dataesr/scientific-paragraphs-categorization huggingface dataset:
MUC4 and patient survival To establish a correlation between MUC4 expression and patient survival, we have compared survival analysis and hazard ratio in population designated as MUC4 high risk and low risk in every organ from TCGA datasets (Table  4 ). We have used SurvExpress optimized algorithm that generates risk group by sorting prognostic index (higher value of MUC4 for higher risk). The algorithm splits the populations where the p-value testing the difference of MUC4 expression is minimal  [4]. Pancreatic cancer presented the most important hazard ratio for MUC4 (HR = 3. 94 [CI 1. 81-8. 61] p = 0. 0005756) (Fig. 5a ). MUC4 high risk was also significantly associated with survival in bladder cancer (HR = 1. 48), colon cancer (HR = 2. 1), lung adenocarcinoma (HR = 1. 7), lung squamous carcinoma (HR = 1.
69), ovarian cancer (HR = 1. 33), skin cancer (HR = 1. 87) and stomach cancer (HR = 1. 58) (Fig. 5a ). Acute myeloid leukemia (HR = 1. 59) and liver cancer (HR = 1. 4) almost reach statistical significance. Other datasets did not show any statistically significant differences. A significant reduction in patient's survival was observed in bladder cancer (p = 0. 01135), colon cancer (p = 0. 00891), lung adenocarcinoma (p = 0. 008187), lung squamous carcinoma (p = 0. 03586), ovarian cancer (p = 0. 0186), pancreatic cancer (p = 0. 000219), skin cancer (p = 0. 02384) and stomach cancer (p = 0. 04751) as illustrated in Kaplan-Meier curves (Fig. 5b ). Strikingly, pancreatic median survival was 593 days in MUC4 high cohort (n = 149) whereas the 50% survival was not reached in MUC4 low cohort (n = 27).
We also compared the survival and hazard ratio, in the same cancers whose survival is associated with MUC4    S1  (GO 0031424: keratinization, GO 0007155: cell adhesion, GO 0019897: extrinsic component of plasma membrane, GO 0016323: basolateral plasma membrane and GO 0016324: apical plasma membrane) (Fig. 6a , Additional file 6: Table  S2 ). These gene signatures were all significantly associated with survival in the TCGA dataset tested. The "keratinization" (GO 0031424) and "cell adhesion" (GO 0007155) signature are associated with HR comprised between 1. 65 and 3. 76 and between 2. 15 and 3. 23, respectively. The GO 0019897 signature is associated with weaker HR (1. 55-2. 30). "basolateral" (GO 0016323) and "apical plasma membrane" (GO 0016324) signatures harbor more increased HR (2. 21-4.
5 and 1. 77-4. 42, respectively) in these datasets. We performed a similar analysis according to the top genes (ADGRF1, LCN2, MUC20, C1ORF116, SCEL, STEAP4) that harbored Pearson's correlation with MUC4 superior to 0. 5 (Fig. 6b , Additional file 7: Table  S3 ). This signature is associated with survival in all TCGA dataset tested (HR comprised between 1. 91 and 8. 77). Notably, pancreatic cancer harbored the strongest association with survival according to this signature (HR = 8. 77 ). Overall, these bigger signatures harbored higher hazard ratio compared to MUC4 alone.
broad insti tute. org/ccle) of MUC4. We analyzed the survival curves of the high risk group (MUC4/MUC16/MUC20 high , n = 159) and low risk group (MUC4/MUC16/MUC20 low , n = 17) from the pancreas TCGA dataset. The MUC4/MUC16/MUC20 high risk group was associated with an increased hazard ratio (HR = 6. 5  [2. 04-20. 78 ], p = 0. 001582) and a shorter overall survival (p = 0. 0003088) (Fig. 7a ). Median survival was similar as in MUC4 high cohort (593 days). The MUC4/MUC16/MUC20 high group harbored a statistically significant increase of MUC4, MUC16 and MUC20 expression (Fig. 7b ). We also analyzed overall survival in every other PDAC database available in Surexpress. We show that MUC4 high group was associated with a statistically significant reduced overall survival and increased hazard ratio in both ICGC and Stratford (GSE21501) cohorts (Fig.
