Molecular dating of species divergences has become an important means to add a temporal dimension to the Tree of Life. Increasingly larger datasets encompassing greater taxonomic diversity are becoming available to generate molecular timetrees by using sophisticated methods that model rate variation among lineages. However, the practical application of these methods is challenging because of the exorbitant calculation times required by current methods for contemporary data sizes, the difficulty in correctly modeling the rate heterogeneity in highly diverse taxonomic groups, and the lack of reliable clock calibrations and their uncertainty distributions for most groups of species. Here, we present a method that estimates relative times of divergences for all branching points (nodes) in very large phylogenetic trees without assuming a specific model for lineage rate variation or specifying any clock calibrations. The method (RelTime) performed better than existing methods when applied to very large computer simulated datasets where evolutionary rates were varied extensively among lineages by following autocorrelated and uncorrelated models. On average, RelTime completed calculations 1,000 times faster than the fastest Bayesian method, with even greater speed difference for larger number of sequences. This speed and accuracy will enable molecular dating analysis of very large datasets. Relative time estimates will be useful for determining the relative ordering and spacing of speciation events, identifying lineages with significantly slower or faster evolutionary rates, diagnosing the effect of selected calibrations on absolute divergence times, and estimating absolute times of divergence when highly reliable calibration points are available.bioinformatics | timescales | relaxed clocks T housands of research studies have reported the use of molecular dating techniques in establishing the timing of species divergences (e.g., refs. 1-5). With the availability of fast and cheap genome sequencing, molecular dating is being applied to increasingly larger datasets that span a much greater diversity of species and harbor extensive heterogeneity of evolutionary rates among lineages. This complexity poses many challenges that limit modern scientific investigations from truly leveraging the genome revolution. First, the application of the fastest molecular dating tools available already requires a very large amount of computational time for datasets containing only a few hundred sequences, which are modest for today's standards (6, 7). Second, current approaches require a priori selection of statistical distributions to model the heterogeneity of rates among branches in the evolutionary tree (e.g., autocorrelated versus uncorrelated rates, 8-12). Use of an incorrect statistical distribution is known to introduce significant bias in such analyses (10,(13)(14)(15). With increasingly larger datasets, it is unlikely that the same rate model will fit evolutionarily distant groups in the same large phylogeny, which exacerbates the...

This is the largest series of PJI by S. aureus managed with DAIR reported to date. The success rate was 55%. The use of rifampin may have contributed to homogenizing MSSA and MRSA prognoses, although the specific rifampin combinations may have had different efficacies.

Graphical Abstract Highlights d The exRNA Atlas provides access to human exRNA profiles and web-accessible tools d Atlas analysis reveals six exRNA cargo types present across five human biofluids d Five of the cargo types associate with specific vesicular and non-vesicular carriers d These findings and resources empower studies of extracellular RNA communication An extracellular RNA atlas from five human biofluids (serum, plasma, cerebrospinal fluid, saliva, and urine) reveals six extracellular RNA cargo types, including both vesicular and nonvesicular carriers. SUMMARY To develop a map of cell-cell communication mediated by extracellular RNA (exRNA), the NIH Extracellular RNA Communication Consortium created the exRNA Atlas resource (https://exrna-atlas.org). The Atlas version 4P1 hosts 5,309 exRNA-seq and exRNA qPCR profiles from 19 studies and a suite of analysis and visualization tools. To analyze variation between profiles, we apply computational deconvolution. The analysis leads to a model with six exRNA cargo types (CT1, CT2, CT3A, CT3B, CT3C, CT4), each detectable in multiple biofluids (serum, plasma, CSF, saliva, urine). Five of the cargo types associate with known vesicular and non-vesicular (lipoprotein and ribonucleoprotein) exRNA carriers. To validate utility of this model, we re-analyze an exercise response study by deconvolution to identify physiologically relevant response pathways that were not detected previously.To enable wide application of this model, as part of the exRNA Atlas resource, we provide tools for deconvolution and analysis of user-provided case-control studies.

It is important to know the spectrum of the microbial aetiology of prosthetic joint infections (PJIs) to guide empiric treatment and establish antimicrobial prophylaxis in joint replacements. There are no available data based on large contemporary patient cohorts. We sought to characterize the causative pathogens of PJIs and to evaluate trends in the microbial aetiology. We hypothesized that the frequency of antimicrobial-resistant organisms in PJIs has increased in the recent years. We performed a cohort study in 19 hospitals in Spain, from 2003 to 2012. For each 2-year period (2003-2004 to 2011-2012), the incidence of microorganisms causing PJIs and multidrug-resistant bacteria was assessed. Temporal trends over the study period were evaluated. We included 2524 consecutive adult patients with a diagnosis of PJI. A microbiological diagnosis was obtained for 2288 cases (90.6%). Staphylococci were the most common cause of infection (1492, 65.2%). However, a statistically significant rising linear trend was observed for the proportion of infections caused by Gram-negative bacilli, mainly due to the increase in the last 2-year period (25% in 2003-2004, 33.3% in 2011-2012; p 0.024 for trend). No particular species contributed disproportionally to this overall increase. The percentage of multidrug-resistant bacteria PJIs increased from 9.3% in 2003-2004 to 15.8% in 2011-2012 (p 0.008), mainly because of the significant rise in multidrug-resistant Gram-negative bacilli (from 5.3% in 2003-2004 to 8.2% in 2011-2012; p 0.032). The observed trends have important implications for the management of PJIs and prophylaxis in joint replacements.

No abstract