dataesr/scientific-paragraphs-categorization huggingface dataset:
However, in this as in many other cases, the original sense co-exists with the new one, and specific contexts or genres will select one over the other. This is known as synchronic variation, and can be successfully modelled probabilistically, as advocated by several authors (see e. g. Jenset and McGillivray (2017) ). The close relationship between innovation and variation is well-known in historical linguistics, and critical to ancient languages. Indeed, the unavailability of balanced corpora due to the limited amount of data at our disposal makes it crucial for models to explicitly account for confounding variables like genre, so as to enable them to use all existing data. To address these challenges, we introduce GASC (Genre-Aware Semantic Change), a novel dynamic Bayesian mixture model for semantic change.
In this model, the evolution of word senses over time is based not only on distributional information of lexical nature, but also on additional features, specifically genre. This allows GASC to decouple sense probabilities and genre prevalence, which is critical with genre-unbalanced data such as ancient languages corpora. The value of incorporating genre information in the model goes beyond literary corpora and historical language data and can be applied to recent data spanning over a period of time where text type information is critical, for example in specialized domains. Explicitly mod-elling genres also makes it possible to address a number of additional questions, revealing the genre most likely associated to a given sense, the most unusual sense for a genre, and which genres have the most similar senses.
Although historical dictionaries and traditional philology describe the evolution of word senses over time, they do not necessarily reflect the evidence from corpora on which models can be evaluated, and often only provide insights into the appearance of a new sense, rather than the relative predominance of a word's senses across time. These reasons led us to craft a novel evaluation dataset and framework, which reflects the data on which the model is evaluated, and allows for a finer-grained evaluation of the predominance of word senses across time.
Log-likelihood evaluation First, we compared GASC with the state-of-the-art (SCAN) in terms of held-out data log-likelihood. We chose 50 targets that could be identified as polysemous (e. g. the verb legō, whose senses are 'gather' and 'tell') based on two criteria: high frequency and a a suitably clear-cut range of meanings. We initially based our selection on the secondary literature and chose 17 words from the well-studied vocabulary of Ancient Greek aesthetics  (Pollitt, 1974). We complemented this selection with the inclusion of the 33 most frequent clearly polysemous words identified by an Ancient Greek expert in a frequency-ranked word list extracted from the Diorisis corpus. The necessity to identify manually suitable words led us to limit their number to 50. For each one of these target words, we randomly divided the corpus into a train (80%) and test set (20%).
One expert selected the correct sense for each occurrence of mus and harmonia, and the other expert performed the same task on kosmos. The results of each expert's annotation task were not reviewed by the other expert  (Vatri et al. 2019  for the dataset). Table  1  shows an example from the annotated dataset for the word kosmos. The annotators also marked when the semantic annotation was purely based on the target word context, which is the evidence on which the model can rely (category "collocates"). Only annotations based on collocates were retained in the evaluation. Using this information, the relative frequency of each sense for each target word in any time slice becomes computable, and was used to create ground-truth data on the diachronic predominance of a word's senses as reflected in the corpus.
Experiments Predictions on held-out words Considering the 50-word dataset described in Section 4, we evaluated the predictive performance in terms of loglikelihood of held-out data for SCAN (not using any genre information), GASC-all (GASC with all the G = 10 available genres) and GASC-narr (GASC with 2 genres, Narrative vs. non Narrative). Narrative and Technical are such that all 50 words occurred at least once in the training and test sets, and analogous results are obtained when GASC with Technical vs. non Technical. For each model, we compared the 3 hyperparameter settings previously reported, with higher scores indicating that a model is better at explaining unseen data. Figure  3  shows predictive log-likelihood scores for a range of K, with the results averaged over 50 leave-one-out folds.
Each time, the scores were averaged under the final 10 samples of the latent variables, out of 1000 MCMC iterations. On average, GASC-narr consistently outperforms SCAN across every K and hyperparameter setting. On the other hand, SCAN has a higher held-out log-likelihood than GASC-all. Exploiting some information on the genre yields better predictions, while using all genres attested in the corpus is not effective as some genres are not sufficiently represented by the data. Figure  3  also shows that the best predictions over unseen data are obtained for K between 10 and 15. Higher K values tend to introduce noisy senses with no improvement for the model output. In addition, Setting 3 worked better or on par with other settings. In the next section, we fix the hyperparameters and use a validation set of words that were not part of the 50 targets of this experiment.