7c ). In Zhang cohort (GSE28735), MUC4 high group was associated with a reduced overall survival that was close to statistical significance (p = 0. 08971). In other organs, the MUC4/MUC16/MUC20 high group was associated with an increased hazard ratio and reduced overall survival in bladder cancer, colon cancer, lung adenocarcinoma, lung squamous adenocarcinoma, skin cancer, stomach cancer (Additional file 8: Figure  S5A ). Notably, the MUC4/MUC16/MUC20 high group in colon cancer (HR = 2. 26  [1. 51-3. 4] ) showed a median survival of 1741 days whereas the low risk group did not reach the 50% survival. Similarly, the MUC4/MUC16/MUC20 high group in stomach cancer showed a median survival of 762 days whereas the low risk had a median survival of 1811 days. No significant difference was observed for ovarian cancer (p = 0.
2081). Moreover, a reduced overall survival was observed in liver cancer (p = 0. 04789) and acute myeloid leukemia (AML) (p = 0. 02577) (Additional file 8: Figure  S5B ) in which we did not show any statistical difference when sorting the patients for MUC4 alone. Overall, we observed that MUC4/MUC16/MUC20 signature harbored an increased hazard ratio compared with MUC4 alone for pancreatic cancer and to a lower extent in bladder cancer, colon cancer, lung squamous cancer and stomach cancer. We analyzed MUC4, MUC16 and MUC20 expression in pancreatic tumor (T) and paired adjacent non tumoral tissues (ANT) from GSE28735 (Fig. 6 ) and GSE16515 (not shown) datasets  [25, 26]. We confirmed MUC4 overexpression in tumor tissues (p < 0. 0001). MUC16 and MUC20 mRNA level were also increased (p < 0. 0001 and p = 0.
01 and p < 0. 05 respectively). ROC curves of MUC4, MUC16, MUC20 and MUC4 + MUC16 + MUC20 combination were established using GSE28735 dataset. The combination of MUC4 + MUC16 + MUC20 produced a high specificity of 97. 78% (88. 23-99. 94) and a mild sensitivity of 55. 56% (40-70. 36) (likelihood ratio = 25) (Fig. 8b ). Similar results were obtained for GSE16515 with 93. 75% specificity and 69. 44% sensitivity (LR ± 11. 11) (not shown). MUC16 AUROC was similar to that of MUC4 + MUC16 + MUC20 in GSE28735 dataset but harbored a lower specificity/sensitivity in GSE16515. Altogether, this suggests that MUC4/MUC16/ MUC20 high signature would be useful in stratification of patients with worst prognosis in several carcinoma and notably pancreatic, stomach and colon cancers.
Based on available TCGA datasets, mucin mutation map was generated by cBioPortal Mutation Mapper  [35]. MUC4 mutations were notably observed in Kidney Clear Cell Renal Carcinoma (20-45%) and were correlated with survival outcomes. Rare mutations were described in the main overexpressing model that is pancreatic cancer. Because of the very large size of MUC4 gene, probability of acquiring mutation could be increased. MUC4 belongs to the most mutated genes upon stress exposure such as nicotine treatment or aging  [36, 37]. The enrichment of mutation of MUC4 could be related with the fact that the first risk factor of kidney cancer is smoking  [38]  and that kidney cancer diagnosis is occurring at elder ages (65 years)  [39]. Pancreatic cancer shares these characteristics but harbors a very rare mutation occurrence (3%) suggesting that aging could be specific of cancers such as kidney or lung and that overexpression is more important for other cancers.
The present work highlights the relationship between MUC4/MUC16/MUC20 expression and overall survival. This signature could be proposed as a prognostic marker. Moreover, MUC4 is expressed in the earliest stage (PanIN1A) of pancreatic cancer but is not specific enough. The potential of the combination MUC4/MUC16/MUC20 as a diagnosis marker is not known and remains to be investigated in the future. Moreover, development of unsupervised algorithm will allow the identification of new non intentional bigger signatures leading to better prognostic and predictive performances. Genome wide computational unsupervised procedures from discovery datasets will help to determine hypothesis signature. The signature will be subsequently validated on a number of independents datasets. Thus, multi-platform analysis using TCGA datasets helped to characterize the complex molecular landscape of PDAC  [54].
Another metaanalysis approach based on PDAC datasets allowed the identification of a 5 genes classifier signature (TMPRSS4, AHNAK2, POSTN, ECT2, SERPINB5) with 95% sensitivity and 89% specificity in discriminating PDAC from non-tumor samples  [55]. Interestingly, TMPRSS4 and SERPINB5 are two genes belonging to the gene list correlated with MUC4 expression.
