Improved model identification for non-linear systems using a random subsampling and multifold modelling (RSMM) approach

In non-linear system identification, the available observed data are conventionally partitioned into two parts: the training data that are used for model identification and the test data that are used for model performance testing. This sort of 'hold-out' or 'split-sample' data partitioning method is convenient and the associated model identification procedure is in general easy to implement. The resultant model obtained from such a once-partitioned single training dataset, however, may occasionally lack robustness and generalisation to represent future unseen data, because the performance of the identified model may be highly dependent on how the data partition is made. To overcome the drawback of the hold-out data partitioning method, this study presents a new random subsampling and multifold modelling (RSMM) approach to produce less biased or preferably unbiased models. The basic idea and the associated procedure are as follows. First, generate K training datasets (and also K validation datasets), using a K-fold random subsampling method. Secondly, detect significant model terms and identify a common model structure that fits all the K datasets using a new proposed common model selection approach, called the multiple orthogonal search algorithm. Finally, estimate and refine the model parameters for the identified common-structured model using a multifold parameter estimation method. The proposed method can produce robust models with better generalisation performance

GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs

This work studies the problem of stochastic dynamic filtering and state propagation with complex beliefs. The main contribution is GP-SUM, a filtering algorithm tailored to dynamic systems and observation models expressed as Gaussian Processes (GP), and to states represented as a weighted sum of Gaussians. The key attribute of GP-SUM is that it does not rely on linearizations of the dynamic or observation models, or on unimodal Gaussian approximations of the belief, hence enables tracking complex state distributions. The algorithm can be seen as a combination of a sampling-based filter with a probabilistic Bayes filter. On the one hand, GP-SUM operates by sampling the state distribution and propagating each sample through the dynamic system and observation models. On the other hand, it achieves effective sampling and accurate probabilistic propagation by relying on the GP form of the system, and the sum-of-Gaussian form of the belief. We show that GP-SUM outperforms several GP-Bayes and Particle Filters on a standard benchmark. We also demonstrate its use in a pushing task, predicting with experimental accuracy the naturally occurring non-Gaussian distributions.Comment: WAFR 2018, 16 pages, 7 figure

Effect of spatial configuration of an extended nonlinear Kierstead-Slobodkin reaction-transport model with adaptive numerical scheme

In this paper, we consider the numerical simulations of an extended nonlinear form of Kierstead-Slobodkin reaction-transport system in one and two dimensions. We employ the popular fourth-order exponential time differencing Runge-Kutta (ETDRK4) schemes proposed by Cox and Matthew (J Comput Phys 176:430-455, 2002), that was modified by Kassam and Trefethen (SIAM J Sci Comput 26:1214-1233, 2005), for the time integration of spatially discretized partial differential equations. We demonstrate the supremacy of ETDRK4 over the existing exponential time differencing integrators that are of standard approaches and provide timings and error comparison. Numerical results obtained in this paper have granted further insight to the question "What is the minimal size of the spatial domain so that the population persists?" posed by Kierstead and Slobodkin (J Mar Res 12:141-147, 1953 ), with a conclusive remark that the popula- tion size increases with the size of the domain. In attempt to examine the biological wave phenomena of the solutions, we present the numerical results in both one- and two-dimensional space, which have interesting ecological implications. Initial data and parameter values were chosen to mimic some existing patternsScopus 201

Recurrent Urinary Tract Infection: A Mystery in Search of Better Model Systems

Urinary tract infections (UTIs) are among the most common infectious diseases worldwide but are significantly understudied. Uropathogenic E. coli (UPEC) accounts for a significant proportion of UTI, but a large number of other species can infect the urinary tract, each of which will have unique host-pathogen interactions with the bladder environment. Given the substantial economic burden of UTI and its increasing antibiotic resistance, there is an urgent need to better understand UTI pathophysiology – especially its tendency to relapse and recur. Most models developed to date use murine infection; few human-relevant models exist. Of these, the majority of in vitro UTI models have utilized cells in static culture, but UTI needs to be studied in the context of the unique aspects of the bladder’s biophysical environment (e.g., tissue architecture, urine, fluid flow, and stretch). In this review, we summarize the complexities of recurrent UTI, critically assess current infection models and discuss potential improvements. More advanced human cell-based in vitro models have the potential to enable a better understanding of the etiology of UTI disease and to provide a complementary platform alongside animals for drug screening and the search for better treatments

Radiocarbon dating of methane and carbon dioxide evaded from a temperate peatland stream

Streams draining peatlands export large quantities of carbon in different chemical forms and are an important part of the carbon cycle. Radiocarbon (14C) analysis/dating provides unique information on the source and rate that carbon is cycled through ecosystems, as has recently been demonstrated at the air-water interface through analysis of carbon dioxide (CO2) lost from peatland streams by evasion (degassing). Peatland streams also have the potential to release large amounts of methane (CH4) and, though 14C analysis of CH4 emitted by ebullition (bubbling) has been previously reported, diffusive emissions have not. We describe methods that enable the 14C analysis of CH4 evaded from peatland streams. Using these methods, we investigated the 14C age and stable carbon isotope composition of both CH4 and CO2 evaded from a small peatland stream draining a temperate raised mire. Methane was aged between 1617-1987 years BP, and was much older than CO2 which had an age range of 303-521 years BP. Isotope mass balance modelling of the results indicated that the CO2 and CH4 evaded from the stream were derived from different source areas, with most evaded CO2 originating from younger layers located nearer the peat surface compared to CH4. The study demonstrates the insight that can be gained into peatland carbon cycling from a methodological development which enables dual isotope (14C and 13C) analysis of both CH4 and CO2 collected at the same time and in the same way

