dataesr/scientific-paragraphs-categorization huggingface dataset:
Data was imported and processed using the RnBeads v2. 2R package pipeline. For the preprocessing of the data, the normalization method was set to "illumina" and the background correction method to "methylumi. noob". Regulon analysis. We reconstructed transcriptional regulatory networks (regulons) using the R package RTN  78. We investigated 23 transcription factors previously associated with bladder cancer  33  and 78 candidate regulators associated with chromatin remodeling in cancer  34. Gene lists can be found in Supplementary Table  8. Potential associations between a regulator and all possible target genes were inferred from the expression matrix by Mutual Information and Spearman's correlation, and permutation analysis was used to remove associations with a BH-adjusted p-value > 1 × 10 -5.
Primer sequences for the 12 genes can be found in Supplementary Table  9. Construction of single-sample transcriptomic classifier. We constructed a Pearson nearest-centroid classifier for NMIBC based on the recently published classifier for the MIBC consensus subtypes  27. Only samples with positive silhouette scores were used for feature selection (n = 505). We filtered the expression matrix to include genes with a median expression > 0 in at least one of the four classes and used a step-wise ANOVA approach to identify genes with significantly different expression levels across classes. ANOVA between all four classes resulted in 13,650 significant genes (BH-adjusted p-values < 0. 05). Genes highly expressed in class 2b dominated the list, so we removed class 2b samples and previously significant genes from the dataset and performed a second round of ANOVA on the remaining classes.
We computed four centroids corresponding to the four NMIBC classes (i. e. , the mean gene expression profile of the 1942 chosen feature genes for each class), and class labels are then assigned to single NMIBC samples based on the Pearson correlation between a sample's expression profile and the four-class centroids. The NMIBC classifier is available as a web application at http://nmibc-class. dk, as an R package at https://github. com/sialindskrog/ classifyNMIBC or in Supplementary Software 1. A similar approach was used to construct a Pearson nearest-centroid classifier for the T1HG subtypes, resulting in 883 chosen feature genes. Proteomics. Formalin-fixed paraffin-embedded (FFPE) tissue from transurethral resection of bladder tumors (TURB) was obtained from 167 Danish patients at Skejby and Frederiksberg hospital.
Digital pathology. For digital pathology, we utilized the Visiopharm image analysis software version 2018. 9. 5. 5952 (Visiopharm A/S, Hørsholm, Denmark). The Tissue Array module was used to identify and extract individual cores on the TMAs and the Tissue Align module to align the pan-cytokeratin stained image with its corresponding fluorescence image (Supplementary Fig. 7a , step 1). Image analysis protocol packages (APPs) developed by our group  41  were used to automatically: (1) Define parenchymal and stromal regions as pan-cytokeratin positive and negative, respectively (Supplementary Fig. 7a, step 2 ). Calculate the proportion of immune cell subsets based on co-localization of selected markers (Supplementary Fig. 7a, step 3 ). Calculate the proportion of PD-L positive cells. In addition, we designed an APP using the Tissue Author module, in order to calculate the proportion of GATA3, CK5/6 or double-positive positive cells.
For GATA3 and CK5/ CK6 detection, tumors were classified as positive if more than 50% of the carcinoma cells expressed the marker. For all markers, we selected a threshold visually to differentiate between positive and unspecific staining. The threshold was verified by an experienced pathologist. We applied the following scoring algorithm to calculate cell fractions (here shown for T helper cells): The proportion of infiltrating immune cells was consistent across the 3 tissue cores from the same tumor; average Pearson correlation coefficient: 0. 67 (panel 1) and 0. 77 (panel 2). Independent transcriptomics datasets used for validation. Transcriptomics data from 11 historical cohorts (Kim 80 , Lindgren  45  , Sjödahl2012 7 , CIT  72  , Choi  81  , Sjödahl2017 26 , Song  82  , Sjödahl2019 83 , Aarhus microarrays  5,54,84,85,  , Meeks  35  ) were used for the validation of the four-class NMIBC classification and T1HG subtypes.