Conclusion We introduced GASC, a Bayesian model to study the evolution of word senses in ancient texts. We performed this analysis conditional on the text genre, demonstrating that the ability to harness genre metadata addresses a fundamental challenge in disambiguating word senses in ancient Greek. In experiments we showed that GASC provides interpretable representations of the evolution of word senses, and achieves improved predictive performance compared to the state of the art. Further, we established a new framework to assess model accuracy against expert judgement. To our knowledge, no previous work has systematically compared the estimates from a statistical model to manual semantic annotations of ancient texts. SCAN and GASC (bottom). Each stacked bar represents all kosmos occurrences in a given time.
Acknowledgements This work was supported by  The Alan Turing Institute  under the  EPSRC grant   EP/N510129/1  and the seed funding grant  SF042. We would also like to thank  Viivi Lähteenoja  for her work on the annotation of the word kosmos,  Dr Lea Frermann  for her advice,  Prof Geoff Nicholls  for his insights on the model, as well as the anonymous reviewers for their helpful comments.
To examine the functional impact of TP53 mutations in the TCGA cohort, we analysed TCGA for overall cancer patient survival, for the 12 disparity cancers across 14 years. Together, patients with mutated TP53 from these 12 disparity cancers had significantly poorer survival outcomes than their wt TP53 counterparts (Log-rank test; p-value = 0. 0002, Fig. 1b ). It is relevant to add that this finding was validated recently in a study of individual cancers, where nine of the cancers in this study showed poorer survival for patients with TP53 mutation, while individual data analyses for the other three cancers were not included  24. Given our finding that TP53 mutations occur at higher frequency in males than in females in the 12 disparity cancers, we expect TP53 mutational status to contribute significantly to the higher incidence of cancer deaths in males in the general population, over females.
KEGG  16  ); (3) X-chromosome analysis is frequently avoided in sporadic diseases because X-inactivation (Xi) silencing in females complicates data interpretation (reviewed in refs. 27, 28 ) and (  4 ) males are at greater disease risk than females from X-chromosome mutations (reviewed in ref. 29  ). To identify coding genes that interact with p53 on the X chromosome  28, 30  , we adopted the unbiased Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database tool  31. A reproducible p53-STRING set of 90 genes was defined (with confidence limits ≥ 0. 3) from among the ~800 coding genes on the X chromosome (Supplementary Data 1, TAB1; Supplementary Fig. 1a ). We note that due to the rapid pace of new discoveries, some partnerships remain to be captured. Among the STRING set are key p53 modifiers (kinases, E3-ligases and a deubiquitinases).
Gene Ontology (GO) analysis shows that the p53-STRING set is rich in fundamental p53 regulatory processes, including regulation of apoptosis, cell cycle, DNA structural integrity and hypoxic response. Additional notable processes included are regulation of protein acetylation and histone modifications, among others (Supplementary Fig. 1b ; with analyses shown for both the inclusion, or not, of TP53, respectively, Supplementary Data 1, TAB2, 3). The specific example of the p53-negative regulator HUWE1 supports this link, where reduction of its levels corresponds with diminished cell growth only in cells with wt TP53, but not mutant (among 135 lines of the Achilles project, Supplementary Fig. 1c ); consistent with the relief of wt p53 from constraints resulting in growth inhibition. Fig. 1  The frequency of cancer with TP53 mutation is greater for males than females in multiple non-reproductive cancer types with TP53 mutation corresponding to poorest survival.
a Predicted TP53 mutation incidence in male and female cancers in the US population as calculated from combined SEER and TCGA data for the13 most abundant non-reproductive cancers: oesophageal carcinoma (ESCA), liver hepatocellular carcinoma (LIHC), bladder urothelial carcinoma (BLCA), head and neck squamous cell carcinoma (HNSC), lung squamous cell carcinoma (LUSC), skin cutaneous melanoma (SKCM), lower grade glioma (LGG), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), pancreatic adenocarcinoma (PAAD) and lung adenocarcinoma (LUAD and the exception (demarcated by a dashed box), kidney renal clear cell carcinoma (KIRC). b Overall survival (OS) across 14 years, for patients from the 12 disparity cancers in TCGA is stratified for cancers with wt TP53 (aquamarine) and mutant TP53 (turquoise).
