dataesr/scientific-paragraphs-categorization huggingface dataset:
At each tree node, one and two environmental predictors were evaluated (m try ) for pteropods and foraminifers, respectively, and the minimum number of rows at each final node (min rows ) was set to three and two. The maximum tree size was constrained to 30 for pteropods and 10 for foraminifers. For each bootstrap replicate of the tree, we chose a fraction (r sample ) of 0. 8 and 0. 632 of the total data set. For the GBM, we determined a maximum depth (max depth = 5) and minimum number of observations per terminal node (min rows = 1) for each individual tree for both plankton groups (see also Table  S7  in Supporting Information S1). The learning rate (r learn ) was determined to be 0. 01 and each individual tree is trained on a fraction of 0. 75 and 0. 5 of the total data set for pteropods and foraminifers, respectively, using all of the predictor columns (r samplecolumns ).
, 2013; Strömberg et al. , 2009) , which also show peaks in the tropical ocean. Earlier studies also found the high latitudes to be regions of high biomass for both plankton groups  (Bednaršek, Mozina, et al. , 2012; Hunt et al. , 2008; Lalli & Gilmer, 1989; Schiebel & Movellan, 2012). Contrary to previous studies  (Bednaršek, Mozina, et al. , 2012; Hunt et al. , 2008; Lalli & Gilmer, 1989) , the Southern Ocean was not identified as a region of major pteropod productivity in our study. This is likely due to the influence of the SO-CPR data set, which included a high fraction (95. 8%) of absences. Removing all CPR data from our training data set (i. e. , SO-CPR and Aus-CPR) significantly increases biomass concentrations for pteropods by a factor of 4-8 in the Southern Ocean but not in other basins  (Figures S20 and S21  in Supporting Information S1).
Data Availability Statement The observational datasets used to train the models as well as the model outputs are publicly available on the data platform PANGAEA (https://doi. org/10. 1594/PANGAEA. 957258;  Knecht et al. , 2023). An adapted version of the modelling pipeline, applicable to any species abundance or biomass dataset in the AtlantECO format is available on Zenodo (https://doi. org/10. 5281/zenodo. 7888452;  Knecht, 2023).
To check for DNA contamination, DNA extractions were also performed on negative controls, which consisted of three field blanks and three lab blanks (of Ultra Clean DNA free water). DNA concentration of all extractions were quantified with a Qubit™ 1X dsDNA HS Assay. DNA replicates from each sample point were pooled prior to sequencing, with a total of 16 samples and two blanks (one field and lab blank) used for library construction. As multi-primer approaches better capture community diversity  [25] , we utilised both mtDNA COI and 12S rRNA primers to specifically target the eukaryote community  [26] , as well as fishes  [27] , respectively. Library preparation methods, sequencing, as well as bioinformatic analyses are provided in the Supplementary Materials Methods 1, 2. Only OTUs that matched multi-cellular eukaryotes were retained in the final dataset, with non-marine and OTUs matching algae removed.
(c) Statistical analyses Analyses were performed using the R package vegan  [29]  in the R statistical environment (version 4. 2. 1), following  [12]  and carried out at the OTU level, in order to overcome the limitation of incomplete taxonomic association between genetic sequence and a taxonomic identification. OTU accumulation curves were constructed with the function 'specaccum'. To investigate whether eDNA signatures showed variation over time and depth, OTU richness, the total number of OTUs per sample, was calculated. The OTU richness was compared with twoway ANOVAs using the model OTU_richness~ Depth + Time, after the data was tested for the appropriate assumptions. Between-sample distances were computed using the Bray-Curtis dissimilarity index and distances used to obtain ordinations of the samples in nonmetric multidimensional scaling (nmMDS) representations using the function 'metaMDS'.
(a) Sequencing results Post sequencing, the number of reads ranged between 2 and 42,925 per sample for the COI and 95,326 and 180,429 for 12S (Supplementary Materials, table  S2 ). Of these, two samples of the CO1 dataset, T0 at 1 m and T6 at 1 m, did not have any reads and one sample in the 12S dataset only resolved five OTUs (T3 at 8 m). For the remainder of the samples and prior to filtering, we obtained a total of 167,740 reads belonging to 1290 operational taxonomic units (OTUs) for the COI dataset (12 samples), and 1,925,206 reads belonging to 68 OTUs for the 12S dataset (14 samples). The extraction controls and blanks (field and lab) had a negligible number of reads that were removed from the total dataset (0. 2 ± 0. 5 and 0. 67 ± 0. 46; mean ± SE), OTU accumulation curves for both the CO1 and 12S datasets revealed that the dataset (n = 14) was insufficient to capture OTU diversity (Supplementary Materials, fig2).
