Streptococcus pneumoniae (the pneumococcus) is the world’s foremost bacterial pathogen in both morbidity and mortality. Switching between phenotypic forms (or ‘phases’) that favour asymptomatic carriage or invasive disease was first reported in 1933. Here, we show that the underlying mechanism for such phase variation consists of genetic rearrangements in a Type I restriction-modification system (SpnD39III). The rearrangements generate six alternative specificities with distinct methylation patterns, as defined by single-molecule, real-time (SMRT) methylomics. The SpnD39III variants have distinct gene expression profiles. We demonstrate distinct virulence in experimental infection and in vivo selection for switching between SpnD39III variants. SpnD39III is ubiquitous in pneumococci, indicating an essential role in its biology. Future studies must recognize the potential for switching between these heretofore undetectable, differentiated pneumococcal subpopulations in vitro and in vivo. Similar systems exist in other bacterial genera, indicating the potential for broad exploitation of epigenetic gene regulation.

Accelerometers are increasingly being used to assess physical activity in large-scale surveys. Establishing whether key physical activity outcomes can be considered equivalent between three widely-used accelerometer brands would be a significant step towards capitalising on the increasing availability of accelerometry data for epidemiological research. Twenty participants wore a GENEActiv, Axivity AX3 and ActiGraph GT9X on their non-dominant wrist and were observed for two-hours in a simulated living space. Participants undertook a series of seated and upright light/active behaviours at their own pace. All accelerometer data were processed identically using open-source software (GGIR) to generate physical activity outcomes (including average dynamic acceleration (ACC) and time within intensity cut-points). Data were analysed using pairwise 95% equivalence tests (±10% equivalence zone), intra-class correlation coefficients (ICC) and limits of agreement. The GENEActiv and Axivity could be considered equivalent for ACC (ICC=0.95, 95% confidence interval (CI) 0.87 to 0.98), but ACC measured by the ActiGraph was approximately 10% lower (ICC: GENEActiv/ActiGraph 0.86, 95% CI 0.56 to 0.95; Axivity/ActiGraph 0.82, 95% CI 0.50 to 0.94). For time spent within intensity cut-points, all three accelerometers could be considered equivalent to each other for over 85% of outcomes (ICC≥0.69, lower 95% CI≥0.36), with the GENEActiv and Axivity equivalent for 100% of outcomes (ICC≥0.95, lower 95% CI≥0.86). GENEActiv and Axivity data processed in GGIR are largely equivalent. If comparing GENEActiv or Axivity to the ActiGraph, time spent within intensity cut-points has good agreement. These findings can be used to inform selection of appropriate outcomes if comparing outputs from these accelerometer brands.

Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been suggested for the purpose of estimating ID, but no standard package to easily apply them one by one or all at once has been implemented in Python. This technical note introduces scikit-dimension, an open-source Python package for intrinsic dimension estimation. The scikit-dimension package provides a uniform implementation of most of the known ID estimators based on the scikit-learn application programming interface to evaluate the global and local intrinsic dimension, as well as generators of synthetic toy and benchmark datasets widespread in the literature. The package is developed with tools assessing the code quality, coverage, unit testing and continuous integration. We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation for real-life and synthetic data.

Multidimensional datapoint clouds representing large datasets are frequently characterized by non-trivial low-dimensional geometry and topology which can be recovered by unsupervised machine learning approaches, in particular, by principal graphs. Principal graphs approximate the multivariate data by a graph injected into the data space with some constraints imposed on the node mapping. Here we present ElPiGraph, a scalable and robust method for constructing principal graphs. ElPiGraph exploits and further develops the concept of elastic energy, the topological graph grammar approach, and a gradient descent-like optimization of the graph topology. The method is able to withstand high levels of noise and is capable of approximating data point clouds via principal graph ensembles. This strategy can be used to estimate the statistical significance of complex data features and to summarize them into a single consensus principal graph. ElPiGraph deals efficiently with large datasets in various fields such as biology, where it can be used for example with single-cell transcriptomic or epigenomic datasets to infer gene expression dynamics and recover differentiation landscapes.