dataesr/scientific-paragraphs-categorization huggingface dataset:
Rh1 with its low projected worm burden was slower to produce detectable eggs (Fig. 2e ) and showed only 6. 1% weight loss at Wk10, returning to the initial weight by Wk12. Self-cure follows a negative exponential trajectory. The negative exponential decline in both CAA and EPG levels from Wk10 denotes the start and form of the self-cure process (Fig. 2a ). A linear regression on an Ln-transform of each CAA dataset between Wk10 and Wk42, provided a good model (Fig. 3a ), allowing the cure rate to be estimated (Fig. 3b ). The demise of the adult worms was a protracted process, taking a mean of 5 weeks for half to be eliminated, and so on in the manner of radioactive decay, out to Wk42. Rh1 with its lower predicted burden had a half-life of 15 weeks, while Rh6 had the most difficulty in dealing with its worm population (t½ = ~9 weeks).
7a ). Taken together, the viability of schistosomula incubated with plasma in all cultures did not decline significantly prior to Wk12 (Wk12 < Wk0, P < 0. 05, n = 11). However, when Fast and Slow group cultures were analysed separately, a significantly greater killing capacity was evident in the Fast group by Wk8 (P < 0. 05; Fig. 7a ). The differential killing continued through Wk10 (P < 0. 001), Wk12 (P < 0. 02) and Wk16 (P < 0. 05) before the capacity of the Slow group plasma caught up, and a plateau was reached at Wk24. After the challenge, there were negligible differences in killing capacity between Fast and Slow group plasma samples, but analysis of the total dataset revealed a further increment in killing over the level at challenge, significant by Wk1pc (Fig. 7b ; P < 0. 01). The value at Wk4pc represents a 50% reduction in schistosomula viability compared to the post-infection levels (from 63 to 31%; P < 0.
A modified Packard Fluorocount microtiter plate reader was used to measure UCP signals. Standard curves were constructed to determine CAA levels in the rhesus samples and a negative human serum was included. The increasing CAA levels up to Wk10 were analysed by fitting a simple logistic curve to the data using Microsoft Excel (version 15. 0. 5371. 1000) and GraphPad Prism v. 8. 0 (Supplementary Fig. 1 ). The levels after Wk10 followed a negative exponential decline and the dataset for each animal between Wk10 and Wk42 was linearised by an Ln-transform, followed by linear regression. This allowed the cure rate to be estimated, where t1/2 = ln2/ λ = 0. 693/regression slope. The same formula was used to estimate the rate at which egg excretion decreased between Wk10 and Wk24. An estimate of worm burden in adult worm equivalents was back-calculated from data in the literature  43  on five adult macaques perfused at Wk8, which had means of 424 worms and 5780 pg/ml CAA, respectively, amounting to 13.
Reads were mapped using bowtie2 (v. 2. 2. 9) 77 against the S. mansoni genome PRJEA36577 (v7) retrieved from WormBase (schistosoma_mansoni. PRJEA36577. WBPS14. genomic_softmasked. fa) and the overall average mapping rate of ChIP-Seq reads to the genome was 93% (Supplementary Data 10). Default parameters were used to report only the best alignment of each paired-end read. Samtools 78 (v. 1. 8) and picard-tools MarkDuplicates (v. 1. 95) (https://broadinstitute. github. io/picard/) were used to filter and remove PCR and optical duplications; filtering resulted in ~58% of mapped reads remaining for further analysis (Supplementary Data 10). Qualimap 79 (v. 2. 2. 1) was used for mapping quality control. After removal of reads mapping to mitochondria, peak calling was performed with MACS2 (v 2. 1.
1)  80 using the AQUAS pipeline (https://github. com/NHLBI-BCB/TF_chipseq_pipeline). Peak analyses showed that on average ~2500 peaks were identified per sample with an average peak length of 170 bp (Supplementary Data 11), at a significance threshold p value of 0. 01. H3K4me3 peaks can be visualised in the genome browser at http:// schistosoma. usp. br. H3K4me3 mark peaks were assigned to genes according to the annotation published by our group 81 comprising 16,583 lncRNAs and 14,520 protein-coding genes annotated in the S. mansoni genome (v7). Peaks were assigned only when located within 2 kb upstream from the TSS or along the gene body. Peaks were assigned to three categories: TSS peaks (located within 2 kb up or downstream from gene TSS), Gene Body peaks (peaks located downstream >2 kb from gene TSS) and IGR peaks (intergenic region peaks, not present in TSS or Gene Body peak groups).