Restricted expression of X-linked gene mutations in females. Given the lower incidence of non-reproductive cancers in females, we hypothesised that they are preferentially protected from the expression of cancer-risk gene mutations, compared with males. As we were unable to test this in healthy people, we examined data from cancer biopsies, aware that we were searching for remnants of a broken protective system. We compared the expression of X-chromosome mutations between cancers of females and males, where X-chromosome exome mutations that were not detected at the mRNA level (RMAF ≤ 0. 20; Fig. 3 ) are referred to as non-expressed mutations (NEMs, see the Methods section). The incidence of NEMs reached significance only in females (Fig. 4 ; Weighted chi-square test, Benjamini-Hochberg adjusted p-value ≤ 0.
3, data not shown). Strikingly, our analysis revealed an unexpectedly high incidence of NEMs in the p53-STRING gene set (Wilcoxon rank test p-value = 0. 001 Supplementary Data 3, TAB4, 5). The most significant NEMs in these cancers occurred in the p53-STRING genes FLNA, MED12, HUWE1, ATRX (Fig. 4 ). In total, over half the p53-STRING genes (50 out of 90) were identified with a significantly disproportionate NEM incidence between males and females (adjusted p-value were ≤ 0. 05). GO analysis of these 50 genes identified their involvement in fundamental pathways: histone modifications (ubiquitination, H3-H9 modification and   S Table  6  Fig. 5  S. Tables  4, 5  p-value = 0 Fig. 3  RNA-mutated allele frequency (RMAF) for X-chromosome genes was particularly high in male cancers, while in female cancers most mutations were not expressed.
RMAF was calculated for the 12 disparity cancers, for females and males independently (and pertinently this combined data reflects the individual cancers as evident in Supplementary Fig. 2c ). RMAF was quantified using a scale between 0 and 1. For a specific exome nucleotide mutation, 0 indicates the complete absence of mRNA for this particular mutation, where the gene is expressed at the mRNA level; 1 defines mutation in this nucleotide in the corresponding mRNA in 100% of cases. Females are indicated in pink and males in blue. trimethylation), protein localisation (to chromosome and telomeric region), regulation of telomere maintenance, and cell cycle DNA replication as key pathways (Supplementary Data 3, TAB6; Supplementary Fig. 3b ). The functional association of these 50 genes with p53 was confirmed by extensive literature mining (Supplementary Data 3, TAB7).
Of note, analysis of the KIRC data set did not identify significant NEMs for the X chromosome (Supplementary Data 3, TAB8). However, analyses of larger KIRC data sets will be required for validation. Overall, this exceptional incidence of NEMs among these p53-STRING genes supports our hypothesis that resistance to cancers of the disparity set is conferred in females through selective protection of the p53-Xchromosome network. While NEMs are relevant to cancer protection, it is the expressed mutations that pose cancer risk. This defines our third cornerstone that compared with males, females express a significantly lower proportion of their X-linked gene mutations, and this is outstanding for the p53-STRING set. Frequent expression of X-linked gene mutations in cancers. In male and female cancers, X-chromosome expressed mutations (EMs, RMAF ≥ 0.
75, see Methods) were numerous (Supplementary Data 4, TAB1). Consistent with the relevance of the p53-STRING set in cancer, these genes were among those with the most frequent EMs. Specifically, more than half of the p53-STRING set genes (47) were among the 299 genes with EMs in female cancers (Supplementary Data 4, TAB2,3). Similarly, in male cancers, 53 genes of the p53-STRING set were among the 341 genes with EMs (Supplementary Data 4, TAB4,5). This abundance of p53-STRING set EMs in males and females supports the disruption of p53 networks in cancers of both sexes (Supplementary Data 4, TAB6). GO analysis of the EM genes in females (299) and males (341) identified distinct pathway vulnerabilities between the sexes (Supplementary Fig. 4a, b , respectively; Supplementary Data 4, TAB7). In females, EM genes were prevalent in processes associated with internal amino acid acetylation and covalent chromatin modification, while in males the processes of glycosyl compound metabolic process, histone modification and response to iron(III) ion were pronounced.
