dataesr/scientific-paragraphs-categorization huggingface dataset:
Distribution estimation using EM-scheme We estimated the parameters of the distributions p(C i ) and p(L(x)|C i ) with the EM-scheme on the four year dataset, again using the manually segmented and the SPoCA segmented images. For both masks the EM-iteration converges to a maximum relative change of the values of p(C i ) of less than 1% in about five iterations. With our implementation, each iteration takes approximately 5 min of computation time on a server with two Intel Xeon E5-2680v2 processors with 10 cores each. The computation time can be decreased by subsampling the images in time. However, care must be taken that the subsampling does not interact with the solar rotation as it could bias the results toward certain Carrington longitudes. A good alternative to subsampling at regular intervals would be a random sampling.
For the estimation of p(L(x)|C i ) it is important that the dataset spans over a time range that is long enough (multiple Carrington rotations) and is of a high enough cadence (at least one image per day). Once these distributions are estimated, they can be applied to all images in the dataset using  Eq. (  4 ). This way their final segmentation can be calculated in only one step. The area coverage is computed as the number of pixels belonging to a particular class to the number of on-disk pixels in an area-preserving sinusoidal reprojection of the original helioprojective image. With the manually segmented training mask, estimation of P(C i ) over the four year data set results in an area coverage of 8. 55%, 81. 96% and 9. 49% for the AR, QS and CH class, respectively. With the SPoCA training mask, those values are 2.
Validation using a test dataset Performance evaluation of a supervised method is classically done against a test dataset. In our case, the test dataset is constituted of new manually segmented images. While  Rigler et al. (2012)  studied the effect of having different experts specify the training and the test segmentation masks, in this work the test masks were created by the same person who made the initial training mask in order to avoid personal biases from influencing the results. Note that they still do not provide an ideal ground truth since these manual segmentations may contain errors themselves. We provided rough segmentations for nine new randomly selected images from the four year dataset. In these segmentations, we included only those regions that were least likely to result in errors in the manual segmentation, i.
Consistent with the manually provided training masks: changes in the training mask should reflect in the resulting segmentations. To show these properties we use the high cadence three month dataset and the same training masks as before. We reused the radial intensity profile as well as the distributions p(C i ) and p(L(x)|C i ) estimated on the four year dataset. In the absence of major solar activity (e. g. flares), and on shorter timescales (on the order of hours) the observed size of CHs does not show large variations. The first property says that in these cases the area coverage computed from the segmentation should also show only minor variations over time. Figure  9  shows that this is indeed the case: the median variance in CH fractional area over a 24 h sliding window is 8. 1 • 10 À6 , compared to a variance of 6.
The segmentations resulting from the various training sets can also be compared against stability and consistency criteria such as the ones defined in Section 5. 2. From such a study, one could derive a commonly accepted standard, e. g. defining how far an active region should extend. This would provide a benchmark for comparing different feature detection algorithms. The software implementing this method along with a script to reproduce all results outlined in this paper is available at http://bitbucket. org/rubendv/bayesian_segmentation_code.
Acknowledgements. The authors would like to thank  Benjamin Mampaey  for providing the SPoCA masks. RDV and VD acknowledge support from the  Belgian Federal Science Policy Office  through the  ESA-PRODEX program , Grant No. 4000103240. RDV further acknowledges support from the  BRAIN. beprogram of the  Belgian Federal Science Policy Office. Thanks are extended to the two referees for their pertinent comments that helped improving this manuscript. The editor thanks  Michael Kirk  and an anonymous referee for their assistance in evaluating this paper.
HRM primers were designed with the online uDesign SM application (www. dna. utah. edu/udesign/index. html) [50]. Their alignment with the genomic reference sequence is shown in Figure  4. A 5′ tail was added to the rs12979860 forward HRM primer for a better separation of the melting temperature domains of the rs12979860 and rs738409 amplicons. Allele-specific, cloning and sequencing primers were designed using Primer3 software  [51]  and verified for specificity with Primer-BLAST  [52]. OligoAnalyzer 3. 0 (https://eu. idtdna. com/calc/analyzer) [53]  was used to assess the propensity of primers to form loops or dimers. In the case of allele-specific primers, a nucleotide mismatch was introduced in the penultimate 3′ position to increase specificity, and a GC tail was added to the 5′ end of one primer per SNP to facilitate visual discrimination of the amplicons by melting curve.
