dataesr/scientific-paragraphs-categorization huggingface dataset:
The Tothill dataset also comprises the follow-up time, survival status and microarray gene expression of 278 patients and 19,816 gene expression levels  [22]. These three datasets have 6,965 genes in common that were therefore adopted for all the experiments using the Gene Co-expression Network. The same number of genes are present in the Gene Functional Network, which will be considered the benchmark to determine and confirm the weighted degree as the best centrality measure to be used in DEGREECOX. High edge weights imply a strong connection between the corresponding genes/vertices. This is desirable for centrality measures such as the weighted degree. However, for the betweenness and closeness centrality measures, this would lead to more highly connected vertices having lower betweenness, since they will not be present in the shortest paths.
Performance evaluation of the Cox models With the best candidate metric selected, experiments were carried out with DEGREECOX using the weighted degree of the network and compared against three existing models: NET-COX, LASSO and RIDGE. The latter two are sub-cases of the elastic net with regularization parameters α = 1 and α = 0, respectively. The other parameters for the models were selected using five-fold cross-validation, following the same procedure previously used  [14]. In the cross-validation procedure, the dataset is partitioned in 5 different folds, where four of them are used in model training to find the model's coefficients ( β(-i) λα ) and the i-th set is left out. This procedure is performed 5 times for each (λ, α) parameter combination, or (λ), depending on the model. The test itself will determine the parameters that best fit the training data and perform best to new unseen data.
This is done by maximizing the partial likelihood (pl) between the full dataset (X) and the pl of the test set (X (-i) ). CV (λ, α) = 5 i=1 pl X, β(-i) λα -pl X (-i) , β(-i) λα. ( 16 ) Three different analytical methods were used to evaluate the models: the root mean squared error (RMSE); the concordance index (c-index); and the Kaplan-Meier estimator. The residuals used to calculate the RMSE were the deviance residuals  [37] , that calculate the difference between the log-likelihood (Eq. 6) for each individual in the dataset using the global inferred model ( β) and a saturated, or full model, ( β). The saturated model is a perfect fit for the data, as the β coefficients are allowed to be different for each individual. This residual is centered in zero and can be regarded as the generalization of the residual sum of squares  [37] : res Deviance = -2 log(l( β)) -log(l( β)).
It allows for right censored data and, when calculated for two different groups, we use the log-rank test  [39]  to compare survival distributions. In order to test the predictability of the models the following procedure was used: find the best parameters for a training dataset using 5-fold cross validation and then test on the same dataset and 2 others. For example train a model with Bonome to test with the TCGA and Tothill dataset. The results obtained are summarized in Tables  1  and 2  to assess the generalization capability of the methods with new data and how it fits with the training set. We observe that DEGREECOX, NET-COX and RIDGE regression perform very similarly across all three evaluation measurements. Regarding the deviances, as measured by RSME, we can conclude that network information improves the results in all the datasets except for TCGA tested on TCGA, where RIDGE achieves lower deviances.
For the Bonome and Tothill datasets, DEGREECOX has the best results. When using cross-testing, NET-COX has the best results for the Bonome and Tothill datasets and DEGREECOX for the TCGA dataset. NET-COX determines a very good model using the Tothill dataset as training, but then alternates with RIDGE and DEGREECOX Table  1  Deviance and C-index results for models by 5-fold cross-validation and tested on all 3 datasets (including 2 that were hidden from the training phase). The LASSO and RIDGE methods do not use network information so the values for GCN and GFM are the same, they are only shown in both networks when they are better than DEGREECOX and NET-COX   on the other datasets. Such similar results are expected, as both NET-COX and DEGREECOX use the same additional information, the GCN and GFM networks.
5 ) and log-rank tests performed, all available as Additional file 1. The analysis was done for each model and shows that when testing with the Bonome and TCGA datasets, there is a statistically significant difference between the survival functions of the two groups across all models. The dataset that had the worst separation was the Tothill one, as LASSO and RIDGE perform in a similar way to the other methods up until month 30, which can be seen in Fig. 5c  and d. Afterwards, both curves start to converge to each other. This observation is coherent with the p-value results of the log-rank test in Table  2. This result in particular shows that enriching the models with network-based information can lead to better predictive models. When measuring the separation between two groups by assessing the p-value of the log-rank test, there is a slight improvement in the results of DEGREECOX for the 50 %-50 % partition over the top 40 %-lower 40 % case (where 20 % of the observations are excluded), which might indicate a better performance in the presence of noisy information.
