dataesr/scientific-paragraphs-categorization huggingface dataset:
Globally representative whole genome sequencing datasets for RSV-A and RSV-B Reference datasets of RSV WGS for both subtypes were created. For RSV-A, a total of 1010 complete genomes and for RSV-B a total of 633 complete genomes, available up to 3 May 2019, were F I G U R E 1 (A) Respiratory syncytial virus (RSV) genome schematic organisation, and location of the CDC detection (M gene) and Victorian Infectious Disease Reference Laboratory (VIDRL) (L gene) subtyping primers/probes. (B) Mismatches between the CDC detection pan RT-PCR primers/probe and the M gene for the 2019 EQA sequences. The forward primer matches all 8 RSV-A and RSV-B M gene sequences.
ACKNOWLEDGEMEN TS The authors are grateful to the  National Influenza Centres, Public Health Laboratories , and the  Ministries of Health  of the participating countries for participating in Phase II of the WHO Global RSV Surveillance EQA 2019-2020. We thank the  Respiratory Viruses Branch, Division of Viral Diseases, CDC, Atlanta ; the  Public Health England at Colindale ; the  National Institute of Communicable Diseases, Johannesburg  and  The Peter Doherty Institute for Infection and Immunity, WHO Collaborating Centre  for Reference and Research on Influenza, VIDRL,  Australia , and the  International Reagent Resources (IRR) ,  Centers for Disease Control and Prevention, Atlanta, GA, USA , for providing support for the implementation of Phase II. We would like to thank  Tiina Talts  for performing whole genome sequencing of the PHE/UKHSA RSV genomes included in this analysis; and  Boitshoko Mahlangu ,  Thabo Mohale ,  Daniel Amoako  and  Josie Everatt  for processing and analysing the NICD RSV genomes.
2. 1 | 1 Globally representative whole genome sequencing datasets for RSV-A and RSV-B Reference datasets of RSV WGS for both subtypes were created. For RSV-A, a total of 1010 complete genomes and for RSV-B a total of 633 complete genomes, available up to 3 May 2019, were F I G U R E 1 (A) Respiratory syncytial virus (RSV) genome schematic organisation, and location of the CDC detection (M gene) and Victorian Infectious Disease Reference Laboratory (VIDRL) (L gene) subtyping primers/probes. (B) Mismatches between the CDC detection pan RT-PCR primers/probe and the M gene for the 2019 EQA sequences. The forward primer matches all 8 RSV-A and RSV-B M gene sequences.
isolate composition of the 2019-2020 WHO RSV EQA panel Scoring system for the 2019-2020 WHO RSV EQA Description Report text Score RSV identified RSV 1 RSV-A/B identified RSV-A/B 1 RSV negative RSV negative 2 No virus No virus 0 RSV wrong subtype RSV wrong subtype 0 Indeterminate Indeterminate 0 Named virus other than that Named virus 0 specified RSV + an additional pathogen Additional 0 pathogen Sample number Sample designation of RSV isolates Subtype GISAID accession ID 1 HRSV/A/England/174460397/2017 A EPI_ISL_732338 2 HRSV/A/Australia/VIC-RCH010/2018 A EPI_ISL_1834085 3 HRSV/A/Australia/VIC-VIDRL002/2018 A (control) EPI_ISL_4602779 4 HRSV/A/South Africa/NICD-R06229/2019 A EPI_ISL_9003918 5 HRSV/B/England/154680653/2015 B EPI_ISL_4848359 6 HRSV/B/England/180440410/2018 B EPI_ISL_732354 7 HRSV/B/Australia/VIC-VIDRL003/2015 B (control) EPI_ISL_4569432 8 HRSV/B/South Africa/NICD-R05898/2019 B EPI_ISL_9003919 9 HRSV/A/England/0709161v/2007 A EPI_ISL_11901829 10 HRSV/B/South Africa/NICD-R06224/2019 B EPI_ISL_9003920 11 RSV negative specimen (freeze-dried matrix only) NA NA 12 RSV negative specimen (Influenza B NA EPI_ISL_168822 B/Phuket/3073/2013) Note: Two labelled controls, one RSV-A and one RSV-B (sample Numbers 3 and 7, respectively) were also provided to participating laboratories as part of the panel in order to facilitate implementation of RSV detection and typing assays.