Real-Time PCR and High-Resolution Melting Analysis Real-time PCR and melting experiments were performed in white plates on the PikoReal 96 (Thermo Fisher Scientific, Vantaa, Finland) instrument or in 100 µL low profile clear tubes on the LightCycler Nano (Roche Diagnostics, Mannheim, Germany), with 10 µL reaction volumes. Default temperature ramp rates (7 °C/s, as indicated by the instrument software) were used throughout the study. Allele-specific real-time PCR mixture contained 2 µL DNA, 5 µL of Universal FastStart SYBR Green master mix with ROX (Roche Diagnostics, Mannheim, Germany), and primers at a final concentration of 400 nM for rs12979860 and 200 nM for rs738409. The temperature steps were 95 °C for 10 min. , followed by seven cycles of 95 °C for 10 s and 67 °C for 30 s, with 0. 5 °C decrease at each cycle, then 45 cycles at 95 °C for 10 s and 63 °C for 25 s.
5 °C/cycle, then 40 cycles at 95 °C for 10 s and 60 °C for 25 s. Standard templates, positive controls consisting of DNA samples with known genotypes, and PCR-grade water as no template control were tested in all analytical series, along with clinical samples. High resolution melting was preceded by amplicon denaturation at 95 °C for 1 min and heteroduplex formation at 40 °C. Reaction products were then gradually heated from 60 to 95 °C in steps of 0. 2 °C per 4 s, with fluorescence readings at every step. The PikoReal instrument software (ver. 2. 2, Aug 2013, Thermo Fisher Scientific, Vantaa, Finland) is able to assign a call to each melting curve, using a hierarchical clustering algorithm and input calls of standard melting templates. First, fluorescence values are normalized according to user-adjustable pre-melting and post-melting temperature intervals, wherein the fluorescence of each curve is considered 100% and 0% respectively.
Then, fluorescence curves can be transformed by three different methods to facilitate clustering. The first method (RFU difference) relies on the calculation of the difference between fluorescence of all samples and a reference calculated automatically. In the second (dF/dT difference), the difference between the (negatively transformed) first derivative of the fluorescence of each sample and that of a reference is calculated. The third method only considers the melting peaks of the normalized curves, resulting in a scatter plot with temperature and peak height as coordinates. The LightCycler Nano software (ver. 1. 0, June 2011, Roche Diagnostics, Mannheim, Germany) only supports the relative fluorescence difference algorithm and is not able to automatically assign genotypes if more than one melting domain is present in one sample, as in a duplex assay.
In order to efficiently study and understand the molecular mechanisms triggered by gastrin and cholecystokinin, detailed knowledge concerning the signaling pathways they regulate is paramount. Information concerning intracellular signaling is commonly retrieved from databases such as Reactome  [14]  and KEGG  [15]. However, none of these resources currently specify which specific molecular events are known to take place in response to gastrin or CCK. Researchers in need of such knowledge therefore must spend significant time reviewing current literature in order to gain an exhaustive and up-to-date understanding of the signaling network. A comprehensive map of gastrin and CCK intracellular signaling pathways would significantly assist in the study of normal or aberrant cholecystokinin receptor (CCKR) signaling.
In the past decade, several manually constructed maps of signaling events have been published  [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] , each providing solid foundations for a systems understanding of the signaling mechanisms. The present work extends this approach to the domain of CCKR signaling by providing a comprehensive literature-based CCKR signaling map that comprises 530 molecular species and 431 reactions, considerably extending previously compiled knowledge on CCK2R signaling  [10, 26]  including CCK1R downstream events. Partitioning the total CCKR signaling map into subnetworks using the BiNoM tool  [27]  resulted in 18 modules that coordinate with each other to elicit the diverse intracellular signaling responses to gastrin and/or CCK. Finally, we used the CCKR map as a scaffold for proteinprotein interaction (PPI) data integration assisted by PathExpand  [28] , in order to predict novel components of the signaling network.