Chemokine (C-C motif) ligand 2 mediates direct and indirect fibrotic responses in human and murine cultured fibrocytes

<p>Abstract</p> <p>Background</p> <p>Fibrocytes are a population of circulating bone-marrow-derived cells that express surface markers for leukocytes and mesenchymal cells, and are capable of differentiating into myofibroblasts. They have been observed at sites of active fibrosis and increased circulating numbers correlate with mortality in idiopathic pulmonary fibrosis (IPF). Inhibition of chemokine (C-C motif) receptor 2 (CCR2) during experimental models of lung fibrosis reduces lung collagen deposition, as well as reducing lung fibrocyte accumulation. The aim of the present study was to determine whether human and mouse fibrocytes express functional CCR2.</p> <p>Results</p> <p>Following optimized and identical human and murine fibrocyte isolation, both cell sources were shown to be positive for CCR2 by flow cytometry and this expression colocalized with collagen I and CD45. Human blood fibrocytes stimulated with the CCR2 ligand chemokine (C-C motif) ligand 2 (CCL2), demonstrated increased proliferation (<it>P </it>< 0.005) and differentiation into myofibroblasts (<it>P </it>< 0.001), as well as a chemotactic response (<it>P </it>< 0.05). Murine fibrocytes also responded to CCR2 stimulation, with CCL12 being more potent than CCL2.</p> <p>Conclusions</p> <p>This study directly compares the functional responses of human and murine fibrocytes to CCR2 ligands, and following comparable isolation techniques. We have shown comparable biological effects, strengthening the translatability of the murine models to human disease with respect to targeting the CCR2 axis to ameliorate disease in IPF patients.</p

Subbarrel patterns in somatosensory cortical barrels can emerge from local dynamic instabilities

Complex spatial patterning, common in the brain as well as in other biological systems, can emerge as a result of dynamic interactions that occur locally within developing structures. In the rodent somatosensory cortex, groups of neurons called "barrels" correspond to individual whiskers on the contralateral face. Barrels themselves often contain subbarrels organized into one of a few characteristic patterns. Here we demonstrate that similar patterns can be simulated by means of local growth-promoting and growth-retarding interactions within the circular domains of single barrels. The model correctly predicts that larger barrels contain more spatially complex subbarrel patterns, suggesting that the development of barrels and of the patterns within them may be understood in terms of some relatively simple dynamic processes. We also simulate the full nonlinear equations to demonstrate the predictive value of our linear analysis. Finally, we show that the pattern formation is robust with respect to the geometry of the barrel by simulating patterns on a realistically shaped barrel domain. This work shows how simple pattern forming mechanisms can explain neural wiring both qualitatively and quantitatively even in complex and irregular domains. © 2009 Ermentrout et al

Determinants of adults' intention to vaccinate against pandemic swine flu

This article has been made available through the Brunel Open Access Publishing Fund.This article has been made available through the Brunel Open Access Publishing Fund.Background: Vaccination is one of the cornerstones of controlling an influenza pandemic. To optimise vaccination rates in the general population, ways of identifying determinants that influence decisions to have or not to have a vaccination need to be understood. Therefore, this study aimed to predict intention to have a swine influenza vaccination in an adult population in the UK. An extension of the Theory of Planned Behaviour provided the theoretical framework for the study. Methods: Three hundred and sixty two adults from the UK, who were not in vaccination priority groups, completed either an online (n = 306) or pen and paper (n = 56) questionnaire. Data were collected from 30th October 2009, just after swine flu vaccination became available in the UK, and concluded on 31st December 2009. The main outcome of interest was future swine flu vaccination intentions. Results: The extended Theory of Planned Behaviour predicted 60% of adults’ intention to have a swine flu vaccination with attitude, subjective norm, perceived control, anticipating feelings of regret (the impact of missing a vaccination opportunity), intention to have a seasonal vaccine this year, one perceived barrier: “I cannot be bothered to get a swine flu vaccination” and two perceived benefits: “vaccination decreases my chance of getting swine flu or its complications” and “if I get vaccinated for swine flu, I will decrease the frequency of having to consult my doctor,” being significant predictors of intention. Black British were less likely to intend to have a vaccination compared to Asian or White respondents. Conclusions: Theoretical frameworks which identify determinants that influence decisions to have a pandemic influenza vaccination are useful. The implications of this research are discussed with a view to maximising any future pandemic influenza vaccination uptake using theoretically-driven applications.This article is available through the Brunel Open Access Publishing Fund