dataesr/scientific-paragraphs-categorization huggingface dataset:
The idea is hence to provide an underapproximation of the model that only contains counterexample traces. The underapproximation is obtained by a search algorithm that segments the execution trace into a sequence of regions, whose states are eventually "funneled" into the following one, so that progress towards a fair region condition is ensured  [14, 15]. All the above algorithms have been implemented in the nuXmv model checker  [16] , a successor of the NuSMV model checker  [17] , born from a joint project between FBK and CMU. nuXmv integrates at its core the MathSAT  [18]  SMT solver to support a number of SMT-based techniques for VMT, and supports the standardized VMT language  [19]. In turn, nuXmv is at the core of the HyCOMP  [20]  model checker for hybrid automata, the xSAP  [21]  tool for safety assessment, and of the OCRA  [22]  tool for contract-based design.
The techniques described in this paper have been applied in several industrial settings: nuclear  [23] , biological  [24] , railways  [25, 26] , avionics  [27] , space  [28, 29] , software engineering  [30, 31] , and multi-core design  [32]. This paper is structured as follows. In Sect. 2, we overview Satisfiability Modulo Theories. In Sect. 3, we define the problem of Verification Modulo Theories. In Sect. 4, we discuss implicit predicate abstraction and refinement. In Sect. 5, we overview the abstractionbased algorithms for invariant checking, and, in Sect. 6, we discuss the extension to the nonlinear case by way of incremental linearization. In Sect. 7, we present the algorithms for LTL(T) model checking. In Sect. 8, we overview the NuSMV and nuXmv verification engines. In Sect. 9 we draw some conclusions.
6  In some sense, therefore, nuXmv carries on the legacy of Ed Clarke and his message of combining theoretical results with strong practical applications. From a technical standpoint, nuXmv integrates, extends and complements the functionalities of nuSmv with multiple functionalities to facilitate its deployment in several operational industrial and research settings. For finite-state systems, it complements the basic verification techniques available in nuSmv with a family of new state-of-theart SAT-based techniques, including interpolation and IC3. For infinite-state systems, ⟨{c, n, o}, ⊤, (n > o) ∧ ((c < n ∧ c � = c + 1 ∧ n � = n ∧ o � = o) ∨ (c ≤ n ∧ o � = n)), c = 0⟩. {0, 1, 0}, {1, 1, 0}, {0, 2, 1}, {1, 2, 1}, … , {k -1, k, k -1}, {k, k, k -1}, {0, k + 1, k}, … nuXmv extends the nuSmv language with new data types, namely integers, reals and unbounded arrays.
The interested reader can refer to  [16]  for detailed discussion of all the functionalities provided by nuXmv. nuXmv has been used in a wide range of applications, both at academic and at industrial level, in different application domains including avionics, railways, automotive, space, and biological (e. g. [24, 31, 32, [80] [81] [82] [83] [84]. ) Finally, nuXmv is also the back-end of several other tools, including the KraToS  [85]  software model checker, the RATSY  [86]  tool for temporal logic synthesis, the OCRA  [22]  platform for contract-based verification, the xSAP  [87]  for model-based safety assessment, and the HyCOMP  [20]  model checker for the verification of hybrid systems.
Conclusions In this paper we presented a retrospective on the verification of infinite-state transition systems expressed symbolically in SMT. This line of work was substantially influenced by the work of Ed Clarke, based on the ideas on SAT-based model checking and abstraction refinement. At the methodological level, Clarke always underlined the importance of developing strong tools and applying them in practical case studies, being enthusiastic about the work on the NuSMV model checker. This paved the way to the development of the nuXmv model checker, and its applications to practical industrial case studies. Challenges for future research include devising effective verification and falsification algorithms and tools for more expressive classes of VMT problems: parameterized systems expressed in the quantified theory of arrays  [88] , timed and hybrid systems  [40] , sequential and concurrent software, recurrent neural networks, and the closed-loop combination of physical systems and control software.
