dataesr/scientific-paragraphs-categorization huggingface dataset:
However, this analysis was limited to the structure of the transcriptional networks only, the signaling part of the network was completely neglected. In order to deal with combined signaling+transcriptional networks, we merged each of the transcriptional network to the global reconstruction of signaling taken from the SIGNOR database  [7]  (version from February 2016). As a simplifying modeling hypothesis, we assume that the structure of the global signaling network depends less on the biological context than the structure of the transcriptional regulation layer (Fig  1 ). Although this is a relatively strong hypothesis, it can be qualitatively justified here by the following considerations. First, in the textbooks on the biology of cancer, it is frequently assumed that rapid and coarse-grained evolution taking place during cancerogenesis does not have time to define new interactions between protein domains  [41].
Biological interpretation of PageRank and CheiRank centrality measures and their changes in cancer Distribution of proteins on PageRank vs CheiRank plane. For the three directed biological networks described above (SIGNOR alone and two merged signaling+transcriptional regulatory networks), we applied the Google matrix methodology as described in Materials and Methods section, and determined the values of PageRank and CheiRank for all proteins. The distribution of proteins in PageRank vs CheiRank plane is shown in  Fig 2. This figure shows that in the case of these networks PageRank and CheiRank measures are not correlated which reflects quite distinct biological role of proteins with many incoming and with many outgoing directed interactions. It can be easily demonstrated (data not shown) that most of the proteins simultaneously having high values of PageRank and CheiRank (such as AKT1, NOTCH1, CTNNB1, TP53, CDKN1A, ATM, MAPK3, CDK1, EGFR) play an important role in cancer biology.
https://doi. org/10. 1371/journal. pone. 0190812. g004 some predefined reference protein set by random chance. For this purpose we used the toppgene bioinformatics package  [45]. For the enrichment analysis, we took those proteins deviating from zero by two standard deviations of the distributions of PageRank of CheiRank log ratios, and analyzed the positive and negative sides of the distribution separately. The results of the analysis are presented in Table  1  and online at  [46]. We've noticed, however, that the results of this analysis can be biased by a simple fact that a protein can be included in the "normal" transcriptional network GM12878 (thus having, for example, many transcriptional out-going interactions) and not at all included in the "cancer" transcriptional network K562 (thus having no transcriptional out-going interactions at all).
However, it did not distinguish between the nature of the changes (excess/deficit of incoming or outgoing regulations in cancer) and was not able to identify groups of genes related to cell cycle (which was significant in the PageRank analysis). By contrast, in-closeness was able to capture the disregulation of the cell cycle (pvalue 10 -5 for G2/M Transition term from REACTOME pathway database) while neither inor out-closeness identified the nature of the disease (leukemia). Both authority and hub centrality measures were not informative in their changes in cancer compared to normal cells and collected very few and unrelated enriched terms. The two above-mentioned analyses allow us to conclude that PageRank and CheiRank represent informative measures of network centrality which can be applied to identify structural changes in the oriented biological networks in diseases such as cancer.
We start with a definition of a biological pathway, which play one of the most central roles in all cancer types: AKT-mTOR pathway. It is a molecular cascade downstream of PI3K kinase which is important in regulating the cell cycle and cell survival, controlling a number of normal physiological processes, and being dysregulated in many diseases including cancer. We take the definition of the AKT-mTOR pathway from the external pathway database Atlas of Cancer Signaling Network (ACSN)  [47] , based on manual mining for interactions from thousands of molecular biology publications, so the definition of this subset of proteins can be called "knowledge-driven". Second analyzed subset by contrast was chosen to be purely "data-driven" and corresponds to a particular gene expression signature (set of genes), shown to be connected to cell proliferation in multiple cancer studies through data analysis.
