dataesr/scientific-paragraphs-categorization huggingface dataset:
We recognise that the preparation of each SurPass might be quite time consuming. This is the case particularly if the treatment summary is entered manually by retrieving data from hard copies of the clinical record. However, we have shown that recovering data from databases used during the treatment period (e. g. electronic clinical records, databases of clinical trials in which the former patient was enrolled or cancer registries) is also feasible. This procedure requires that a mapping procedure be performed to associate variables between each clinical trial or institutional electronic clinical record database and the SurPass. This process may require some extra time but, once done, it can be useful for all former patients whose data are stored in that specific database. Importantly, because of quality control issues, final data from clinical trials might not be available until several years after treatment completion.
Information about the radiotherapy given to survivors treated several years ago can be imprecise, but even 'basic' information is important (e. g. to know that some radiotherapy to the chest was received). Current guidelines do not use the OAR dose to estimate the risk for a specific side-effect (e. g. secondary breast cancer) but mostly use broad areas of the body that received radiotherapy (e. g. chest). When more detailed information on exposures to certain OAR is available, this can already be uploaded into the SurPass platform. This information might be of interest in the future for clinical purposes, e. g. local tumour relapse or for new guideline-specific risk stratification and surveillance recommendations. Another important issue we had to deal with was that the SurPass platform contains personal data that need protection according to national and international privacy regulations.
Based on local/national circumstances, it is possible to have data stored in the Cineca data centre or in a local certified data centre in the country that wants to adopt the SurPass platform. Any data center should anyway guarantee the highest level standards of security and privacy according to the European Directive 2016/ 679. In addition, ad hoc solutions can be implemented in the case of constraints that are more stringent. During the ExPO-r-Net project, a pseudo-anonymisation service, called EUPID (European Patient Identity Managementdhttps://eupid. eu), was developed by project partner Austrian Institute of Technology, and it has been integrated into the SurPass platform  [29]. The European Society for Paediatric Oncology (SIOPE) has continuously supported the SurPass initiative since its inclusion in the ENCCA, ExPO-r-Net and PanCar-eSurFup projects and has included the SurPass in the SIOPE Strategic Plan 'A European Cancer Plan for Children and Adolescents' (https://www.
Acknowledgements The authors acknowledge the following professionals for their input during the SurPass development:  Frieda Clinton ,  Our Lady's Children's Hospital, Crumlin ,  Dublin 12 , Ireland;  Arja Harila-Saari ,  Oulu University Hospital, Finland ;  Colin Kennedy ,  University of Southampton, UK ;  Odile Oberlin ,  Institute Gustave Roussy, Paris France ;  Maria Filomena Pereira ,  Portuguese Institute of Oncology, Lisbon, Portugal ;  Giulia Petrarulo , Communication Consultant, Italy and  Monica Terenziani ,  National Cancer Institute, Milan, Italy , In addition, the authors acknowledge survivors and their families for their input and willingness to collaborate on the test phase of the SurPass prototype development. This study was partially funded by the  FP7-HEALTH-2010  Grant agreement no:  261474   ENCCA  Project, the  Ex-PO-RNet (European Expert Paediatric Oncology Reference Network for Diagnostics and Treatment).
Splice-Site-Creating Mutation Discovery We collected high-quality mutation calls from 8,656 tumors across 33 cancer types derived from The Cancer Genome Atlas having available TCGA RNA-seq data (STAR Methods). For every mutation, we defined a set of control samples in the same cancer cohort that lacked the same mutation in the gene of interest. We sought to assess the landscape of SCMs across cancer genomes by evaluating all mutations already having conventional annotations and their potential splice-site-creating effects (Figure  1A ). To achieve this goal, we conducted analysis using a bioinformatic tool, MiSplice (mutation-induced splicing), that systematically evaluates mutations in a splicing context using RNA-seq data (Figure  1B ). MiSplice manages large analyses using parallel computation to search for alternative splice junctions within windows of ±20 bp from the mutation of interest.
Neoantigens Introduced by SCMs We have further investigated neoantigens produced by SCMs. By using the RefSeq transcript database, a total of 2,993 protein sequences were translated for transcripts containing mutationinduced alternative splice forms (Table  S3 ). In the translation, we allowed for different transcripts from each SCM. The HLA types for each sample were adopted from the TCGA pancan immune working group (Synapse ID: syn5974636). NetMHC4 and NetMHCpan-3. 0 (Andreatta and Nielsen, 2016)  were used to predict the binding affinity between epitopes and the major histocompatibility complex (MHC) and showed a high concordance in total predicted neoantigens (Pearson = 0. 94; Figure  S2 ). We found that alternative splice forms for some important genes related to tumorigenesis, including SMARC1, KDM6A, and NOTCH1, are highly immunogenic and can contain 40 or more unique neoantigens (Figure  6A )  (Dalgliesh et al.