ACKNOWLEDGEMENTS This work was supported in part by the  French  national research agency grants FLAGEL-OME:  ANR-19-CE17-0014  and  OLIGO-SPERM :  ANR-21-CE17-00XX. We would like to sincerely thank  Roxane DOMINGUEZ  and  Aurélien SIMON  for their assistance with the software and IT solutions. We are also grateful to  Charlotte GUYOT  and  Marlène GANDULA  for technical assistance.
On average, the core radii are about 40 Å; considering the belt thickness results in the total bicelle diameter of ~ 100 Å (see Section "Differences in bicelle radii" in Text Document S2). The diameter of the bicelles observed in work  24  for the analogical system (DMPC/CHAPSO mixture, Q = 3) and obtained using electron microscopy was in a range of 100-500 Å. In accordance with their SANS model fit, the diameter was about 420 Å. However, in the mentioned work, the total lipid/detergent concentration was 0. 25 wt %, whereas, in our experiments, it was in a range from 5. 6 to 14%. As shown in  38  , the average size of bicelles critically depends on the total lipid/detergent concentration: the pseudo-hydrodynamic radius decreases by several times when the total lipid The datasets corresponding to the mixture of the bicelles with PMs (i.
An additional SAXS experiment for characterizing unit cells of BR crystals in one sample was performed at the bioSAXS beamline BM-29 at the ESRF (Grenoble, France)  99. The working energy of BM-29 (ESRF) was 12. 5 keV, the experimental hutch was equipped with a marble table housing a modular-length flight tube (3. 5 m sample to detector distance used for this experiment), a 2D detector (Pilatus 1 M), and an automated sample changer, and the achievable q value range was 0. 0025-0. 5 Å -1. The data collection, processing, and initial analysis were performed in an automated manner using BsxCuBE and the dedicated beamline software within the EDNA framework  100. SANS measurement and data reduction. SANS measurements were performed at the YuMO time of a flight spectrometer (IBR-2, Dubna, Russia) with a two-detector system  101, 102.
Calculation of unit-cell dimensions for BR crystals. For the recalculation of the unit-cell dimensions from SAXS data, the following sum of squares of the related discrepancies was minimized: where q exp i is the position of the ith experimental peak, q theor ([hkl] i ) is the theoretical peak position that corresponds to the Miller indexes [hkl] i giving the closest peak position to q exp i (see Table  S4 ). The theoretical peak positions for a crystal with a monoclinic unit cell are given by the following expression (2): (1) i q i exp -q theor ([hkl] i ) q theor ([hkl] i ) 2 → min, SAS data analysis and modeling. The Primus program from the ATSAS software suite  104  was used for the primary processing of SAXS and SANS 1D profiles I(q). SAXS data for water solutions of amphiphilic molecules (unilamellar liposomes, bicelles, nanodiscs, detergent micelles and solubilized membrane proteins) typically demonstrate the intensity maximum in the range 0.
4 Å. Model 2 (used for the ribbons) is an elliptical cylinder with a core-shell scattering length density profile (see Fig. 8B , we used a plugin model based on two SasView models from the "cylinder" category: a core_shell_cylinder and an elliptical_cylinder). The SAS curves fitting with the models of bicelles and ribbons was done using the SasView 4. 2. 2 program  109. The optimization of the structural parameters in the mentioned models was done by minimization of the following expression: where N exp and N param are the numbers of the experimental points and the fitted parameters, respectively; (q i , I i , σ i ) is a set of the SAS experimental data. Equations (4-7) for the model curve I model (q i ) are given in the Text Document S1. The calculations were carried out on the assumption that the structure factor can be neglected (S(q) = 1).
