dataesr/scientific-paragraphs-categorization huggingface dataset:
On both datasets, the best models were UNet and CNNet models trained from boobies data with respectively AUC scores of 0. 98 and 0. 87. In particular, they outperformed the HMM that were specifically fitted to Guañape and Fernando de Noronha data. By contrast, the FCNet used by  [17]  that obtained better results than HMM on the Pescadores dataset (AUC of 0. 89 vs 0. 86 for HMM) did not predict better than HMM when used at Guañape (e. g. AUC of 0. 89 vs 0. 92 for HMM). The MSE for the estimated dive distribution maps stressed the greater relevance of UNet predictions with a MSE value 1. 6 times smaller than the one derived from CNNet estimations and 1. 9 times smaller than the one derived from HMM estimations (Table  3 ). As illustrated in Fig  6 , only the Unet did not overestimate the number of dives in the vicinity of the colony as well as an other foraging area southward from the colony.
(c) Network fine-tuning. In this section, we evaluated the benefits of a fine-tuning strategy for the prediction of masked boobies dives. As expected, all deep networks initialized using previous models converged more quickly than deep networks trained from scratch. In particular, a dataset of 15 foraging trips (i. e. around 30k GPS positions) was enough for convolutional networks to obtain AUC of 0. 9 using fine-tuning, whereas deep networks trained from scratch needed twice as many trips for the same predictive performance (Fig  7 ). The improvement issued from a fine-tuning was notably important for small-to-medium datasets (5-10 foraging trips, i. e. 10k to 20k GPS positions), and for the CNNet. It decreased as the size of the dataset increased. From our experiments, at least 5 trips were necessary to fine-tune a relevant network compared with the HMM baseline (see for instance bottom-center in  Fig 7).
We may also stress that Cormorants trajectories are characterized by relatively long gaps in the regularly sampled sequence of locations, since these devices do not receive a satellite signal while submerged  [26, 58]. This may in turn make more complex the analysis of Cormorants trajectories. In this respect, UNet showed a greater ability to discriminate the resting/bathing behaviours from dives, and a greater robustness to the presence of linearly-interpolated segments. Whereas HMM are mostly driven by fine-scale features (w. r. t. the considered time resolution), UNets exploit a multi-scale analysis of trajectory data and can extract relevant multi-scale information to retrieve dive. Future work could investigate further the key features extracted by UNets. As shown in Fig  3 , the performance of the deep networks was closely related to the temporal resolution of the sampled dataset.
Beyond the evaluation of dive prediction performance on a trajectory dataset, which is independent from the training dataset, the question whether a model trained on a given dataset, e. g. for a given species, colony and time period, may apply to other species, colonies and/or time periods, naturally arises as a key question. Numerous studies in the deep learning literature  [61, 62]  have highlighted that some neural architectures show relevant generalization properties whereas others may not. Here, we evaluated the generalization performance of the three benchmarked deep networks. Thus we demonstrate the ability of deep networks trained at a colony for one species to also apply to an another colony (of the same ecosystem) for the same species. In our example, Peruvian boobies from Guañape Island did have different foraging strategies from their counterparts from Pescadores island, with trips two times longer and dives slightly longer (see Table  1 ).
However, the UNet reached similar dive prediction performance when applied to Guañape data. This suggests that dive patterns are highly similar between Peruvian boobies from both colonies. We also show the great ability of the CNNet to generalize dive prediction to a seabird of same genus but from a totally distinct ecosystem. When applied to masked boobies trajectories from a Brazilian colony the CNNet trained from Peruvian boobies data obtained an AUC of 0. 87 despite the important difference in foraging strategies (Table  3 ). The same model trained on masked boobies data reached an AUC of 0. 93 (Fig  7 ), suggesting that diving characteristics are slightly different. Masked boobies from the Brazilian colony feed indeed on different preys, and spend way more time resting at the surface (Table  1 ).
As deep networks trained on cormorants unsurprisingly led to less accurate prediction when used to predict boobies dives, we suggest that the CNNet may capture genus-specific features. These results then support the relevance of deep learning schemes as 'ready-to-use' tools which could be used by ecologists to predict seabirds dives on new (small) datasets, including when these datasets do not include groundtruthed dive data for a supervised training. To make easier such applications, we share online the different models we trained on the considered datasets (https://github. com/AmedeeRoy/BirdDL/models). Beyond such a direct application, trained models are also of key interest to explore transfer learning strategies, which refer to the ability of exploiting some previously trained models to address a new task or dataset rather than training a new model from scratch.
