dataesr/scientific-paragraphs-categorization huggingface dataset:
To identify interaction differences between TSCs and ESCs in a stringent manner, we developed a statistical model (see Methods) that performs a direct comparison of the mapped data from both cell types, yielding a list of statistically significant differential interactions. These differential interactions also displayed strong differences in contact frequency in a genome-wide Hi-C dataset from an independently derived ESC line (Supplementary Fig. 2b )  31. Notably, genes involved in TSC-specific interactions included a number of essential trophoblast-associated genes such as Tfap2c (Fig. 1b ), Eomes, Tead4, Id2 and Fgfr2. ESC-specific interactions included various developmental genes such as Six3, Pax1 (Fig. 1b ), Evx1 and Myocd, as well as the Hox gene clusters (Fig. 1c , Supplementary Fig.
3e ). We then focused specifically on TSC-expressed genes that were involved in H3K27me3 homotypic interactions in ESCs. Strikingly, among these we uncovered an ESC-specific long-range interaction between Gata3 and Sfmbt2 (Fig. 2e , Supplementary Fig. 4a ). Gata3 encodes a key transcription factor involved in the c Genome browser view of the Hoxa gene cluster highlighting a reduction in intra-cluster interactions in TSC cells establishment of the trophoblast lineage  11  , and Sfmbt2 is an imprinted gene essential for maintenance of the trophoblast lineage  32. Other examples of TSC-expressed genes that are involved in such long-range repressive interactions in ESCs included Nfatc1 and the homeobox genes Irx1, Irx2 and Six2 (Fig. 2f , Supplementary Fig. 4a ). These interactions could also be robustly detected in a genome-wide Hi-C dataset (Supplementary Fig.
5. 8), which maps the ditags against the mouse genome (mm10), filters experimental artefacts, such as circularised reads and religations, and removes duplicate reads  51. For read yields and quality control metrics see Supplementary Fig. 1. To maintain the statistical power even between the two cell types, reads from the TSC dataset were randomly subset to match the number of reads in the ESC dataset. Significant promoter-genome interactions were called using the GOTHiC BioConductor package (v1. 14. 0). To exclude low frequency contacts, which are likely to be non-functional or have a role only in a very small proportion of cells, we established stringent q-value thresholds that ensured only visibly strong contacts with a given promoter were kept-based on empirical assessment of 4C-like tracks.
For multiple testing correction we use Independent Hypothesis Weighting (IHW v. 1. 6. 0) and a false discovery rate of 5%. For the sole purpose of comparing our interactions with CHiCAGO-processed PCHi-C data from human blood cell types  30  (Supplementary Data 1) we also identified ESC and TSC interactions using the CHiCAGO Bioconductor package (v1. 6. 0)  52. Interactions were merged from biological replicates using CHiCAGO and those with score >5 were considered in our analysis. For Ring1A KO and Ring1A/B DKO PCHi-C data  27  (Supplementary Data 1), processed GOTHiC calls were downloaded and lifted over from mm9 to mm10. For genome-wide ESC Hi-C data  31  (Supplementary Data 1), Knight-Ruiz normalised matrices for regions of interest were extracted using Juicer  53. ChIP-seq and DNase-seq data processing.
Fastq files from publicly available datasets (Supplementary Data 1) were downloaded via the EMBL-EBI European Nucleotide Archive. Reads were trimmed using Trim_galore! and aligned to the mm10 genome assembly using Bowtie2 v2. 1. 0 (ref. 54 ), followed by filtering of uniquely mapped reads. ChIP-seq peak detection was performed using MACS2 v2. 1. 1 (ref. 55 ) with -q 0. 05; for histone marks the option --broad was used. DNase-seq peak detection was performed using F-seq v1. 84 (ref. 56 ) with options -f 0 -t 6. ChIP-seq signal densities were calculated by normalising read counts at each peak by the total read counts and peak length. The MatchIt R library was used to identify matched peaks (in length and ChIP-seq signal density) between ESCspecific interactions and shared interactions. Enhancers were first defined based on the H3K4me1 peaks in each cell type: (1) H3K4me1 peaks overlapping H3K27ac but not H3K27me3 were classified as active enhancers, (2) those overlapping H3K27me3 as poised and (3) those overlapping neither mark as intermediate.