melilotiinoculated plants (EFE and PSE) (Fig. 4C ). Seeds from field-grown barley plants were used to verify our findings. Similarly, Proteobacteria were the prevalent endophytic bacteria in seeds from field-grown barley (Fig. 4B ). The number of ASVs related to Pantoea and Raoultella increased in seeds originating from field-grown plants after bacterial treatment with E. meliloti (FGE) (Fig. 4C ). It must be noted that the usual methods such as principal component analysis to analyze such data were not suitable here due to the extremely small number of ASVs. These results demonstrated that bacterial inoculation influences the composition of seed endophytic microbiota.
Transcriptome analysis For the transcriptome analysis assay, the regenerated barley plants were treated with bacterial suspensions (10 7 CFU/ml), and 10 mM MgCl 2 was used as the solvent control. The samples were collected before (0 h) and 24 h after bacterial inoculation. Each treatment contained three independent biological replicates. Total RNA was extracted with the RNeasy Plant Mini Kit (Qiagen, Germany). Library construction was performed by using the stranded mRNA enrichment method, and 20 M paired end (100 bp length) reads per sample were sequenced by Beijing Genomics Institute (BGI, China). The data analysis, including read mapping and feature counting, was performed within the R package Rsubread version 2. 12. 3  [75]  with the default settings. The updated reference genome, MorexV3_pseudomol-ecules_assembly  [76] , was used for data analysis.
Amplicon sequencing libraries of the DNA samples were performed with a two-step PCR targeting the 16S rRNA gene's V4 region, as described previously  [41, 74, 79]. Sequencing was performed on an Illumina MiSeq platform with a Reagent Kit v2 (2 × 250 cycles) (Illumina, San Diego, CA, United States). The identification of amplicon sequence variants (ASVs) was performed by using the DADA2 version 1. 10. 0 plugin for QIIME2 (truncL = 0, truncR = 0; trimL = 8, trimR = 8, a minimum overlap of 12 bp)  [79]. Each ASV sequence was annotated by using the q2-feature-classifier classifysklearn module trained with SILVA SSU rel. 132 database  [80]. The final ASVs per sample were obtained by dividing each count of ASV per sample by the total amount of DNA per sample. Figure creation was performed by the ComplexHeatmap R package (version 2.
Acknowledgements The authors thank  Maximilien Beuret  for the ICP-MS analysis performed in the  Pôle de Compétences Chimie Analytique Environnementale, ANATELo ,  LIEC laboratory ,  UMR 7360 CNRS -Université de Lorraine. We acknowledge  Kornelia Smalla  for discussion on this manuscript. The figures were created with the help of BioRender.
Quantifications of the gene targets were performed using the CFX Manager 3. 0 software (Bio-Rad, Marnes-la-Coquette, France). Total bacteria were quantified by qPCR using the 338F and 518R primers targeting the 16S rRNA gene according to  [26]. Total Bacteroidales were quantified using the AllBac296F and AllBac467R primers according to  [27]. Relative contents in Human fecal bacteria of the collected samples were inferred using the HF183 qPCR assay  [28]. Mobile genetic elements (MGEs) named integrons were quantified by targeting integrase genes of class I, II, and III according to  [29].
The tpm Metabarcoding Analytical Scheme The tpm phylogenetic marker allows an allocation of amplicon gene sequences to bacterial species of, mainly, a group of γ-proteobacteria including the Aeromonas and Pseudomonas. PCR products were generated as already described  [21]. PCR products were purified and sequenced by Biofidal (Microsynth, Vaux-en-Velin, France). Raw reads are available on the European Nucleotide Archive with the accession number PRJEB36958. tpm raw reads were processed using the dada2 package for R  [30]. Linker, barcode, and primers were removed using the Trimgalore v0. 6. 5 software (https://www. bioinformatics. babraham. ac. uk/projects/trim_galore/ (accessed on 16 February 2023)). The SOP (standard operating procedure) at https://benjjneb. github. io/dada2/bigdata_paired.
html (accessed on 16 February 2023) for paired-end reads was used to process the tpm raw reads. The decontam package for R  [31]  was used to detect contaminant amplicon sequence variants (ASV) from control samples. Only 21 contaminant ASV representing 0. 27% of the total reads were detected. The tpm reads were taxonomically affiliated using the tpm metabarcoding sequences database  [32].