(b) OTU and taxonomic diversity Post-filtering, taxonomic assignment for the CO1 metabarcoding revealed 880 OTUs, assigned across various taxonomic levels (Supplementary Figure  S3 ), including 75 families with 19 OTUs identified to species. The most diverse taxonomic group were Cnidaria (104 OTUs), Arthropoda (42), Porifera  (35) , Annelida  (33)  and Mollusca  (30). The least diverse groups were the Chaetognatha and Placozoa, both representing one OTU. Taxonomic assignment for 12S identified 44 OTUs across 24 families and 11 species (Supplementary Figure  S3 ). Two species, Chorismoschismus dentex and Cheilodactylus fasciatus were detected by both methods. (c) Spatio-temporal patterns of eDNA OTU richness for the COI dataset was 140. 7 (± 25. 5) and 18. 4 (± 1. 4) for 12S. A two-way ANOVA showed that OTU richness was not significantly different across time (COI: F6= 0.
414, p= 0. 8462; 12S: F6= 1. 097, p= 0. 457) or depth (COI: F1= 4. 638, p= 0. 0747; 12S: F1= 2. 044, p= 0. 203). Maximum and minimum OTU richness for both datasets were at T2 (16:00) and T3 (20:00). Overall, almost three-fold the number of OTUs were detected at 8 m compared to the 1 m with CO1, whereas OTUs detected for the 12S data were more evenly distributed across depths (figure  1 ). Heatmaps of OTUs reflect the dynamic nature of OTUs detected across space and time (Supplementary Materials, figure  S4 ). Non-metric multidimensional scaling plots using the Bray Curtis dissimilarity index of the abundance data for the COI dataset (figure  2a, c ), reflect partial overlap among the different communities sampled. The plots showed no distinct community trends. PERMANOVAs revealed that community composition did not significantly differ across depth (F1= 0.
93, p = 0. 52) or time (F6= 0. 86, p = 0. 80). Time of sampling accounted for most of the variation in community composition (R2 = 51%), with depth explaining the least (R2 = 9. 2%). NMDS scaling plots for the 12S dataset (figure  2b, d ) showed no clear separation between samples taken across the different time points or across the different depths. Community composition also did not significantly differ across depth (PERMANOVA; F1 = 1. 36; p = 0. 24) (figure  5 ) as well as time (PERMANOVA; F6 = 0. 93; p = 0. 57), with time explaining most of the variation (R2 = 43%) compared to depth (R2 = 11%).
Our study also provides support for multiple primer assays as COI detected the Onefin electric ray, Narke capensis, that was not recovered with MiFish, demonstrating that multi-marker approaches are needed to provide the most comprehensive community assemblage  [32]. As with many eDNA biodiversity surveys globally, the lack of reference databases limited taxonomic identification, with only 19 OTUs for the COI data assigned to species level, of which seven were sponges. Of the teleost community, 11 OTUs were assigned to species, highlighting that continued efforts for barcoding regional marine biodiversity are essential  [24, 32, 33] , including for fishes that generally have incomplete 12S rRNA databases  [32, 34]. (b) Limited fine-scale spatial and temporal variation of environmental DNA signatures There were no significant differences in OTU richness across depth or time, despite previous studies reporting significant variation in eDNA community assemblages at fine spatio-temporal scales  [12] [13] [14] 35].
In our study this was driven partially by sessile benthic taxa such as crinoids, bivalves and sponges that were detected in the shallower samples. For fishes, benthic species such as Bathytoshia brevicaudata and Cynoglossus zanzibarensis were also detected at 1 m. These findings may result from 1. presence of pelagic larval stages, or 2. mixing of the water column, which may homogenise the samples through eDNA transport. Besides abiotic factors such as water movement and wave-action, biotic factors such as ecological behaviour could also drive the fluctuation eDNA signals, as kelp fauna display diurnal, horizontal and vertical dispersal  [36]. Although the PERMANOVA suggested that time partly explains the distribution of eDNA in our study site, it is unlikely that tidal cycles significantly shape marine eDNA signals as this study and Jeunen et al.