Early disseminating T1 patient cohort Utilising the Northern Ireland Cancer Registry (NICR) database, we identified patients (n = 10), diagnosed with T1 CRC (SNOMED codes C18-C20) between 2007-2012, who subsequently relapsed or died from CRC (defined hereafter as 'relapse' cases). Most of these patients had undergone radical surgery rather than local excision. Using a nested case-control methodology, CRC deaths/recurrences within the T1 cohort were matched by gender, 10-year age-band, and diagnostic hospital lab to control patients (n = 2-3), who had also been diagnosed with T1 CRC but who did not experience disease relapse or CRC-related death (defined hereafter as 'non-relapse' cases). Following pathological verification of T1 status (pT1), alongside exclusions due to limited availability of tumour tissue and extracted RNA/DNA, transcriptional profiles were generated for 27 patients (10 relapse, 17 non-relapse; Supplementary Table  1 ) and mutational profiles were determined for 21 patients (8 relapse, 13 non-relapse, Supplementary Fig.
1a ). To enumerate the stromal cell content in situ, we further analysed haematoxylin and eosin-(H&E)-stained whole slide images of our pT1 samples using the open-source quantitative pathology software QuPath  26. These analyses revealed a significant positive correlation between transcriptome-based stromal (fibroblast) MCP-counter scores and the digital pathology-based stromal scores (Fig. 1b , rho = 0. 69, p = 0. 00009), validating our in silico approach to quantifying stromal cell abundance in the TME. Together, these findings argued against an early prognostic value for stromal content in our pT1 cohort and prompted us to investigate tumour-intrinsic factors that may contribute to relapse. As the development of CRC entails the accumulation of mutations in key driver genes, which often correlate with distinct stages of tumour development  27  , we next compared the mutational profiles of relapse and non-relapse cases in our pT1 patient cohort.
Methods pT1 patient cohort The pT1 samples were collected via the Northern Ireland Biobank (study reference: NIB16-0215), which has approval from ORECNI (16/NI/0030) to collect, store and distribute samples to researchers  68. Written informed consent from patients was obtained prior to acquisition of the tissue sample for research use. Gene expression profiles for pT1 cohorts were generated using Almac Diagnostics Xcel Array platform as part of the S:CORT consortium  69. The annotation file 'Xcel Annotations, CSV format, Release 36', (http://www. affymetrix. com/ support/technical/byproduct. affx?product=xcel) was downloaded from Affymetrix. Raw microarray. CEL file data were normalised using Robust Multi-array Average (RMA) in Partek Genomics Suite software (v6. 6; Partek Inc. ). Principal components analysis (PCA) was used to assess the quality of the data.
Prior to analysis, data were collapsed to the probeset with the highest mean value for each gene using the 'collapseRows' function from the WGCNA R package (v1. 68). Differential gene expression analysis was performed using limma (v3. 40. 6) R package. pT1 gene expression dataset and clinicopathological information are provided from S:CORT, with transcriptional data available on GEO under accession number GSE162667. Mutation data were generated by DNA target capture (SureSelect, Agilent) spanning all coding exons of 80 CRC driver genes followed by next generation sequencing using the Illumina HiSeq2000 platform at the Sanger Institute. Variant calling was performed with Caveman for point mutations and Pindel for indel mutations. Mutations were visualised using the ggplot2 (v3. 3. 5) R package.
Memorial Sloan Kettering stage II CRC IHC cohort The CRC IHC cohort was comprised of 283 stage II patients and was previously described  71. For tissue microarrays (TMAs), three separate 2-mm tissue cores each from tumour tissue and matched normal colon mucosa were removed from each donor paraffin block and transferred to tissue array blocks using a robotic TMA arrayer (TMA Grand Master, 3DHistech). The study protocol was reviewed and approved by the International Review Board of Memorial Sloan Kettering Cancer Center. Data were acquired retrospectively and in an anonymized manner such that patient consent was not required as determined by the IRB.
