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Objectives Decades of empirical research demonstrate that crime is concentrated at a range of spatial scales, including street segments. Further, the degree of clustering at particular geographic units remains noticeably stable and consistent; a finding that Weisburd (Criminology 53:133–157, 2015 ) has recently termed the ‘law of crime concentration at places’. Such findings suggest that the future locations of crime should—to some extent at least—be predictable. To date, methods of forecasting where crime is most likely to next occur have focused either on area-level or grid-based predictions. No studies of which we are aware have developed and tested the accuracy of methods for predicting the future risk of crime at the street segment level. This is surprising given that it is at this level of place that many crimes are committed and policing resources are deployed. Methods Using data for property crimes for a large UK metropolitan police force area, we introduce and calibrate a network-based version of prospective crime mapping [e.g. Bowers et al. (Br J Criminol 44:641–658, 2004 )], and compare its performance against grid-based alternatives. We also examine how measures of predictive accuracy can be translated to the network context, and show how differences in performance between the two cases can be quantified and tested. Results Findings demonstrate that the calibrated network-based model substantially outperforms a grid-based alternative in terms of predictive accuracy, with, for example, approximately 20 % more crime identified at a coverage level of 5 %. The improvement in accuracy is highly statistically significant at all coverage levels tested (from 1 to 10 %). Conclusions This study suggests that, for property crime at least, network-based methods of crime forecasting are likely to outperform grid-based alternatives, and hence should be used in operational policing. More sophisticated variations of the model tested are possible and should be developed and tested in future research.

Deregulation of chromatin modifiers plays an essential role in the pathogenesis of medulloblastoma, the most common paediatric malignant brain tumour. Here, we identify a BMI1-dependent sensitivity to deregulation of inositol metabolism in a proportion of medulloblastoma. We demonstrate mTOR pathway activation and metabolic adaptation specifically in medulloblastoma of the molecular subgroup G4 characterised by a BMI1 High ;CHD7 Low signature and show this can be counteracted by IP6 treatment. Finally, we demonstrate that IP6 synergises with cisplatin to enhance its cytotoxicity in vitro and extends survival in a pre-clinical BMI1 High ;CHD7 Low xenograft model. BMI1 and CHD7 are chromatin remodelling genes with a role in medulloblastoma pathogenesis. Here, the authors demonstrate that the BMI1 High /CHD7 Low signature mediates metabolic adaptation in G4 MB and predicts response to inositol treatment either alone or in combination with chemotherapy.

Epigenetic mechanisms which play an essential role in normal developmental processes, such as self-renewal and fate specification of neural stem cells (NSC) are also responsible for some of the changes in the glioblastoma (GBM) genome. Here we develop a strategy to compare the epigenetic and transcriptional make-up of primary GBM cells (GIC) with patient-matched expanded potential stem cell (EPSC)-derived NSC (iNSC). Using a comparative analysis of the transcriptome of syngeneic GIC/iNSC pairs, we identify a glycosaminoglycan (GAG)-mediated mechanism of recruitment of regulatory T cells (Tregs) in GBM. Integrated analysis of the transcriptome and DNA methylome of GBM cells identifies druggable target genes and patient-specific prediction of drug response in primary GIC cultures, which is validated in 3D and in vivo models. Taken together, we provide a proof of principle that this experimental pipeline has the potential to identify patient-specific disease mechanisms and druggable targets in GBM. The identification of patient-specific disease mechanisms and druggable targets is crucial for precision medicine in glioblastoma. Here, the authors show that comparing patients-matched glioma-initiating cells with neural stem cells enables the discovery of patient-specific mechanisms of disease and the identification of effective drugs

Most biological processes are performed by multiprotein complexes. Traditionally described as static entities, evidence is now emerging that their components can be highly dynamic, exchanging constantly with cellular pools. The bacterial flagellar motor contains ∼13 different proteins and provides an ideal system to study functional molecular complexes. It is powered by transmembrane ion flux through a ring of stator complexes that push on a central rotor. The Escherichia coli motor switches direction stochastically in response to binding of the response regulator CheY to the rotor switch component FliM. Much is known of the static motor structure, but we are just beginning to understand the dynamics of its individual components. Here we measure the stoichiometry and turnover of FliM in functioning flagellar motors, by using high-resolution fluorescence microscopy of E. coli expressing genomically encoded YPet derivatives of FliM at physiological levels. We show that the ∼30 FliM molecules per motor exist in two discrete populations, one tightly associated with the motor and the other undergoing stochastic turnover. This turnover of FliM molecules depends on the presence of active CheY, suggesting a potential role in the process of motor switching. In many ways the bacterial flagellar motor is as an archetype macromolecular assembly, and our results may have further implications for the functional relevance of protein turnover in other large molecular complexes.