Statistical analyses To analyse the differences between the salt marsh and tidal flat, we compared the density, biomass and P/B ratio data at both habitats using Student t tests. Prior to analysis, some data were transformed (logarithm for the biomass and density of Polychaeta, total of macrobenthos biomass) to fulfil the requirements of normality. The data of Ciliophora density and density and biomass of Decapoda, Priapulida and Bivalvia (whether transformed or untransformed) did not follow a normal distribution (Shapiro-Wilks normality test), so the nonparametric Wilcoxon test was applied. Only P/B ratio data of Nematoda, Ciliophora, Gastropoda and Polychaeta were analysed because they were the most representative taxa and they were present in both habitats, thus allowing comparisons to be made.
2003 , Giere 2008). miliano Arena and Ana Delgado, who helped with the fieldwork, and Michel Sciberras for his comments. This study was supported by the Agencia Nacional de Promoción Científica y Tecnológica under Grant PICT-2016-817 and by the Universidad Nacional del Sur under Grant PGI24/B236 from Argentina. Also, the meiobenthos is considered to be primarily linked to the detritus/bacteria-based food chain and the energy fixed by the meiobenthos is often transmitted to higher trophic levels, such as the macrobenthos. Thus, small macrofauna and nekton (usually juvenile stages of larger fishes or crustaceans) can use meiobenthos as a food source  (Heymans and Baird 1995). According to the different roles that these groups play in the energy balance and the carbon cycle, the expansion of salt marshes over tidal flats in the study area may involve a faster flow of energy and carbon transfer by the meiobenthos, along with a greater increase in food web complexity  (Giere 2008).
Objectives: Genome-wide association studies have revealed over 200 genetic susceptibility loci for prostate cancer (PCa). By combining them, polygenic risk scores (PRS) can be generated to predict risk of PCa. We summarize the published evidence and conduct meta-analyses of PRS as a predictor of PCa risk in Caucasian men. Patients and methods: Data were extracted from 59 studies, with 16 studies including 17 separate analyses used in the main meta-analysis with a total of 20,786 cases and 69,106 controls identified through a systematic search of ten databases. Random effects meta-analysis was used to obtain pooled estimates of area under the receiver-operating characteristic curve (AUC). Metaregression was used to assess the impact of number of single-nucleotide polymorphisms (SNPs) incorporated in PRS on AUC.
Electronic and manual search strategy Ten electronic databases (Medline (Ovid), Web of Science, the Cochrane Library, PubMed, Google Scholar, Medline (STN), Embase, Biosis, SciSearch and Drugu) were screened using selected search terms (provided in Supplement Table  1  ). Publication language was limited to English and publication dates from Jan 2009 to Sep 2021. To our knowledge, the first article using a PRS approach to evaluate the risk of PCa was published in 2009. Study identification included both electronic searching strategies combined with manual search of the reference lists of the eligible publications.
Data extraction and quality assessment Three reviewers independently screened the titles and abstracts of all studies initially identified using the selection criteria to identify studies for full text screening. In case of discrepancy, a consensus was reached by a discussion. If a consensus could not be attained by the original three reviewers, an additional reviewer (AA) was brought in to make the final decision. Three authors (RL, KP, KS) independently extracted the data from one third of the publications and one author (AS) extracted the data from all the publications using a pre-designed data extraction form to collect the following items: number of cases and controls overall; number of cases and controls by PRS subgroups; age groups covered; ethnicity; source of the subjects; number of SNPs used for the PRS; method for PRS construction; and reported area under the receiver operating curve (AUC) with 95% confidence intervals (CI).
We used the AUC values reported in the original publications instead of calculating those from numbers of cases and controls, because an accurate AUC estimate cannot be calculated from grouped data but needs to be generated over full range of sensitivity and (one minus) specificity (for all cutoff values). In cases with missing data, the corresponding authors of original publications were contacted by email. Risk of bias in individual studies was independently evaluated by two authors (AS and AA) and in case of disagreement, an additional author assessed the risk of bias and made the final decision. The following characteristics were used to evaluate bias based on reporting guidelines for case-control studies: sources of the cases and controls; matching of cases and controls; exposure assessment and outcome definition consistent with the CLARITY criteria (Evidence Partners/McMaster).
ResultsStudy selection Altogether 1,290 publications were identified from the 10 databases searched with addition of 16 articles from manual search ( Fig. 1  ). After removal of duplicates and selection based on the predefined criteria, 104 articles were entered for full-text screening. Forty-five articles were excluded for the following reasons: did not evaluate germline mutations (n = 6); evaluated only a single SNP or did not report PRS (n = 27); did not use controls free of prostate cancer (n = 3); too narrow patient group such as only metastatic cases or early onset cases (n = 7); and did not evaluate PCa risk (n = 2). Data was extracted from 59 publications (Supplemental Table  3 ), of which 16 studies were included in the meta-analysis ( Fig. 1  , Table  1  ).