We illustrated how fine-tuned CNNet and UNet models could outperform HMM with smaller training datasets. For instance, the fine-tuned CNNet for the prediction of masked boobies' dive was able to converge and outperform HMM with a dataset twice as small as the dataset required to reach same performance without fine-tuning (Fig  7 ). Such a result was even possible by initializing neural networks with the model trained with cormorant data. This further supports the ability of deep networks to generalize their prediction from deep diving seabirds (e. g. cormorants) to plunge divers (e. g. boobies). Fine-tuning is thus particularly relevant when the training dataset may not be sufficiently large to train a model from scratch. While the need of large dataset is often presented as a drawback for supervized techniques, we demonstrated that relatively small datasets (5-10 foraging trips, i.
Deep network testing. The deep networks fitted on the dataset from Pescadores have been used for dive prediction in Guañape and in Fernando de Noronha. Deep networks are described by their reference name (see Table2). AUC is for area under the roc curve. BCE is the binary cross entropy. MSE corresponds to the mean square error of the diving distribution maps estimated with kernel density estimations and plotted in Fig 5 to the correct diving distribution. SV is for Peruvian boobies (Sula variegata) https://doi. org/10. 1371/journal. pcbi. 1009890. t003.
Acknowledgments We thank all people involved in fieldworks:  H. Weimerskirch ,  K. Delord ,  C. Barbraud ,  Y. Tremblay ,  J. Silva ,  G. Passuni ,  C. Boyd ,  C. Saraux ,  A. Brunel ,  J. Jacoby ,  L. Figuereido  and  G. T. Nunes. We thank Proabonos for permission to work on Guañape and Pescadores Islands. We thank the  Brazilian Ministry of Environment (ICMBio)  and  Fernando de Noronha's firemen  for the authorization and logistical support to fieldworks in Brazil.
Bacterial Strain The bacterial strain selected for the bacteriophages' isolation, K. pneumoniae QAMH 130326/0185, was previously isolated from a urine sample of a Belgian diabetic patient with a UTI. Its capsular type (K-type, K) was identified by wzi gene PCR  [5] , and the phenotypic antibiotic susceptibility profile of this strain was assessed by disk diffusion assay using Mueller-Hinton plates (Becton Dickinson, Erembodegem, Belgium), Axonlab disks (Belgium), and the SIRscan micro software (Axonlab, Machelen, Belgium). The determination of the colistin minimum inhibitory concentration (MIC) was performed by broth dilution assay in FRCOL 96-well microtiter plates, according to the manufacturer's instructions (Sensititre, Fisher Scientific, Merelbeke, Belgium)  [26]. The results were interpreted using EUCAST (European Committee on Antimicrobial Susceptibility Testing) clinical breakpoints (2021 edition) and CASFM VET (Comité de l'Antibiograme de la Société Française de Microbiologie) standards (2019 edition).
To analyze the genome of this bacterium, bacterial genomic DNA was obtained using the DNeasy Blood & Tissue kit (Qiagen, Crawley, UK), following the manufacturer's recommendations. Sequencing libraries were constructed using Nextera XT Illumina kit (Illumina, San Diego, CA, USA). The libraries were then sequenced on a Miseq system paired-end 2 × 300 bp read length (Illumina, San Diego, CA, USA). Contig assembly and scaffolding were performed using SPAdes Assembler V3. 10  [27]. A first automatic genome annotation was performed using RAST server  [28]. Virulence factor search and Viruses 2022, 14, 1079 3 of 15 pathogenic typing were performed using the Kleborate tool (https://github. com/katholt/ Kleborate, accessed on 23 February 2022)  [29]  available on the Pathogenwatch platform (https://pathogen.
Bacteriophages Genome Sequencing To analyse the genomes of these bacteriophages, genomic DNA was extracted from CsCl purified bacteriophages using the DNeasy Blood & Tissue kit (Qiagen, Crawley, UK). The sequencing libraries were obtained using the Illumina Nextera XT DNA sample preparation kit and sequenced using a MiSeq Illumina Next instrument (Illumina, San Diego, CA, USA). The bacteriophage genomes were assembled into single contigs using SPAdes Assembler V3. 10 [27]. The web server RAST was used for genome annotation with coding DNA sequences (CDS) outputs. These results were then compared with the web server HHpred (https://toolkit. tuebingen. mpg. de/tools/hhpred, accessed on 1 February 2022)  [32]. Homologous related bacteriophages were found in GenBank using BLASTn analysis (https://blast.