MiSplice Pipeline The MiSplice pipeline was developed to detect mutation-induced splicing events from RNA-seq data. It is written in Perl and incorporates two standard tools, samtools and MaxEntScan. The pipeline is fully automated and can run multiple jobs in parallel on LSF cluster. It executes the following steps: 1) Splitting large maf file into multiple smaller files with less mutations (currently, the default setting is 200). 2) Discovering splicing junctions within 20bps of the mutation with at least 5 supporting reads with mapping quality Q20 and then filtering canonical junctions by using the Ensembl 37. 75 database. We selected 20bp as a cut-off since it is the farthest distance from the splice junction in a splice region. 3) Computing the number of supporting reads of above cryptic splice sites for control samples without mutations (Table  S1 ).
Neoantigen Prediction For each predicted SCM, we use a curated RefSeq transcript database (version 20130722) to obtain the translated protein sequences for transcript containing alternative splice forms induced by SCMs. Different length of epitopes (8-mer, 9-mer, 10-mer and 11-mer) are constructed from the translated protein sequence. We use NetMHC3pan  (Nielsen and Andreatta, 2016)  and NetMHC4  (Andreatta and Nielsen, 2016)  to predict the binding affinity between epitopes and MHC. Epitopes with binding affinity % 500nM which are also not present in the wild-type transcript are extracted from the following neoantigen analysis.
Code Availability MiSplice is written in Perl and is freely available from GitHub at https://github. com/ding-lab/misplice under the GNU general public license. MiSplice uses several independent tools and packages, including SamTools and MaxEntScan, all of which are likewise freely available, but which must be obtained from their respective developers. The MiSplice documentation contains complete instructions for obtaining and linking these applications into MiSplice.
ACKNOWLEDGMENTS Funding supported by  U54 HG003273 ,  U54 HG003067 ,  U54 HG003079 ,  U24 CA143799 ,  U24 CA143835 ,  U24 CA143840 ,  U24 CA143843 ,  U24 CA143845 ,  U24 CA143848 ,  U24 CA143858 ,  U24 CA143866 ,  U24 CA143867 ,  U24 CA143882 ,  U24 CA143883 ,  U24 CA144025 , and  P30 CA016672.
Lawrence Kwong receives research support from Array BioPharma. Sharon E. Plon is a member of the Scientific Advisory Board for Baylor Genetics Laboratory. Beth Y. Karlan serves on the Advisory Board of Invitae. Aligned RNA-seq bam files were analyzed using the ISB google. These cancer types are Acute Myeloid Leukemia [LAML], Adrenocortical carcinoma [ACC], Bladder Urothelial Carcinoma [BLCA], Brain Lower Grade Glioma [LGG], Breast invasive carcinoma [BRCA], Cervical squamous cell carcinoma and endocervical adenocarcinoma [CESC], Cholangiocarcinoma [CHOL], Colon adenocarcinoma [COAD], Esophageal carcinoma [ESCA], Glioblastoma multiforme [GBM], Head and Neck squamous cell carcinoma [HNSC], Kidney Chromophobe [KICH], Kidney renal clear cell carcinoma [KIRC], Kidney renal papillary cell carcinoma [KIRP], Liver hepatocellular carcinoma [LIHC], Lung adenocarcinoma [LUAD], Lung squamous cell carcinoma [LUSC], Lymphoid Neoplasm Diffuse Large B cell Lymphoma [DLBC], Mesothelioma [MESO], Ovarian serous cystadenocarcinoma [OV], Pancreatic adenocarcinoma [PAAD], Pheochromocytoma and Paraganglioma [PCPG], Prostate adenocarcinoma [PRAD], Rectum adenocarcinoma [READ], Sarcoma [SARC], Skin Cutaneous Melanoma [SKCM], Stomach adenocarcinoma [STAD], Testicular Germ Cell Tumors [TGCT], Thymoma [THYM], Thyroid carcinoma [THCA], Uterine Carcinosarcoma [UCS], Uterine Corpus Endometrial Carcinoma [UCEC], Uveal Melanoma [UVM] METHOD DETAILS Dataset Description.