Acknowledgments We are grateful for  Dr. Sarah Donegan, PhD ,  University of Liverpool, Institute of Population Health , for providing valuable comments on the manuscript. Pinsent Masons, Leeds, UK,  15. Weizmann Institute, Rehovot, Israel, 16. Academic Urology Unit, University of Aberdeen, Aberdeen, UK , 17. Technische Universität Dresden (TUD), Dresden, Germany ,  18. Fraunhofer-Gesellschaft, München, Germany ,  19. Goeteborgs Universitet (UGOT) ,  Gothenburg, Sweden ,  20. Radbound University Medical Center, Nijmegen , The Netherlands, 21. The  Swedish Institute for Health Economics (IHE), Stockholm, Sweden, 22. Bayer AG, Berlin, Germany ,  23. Sanofi, Chilly-Mazarin, France ,  24. Astellas Pharma Europe Ltd, Surrey ,  United Kingdom ,  25. SAS Institute, Tervuren, Belgium ,  26. Janssen Pharmaceutica NV, Beerse, Belgium ,  27.
CI) when only PRS was added to the model AUC + 95% CI when PRS and clinical variables were added to the model Clinical variables added to the model 316. e4 Clinical Genitourinary Cancer April 2023 Lecarpentier et al. 2017 30 212 1313 dns dns Caucasian/Data from the Surveillance, 103 SNPs 0. 620 (0. 581-0. 661) Epidemiology, and End Results (SEER) database (U. S. Cancer registries) Lello et al. 2019 9 379 24733 dns dns Caucasian/UK biobank 448 SNPs 0. 640 (0. 625-0. 655) Sipeky et al. 2020 32 2738 2400 dns dns Caucasian/Data from Caucasian/ Data from 55 SNPs 0. 600 (0. 571-0. 631) 0. 62 (0. 61-0. 63) PSA, age FinRSPC and FinRSPC Tampere University Hospital cohort Black et al. 2020 33 1972 1919 59. 5 ± Caucasian/ Data from Johns Hopkins 72 SNPs 0. 640 (0. 620-0. 660) 7. 2 57. 2 ± 13. 0 University Hospital, Ambry Genetics, and NorthShore University HealthSystem's Genomic Health Initiative Zhang et al.
Analysis Statistical analysis was conducted using SAS (Version 9. 4, SAS Institute, Inc. , Cary, NC, USA), and included descriptive statistics, post-hoc exploratory analyses, and sensitivity analyses. Continuous variables were presented as mean, standard deviation (SD), median and interquartile range (IQR). Categorical variables were presented as frequencies (n) and percentages (%). Post-hoc exploratory analyses were conducted to assess differences between the two countries and by time since diagnosis, for variables that could be impacted by the country context. A programming error led to seven of the de novo survey QoL items not being visible to the participants completing the survey on mobile phones (n=46; missing items as indicated in Supplementary Table  1 ). Sensitivity analyses compared sociodemographic and medical characteristics for participants unable to complete these items versus those who did.
Determination of the Afliation of the Presumptive B. cereus Isolates. Te genotypic group of the presumptive B. cereus was determined as defned previously by Guinebretière et al. [15]. Amplifcation and partial sequencing of the panC gene were performed using Forward (5′-GAG GCG AGA GAA TAC GGA ATA CG-3′) and reverse (5′-GCC CAT TTG ACT CGG ATC CAC T-3′) primers  [15]. Te following PCR conditions were applied: 94 °C for 5 min as an initial denaturation step, followed by 30 cycles of 15 s at 94 °C, 30 s at 55 °C, 30 s at 72 °C, and 7 min at 72 °C as a fnal extension  [36]. Amplicons were purifed and sequenced as mentioned above. Te sequences were analyzed using the Sym'Previus online software (https://www. tools. symprevius. org/bcereus/), a tool that allows a rapid classifcation of B. cereus strains in four groups (from I to IV) by comparing the panC gene sequences of new isolates with reference strains published by Guinebretière et al.
