State-Space Inference and Learning with Gaussian Processes

State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model. Copyright 2010 by the authors

Robust Filtering and Smoothing with Gaussian Processes

We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing, machine learning, robotics, and control for representing unknown system functions by posterior probability distributions. This modern way of "system identification" is more robust than finding point estimates of a parametric function representation. In this article, we present a principled algorithm for robust analytic smoothing in GP dynamic systems, which are increasingly used in robotics and control. Our numerical evaluations demonstrate the robustness of the proposed approach in situations where other state-of-the-art Gaussian filters and smoothers can fail.Comment: 7 pages, 1 figure, draft version of paper accepted at IEEE Transactions on Automatic Contro

Instagram use is linked to increased symptoms of orthorexia nervosa

PURPOSE: Social media use is ever increasing amongst young adults and has previously been shown to have negative effects on body image, depression, social comparison, and disordered eating. One eating disorder of interest in this context is orthorexia nervosa, an obsession with eating healthily. High orthorexia nervosa prevalence has been found in populations who take an active interest in their health and body and is frequently comorbid with anorexia nervosa. Here, we investigate links between social media use, in particularly Instagram and orthorexia nervosa symptoms. METHODS: We conducted an online survey of social media users (N = 680) following health food accounts. We assessed their social media use, eating behaviours, and orthorexia nervosa symptoms using the ORTO-15 inventory. RESULTS: Higher Instagram use was associated with a greater tendency towards orthorexia nervosa, with no other social media channel having this effect. In exploratory analyses Twitter showed a small positive association with orthorexia symptoms. BMI and age had no association with orthorexia nervosa. The prevalence of orthorexia nervosa among the study population was 49%, which is significantly higher than the general population (<1%). CONCLUSIONS: Our results suggest that the healthy eating community on Instagram has a high prevalence of orthorexia symptoms, with higher Instagram use being linked to increased symptoms. These findings highlight the implications social media can have on psychological wellbeing, and the influence social media 'celebrities' may have over hundreds of thousands of individuals. These results may also have clinical implications for eating disorder development and recovery

A generative model for natural sounds based on latent force modelling

Generative models based on subband amplitude envelopes of natural sounds have resulted in convincing synthesis, showing subband amplitude modulation to be a crucial component of auditory perception. Probabilistic latent variable analysis can be particularly insightful, but existing approaches don’t incorporate prior knowledge about the physical behaviour of amplitude envelopes, such as exponential decay or feedback. We use latent force modelling, a probabilistic learning paradigm that encodes physical knowledge into Gaussian process regression, to model correlation across spectral subband envelopes. We augment the standard latent force model approach by explicitly modelling dependencies across multiple time steps. Incorporating this prior knowledge strengthens the interpretation of the latent functions as the source that generated the signal. We examine this interpretation via an experiment showing that sounds generated by sampling from our probabilistic model are perceived to be more realistic than those generated by comparative models based on nonnegative matrix factorisation, even in cases where our model is outperformed from a reconstruction error perspective

RocA truncation underpins hyper-encapsulation, carriage longevity and transmissibility of serotype M18 group A streptococci

Group A streptococcal isolates of serotype M18 are historically associated with epidemic waves of pharyngitis and the non-suppurative immune sequela rheumatic fever. The serotype is defined by a unique, highly encapsulated phenotype, yet the molecular basis for this unusual colony morphology is unknown. Here we identify a truncation in the regulatory protein RocA, unique to and conserved within our serotype M18 GAS collection, and demonstrate that it underlies the characteristic M18 capsule phenotype. Reciprocal allelic exchange mutagenesis of rocA between M18 GAS and M89 GAS demonstrated that truncation of RocA was both necessary and sufficient for hyper-encapsulation via up-regulation of both precursors required for hyaluronic acid synthesis. Although RocA was shown to positively enhance covR transcription, quantitative proteomics revealed RocA to be a metabolic regulator with activity beyond the CovR/S regulon. M18 GAS demonstrated a uniquely protuberant chain formation following culture on agar that was dependent on excess capsule and the RocA mutation. Correction of the M18 rocA mutation reduced GAS survival in human blood, and in vivo naso-pharyngeal carriage longevity in a murine model, with an associated drop in bacterial airborne transmission during infection. In summary, a naturally occurring truncation in a regulator explains the encapsulation phenotype, carriage longevity and transmissibility of M18 GAS, highlighting the close interrelation of metabolism, capsule and virulence

Elementary processes governing the evolution of road networks

Urbanisation is a fundamental phenomenon whose quantitative characterisation is still inadequate. We report here the empirical analysis of a unique data set regarding almost 200 years of evolution of the road network in a large area located north of Milan (Italy). We find that urbanisation is characterised by the homogenisation of cell shapes, and by the stability throughout time of high-centrality roads which constitute the backbone of the urban structure, confirming the importance of historical paths. We show quantitatively that the growth of the network is governed by two elementary processes: (i) `densification', corresponding to an increase in the local density of roads around existing urban centres and (ii) `exploration', whereby new roads trigger the spatial evolution of the urbanisation front. The empirical identification of such simple elementary mechanisms suggests the existence of general, simple properties of urbanisation and opens new directions for its modelling and quantitative description.Comment: 10 pages, 6 figure