Annealing temperatures, fluorescent label-primer combinations, and pooling of amplified microsatellite loci for fragment analysis have been described elsewhere  [24]. Amplified fragments were combined with an internal size standard, GeneScan TM 500 LIZ TM , Hi-Di TM formamide (Applied Biosystems Inc. , Carlsbad CA) and deionized water to a final volume of 10 μL. Fragments were run on an ABI 3730xl DNA Analyzer (Cornell University Life Sciences Core Laboratories Center, Ithaca NY). Allele calling was done in Geneious R6 (https://www. geneious. com) [37]  using the Microsatellite plug-in. MICROCHECKER (http://www. microchecker. hull. ac. uk)  [38]  was used to check the microsatellite data for genotyping errors such as large allele drop-out, and to detect the possible presence of null alleles.
Genotype cluster identification Two approaches were used to identify genotype clusters from the microsatellite data. Correspondence analysis (CA), as implemented in the software GENETIX v. 4. 05 (http://www. genetix. univ-montp2. fr/genetix/genetix. htm)  [39]  was run to visualize the distribution of individual microsatellite genotypes in multidimensional space, and identify clusters of individuals with similar genotypes. We also did Bayesian structure analysis using STRUCTURE v. 2. 3. 4 (http://pritchardlab. stanford. edu/structure. html) [40] , to assign individuals to clusters which minimize departures from Hardy-Weinberg equilibrium and linkage disequilibrium. Bayesian structure analysis also allows the estimation of the individual ancestry coefficient q, which is the proportion of an individual genotype having ancestry from each of K defined clusters.
Ten replicate MCMC simulations were run for each value of K (K = 2 to 8). Each run comprised one million iterations, with an initial burn-in of 100,000 steps. An admixture model with correlated allele frequencies was chosen, using sampling location as a prior  [41]. The most likely number of clusters was inferred based on the ΔK method  [42] , as implemented in the Structure Harvester tool (http://taylor0. biology. ucla. edu/structureHarvester/) [43]. Ancestry coefficients for each K value were averaged across replicate runs using CLUMPP  [44]. To assign an individual to a cluster, we initially used a threshold value of q > 0. 9  [45] , while individuals having q values > 0. 10 and < 0. 9 were categorized as admixed. Results were plotted using DISTRUCT v. 1. 1  [46]. To complement STRUCTURE results in identifying admixed individuals, we ran additional analyses using the program NEWHYBRIDS v.
Amplified fragments were resolved on 2% TBE-agarose gels and detected following ethidium bromide staining, with fragment profiles diagnostic for mtDNA lineage (S1 Fig). To test the null hypothesis of random association between S. fuscescens mitochondrial clades and the nuclear genotype clusters, we used the CNDm program  [50]. This program estimates the normalized cytonuclear disequilibrium (D Ã ), a measure of cytonuclear association, based on observed counts of mtDNA clade-microsatellite cluster combinations.
Genetic diversity and differentiation Microsatellite genotype and allele frequencies were used to obtain estimates of genetic diversity and differentiation for groups of individuals. To estimate diversity, we calculated the mean number of alleles, observed and expected heterozygosity (H O , H E ) and allele frequencies using GENEPOP  [51]. Allelic richness standardized for minimum sample size was calculated using the package hierfstat v. 0. 04-22 for R  [52]. Exact tests of deviations from Hardy-Weinberg equilibrium, the inbreeding coefficient (F IS ), and exact tests of linkage disequilibrium between microsatellite loci were assessed using GENEPOP. To estimate levels of genetic differentiation we did an analysis of molecular variance (AMOVA) as implemented in ARLEQUIN v. 3. 5  [53] , to calculate hierarchical fixation indices, F ST for different hypothesized groupings of individuals.
We used G" ST calculated using the GENALEX software package  [54]  as an unbiased estimator of F' ST , i. e. the value of F ST standardized by the maximum obtainable value accounting for within-population heterozygosity  [55, 56]. To visualize spatial patterns of genetic differentiation based on nuclear and mitochondrial markers, we used GENALEX for multidimensional clustering of populations using principal coordinates analysis (PCoA). We estimated population-pairwise F ST and F ST values calculated from microsatellite genotypes and mtDNA CR sequences, respectively. We tested for correlations between matrices of genetic distance (F ST or F ST ) and geographic distance using a Mantel test as implemented in the package IBDWS v. 3. 2. 3 (http://ibdws. sdsu. edu/~ibdws/) [57] , with 1000 random permutations to test for significance.