Statistical Analyses Alpha (Shannon, Simpson, and Evenness) and beta diversity (Bray-Curtis dissimilarity distances) indices and rarefaction curves were computed using the R software v3. 5. 3 with the Vegan package v2. 5. 6 as carried out in  [33]. All Wilcoxon tests were also computed using the R software. Distribution biases of the tpm inferred taxa between the b-HTWW, the b-DTWW, and the river biofilms samples were investigated using the DESeq2 package for R  [34]. Spearman correlation matrices between the inferred tpm species and the concentration of pharmaceuticals were computed using the Corrplot R package v0. 84. Spearman correlation matrices were illustrated by a heatmap computed on the Excel software. Coalescence phenomena between bacterial communities of the treated wastewater effluents (contamination sources) and the river ones (receiving environment) were investigated using the SourceTracker computer package described by  [22].
Monitoring of Pharmaceutical Loads, Physico-chemical, and Classical Microbial Parameters Pharmaceuticals, physicochemical, and classical bacterial parameters were monitored for the rock biofilms and water samples from the river, and domestic and hospital treatment lines of the WWTP of Bellecombe (Table  S1 ). Principal component analyses (PCA) were performed on these datasets (Figures  2  and S1 ). These PCA clearly segregated b-HTWW and b-DTWW rock biofilms (b) according to their contents in pharmaceuticals (Figure  2 ). Atenolol, diclofenac, propranolol, and trimethoprim concentrations were higher in the b-DTWW biofilms than the hospital ones (termed b-HTWW). Wilcoxon tests confirmed these trends with significantly higher concentrations of atenolol in the b-DTWW (p-value < 0. 05). Iohexol, ranitidine, levofloxacin, and roxithromycin were found in significantly higher concentrations in b-HTWW than in b-DTWW (Figure  2 ) (Wilcoxon test: p-value < 0.
It is to be noted that pharmaceuticals could not be quantified among the river biofilms because of contents below the levels of detection (Figure  S1 ). Higher concentrations of phosphate in the b-HTWW and ammonium in the b-DTWW were observed (Wilcoxon test: p-value < 0. 05). Interestingly, higher caffeine contents were observed among the DTWW than HTWW (Wilcoxon test: p-value < 0. 05) but no major differences were observed between the b-HTWW and h-DTWW (Wilcoxon test: p-value > 0. 05). S1 ). The PCA was computed from datasets generated from biofilms recovered from the clarifiers of the domestic (b-DTWW) and hospital (b-HTWW) treatment lines of the SIPIBEL experimental site (Figure  1 ). Asterisks highlight significant (Wilcoxon non-parametric tests, p < 0. 05) differences between the monitored parameters among the b-HTWW and b-DTWW.
S1 ). The PCA was computed from datasets generated from biofilms recovered from the clarifiers of the domestic (b-DTWW) and hospital (b-HTWW) treatment lines of the SIPIBEL experimental site (Figure  1 ). Asterisks highlight significant (Wilcoxon non-parametric tests, p < 0. 05) differences between the monitored parameters among the b-HTWW and b-DTWW. Microbial contents of the biofilm samples were monitored by culture-and DNA-based methods including quantitative PCR assays. This highlighted high 16S rRNA gene copy numbers from 1. 41 × 10 5 to 1. 8 × 10 7 per g biofilm (dry weight) (Table  S1 ). River biofilms and b-HTWW/DTWW did not show significant differences in these copy numbers over time (Wilcoxon tests; p-value > 0. 05). Total Bacteroidales per g biofilm ranged from 0 to 1. 4 × 10 3 copies per g biofilm (dry weight).