All three subsets are central to the studied in this paper cancer progression and its influence on the structure of biological networks. In all three cases, we roughly equilibrated the sizes of the protein sets, limiting them to approximately 50 proteins. It happened that the direct interactions between the members of all three sets of proteins are described in SIGNOR pathway database, and not in the transcriptional networks. Therefore, one might consider that the wiring of direct connections between the set members are not affected by the changes in the transcriptional program. However, we further show that the structure of indirect connections might change accordingly to the changes in the global context created by the transcriptional network. AKT-MTOR pathway. From SIGNOR database, we've retrieved 138 direct regulatory connections between 63 proteins of AKT-mTOR pathway.
It can be seen that such "emergent oncogenic" indirect interactions also underline existence of a "hidden" causal relation between RBX1 and MAPK1 proteins. Data-driven signature of proliferation-related proteins. We analyzed a set of 49 proteins found in SIGNOR database whose expression was shown to significantly change between fast proliferative and slow proliferative tumors in 9 cancer types  [49]. We found 47 direct interactions connecting them into one large connected component consisting of 31 proteins who were predominantly the phosphorylation targets of the cyclin-dependent kinase CDK1, so the structure of the network of direct interactions is fan-like organized around one large hub protein. As in the previous example, no direct transcriptional connections were found between the members of this protein set.
For 76 such proteins found in SIGNOR pathway database, we find 103 direct interaction connecting these proteins into the largest connected component comprising 49 proteins. One additional direct transcriptional regulation was found between MYC and CBX5 proteins, but only in "cancer" K562 network. The reduced Google matrix analysis revealed 84 indirect regulations for G qr > 0. 01 in the case of the "normal" network (Fig  8A ), all of which were also present in the "cancer" network. We did not find any additional indirect interactions from the analysis of the "cancer" network. The majority of strong indirect interactions pointed to the 3 key apoptosis proteins CASP9, CASP3 and APAF1 (apoptotic protease activating factor) whose local PageRanks decreased in the "cancer" network (which means they become more regulated).
Similar to the PageRank vector the CheiRank vector gives probabilities to find a random surfer on a given node (for a network with inverted direction of links) in the limit of very large number of jumps. On average these probabilities are proportional to the number of outgoing links. At α < 1 only the PageRank vector have λ = 1 while all other eigenvectors of G have |λ| α  [18, 33]. For Wikipedia is was shown that the eigenvectors with a large modulus of λ select some specific communities of Wikipedia network  [18]. However, a priory it is not possible to know what are the meanings of these communities. Thus other methods are required to determine effective interactions between N r nodes of a specific subset (group) of the global network of a large size N ) N r. In this work we apply the Google matrix analysis to the directed network of protein interactions from the cancer database SIGNOR  [7] , and two hybrid networks, constructed by merging SIGNOR to two transcriptional networks measured in normal blood cells and in cancer (leukemia).
Definitions of functionally related groups of proteins The composition of AKT-mTOR pathway and the set of direct transcriptional targets of E2F1 protein were downloaded from the Atlas of Cancer Signaling Network (ACSN) database  [47] , by using GMT files of version 1. 1 available from the ACSN web-site http://acsn. curie. fr (sets E2F1-TARGETS and AKT-mTOR gene sets). The group of proteins related to proliferation was determined as a set of 50 gene names top-contributing to the transcriptomic signature associated to the cell cycle through a largescale pan-cancer analysis of transcriptomic data  [49]. All protein lists are provided in the S1 File and in  [46].
Performance and method implementation Due to analytical derivation of the reduced Google matrix, the application of the formalism to the networks with several thousands or even tens of thousands of nodes takes only seconds on a standard computer (Intel(R) Core(TM) i7-6600U CPU @ 2. 60GHz-8Gb RAM). The code for the method is highly optimized to be used with much larger networks. For computational experiments in this study, we used FORTRAN-based implementation of the method. In the nearest future, we plan to develop a Cytoscape plugin performing the reduced Google matrix computation, which will require re-implementing the method in Java. The results of PageRank and CheiRank calculations are provided in S3 File. The results of reduced Google matrix method application are provided in S4 File.