In this individual cancer approach, we did not further stratify for patient sex to avoid diminishing statistical power, which is a risk associated with too few patients per cohort. We identified significantly reduced survival in a total of five cancers types that expressed high levels of one of these three genes (Fig. 5b ). Sex disparity of p53-STRING gene expression in normal adults. We next examined the relative gene expression levels between males and females of the X-linked p53-STRING set in tissues classified as normal and discriminated as cancer-free, from the TCGA cohorts across all the disparity cancers. While these are referred to as matched normals, we note that these may be affected by the existence of cancer cells and/or by adjacent tumour tissues in these patients (Supplementary Data 5, TAB2).
In the matched normals, KDM6A, DDX3X and UBA1 were significantly differentially expressed among the p53-STRING set. For comparison, we extended this test to the healthy samples of the 1000 genomes project, which are lymphoid in origin. Among these, statistically significant differential expression (DE) was identified between the sexes for 13 of the p53-STRING set genes (among the 95 X -linked DE genes with adjusted p-value ≤ 0. 05, Limma, Benjamini-Hochberg adjustment). All 13 genes were expressed at higher levels in females than in males (Supplementary Fig. 5 ; Supplementary Data 5, TAB3) with KDM6A, DDX3X and UBA1 prominent, which complements our findings in the normals matched to the tumours.
Kaplan-Meier survival curves were generated using the top and bottom medians of gene expression, and differences in survival distributions between medians were tested using a log-rank test. Exome mutations, RMAF and mutation classification. For these nonreproductive cancers, a full list of exome level somatic mutations was downloaded from Genomics Data Commons Data Portal and GDAC Firehose, more precisely, the Oncotator 81 ; annotated mutations were analysed. Mutations classified as "3'UTR", "5'UTR", "Frame_Shift_Del", "Frame_Shift_Ins", "Missense_Mutation", "Nonsense_Mutation", "Splice_Region" and "Splice_Site" were used to focus on mutations with the potential of having a more profound functional impact. Patients with available sex annotation and <15,000 somatic mutations were analysed. To assess whether DNA mutations in the X chromosome were transcribed into RNA, accounting for Xi, we classified mutations into two kinds: NEMs and EMs, and tested differences in numbers between males and females.
Secondly, we calculated a z-score based on the gene expression of the corresponding sample relative to its corresponding cohort (male or female). If the expression of the gene was substantially diminished (i. e. , 4 standard deviations below the mean; z-score ≤ -4) we concluded that the mutation had a functional effect in that sample (a knockdown effect) and assigned a RMAF of 1. Otherwise (z-score > -4), we discarded the mutation as its impact cannot be assessed. When mRNA reads were sufficiently abundant, RMAFs were calculated using Python and the Pysam library (https://github. com/pysam-developers/pysam), a wrapper of htslib and samtools  82. Based upon the relative RMAF distributions in the male and female cancer samples, we established two thresholds in an effort to account for tumour purity and heterogeneity and classified mutations with an RMAF greater or equal to the threshold of 0.
We assigned a plus sign if the proportion of NEM in females was larger than the proportion of NEM in males; and the converse was discriminated with a minus sign. These chiscores should be asymptotically distributed as standard normal (mean 0, standard deviation 1) under the null hypotheses, for both the p53-STRING set and other genes. We tested (Wilcoxon rank test) the difference in distribution between these two groups and found that p53-STRING set genes host significantly larger number of NEMs compared with other genes in the X chromosome. Differential expression. Raw gene-level counts for each cancer type were downloaded from The Broad Institute GDAC Firehose  79. Reads were converted to counts per million (CPM). Lowly expressed genes (fewer than 5 CPMs) were discarded. CPM counts were adjusted and normalised by library size using the R package edgeR  83.