Generally, fecal indicator bacteria (intestinal enterococci, E. coli, and total coliforms) were found in higher numbers among the b-DTWW samples than b-HTWW (Wilcoxon-test; p-value < 0. 05). A PCA was computed from this dataset (Figure  S1 ). b-DTWW samples were characterized by higher counts of class I and III integrons (Wilcoxon test; p-value < 0. 05). Interestingly, b-RU_far5 (8 km upstream of the WWTP) river biofilms showed higher numbers of integron II DNA sequences (Figure  S1a ). Most microbial parameters of the river biofilms and b-HTWW samples could not be differentiated by PCA. P. aeruginosa and A. caviae MPN counts showed homogeneous distribution patterns over the study among the b-HTWW and h-DTWW (Wilcoxon-test; p-value > 0. 05). Nevertheless, high numbers of P. aeruginosa gene targets of 3.
67 × 10 1 to 8. 05 × 10 4 and 6. 83 × 10 3 to 7. 04 × 10 6 MPN/g biofilm were detected among, respectively, the b-HTWW and b-DTWW. P. aeruginosa numbers were below the limit of detection for the biofilms sampled from the Arve River. Relationships between the above bacterial datasets and pharmaceutical concentrations recorded among biofilm samples were explored by Spearman correlation tests (Figure  S3 ). All bacterial counts except the ones of the total heterotrophic bacteria and integron III were found to be positively correlated to concentrations of drugs most often affiliated to the b-DTWW treatment line (e. g. , atenolol, diclofenac, propanolol) and negatively to the b-HTWW samples (Figures  2  and S3 ). Integron III numbers showed positive correlations with drug concentrations most often associated with the b-HTWW (iohexol, levofloxacin, ranitidine, roxithromycin).
General Features The tpm Illumina PCR primers defined in Aigle et al. [21]  generated PCR products from all DNA samples, and these could be sequenced by the Illumina MiSeq v3 technology. Processing of the sequenced PCR products generated a dataset of 1 034 771 sequences distributed into 3082 ASV (Tables  S2  and S3 ). Biofilm river samples b-RD_far6, b-RD_med6, and b-RU_med_6 showed the highest ASV numbers with respectively 826, 807, and 730 ASV (Figure  S4 ). Conversely, biofilm samples b-HTWW3 (124 ASV), b-HTWW1 (161 ASV), and b-HTWW4 (180 ASV) showed the lowest ASV numbers (Table  S2 ; Figure  S4 ). Biofilms sampled from river Blueschist rocks located upstream (b-RU) or downstream (b-RD) of the WWTP outlet showed significantly higher ASV numbers than those of the b-HTWW and b-DTWW (Wilcoxon test; p-value < 0.
001). However, ANOSIM tests did not show significant differences between river biofilms sampled upstream and downstream of the outlet of the WWTP (p-value > 0. 05). Taxonomic affiliations of the ASV were performed. This allowed the classification of more than 99% of the reads at the phylum level (Table  S3 ). As expected, Proteobacteria were dominant in the tpm dataset and represented 96. 4% of the tracked taxa. The tpm marker also allowed the detection of Bacteroidetes (0. 01%) and Cyanobacteria but in low numbers. At the genus level, taxonomic affiliations allowed the classification of about 80% of the tpm reads into 24 well-defined genera (Table  S5 ). value > 0. 05). Taxonomic affiliations of the ASV were performed. This allowed the classification of more than 99% of the reads at the phylum level (Table  S3 ).
As expected, Proteobacteria were dominant in the tpm dataset and represented 96. 4% of the tracked taxa. The tpm marker also allowed the detection of Bacteroidetes (0. 01%) and Cyanobacteria but in low numbers. At the genus level, taxonomic affiliations allowed the classification of about 80% of the tpm reads into 24 well-defined genera (Table  S5 ). S3 ) of biofilms (b-) sampled from HTWW (hospital sewer treated wastewaters), DTWW (domestic sewer treated wastewaters) and the river compartments upstream (b-RU) or downstream (b-RD) the WWTP outlet. Pairwise ANOSIM statistical tests showed significant differentiations between the tpm genetic profiles according to the origin of the samples (p-value < 0. 01). River biofilm sampling sites were at 10 m from the WWTP outlet (close), at 500 m (medium (med)), and 8 km (far).
