clarifying such important questions such as determining the optimal number of components to retain or establishing the biological significance of the extracted components. Most of the existing applications of ICA have been done so far for transcriptomic data even if the interpretation of the components used other types of molecular data (such as mutations, copy number alterations). Since recently, ICA started to be applied to other types of omics profiles, including methylation profiles and proteomics datasets. It seems interesting to determine, using ICA, independent sources of variance in newly emerging omics data types, such as systematic Chip-Seq datasets mapping the state of histone modifications or mutational signature profiles. More experience, standardization and assessment are required to use ICA in the most optimal way for the analysis of single-cell and multi-omics datasets.
Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 15 of 27PPARG in differentiated bladder tumors which was validated experimentally. In Reference [38] , 14 non-redundant colon cancer transcriptomic datasets were analyzed by ICA, and the resulting sk vectors were matched with each other through correlation in order to reveal the functional modules implicated in colon cancer tumor cells' and the variability of tumoral microenvironment.
caballi were developed targeting a 2,930-bp sequence of the 28S gene. All primers and probe for real time PCR and conventional PCR assays were designed using free web Primer3 software, version 4. 0 (http://frodo. wi. mit. edu/primer3/); their sequences were listed in Table  1. The specificity and sensitivity of all PCR assays were tested in silico using primer-BLAST (NCBI, USA) and were validated using a panel of DNA extracts from several species of piroplasms, arthropods and bacteria, as well as from human, donkey, horse, cattle, mouse and dogs (Table  S2 ).
The amplicons were sequenced using the Big Dye Terminator Cycle Sequencing Kit (Perkin Elmer Applied Biosystems, Foster City, CA, USA) with an ABI automated sequencer (Applied Biosystems). The obtained sequences were assembled and edited using ChromasPro software (ChromasPro 1. 7, Technelysium Pty Ltd. , Tewantin, Australia). Then, sequences were compared with those available in the GenBank database by NCBI BLAST (http://blast. ncbi. nlm. nih. gov/Blast. cgi). Our sequences obtained from positive samples in addition to sequences of validated piroplasms species already available in Genbank were aligned using Bioedit software version 7. 0. 5. 3 (ClustalW multiple alignment)  (Tom Hall, 2011). For taxonomic analyses, the Maximum-likelihood phylogenetic trees constructed using MEGA software version 7.
In Chad, the prevalence was significantly higher in 2016 (43/59, 72. 9%) than in 2012 (20/96, 20. 8%), p<0. 0001(Table S3 ). Considering the species of Equidae we studied, the prevalence of piroplasm was significantly higher in horses (160/484, 33. 06%) than in donkeys (2/64, 3. 12%), p<0. 0001. The infection was also observed more often in females (46/124, 37,09%) than in males (116/424, 27. 36%), p<0. 20. More detailed data are given in Table  3. Of the 97 ticks collected in Chad (2016), only 9 (8 female and one male ticks) were positive (9. 27%). Interestingly, all positives ticks were Rhipicephalus decoloratus, which were collected from two horses (coded MGM04 and MGM10) that were also found positive for piroplasms.
Piroplasms species identification To identify piroplasms species, all positive samples were subjected to standard PCR (targeting 18S rRNA gene) coupled with sequencing. Sequencing results showed the existence of a large genetic diversity (Table  4 ) allowing us to identify four piroplasms species: T. equi, B. caballi and two potential new species; provisionally named here Theileria sp. "Africa" and Theileria sp. "Europa". The highest prevalence was observed for Theileria sp. "Africa" (51. 85%, 84/162) followed by T. equi (28. 39%, 46/162), Theileria sp. "Europa" (19. 14%, 31/162), and B. caballi (0. 62%, 1/162). Only one horse from Senegal was positive for B. caballi. We selected some samples corresponding to each 18S genetic variant to sequence using 28S and cox1 genes (Table  1 ). All sequences were submitted to the Genbank and their accession numbers were obtained (Table  S3 ).