Evolutionary radiations are fascinating phenomena corresponding to a dramatic diversification of taxa and a burst of cladogenesis over short periods of time. Most evolutionary radiations have long been regarded as adaptive but this has seldom been demonstrated with large-scale comparative datasets including fossil data. Originating in the Early Jurassic, irregular echinoids are emblematic of the spectacular diversification of mobile marine faunas during the Mesozoic Marine Revolution. They diversified as they colonized various habitats, and now constitute the main component of echinoid fauna in modern seas. The evolutionary radiation of irregular echinoids has long been considered as adaptive but this hypothesis has never been tested. In the present work we analyze the evolution of echinoid species richness and morphological disparity over 37 million years based on an extensive fossil dataset.
Materials and methods A list of 664 fossil species of echinoids recorded worldwide (S1 File) was established based on the literature  [74, 75, 76, 77]. Taxonomic relevance was optimized by discarding poorly defined or ill-known taxa as well as para-taxa established on the basis of test fragments and isolated spines  [78]. Of the 910 nominal species recorded, 246 were discarded in this way. Based on the literature, the stratigraphic range of each species was documented at stage level within the Toarcian-Tithonian interval for which enough data were available for statistical inferences. In total, species belong to 12 different clades that are all monophyletic for the Jurassic based on the robust morphological and molecular phylogenies (Fig 1 ) previously reconstructed  [61, 71, 72, 73]. Species richness values were calculated on the basis of this revised database.
The main feeding strategies of taxa could be inferred from the morphology of the peristome and the associated organs: the Aristotle's lantern, the size, shape and position of the peristome, and the differentiation of buccal pores, interpretations relying on our knowledge of extant representatives with similar morphologies  [60, 61, 64, 79, 80]. The mode of life was partly inferred based on morpho-functional arguments such as the general shape of the test and the number and size of spines employed for locomotion. It was also deduced from paleo-environmental and geological data of deposits in which fossils were reported, such as grain size of sediments from which species used to feed  [61, 72]. For each species, drawings and pictures of the oral side of reference specimens available in the literature (S1 File) were used to draw the outline of the peristome, the ambitus of the test, and the position of perradial suture lines of each ambulacrum.
The test outline was depicted using five sets of 39 semi-landmarks  [82]  positioned in between the five successive landmarks. In total, the test outline was described by 195 semi-landmarks (39 × 5) and 5 landmarks (S2 File). A similar protocol was used for depicting the peristome outline with five sets of seven semi-landmarks and a total of 35 semi-landmarks and 5 landmarks (S2 File). Landmark and semi-landmark configurations were fitted using the generalized Procrustes algorithm minimizing the bending energy, and projected onto the tangent space to the mean shape  [83]. Coordinates of shapes were used to calculate a Principal Component Analysis (PCA) based on the variance/covariance matrix and compute a morphospace plot using Geomorph R package version 3. 0. 0 [84, 85]. The first eight principal components (PCs) accounting for 95% of the total variance of the PCA were used to define morphospace and describe shape variation.
Disparity was computed using two metrics: the Partial Disparity Analysis (PDA) and the Mean Pairwise Distance (MPD). PDA  [54, 86]  gives an overview of the relative contribution of each clade to the total disparity and its evolution through time  [18]. PDA simply expresses the disparity of a given clade for a given time interval as a part of the total variance (i. e. total disparity for the considered time interval) attributable to the clade. MPD  [87]  is the mean of all pair-wise Euclidean distances between species of a clade computed for each time interval. Both disparity metrics were computed for clades with at least five species per time interval using the Morphospace-Disparity Analysis (MDA) package  [88]. A bootstrapping procedure (n = 500) was performed to estimate a confidence interval associated with each value.