Regarding the antibiotic resistance elements, 3 acquired (blaOXA-1, sul-1 and aadA1) and 3 intrinsic (fosA, oqxAB and blaSHV-1) genes were found. At the protein level, proteins FimA, B, D, F, G, and H were predicted. Sequencing was submitted as NCBI BioProject PRJNA821689.
LncRNAs constitute the most prevalent family in human transcriptome and can exert a plethora of functions, such as epigenetic regulation, chromatin remodelling, regulation of proteins' activity and stability, mRNA stability and translation. They make an intimate part of mechanisms controlling cell/tissue homeostasis as well as various diseases including cancers  (Rinn & Chang, 2012)  now nicely reported in the CLC2 lncRNA database  (Vancura et al. , 2021). Although lncRNAs were initially defined as noncoding, several contain open reading frames (ORFs) that are translated into functional peptides and subjected to translation-dependent decay  (Andjus et al. , 2021; Chen, 2020 ). Among lncRNAs, circRNAs are circular molecules with a covalently closed loop structure lacking 5′cap and 3′polyadenylated tail, formed through noncanonical splicing  (Kristensen et al.
In conclusion, in this study, most of the urine and EVs collection methodologies (urine collection, storage, RNA extraction and quality controls, see methods) were in line with the gold standard very recently described by the urine EVs task force  (Erdbrügger et al. , 2021). Thus, our study provided an extensive resource for methodologies, tools, and datasets to determine the lncRNA content of uEVs paving a way for future studies of their role in prostate cancer.
16). Similar correlations were found in each patient comparing tumours RNA expression and uEVs (Extended Data Figure  4a ) supporting a general tendency for enrichment of circRNAs in uEVs observed in previous analyses  (Everaert et al. , 2019). We next identified the most differentially overrepresented ncRNAs in uEVs and tumours. Differential expression analyses on the whole transcriptome (Extended Data Figure  4b ) and hierarchical clustering by Euclidean distance between pooled uEVs and tumour tissues datasets (Extended Data Figure  4c ) revealed 4925 enriched RNAs in EVs and 8336 enriched in tumours (Extended   (d). Interaction between the normalized number of counts of the 311 upregulated circRNAs (top) and 274 upregulated lncRNAs (bottom) in paired Tumour and uEVs for each patient (P1 to P6).
.  Mature lncRNAs and mRNAs are enriched in uEVs Next, to further characterize the uEVs RNA content, we compared the genic structures of transcripts in three types of samples, frozen, FFPE and uEVs, based on read counts across six gencode v32 features: exon, intron, 5′-UTR, 3′-UTR, promoter, and intergenic region in tumours and uEVs specimens (Figure  2a , Extended Data Table  5 ). We found that intergenic and promoter mapped reads were extremely low in uEVs datasets (less than 1% in uEVs vs. 4% in FFPE tumours). This is in striking contrast with 5′, 3′ untranslated regions and exonic regions all enriched in uEVs (3. 2% and 8. 3% for 3′UTR, 14. 2% and 27% for 5′UTR, and 32% to 62% in FFPE tumours and EVs, respectively). Although the three fractions were sequenced using the same total RNAseq protocol, intronic counts were very low in uEVs (2.
.  Nuclear lncRNAs are under-represented in uEVs To directly prove that uEVs are devoid of nuclear transcripts, we analysed public strand-specific RNA-seq data from cytoplasmic and nuclear fractions of 22Rv1 PCa cells  (Kaul et al. , 2020). Nuclear and cytoplasmic transcripts were defined by differential expression analysis of nuclear versus cytoplasmic datasets (see method). In total, 4518 transcripts were considered as highly enriched in nucleus and 6419 transcripts as enriched in cytoplasm. It is important to note that the public dataset was produced from poly(A) RNA-seq, thus missing circRNAs and nonpolyadenylated lncRNAs. Nevertheless, the list of 22Rv1 cytoplasmic and nuclear-enriched RNAs was intersected with those enriched in tumours (n = 8336) and uEVs (n = 4925) (Figure  3a , Extended Data Table  6 ).
The assumed cytoplasmic RNAs represented 58% of uEVs-enriched RNAs, whereas the nuclear transcripts were mostly depleted (representing only 3%) from uEVs (Figure  3b ). In contrast, tumours mainly contained transcripts from both compartments (57%) and nuclear (24%) whereas cytoplasmic RNA fraction was of only 15% (Figure  3b ). To distinguish what type of transcripts were specifically depleted or enriched in the cytoplasmic and nuclear compartments in uEVs and tumours, we inferred the subcellular localization of mRNAs, pseudogenes and lncRNAs from 22Rv1 datasets. The density distribution of the ratio cytoplasmic/nuclear reads showed that tumour libraries preferentially contained nuclear lncRNAs and pseudogene transcripts. As expected, the mRNAs were both nuclear and cytoplasmic. On the contrary, in uEVs the majority of enriched lncRNAs, pseudogenes and mRNA were all detected within the cytoplasmic fraction (Figure  3c ).
