dataesr/scientific-paragraphs-categorization huggingface dataset:
Of these reads, we classified 1607 as unlinked, or 10. 1% of the dataset (Figure  2B ). These long-read metagenomic analyses showed that unlinked rRNA genes are not equally distributed across environments -we found that up to 41% of the taxa in soil had unlinked rRNA genes, whereas other environments had much lower proportions, most notably the human gut, where all sequenced rRNA genes were linked (Figure  3 ). The results from our analyses of the long-read dataset generally mirrored the corresponding results from the complete genome dataset, in that many of the long reads classified as unlinked belonged to the same phyla where unlinked rRNA genes were prevalent in the complete genome dataset (Figure  2 ). The long-read metagenomic dataset confirmed that members of the phyla Deinococcus-Thermus, Planctomycetes, Chloroflexi, Spirochaetes, and Euryarchaeota frequently have unlinked rRNA genes (Figure  2B ).
The long-read dataset also allowed us to provide additional evidence for unlinked rRNA genes in poorly studied phyla that were represented by only a handful of genomes in our complete genome dataset, such as candidate phyla Acetothermia (1 genome and 64 longread sequences) and Patescibacteria (3 genomes and 330 long-read sequences). Using the long-read dataset, we identified 18 additional phyla where unlinked rRNA genes are common, including several candidate phyla (BRC1, GAL15, WS1, WS2) and members of the Candidate Phyla Radiation (Patescibacteria, Figure  2 ). We also found several clades with high proportions of unlinked rRNA genes that had no representation in our complete genome dataset, including Rikenellaceae RC9 gut group (334/624), Verrucomicrobia genus Candidatus Udaeobacter (80/80), Atribacteria order Caldatribacteriales (37/37), Cyanobacteria order Obscuribacterales (4/4), Acidobacteria Subgroup 2 (27/27), Planctomycetes order MSBL9 (40/40), and Chloroflexi class GIF9 (7/7).
Unlinked rRNA genes are strongly conserved We found that taxa with unlinked rRNA genes are not randomly distributed across bacterial and archaeal lineages -rather, we observed a strong phylogenetic signal for this trait, which we confirmed by calculating Pagel's lambda (lambda = 0. 96, p > 0. 001). To highlight this point, we assembled a phylogenetic tree from full-length 16S rRNA gene sequences representing both the complete genome dataset and the long-read metagenomic dataset. We found clusters of related taxa with exclusively unlinked rRNA genes (Figure  4 ) including: Euryarchaeota class Thermoplasmata, the vast majority of Deinococcus-Thermus, CPR division Patescibacteria, Verrucomicrobia DA101 group, Chloroflexi class Dehalococcoidia, and Alphaproteobacteria class Rickettsiales.
Genomic attributes associated with unlinked rRNA genes Given that there are numerous bacterial and archaeal lineages where unlinked rRNA genes are commonly observed, we next sought to determine what other genomic features may be associated with this non-standard rRNA gene arrangement. We treated the presence of unlinked rRNA genes as a binary trait -if a genome had at least one unlinked rRNA gene we counted the genome as "unlinked". In our NCBI complete genome dataset, we found rRNA gene status to be conserved strongly at the species level -meaning that the majority of species had either exclusively linked or unlinked rRNA genes among their members (Supplemental Figure  S2 ). Therefore, for the following tests, we used a subset of our NCBI complete genome dataset -retaining only a single representative of each species, unless the species had heterogeneous rRNA gene status (0.
Interestingly, genomes with unlinked rRNA genes were predicted to have significantly longer minimal generation times (Figure  5 , phyloglm p=0. 028, means of groups: 0. 23 linked, 0. 18 unlinked). Additionally, in our long-read dataset we found that unlinked rRNA genes were more common in environments typified by slow growth rates; soil and sediment samples had higher proportions of unlinked rRNA genes than samples from anaerobic digesters and the human gut (Figure  3 ). RNaseIII separates the precursors of the 16S and 23S rRNA from their common transcript for subsequent maturation and inclusion in the ribosome (2). RNaseIII is not an essential protein in most Bacteria and Archaea, and several phyla in which unlinked rRNA genes are common do not encode RNaseIII (e. g. Deinococcus-Thermus and Euryarchaeota; 41).