-values for log-rank test results for models chosen by 5-fold cross-validation and tested on all 3 datasets (including that were hidden from the training phase). The log-rank tests the separation in two categories of patients, high and low risk based on the expression dataset, using the top and lower 40 % PI groups and the top and lower 50 % PI groups. The LASSO and RIDGE methods do not use network information so the values for GCN and GFM are the same, they are only shown in both networks when they are better than DEGREECOX and NET-COX. The p-values when the model is tested on the same dataset used in training are always 0 and are ommited from the table.
Funding This work was partially supported by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 633974  ( SOUND  project), and  FCT , through IDMEC, under LAETA, projects  UID/EMS/ 50022/2013  and  PERSEIDS (PTDC/EMS-SIS/0642/2014 ). SV acknowledges support by  Program Investigador FCT  ( IF/00653/2012 ) from  FCT , co-funded by the  European Social Fund (ESF)  through the  Operational Program Human Potential (POPH ). AV acknowledges support from  FCT  ( SFRH/BD/97415/2013 ).
864E-5 0. 5630 0. 0033 0. 0045 4. 302E-4 5. 264E-7 7. 183E-7 0. 0026 8. 138E-4 1. 105E-8 1. 632E-7 0. 0050 3. 499E-5 0. 6464 0. 0050 TCGA Tothill GCN GFM 6. 547E -6 5. 822E-6 4. 241E-5 1. 432E-6 1. 765E-5 0. 0036 5. 781E-5 1. 309E-4 1. 247E-4 3. 126E-4 0. 0029 0. 0193 Train Bonome Test TCGA Tothill Bonome Network GCN GFM GCN GFM GCN GFM 40 % PI Thres. DEGREECOX 2. 084E-5 2. 124E-5 0. 0013 4. 390E-4 2. 046E-4 3. 990E-4 NET-COX 1. 082E-4 2. 791E-5 7. 726E-4 1. 514E-4 2. 815E-4 1. 185E-4 RIDGE 1. 594E-6 2. 537E-4 4. 233E-4 LASSO 0. 0364 0. 0048 7. 436E-5 50 % PI Thres. DEGREECOX 3. 332E-4 5. 284E-5 0. 0076 0. 0084 4. 394E-4 0. 0090 NET-COX 2. 169E-5 5. 086E-5 0. 0170 0. 0179 0. 0036 0. 0015 RIDGE 1. 795E-5 0. 0013 3. 193E-4 LASSO 0. 0720 0. 0048 0. 0022 Values in bold represent the best method for the dataset/network combination (per 40 % and 50 % separation).
Introduction Analysis and visualization of massive genomics datasets are an ongoing trend in plant sciences, especially for tropical crops where this subject can help to tackle challenges linked to human activities and Climate Change: conservation and analysis of biodiversity (loss because of human activity), food security (the 9 billion people question), new usage of plant material, etc. In the recent years, because of successively lower costs of genomic sequencing, a large number of groups have developed massive resources and data which require high performance computational resources and new analytical approaches  [1, 2]. The South Green portal is an ecosystem of tools that were originally developed as independent entities to fulfill the need for specific projects or crops, but have evolved over time to generic tools to comprehensively study crop genomics.
Specialized resources Some resources from the South Green portal are dedicated to specialized datasets. Among the Gene-based databases, Green-PhylDB  [3]  provides access to resources for comparative genomics and orthology identification in plant genomes, and OryGenesDB  [4]  is a database developed for rice reverse genetics, and containing FSTs (flanking sequence tags) of various mutagens and functional genomics data, collected from both international insertion collections and the literature. Other resources are -(i) Markerbased databases like TropGeneDB  [5]  which connects data on molecular markers (e. g. Simple Sequence Repeats, Diversity Arrays Technology), Quantitative Trait Loci, genetic and physical maps, phenotyping studies, and information on genetic resources (geographic origin, parentage, collection), (ii) SNiPlay  [6]  which allows querying both SNPs (Single-Nucleotide Polymorphism) and InDels derived from NGS (Next-Generation Sequencing) projects (Whole-Genome Sequencing, Genotyping by Sequencing, RNA-Seq) and computing a set of web analytical workflows for the resulting variants (diversity, population stratification, Genome-Wide Association Study), and proposes graphical representations of the results, and (iii) Gigwa  [7]  providing exploration of very large genotyping studies by filtering them based on not only variant features (SNP, Indels), including functional annotations, but also genotype patterns, in order to extract subsets in various popular export formats.