To test for the significance of MPD differences between clades, a set of null models was computed using Matlab1. Null distribution models were computed for each clade and each time interval following the procedure described by Losos and Miles  [24]  and adapted to fossil data by Neige et al. [18]. For each clade and time interval, bootstrapping was performed with 10,000 replicates by sampling with replacement m species from a total set of species. For each time interval, the total number of species corresponds to the number of species truly recorded at that time, value-centered to remove the inter-clade component of the total disparity, and m corresponds to the number of species for a given clade and time interval. Finally, MPD values of each clade and each time interval were tested against the null distributions.
Discussion Three echinoid clades display taxonomic diversity values that are congruent with a pattern of evolutionary radiation, that is, a sudden, relative increase in species number compared to For each clade, taxonomic diversity is expressed as species richness for each geological stage, the wider the rectangles, the higher the number of species. Colors correspond to the tested disparity values relative to other clades: unusually high (red), normal (orange), unusually low (yellow), not tested due to lack of data (white). Main feeding strategies and modes of life were interpreted for each clade. Ages in million of years Before Present (after Gradstein et al. [73] ). https://doi. org/10. 1371/journal. pone. 0194575. g005 other clades: one clade of regular echinoids, the Cidaridae, and two clades of irregular echinoids, the Neognathostomata and the Atelostomata.
The mixtures (1 μL) were then spotted on the target plate and air dried. MS survey data comprises a total of 50 sub-spectra of 1500 laser shots. Peaks observed in the MS spectra were selected for further MS/MS. CID MS/MS data comprises a total of 100 sub-spectra of 3000 laser shots. Two or more spectra can be combined post-acquisition with mass tolerance set at 0. 1 Da to improve S/N ratio. The potential difference between the source acceleration voltage and the collision cell was set to 1 kV and argon was used as collision gas.
Cells were washed with PBS then with deionized water, and finally mounted in Fluorescent mounting medium (Dako, Carpenteria, CA, USA). The nuclei were stained with DAPI. Slides were examined under a Zeiss LSM 700 confocal microscope. The image acquisition characteristics were the same throughout the different conditions to ensure the comparability of the results and to allow the comparison of the fluorescence levels. For statistical analysis, images of ! 100 cells for each condition were collected based on the DAPI signal and were then analyzed for G D3 and G D2 expression using an automated algorithm on ImageJ software. Anova was used for statistical analysis. A p-value < 0. 05 was considered statistically significant.
Effect of TNF on the expression of ganglioside GTs in breast cancer cell lines The effect of TNF on the expression of glycosyltransferase genes involved in ganglioside biosynthesis was determined by qPCR for the three cell lines. The mRNA levels for each GT gene, i. e. ST3GAL5 encoding the GM3 synthase, ST8SIA1 encoding the GD3 synthase, B4GALNT1 encoding the GM2/GD2 synthase and B3GALT4 encoding the GM1/GD1b synthase, were normalized to the expression of HPRT and reported to the expression of GT genes in non-treated cells (Fig  3 ). Our results confirmed the increased expression of ST8SIA1 after TNF treatment in MCF-7 and Hs578T as previously reported  [19]  but no change in the expression of ST8SIA1 was observed in MDA-MB-231 cells. We also confirmed by qPCR the expression of TNFR1, the gene encoding the TNF receptor 1, in the three cell lines (data not shown).
To quantify variations in ganglioside expression in MCF-7 cells after TNF treatment, we analyzed the population expressing G D3 and G D2 using an automated algorithm on ImageJ software. For each condition, images of ! 100 control and treated-cells were collected based on the DAPI signal for total cell counting. In the control cell population, G D3 and G D2 expressing cells were estimated at 4. 0 ± 4. 0% and 3. 4 ± 2. 3% of total cells, respectively, whereas TNF treatment extended these populations to 12 ± 3. 5% and 12. 7 ± 3. 0% (Fig 5C ).