Construction of the CCKR map from literature The CCKR map was constructed using CellDesigner 4. 2, a structured diagram editor for drawing gene-regulatory and biochemical networks, following the Systems Biology Graphical Notation (SBGN) standard for process diagrams  [29]  and the Systems Biology Mark-up Language (SBML) for model representation  [30]. The MIRIAM (Minimum Information Requested In the Annotation of Models) guidelines were followed to characterize each species in the map  [31]. i). Knowledge encoded in the CCKR map was obtained from scientific publications that were identified by searching for different combinations of cholecystokinin (CCK)/CCK1R and gastrin (G-17)/CCK2R in PubMed or through various literature mining tools, e. g. LitInspector (http://www. genomatix. de/solutions/ genomatix-software-suite.
payaologue. org)  [34] , enabling efficient exchange of comments and tags. Consensus and critical comments from each annotator about the precise representation of reactions, components, and their cellular localization were discussed and implemented (Fig. 1 ). Finally, the CCKR map was published with an open source license for the whole scientific community through PAYAO  [34]  (map available at: http://sblab. celldesigner. org: 18080/Payao11/bin/). The CCKR map is also available in the SBML data exchange format (Additional file 1), and we are also in the process of submitting this map to the PANTHER database  [35]. We welcome comments and tags from a community of curators to continue increasing the quality of this CCKR signaling map and will keep it up to date with our growing biological understanding.
BiNoM decomposition of CCKR map into modules We used the 'prune the graph' function of BiNoM to automatically separate the strongly connected component (SCC), i. e. the central cyclic motif of the map, from more loosely connected upstream and downstream species. The SCC was further decomposed into smallest sub-networks with the function 'extract material components'. Next, some manual curation work was performed: sub-networks with 50 % or more overlapping nodes were clustered together while the large sub-networks were decomposed further until each sub-network contained a unique central cyclic motif. Any unconnected nodes were removed from the subnetworks, yielding a total of 18 sub-networks or modules. Next, these 18 modules were merged together and the resulting network was compared with the initial map to check for completeness in terms of any missing interactions or nodes.
Protein-protein interaction based expansion of the CCKR map PPI data were downloaded using PSICQUIC (all databases, version June 2012), and filtered for binary physical interactions based on PSI-MI controlled vocabulary experimental method descriptions, following the procedure in  [38]  (Charles E. Chapple, personal communication). We then tested each of the 4119 proteins found to interact with CCKR signaling map proteins with the PathExpand method  [28]  to seek for tightly linked protein interactors that enhance the network compactness of the CCKR map by leading to higher node degree, betweenness and average local clustering as well as decreasing the shortest path lengths (for details see  [28] ).
Segmentation of the CCKR signaling map into modules The complete CCKR signaling map (Fig. 2 ) is very large. We therefore set out to identify structural and functional subdomains of this map. We used the BiNoM software to import the CCKR map in Cytoscape and built a modular view of the CCKR map similar to what has been previously reported for the E2F-Retinoblastoma signaling pathway  [27, 68]. The resulting 18 modules (Methods, Table  2 , Fig. 4a , Additional file 2) are fully based on the underlying detailed map and help to comprehend higher order map structure, navigate through the map and work on functional modules. Each of the modules represents a structural and functional signaling subunit, combining a set of closely coordinated molecular events concerning a particular protein or a protein complex (see e.
4a ). For this, we analyzed each of the 4119 proteins interacting with the CCKR map proteins using the PathExpand approach  [28]. PathExpand checks each interactor to see if it satisfies a number of topological criteria leading to increased compactness of the complete CCKR map. We identified a total of 102   3. Interestingly, the set of 70 new CCKR map candidates include 30 proteins that are not known to participate in any signaling pathways listed in the pathway databases KEGG, Reactome, PAN-THER and Biocarta (Table  3 ). A GO term overrepresentation analysis  [83]  showed that the set of map candidates is enriched in molecular functions relating to protein kinases, protein phosphatases and GTPase-regulators, indithat many of them could potentially regulate the CCKR pathway via phosphorylation-dephosphorylation mechanisms and by interfering with small GTPase signaling.