MxIF data analysis After the pre-processing steps, unsupervised k-means clustering with k = 2-15 was applied on the intensity of the all 15 markers (key components of mTOR and TGFβ pathways) for epithelial cells: p-S6, S6, β-catenin, ALDH1, 4EBP1, p-4EBP1, ERK, p-ERK, TGFB1, EGFR, p53, p-mTOR, Wnt5, p38MAPK and EZH2. Extreme values (less than 1%-tile and greater than 99%-tile) were capped, and marker intensities were standardised to zero mean and single standard deviation. Consensus clustering  73  and proportional of ambiguously clustered (PAC)  74  were used to determine the optimal number of groups that consistently separated the cells. Following cluster analysis, the proportion of cells in each cluster, was determined for each patient (patient cluster profile). The median staining intensities of TGFB1, p-mTOR and p-S6 and 4EBP1 markers at single cell level was used to visualise the association of each cluster of stage II CRC patients with pT1 signature.
Patient survival analysis Kaplan-Meier survival analysis was performed using the function survfit() of the R package 'survival' (v3. 2-13). Stage II CRC patients were ranked according to the proportion of single cells associated with cluster 4. Patients with more than 50% of their epithelial cells associated with cluster 4 were classified as 'high' (n = 42) and those with fewer than 50% of their epithelial cells associated with cluster 4 were classified as 'low' (n = 240) (Supplementary Table  2 ). Differences in relapse-free survival (RFS) were compared using the log-rank test. Cox proportional hazards regression was performed using the function coxph() from the R package 'survivalAnalysis' (v0. 2. 0) to calculate hazard ratios (HR) and 95% confidence intervals (95% CI). Survival curves were generated with ggsurvplot from the R package 'survminer' (v0.
Quantification of immune cell and fibroblast content Transcriptome-based stromal and immune scores were generated using the 'MCPcounter. estimate' function in MCPcounter R package (v1. 2. 0) and compared to the digital pathology-based stromal scores calculated using QuPath. Correlation analyses were performed using the 'cor. test' function from the R stats package, with the 'Spearman' method for the T1 cohort and with the 'Pearson' method for the FOCUS cohort. The default setting was used for all other parameters. These statistical analyses were performed using stats package R (v4. 1. 2) and RStudio (v1. 1. 447).
Classification of of the pT1 cohort using the CMS and CRIS classifiers The consensus molecular subtype (CMS) of each patient tumour was determined using CMS classifier (v1. 0. 0); the 'classifyCMS. RF' function R package. CRC intrinsic subtype (CRIS) classifications  21  were predicted using the Nearest Template Prediction (NTP) module (v4) on GenePattern (https://genepattern. broadinstitute. org/). To account for the small sample sizes, we also utilised the CRIS and CMS classifiers in conjunction with gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA) to determine the degree of enrichment of the classifier gene sets in individual pT1 patient samples.
Gene set enrichment analysis A fast implementation of pre-ranked Gene Set Enrichment Analysis (FGSEA) using the fgsea (v1. 18. 0) R package was performed on log expression ratio of relapse vs non-relapse cases to assess enrichment of CMS/CRIS subtypes and also enrichment of CAF and stromal gene sets  21  and Hallmark gene set of MSigDB 28 resource. R package msigdbr (v7. 4. 1) was used to retrieve the Hallmark gene set. Benjamini-Hochberg FDR <0. 2 considered as significant.