Conclusion We analyzed MUC4 expression systematically in all organs in TCGA and CCLE large scale databases and confirmed its aberrant expression in associated carcinoma and the MUC4 impact on patient's survival. Moreover, 187 genes (involved in cell adhesion, cell-cell junctions, glycosylation and cell signaling) were correlated with MUC4. Among them, MUC16 and MUC20 membrane-bound mucins and their combination MUC4/ MUC16/MUC20 is associated with a poorer overall survival in different cancers including pancreatic, colon and stomach cancers suggesting MUC4/MUC16/MUC20 as a poor prognostic signature for these cancers. This potential as new biomarkers remains to be investigated in the future.
De novo draft assembly of the F2 genome: A powerful tool to identify B73 and F2 non shared regions To discover new genome-specific regions in European maize, we produced a draft genomic sequence of F2, a French maize line of genome size similar to this of B73 (1C genome size of 2. 46 ± 0. 01 Mb, see Additional file 1: Table  S1 ). This de novo whole genome assembly (WGA) of the F2 genome was conducted by processing a 90X short-reads dataset with a combination of ABySS and SSPACE (see Materials and Methods). F2 genome size was estimated by K-mer frequency analysis to 2. 47 GB, in agreement with flow cytometry estimation. This assembly represents 1597 Mb (i. e. , 64. 8% of the F2 genome) with a scaffold N50 of 13,895 bp and NG50 of 4289 bp. Quality of this assembly was assessed by searching for 248 ultra-conserved Core Eukaryotic Genes (CEGs) using CEGMA  [36].
Homology searches using TE databases indeed revealed that 60% of our F2 WGA corresponds to TE sequences (56% for contigs and scaffolds > 1 kb). To further assess the quality of the assembly in gene-rich and TE-rich regions we aligned F2 scaffolds onto three F2 BAC sequences assembled from Roche 454 or PacBio SMRT sequencing (C. Vitte, personal communication). One of these BACs corresponds to the Bronze region while the two others are totally depleted of gene and contain mainly nested TEs which are known to be among the most challenging regions of the genome to assemble. Coverage of BACs by F2 WGA scaffolds range from 44 to 80% (Additional files 2, 3 and 4: Figures  S1, S2  & S3 ) and gene space is fully covered. With no exception, contigs are correctly ordered and oriented within scaffolds, and no evidence of chimeric scaffolds or contigs was detected, thus highlighting the quality of the scaffolding and showing that the F2 scaffolds cover large TE-rich regions.
F2-specific regions contain non-ubiquitous genes To further analyze the set of new genes found in the F2specific regions, we used our set of 417 new putative genes to analyze their coding properties and expression patterns. First, we expected that some large PAVs overall specific to one line might contain short coding sequences shared between B73 and F2. So we filtered out PAV predicted coding sequences exhibiting > 99% identity with B73 mRNA over their full length. Twenty-two of such genes were filtered out, thus leading to a final set of 395 novel coding sequences (average size: 1. 1 kb). A putative function was then assigned to 91 of these proteins using protein domain annotation and similarity search against UniProtKB/Swiss-Prot sequence database (Additional file 1: Table  S4 ). Annotation of protein functions revealed that 17 of them (20%) are putatively For each chromosome, top panel: fraction of window covered by B73 genes, number of F2/B73 SNPs per window, PAVs (F2-specific and B73-specific regions).
Conservation of F2-specific genes in maize and other grasses Finally, we investigated the conservation of the 395 F2 novel genes with other maize inbred lines and closely related species using BLASTN analyses onto the NCBI non-redundant nucleotide database (Additional file 1: Table  S6 ). We focused only on BLAST best hits to determine whether the sequence is present in other maize or in close maize relatives. Of these sequences, 358 (90. 6%) have a best hit in maize and 31 (7. 8%) in other related grasses (20 in Sorghum, 3 in Setaria, 3 in Saccharum, 1 in Miscanthus, 1 in Panicum, 1 in Tripsacum, and 2 in Oryza). Among the 358 genes with a maize homology, only 22 (6. 1%) shared > 95% nucleotide identity over > 85% of their sequence with B73, and 31 (8. 6%) with other maize lines. The remaining 305 sequences exhibit significant similarity (e-value < 1.
As shown in Fig. 5 , all three analyses separated the 25 lines into the four groups to which they belong, with slight overlap between Northern Flints and European Flints when SNPs and F2-specific regions are considered. No major difference was found between PAVs and SNPs clustering. Most PAVs are shared among maize groups but some are found only in European germplasm Hierarchical clustering performed on 4218 PAVs present in F2 and absent in B73 with no missing data (Fig. 6a ) revealed a majority of cases with high frequency in European Flints and lower frequencies in other groups. Among these, 396 variants (9. 3%) are found only in European Flints among which 134 (3. 2%) variants are found only in F2 and no other inbred line. On the other extreme, 1064 (25%) of the PAVs present in F2 and absent in B73 are shared among all groups and may correspond to ancient variants with recent losses more or less specific to B73.
