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Background: Pediatric osteoarticular infections (POAIs) are serious diseases requiring early diagnosis and treatment., Methods: In this prospective multicenter cohort study, children with POAIs were selected from the European Union Childhood Life-threatening Infectious Diseases Study (EUCLIDS) database to analyze their demographic, clinical, and microbiological data., Results: A cohort of 380 patients with POAIs, 203 with osteomyelitis (OM), 158 with septic arthritis (SA), and 19 with both OM and SA, was analyzed. Thirty-five patients were admitted to the Pediatric Intensive Care Unit; out of these, six suffered from shock, one needed an amputation of the right foot and of four left toes, and two had skin transplantation. According to the Pediatric Overall Performance Score, 36 (10.5%) showed a mild overall disability, 3 (0.8%) a moderate, and 1 (0.2%) a severe overall disability at discharge. A causative organism was detected in 65% (247/380) of patients. Staphylococcus aureus ( S. aureus ) was identified in 57.1% (141/247) of microbiological confirmed cases, including 1 (0.7%) methicillin-resistant S. aureus (MRSA) and 6 (4.2%) Panton-Valentine leukocidin (PVL)-producing S. aureus , followed by Group A Streptococcus (18.2%) and Kingella kingae (8.9%). K. kingae and PVL production in S. aureus were less frequently reported than expected from the literature., Conclusion: POAIs are associated with a substantial morbidity in European children, with S. aureus being the major detected pathogen. In one-third of patients, no causative organism is identified. Our observations show an urgent need for the development of a vaccine against S. aureus and for the development of new microbiologic diagnostic guidelines for POAIs in European pediatric hospitals., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Trobisch, Schweintzger, Kohlfürst, Sagmeister, Sperl, Grisold, Feierl, Herberg, Carrol, Paulus, Emonts, van der Flier, de Groot, Cebey-López, Rivero-Calle, Boeddha, Agapow, Secka, Anderson, Behrends, Wintergerst, Reiter, Martinon-Torres, Levin, Zenz and the EUCLIDS consortium.)

Healthcare costs have been dramatically rising in developed economies worldwide. A key driver of cost increases has been high-cost drugs. The current model of reimbursement is not configured for drugs with uncertain outcomes. Future reimbursement will require better allocation of available healthcare system funds. Technological advancements have opened the door to a new type of outcomes-based reimbursement, enabling value exchange between payers and pharmaceutical companies, which we term precision reimbursement. Precision reimbursement extends beyond value-based contracts, with decisions at individual rather than aggregate level. For precision reimbursement to be adopted, there are data, computation and infrastructure requirements. All stakeholders benefit in moving to precision reimbursement for optimal resource allocation, risk sharing and, ultimately, improved outcomes. There are implementation challenges including cost, change management, information governance and development of surrogate markers. The overarching trend in medicine is toward personalised interventions, with precision reimbursement as the logical consequence., (© Royal College of Physicians 2021. All rights reserved.)

[This corrects the article DOI: 10.1371/journal.pone.0179553.].

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine., (© 2018 The Authors.)

Mycobacterium tuberculosis (M. tuberculosis) survives and multiplies inside human macrophages by subversion of immune mechanisms. Although these immune evasion strategies are well characterised functionally, the underlying molecular mechanisms are poorly understood. Here we show that during infection of human whole blood with M. tuberculosis, host gene transcriptional suppression, rather than activation, is the predominant response. Spatial, temporal and functional characterisation of repressed genes revealed their involvement in pathogen sensing and phagocytosis, degradation within the phagolysosome and antigen processing and presentation. To identify mechanisms underlying suppression of multiple immune genes we undertook epigenetic analyses. We identified significantly differentially expressed microRNAs with known targets in suppressed genes. In addition, after searching regions upstream of the start of transcription of suppressed genes for common sequence motifs, we discovered novel enriched composite sequence patterns, which corresponded to Alu repeat elements, transposable elements known to have wide ranging influences on gene expression. Our findings suggest that to survive within infected cells, mycobacteria exploit a complex immune "molecular off switch" controlled by both microRNAs and Alu regulatory elements.