Phylogenetical analysis The phylogenetic trees constructed on the base of 18S, 28S and cox1 genes allowed the position of the identified species to be visualized  (Figures 2, 3, and 4, respectively). Both Theileria sp. "Africa" and Theileria sp. "Europa" formed separate clades with samples of different geographical origins grouped together with a good bootstrap support (Figures  2, 3,  and 4 ). In a second time, we compared sequences of three genes of Theileria spp. identified in the present study with T. equi sequences of WA strain and USDA strains available in the Genbank (Table  S4 ). We discovered that the differences among T. equi and the two potential new species varied between 4 to 14%.
Acknowledgements This study was supported by the  Institut Hospitalo-Universitaire (IHU) Méditerranée Infection , the  National Research Agency  under the program « Investissements d'avenir », reference  ANR-10-IAHU-03 , the  Région Provence Alpes Côte d'Azur and European funding FEDER PRIMI. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript. We thank  Masse Sambou ,  Maxence Aubadie  ( †),  Raphaël Tine ,  Mamadou Diara ,  Pierre Verhaeghe ,  Bertrand Lafrance ,  Mustapha Dahmani ,  Younes Laidoudi  and  Rima Saad-Eddine  for their assistance during the field and laboratory work. We also want to thank the  command of National and Nomadic Guard (GNNT) of Chad , the  French Armed Forces Medical Service , in particular  Jean-Lou Marié  and the  « Association Pour la Recherche en Infectiologie» (APRI)  for their financial support.
All data used in this work are available for public by following this link : "http://bit. ly/2Icag5E". This link provides for each test corpus the degraded images, the noisy texts extracted by the OCR and their aligned version with clean data at the word and the character levels. In order to evaluate the quality of the text generated by an OCR engine, one of the most popular measures is Character Error Rate (CER)  [11]. The CER is the proportion of errors made on characters compared to the original text. There are three types of errors: -Substitution where one character has been replaced by another. -Deletion where a character has simply not been recognized by the OCR. -Insertion where an additional character has been wrongly added. The CER varies between 0. 4% and 5. 4% according to the documents and the type of degradation added.
Training settings In order to analyze the effects of OCR degradation, we tested the systems of Ganea and Hofmann 2017's  [6]  and Le and Titov's  [14]. We used the pre-trained models of each system. More precisely, Ganea and Hofmann trained their entity embeddings on the Wikipedia (Feb 2014) corpus and they used the pre-trained Word2Vec word vectors with 300 dimensions 13. Le and Titov used GloVe  [19]  word embeddings trained on 840B tokens and they selected the best 7 relations on the development scores. Both of them trained their systems on the AIDAtrain data set.
Despite these high error rates, the combination of these OCR degradations does not appear to have a significant change in the performance of two NEL systems. Both systems achieved satisfactory results (average micro F1-score greater than 0. 75) for all OCRed versions. The analysis of the relations of mentions as latent variables improved the analysis of candidates mentions and increased F1 scores for almost all clean   4. NEL results (micro F1 scores) using the Le and Titov's system  [14]  on the OCRed versions of data sets. data sets (except AQUAINT and WIKI data sets). However, errors produced by the OCR degradation were more critical for this analysis. Indeed, the mentions affected by OCR errors degraded the analysis of the relationships between other mentions that led to a decrease in the performance of this approach.
We were able to provide the DL community with recommendations on the OCR quality that is required for a given level of expected NEL performance. Further work is under progress to analyze the performance of end-to-end NEL systems on OCRed data sets. More precisely, we want to investigate how different approaches can overcome the problem of OCR degradation and provide correct predictions. We also intend to analyze and to test the performance of these systems using real data on low-resources languages.
[3,8]]ed on CoNLL 2003 data that was used for NER task. This data set is divided into AIDA-train for training, AIDA-A for validation, and AIDA-B for testing. This data set contains 1393 Reuters news articles and 27817 linkable mentions. -AQUAINTdataset[18,8]iscomposed of 50 short news documents (250-300 words) from the Xinhua News Service, the New York Times, and the Associated Press. This data set contains 727 mentions. -ACE2004dataset[21,8]isa subset of the ACE2004 coreference documents with 57 articles and 306 mentions, annotated through crowdsourcing. -MSNBCdata set[3,8]is composed of 20 news articles from 10 different.