Among these are the protein kinases STK4, CSK21, CSK22, ITPKA, FLT4, DDR2, KS6KA4 and MAPK15 and a high number of Dual specificity phosphatases (DUSP1, 2, 4, 5, 7, 9, 22) in addition to phosphatases PHLPP1 and PTPRR. Inspection of the expanded networks resulting from the combination of PathExpand protein interactors with 7 CCKR modules (Fig. 4b , transcription factor centered modules excluded) shows that 11 of the protein interactors are linked to more than one CCKR module and can contribute both to PKC-independent (e. g. ARHGEF25, ARHGAP31, TBC1D3F) and PKC dependent (e. g. ICMT) signaling routes. In contrast, 21 protein interactors are linked to only one module, suggesting that they may act as preferential regulators of this module. For instance, the Mitogen activated kinase 15 (MAPK15) is a compactness increasing protein only for the SRC module, where it interacts with the two kinases SRC and CSK.
The small GTPases RHOA, RAC1, and CDC42 are documented to activate kinases ROCK1 and PAK1, which are both part of the gastrin-regulated Rho GTPase module, and of kinase AKT1, part of the gastrin-regulated AKT1 signaling module. The inactive  form of the small GTPases is restored by GTPaseactivating proteins (GAPs) that enhance hydrolyzation of the bound GTP. In gastrin-mediated signaling deactivation of RHOA and RAC1 is effectuated by GAPs, ARHGAP4 and RGS2, respectively. Eight of the ten PathExpand protein interactors (Fig. 5b , Additional file 5) of Rho GTPase module components interact with one or more of the small GTPases RHOA, RAC1, and CDC42. All of these protein interactors are annotated with GO-terms indicating a known role in regulation of small GTPases, and four of them (IQGAP2, TBC1D3F, ARHGAP31, OPHN1) are known as GTPase activators.
We suggest that these eight proteins are strong candidates for potentially novel modulators of gastrin-mediated Rho GTPase module signaling. Three of the proteins (TBC1D3F, ARHGAP25, and ICMT) are not yet present in any of the common pathway databases (see Table  3 ). The evidence presented here, indicating that the three proteins have tight interaction with multiple proteins involved in gastrin signaling, demonstrates that our approach is well suited to identify testable hypotheses, even for candidates for which knowledge in public databases is still sparse. The PathExpand interactor BNIP1 (BCL2/adenovirus E1B 19 kDa interacting protein 1) interacts with two downstream BCL module components BCL2L1 and BCL2, both involved in effectuating the gastrin anti-apoptotic signal. Since BNIP1 is known to be involved in SNARE vesicular transport, our findings indicate that it may be an interesting candidate to follow up with investigating a potential role of vesicular transport in modulating BCLlinked signaling mechanisms in the gastrin signaling pathway.
Some pathway proteins and reactions of the CCKR signaling map are also described in the Reactome  [14]  and KEGG  [15]  databases. However, the CCKR map constitutes a vastly more comprehensive and integrated model, providing detailed signaling reactions linking the receptors CCK1R and CCK2R all the way to regulated genes and cellular responses. For comparison, the recently published gastrin signaling map  [26]  comprises approximately half of the molecules and only a quarter of the reactions presented in our CCKR map. Similarly, knowledge currently in Reactome covers less than 5 % of the pathway details presented here. The CCKR map therefore represents a significant increase in curated signaling information. The provided SBML version of the CCKR map can serve as a starting point to generate quantitative mathematical models  [88]  for simulation and prediction of cellular outcomes in response to perturbations of the network.
Conclusion Our work demonstrates how publicly available tools can aid in the manual curation of an extensive network topology, in order to build a foundation for a systems understanding of CCKR-mediated cellular responses. Furthermore, the integration of a comprehensive model of complex biological networks with genome scale data can provide new knowledge and hypotheses on molecular mechanisms underlying cellular processes. Further development of the resources presented here should be of high interest in translational research aimed at identifying new targets and biomarkers for treatment and diagnostics of gastrin-and/or cholecystokinin-related disease, including cancer. manuscript writing. KC participated in modular decomposition with BiNoM analysis, carried out network topology studies and helped in manuscript writing.