The kind and duration of phylogenetic topological "signatures" left in the wake of macroevolutionary events remain poorly understood. To this end, we examined a broad range of simulated phylogenies generated using trait-biased, heritable speciation probabilities and mass extinction that could be either random or selective on trait value, but also using background extinction and diversity-dependence to constrain clade sizes. In keeping with prior results, random mass extinction increased imbalance of clades that recovered to pre-extinction size, but was a relatively weak effect. Mass extinction that was selective on trait values tended to produce clades of similar or greater balance compared to random extinction or controls. Allowing evolution to continue past the point of clade-size recovery resulted in erosion and eventual erasure of this signal, with all treatments converging on similar values of imbalance, except for very intense extinction regimes targeted at taxa with high speciation rates. Return to a more balanced state with extended post-extinction evolution was also associated with loss of the previous phylogenetic root in most treatments. These results further demonstrate that while a mass extinction event can produce a recognizable phylogenetic signal, its effects become increasingly obscured the further an evolving clade gets from that event, with any sharp imbalance due to unrelated evolutionary factors.

The effect of mass extinctions on phylogenetic diversity and branching history of clades remains poorly understood in paleobiology. We examined the phylogenies of communities of digital organisms undergoing open-ended evolution as we subjected them to instantaneous "pulse" extinctions, choosing survivors at random, and to prolonged "press" extinctions involving a period of low resource availability. We measured age of the phylogenetic root and tree stemminess, and evaluated how branching history of the phylogenetic trees was affected by the extinction treatments. We found that strong random (pulse) and strong selective extinction (press) both left clear long-term signatures in root age distribution and tree stemminess, and eroded deep branching history to a greater degree than did weak extinction and control treatments. The widely-used Pybus-Harvey gamma statistic showed a clear short-term response to extinction and recovery, but differences between treatments diminished over time and did not show a long-term signature. The characteristics of post-extinction phylogenies were often affected as much by the recovery interval as by the extinction episode itself.

Virus gene sequencing and phylogenetics can be used to study the epidemiological dynamics of rapidly evolving viruses. With complete genome data, it becomes possible to identify and trace individual transmission chains of viruses such as influenza virus during the course of an epidemic. Here we sequenced 153 pandemic influenza H1N1/09 virus genomes from United Kingdom isolates from the first (127 isolates) and second (26 isolates) waves of the 2009 pandemic and used their sequences, dates of isolation, and geographical locations to infer the genetic epidemiology of the epidemic in the United Kingdom. We demonstrate that the epidemic in the United Kingdom was composed of many cocirculating lineages, among which at least 13 were exclusively or predominantly United Kingdom clusters. The estimated divergence times of two of the clusters predate the detection of pandemic H1N1/09 virus in the United Kingdom, suggesting that the pandemic H1N1/09 virus was already circulating in the United Kingdom before the first clinical case. Crucially, three clusters contain isolates from the second wave of infections in the United Kingdom, two of which represent chains of transmission that appear to have persisted within the United Kingdom between the first and second waves. This demonstrates that whole-genome analysis can track in fine detail the behavior of individual influenza virus lineages during the course of a single epidemic or pandemic.

The emergence of the influenza (H1N1) 2009 virus provided a unique opportunity to study the evolution of a pandemic virus following its introduction into the human population. Virological and clinical surveillance in the UK were comprehensive during the first and second waves of the pandemic in 2009, with extensive laboratory confirmation of infection allowing a detailed sampling of representative circulating viruses. We sequenced the complete coding region of the haemagglutinin (HA) segment of 685 H1N1 pandemic viruses selected without bias during two waves of pandemic in the UK (April-December 2009). Phylogenetic analysis showed that although temporal accumulation of amino acid changes was observed in the HA sequences, the overall diversity was less than that typically seen for seasonal influenza A H1N1 or H3N2. There was co-circulation of multiple variants as characterised by signature amino acid changes in the HA. A specific substitution (S203T) became predominant both in UK and global isolates. No antigenic drift occurred during 2009 as viruses with greater than four-fold reduction in their haemagglutination inhibition (HI) titre ("low reactors") were detected in a low proportion (3%) and occurred sporadically. Although some limited antigenic divergence in viruses with four-fold reduction in HI titre might be related to the presence of 203T, additional studies are needed to test this hypothesis.