This pan-genome includes the B73 RefGen V2 sequence plus two additional pseudomolecules containing respectively the F2-specific sequences anchored and not anchored to B73 sequence. The methodology can be easily applied to include additional genome sequences or future improved version or B73 and F2 genome assemblies. While neither B73 or F2 genome assembly are complete, the provided B73-F2 pan-genome sequence is an unprecedented material to better characterize and use the European maize germplasm. It should for instance help in completing reference database in view of genotyping by sequencing (GBS) or contribute to the definition of new genotyping arrays to increase the power of genome-wide association studies (GWAS). Considering the history of maize, we expect that sequences from F2 will not be sufficient to well represent the genomic diversity of the European germplasm.
Detection of genome sequences not shared between B73 and F2 using read mapping The bioinformatics workflow for B73/F2 pangenome building is summarized in Additional file 8: Figure  S7. Structural variants showing presence in F2 and absence in B73 were detected using Pindel v0. 2. 4  [64]  and BreakDancer v1. 4. 5  [65]. First, B73 reads (40X dataset downloaded from NCBI Sequence Read Archive (SRA: SRR404240)) were aligned using Stampy onto F2 WGA. Aligned reads were then used as input for Pindel and BreakDancer, with a requirement of minimum local coverage of 3 reads with Q20 mapping quality. To discard possible false positives SVs due to F2 assembly errors or presence of stretches of Ns, we then performed the same analysis using reads from F2 with no SV as expectation (40× from our original dataset), and variants supported by both B73 and F2 reads were discarded.
Sequence annotation of novel F2 regions and expression of novel F2 genes Novel F2-specific sequences were annotated for genes and transposable elements. F2-specific regions were searched for TE using RepeatMasker (A. F. A. Smit, R. Hubley & P. Green RepeatMasker at http://repeatmasker. org) with database Repbase RELEASE 20150807  [68]. Gene annotation was based on mRNA-seq data analysis. mRNA-seq from B73, MO17, EP1, KUI3 and F331 leaves (C. Vitte, personal communication) and from each of the 12 F2 tissue samples from this study were subjected to sequence quality checking with Fastqc (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Contaminant sequences were removed from the raw data using cutadapt (link: https://cutadapt. readthedocs. io/en/stable/, version: v1. 0) with options: -discard -O 10 -q 1 -m 24).
Genes overlapping RepeatMasker-based TE annotations over > 80% of their length were discarded. Novel F2 genes with CDS coding proteins with over 100 aa, with no significant blast hit (> 95% identity over 90% of the query length) with B73 sequences (NCBI no-redundant nucleotide bank) and FPKM > 1 at least in one library were selected for further expression analysis. F2 novel CDS were searched for known protein domains and sequence similarity using InterProscan 5  [71]  and Blastp with the UniProt protein sequence database.
Acknowledgements We are grateful to  Jacques Laborde  and  Carine Palaffre  of  INRA Saint Martin de Hinx  for providing seed stocks, to  Cyril Bauland  for historical information of the maize accessions, to  Delphine Madur  for help with microsatellite-based F2 seed lot validation, to  Pierre Chambrier  for help with RNA extractions, and to  Genseric Beydon  for BAC isolation. We thank  Pr Olivier Panaud  for helpful discussions on the original manuscript and  Dr. Domenica Manicacci  for helpful comments on LD analyses. We thank  Matthieu Labernardière  and  Cyril Pommier  at  INRA URGI  for the  B73/ F2  pan-genome JBrowse setting as well as the  Cornfed Plant-KBBE program  ( P. I. A. Charcosset ) for providing sequences of the 23 maize accessions. The present work has benefited from the core facilities of  Imagerie-Gif , (http://www.
Availability of data and materials The B73/F2 pan-genome sequence and annotation is available through a genome browser at https://urgi. versailles. inra. fr/jbrowse/gmod_jbrowse/ ?data=myData/Amaizing_Pangenome. The F2 whole genome assembly as well as the full pB73/F2 pan-genome sequence and annotation are available from the Dryad repository: https:// doi. org/10. 5061/dryad. f81sj. DNA-seq and mRNA-seq reads from this study were deposited in the European Nucleotide Archive (ENA) under the study accession number PRJEB13732 http://www. ebi. ac. uk/ena/data/view/PRJEB13732.