The resulting cDNA was hybridized onto Affymetrix Human Transcriptome 2. 0 microarrays. Quality control checks, gene level normalization (using SST-RMA) and signal summarization was conducted using Affymetrix Expression Console Software. Unpublished cohort 2 provided by Richard Bryan. RNA-Seq based analysis of 78 tumors from the West Midlands Bladder Cancer Prognosis Programme (BCPP, ethics approval 06/MRE04/65). All ethical regulations for work with human participants were followed. RNA libraries were prepared using the Truseq Stranded Total RNA with Ribo-zero Gold kit (Illumina) and 2 × 100 bp PE sequenced (Hiseq, n = 26) or 2 × 75 bp PE sequenced (Nextseq, n = 52). The data were aligned to GRCh37 and reads counted with STAR aligner (v2. 5. 2b). Log2(Read count+1) for each gene has been used as input for the class prediction.
Unpublished cohort 3 provided by Trine Strandgaard. RNA-Seq based analysis of 47 fresh frozen tumors from patients enrolled at Aarhus University Hospital with high-risk NMIBC, and analyzed following approval by the Danish National Committee on Health Research Ethics (#1708266). All ethical regulations for work with human participants were followed. RNA-Seq data was generated using analysis pipelines described above for the additional samples included in the discovery cohort in this work. Pathway enrichment analysis. Pathway enrichment analysis was performed independently in the UROMOL cohort and each historical cohort that contained representatives of all classes. First, we collected pathway annotation from the Reactome (using R package reactome. db v1. 68. 0) and KEGG (using the R package KEGGREST v1.
24. 1) databases. We joined these annotations and performed geneset variation analyses (using the R package GSVA v1. 32. 0) to obtain single-sample enrichment scores for each pathway. To find associations between pathways and classes, we performed Mann-Whitney U-tests using the pathway enrichment scores between samples in each class versus samples in other classes in each cohort separately. P-values were BH-adjusted. For the pathway visualizations, we first filtered pathways that were enriched in the same class in the UROMOL cohort and in at least four other datasets and then manually selected pathways from the filtered list. Pathway enrichment scores were grouped using hierarchical clustering with correlation distances (1r) and Ward clustering using the enrichment scores in the UROMOL cohort and the same pathway order was then used for the independent cohorts.
Regulon activity in validation cohorts. The regulons from the transcriptional networks calculated from UROMOL data were used to derive differential enrichment scores in each cohort separately using the two-tail GSEA method (R package RTNsurvival). We discretized the activity scores into "active" and "repressed" status, aggregated the regulon status in all cohorts, and used Fisher's exact tests to find the association of regulon status with each class. P-values were BH-adjusted. Weighted in silico pathology (WISP) analysis. To approximate intra-tumor heterogeneity, we used the bulk transcriptomic profiles and the consensus clustering results for the UROMOL cohort and applied the Weighted in silico pathology (WISP, R package v. 2. 3) method with default settings. Only samples with a positive silhouette score were used for the WISP analysis (n = 505).
AUCs and associated 95% CIs (computed with 2000 stratified bootstrap replicates) were calculated using the R package pROC. Likelihood ratio tests were used to assess model improvement (all models were compared to the EORTC model). P-values below 0. 05 were considered significant across all tests and BH-adjustment of p-values was performed to control for multiple testing when necessary (otherwise unadjusted p values are reported). The R packages tidyverse, ggplot2, reshape2, and ComplexHeatmap were used for data analysis and figure creation. All statistical and bioinformatics analyses were performed with R (v3. 6. 0 or 3. 6. 1).
Acknowledgements We thank  K. K. Cheng ,  Maurice P. Zeegers ,  Nicholas D. James ,  Naheema S. Gordon ,  Ben Abbotts  and  Roland Arnold  for work involved in generating the Birmingham RNA-Seq validation cohort. S. V. L. , F. P. , and L. D. are supported by the following funding sources:  Aarhus University , The Danish Cancer Biobank, The  Health Research Foundation of Central Denmark Region , The  Danish Cancer Society. N. M. and F. X. R. are supported by  Fondo de Investigaciones Sanitarias (FIS) ,  Instituto de Salud Carlos III, Spain  (# PI18/ 01347 ),  Asociación Española Contra el Cáncer  (AECC, #  GB28012014 ). D. J. D. is supported by the following funding sources:  RSG 17-233-01-TBE  from the  American Cancer Society , the  W. W. Smith Charitable Trust , the  Pennsylvania Department of Health via Tobacco CURE Funds , the  Ken and Bonnie Shockey Fund for Urologic Research and the Bladder Cancer Support Group at Penn State Health.
