Methods for comparing and constraining models of dendritic spines

Physiological evidence using Infrared Video Microscopy during the uncaging of glutamate has proven the existence of excitable calcium ion channels in spine heads, highlighting the need for reliable models of spines. In this study we compare the three main methods of simulating excitable spines: Baer & Rinzel's Continuum (B&R) model, Coombes' Spike-Diffuse-Spike (SDS) model and paired cable and ion channel equations (Cable model). Tests are done to determine how well the models approximate each other in terms of speed and heights of travelling waves. Significant quantitative differences are found between the models: travelling waves in the SDS model in particular are found to travel at much lower speeds and sometimes much higher voltages than in the Cable or B&R models. Meanwhile qualitative differences are found between the B&R and SDS models over realistic parameter ranges. The cause of these differences is investigated and potential solutions proposed.

Comparison of subspace and ARX models of a waveguide's terahertz transient response after optimal wavelet filtering

A quasi-optical deembedding technique for characterizing waveguides is demonstrated using wide-band time-resolved terahertz spectroscopy. A transfer function representation is adopted for the description of the signal in the input and output port of the waveguides. The time-domain responses were discretized and the waveguide transfer function was obtained through a parametric approach in the z-domain after describing the system with an AutoRegressive with eXogenous input (ARX), as well as with a state-space model. Prior to the identification procedure, filtering was performed in the wavelet domain to minimize both signal distortion, as well as the noise propagating in the ARX and subspace models. The optimal filtering procedure used in the wavelet domain for the recorded time-domain signatures is described in detail. The effect of filtering prior to the identification procedures is elucidated with the aid of pole-zero diagrams. Models derived from measurements of terahertz transients in a precision WR-8 waveguide adjustable short are presented.

Intelligent agents and distributed models for cooperative task support

Purpose – To describe some research done, as part of an EPSRC funded project, to assist engineers working together on collaborative tasks. Design/methodology/approach – Distributed finite state modelling and agent techniques are used successfully in a new hybrid self-organising decision making system applied to collaborative work support. For the particular application, analysis of the tasks involved has been performed and these tasks are modelled. The system then employs a novel generic agent model, where task and domain knowledge are isolated from the support system, which provides relevant information to the engineers. Findings – The method is applied in the despatch of transmission commands within the control room of The National Grid Company Plc (NGC) – tasks are completed significantly faster when the system is utilised. Research limitations/implications – The paper describes a generic approach and it would be interesting to investigate how well it works in other applications. Practical implications – Although only one application has been studied, the methodology could equally be applied to a general class of cooperative work environments. Originality/value – One key part of the work is the novel generic agent model that enables the task and domain knowledge, which are application specific, to be isolated from the support system, and hence allows the method to be applied in other domains.

A review and implementation of swarm pattern formation and transformation models

Purpose: The purpose of this paper is to address a classic problem – pattern formation identified by researchers in the area of swarm robotic systems – and is also motivated by the need for mathematical foundations in swarm systems. Design/methodology/approach: The work is separated out as inspirations, applications, definitions, challenges and classifications of pattern formation in swarm systems based on recent literature. Further, the work proposes a mathematical model for swarm pattern formation and transformation. Findings: A swarm pattern formation model based on mathematical foundations and macroscopic primitives is proposed. A formal definition for swarm pattern transformation and four special cases of transformation are introduced. Two general methods for transforming patterns are investigated and a comparison of the two methods is presented. The validity of the proposed models, and the feasibility of the methods investigated are confirmed on the Traer Physics and Processing environment. Originality/value: This paper helps in understanding the limitations of existing research in pattern formation and the lack of mathematical foundations for swarm systems. The mathematical model and transformation methods introduce two key concepts, namely macroscopic primitives and a mathematical model. The exercise of implementing the proposed models on physics simulator is novel.