In this paper, we investigate the spelling conventions on the Twitter microblogging platform. In order to gain insight into the universalities and specificities of communication on social media, we perform a comparative analysis of three closely related languages: Slovene, Croatian and Serbian. The data collection and annotation protocols were developed jointly for all three languages, allowing for maximum interoperability and comparability of results. The analysis reveals differences in the amount of deviation from the norm in the three languages, with Slovene twitterese being the most inclined to using non-standard spelling, and Serbian the least. Overall, closed word classes, especially interjections and abbreviations, are found to be more non-standard than the open classes. In terms of types of standard > non-standard transformations, character deletions are more frequent than insertions or replacements, and transformations mostly occur in word-final positions.
Overall, even though some quantitative data have been reported, qualitative analysis and survey questionnaires prevail in these studies. The two studies that are most directly related to the work presented in this paper are  Fišer et al. (2015)  and  Miličević and Ljubešić (2016). The first compares tweets published in Slovene, Croatian and Serbian. It finds that, contrary to popular belief, most of the language used in tweets is fairly standard, especially in Slovene and Croatian. Another interesting finding was that the key characteristic of non-standard Slovene tweets is non-standard orthography, while non-standard lexis is more typical of Croatian, and especially Serbian. The second study looked only at Croatian and Serbian, detecting both similarities and differences between them. While some of the discrepancies were interpreted as being due to linguistic differences between the two languages (e.
g. Croatian tends to drop final vowels to a higher extent than Serbian), others appear to be better explained by looking at extra-linguistic factors, such as user age, which seems to be lower in the case of Serbian, leading to a more chat-like format of messages. Both studies shared the finding that diacritics on letters such as č, ć, š, ž and đ are omitted more often in Serbian than in Croatian and Slovene. In the present paper, we focus on posts from the Twitter microblogging platform written in Slovene, Croatian and Serbian. As one of the most widely used CMC platforms, Twitter has already received a lot of attention in linguistics. The average number of tweets published per day amounts to about 500 million, 1  and the content ranges from news broadcasts and official announcements by companies and institutions, to personal thoughts and opinions the users share, making Twitter a rich and easily accessible source of data for a wide range of (socio)linguistic inquiries.
An additional component influencing the structural properties of its language is that tweets are limited to only 140 characters. The analysis we report on is based on manually normalised, lemmatised and part-of-speech tagged samples of tweets in Slovene, Croatian and Serbian, created with the goal of developing tools for automatic CMC normalisation and tagging. In the remainder of the paper we first describe the corpora the tweets were sampled from and the samples themselves, moving on to the procedure and guidelines used in the manual normalisation. We then present the results of the analysis of normalisation. Specifically, we look at the distribution of standard-to-non-standard transformations across parts of speech and lemmas, as well as at the distribution of transformation types (deletions, insertions, and replacements), and compare these phenomena across the three datasets.
2016 ). These datasets were created to facilitate the development of processing tools for nonstandard language, and for this reason they were sampled to represent tweets with different levels of technical and linguistic (non-)standardness (see  Ljubešić et al. 2015). However, since the focus of this paper is on non-standard spelling variants, we only take into account the linguistically non-standard portion of the dataset, resulting in 1,983 tweets  (54,688 tokens)   1. These features include phenomena typical of CMC in general, such as phonetic spelling of foreign words (e. g. , lajk for like), omission of diacritics (e. g. , razrednicarka for razredničarka -teacher), or shortenings (e. g. , yt for YouTube), Twitter-specific phenomena like hashtags, @ name mentions and emoticons/emoji, as well as phenomena common in informal communication settings, such as the use of colloquial and dialectal non-standard forms (e.
Linguistic normalisation In this paper we are most interested in the level of linguistic normalisation. In our case, the main goal of manual normalisation was to provide training data for building tools for automatic normalisation of CMC data. However, normalisation is also important for the end users of CMC corpora, as it enables them to perform queries based on standard forms, much along the lines of dialectal or diachronic data. Normalisation was restricted to the word level, while word order, syntax, punctuation, ellipses, usernames, hashtags, emoticons/emoji and lexical choice (e. g. , colloquial komp for kompjuter -computer) were not normalised. Normalisation included the standardisation of non-standard spelling variants (e. g. , in Slovene jst > jaz -I), as well as spelling and typing errors (e.