J. I. W. is supported by the  Laurence M. Demers Career Development Professorship in Pathology and Medicine, Pennsylvania State University. J. D. R. is supported by the  Ken and Bonnie Shockey Fund for Urologic Research at Penn State Health. J. M. is funded by a  SEED Award  from the  HOPE Foundation , the  Department of Defense  ( W81XWH-18-0257 ), and the  VHA  ( BX003692-01 ).
Data availability Raw sequencing and SNP data are deposited and available under controlled access at The European Genome-phenome Archive (EGA), which is hosted by the European Bioinformatics Institute (EBI) and the Centre for Genomic Regulation (CRG). The RNA-Seq data are available under accession code: EGAS00001004693 and the SNP data are available under accession code: EGAS00001004862. The data are available under controlled access at EGA. Due to privacy laws, data will be available following new approvals by ethical committees and data protection agencies. The data release process can be initiated by contacting the corresponding author (lars@clin. au. dk). Processed normalized mRNA read counts are available in Supplementary Data 2, processed proteomics data are available in Supplementary Data 3 and processed EPIC BeadChip methylation data are available in Supplementary Data 4.
Data are available within the Article file, Supplementary Information or from the authors upon request. The expression data used for validation are available under the following accession codes: Kim  80  , microarray, GEO: GSE13507; Lindgren  45  , microarray, GEO: GSE32549; Sjödahl2012 7 , microarray, GEO: GSE32894; CIT  72  , microarray, ArrayExpress: E-MTAB-1803; Choi  81  , microarray, GEO: GSE48075; Sjödahl2017 26 , microarray, GEO: GSE83586; Song  82  , microarray, GEO: GSE120736; Sjödahl2019 83 , microarray, GEO: GSE128959; Aarhus microarrays  5, 54, 84, 85  , GEO: GSE3167 and GSE5479; Meeks  35  , RNA-Seq, GEO: GSE154261; unpublished cohort 1 provided by Margaret Knowles, microarray, GEO: GSE163209; unpublished cohort 2 provided by Richard Bryan, RNA-Seq, EGA: EGAS00001004358; unpublished cohort 3 provided by Trine Strandgaard, RNA-Seq, EGA: EGAS00001005050.
However, the ranges and adopted quality scores were based upon a priori operational knowledge and experience of each site. The objective of this study is to evaluate the spatial variability of aerosols at the scales typical for the observations required to support the Copernicus Anthropogeneic CO 2 Monitoring (CO2M) mission. Specifically, we focus on evaluating differences in spatial variability between 2 km and 4 km spatial resolution considered for the MAP instrument  [11, 42]. As such, the explicit objective of this study is to determine whether characterizing aerosol with a MAP at a spatial resolution of 4 km is sufficient for the CO2M mission. We aim to derive the mean aerosol varibility at 2 km and 4 km and to evaluate possible differences in aerosol characterization. The analysis relies on the comprehensive dataset and frequent aerosol measurments collected during more than 30 years by AERONET ground-based measurements  [39].
AERONET aerosol dataset AERONET is a global distributed network of well-calibrated sun photometers  [39]. By measuring sun direct radiances, AERONET provides high temporal (every ∼15 minutes in daytime) multiwavelength AOD products with high accuracy ( ±0. 01 ∼0. 02). In addition, almucantar measurements of sky radiances can be converted by an inversion algorithm  [44] [45] [46]  to retrieve aerosol microphysical properties, e. g. aerosol size distribution, complex refractive index, particle sphericity, aerosol single scattering albedo (SSA or ω), etc. [2]. Generally, the accuracy of AERONET inversion products is related to the total aerosol loading. The SSA accuracy is estimated to be ±0. 03 when AOD (440 nm) is higher than 0. 2, and ±0. 05-0. 07 when AOD is lower than 0. 2  [45]. In this study, we selected 30 AERONET sites, which represent very different aerosol conditions to best represent the aerosol temporal and spatial variability.