Mouse RNA isolation and qPCR RNA isolation was performed using the RNeasy mini kit (Qiagen, #74104) and cDNA was synthesised using the high capacity cDNA reverse transcription kit (Thermo Fisher Scientific), #4368813). SYBR Green (Thermo Fisher Scientific, #F410XL) qPCR reactions were performed with the BioRad CFX96 Real-Time System PCR system using BioRad software using CFX Maestro Software 2. 3. Relative fold change in gene expression was calculated using the 2 -ΔΔCt method. All ΔΔ Ct values were normalised to the housekeeping gene Gapdh. The sequences of the primers used for qPCR were as follows: Gapdh forward, 5′-GAAGGCCGGGGCCCACTTGA-3′; Gapdh reverse, 5′-CTGGG TGGCAGTGATGGCATGG-3′; Alk5 CA forward, 5′-TTGTGAACAGAA GTTAAGGC-3′; Alk5 CA reverse, 5′-AGCATAATCAGGAACATCAT-3′; Alk5 forward, 5′-TCTGCATTGCACTTATGCTGA-3′; Alk5 reverse, 5′-AAAGGG CGATCTAGTGATGGA-3′; Smad7 forward, 5′-GGCCGGATCTCAGGCA TTC-3′; Smad7 reverse, 5′-TTGGGTATCTGGAGTAAGGAGG-3′; SerpA1 forward, 5′-CCCCACGGAGATGGTTATAG-3′; SerpA1 reverse, 5′-ATCAC TTGCCCCATGAAGAG-3′; Puma forward, 5′-AGCAGCACTTAGAGTC GCC-3′; Puma reverse, 5′-CCTGGGTAAGGGGAGGAGT-3′; Noxa forward, 5′-GCAGAGCTACCACCTGAGTTC-3′; Noxa reverse, 5′-CTTTTG CGACTTCCCAGGCA-3′; Bax forward, 5′-TGAAGACAGGGGCCTTTTTG-3′; Bax reverse, 5′-AATTCGCCGGAGACACTCG-3′.
Mouse RNA sequencing and analysis Total RNA was extracted from whole small intestinal tissue. RNA integrity was analysed with a NanoChip (Agilent Technologies RNA 6000 Nanokit #5067-1511). A total of 2 μg of RNA was purified via poly(A) selection. The libraries were run on an Illumina NextSeq 500 sequencing system using the High-Output kit v2. 5 (75 cycles; Illumina #20024906). GSEA analysis was performed using the fgsea (v1. 10. 1) R package on shrunken log expression ratio of VilCre ER ;Apc fl/fl ;Kras G12D/+ ;Alk5 CA (AKA CA ) vs WT models. The comparison gene sets were obtained from refs. 22,31. A murine CRIS-B gene set was created from the human genes associated with CRIS-B from the original study which were converted into their mouse orthologues using data obtained from the HUGO Gene Nomenclature Committee (HGNC) and Mouse Genome Informatics (MGI) databases.
Single-sample gene set enrichment analysis (ssGSEA) was run, using the ssGSEA method in GSVA R package (v1. 32. 0) with the murine CRIS-B consisting of 63 genes to assign CRIS-B scores to normalised count RNA-Seq data from the various mouse models which had a pseudocount of 1 and been log2 transformed. The R package pamr (v1. 55) was used to generate a classifier from transcriptional data from VilCre ER ;Alk5 CA and VilCre ER ;Alk5 +/+ mice. Using 3-fold cross-validation and a threshold of 4. 98 (misclassification error = 0), a classifier containing 60 genes was created. To allow this classifier to be applied to human samples, the mouse genes were converted into their human orthologues using the R package biomaRt (v2. 40. 5). Using the CMScaller package (v0. 99. 1), the NTP method was then applied to the human gene template (54 genes) to classify the samples in the pT1 cohort into WTlike (n = 17) and ALK5 CA -like (n = 10).
Statistical analyses The smallest sample size that could give a significant difference was chosen in accordance with the 3Rs. Given the robust phenotype of the Apc fl/fl mice and assuming no overlap in the control versus the experimental group, the minimum sample size was 3 animals per group. For analysis of organoid cultures, investigators were blinded when possible. Investigators performed all histological quantification blinded to genotype or treatment. Statistical analysis was performed with GraphPad Prism v8. 0. 0 for Windows (GraphPad Software) using one-tailed Mann-Whitney U-tests unless otherwise stated. Statistical comparisons of survival data were performed using the log-rank (Mantel-Cox) test. For individual value plots, data are displayed as mean ± standard error of the mean (s.