In order to avoid redundant GOs the algorithm REVIGO  85  was used with the whole UniProt GO terms as database. RNA-seq. For RNA-Seq assays, 3-day-old schistosomula were cocultured for 72 h with a pool of plasma from all rhesus macaques collected at Wk0, Wk8 or Wk43 (i. e. Wk1pc). Schistosomula not exposed to plasma were used as controls. Three biological replicates from each condition were assayed. Total RNA from schistosomula was extracted using the RNeasy Micro Kit (Qiagen, 74004). RNA samples were quantified using the Qubit RNA HS Assay Kit (Thermo Fisher Scientific, Q32852); purity was evaluated using NanoDrop ND-1000 Spectrophotometer (NanoDrop Technologies) and the integrity was verified using the Agilent RNA 6000 Pico Kit (Agilent Technologies, 5067-1513) in the 2100 Bioanalyzer Instrument (Agilent Technologies).
Read counting was performed with RSEM 87 (v 1. 3. 1) using a previously published transcriptome annotation  81. Read count values (Supplementary Data 6) are shown as log2CPM normalised across all conditions with Trimmed Mean of M-values (TMM) method implemented in the edgeR package within the R platform (v 3. 6. 2)  60. A manual search for the genes of the early autophagy pathway  88  was performed using curated sequences for yeast and human on Uniprot (https:// www. uniprot. org/) searched by BLASTp against NCBInr (https:// blast. ncbi. nlm. nih. gov) and WormBase Parasite (https://parasite. wormbase. org). Genewise dispersion estimation was calculated with edgeR using all 12 samples, and statistical analysis for identification of differentially expressed genes (DEGs) was performed by comparing each cocultured group to control with two different algorithms: (i) limma+voom  89 (v 3.
Acknowledgements We would like to acknowledge  Professor Richard Law ,  York Centre for Complex Systems Analysis , for advice on the analysis of datasets. We thank the technical and veterinary staff from the  Central Animal Facility from Butantan Institute, São Paulo, Brazil , for proper and careful handling of the animals throughout the study. Epigenetics experiments were done on the  Environmental Epigenetics facility of IHPE  with the support of  LabEx CeMEB , an  ANR 'Investissements d'avenir' programme  ( ANR-10-LABX-04-01 ). We thank  Jean-François Allienne  and the ' Bio-Environnement ' platform at the  University of Perpignan Via Domitia  for support in NGS library preparation and sequencing. We gratefully acknowledge the stimulating discussions throughout the entire project of  Dr.
Ricardo DeMarco ,  Universidade de São Paulo at São Carlos , in memoriam. This work was supported by grants from  Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)  (Grant numbers  15/06366-2 ,  18/15049-9  and  18/23693-5  to S. V. -A). D. W. S. received a fellowship from  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) ,  Brazil -Finance Code 001  and subsequently from  FAPESP  ( 19/ 09404-3 ); A. S. A. P. is a fellow of  FAPESP  ( 16/10046-6 ); J. V. M. M. is a fellow of  FAPESP  ( 18/18117-5 ); C. G. received a grant from  EPICURE  (projet de recherche conjoints CNRS Brésil  FAPESP -2018 ) and the support of  LabEx CeMEB , an  ANR 'Investissements d'avenir' programme  ( ANR-10-LABX-04-01 ). SVA laboratory was also supported by institutional funds from  Fundação Butantan ; S.
Radial velocity data set The radial velocity spectrograph data processing relies on the preliminary astrometric solution (AGIS-3. 1 in  Lindegren et al. 2021b ) in order to have sufficiently accurate source positions to fix the correct wavelength scale of the RVS spectra. This means that the RVS processing can only start later during a processing cycle. In the planning of the Gaia EDR3 and Gaia DR3 releases it was not possible to accommodate the full sequence of RVS processing, radial velocity estimation, and validation, to allow new radial velocities to be published as part of Gaia EDR3. These will instead appear with Gaia DR3 in 2022. At the time of writing, the Gaia DR3 RVS processing is finished and the results are undergoing internal validation through the use of epoch radial velocities in the non-single star pipeline, and of RVS spectra in the astrophysical characterisation of Gaia sources.