Estimating gene flow: Isolation-with-migration model The Isolation-with-Migration model (IM)  [58]  was used to examine the relative contribution of competing demographic scenarios that explain the sharing of mitochondrial haplotypes between genotype clusters: (1) divergence under complete isolation (incomplete lineage sorting) and (2) divergence with gene flow (hybridization and introgression), by estimating the posterior probability of migration since the time of population splitting  [59, 60]. We used the Bayesian Markov-Chain Monte Carlo method implemented in the program IMa2p  [61] , a parallel implementation of the IMa2 program  [59] , to jointly estimate posterior distributions of six demographic parameters, scaled by the mutation rate μ: population sizes for ancestral (q 2 ) and descendant (q 0 , q 1 ) populations, bidirectional migration rates (m 1>2 and m 2>1 ), where m is the migration rate per generation into the specified population, and divergence time (t) for pairs of populations.
The IM model assumes that the populations under study are panmictic, markers are selectively neutral, and free from recombination. We tested the selective neutrality of nucleotide variation in CR sequences using Tajima's D  [62] , and Fu's Fs  [63] , estimated using ARLEQUIN. Tests for recombination were done on the CR sequence alignment using the RDP method  [64]  as implemented in the RDP3 package  [65] , and a pairwise homoplasy index (PHI) test  [66]. None of these methods detected evidence of recombination in the control region sequence alignment. While IMa2p can analyse multiple populations, a large number of loci are required for better estimation, and require a well resolved, rooted phylogeny of populations as input  [67]. Consequently, we performed multiple population pairwise analyses instead, with mtDNA data analysed as three separate pairwise comparisons in which individuals were assigned to three genetic groups according to the microsatellite data (see results).
Morphological differentiation and geometric morphometric analysis Morphological identification of the specimens as S. fuscescens followed Woodland  [14]. The left side of each fish was photographed using a mounted digital camera. The dorsal, pelvic, anal and caudal fins of each fish were spread out and pinned in place prior to photo documentation. Landmarks were chosen in accordance with the guidelines of Zelditch et al. [73]. A total of 22 landmarks were selected for their capacity to represent overall body shape (S2 Fig). The Cartesian coordinates of each landmark were digitized using tpsDig2 v. 2. 05 (http://life. bio. sunysb. edu/morph/) [74]. Geometric morphometric analyses were subsequently conducted using the PAleontological STatistics (PAST) software v. 1. 72 [75]. To eliminate the confounding effect of size differences, the digitized raw landmarks were transformed using the generalized least squares Procrustes superimposition  [76, 77] , by initially scaling landmark configurations of all specimens to unit size, then aligning all landmarks as closely as possible.
Funding for this research was provided by the  Global Environment Facility-Coral Reef Targeted Research Capability Building for Management Project (GEF-CRTR)  through the  Southeast Asia Center of Excellence grant  to the  University of the Philippines Marine Science Institute. Additional funding was provided by the  Office of the Vice Chancellor for Research and Development, University of the Philippines Diliman  (OVCRD Grant PhDIA #  121206  to RRG) and the  University of the Philippines Marine Science Institute (DRP, RRG). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Genome sequencing and gene family evolution The genome size of U. mutabilis was estimated by flow cytometry and k-mer spectral analysis to be around 100 Mbp. In total, 6. 9 Gbp of PacBio long reads were assembled into 318 scaffolds (98. 5 Mbp), covering 98. 5 % of the estimated nuclear haploid genome (Figure  2 , Table  S1 ). To increase the accuracy of the genome sequence at single-base resolution, the scaffolds were polished using PacBio, and Illumina paired-end reads. We predicted 12,924 protein-coding genes, of which 91. 8 % were supported by RNA-Seq data. Analyses of genome completeness indicated that the genome assembly captures at least 92% of the eukaryotic BUSCO dataset. Analyses of pico-PLAZA Core Gene Families resulted in a completeness score of 0. 968 of the protein-coding genes (Table  S2 ).
A phylogenetic tree inferred from a concatenated alignment of 58 nuclear protein-coding genes (totaling 42,401 amino acids) supports a sister-group relationship of Ulva with the Chlorophyceae in the crown chlorophytes (Figure  3 ). This topology corroborates earlier phylogenetic hypotheses based on multigene organelle datasets (reviewed in  [8] ). The divergence of Ulva and the Chlamydomonas -Gonium -Volvox clade (Chlorophyceae, Volvocales) from their common ancestor coincided with substantial gain and loss of gene families in both lineages (Figure  3 ). Gain and loss, however, do not seem to be correlated with multicellularity.