.  Cancer lncRNAs census are found both in uEVs-enriched lncRNAs and in PCa cell lines EVs In addition to circRNAs, lncRNAs have been shown to be associated to cancer promoting or suppressing the tumour progression  (Huarte & Rinn, 2011). Within the last decade enormous efforts have been made not only to list cancer-related lncRNAs but to provide their mechanistic functions. Several databases have been released reporting their misregulation but also their functional significance (Mitranscriptome  (Iyer et al. , 2015)  or RNA atlas  (Lorenzi et al. , 2021)  among others). Recently, a nice curated list of lncRNA-mediating cancer progression has been publicly released as the cancer lncRNA census (CLC2)  (Vancura et al. , 2021). To define which of the uEV-enriched lncRNAs could play a role into the hosting cell, we intersected the list of uEVs-enriched, PCa cellular and cEVs lncRNAs with the 492 CLC2 lncRNAs list (Figure  5a , Extended Data Table  8 ).
, 2017). This result showed that both EVs-lncRNAs and tumour-lncRNAs have similar capacities (and proportion) to encode peptides with high affinity binding to MHC. In conclusion, among the uEVs-enriched lncRNAs, we identified 15 transcripts (called EVs-neoLncRNA hereafter) that could potentially serve as templates for the translation of 9-mer peptides with potent neoantigens properties, if internalized and translated into the host cells. To determine whether the uEV-LncRNAs could indeed encode for detectable and stable peptides, we performed proteomic mass spectrometry on PC3, LNCaP and HCT116 cells and compared MS-detected peptides with the predicted encoded peptides from the ORF of PC3-LncRNAs, of the upregulated uEVs-lncRNAs and the UniProt database (see method). The observed retention times for these MS-detected peptides were correlated with the calculated hydrophobicity of each unmodified MS-detected peptide (Figure  6b ).
The results indicated a correlation for 616 PC3 cellular-lncRNA peptides and eight uEV-LncRNA peptides (Figure  6b ). Strikingly, three MS-peptides found in both aforementioned groups were derived from ORFs of EVs-neoLncRNAs for which peptides were predicted to be high affinity MHC classI neoantigens, suggesting that these peptides might be stable enough to be detected (Figure  6b ). To confirm that uEV-neolncRNAs showed active translation features, we first quantified their expression in PC3, DU145 and LNCaP PCa cell lines and corresponding cEVs. As shown in the heatmap, all the 15 EVs-neolncRNAs are expressed, at least in one of the three cell lines, at lower but comparable levels as the POLR2A mRNA used here as a moderate expressed control (Figure  6c ). We then reanalysed a public ribosome profiling dataset for the PC3 cell line, to determine ribosome occupancy on the 15 EVs-neolncRNAs  (Hsieh et al.
Read count genomic distribution We created from the human gene annotation gencode32 (https://www. gencodegenes. org/human/release_32. html) six classes of genomic features (exon, intron, 5′-UTR, 3′-UTR, promoter, intergenic), as follows: The exons from all the transcripts of each gene were merged by location, in order to have nonredundant segments of exons, using the merge command of BEDTools 2. 29. Introns were inferred from these exons using the R package GenomicFeatures from bioconductor. The 5′ and 3′ UTRs from gencode32 were merged and were subtracted from the previous features. Promoters were inferred by using 1 kb upstream of the first exon (when possible), in respect of the strand. Intergenic parts were constructed after the concatenation of all the previous features, using the complement command of bedtools.
Exon-intron read count ratio The exons from all the transcripts of each gene of the human gene annotation gencode32 (https://www. gencodegenes. org/human/ release_32. html) were merged by location using the merge command of BEDTools 2. 29, in order to have nonredundant segments of exons. Introns were inferred from these exons using the R package GenomicFeatures from bioconductor. Genes with no introns were discarded. The alignment files were converted to BEDPE format and were intersected with the new formed annotation to determine their status (exonic or intronic), using the count command of BEDTools 2. 29. The number of exonic and intronic counts for each gene was then determined, using the same command. The counts were normalized using the length of each feature, and the ratio exonic count over intronic count was computed (genes that had 0 count on numerator and denominator before calculation were discarded.