05) than the b-TWW related ones. Nevertheless, Aeromonas, Pseudomonas, and Herbaspirillum were found among all the biofilm samples (Table  S5 ). Taxonomic affiliations of tpm ASV at the species level allowed the tracking of 91 well-defined species representing about 25% of the tpm harboring bacteria resolved by the metabarcoding approach deployed in this work (Table  S6 ). Five species dominated the tpm dataset with relative abundances higher than 1%: P. anguilliseptica (6. 85%), P. anguilliseptica-like (4. 80%), H. aquaticum (2. 41%), A. sobria (1. 45%), P. fluorescens (1. 19%) and P. koreensis (1. 14%). Several species of health concern were detected among the tpm dataset including A. sobria, P. aeruginosa, A. caviae, and A. hydrophila. Infra-specific diversity of the P. aeruginosa reads was explored according to  Aigle et al.
(2021). ASV_1211 and ASV_1373 were found to match the tpm type-G08-24 of the P. aeruginosa PA14 sub-clade. ASV_1664 and ASV_1682 matched the tpm type-G12_15_17_18_35 sequences of the PAO1 sub-clade. Two fish pathogens, P. anguilliseptica and A. salmonicida, could be tracked using the tpm dataset.
Relationships between the Relative Content of tpm-Harboring Species and Pharmaceuticals among b-HTWW and b-DTWW A heat map illustrating relations between the tpm metabarcoding dataset and the pharmaceutical concentrations was computed and highlighted significant Spearman correlations (Table  S9 ). Among the most significant correlations (r > 0. 8, r < -0. 8), reads allocated to L. purpurea were found positively correlated to the concentrations of atenolol (D) (D indicates higher concentrations in the domestic line biofilms, and H in the hospital one according to Figure  2 ), propranolol (D) and diclofenac (D) (Figure  4 ), and those allocated to Pseudomonas_Gp_BR were correlated to caffeine concentrations. However, A. popoffii and S. humi tpm reads were negatively correlated to atenolol (D), those of Pseudomonas_Gp_BR to bezafibrate, of A.
anguilliseptica was found to be negatively correlated to concentrations of bezafibrate, levofloxacin (H), and roxithromycin (H), and positively with caffeine. P. anguilliseptica-like tpm reads showed negative relations with trimethoprim, which was in higher concentrations in the b-DTWW but also clofibrate, and diazepam. H. aquaticum tpm read numbers were found correlated to bezafibrate and trimethoprim (D), and negatively with diazepam and levofloxacin (H). Table  1. Relative contributions of tpm-harboring bacterial ASV from b-HTWW (biofilms developing from hospital sewer treated wastewaters), b-DTWW (biofilms developing from domestic sewer treated wastewaters), and biofilm river taxa upstream (b-RU) the SIPIBEL WWTP outlet, in the build-up of downstream river biofilms (b-RD) as computed by a SourceTracker Bayesian analysis using datasets of Table  S3.
bers. B-RU_close and b-RD_close biofilms showed significantly lower numbers of ASV than those sampled from b-RU_med, b-RU_far, b-RD_med, and b-RD_far (Wilcoxon tests; p-value < 0. 01). b-HTWW biofilms showed significantly lower ASV numbers than those from the b-DTWW ones (Wilcoxon test; p-value < 0. 05). Shannon index computed from the tpm ASV datasets showed heterogeneous values going from 5. 14 to 1. 11 (Table S2). Sample b-RD_med4, b-RD_med1, and b-RD_med5 showed the highest Shannon values with respectively 5. 14, 5. 12, and 5. 02 while b-RU_far3, b-RU_far4, and b-HTWW6 showed the lowest ones with 1. 11, 2. 75 and 3. 10. b-DTWW biofilms showed significantly lower Shannon indices than those of the river and the hospital line (Wilcoxon test; p-value < 0. 01). Evenness indices showed values from 0.
Acknowledgments: We thank the  SIPIBEL field observatory (Bellecombe WWTP)  for their technical assistance and access to the site. We also thank the  School of Integrated Watershed Sciences H2O'LYON  ( ANR 17-EURE-0018 ), and the  Lyon Urban School (LUS)  ( ANR-17-CONV-0004 ) for their supports in the elaboration of this multi-disciplinary research initiative.