Current developments are on the way for databases aimed to host/pathogen interactions, catalogs of plant pathogens strains and so on. Recently, based on our experience with Web Semantic technologies  [8] , we developed AgroLD (www. agrold. org) an RDF (Resource Description Framework) knowledge base that consists of integrated data from a variety of plant resources and ontologies. A complete summary of the South Green databases with their description is available at http://www. southgreen. fr/databases.
Genome Hubs Our participation in several reference genome sequencing projects  [9] [10] [11]  has led us to develop crop-specific information systems, so called Genome Hubs, to manage the corresponding genome annotations and linked datasets. Data available are under different forms, from complete genome sequence along with gene structure, gene product information, metabolic pathways, gene families, transcriptomic assays (Expressed Sequence Tags, RNA-Seq), genetic markers as well as genetic and physical maps. Several Genome Hubs were released: Banana  [12] , Cassava, Cacao, Coffee  [13] , and others (e. g. Rice, Magnaporthae) are currently under development. Genome Hubs are powered by major GMOD (Generic Model Organism Database) components (i. e. Chado, Cmap, Jbrowse, Tripal, Galaxy, Pathway tools) and complemented by resources and tools developed within the South Green framework such as Green-PhylDB, SNiPlay and TropGeneDB.
Galaxy workflows Galaxy  [14]  is a web-based service that allows an easy access to the bioinformatic applications and strongly supports reproducibility of analysis steps. Through its graphical interface, non-bioinformatician scientists are able to conduct small scale as well as medium range analyses in a user-friendly manner. In addition to the generic tools provided with the standard installation of Galaxy, the South Green Galaxy instance (http://galaxy. southgreen. fr/galaxy/) contains a large collection of exclusive tools. The Galaxy Tool Shed allows these modules to be easily shared with any other Galaxy instance. In addition, preconfigured workflows were designed for recurrent analyses in plant genomics such as NGS mapping/cleaning, SNP calling and filter-ing, Genome-Wide Association Study, basic population genetics, structural variations and phylogenetics.
TOGGLE TOGGLE  [15]  is a command-line tool designed to allow biologists to create large workflows without knowing how to code it (https://github. com/SouthGreenPlatform/). Using editable configuration files, it proposes a more in-depth control of customizable pipelines and allows the management of many more samples than Galaxy (ex: massive resequencing projects, more than 50 samples), as well as the management of tools versions. It can be used on wide array of analyses, such as NGS data cleaning, DNAseq, RNAseq, restriction site-associated DNA sequencing (RADseq)/Genotyping by Sequencing (GBS), genome and transcriptome assembly, SNP calling, structural variations detection, and so on. The current version can be deployed on laptop, large machine as well as a HPC cluster, using normal system of Docker machines, and is scheduleraware.
Conclusion The South Green portal is developed by a local network of scientists gathering bioinformatics and genomics skills located on the Agropolis campus from Montpellier as a joint collaboration between CIRAD, IRD, INRA, SupAgro and Bioversity international. Based on this strong local community in the field of agriculture, food and biodiversity, various bioinformatics applications and resources dedicated to genomics of tropical and Mediterranean plants have been developed, published and provided to the international community. Source code can be also found on GitHub (https://github. com/SouthGreenPlatform).
Funding This work was supported by  Agropolis Fondation/AGRO Labex  ( ID ARCAD 0900-001 ,  ID RTB Multi-Genomes Hub   1403-018 ), the  French Institute of Bioinformatics (IFB) ,  ANR  (GNPAnnot, MusaTract, CoffeaSeq, AfriCrop # NR-13-BSV7-0017 ),  CIRAD  and  IRD  (computer infrastructures), the  CGIAR Research Programs on Roots, Tubers and Bananas and GRiSP/RICE, IRIGIN/France Génomique  (National infrastructure, funded as part of " Investissement d'avenir" program  managed by  ANR  #  ANR-10-INBS-09 ), and  NUMEV Labex  (LandPanToggle # 2015-1-030-LARMANDE ).