Modeling entangled dynamics: comparison between stochastic single-chain and multichain models

To test the effectiveness of stochastic single-chain models in describing the dynamics of entangled polymers, we systematically compare one such model; the slip-spring model; to a multichain model solved using stochastic molecular dynamics(MD) simulations (the Kremer-Grest model). The comparison involves investigating if the single-chain model can adequately describe both a microscopic dynamical and a macroscopic rheological quantity for a range of chain lengths. Choosing a particular chain length in the slip-spring model, the parameter values that best reproduce the mean-square displacement of a group of monomers is determined by fitting toMDdata. Using the same set of parameters we then test if the predictions of the mean-square displacements for other chain lengths agree with the MD calculations. We followed this by a comparison of the time dependent stress relaxation moduli obtained from the two models for a range of chain lengths. After identifying a limitation of the original slip-spring model in describing the static structure of the polymer chain as seen in MD, we remedy this by introducing a pairwise repulsive potential between the monomers in the chains. Poor agreement of the mean-square monomer displacements at short times can be rectified by the use of generalized Langevin equations for the dynamics and resulted in significantly improved agreement.

Integrated motivational interviewing and cognitive behavioural therapy for people with psychosis and comorbid substance misuse: randomised controlled trial

Objectives To evaluate the effectiveness of integrated motivational interviewing and cognitive behaviour therapy in addition to standard care for patients with psychosis and a co-morbid substance use problem. Design Two-centre, open, rater-blind randomised controlled trial Setting UK Secondary Care Participants 327 patients with clinical diagnoses of schizophrenia, schizophreniform or schizoaffective disorder and DSM-IV diagnoses of drug and/or alcohol dependence or abuse Interventions Participants were randomly allocated to integrated motivational interviewing and cognitive behaviour therapy or standard care. Therapy has two phases. Phase one – “motivation building” – concerns engaging the patient, then exploring and resolving ambivalence for change in substance use. Phase two –“Action” – supports and facilitates change using cognitive behavioural approaches. Up to 26 therapy sessions were delivered over one year. Main outcomes The primary outcome was death from any cause or admission to hospital in the 12 months after therapy. Secondary outcomes were frequency and amount of substance use (Timeline Followback), readiness to change, perceived negative consequences of use, psychotic symptom ratings, number and duration of relapses, global assessment of functioning and deliberate self harm, at 12 and 24 months, with additional Timeline Followback assessments at 6 and 18 months. Analysis was by intention-to-treat with robust treatment effect estimates. Results 327 participants were randomised. 326 (99.7%) were assessed on the primary outcome, 246 (75.2%) on main secondary outcomes at 24 months. Regarding the primary outcome, there was no beneficial treatment effect on hospital admissions/ death during follow-up, with 20.2% (33/163) of controls and 23.3% (38/163) of the therapy group deceased or admitted (adjusted odds-ratio 1.16; P= 0.579; 95% confidence interval 0.68 to 1.99). For secondary outcomes there was no treatment effect on frequency of substance use or perceived negative consequences, but a statistically significant effect of therapy on amount used per substance-using day (adjusted odds-ratios: (a) for main substance 1.50; P=0.016; 1.08 to 2.09, (b) all substances 1.48; P=0.017; 1.07 to 2.05). There was a statistically significant treatment effect on readiness to change use at 12 months (adjusted odds-ratio 2.05; P=0.004; 1.26 to 3.31), not maintained at 24 months. There were no treatment effects on assessed clinical outcomes. Conclusions Integrated motivational interviewing and cognitive behaviour therapy for people with psychosis and substance misuse does not improve outcome in terms of hospitalisation, symptom outcomes or functioning. It does result in a reduction in amount of substance use which is maintained over the year’s follow up. Trial registration Current Controlled Trials: ISRCTN14404480

Eyes, amygdala, and other models of face processing: Questions for the SIMS model

This commentary raises general questions about the parsimony and generalizability of the SIMS model, before interrogating the specific roles that the amygdala and eye contact play in it. Additionally, this situates the SIMS model alongside another model of facial expression processing, with a view to incorporating individual differences in emotion perception.