Peaks within 1 kb of RefSeq transcriptional start sites were excluded. A second list of active enhancers in TSCs was defined by finding the intersection between DNase I, H3K4me1 and H3K27ac peaks, merging enhancers within 300 bp of each other, and filtering out particularly small (<100 bp) or large (>3 kb) enhancers. RNA-seq data processing. Fastq files from publicly available datasets (Supplementary Data 1) were downloaded via the EMBL-EBI European Nucleotide Archive. Reads were trimmed using Trim_galore! and aligned the mm10 genome assembly with Tophat v2. 0. 9 (ref. 57 ) using a transcriptome index from Illumina's iGenomes. For Ring1A KO and Ring1A/B DKO RNA-seq, mapped bam files were directly downloaded from the respective data repository. Transcript FPKM (fragments per kilobase of transcript per million mapped reads) values were extracted using Seqmonk.
Acknowledgements We thank  Sarah Elderkin  for ESC culture,  Kristina Tabbada  and  Clare Murnane  for nextgeneration sequencing;  Steven Wingett  for initial PCHi-C data processing;  Boyan Bonev  for help with extracting Hi-C data and  Sarah Burge  for useful discussions. S. S. was supported by the  Biotechnology and Biological Sciences Research Council  ( BB/J004480/ 1 ) and a  Career Progression Fellowship  from the  Babraham Institute. B. M. holds an  MRC eMedLab Medical Bioinformatics Career Development Fellowship , funded from award  MR/L016311/1. C. D. T. was supported by  The Medical College of Saint Bartholomew's Hospital Trust. M. R. B. is a  Sir Henry Dale Fellow  ( 101225/Z/13/Z ), jointly funded by the  Wellcome Trust  and the  Royal Society. This project was enabled through access to the  MRC eMedLab Medical Bioinformatics infrastructure , award  MR/L016311/ 1.
Some authors have started employing the GMI as primary endpoint. At the time of writing (April 2017), there were a total of ten oncology trials registered in the European Union Clinical Trial Register and eleven oncology trials registered on the https://www. clinicaltrials. gov website as using GMI. For example, Von Hoff et al. [3]  used the GMI to measure the activity of a targeted therapy selected by molecular profiling in patients having failed all effective treatments. Eighteen out of 66 patients (27%) had a progression-free survival (PFS) ratio superior to 1. 33 (95% confidence interval: [17%; 38%]). Several others published trials  [4] [5] [6]  used a GMI-based approach to assess the activity of second-line treatments, but the estimation did not account for patients with censored times to progression.
This distribution corresponds to a gamma distribution with mean and variance equal to 1 and. Shared frailty models allow estimating the intrapatient dependence via Kendall's , which is a rank correlation measure of the concordance between time pairs. In the case of a gamma frailty model, Kendall's  is equal to /(+2) and can thus be estimated by plugging in the estimate of. Different distributions can be assumed for the baseline hazard  [11] ; we chose a Weibull distribution because it was the one which fitted the best our advanced colorectal data. We fitted and compared the parametric frailty models using the parfm package in R  [11].
Probabilistic Neighbourhood Tractography (PNT) is an automatic segmentation method, which provides whole tract-averaged measures of FA and MD in major white matter fasciculi. This method, first described by Clayden et al  18, 19  , has been optimized for neonatal data  20. It involves single seed point tractography to segment a tract of interest by modelling the length and shape variation of individual tracts compared with a pre-defined reference tract. We aimed to compare FA and MD values measured from geometric ROIs with tract-averaged values obtained using PNT in the genu of the corpus callosum (gCC) and the CST. The secondary aim was to evaluate intra-rater variation in FA and MD values obtained by ROI placement within these tracts.
Magnetic Resonance Imaging A Siemens Magnetom Verio 3 T MRI clinical scanner (Siemens Healthcare GMbH, Erlangen, Germany) and 12-channel phased-array head coil were used to acquire: T1-weighted MPRAGE volume (~1 mm 3 resolution), T2weighted STIR (~0. 9 mm 3 resolution), T2-weighted FLAIR (~1 mm 3 resolution), and diffusion MRI (11 T2-and 64 diffusion encoding direction (b = 750 s/mm 2 ) single-shot spin-echo echo planar imaging (EPI) volumes with 2 mm isotropic voxels) data. Infants were examined in natural sleep with pulse oximetry, temperature and electrocardiography data monitoring. Ear protection was used for each infant, comprising earplugs placed in the external ear and neonatal earmuffs (MiniMuffs, Natus Medical Inc. , CA).