Biodiversity assessment demands objective measures, because ultimately conservation decisions must prioritize the use of limited resources for preserving taxa. The most general framework for the objective assessment of conservation worth are those that assess evolutionary distinctiveness, e.g. Genetic (Crozier 1992) and Phylogenetic Diversity (Faith 1992), and Evolutionary History (Nee & May 1997). These measures all attempt to assess the conservation worth of any scheme based on how much of the encompassing phylogeny of organisms is preserved. However, their general applicability is limited by the small proportion of taxa that have been reliably placed in a phylogeny. Given that phylogenizaton of many interesting taxa or important is unlikely to occur soon, we present a framework for using taxonomy as a reasonable surrogate for phylogeny. Combining this framework with exhaustive searches for combinations of sites containing maximal diversity, we provide a proof-of-concept for assessing conservation schemes for systematized but un-phylogenised taxa spread over a series of sites. This is illustrated with data from four studies, on North Queensland flightless insects (Yeates et al. 2002), ants from a Florida Transect (Lubertazzi & Tschinkel 2003), New England bog ants (Gotelli & Ellison 2002) and a simulated distribution of the known New Zealand Lepidosauria (Daugherty et al. 1994). The results support this approach, indicating that species, genus and site numbers predict evolutionary history, to a degree depending on the size of the data set.

The flavin-containing monooxygenase (FMO) gene family is conserved and ancient with representatives present in almost all phyla so far examined. The genes encode FAD-, NADP- and O(2)-dependent enzymes that catalyse oxygenation of soft-nucleophilic heteroatom centres in a range of substrates. Although usually classified as xenobiotic-metabolising enzymes, examples of FMOs exist that have evolved to metabolise specific endogenous substrates as part of a discrete physiological process. The genome of Caenorhabditis elegans contains five predicted genes encoding putative homologs of mammalian FMOs, K08C7.2, K08C7.5, Y39A1A.19, F53F4.5 and H24K24.5, which we have named fmo and numbered fmo-1 to fmo-5, respectively. As a first step towards determining their functional role(s), we have experimentally characterised these C. elegans fmo genes including analysing reporter gene expression patterns and RNAi phenotypes. Two major gene expression patterns were observed, either intestinal or hypodermal, but no gross RNAi phenotypes were found possibly due to functional redundancy. The internal structures of fmo-2, fmo-3 and fmo-4 have been compared with orthologs identified in the related nematode C. briggsae. For each orthologous pair, a global comparison of the paired upstream intergenic regions was performed and a number of conserved noncoding sequences, which may represent potential cis-regulatory elements, identified. Phylogenetic analysis reveals that several of the fmo homologs are the result of gene duplication along the lineage leading to the nematodes.

Developmental processes are thought to be highly complex, but there have been few attempts to measure and compare such complexity across different groups of organisms. Here we introduce a measure of biological complexity based on the similarity between developmental and computer programs. We define the algorithmic complexity of a cell lineage as the length of the shortest description of the lineage based on its constituent sublineages. We then use this measure to estimate the complexity of the embryonic lineages of four metazoan species from two different phyla. We find that these cell lineages are significantly simpler than would be expected by chance. Furthermore, evolutionary simulations show that the complexity of the embryonic lineages surveyed is near that of the simplest lineages evolvable, assuming strong developmental constraints on the spatial positions of cells and stabilizing selection on cell number. We propose that selection for decreased complexity has played a major role in moulding metazoan cell lineages.

Species are defined using a variety of different operational techniques. While discussion of the various methodologies has previously been restricted mostly to taxonomists, the demarcation of species is also crucial for conservation biology. Unfortunately, different methods of diagnosing species can arrive at different entities. Most prominently, it is widely thought that use of a phylogenetic species concept may lead to recognition of a far greater number of much less inclusive units. As a result, studies of the same group of organisms can produce not only different species identities but also different species range and number of individuals. To assess the impact of different definitions on conservation issues, we collected instances from the literature where a group of organisms was categorized both under phylogenetic and nonphylogenetic concepts. Our results show a marked difference, with surveys based on a phylogenetic species concept showing more species (48%) and an associated decrease in population size and range. We discuss the serious consequences of this trend for conservation, including an apparent change in the number of endangered species, potential political fallout, and the difficulty of deciding what should be conserved.