For further insights into the increased data quality see  Fabricius et al. (2021)  and the papers cited above. The overall completeness of the catalogue at the faint end has increased somewhat as can be appreciated from Fig. 1. At the bright end Gaia EDR3 has a similar level of incompleteness as Gaia DR2. The spatial resolution of Gaia EDR3 has slightly improved with respect to Gaia DR2. This can be seen from the number counts of source pairs as a function of their separation presented in  Fabricius et al. (2021). The counts drop below the expected line for a random source distribution at ∼ 1. 5 arcsec (2. 2 arcsec for Gaia DR2). The improved resolution can also be seen in the increased completeness of visual pairs from the Washington Double Star catalogue  (Fabricius et al. 2021). The completeness in close source pairs decreases rapidly below about 0.
7 arcsec which is to be expected as the treatment of sources in crowded fields has not fundamentally changed between Gaia DR2 and Gaia EDR3, even if the crowding effects were better characterised during the data processing. The better treatment of high proper motion stars at the source list creation stage and the generally cleaner source list has led to a much more reliable sample of high proper motion stars in Gaia EDR3. This is demonstrated in  Fabricius et al. (2021)  where the diagram of proper motion vs. parallax shows a striking improvement from Gaia DR2 to Gaia EDR3, with most of the high proper motion stars now located on the positive parallax side of the 500 km s -1 tangential velocity locus. The suppression of spurious parallaxes has also removed a lot of unrealistically high proper motions for stars with negative parallaxes.
Our approach is based on the method of super-statistical analysis  [15] [16] [17]  , which we have originally developed to analyze the random migration patterns of individual cancer cells  18  , revealing that their average migration speed, the directional persistence of the cell trajectories, and other hyper-parameters are time-dependent, reflecting internal mode changes such as the cell cycle. In subsequent work, we have demonstrated that the method can also be used to extract and model gradual or abrupt hyper-parameter changes in other complex dynamical systems, such as the climate or the stock market  19. In the present study, we apply a simplified version of superstatistical analysis to a set of full-night EEG recordings. Each 30 s epoch of these recordings has been visually scored by a sleep specialist, according to the AASM (American Academy of Sleep Medicine) rules, so that the data is categorized into four different sleep stages (REM, N1, N2, and N3) and the wake state.
Results The results presented throughout this study are based on 68 independent EEG data sets from sleeping human subjects, each recorded during a full night in the sleep lab of the University Hospital Erlangen. The signals from the three EEG channels can be analyzed, in principle, on at least three different time scales: The shortest scale corresponds to the individual time steps t of the raw signals, which in our case have been recorded with a rate of 256 values per second and channel. The medium scale is that of epochs n, each with a duration of 30 s, corresponding to 7680 successive EEG values per channel. A sleep stage label S n 2 Wake; REM; N1; N2; N3 f ghas been assigned to each of these epochs by a specialist. It is noteworthy that, for simplicity, we include Wake to the list of sleep stages.
For occasional epochs in which the raw EEG data are not reliable due to obvious measurement artifacts, Bayesian updating proceeds only on the basis of the prior Π. Separability of sleep stages. The accuracy of automatic sleep-stage detection depends on how well data clusters from different stages separate in the embedding space, which in our case corresponds to the three-dimensional space of the hyperparameters STD, KUR, and SKE. In order to assess this mutual separability of sleep stages in a quantitative way, we use two related measures: the GDV 8,9,11 and the CSI. Both measures take as an input a list of N labeled D-dimensional data vectors (points), belonging to L distinct classes (clusters) and produce as an output a single number that characterizes the degree of separability of these classes.