Diffusion MRI Data Processing After conversion from DICOM to NIfTI-1 format, dMRI data were preprocessed using FSL tools (FMRIB, Oxford, UK; http://www. fmrib. ox. ac. uk). This included brain extraction and removal of bulk infant motion and eddy current induced artifacts by registering subsequent diffusion-weighted volumes to the first T2-weighted EPI volume for each subject. Using DTIFIT, MD and FA volumes were generated for every subject. Underlying connectivity data was provided by FSL's BedpostX/ProbTrackX algorithm run with its default parameters: 2-fiber model per voxel, 5000 probabilistic streamlines for each tract with a fixed separation distance of 0. 5 mm between successive points  21, 22. For ROI analysis, values were extracted from a 8 mm 3 cubic ROI placed at the centre of the genu of corpus callosum (manual visual placement in axial plane being midline between the superior margins of the lateral ventricles, below the cerebral cortex; Figure  1c ), and right and left PLIC (manual visual placement in axial plane being inferior-lateral on the PLIC, seen as 2 diagonal bands of increased signal intensity within the basal ganglia; Figure  1f ) on the T2-weighted EPI volume.
'In-house' MATLAB (http://uk. mathworks. com) code was written to allow the user to interactively place these cubic ROIs using a mouse-driven GUI and generate FA and MD values from them; FA and MD parametric volumes are, by definition, co-registered to the T2-weighted volume for each subject. For PNT analysis, we used the method first described by Clayden et al  18, 19  and refined by Anblagan et al  20  for the neonatal brain. Reference tracts for the gCC (Figure  1a ) and CST (Figure  1d , e) were generated from a previous study of 20 term-control infants  20. Individual tracts of interest (gCC, right and left CST) were segmented using PNT as implemented in TractoR (http://www. tractor-mri. org. uk). The resulting tractography mask was applied to each subject's MD and FA maps and tract-averaged values were calculated (see Figure  1b  for an example segmentation of the genu in a representative subject, and Figure  2a-c  for examples of segmented CC and CST in 3D space).
Acknowledgements We are grateful to the families who consented to take part in the study and to the nursing and radiography staff at the  Clinical Research Imaging Centre , the  University of Edinburgh  (http://www. cric. ed. ac. uk) who scanned the infants. We thank  Thorsten Feiweier  at  Siemens Healthcare  for collaborating with DTI acquisitions ( Works-in-Progress Package for Advanced EPI Diffusion Imaging ).
It should be noted that both ESD and EDD of fhIR hearts treated with 10 µM paclitaxel were statistically different from ESD and EDD of wild-type control hearts when statistical analysis was performed on the full set of data presented in Table 1 (P<1×10 -4 ), showing that paclitaxel treatment only led to a partial rescue at this concentration. We could not determine whether higher concentrations of paclitaxel would be able to improve DD as efficiently as MB at 30 μM, due to the narrow therapeutic window of this compound. We also studied the effects of paclitaxel Fig. 1. Primary screen of the Prestwick Chemical Library. (A) Scheme of the screening procedure. F0 flies were UAS-fhIR males and UAS-mitoGFP; Hand-GS females. F1 male flies were UAS-mitoGFP/UAS-fhIR; Hand-GS/+ (named fhIR flies). F1 individuals developed on medium containing RU486, to induce frataxin inactivation, and the drugs to be tested.
In vivo imaging of fly hearts and movie analysis Flies were anesthetized with FlyNAP (Carolina Biological Supply Company). The anterior parts of heart (abdominal segments A1/A2) were observed with a Zeiss SteREO Lumar. V12 stereomicroscope, with a NeoLumar S 1. 5× objective. Video movies were acquired with a Hamamastu Orca Flash 4. 0 LT camera (50 frames per second, 501 frames per movie). For the primary screen, each video was analyzed as described in  Seguin et al. (2015)  using ImageJ to estimate the diastolic diameter (DD), from a picture generated by flattening all frames into one (code available at https://github. com/MichaelRera/autoMmodeGen). This method was used at this step because it was appropriate and fast enough to analyze several thousand movies, as required during the primary screen.