By accessing the clinical data and mutation lists, two groups were analysed: Wt-TP53 Tumours and Mt-TP53 tumours (all samples with pathogenic TP53 mutations), each one stratified by patient sex. In addition, RNAseq count data from 465 healthy lymphoblastoid cell lines (derived from normal bloods) was downloaded from the 1000 Genomes project (https://www. ebi. ac. uk/arrayexpress/ experiments/E-GEUV-1)  84. Normalisation was also performed using edgeR  83. By accessing the available clinical data two groups were analysed: males and females. Differences in gene expression between these groups were computed with the R package Limma  85  , and significant changes were inferred by obtaining p-values and by adjusting for multiple hypothesis testing (Benjamini-Hochberg adjustment), false discovery rates were calculated.
Propensity scores were also considered in the differential expression analysis. Gene set enrichment analysis. To further investigate the role of p53-STRING genes in the disparity set, after performing differential expression analysis (normal versus wt TP53 tumours), the corresponding gene lists ranked by fold change were analysed for enrichment of negative X-linked p53 regulators using the fgsea package in R  86. Normalised enrichment scores (NES) and adjusted p-values (adjustment by FDR) were used to highlight the enrichment of these genes in wt-p53 tumours. ClueGO cytoscape analysis. Gene Ontology (GO) analysis of the gene sets was performed in ClueGO Cytoscape. The following parameters were selected: GO tree interval between levels 3 and 8; GO terms with at least three proteins and 4% of proteins; a kappa score of 0.
4 and only GO terms with a p-value ≤ 0. 05 were selected. STRING analysis. Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database tool 31 was adopted for analysis of X-linked genes that link to TP53 (version 10. 5; https://version-10-5. string-db. org/cgi/network. pl? taskId=PpCASJpZQ8Du). TP53 was coincidently included in the gene input, to define association. Parameters of selection included: textmining, experiments coexpression, gene-fusion, and co-occurrence. We ignored genes that sorted into a confidence score <0. 3.
Acknowledgements Prof Arnie Levine  for his helpful comments and suggestions. Simon Keam  for assistance with the graphic software package. Jason Li  and  Richard Tothill  for bioinformatics advice. The Haupt lab acknowledges funding from the following sources:  NHMRC  ( 1123057 ),  NBCF  ( IN-16-042 ),  Sister Institution Network Fund (SINF) :  MD Anderson -Peter Mac , and the  Peter MacCallum Foundation. T. P. S. acknowledges funding from  NHMRC Program  Grant  1054618.
Participant Study includes all consecutive patients treated with the FOLFIRI3 regimen for mCRC in our centre. From January, 2008 to December, 2017, patients were identified through the chemotherapy prescription computer software programme used at the cancer centre (CHIMIO ® , Computer Engineering). The database was declared to the National French Commission for bioinformatics data and patient liberty (CNIL). The study was performed in agreement with French regulations with approval from the local institutional review boards. A general informed consent was signed by all cancer patients at the time of their first hospitalization in the cancer centre, enabling patient clinical and biological data analysis in this cohort study. Demographics, cancer history, toxicity according to the Common Toxicity Criteria [Common Toxicity Criteria (CTC) v2.
0 (http://cancer. gov/)], and treatment outcomes, as well as pathological, clinical, biological, and radiological data [tumour response according to the Response Evaluation Criteria in Solid Tumours (RECIST) v1. 1 criteria], were retrospectively collected from medical records. To be evaluable, all patients must have received at least four cycles of chemotherapy. Patients were classified as follows: complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD). For statistical analysis, the best tumour response was selected. Patients with either CR, PR, or SD were classified as responders and patients with PD as nonresponders.
The ideal software should cover all key business processes of the registry daily work, including donor and patient data management, export to Search & Match Service of WMDA (previously known as BMDW), international donor search processes, management of requests, finances, transplant records and donor/ patient follow-up. Table  3  outlines important elements for ideal registry software. 6. Emerging alternatives: outcomes after HCT from related haploidentical donors (siblings, children, and parents) have improved significantly over the past few years, with results approaching those of MUD and MSD transplants. The use of haploidentical HCT will extend the availability of allo-HCT to virtually all patients in need, as almost all patients will have an available haploidentical related donor. This advantage, in addition to the low cost and immediate availability, is particularly important for countries with no donor registries and where cost is prohibitive for acquiring unrelated donor cells from international registries or for establishing a national registry.