Therefore, we checked if there was a significant association between unlinked rRNA genes and the presence of RNaseIII genes. Interestingly, we found that genomes with unlinked rRNA genes were significantly less likely to encode the bacterial form of RNaseIII genes (Figure  5  and Supplemental Figure  S3 , PF00636: phyloglm p < 0. 001, means of groups: 1. 0 linked, 0. 71 unlinked; PF14622: phyloglm p = 0. 007, means of groups: 0. 86 linked, 0. 66 unlinked). We were unable to check this relationship for archaeal RNaseIII, due to the size of our archaeal dataset (phyloglm failed to converge, only 39 genomes in our dataset had this gene). However, we note that the archaeal RNaseIII PF11469 was found in only two clades that feature exclusively linked rRNA genes (Euryarcheaota family Thermococcaceae and Crenarchaeota family Thermofilaceae).
Discussion While unlinked rRNA genes have been documented previously, we have demonstrated that they are far more widespread among Bacteria and Archaea than expected. We found that unlinked rRNA genes consistently occur in 12 phyla using a dataset of complete genomes (Figure  2C ), and 18 additional phyla using a dataset of longread metagenomic sequences obtained from environmental samples (Figure  2D ). Interestingly, some phyla were classified as exclusively linked in our complete genome dataset, yet had many members with unlinked rRNA genes in our long-read dataset. For example, while there were no complete genomes in the phylum Verrucomicrobia with unlinked rRNA genes (0/32), 38% of verrucomicrobial rRNA sequences were unlinked in our long-read dataset (82/217), with the majority of this group closely related to the bacterium Ca.
Udaeobacter copiosus from the DA101 soil group  (42). This imbalance is likely due to the strong bias towards faster-growing organisms when using traditional cultivation methods  (43) , and the fact that cultivated Bacteria and Archaea still make up the majority of high-quality genomes in public databases  (13). Our results highlight the importance of using a combination of complete genomes, where genetic organization and traits can be assessed rigorously, with metagenomic data that allows us to sample the diversity found in selected environments in an unbiased manner. Together, these independent datasets show that unlinked rRNA genes occur across many bacterial and archaeal phyla. The widespread prevalence of unlinked rRNA genes in many environmental samples has important implications for the use of community analysis methods that require the 16S and 23S rRNA genes to be in close proximity.
For instance, before 16S rRNA gene sequencing became common practice, the ITS region of the 16S and 23S rRNA operon was routinely used to fingerprint microbial communities  (44). Likewise, the increasing popularity of long-read sequencing technologies has led to bacterial genotyping methods that target the full rRNA operon. While sequencing from the 16S rRNA gene into the 23S rRNA gene (thus including the ITS region of the rRNA operon) can increase taxonomic resolution and allow strain level identification (45), our work shows that amplicon-based studies dependent on 16S and 23S rRNA genes being located in close proximity may miss a large portion of bacterial and archaeal diversity. We found the average distance between unlinked 16S and 23S rRNA genes in our complete genome dataset to be ~410 Kbp, a rather impractical distance to amplify by PCR.
While strategies which use reads spanning the 16S and 23S rRNA genes to improve taxonomic resolution (e. g. 45, 46)  are less likely to introduce biases in some environments (e. g. human gut), they will miss many phylogenetic groups in other environments like soil and sediment, where a significant fraction of taxa have unlinked rRNA genes (Figure  3 ). We used our long-read metagenomic dataset to not only bypass the cultivation bias of our complete genome dataset, but to also estimate the abundance of unlinked rRNA genes in a range of microbial community types. Our analyses of the long-read metagenomic dataset show that taxa with unlinked rRNA genes are far more abundant in some environments than others. Most notably, unlinked rRNA genes were far more common in soil (where as many as 41% of rRNA genes detected were unlinked) than the human gut (where no unlinked rRNA genes were detected, Figure  3 ).
Conclusions Unlinked rRNA genes are far more prevalent than expected, especially among those Bacteria and Archaea found in environmental samples for which complete genomes are not yet available. While this rearrangement appears to occur more frequently in slowergrowing taxa and may be related to the presence of RNaseIII, it remains to be determined if unlinked rRNA genes confer any specific advantages. Regardless, we have shown that 52% of the phyla included in our combined datasets (37/71) have at least one member with unlinked rRNA genes, that unlinked rRNA genes occur in taxa that are abundant and ubiquitous, and that up to 41% of rRNA genes in some environments are unlinkedmeaning unlinked rRNA genes are far from atypical anomalies. Indeed, unlinked rRNA genes function as a reminder that the metabolisms of poorly-studied environmental Bacteria and Archaea sometimes differ from conventions derived from model organisms.