The FOCUS clinical trial was performed in accordance with the Declaration of Helsinki. All subjects provided written informed consent for further research on their samples at the time of consent to the clinical trial. Both the original clinical trial (FOCUS Ref: 79877428) and the data reported here (S:CORT ref 15/EE/0241) were approved by the National Research Ethics Service in the United Kingdom. Transcriptional data of 361 samples used in this study is available on GEO under accession number GSE156915.
Acknowledgements We thank the  Core Services and Advanced Technologies at the Cancer Research UK Beatson Institute  ( C596/A17196  and  A31287 ), and particularly the  Biological Services Unit, Histology Service and Molecular Technologies. We are thankful to members of the  Sansom and Dunne labs  for discussions of the data and manuscript. O. J. S. and his lab members were supported by  Cancer Research UK  ( A28223 ,  A21139 ,  A12481 ,  A17196  and  A31287 ). D. J. F and J. D. G. L were supported by  UK Medical Research Council  ( MR/R017247/1  and MR/NO21800/1 respectively). P. D. D and the Belfast team were supported by  Cancer Research UK  ( ACRCelerate award ;  A26825  and  Early Detection award ;  A29834 ), a joint  Cancer Research UK  and  UK Medical Research Council  programme (S: CORT MR/M016587/1 ), the  NI HSC R&D Experimental Cancer Medicine Centre (ECMC)  programme ( COM/5535/19 ) and the  CRUK Infrastructure Oversight Committee  ( 25176 ) for Belfast ECMC.
Data availability Raw and processed RNA sequencing data that support the findings in this study and available on GEO under accession number GSE182019. Large raw image/micrograph files that support the findings in this study are not provided with the source data and are available from the corresponding authors upon request. pT1 clinicopathological information are provided in Supplementary Table  1 , and raw and processed transcriptional data is available on GEO under accession number GSE162667. Raw and processed transcriptional data from FOCUS cohort data are available on GEO under accession number GSE156915. The CRC cohort was comprised of 282 stage II patients and was previously described in Oncotarget. 2020 Feb 25; 11(8): 813-824; https://doi. org/10. 18632/ oncotarget. 27491. Source data are provided with this paper.
, 2016). Intriguingly, histone mark writers, such as p300, have recently been shown to modulate alternative splicing by post-translationally acetylating the splicing factors themselves, which can impact their RNA binding capacity and activity  (Siam et al. , 2019). There is evidence of a p300-mediated acetylation of PTB  (Weinert et al. , 2018) , although its functional impact, as well as the existence of other post-translational modifications such as methylation, remain unclear. Finally, we expect this chromatin-mediated regulation of alternative splicing to be gene-and context specific. In a recently published genome-wide analysis, using the most extensive epigenomic and transcriptomic datasets publicly available from the ENCODE and Epigenomic Roadmap projects, we showed that exons differentially marked by specific chromatin modifications share common functional and regulatory pathways, suggesting a coordinating role for histone marks in regulating the alternative splicing of functionally related genes  (Agirre et al.
RNA extraction and RT-qPCR Quantitative RT-PCR analysis was performed in biological triplicates or quadruplicates. Total RNAs were prepared from cells with the GeneJET RNA Purification Kit (Thermo Scientific #K0732). All samples were eluted into 30 mL RNAse-free water. DNAs was remove from RNA by using RQ1 RNase-Free DNase (Promega #M6101), briefly, 1 mg of RNA was mixed with RQ1 DNase and RQ1 DNase 10X Reaction Buffer and incubated 30 min at 37 C. RQ1 enzyme was inactivated by adding Stop Solution 10 min at 65 C. cDNAs were generated using the Transcriptor First Strand cDNA Synthesis kit (Roche 04 897 030 001) according to the manufacturer's instructions. For each biological replicate, quantitative PCR reactions were performed in technical duplicates using the iTaq Universal Syber Green supermix (Bio-Rad) on the Bio-Rad CFX-96 Touch Real-Time PCR System, and the data normalized to TBP.