Genomic DNA was extracted and purifed from a 48 h bacterial culture on Mueller-Hinton blood agar plates (Oxoid Deutschland GmbH, Wesel, Germany) using the QIAGEN ® Genomic-tip 20/G Kit (QIAGEN ® , Hilden, Germany) according to the manufacturer's instructions. Te DNA was eluted in 200 μl elution bufer. DNA was quantifed spectrophotometrically using a Nanodrop ® ND-1000 (Fisher Scientifc GmbH, Schwerte, Germany). Sequencing libraries were created using the Nextera XT DNA Library Preparation Kit (Illumina Inc. , San Diego, CA) and 300 pb-long aired-end sequences were generated on an Illumina MiSeq instrument Raw sequencing data from this study were deposited in the European Nucleotide Archive (ENA) under BioProject PRJEB64867. Te Linux-based bioinformatics pipeline WGSBAC v. 2. 0. 0 (https://gitlab.
com/FLI_Bioinfo/WGSBAC) was used for data analysis. FastQC v. 0. 11. 7  [46]  was used for quality control, and raw read coverage was calculated. Next, WGSBAC performed assembly using Shovill v. 1. 0. 4 (https://github. com/ tseemann/shovill). Te quality of the assembled genomes was controlled by QUAST v. 5. 0. 2  [47] , while Kraken 2 v. 1. 1  [48]  and the database Kraken2DB were used to determine potential contamination. For the investigation of antimicrobial resistance genes and virulence determinants, WGSBAC used the software ABRicate (v. 0. 8. 10) (https:// github. com/tseemann/abricate) and the databases: Res-Finder  [49] , NCBI  [50]  and virulence factor database (VFDB)  [51]. In addition, AMRFinderPlus (v. 3. 6. 10)  [52]  was utilized for the detection of chromosomal point mutations leading to AMR and organism-specifc acquired resistance genes.
For genotyping, classical multilocus sequence typing (MLST) based on assembled genomes was performed with the software mlst v. 2. 16. 1 that incorporates the PubMLST database for B. cereus typing database (https://pubmlst. org/ bigsdb?db=pubmlst_bcereus_seqdef ) which was also used to defne clonal complexes. In addition, WGSBAC performed mapping-based genotyping using core-genome single nucleotide polymorphisms (cgSNPs) identifed by Snippy v. 4. 3. 6 (https://github. com/tseemann/snippy) with standard settings. As a reference, the genome of B. cereus strain FORC_047 (accession ASM222028v1) was used. For phylogenetic tree construction based on cgSNP analysis, RAxML (Randomized Axelerated Maximum Likelihood) v. 8  [53]  was utilized. Te phylogenetic tree was rooted to the reference genome and visualized using the interactive Tree of Life (iTOL) v.
Te fndings of the present research show that the B. cereus investigated in this study are likely to represent a signifcant health risk because these isolates contain the enterotoxin gene and have the ability to adhere to stainless steel surfaces. In addition, the presence of proteolytic and lipolytic enzymes in these isolates increases the chances of cross-contamination and hinders the decontamination of food industry surfaces. In this study, the detection of genetic factors for AMR was performed using diferent tools and databases (AMR-Finder, ResFinder and NCBI) in order not to miss any loci, as there is no single method that might be sufcient for the purpose alone (Table  4 ) and we found that AMRFinder appears to be a highly accurate AMR gene detection system, as previously mentioned  [88].
Results Average read length ranged closely for TAG and LIG (> 5,000 bp), while being drastically smaller for PCR (< 1,100 bp). LIG produced the largest output with 33. 62 Gbp vs. 11. 72 Gbp for TAG and 4. 79 Gbp for PCR. PCR produced the most sequencing noise with only 22. 7% of reads mappable to the curated genomes, vs. 92. 9% for LIG and 87. 3% for TAG. Output per channel was most homogenous in LIG and most variable in PCR, while intermediate in TAG. Artifactual tandem content was most abundant in PCR (22. 5%) and least in LIG and TAG (0. 9% and 2. 2%). Basecalling and demultiplexing of barcoded libraries resulted in ~ 20% data loss as unclassified reads and 1. 5% read leakage.
Interestingly, we noted that in Flye/Metaflye assemblies, several plasmids represented concatemers (two successive copies stitched together), for which we found no evidence in the raw data via mapping. This seemed to happen more often for plasmids < 25 Kbp. Overall, the hybrid assembly with TAG performed the best for plasmids since most were complete and did not represent concatemers (see Table  2 's indices). It is to be noted that Illumina (PE) reads alone assembled several circular plasmids correctly. Regarding chromosomes, comparison of mapped nanopore reads in the Integrated Genome Viewer (IGV) on the hybrid and natively assembled chromosomes revealed occasional missassemblies, one large inversion (in SAF3333 with hybrid PE + TAG), tandem repeat length variation (e. g. HIS2824) as well as the stitching of artifactual tandem repeats (in MIP2461 with TAG).