Integration of chaos theory and mathematical models in building simulation: Part I: Literature review

Current mathematical models in building research have been limited in most studies to linear dynamics systems. A literature review of past studies investigating chaos theory approaches in building simulation models suggests that as a basis chaos model is valid and can handle the increasingly complexity of building systems that have dynamic interactions among all the distributed and hierarchical systems on the one hand, and the environment and occupants on the other. The review also identifies the paucity of literature and the need for a suitable methodology of linking chaos theory to mathematical models in building design and management studies. This study is broadly divided into two parts and presented in two companion papers. Part (I) reviews the current state of the chaos theory models as a starting point for establishing theories that can be effectively applied to building simulation models. Part (II) develops conceptual frameworks that approach current model methodologies from the theoretical perspective provided by chaos theory, with a focus on the key concepts and their potential to help to better understand the nonlinear dynamic nature of built environment systems. Case studies are also presented which demonstrate the potential usefulness of chaos theory driven models in a wide variety of leading areas of building research. This study distills the fundamental properties and the most relevant characteristics of chaos theory essential to building simulation scientists, initiates a dialogue and builds bridges between scientists and engineers, and stimulates future research about a wide range of issues on building environmental systems.

Integration of chaos theory and mathematical models in building simulation: Part II: Conceptual frameworks

Current mathematical models in building research have been limited in most studies to linear dynamics systems. A literature review of past studies investigating chaos theory approaches in building simulation models suggests that as a basis chaos model is valid and can handle the increasing complexity of building systems that have dynamic interactions among all the distributed and hierarchical systems on the one hand, and the environment and occupants on the other. The review also identifies the paucity of literature and the need for a suitable methodology of linking chaos theory to mathematical models in building design and management studies. This study is broadly divided into two parts and presented in two companion papers. Part (I), published in the previous issue, reviews the current state of the chaos theory models as a starting point for establishing theories that can be effectively applied to building simulation models. Part (II) develop conceptual frameworks that approach current model methodologies from the theoretical perspective provided by chaos theory, with a focus on the key concepts and their potential to help to better understand the nonlinear dynamic nature of built environment systems. Case studies are also presented which demonstrate the potential usefulness of chaos theory driven models in a wide variety of leading areas of building research. This study distills the fundamental properties and the most relevant characteristics of chaos theory essential to (1) building simulation scientists and designers (2) initiating a dialogue between scientists and engineers, and (3) stimulating future research on a wide range of issues involved in designing and managing building environmental systems.

Dynamic integrated system optimization and parameter estimation for discrete time optimal control of nonlinear systems

An algorithm for solving nonlinear discrete time optimal control problems with model-reality differences is presented. The technique uses Dynamic Integrated System Optimization and Parameter Estimation (DISOPE), which achieves the correct optimal solution in spite of deficiencies in the mathematical model employed in the optimization procedure. A version of the algorithm with a linear-quadratic model-based problem, implemented in the C+ + programming language, is developed and applied to illustrative simulation examples. An analysis of the optimality and convergence properties of the algorithm is also presented.

Model predictive control using dynamic integrated system optimisation and parameter estimation (DISOPE)

DISOPE is a technique for solving optimal control problems where there are differences in structure and parameter values between reality and the model employed in the computations. The model reality differences can also allow for deliberate simplification of model characteristics and performance indices in order to facilitate the solution of the optimal control problem. The technique was developed originally in continuous time and later extended to discrete time. The main property of the procedure is that by iterating on appropriately modified model based problems the correct optimal solution is achieved in spite of the model-reality differences. Algorithms have been developed in both continuous and discrete time for a general nonlinear optimal control problem with terminal weighting, bounded controls and terminal constraints. The aim of this paper is to show how the DISOPE technique can aid receding horizon optimal control computation in nonlinear model predictive control.