Moreover, the CO-like TFs are one of the families potentially involved in the establishment of complex multicellularity in green algae and land plants  [30]. Genome-wide mapping of Ulva CO-like genes and functionally related genes indicates that the majority (60%) originated through tandem duplication in Ulva (Figure  4C ). Although the functions of these proteins will need to be confirmed experimentally, the CO-like and CMF genes in Ulva could be involved in the integration of a multitude of environmental signals in a highly dynamic intertidal environment, of a kind to which the other sequenced green algae are not regularly subjected. A total of 441 protein kinases were identified in the Ulva genome, representing ca. 3-4% of all protein coding genes. The largest subfamily of Ulva kinases has similarity to PKnB kinase (Data S1), a "eukaryoticlike" serine/threonine kinase originally discovered in bacteria.
Around 20 of the PKnB kinases possess a transmembrane (TM) domain and an extracellular/adhesion domain: either Kringle (IPR000001), FAS1 (fasciclin-like; IPR000782), or Pectin lyase fold (IPR012334, IPR011050), and so represent good candidates for Ulva receptor-kinases, with potential roles in environmental sensing and/or developmental signalling. By acting on Ulva cell wall components, for example, the pectin lyase fold domains may contribute to desiccation resistance  [31]  and to the growth and development of a multicellular thallus  [32]. We note that although the three aforementioned extracellular domains are present in many green algae, including the green seaweed Caulerpa (Figure  5 ), it is unusual to see them linked to a kinase, and this coupling is only seen in a few other eukaryotic species.
The pectin lyase-TM-kinase family proteins, on the other hand, are more divergent in sequence and structure (including kinase-TM-pectin lyase proteins) and are more likely to have arisen by dynamic gene fusions or conversions. In addition to containing extracellular protein domains that are linked to intracellular kinases, Ulva also shows a significantly enriched diversity of the protein domains associated with the extracellular matrix (ECM) and cell surface, relative to its sequenced sister taxa. Notable examples of these enriched ECMassociated domains include Scavenger Receptor and Cysteine Rich domain (SRCR) proteins (IPR001190, IPR017448), which are absent from land plants but present in animals and Volvocales  [34]. In Metazoans, the SRCR proteins have diverse roles that include the recognition of the pathogen-associated molecular patterns (PAMPs) that mediate bacterial interactions  [35] , and possibly encompass an early evolutionary role in cell-cell recognition or aggregation  [36].
The germin (IPR001929) and RmlC-Like cupin domain folds (IPR011051) are also among the ECM-associated domains and occur ubiquitously in streptophytes, where they are linked to the regulation of cell wall properties such as extensibility and defense  [37]. The gametolysin/MEROPS peptidase family M11 of VMPs (Volvox matrix metalloproteases) (IPR008752), similar to mammalian collagenases and crucial to ECM remodeling (Hallman et al. 2001), is enriched in Volvox relative to Chlamydomonas  [6]. While this M11 class of peptidases is itself absent in Ulva, the related MEROPS M8/leishmanolysin peptidase domain (IPR001577) is 23-fold enriched compared to Chlamydomonas. Additionally, 28 collagen triple helix repeat (IPR008160) proteins, with predicted extracellular locations, have been detected, each with a G-N/D-E repeat, rather than the more usual G-P-Hyp, which suggests the presence of collagen-like innovations in the ECM of Ulva.
The combination of comparative analyses and expression data make UM036_0102 a credible candidate for the enzyme that mediates the methylation step in the biosynthesis of DMSP, as an alternative to DSYB in eukaryotes. Also relevant when considering DMSP synthesis are four putative BCCT-type family symporters/antiporters (IPR000060), which have been demonstrated to be involved in the import of DMSP in several bacteria  [51]. In Ulva, which shifts from DMSP synthesis towards DMSP uptake under conditions of sulfur deficiency, a putative DMSP transporter has been shown to be a Na + ⁄DMSP symporter, which points towards BCCT transporters  [52]. Homologs of the putative Ulva BCCT transporters are also present in diatoms, some marine prasinophytes and opisthokonts, but are notably absent from the fresh water Trebouxiophyceae and Chlorophyceae.
Data S1. Annotated genes of Ulva mutabilis transcription associated genes, cell cycle related genes, protein kinases, phytohormone synthesis and signalling, cell wall synthesis, and iron uptake. pbalign v0. 3. 1 (from SMRT Link v5. 0. 1). The PacBio-polished scaffolds were further improved using the paired-end Illumina reads with Pilon  [79]. To eliminate putative bacterial contamination super-scaffolds were searched against the NCBI nucleotide (nt) database using MegaBLAST with an (E-value < 1e-65).