In contrast with observations made in MCF-7 cells, TNF had no effect on the expression of the GM3 synthase. Surprisingly, no change in the expression of all GTs genes tested was observed for the triple negative MDA-MB-231 cells, which are of the same molecular subtype than Hs578T cells, and known to express the TNF receptor 1 (TNFR1)  [36]. We also confirmed the expression of TNFR1 by qPCR in the three cell lines (data not shown). These results indicate that inflammation differently modulates the expression of ganglioside-specific GT genes in breast cancer cells. The effect of TNF treatment on cell surface ganglioside expression was in parallel analyzed by flow cytometry and confocal microscopy. As expected, an increased expression of G M3 and G M2 was observed in TNF-treated MCF-7 cells that fitted well with the increased of GM3 synthase gene expression.
Acknowledgments This work was supported by the  University of Lille and La Ligue Regionale contre le Cancer. We are indebted to the  Research Federation FRABio (Univ. Lille, CNRS ,  FR 3688 ,  FRABio, Biochimie Structurale et Fonctionnelle des Assemblages Biomoleculaires ) for providing the scientific and technical environment conducive to achieving this work.
processing methods used for miRNA analyses were performed on the Cy3 Median Signal in Agilent Feature software v10. 7 (Agilent Technologies, Inc. ). For gene expression, analyses were performed on the Cy3 and Cy5 Median signal in Agilent Feature software v10. 7 (Agilent Technologies, Inc. ). Raw data files were extracted using functions in Bioconductor  (17). Flagged spots, as well as control spots, were systematically removed, and data were log 2 transformed. Quantile normalization was performed using the normalizeBetweenArray function from R package 'LIMMA' (version no. 3. 34. 9) (18). The median of each probe for a given miRNA was computed and the corresponding value was assigned to the miRNA. Data were then filtered according to the maximum number of missing values allowed for each miRNA (30%).
miRNAs and genes that were significantly differentially expressed between the groups at P<0. 05 using BRB ArrayTools v2 (https://brb. nci. nih. gov/BRB-ArrayTools/), as determined using two-way analysis of variance and multiple correction Benjamini-Hochberg test, were selected for further analysis. Significantly differentially expressed miRNAs were used to build a hierarchical cluster using Gene-E (https://software. broadinstitute. org/GENE-E/index. html). For biological interpretation of significant genes, the Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david. ncifcrf. gov/) was employed to perform the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Significant KEGG pathways (P<0. 05) were selected in VHL-associated and sporadic ccRCC tumors.
For patient 1674, a total of 1,282 genes and 56 miRNAs were differentially expressed (Table  II ). In addition, as shown in Fig. 2, a   -------------------------------------------------------------------------------------------- - No obvious differences were detected between the samples, but the two subclusters could be distinguished according to the nuclear grade of these tumors. When tumors were compared two by two, no significant difference was found, thus supporting the hypothesis of a similar molecular evolution between independent tumors. KEGG biological pathway analysis was conducted using DAVID software. This analysis globally identified three classes of pathways that were overrepresented with dysregulated genes. Notably, in the two patients, the most significant pathways were similar, and were implicated in 'immunity' and 'metabolism' (Table  III ).
Acknowledgements The authors of the present study would like to thank  Dr Vladimir Lazar  and  Mrs Véronique Roux  ( Genomic Platform, Gustave Roussy, Villejuif, France ) for helping to design the microarray experiments. The authors are also grateful to  Centre de Ressources Biologiques from Necker and Saint Joseph Hospitals (Paris, France) and Bicêtre Hospital (Le Kremlin-Bicêtre, France)  for the frozen samples used to perform the present study.
Introduction Natural language processing and semantic applications that depend on a machine learning component require training data, i. e. examples from which the machine learning algorithm learns from. The training datasets require, most of the time, manual annotation. Usually, such annotations are conducted in a predefined cycle of annotation activities. Once the annotation problem is identified, a standalone annotation tool along with the annotation guideline is developed. At the end of the annotation cycle, the collected dataset is fed to the machine learning component, which produces a static model that can be used thereafter in an application. Possible limitations of these annotation approaches are: 1) Developing a standalone annotation tool is costly, sometimes expert or specially trained annotators are required.