Statistical analysis For selection of drugs in the primary screen, comparison of DDs of treated and untreated fhIR flies were carried out independently for each drug subset to ensure that control flies experienced the same conditions as drug-treated flies. Analysis was performed with R (https://r-project. org) under RStudio environment (https://rstudio. com) and the statistical significance assessed by non-parametric Wilcoxon analysis. Additionally, a global analysis was performed at the end of the whole screen on normalized data, and compounds corresponding to a Z-score >2 were added to the initial selection for further validation. For the validation step, since several experiments were performed on each selected compound, we included all the experimental values in an Access database to facilitate subsequent analysis.
To take into account small variations occurring between independent experiments, we first normalized diastolic and systolic diameters in each experiment (i) with a coefficient N i calculated as: N i ¼ medianðDD fhIR;untreated i Þ medianðDD fhIR;untreated all experiments Þ : This procedure allowed us to perform a statistical analysis of all these normalized values with the GraphPad Prism 6 software. Statistical significance of each selected compound was assessed with non-parametric ANOVA (Kruskal-Wallis test with Dunn's post hoc test) and compounds with P-values <5×10 -2 were retained.
To control the application of rewrite rules, Porgy provides a strategy language. The latest version of Porgy can be downloaded from http://porgy. labri. fr either as source code or binaries for MacOS, Windows or Linux machines. Building on these previous works, this paper focuses on the formal structure of labelled port graph and explains its role in the design of executable specifications of complex systems. Labelled port graphs are port graphs where ports, nodes and edges carry a label, which is an expression from a formal language given as a parameter together with its interpretation domain. The notion of attribute used in Porgy to define properties of nodes, ports and edges can be seen as a particular case of label, where a set of built-in function symbols and predicates are available (for example, arithmetic operators, and predicates interpreted over a graph domain, such as edge(n, n ), which holds if there exists an edge linking the nodes n and n in a given graph).
), or symbolic terms built on a signature Σ = (S, Op) of an abstract data type and a set X S of variables of sorts S. We denote by T (Σ, X S ) the set of terms over Σ and X S. We use a set Pred of predicates involving equality, disequality and ordering on numerical values. Definition 2. 4 [Ground and symbolic labelled port graph] Given a formal language L defined by a signature Σ = (S, Op, Pred) and a set X S of variables of sorts S, we call symbolic port graph a labelled port graph whose labels contain variables, and ground port graph a labelled port graph without variables. Sorted variables are instantiated in the sorted semantic domain associated to L. In Porgy, labels with abstract values (i. e. , expressions v i ∈ T (Σ, X S ) that may contain variables), allow us to define generic patterns in rewrite rules: abstract values in left-hand sides of rewrite rules are matched against concrete data in the graphs to be rewritten.
Different types of edges can be visually distinguished thanks to labels Colour and Shape. For example, additional edges can be created as shortcuts between two states, for instance to represent a derivation in a more concise way. Such a shortcut has a label that records the sequence of steps involved in the shortcut. Figure  9  gives an example of a derivation graph, where shortcut edges are depicted in green. Strategies are used to control the application of rules (including probabilistic application) and to focus on points of interest: strategies define which rule(s) should be applied and where (see  [17, 5, 18]  for general definitions). They may also express shortcuts between two states as shown in Figure  9. The language used in Porgy to write strategies for port graph rewriting is defined in  [13].
Related works Graphs are used in many forms and contexts in computer science and the need to generate, visualise and transform them led to the development of a variety of tools implementing labelled graph transformation and rewriting. With the aim of promoting these concepts in the software developers community using UML and Java, the language of Story diagrams  [15] , embedded in the Fujaba Tool Suite  [24] , adopts most of the features of Progres  [31]  but avoids the backtracking mechanism related to the non-determinism of graph rewriting. GROOVE  [30]  is another graph transformation system closely-related to Porgy. It uses labelled graphs, and transformations are specified by rules and control. GROOVE has been used to model and analyse complex systems in various domains as illustrated in  [16].
It is a versatile tool; however, it does not provide the visualisation and animation features available in Porgy. GP  [28]  is also a closely related rule-based, non-deterministic programming language, where programs are defined by sets of graph rewrite rules and a textual strategy expression. The strategy language has three main control constructs: sequence, repetition and conditional. Since the aim is to execute graphs programs efficiently, GP builds only one rewriting derivation, although early versions of GP used a Prolog-like backtracking technique to explore the whole derivation graph. GP does not provide mechanisms to visualise the derivation tree, unlike Porgy, where users can interactively navigate on the tree, visualise alternative derivations, follow the evolution of specific redexes, etc.