The solvent gradient was set  [52] , with a 0. 5 mL min -1 flow rate. Identification and calibration of the HPLC peaks were performed with antheraxanthin, ββ-carotene, canthaxanthin, chlorophyll a, chlorophyll b, chlorophyll c2, diatoxanthin, diadinoxanthin, fucoxanthin, and pheophytin a standards. We identified all detected peaks by their absorption spectra and relative retention times using the Agilent OpenLab software. Quantification was performed using standard calibration curves built with repeated injections of standards over a range of dilutions. Xanthophylls, carotenes, and chlorophyll b and c were quantified at 470 nm, and chlorophyll a and their derivatives as well as pheopigments were quantified at 665 nm. The relative abundance of each pigment (%) was calculated from its respective concentration in the sample (µg mg -1 ).
e. , biological, physical, biogeochemical, and meiofauna variables on the same sediment core, and/or at the same sediment depth). The overall analysis consisted here in introducing several active groups (for instance: Physico-chemical parameters, metals, contaminants, prokaryotic biomass, organic matter, pigments) in a factorial analysis in order to analyze similarities and discrepancies between groups [64]. Two MFAs were carried out, one with environmental factors and the other with the density and biomass of the meiofauna. On the other hand, the studied entities came from the same samples (in the same sample were extracted several data), which allowed us to compare the two MFA between them thereafter. Separated PCAs were then performed on each group of variables: One with environmental factors, one with the density of nematodes, and one with the biomass of nematodes and then a weight equal to the inverse of the first Eigen value of the PCA was assigned to each group before concatenating them in a single dataset.
Additionally, in order to explore the weight of a given factor (e. g. , sediment depth or the interaction between sampling station and sediment depth), we performed a supervised Between Class Analysis (BCA) by decomposing the total inertia of the MFA dataset according to a given instrumental variable (i. e. , sampling stations, and/or sediment depth). Percentage of total inertia explained by the instrumental variable was systematically calculated (  [63] ; hereafter called bca-ratio). The above mentioned statistical procedure was previously detailed  [65]  and was performed (i) on the sediment parameters measured at each sampling stations (i. e. , environmental variables: 3 variables (granulometry, pH, redox potential); pigments: 31 variables; prokaryotic biomass: 2 variables (archaeal and bacterial); organic matter quality: 2 variables (C:N ratio and organic carbon content); contaminants: 18 variables (organic and inorganic contaminants); (ii) on the nematodes density and iii) on the nematodes biomass, grouped by their feeding behavior (i.
Environmental Characteristics The first axis of the Multiple factor analyses (MFA), based on the environmental variables at each depth and station, explained 36. 6% and the second axis explained 19. 7% of the variance (Figure  2A ). MFA analysis showed that the three layers of each station (L1, L2, L3) were grouped together (Figure  2A ). Then, the first axis of the supervised Between Class Analysis (BCA) on the same environmental variables dataset explained 75. 6% and the second axis explained 24. 4% of the variance (Figure  2B ). station 1 was separated from the other stations by the axis 1 and stations 2 and 3 were separated by the axis 2 (Figure  2B ). The supervised analysis (BCA; Figure  2C ) showed that 39. 8% of total inertia (i. e. , bcaratio) was explained by the sampling stations.
Acknowledgments: The authors thank the team of  LEEISA  ( A. Gardel ,  S. Morvan ,  T. Maury ) for their help during the sampling cruise. We thank  J. Devesa  ( LEMAR ) for CHN analysis and  F. Fromard  for constructive discussions at the early stage of the project. We thank  « Guyane   SIG-Plateforme Territoriale »  for providing the DEM used in this study.
Funding: This research was funded by  Office de l'Eau de la Guyane (OEG) ,  Office français de la biodiversité (OFB) , " Investissements d'Avenir " grant managed by  Agence Nationale de la Recherche  (Ceba, ref. ANR-10-LABX-25-01 ). A. E. H and L. E. H. B were supported by the  AXA Research Fund  grant and by the  FEDER  grant number  1166-39417. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
We showed that most of the RNAs enriched in the uEVs are intron-free cytoplasmic RNA but also contain previously identified functional circRNAs and specific lncRNAs encoding potential neoantigens. Finally, to orientate subsequent functional studies, we generated and explored the transcriptomes of 3 common PCa cell lines and their corresponding EVs. In conclusion, in this study, most of the urines and EVs collection methodologies (urine collection, storage, RNA extraction and quality controls, see methods) were in lines with the gold standard very recently described by the urine EVs task force  34. Thus, our study provided an extensive resource for methodologies, tools and datasets to determine the lncRNA content of circulating urinary EVs paving a way for future studies of their role in prostate cancer.
