Conformational effects on the Circular Dichroism of Human Carbonic Anhydrase II: a multilevel computational study

Circular Dichroism (CD) spectroscopy is a powerful method for investigating conformational changes in proteins and therefore has numerous applications in structural and molecular biology. Here a computational investigation of the CD spectrum of the Human Carbonic Anhydrase II (HCAII), with main focus on the near-UV CD spectra of the wild-type enzyme and it seven tryptophan mutant forms, is presented and compared to experimental studies. Multilevel computational methods (Molecular Dynamics, Semiempirical Quantum Mechanics, Time-Dependent Density Functional Theory) were applied in order to gain insight into the mechanisms of interaction between the aromatic chromophores within the protein environment and understand how the conformational flexibility of the protein influences these mechanisms. The analysis suggests that combining CD semi empirical calculations, crystal structures and molecular dynamics (MD) could help in achieving a better agreement between the computed and experimental protein spectra and provide some unique insight into the dynamic nature of the mechanisms of chromophore interactions

Event Timing in Associative Learning: From Biochemical Reaction Dynamics to Behavioural Observations

Associative learning relies on event timing. Fruit flies for example, once trained with an odour that precedes electric shock, subsequently avoid this odour (punishment learning); if, on the other hand the odour follows the shock during training, it is approached later on (relief learning). During training, an odour-induced Ca++ signal and a shock-induced dopaminergic signal converge in the Kenyon cells, synergistically activating a Ca++-calmodulin-sensitive adenylate cyclase, which likely leads to the synaptic plasticity underlying the conditioned avoidance of the odour. In Aplysia, the effect of serotonin on the corresponding adenylate cyclase is bi-directionally modulated by Ca++, depending on the relative timing of the two inputs. Using a computational approach, we quantitatively explore this biochemical property of the adenylate cyclase and show that it can generate the effect of event timing on associative learning. We overcome the shortage of behavioural data in Aplysia and biochemical data in Drosophila by combining findings from both systems

From the Sum of Near-Zero Energy Buildings to the Whole of a Near-Zero Energy Housing Settlement: The Role of Communal Spaces in Performance-Driven Design

Almost a century ago Modernism challenged the structure of the city and reshaped its physical space in order to, amongst other things, accommodate new transportation infrastructure and road networks proclaiming the,nowadays much-debated ‘scientificated’ pursuit of efficiency for the city. This transformation has had a great impact on the way humans still design, move in, occupy and experience the city. Today major cities in Europe, such as Paris and London, are considering banning vehicles from their historic centers. In parallel, significant effort is currently underway internationally by designers, architects, and engineers to integrate innovative technologies and sophisticated solutions for energy production, management, and storage, as well as for efficient energy consumption, into the architecture of buildings. In general, this effort seeks for new technologies and design methods (e.g., DesignBuilder with EnergyPlus simulation engine; Rhicoceros3D with Grasshopper plugin and Ecotect, Radiance and EnergyPlus tools) that would enable a holistic approach to the spatial design of Near-Zero Energy buildings, so that their ecological benefits are an added value to the architectural design and a building’s visual, and material, impact on its surrounding space. The paper inquires how the integration of such technological infrastructure and performance-orientated interfaces changes yet again the structure and form of cities, and to what extent it safeguards social rights and enables equal access to common resources. Drawing from preliminary results and initial considerations of ongoing research that involve the construction of four innovative NZE settlements across Europe, in the context of the EU-funded ZERO-PLUS project, this paper discusses the integration of novel infrastructure in communal spaces of these settlements. In doing so, it contributes to the debate about smart communities and their role in the sustainable management of housing developments and settlements that are designed and developed with the concept of smart territories

Combining Semi-Physical and Neural Network Modeling: An Example of Its Usefulness

. We illustrate the power of combining semi-physical and neural network modeling in an application example. It is argued that some of the problems related to the use of neural networks, such as high dimensionality of the parameter space and problems with local minima, can be alleviated using this approach. Key Words. Nonlinear system identification; semi-physical modeling; neural network modeling. 1. INTRODUCTION System identification as described by, e.g., Ljung (1987) is a well established methodology for designing mathematical models of dynamical systems using input-output data. After experiment design, the problem can be split into two parts: model structure selection followed by parameter estimation. While various least-squares type of algorithms are predominant for parameter estimation, one has a large spectrum of model structure approaches to choose between. Physically parameterized modeling (where all physical insight about the system is condensed into the model) is a quite t..

Combining Semi-Physical and Neural Network Modeling: An Example of Its Usefulness

: This paper illustrates the power of combining semi-physical and neural network modeling in an application example. It is argued that some of the problems related to the use of neural networks, such as high dimensionality of the parameter space and problems with undesired local minima, can be alleviated by this approach. Keywords: Nonlinear system identification; semi-physical modeling; neural nets. 1. INTRODUCTION System identification as it is described by, e.g., (Ljung, 1987) is a well established methodology for designing mathematical models of dynamical systems using input-output data. After experiment design, the problem can be split into two different parts: model structure selection followed by parameter estimation. While various least-squares type of algorithms are predominant for parameter estimation, one has a large spectrum of model structures to choose between. Physically parameterized modeling (where all the physical insight about the system is condensed into the model)..

Does mathematics anxiety moderate the effect of problem difficulty on cognitive effort?

Abstract A negative relationship between mathematics anxiety (MA) and mathematics performance is well documented. One suggested explanation for this relationship is that MA interferes with the cognitive processes needed when solving mathematics problems. A demand for using more cognitive effort (e.g., when performing harder mathematics problems), can be traced as an increase in pupil dilation during the performance. However, we lack knowledge of how MA affects this relationship between the problem difficulty and cognitive effort. This study investigated, for the first time, if MA moderates the effect of arithmetic (i.e., multiplication) problem difficulty on cognitive effort. Thirty-four university students from Norway completed multiplication tasks, including three difficulty levels of problems, while their cognitive effort was also measured by means of pupil dilation using an eye tracker. Further, the participants reported their MA using a questionnaire, and arithmetic competence, general intelligence, and working memory were measured with paper-pencil tasks. A linear mixed model analysis showed that the difficulty level of the multiplication problems affected the cognitive effort so that the pupil dilated more with harder multiplication problems. However, we did not find a moderating effect of MA on cognitive effort, when controlling for arithmetic competence, general intelligence, and working memory. This suggests that MA does not contribute to cognitive effort when solving multiplication problems