Proteomic analysis at the sites of clinical infection with invasive Streptococcus pyogenes

Invasive Streptococcus pyogenes infections are rare, with often-unexplained severity. Prompt diagnosis is desirable, as deaths can occur rapidly following onset and there is an increased, but preventable, risk to contacts. Here, proteomic analyses of clinical samples from invasive human S. pyogenes infections were undertaken to determine if novel diagnostic targets could be detected, and to augment our understanding of disease pathogenesis. Fluid samples from 17 patients with confirmed invasive S. pyogenes infection (empyema, septic arthritis, necrotising fasciitis) were analysed by proteomics for streptococcal and human proteins; 16/17 samples had detectable S. pyogenes DNA. Nineteen unique S. pyogenes proteins were identified in just 6/17 samples, and 15 of these were found in a single pleural fluid sample including streptococcal inhibitor of complement, trigger factor, and phosphoglycerate kinase. In contrast, 469 human proteins were detected in patient fluids, 177 (38%) of which could be identified as neutrophil proteins, including alpha enolase and lactotransferrin which, together, were found in all 17 samples. Our data suggest that streptococcal proteins are difficult to detect in infected fluid samples. A vast array of human proteins associated with leukocyte activity are, however, present in samples that deserve further evaluation as potential biomarkers of infection

Can patient decision aids reduce decisional conflict in a de-escalation of breast radiotherapy clinical trial? The PRIMETIME Study Within a Trial implemented using a cluster stepped-wedge trial design

Background For patients with early breast cancer considered at very-low risk of local relapse, risks of radiotherapy may outweigh the benefits. Decisions regarding treatment omission can lead to patient uncertainty (decisional conflict), which may be lessened with patient decision aids (PDA). PRIMETIME (ISRCTN 41579286) is a UK-led biomarker-directed study evaluating omission of adjuvant radiotherapy in breast cancer; an embedded Study Within A Trial (SWAT) investigated whether PDA reduces decisional conflict using a cluster stepped-wedge trial design.Methods PDA diagrams and a video explaining risks and benefits of radiotherapy were developed in close collaboration between patient advocates and PRIMETIME trialists. The SWAT used a cluster stepped-wedge trial design, where each cluster represented the radiotherapy centre and referring peripheral centres. All clusters began in the standard information group (patient information and diagrams) and were randomised to cross-over to the enhanced information group (standard information plus video) at 2, 4 or 6 months. Primary endpoint was the decisional conflict scale (0-100, higher scores indicating greater conflict) which was assessed on an individual participant level. Multilevel mixed effects models used a random effect for cluster and a fixed effect for each step to adjust for calendar time and clustering. Robust standard errors were also adjusted for the clustering effect.Results Five hundred twenty-one evaluable questionnaires were returned from 809 eligible patients (64%) in 24 clusters between April 2018 and October 2019. Mean decisional conflict scores in the standard group (N = 184) were 10.88 (SD 11.82) and 8.99 (SD 11.82) in the enhanced group (N = 337), with no statistically significant difference [mean difference - 1.78, 95%CI - 3.82-0.25, p = 0.09]. Compliance with patient information and diagrams was high in both groups although in the enhanced group only 121/337 (36%) reported watching the video.Conclusion The low levels of decisional conflict in PRIMETIME are reassuring and may reflect the high-quality information provision, such that not everyone required the video. This reinforces the importance of working with patients as partners in clinical trials especially in the development of patient-centred information and decision aids

RNA Docking and Local Translation Regulate Site-Specific Axon Remodeling In Vivo

Nascent proteins can be positioned rapidly at precise subcellular locations by local protein synthesis (LPS) to facilitate localized growth responses. Axon arbor architecture, a major determinant of synaptic connectivity, is shaped by localized growth responses, but it is unknown whether LPS influences these responses in vivo. Using high-resolution live imaging, we examined the spatiotemporal dynamics of RNA and LPS in retinal axons during arborization in vivo. Endogenous RNA tracking reveals that RNA granules dock at sites of branch emergence and invade stabilized branches. Live translation reporter analysis reveals that de novo ß-actin hotspots colocalize with docked RNA granules at the bases and tips of new branches. Inhibition of axonal ß-actin mRNA translation disrupts arbor dynamics primarily by reducing new branch emergence and leads to impoverished terminal arbors. The results demonstrate a requirement for LPS in building arbor complexity and suggest a key role for pre-synaptic LPS in assembling neural circuits.This work was supported by Cambridge Trust, Croucher Foundation, Sir Edward Youde Memorial Fund (H.H.-W.W.), Gates Cambridge (J.Q.L.), Fundac¸ a˜ o para a Cieˆ ncia e Tecnologia (C.M.R.), Wellcome Trust Senior Investigator Award (100329/Z/ 12/Z) (W.A.H.), EPSRC Grant (EP/H018301/1), MRC Grant (MR/K015850/1 and MR/K02292X/1), Wellcome Trust (089703/Z/09/Z) (C.F.K.), Wellcome Trust Programme Grant (085314/Z/08/Z), and ERC Advanced Grant (322817) (C.E.H.)