None of the languages above has Position constructs. Compared to these systems, Porgy's strategy language clearly separates the issues of selecting positions for rewriting and selecting rules, with primitives for focusing as well as traditional strategy constructs. Graph rewriting is also widely used in chemistry and biology. Systems such as BioNetGen  [11] , RuleBender  [32] , Mosbie  [33]  address the problem of modelling huge graphs. They integrate visualisation with modelling and simulation of rulebased intracellular biochemistry, but do not provide a strategy language. However the rules are quite similar to Porgy's and BioNetGen uses port graphs. The graph transformation approach developed in  [20]  encapsulate in "units" rules and control conditions as in our strategic rewrite graph programs.
Acknowledgement We thank  Oana Andrei ,  Guy Melançon  and  Olivier Namet  for their work in the initial  Porgy  project ( 2009-2012 ); their ideas and enthusiasm were invaluable during the early stages of development of this tool. We also thank  Jason Vallet  for implementing several features of Porgy, writing the documentation and developing the social network propagation influence example. Our thanks also go to the anonymous referees whose valuable remarks and suggestions helped us to improve the first version of this paper.
We identified 54 studies as eligible for inclusion and contacted all authors to obtain missing estimates and individual-level data, where possible. 34 studies availed enough information to be included in a meta-analysis. The meta-analysis consisted of mixed effects regression models, one per each relevant acoustic feature. Results: Of the 37 authors contacted, 59% responded and 5% provided at least some of the requested data. Chief reasons of denials were: i) data loss (n = 8), ii) effort required (n = 5), iii) ethical concerns with data sharing (n = 1). On the results available we found significant meta-analytic effects of schizophrenia in percentage of spoken time (n = 6, d = -1. 16, 95% CIs: -2. 06 -0. 27) and proportion of pauses (n = 5, d = 0. 56, 95% CIs: 0. 15 0. 96). After controlling for influential studies, we found significant differences also in pitch mean (n = 5, d = 0.
Discrepancies do not affect results as we used our pipelines for all genomes, including JGI genomes. We simultaneously assessed the completeness and redundancy of our genome assemblies and gene predictions using BUSCO  (Simão et al. 2015) , which scores the presence and absence of known single copy orthologs in predicted proteomes. Overall, we recovered between 95% and 98% of BUSCO's 1,438 fungal Current literature identifies the loss of PCW degrading enzymes as a potentially defining feature of EM genomes  (Nagendran et al. 2009; Wolfe, Kuo, et al. 2012; Kohler et al. 2015; Martin et al. 2016). To catalogue the depth and breadth of PCW degrading enzyme reductions in EM Amanita, we annotated the full complement of carbohydrate active enzymes in genomes using the CAZy database  (Lombard et al.
We were able to annotate 57% of the expanded clusters with InterPro domains and 44% with GO terms, while 84% and 62% of the contracted clusters were annotated with InterPro domains and GO terms, respectively. Figure  5  plots significantly overrepresented InterPro domains (fig. 5A ) among expanded and contracted gene families, as well as enriched GO terms in the "Biological Process" category (fig. 5B  and C ). To visualize the analysis, GO terms were consolidated into semantically similar terms using REVIGO  (Supek et al. 2011). A comprehensive list of enrichment results is provided in supplementary table  S2 , Supplementary Material online. Gene families expanded early in the evolution of the EM niche, within the MRCA of EM Amanita, are significantly enriched for several domains found in FAD-dependent oxidoreductases, including Berberine Bridge Enzymes (BBEs; IPR006094, IPR016166, IPR016167, IPR016169, IPR012951).
These enzymes are involved in a range of different chemical reactions, and in fungi reactions include oxidation of carbohydrates and the biosynthesis of alkaloids  (Daniel et al. 2017). The enrichment for oxidoreductases is also reflected in the GO term analysis, where the strongest overrepresentation was found for the term "oxidation-reduction process" (GO: 0055114). Other significantly enriched terms suggest expansion of genes involved in nucleosome assembly (GO: 0006334 "nucleosome assembly") and posttranslational regulation (GO: 0006468, "Protein phosphorylation"). Gene families contracted early in the evolution of the EM niche, within the MRCA of EM Amanita, are significantly enriched for genes involved in carbohydrate metabolism, for example, the overrepresentation of InterPro domains IPR017853 "Glycoside hydrolase superfamily" (now obsolete), IPR013781 "Glycoside hydrolase, catalytic domain" and IPR005103 "Glycoside hydrolase, family 61.