On the contrary, the addition of RSD data tightens the constraints significantly. The WL contours, including the ultraconservative cut that removes dependence on nonlinear physics, result in weaker constraints. In the table, µ 0 -1 and η 0 -1 are obtained by reconstructing Eqs. (  46 ) and (  47 ) from E 11 and E 22 at the present time. In addition, the present value of the Σ parameter, defined in Eq. (  5 ), can be obtained from µ and η as Σ = (µ/2)(1 + η) using Eqs. (  4 ) and (6). 46 ) and (47) at the present time in the DE-related parametrization when no scale dependence is considered (see Sect. 5. 2. 2). Σ is obtained as Σ = (µ/2)(1 + η). The time-related evolution would give similar contours. In the labels, Planck stands for Planck TT+lowP. [µ(z, k) -1] + [η(z, k) -1], which corresponds to the (approximate) maximum degeneracy line identified within the 2 dimensional posterior distributions.
The tension can be understood by noticing that the best fit power spectrum corresponds to a value of µ and η (E 11 = -0. 3, E 22 = 2. 2 for Planck TT+lowP) close to the thick long dashed line shown in Fig. 1  for demonstration. This model leads to less power in the CMB at large scales and a higher lensing potential, which is slightly preferred by the data points with respect to ΛCDM. This explains also why the MG parameters are somewhat degenerate with the lensing amplitude A L (which is an 'unphysical' parameter redefining the lensing amplitude that affects the CMB power spectrum). As discussed in Planck Collaboration XIII (2015) (see for ex. Sect. 5. 1. 2), ΛCDM would lead to a value of A L  (Calabrese et al. 2008)  somewhat larger than 1. When varying it in MG, we find a mean value of A L = 1.
(50) This has three extra parameters (α, β 1 , β 2 ), in addition to w. If β 1 = 1 and β 2 = 0 this becomes the generalized k-essence model. An example of this class of models is "kinetic gravity braiding"  (Deffayet et al. 2010 ) and similar to the non-minimally coupled k-essence discussed via EFT in Sect. 5. 2. 1. The α parameter in Eq. (  50 ) can be now interpreted as a sound speed, unconstrained as in results above. There are however two additional degrees of freedom, β 1 and β 2. RSD data are able to constrain them, with the addition of Planck lensing and WL making only a minor change to the joint constraints. As in the LVMG case, we use a new basis γ i = |1 + w| α i β i in the MCMC analysis, where α 1 = 0. 2 and α 2 = 1 were chosen to decorrelate w and γ i. The resulting 2σ upper limits are γ 1 < 0.
As shown in Fig. 19 , there is a degeneracy between the optical depth, τ, and the f (R) parameter, B 0. Adding any structure formation probe, such as WL, RSD or CMB lensing, breaks the degeneracy. Figure  20  shows the likelihood of the B 0 parameter using EFTcamb, where a ΛCDM background evolution is assumed, i. e. , w DE = -1. As the different data sets provide constraints on B 0 that vary by more than four orders of magnitude, we show plots for log 10 B 0 ; to make these figures we use a uniform prior in log 10 B 0 to avoid distorting the posterior due to prior effects. However, for the limits quoted in the tables we use B 0 (without log) as the fundamental quantity and quote 95 % limits based on B 0. In this way the upper limit on B 0 is effectively given by the location of the drop in probability visible in the figures, but not influenced by the choice of a lower limit of log 10 B 0.
3). There is a degeneracy between the two parameters for Planck TT+lowP+BSH. Adding lensing will break the degeneracy between the two. Here Planck indicates Planck TT+lowP. Finally, we note that f (R) models can be studied also with the MGcamb parametrization, assuming the quasi-static limit. We find that for the allowed range of the B 0 parameter, the results with and without the quasi-static approximation are the same within the uncertainties. The 95 % confidence intervals are reported in Table  8. These values show an improvement over the WMAP analysis made with MGcamb (B 0 < 1 (95 % C. L. ) in  Song et al. (2007a) ) and are similar to the limits obtained in  Marchini & Salvatelli (2013) ;  Hu et al. (2013)  (see also  Dossett et al. (2014)  where data from WiggleZ were used,  Cataneo et al.