We have developed hypotheses about the potential advantages of unlinked rRNA genes, hypotheses which could be tested experimentally and represent a promising direction for future research -especially as some taxa with unlinked rRNA genes are relatively easy to manipulate in culture  (61, 62). publications listed in methods. Classifications and details of both the complete genome and long-read datasets are included in Supplemental Dataset S1 and S2, respectively. Figure  1 : In most Bacteria and Archaea, rRNA genes are arranged in the order 16S -23S -5S, and are transcribed and regulated as a single unit. However, in some cases, the 16S is separated from the 23S and 5S, and is referred to as "unlinked". Linked refers to genomes with exclusively linked rRNA genes, unlinked refers to genomes with exclusively unlinked rRNA genes, and mixed refers to genomes with at least one set each linked and unlinked rRNA genes.
Acknowledgements This research was supported in part by the  Chateaubriand Fellowship  awarded to T. E. B. from the  Office for Science & Technology of the Embassy of France in the United States  and a grant to N. F. from the  U. S. National Science Foundation  ( EAR1331828 ). M. A. was supported by a research grant ( 15510 ) from  Villum Fonden. A. E. gratefully acknowledges the support of a  Leverhulme Trust Research Fellowship  ( RF-2017-652\2 ). E. R. was supported by the  INCEPTION  project ( PIA/ANR-16-CONV-0005 ). We thank  Will Trimble  for assistance tracking down publicly available Moleculo sequences,  Michael Engel  for figure design input, and  Eric Johnston  for introducing the lead author to unlinked rRNA genes.
Data availability All genomes used in this study were downloaded from NCBI, with assembly IDs listed in Supplemental Dataset S1. All Nanopore data is available at the Sequence Read Archive (SRA) under Bioproject ID PRJNA553237 or the European Nucleotide Archive (ENA) under PRJEB33278. All Moleculo data has been published previously, with status of rRNA genes. Sequences originating from the long-read dataset cannot be mixed, as we could not distinguish multi-copy rRNA genes. Clades with high proportions of unlinked  Linked genomes feature exclusively linked rRNA genes; unlinked genomes have at least one set of unlinked rRNA genes. We calculated these statistics using a subset of our complete genomes, including one genome per unique species and rRNA gene status. A) Genomes with unlinked rRNA genes have smaller genomes on average, but this difference was not significant after accounting for phylogeny (phyloglm p =0.
Introduction: Hospital patient satisfaction has been a salient policy concern. We examined rurality's impact on patient satisfaction measures. Methodology: We examined patients (age 50 and up) from 65 rural and urban hospitals in Massachusetts, using the merged data from 2007 American Hospital Association Annual Survey, State Inpatient Database and Survey of Patients' Hospital Experiences, utilizing Hierarchical binary logistic regression analyses to examine the rural disparities in patient satisfaction measures. Results: Relative to the urban location, rurality reduced the likelihood of cleanliness of environment (odds ratio ¼ 0. 66, 95% confidence interval: [0. 63-0. 70]); but increased the likelihood of staff responsiveness and quietness. Compared to Caucasian counterparts, Hispanic patients were less likely to reside in a quiet hospital.
Data Source This study utilized merged data from 3 major sources. The first data source was the State Inpatient Database (SID). The SID data are part of the Healthcare Cost and Utilization Project by Agency for Healthcare Research and Quality (AHRQ) that provides patient-level variables. The SID is a state-specific data that contains more than 95% of hospital discharge information in the United States (AHRQ, SID Documentation, 2017). The second data source came from the American Hospital Association (AHA) Annual Survey Database, which provides hospital-level variables  (13, 14). The AHA annual survey is an ongoing survey providing information about hospitals' organizational structure, facilities, and services, as well as the staffing information. The third data source is the CMS-HCAHPS. The HCAHPS survey provides 21 patient-rated items about care, these hospital-level variables are classified by 9 major topics  (1).