Methods Several public datasets were used to analyze the association between the expression level of PHB1 and PCa progression as well as PCa patient outcomes. The expression of PHB1 in human PCa specimens and PCa cell lines was examined by immunohistochemistry (IHC), qRT-PCR, and western blotting. Then both the biological roles of PHB1 in castration resistance and underlying mechanisms were investigated by gain/loss-of-function analyses. Next, in vitro and in vivo a series of experiments were conducted to investigate the anti-cancer effects of FL3 on CRPC cells as well as the underlying mechanisms.
Bioinformatics Analysis The mRNA expression data of PHB1 were extracted from GEO (http://www. ncbi. nlm. nih. gov/geo) and cBioPortal (http://www. cbioportal. org/) database. The data used for Kaplan-Meier survival analysis were from TCGA (https://www. cancer. gov/about-nci/organization/ccg/research/structuralgenomics/tcga/using-tcga) and cBioPortal (http://www. cbioportal. org/) database.
Qrt-pcr Total RNA was isolated using TRIzol reagent (Invitrogen, CA, UAS), and 1 µg of total RNA was used as template for the rst strand cDNA synthesis using the ReverTra Ace qPCR RT Kit (TOYOBO, Osaka, Japan). qPCR reactions were carried out with a FastStart Universal SYBR Green Master Mix (Roche, Mannheim, Germany). Data were normalized by the expression level of β-actin in each sample. The primer sequences used are listed in Supplementary Table  S1.
Immuno uorescence (If) IF was performed as previously described  [32]. Pre-treated LNCaP (4 × 10 4 cells/well) and C4-2B (2 × 10 4 cells/well) cells were seeded on glass coverslips in 24-well plates, respectively. Mitochondrial labelling was performed using MitoTracker Deep Red FM (40743ES, Yeasen Biotech, Shanghai, China) following the manufacturer's instruction. Nucleus was stained with DAPI (H-1200, VECTASHIELD, CA, USA). Antibodies used is summarized in Supplementary Table  S2. Cells were observed under Laser Confocal Microscope LSM 980 (Zeiss, Oberkochen, Germany). Images taken were appropriately processed with the ZEN software program. Experiments were repeated three independent times.
Result PHB1 expression is signi cantly upregulated in CRPC and is associated with poor prognosis. To explore its role in castration resistance, we rst investigated the association between the expression level of PHB1 and PCa progression. By analyzing public datasets, we found that PHB1 mRNA expression was upregulated in localized PCa tissues compared with benign prostate tissues (GSE35988)  [33]  (Fig. S1  A ). Higher mRNA levels of PHB1 were associated with higher Gleason score (P = 0. 0054) (Fig. S1 B ). More importantly, analysis of three independent datasets (GSE35988, GES74367 and Fred Hutchinson Cancer Research Center-2016)  [33] [34] [35]  showed signi cantly increased PHB1 expression in CRPC tissues compared to primary PCa tissues (Fig. 1A ). Using IHC, we examined PHB1 protein expression in 105 PCa and 5 cancer-adjacent normal tissues.
The results showed that PHB1 expression was signi cantly higher in PCa tissues than that in adjacent normal tissues and was positively correlated with the Gleason score (Fig. 1B ). Similarly, the expression of PHB1 was higher in LNCaP cells with ADPC phenotype than that in normal prostate epithelial RWPE-1 cells, while in CRPC cell lines (C4-2B, 22Rv1, DU145, PC3), PHB1 expression was higher than that in LNCaP cells (Fig. 1C ). Finally, we also performed Kaplan-Meier survival analysis using the TCGA database (https://www. cancer. gov/aboutnci/organization/ccg/research/structural-genomics/tcga/using-tcga) and cBioPortal database (http://www. cbioportal. org/) to explore the clinicopathological signi cance of PHB1 expression in PCa patients. As shown in Fig. 1D , compared with the low PHB1 expression group, the high expression group had shorter progression-free survival (TCGA), overall survival (MCTP-2012)  [33]  , and disease-free survival (MSCKK-2010)  [36].