Acknowledgements. It is a pleasure to thank  Luca Amendola ,  Emilio Bellini ,  Diego Blas ,  Sarah Bridle ,  Noemi Frusciante ,  Catherine Heymans ,  Alireza Hojjati ,  Bin Hu ,  Thomas Kitching ,  Niall MacCrann ,  Marco Raveri ,  Ignacy Sawicki ,  Alessandra Silvestri ,  Fergus Simpson ,  Christof Wetterich  and  Gongbo Zhao  for interesting discussions on theories, external data sets and numerical codes. Part of the analysis for this paper was run on the  Andromeda and Perseus clusters of the University of Geneva  and on WestGrid computers in Canada. We deeply thank  Andreas Malaspinas  for invaluable help with the Andromeda and  Perseus Clusters. This research was partly funded by the  DFG TransRegio TRR33  grant on 'The Dark Universe' and by the  Swiss NSF. The Planck Collaboration acknowledges the support of:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MINECO , JA, and  RES (Spain) ;  Tekes, AoF , and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ;  ERC  and  PRACE (EU).
The normalization of V(φ) is computed using Ω m = 0. 3. Here Planck indicates Planck TT+lowP. Parameter Planck+BSH (1-param. ) Planck+BSH (3-param. ) Planck+WL+BAO/RSD Planck+lensing+BSH ∞. -0. 08 +0. 32 -0. 32 fixed = 0 -0. 11 +0. 16 -0. 12 ≤ 0. 76 (95 % CL) 0. 14 +0. 17 -0. 25 ≤ 0. 38 (95 % CL) -0. 03 +0. 16 -0. 17 ≤ 0. 52 (95 % CL) ζ s. fixed = 0 not constrained not constrained not constrained (Doran & Robbers 2006) posterior distributions showing 68 % and 95 % C. L. constraints on Ω m and s for scalar field models (see Sect. 5. 1. 4). The dashed line for s = 0 is the ΛCDM model. Here Planck indicates Planck TT+lowP. Fig. 10. Marginalized posterior distributions for Ω e for the early DE parameterization in (26) and for different combinations of data (see Sect. 5. 1. 5). Here Planck indicates Planck TT+lowP.
Consequently, the current literature cannot be used to distinguish between major mergers, minor mergers and adiabatic expansion. In this paper we study the massive, evolved galaxy cluster SPT-CL J0546-5345. We present the first high-resolution ground-based measurement of the stellar mass -size relation measured in the rest-frame light of the old stellar population at z = 1, and demonstrate the importance of measuring the relation redward of the 4000 Å break by comparing with rest-frame UV stellar mass -size relations for the same sample. In Section 2 we present our GeMS Ks-band imaging and describe our observing and data processing strategies, including successful correction of the optical distortion and faint source residual images. We also present our ancillary data of GMOS spectroscopy and HST archival F606W and F814 band imaging.
Data reduction The basic data reduction steps were conducted with the Gemini IRAF GSAOI package 3 , with data from individual detectors stored as separate extensions within multiextension fits files. Daytime calibration lamp-on and lampoff dome flats were combined to generate a master dome flat. Variance and data quality extensions were populated and non-linear or saturated pixels were flagged. The frames were flat fielded using dome flats, as we found that these gave lower residual structure compared to twilight or dome multiplied by twilight sky flats. 3 The Gemini IRAF software is available at http://www. gemini. edu/sciops/data-and-results/processingsoftware.
Basic reduction was performed with the GSAOI IRAF package. We used the GAIA software package  (Draper et al. 2014 ) to perform a coarse, initial world coordinate solution (WCS) solution for each detector in the GSAOI mosaic. Following that we constructed a catalogue of sources in the astrometric field using SExtractor  (Bertin & Arnouts 1996)  and compared this to the HST catalogue  (Anderson et al. 2008)  with SCAMP  (Bertin 2006). We applied the resulting distortion correction to our science images with SWarp  (Bertin et al. 2002) , which mosaicked the individual detectors to a single image. We provide more detailed instructions at Appendix A. The mosaicked images were reregistered to a common reference by centroiding the single bright star common to our science images. The astrometric rms is 5 mas internally and 8 mas with respect to HST ACS F814W imaging.
Short wavelength imaging observations Archival high angular-resolution imaging data is available for SPT-CL J0546-5345, with HST /ACS imaging in the F606W and F814W broad-band filters observed as part of program 12477 (PI: High). We obtained multi-drizzled HST /ACS data from the Mikulski Archive for Space Telescopes 5. Exposure times were 1920 s for F606W and 1916 s for F814W. Zero points were calculated in a similar manner to § 2. 2. A three-colour composite image of SPT-CL J0546-5345 is shown in Figure  1. The native resolution of the HST /ACS F814W (∼ I-band) image is equivalent to that of the AOassisted Gemini/GeMS Ks image, as is highlighted by the close-up of the cluster core region shown in Figure  2.