We describe a subset of glioblastoma, the most prevalent malignant adult brain tumour, harbouring a bias towards hypomethylation at defined differentially methylated regions. This epigenetic signature correlates with an enrichment for an astrocytic gene signature, which together with the identification of enriched predicted binding sites of transcription factors known to cause demethylation and to be involved in astrocytic/glial lineage specification, point to a shared ontogeny between these glioblastomas and astroglial progenitors. At functional level, increased invasiveness, at least in part mediated by SRPX2, and macrophage infiltration characterise this subset of glioblastoma.

Choroid plexus tumours (CPTs) account for 2–5% of brain tumours in children. They can spread along the neuraxis and can recur after treatment. Little is known about the molecular mechanisms underlying their formation and only few high fidelity mouse models of p53-deficient malignant CPTs are available. We show here that c-MYC overexpression in the choroid plexus epithelium induces T-cell inflammation-dependent choroid plexus papillomas in a mouse model. We demonstrate that c-MYC is expressed in a substantial proportion of human choroid plexus tumours and that this subgroup of tumours is characterised by an inflammatory transcriptome and significant inflammatory infiltrates. In compound mutant mice, overexpression of c-MYC in an immunodeficient background led to a decreased incidence of CPP and reduced tumour bulk. Finally, reduced tumour size was also observed upon T-cell depletion in CPP-bearing mice. Our data raise the possibility that benign choroid plexus tumours expressing c-MYC could be amenable to medical therapy with anti-inflammatory drugs.

Tracking bacteria using video microscopy is a powerful experimental approach to probe their motile behaviour. The trajectories obtained contain much information relating to the complex patterns of bacterial motility. However, methods for the quantitative analysis of such data are limited. Most swimming bacteria move in approximately straight lines, interspersed with random reorientation phases. It is therefore necessary to segment observed tracks into swimming and reorientation phases to extract useful statistics. We present novel robust analysis tools to discern these two phases in tracks. Our methods comprise a simple and effective protocol for removing spurious tracks from tracking datasets, followed by analysis based on a two-state hidden Markov model, taking advantage of the availability of mutant strains that exhibit swimming-only or reorientating-only motion to generate an empirical prior distribution. Using simulated tracks with varying levels of added noise, we validate our methods and compare them with an existing heuristic method. To our knowledge this is the first example of a systematic assessment of analysis methods in this field. The new methods are substantially more robust to noise and introduce less systematic bias than the heuristic method. We apply our methods to tracks obtained from the bacterial species Rhodobacter sphaeroides and Escherichia coli. Our results demonstrate that R. sphaeroides exhibits persistence over the course of a tumbling event, which is a novel result with important implications in the study of this and similar species.

In the original version of this article [1], there was 1 error in the affiliation of the European Institute of Oncology (affiliation 3). In this correction article the updated affiliation is shown for clarification.

There are various cases of animal movement where behaviour broadly switches between two modes of operation, corresponding to a long-distance movement state and a resting or local movement state. Here, a mathematical description of this process is formulated, adapted from Friedrich et al. (Phys Rev E, 74:041103, 2006b ). The approach allows the specification any running or waiting time distribution along with any angular and speed distributions. The resulting system of integro-partial differential equations is tumultuous, and therefore, it is necessary to both simplify and derive summary statistics. An expression for the mean squared displacement is derived, which shows good agreement with experimental data from the bacterium Escherichia coli and the gull Larus fuscus . Finally, a large time diffusive approximation is considered via a Cattaneo approximation (Hillen in Discrete Continuous Dyn Syst Ser B, 5:299–318, 2003 ). This leads to the novel result that the effective diffusion constant is dependent on the mean and variance of the running time distribution but only on the mean of the waiting time distribution.

Choroid plexus tumours (CPTs) account for 2–5% of brain tumours in children. They can spread along the neuraxis and can recur after treatment. Little is known about the molecular mechanisms underlying their formation and only few high fidelity mouse models of p53-deficient malignant CPTs are available. We show here that c-MYC overexpression in the choroid plexus epithelium induces T-cell inflammation-dependent choroid plexus papillomas in a mouse model. We demonstrate that c-MYC is expressed in a substantial proportion of human choroid plexus tumours and that this subgroup of tumours is characterised by an inflammatory transcriptome and significant inflammatory infiltrates. In compound mutant mice, overexpression of c-MYC in an immunodeficient background led to a decreased incidence of CPP and reduced tumour bulk. Finally, reduced tumour size was also observed upon T-cell depletion in CPP-bearing mice. Our data raise the possibility that benign choroid plexus tumours expressing c-MYC could be amenable to medical therapy with anti-inflammatory drugs.