Motivation: Animals build their bodies by altering the fates of cells. The way in which they do so is reflected in the topology of cell lineages and the fates of terminal cells. Cell lineages should, therefore, contain information about the molecular events that determined them. Here we introduce new tools for visualizing, manipulating, and extracting the information contained in cell lineages. Our tools enable us to analyze very large cell lineages, where previously analyses have only been carried out on cell lineages no larger than a few dozen cells., Results: Ales (A Lineage Evaluation System) allows the display, evaluation and comparison of cell lineages with the aim of identifying molecular and cellular events underlying development. Ales introduces a series of algorithms that locate putative developmental events. The distribution of these predicted events can then be compared to gene expression patterns or other cellular characteristics. In addition, artificial lineages can be generated, or existing lineages modified, according to a range of models, in order to test hypotheses about lineage evolution., Availability: The program can run on any operating system with a compliant Java 2 environment. Ales is free for academic use and can be downloaded from http://mbi.dkfz-heidelberg.de/mbi/research/cellsim/ales.

Explaining the uneven distribution of species among lineages is one of the oldest questions in evolution. Proposed correlations between biological traits and species diversity are routinely tested by making comparisons between phylogenetic sister clades. Several recent studies have used nested sister-clade comparisons to test hypotheses linking continuously varying traits, such as body size, with diversity. Evaluating the findings of these studies is complicated because they differ in the index of species richness difference used, the way in which trait differences were treated, and the statistical tests employed. In this paper, we use simulations to compare the performance of four species richness indices, two choices about the branch lengths used to estimate trait values for internal nodes and two statistical tests under a range of models of clade growth and character evolution. All four indices returned appropriate Type I error rates when the assumptions of the method were met and when branch lengths were set proportional to time. Only two of the indices were robust to the different evolutionary models and to different choices of branch lengths and statistical tests. These robust indices had comparable power under one nonnull scenario. Regression through the origin was consistently more powerful than the t-test, and the choice of branch lengths exerts a strong effect on both the validity and power. In the light of our simulations, we re-evaluate the findings of those who have previously used nested comparisons in the context of species richness. We provide a set of simple guidelines to maximize the performance of phylogenetically nested comparisons in tests of putative correlates of species richness.

We used simulations to compare the relative power of eight statistical tests to detect imbalance in phylogenies that is too great to be ascribed to an equal-rates Markov null model. Three of these tests have never had their power assessed before. Our simulations are the first to assess performance under scenarios in which the speciation rates of various lineages can evolve independently. In one of the scenarios explored, rates depend upon the value of an evolving trait, whereas in the other the probability that a species will speciate declines with the time since it last did so. The results indicate that the relative performance of the methods depends upon how the imbalance is generated. Different types of processes lead to different imbalance signatures, i.e., different patterns of imbalance at different depths in the phylogeny, and the measures of tree shape differ in the depth of phylogeny at which they are most sensitive. Relative performance is also affected by tree size but does not appear to depend greatly upon the degree of speciation rate variation among lineages. Two of the indices (Colless's index I(c) and Shao and Sokal's Nmacr;) show reasonable performance throughout, but another (Shao and Sokal's B(2)) is never indicated to be a preferred method. Two tests that do not require completely resolved phylogenies, mean I' and mean I'(10), have reasonable power.

Two lines of evidence indicate that the degree of symmetry in phylogenetic topologies differs at different hierarchical levels. First, in a set of 61 phylogenies with superspecific taxa as their terminals, trees were on average more unbalanced (asymmetric) when the species richness of terminals was considered than when it was not. Second, nodes with a given number of higher taxa descended from them were on average more unbalanced than were nodes with the same number of species as descendants. There are several possible reasons--some biological, some artifactual--for the differences. Whatever the reason, these results caution against treating species-level and higher level phylogenies as equivalent when considering tree shape. The imbalance measure adopted here permits the use of trees that contain polytomies, facilitating a larger sample than has been achieved previously.

The hierarchical nature of phylogenies means that random extinction of species affects a smaller fraction of higher taxa, and so the total amount of evolutionary history lost may be comparatively slight. However, current extinction risk is not phylogenetically random. We show the potentially severe implications of the clumped nature of threat for the loss of biodiversity. An additional 120 avian and mammalian genera are at risk compared with the number predicted under random extinction. We estimate that the prospective extra loss of mammalian evolutionary history alone would be equivalent to losing a monotypic phylum.