Acknowledgements We thank  Charles E. Chapple  for sharing his protein-protein interaction network and  Enrico Glaab  for "a la carte" PathExpand analyses. This work was supported by The  Norwegian Cancer Society , The Liaison Committee between the  Central Norway Regional Health Authority (RHA) , the  Norwegian University of Science and Technology (NTNU)  and  Sør-Trøndelag University College (HiST).
Software implementation The presented method has been implemented using the Rheolef environment 3   [64] , which is a general purpose C++ library for scientific computing, with special emphasis on finite elements and parallel computation. Rheolef provides support for distributed-memory parallelism via MPI  4. Rheolef relies upon the Trilinos 5 , Boost 6 , Blas 7 and UMFPACK 8  libraries for much of its functionality. Rheolef bases on Scotch for distributed mesh partitioning  9. The reduced order problem has been numerically solved with the free software package GNU Octave [24] 10.
The ResidueTCS, ColumnTCS, Se-quenceTCS and AlignmentTCS are available in score ascii output format of the web server. The PairTCS is not a default output (sp ascii) and is only available using the command line version of T-Coffee (http://tcoffee. readthedocs. org/en/latest/TCS. html). Once the metrics have been computed, they are used for filtering out the less reliable columns (or residues), coloring every position in the target MSA and generating bootstrap replicates (for tree reconstruction) using the ColumnTCS as a weight when randomly selecting columns for inclusion in a bootstrap replicate with the same column size of the target MSA  (1). The filtered MSAs and the bootstrap replicates can be either downloaded for further usage or sent to the PhyML web server. PhyML can accept the replicates input as multiple data sets (option 'Number of data sets' in PhyML server) and it will generate an individual tree for each replicate instead of the consensus-bootstrap tree.
TCS WEB SERVER The TCS web server is part of the T-Coffee web platform; its access is free and unrestricted, without login procedure. The server is accessible from http://tcoffee. crg. cat/ tcs with any standard web browser (Mozilla Firefox 5+, Google Chrome, Internet Explorer 8+, Safari 6+ and Opera 11+). All the functions of the server are also available through the command line version of T-Coffee, freeware open-source software available from http://www. tcoffee. org/Projects/tcs/. Detailed usage information can be found on the same page or http://tcoffee. readthedocs. org/en/latest/ TCS. html.
Input MSAs (CLUSTAL, FASTA, MSF, Phylip or Nexus format) can either be cut/pasted or uploaded. For unaligned sequences, users can run any T-Coffee method first and then click a blue button 'Core/TCS' in the 'Send results' section, which will automatically initialize pre-filled TCS service with T-Coffee MSA as input. By default the server is set to compute a Mproba pair T-Coffee pairwise library and to generate the filtered alignments along with its other outputs by removing all columns having a scaled ColumnTCS score (range 0-9) less than 4. Users can change this behavior by triggering the 'Show more options' hyperlink that makes it possible to change the library computation methods, the filtering threshold and/or the filtering on residues rather than columns. The advance mode also makes it possible to retain empty columns after filtering (Figure  1c ).
Computation The main limiting step of TCS is the library computation, which involves computing all possible pairwise alignments on the input sequences. On a MSA of seven sequences, the server takes ∼20 s--most of it being queuing access. It can deal with a MSA of up to 1000 sequences though users are encouraged to select fast library computation modes through the 'Show more options' link (i. e. Mkalign msa, Mmafft msa and Mmuscle msa) when processing MSAs containing more than 100 sequences. This mode is significantly faster. It is used by Ensembl compara  (15)  and takes ∼3 h to process a MSA containing 1000 sequences. The browser does not need to stay open. Thanks to a cookie system private to the user's computer, the 'History' link on the top bar provides access to earlier computation results whenever the TCS page is re-opened.