Metagenes To select the exons and introns features, we have created firstly metatrancripts as follows: The exons from all the transcripts of each gene were merged by location using the merge command of BEDTools 2. 29, to have nonredundant segments of exons, and these exons were numbered; Introns were inferred from these exons. The first exon, first intron, second exon, last intron and last exon were selected for each gene having at least one raw read count (read count from Kallisto). The alignment files were converted in BigWig files with RPM normalization (reads per million of mapped reads), using UCSC tools (http://hgdownload. cse. ucsc. edu/admin/exe/linux. x86_64/), and for each part and each gene, the read coverage was extracted. The obtained signal was scaled on 100 positions using the R base approx function (allows to compute interpolations), with the following parameters: method = "linearties = ,""ordered" The average, minimum, and maximum values were computed at each of the 100 positions, and the result was plotted using the R package ggplot2.
4. 7. 6 Neoantigen analysis HLA allotypes from FFPE and uEVs samples were determined using the tool seq2HLA. Upregulated lncRNAs from FFPE &uEVs were selected, and a research of the ORFs (start codon = AUG) of the most abundant transcript for each gene was performed using TransDecoder (https://transdecoder. github. io/) and the R bioconductor package ORFik https://github. com/Roleren/ORFik. The translation into peptides from the three frames was performed using the SeqinR R package  (Charif & Jean, 2007) , and only the ones with a length > = 8 amino acids were kept. The list of peptides for FFPE and uEVs was given to the tool netMHCpan-4. 1 in order to find neoantigens. Only the peptides with an elution ligand rank < = 2 were kept for downstream analysis.
Data analysis LncRNA and Neoantigen data base prediction. For identification, the data were searched against the Homo Sapiens (UP000005640_9606) UniProt database (https://www. expasy. org/resources/uniprotkb-swiss-prot) and our two predicted databases of PC3 LncRNA ORF encoded peptides and uEV-lncRNA ORF encoded peptides, using Sequest HT through proteome discoverer (PD version 2. 4). These two databases have been built, respectively, from 12,815 lncRNAs with at least one count in PC3 cell line by RNA sequencing, and from the 228 enriched uEV-lncRNAs. The selection of the ORFs (start codon = AUG) of the most abundant transcript for each lncRNA was performed using TransDecoder (https://transdecoder. github. io/) and the R bioconductor package ORFik https://github. com/Roleren/ORFik. We identified 7033 (from PC3) and 167 (from uEVs) lncRNAs containing ORFs.
Translation of these ORFs into peptides from the three frames were performed using the SeqinR R package  (Charif & Jean, 2007) , and only the ones with a length > = 8 amino acids were kept. We then obtained 43,387 putative peptides for the PC3 LncRNAs and 862 putative peptides for enriched uEV-lncRNAs (Extended Data Table  10 ). PD enzyme specificity was set to trypsin and a maximum of two miss cleavages sites were allowed. Oxidized methionine, Met-loss, Met-loss-Acetyl and N-terminal acetylation were set as variable modifications. Carbamidomethylation of cysteins was set as fixed modification. Maximum allowed mass deviation was set to 10 ppm for monoisotopic precursor ions and 0. 02 Da for MS/MS peaks. The resulting files were further processed using myProMS v3. 9. 3  (Poullet et al. , 2007)  (https://github.
uEVs-enriched RNAs show lncRNA-encoding neoantigens shared by prostate cell lines. (a). Workflow of ORFs, peptides and neoantigens prediction from lncRNAs enriched in Tumour (n = 1993) and uEVs (n = 274). (b). Correlation between hydrophobicity and observed retention time for Neoantigen peptides (red dots), PC3 lncRNA peptides (blue dots and regression line with 95% confidence interval, R 2 = 0. 482), peptides from lncRNAs upregulated in uEVs (cyan) and peptides derived from Uniprot-annotated human proteins (grey dots and orange regression line, R 2 = 0. 889) from three cell lines (PC3, LNCAP and HCT116 in N = 5 biological replicate). (c). Heatmap representing the relative expression by log10(TPM+1), of POLR2A mRNA and 15 uEVs-neoLncRNAs, encoding the 351 strong unique neoantigens within uEVs, Tumour, PC3-, LNCaP-and DU145-cells and their respective EVs and RPF from PC3 ribosome profiling dataset.
DATA AVA I L A B I L I T Y S TAT E M E N T The bioinformatic codes, intermediate data and Extended Data Tables are available at: https://github. com/MorillonLab/Prostate_ 6_tumor_FFPE_vs_6_tumor_uEVs_analysis. The raw data are available under GEO accession GSE183070. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org).