Particularly, in the hierarchical model, the first (individual) level of the multilevel logistic regression model is specified in Equation  1 , and the second (facility) level model is specified in Equation (2) (Appendix A). Y ij is the binary patient satisfaction measure for the ith individual in the jth facility (Appendix A). All statistical analyses were conducted using SAS, version 9. 4 (SAS Institute, Inc, Cary, North Carolina) (Table  1 ).
Descriptive Analysis The study sample consisted of older patients (age ! 50) from 65 rural (n ¼ 15 491) and metropolitan hospitals (n ¼ 446 906) in Massachusetts, using the merged data from 2007 AHA, SID, and HCAHPS Survey. In general, about 2 out of 5 sample patients were male. Nearly half of the sample were using Medicare as the primary insurance, about one-third were using private insurance, and about 1 out of 8 used Medicaid to cover services. About 4 out of 5 were white patients, while black and Hispanic patients account for 6. 06% and 5. 90% of the sample, respectively. The average LOS was slightly less than 5 days. Nearly one-third of the sample were surgical patients. The common diagnoses included cancer, anxiety, epilepsy, diabetes, cognitive or psychosis disorders, and stroke. At the facility level, about 3.
Massachusetts. In the future, we will use the merged data sets of AHA, SID, and HCAHPS from various states to be able to generalize the study results. ConclusionsIn summary, this study examined the impact of rurality as well as other individual-and facility-level predictors on hospital quality of care, measured by patients' satisfaction. The findings remained statistically significant after adjusting for other individual-and facility-level covariates, such as the demographic characteristics, the payment sources, the diagnosis, and the facility characteristics. Policy efforts to enhance Medicaid and Medicare payment approaches and levels, nursing staff trained to care for patients with mental health conditions, as well as teaching hospitals will likely decrease the rural-urban disparities in hospital quality of care.
The resulting dsDNA product was then introduced into a library, which was subjected to Illumina sequencing (see Supporting Information). Data processing involved phix removal, mapping the data on the sense strand, and analysing the concordance (Figure  2B ) as well as the number of errors per read (Figure  S4 ). This analysis showed that 99. 2% of the reads have a concordance > 95% and that 95% of all reads have 2 or less errors with no detectable single nucleotide variants (SNVs) in any of the reads. 62 Lastly, the very low rate of base substitution (<0. 5%) further confirms the high fidelity of the DNA-modified DNA-DNA cycle.
Acknowledgements We thank  Cédric Fund  ( Biomics Platform ,  C2RT ,  Institut Pasteur, Paris, France ) for the Illumina sequencing. This work was financially supported by  Institut Pasteur  (starting grant to M. H. ),  France Génomique  ( ANR-10-INBS-09-09 ) and IBISA (to T. C. ), the  Natural Science and Engineering Council of Canada  ( Discovery grant  to MJD), and the  Swiss National Science Foundation  ( 200020-146646  to C. J. L. ).
, 2015; Keller, 2015; Fauchille, 2015; Ma et al. , 2016; Fauchille et al. , 2018). In order to capture all the microstructural features of interest, these imaging techniques produce high resolution images, usually obtained on limited volumes or areas. However, the small size of the field of view reached (typically in the range of a few dozen to a few hundreds of micrometers) may question the representativeness of the petrographic observations. In particular datasets obtained at this scale can be uncertain: are the structural characteristics and petrophysical/numerical properties determined on these small 3D volumes or 2D images representative of the characteristics and properties at the upper scale? In practice, the smallest representative volume or area is identified as the so-called Representative Elementary Volume (REV) (or Representative Elementary Area -REA in 2D), which is required to "separate" the two following space scales: firstly, the scale of heterogeneity, i.
Despite the theoretical link between * and I r , the latter is much easier to calculate in practice because there exist numerous commercial software packages in geostatistics. In other words, this additional microstructural descriptor was introduced, i. e. , the variogarm range, because it is likely easier to determine it in practice than the two-point probability function. In our study, variograms have been calculated using commercial Surfer© software. To  Considering the COx map (Fig. 5 ), the six curves associated with the six starting domains converge to O values in the range [48. 6-52. 4%], including the mean clay fraction, PQB of 50. 4% calculated for the whole map (Table  1 ). The difference between these asymptotic values, O , for a L box of 500 µm and mean clay fraction PQB is less than 4%.