MERRA-2 reanalysis wind speed The newly released MERRA-2  [43]  is a global earth system model based reanalysis dataset with data assimilation. MERRA-2 provides long-term (1980 -onwards) reanalysis dataset of climate system. In this study, we will use MERRA-2 10-m wind speed data (Last access: 2019-04-23) as ancillary data to access the aerosol temporal and spatial variability. The evaluation of MERRA-2 wind speed dataset indicates a clear improvement to its predecessor (MERRA), which tended to underestimate winds over ocean and overestimate winds over land. The errors for both MERRA and MERRA-2 are relatively higher at coastal and land areas than in open ocean  [48]. Fig. 2  shows the global mean (20 0 0-2018) 10-m wind speed and direction climatology from MERRA-2 dataset. Fig. 3  shows the scatter plots of mean AOD (440 nm) versus mean AE (440/870 nm) and the mean 10-m wind speed for all 30 AERONET sites.
e. localtime between 10:00 and 14:00 hrs), we compare the daytime aerosol temporal variability TV AOD (440 nm) derived using all data with the TV AOD derived using data collected during the specific local time window between 10:00 and 14:00 hrs. The results are illustrated in Fig. 9 : blue crosses are daily mean TV AOD (440 nm) derived using all data; red circles are TV AOD (440 nm) derived using data during the specific time window. In general, the TV AOD (440 nm) during local time 10-14 is slightly lower than the dirual mean by 12. 5% (local time 10-14: 0. 021/h; dirual: 0. 024/h). Two urban polluted sites (#2 Mexico_City and #20 Beijing) show that the variability around noon (local time 10-14) is higher than the daily mean variability by 6. 6% (#2) and 1. 8% (#20) respectively. In contrast, over dust sites (e.
Conclusions In this study, we made an attempy to estimate the aerosol maximum temporal variability using full data archives of 30 representative AERONET sites. By exploiting additional information about local wind speed, the maximum and mean aerosol variability was estimated at scales of 2 km and 4 km, and then inverstigated for clear differences that could affect satellite requirements. The results of this study can be summarized by the following conclusions: √ Aerosol maximum temporal variability ( /h) is influenced by local wind speed. For example, the AOD temporal variability is high over urban polluted sites where the local wind speed is low. On the other hand, the aerosol size parameter (AE) shows high temporal variability over oceanic sites where the local wind speed is high. The aerosol temporal variability in 1 hour is generally higher than its spatial variability at 1 km.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (9. 8%) that is slightly higher than the one (5. 5%) derived from AERONET data (using MERRA-2 wind speed), which could partially be explained also by the fact that the MAIAC AOD is slightly lower than AERONET measurements over Beijing. Meanwhile, we got SV AOD_MAIAC (550nm) 0. 004, and the relative change is 5. 5% at REUNION_ST_DENIS ( Fig. A1  ), where the aerosol loading is generally low (AERONET mean AOD at 440 nm is 0. 08). The derived SV AOD using AERONET and MERRA-2 wind speed is 0. 0 0 04 per km (0. 5% in relative change), which is 10 times smaller than the SV AOD_MAIAC (550nm). The higher MAIAC AOD variability can probably be explained that MODIS AOD can be affected by other factors such as surface heterogeneity and topology especially in cases with low aerosol loading.
Acknowledgement This study was supported by the  European Space Agency, ESTEC  under the contract of the Study on Spectral Sizing for CO 2 Obser-vation No. 40 0 0118601/16/NL/FF/gp. We would like to acknowledge the AERONET team and MERRA-2 team for making their data available for the community. All PIs of AERONET sites are gratefully acknowledged.
, 2018) , whereas in colon cancer, Loc90024 encodes the SRSP protein  (Meng et al. , 2020)  and HOXB-AS3 translates the homonym protein  (Huang et al. , 2017). In lung cancer, Linc01420 was found to encode the NoBody protein  (D'Lima et al. , 2017)  and UBAP1-AST6 the homonym protein  (Ye et al. , 2020). If the role of these proteins is not yet fully understood and they are not necessarily referenced in the protein databases, some have been accepted, such as NoBody (non-annotated P-body-dissociating polypeptide) which is referenced and considered as reviewed in the UniprotKB database since March 2017 (accession number A0A0U1RRE5, NBY). NoBody (NBY) is involved in the RNA decapping through its interaction with the protein enhancer of decapping 4 (EDC4), an activator of RNA decapping  (D'Lima et al.
A Historical Gap in the Dogma of Protein Translation It was for long admitted that the DNA is transcribed into a premature RNA (pre-mRNA) and then into several mature RNAs (mRNAs) through alternative splicing, each of these mRNAs being then translated into a single protein. However, modern MSbased proteomics thanks to the high sensitivity and speed of the new instruments has challenged the bases of molecular biology and translation  (Mouilleron et al. , 2016). Indeed, the protein databases were largely established by translation prediction from the first genome sequencing data. Rules have thus been established to determine which transcripts code for proteins and predict protein sequences. Among others, these rules establish that an RNA which is presenting an ORF of less than 100 nucleotides is considered as non-coding.