We next identified the most differentially overrepresented ncRNAs in uEVs and tumors. Differential expression analyses on the whole transcriptome (extended data fig  3b ) and hierarchical clustering by Euclidean distance between pooled uEVs and tumor tissues datasets (extended data fig3c) revealed 4925 enriched RNAs in EVs and 8336 enriched in tumors (Extended Data Table  4 ). From these differential analyses, very few or none pre-miRNAs, snoRNAs, snRNAs and repeats were defined as significantly overrepresented in urines. In contrast, a subgroup of circRNAs (N=311), mRNAs (N=4,120), pseudogenes (N=200) and lncRNAs (N=274) were defined as significantly enriched in uEVs compared to tumors (Fig. 1c ; Extended Data Fig. 3b ). To explore further to which extent each patient' urines contained specific noncoding RNAs, we analyzed the number of normalized counts of lncRNAs and circRNAs by plotting each value in uEVs and tumors and calculated for each linked pair the enrichment value (Fig.
Mature lncRNAs and mRNA are enriched in uEVs Next, to further characterize the uEVs RNA content, we compared the genic structures of transcripts in both types of samples, uEVs and tumors, based on read counts across six gencode v32 features: exon, intron, 5'-UTR, 3'-UTR, promoter, and intergenic region in tumors and uEVs specimens (Fig. 2a , Extended Data Table  5 ). We found that intergenic and promoter mapped reads were extremely low in uEVs datasets (less than 1% in uEVs versus 4% in tumors). Although both fractions were sequenced using the same total RNA-seq protocol, intronic counts were very low in uEVs (2. 6%) in comparison to tumors (46. 3%). 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 26.
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 prostate cancer cells  38. Nuclear and cytoplasmic transcripts were defined by differential expression analysis of nuclear versus cytoplasmic datasets (see method). In total, 4,518 transcripts were considered as highly enriched in nuclear and 6,419 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 non-polyadenylated lncRNAs. Nevertheless, the list of 22Rv1 cytoplasmic and nuclear-enriched RNAs was intersected with those enriched in tumors (n=8336) and uEVs (n=4925) (Fig. 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 (Fig  3b ). In contrast, tumors mainly contained transcripts from both compartments (57%) and nuclear (24%) whereas cytoplasmic RNA fraction was of only 15% (Fig 3b). To distinguish what type of transcripts were specifically depleted or enriched in the cytoplasmic and nuclear compartments in uEVs and tumors, we inferred the subcellular localization of mRNAs, pseudogenes and lncRNAs from 22Rv1 datasets. The density distribution of the ratio cytoplasmic/nuclear reads showed that tumor 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 (Fig.
Most uEVs-circRNAs are present in EVs released by prostate cell lines EVs-RNA have a key role in intercellular communication. While a lot of attention has been focused on miRNA in the past, little is known on the potential functionality of lncRNA and circRNA in this intercellular signaling, raising the need to properly characterize cellular models of EVs cargo. We performed total RNA extraction and sequencing of PC3, DU145, and LNCaP human prostate cancer cell lines and resulting EVs collected from the cell culture media (cEVs). We focused our attention only on circRNAs species as the most abundant type of uEVs-lncRNAs (Extended Data Table  7 ). Strikingly, the intersection of circRNAs present in patients uEVs, cell lines and cEVs (Fig. 4a  and Extended Data Table  7 ) showed 99% (308) cellular and 99,3% (309) cEVs-circRNAs in common with the 311 uEVs-enriched circRNAs.
We retrieved all peptide sequences over 8 aminoacids, resulting in 11,707 tumors and 862 uEVs lncRNA-encoded peptides, with some isoforms being the source of several peptides. Neoantigens were then predicted with NetMHCpan-4, using MHC typing computed by seq2HLA from the RNA-seq file of each patient  43. To best separate ligand from non-ligand peptides, only neopeptides with an elution ligand index lower than 0. 5 were kept, corresponding to 15,677 (5941 unique) for tumors and 768 (351 unique) for uEVs, derived from 244 and 15 transcripts, respectively (see method, Extended Data Table  8 , Fig. 5a  and Extended Data Fig. 4 ). From the top 2% predictions, we estimated that the majority of potential lncRNA-derived neoantigens were 9-mer peptides (Fig. 5b ), the gold standard size for neoantigen recognition  44.