Acknowledgments. We thank  NEEDS-MIPOR -VARAPE (VARiabilité spatiale des propriétés de l'Argilite au travers d'une approche numérique-expérimentale à Plusieurs Echelles ) program for its financial support. A part of this study was also performed in the framework of the research project " ExCiTING " funded by the  French National Research Agency  (grant agreement  ANR-17-CE06-0012 ). The authors also acknowledge financial support from the  European Union (ERDF)  and " Région Nouvelle Aquitaine ".
Study setting, data collection and definitions The setting was a 980 beds Regional University Hospital in Ancona, Central Italy, including five intensive care units (ICUs), 11 medical and 11 surgical wards. All patients (≥18 years old) with BSIs due to KPC-Kp diagnosed between February 2011 and December 2015 were considered. A KPC-Kp BSI was documented by either one or more positive blood culture  [16]. BSIs were defined as hospital-acquired if the index blood culture had been collected > 48 h after hospital admission and no signs or symptoms of infection had been noted at admission. Patient variables included age, sex, presence of acute or chronic comorbidities, Charlson comorbidity index and APACHE II score, previous surgery (≤30 days before BSI onset), any invasive procedures (≤72 h before BSI onset), steroid and/or immunosuppressive therapy taken over the previous 30 days, previous antimicrobial therapy (≤30 days).
Several hundred proteins are thought to be involved in the formation and function of ciliary structures and associated signaling and transport pathways. Between Gene Ontology (GO)  [6]  and the SYSCILIA Gold Standard (SCGS)  [7]  approximately 750 genes have already been associated with ciliary function. However, it is likely that many more ciliary proteins remain to be identified, as new genes for this organelle are still frequently uncovered-often in relation to genetic disorders. [2]. Although omics data sets provide a rich source of information to determine the proteins that constitute an organelle, they are inherently imperfect. It is well established that large scale approaches often miss key players; for example, proteomics fares better at finding intracellular versus extracellular or membrane proteins  [8].
Furthermore, an extensive database, CilDB, contains data from individual genomics, transcriptomics, and proteomics approaches related to the cilium  [17]. Although such databases are invaluable to researchers, it is not obvious how to handle the sometimes-conflicting information presented by independent data sources: for example, what does a negative result in one dataset and a positive result in another mean? A powerful solution lies in statistical and probabilistic integration of data sets. Multiple methods exist to combine genomics data, ranging from simply taking the genes that are reported by most data sources, to machine learning methods that take into account non-linear combinations of the data (reviewed in  [18] ). In this spectrum, naive Bayesian classifiers take a middle ground. They exploit the relative strengths of the various data sets while maintaining transparency of the integration.
Data collection and curation We collected and constructed a total of six new data sets from proteomics, genomics, expression and evolutionary data and complemented these with two public data sets (Table  1 ). The data sets include three protein-protein interaction (PPI) data sets based on three methods: tandem-affinity purification and mass spectrometry (TAP-MS)  [19] , stable isotope labeling of amino acids in cell culture (SILAC), and yeast two-hybrid (Y2H) screens. The latter two data sets are published as part of this work and include 1666 proteins (1301 and 365, respectively) describing 4659 interactions (4160 and 499, respectively. ) and an estimated positive predictive value for discovering new ciliary proteins of 0. 28 and 0. 54 respectively. Because the TAP-MS and SILAC data sets are based on a similar methodology and have a large bait overlap (14 out of 16 SILAC baits were used in the TAP-MS data set), we merged them into a single data set (Mass-spec based PPI) to avoid bias (see methods).
The final log-summed likelihood ratio, which includes the prior expected probability to observe a ciliary gene in the genome, we call the 'CiliaCarta Score'. This score represents the likelihood for a gene to be ciliary, based on all the data sets considered (Bayesian scores for all genes can be found in S1 Table ). A practical interpretation of the log-summed likelihood ratio is that a positive score of 1 means that a gene is twice more likely to be ciliary then non-ciliary. Whilst a score of -1 means that a gene is twice more likely to be non-ciliary then ciliary. The integrated score readily distinguishes between the positive and negative set (Fig 1B, p-value: 2. 8e-85 Mann-Whitney U test). A ten-fold cross-validation.