[3,8]]ed on CoNLL 2003 data that was used for NER task. This data set is divided into AIDA-train for training, AIDA-A for validation, and AIDA-B for testing. This data set contains 1393 Reuters news articles and 27817 linkable mentions. -AQUAINTdataset[18,8]iscomposed of 50 short news documents (250-300 words) from the Xinhua News Service, the New York Times, and the Associated Press. This data set contains 727 mentions. -ACE2004dataset[21,8]isa subset of the ACE2004 coreference documents with 57 articles and 306 mentions, annotated through crowdsourcing. -MSNBCdata set[3,8]is composed of 20 news articles from 10 different topics (two articles per topic: Business, U. S. Politics, Entertainment, Health, Sports, Tech & Science, Travel, TV News, U. S. News, and World News), having 656 linkable mentions in total.
Background: The dysfunction of OPA1, a dynamin GTPase involved in mitochondrial fusion, is responsible for a large spectrum of neurological disorders, each of which includes optic neuropathy. The database dedicated to OPA1 (https://www. lovd. nl/OPA1), created in 2005, has now evolved towards a centralized and more reliable database using the Global Variome shared Leiden Open-source Variation Database (LOVD) installation. Results: The updated OPA1 database, which registers all the patients from our center as well as those reported in the literature, now covers a total of 831 patients: 697 with isolated dominant optic atrophy (DOA), 47 with DOA "plus", and 83 with asymptomatic or unclassified DOA. It comprises 516 unique OPA1 variants, of which more than 80% (414) are considered pathogenic.
Full clinical data for 118 patients are documented using the Human Phenotype Ontology, a standard vocabulary for referencing phenotypic abnormalities. Contributors may now make online submissions of phenotypes related to OPA1 mutations, giving clinical and molecular descriptions together with detailed ophthalmological and neurological data, according to an international thesaurus. Conclusions: The evolution of the OPA1 database towards the LOVD, using unified nomenclature, should ensure its interoperability with other databases and prove useful for molecular diagnoses based on gene-panel sequencing, large-scale mutation statistics, and genotype-phenotype correlations.
This underscores the importance of the OPA1 locus-specific database associating genetic and clinical data, which should facilitate the identification of new genotype-phenotype correlations in OPA1-related disorders. Although to date more than 500 unique OPA1 variants, mostly family-specific, have been reported (see https://www. lovd. nl/OPA1), the original eOPA1 database, published in 2005, contained only variants and references gathered from publications  [33]. The study of over a thousand patients diagnosed in our clinical laboratory  [34]  led us to transform our restricted molecular OPA1 database into a clinico-biological database for DOA, aimed at collecting patient data with a full record of clinical, electrophysiological and biochemical data  [35]. Today, the extensive molecular diagnosis made possible by high-throughput sequencing (HTS) allows us to migrate to a common installation integrating all known human genes, i.
Results and discussion The OPA1 database contains four main independent but interconnected tables labelled "Variants", "Screening", "Individual" and "Phenotype". These tables are visible on a typical web page entry as shown in Fig. 1. The "Variants" table includes information about the sequence variations at the genomic (DNA) and the transcript variant (cDNA) levels, for both transcripts 8 and 1 (not shown), as well as the reported and concluded status for each variant (Fig. 1a ). The "Screening" table gives details of the methods and techniques used for investigating the structural variants and the tissue analyzed (Fig. 1b ). The "Individual" table contains details of the patient examined, including gender, geographic origin, and patient identification, if applicable, as listed in the original manuscript (Fig.
Molecular relevance To date, the database contains 516 unique variants, of which 80% (414) are considered pathogenic sequence variants. These variants, which mainly affect the coding sequence and exon-intron boundaries of the gene, are mainly located in the GTPase and dynamin domains of the protein (exons 10 to 26), highlighting the importance of these domains in OPA1 functions (Fig. 2 ). Among the most frequently observed pathogenic OPA1 variants, 28% are missense variants; 24% are associated with altered splicing, which produces effects that are difficult to predict reliably; 22% are frameshift variants; 15% are nonsense variations; and 7% are deletions (Fig. 3 ). Interestingly, 149 of the unique variants in the database (29%) are unpublished in the literature, i. e. have been submitted to our database only (Additional file 1).