Moreover, for the translation to occur, the presence of a start "AUG" codon as well as a Kozak context is required to enable the fixation of the ribosome  (Kozak, 1999). Besides, it was considered that eukaryotic cells are monocistronic, i. e. , that only a single protein can be translated from a specific coding mRNA FIGURE 3 | (A) The presence of a start codon on an lncRNA induces this translation into protein by the ribosome; this protein, not predicted in conventional databases, is therefore considered to be AltProt. (B) circRNA arbors an IRES site helping the ribosome fixation and thus the expression of some proteins. (C) Pre-miRNAs have on their sequence some start codons involving the ribosome binding and thus the transcription of AltProt. (Breuza et al. , 2016). However, contrary to this dogma, the development of new technologies of RNA sequencing such as polysome profiling, ribosome immunoprecipitation, ribosome affinity purification, ribosome profiling (also known as Riboseq), and ribosome-nascent chain complex sequencing (RNCseq) and MS-based proteomics using databases built from new prediction rules (i.
e. , Open Proteome database) allowed the detection and identification of translated proteins from supposedly ncRNAs or non-coding regions of mRNA. The different studies have revealed that numerous ncRNAs were targeted by the ribosome and translated into proteins unlike what was initially expected  (Menschaert et al. , 2013; Bazzini et al. , 2014; Ingolia et al. , 2014; Pueyo et al. , 2016; Wang et al. , 2019). For a few years now, Ribo-seq is democratizing thanks to the development of one-shot analysis methods such as AHARIBO  (Minati et al. , 2021)  which enables to find proteins derived from RNA so far considered as noncoding. Despite important advances, the implementation of proteogenomic strategies remains and requires a set of skills in the various fields of genomic, transcriptomic, and proteomic and access to often expensive technologies.
Filling the Gap Demonstrating the existence of proteins encoded from ncRNA has highlighted the limits of the conventional protein databases and the need for new protein libraries  (Wu et al. , 2020). In the last 10 years, several new databases have thus been created. Many of them are based on the identification of small ORFs (smORF) in the ncRNAs to integrate these unreferenced sequences, at the protein but also at the genomic level, in new repositories, namely, sORF. org (Olexiouk et al. , 2017) , SmProt  (Hao et al. , 2018) , and RPFdb  (Xie et al. , 2016). On the other hand, some databases are established based on the prediction of the translation sites and the smORF from genomic and transcriptomic data (RefSeq (O'  Leary et al. , 2016)  and ENSEMBL assembly  (Aken et al. , 2017) ) followed by a bioinformatic assembly, close to that initially carried out for the reference proteins (RefProt).
These databases give access to new isoforms as well as to the proteins translated from noncoding sequences which are coined the terminology of alternative proteins (AltProt). The constitution of this public database, initially called HAltORF (2012)  (Vanderperre et al. , 2012)  and now known as OpenProt (2019)  (Brunet et al. , 2019) , was based on different translation rules than that of the classic RefProt databases. The used translation rules are as follows: no maximum length for the translated proteins, a minimum length transcript of 30 codons, and the use of an AUG initiation codon, allowing the generation of a larger number of predicted proteins. In addition, this database crosses the predicted proteins with the known data in Ribo-seq and MS allowing to combine a maximum of predictions for exploratory studies or to use a more restricted database only including proteins already described in the literature.
An Alternative Vision of Proteomics in GBM The translation of circRNAs into proteins has been demonstrated using Ribo-seq, RNC-seq, and RNA-seq which identify the binding of the transcript by the ribosome and the production of the translated nascent chain of proteins. Nevertheless, MS analysis coupled with large-scale interrogation from predicted databases makes it possible to identify a much larger number of new proteins from complex media. In GBM, several large-scale studies of ghost proteins were conducted using MS-based proteomic approaches. A first study, conducted from cell lines and dedicated to the optimization of extraction and enrichment methods to improve AltProt detection, showed the detection of 89 different AltProts representing an average of 1% of total identifications but 70% of the total proteins with a <15-kDa molecular weight (MW).