Reasoning Processes in Propositional Logic

We conducted a computer-based psychological experiment in which a random mix of 40 tautologies and 40 non-tautologies were presented to the participants, who were asked to determine which ones of the formulas were tautologies. The participants were eight university students in computer science who had received tuition in propositional logic. The formulas appeared one by one, a time-limit of 45 s applied to each formula and no aids were allowed. For each formula we recorded the proportion of the participants who classified the formula correctly before timeout (accuracy) and the mean response time among those participants (latency). We propose a new proof formalism for modeling propositional reasoning with bounded cognitive resources. It models declarative memory, visual memory, working memory, and procedural memory according to the memory model of Atkinson and Shiffrin and reasoning processes according to the model of Newell and Simon. We also define two particular proof systems, T and NT, for showing propositional formulas to be tautologies and non-tautologies, respectively. The accuracy was found to be higher for non-tautologies than for tautologies (p < .0001). For tautologies the correlation between latency and minimum proof length in T was .89 and for non-tautologies the correlation between latency and minimum proof length in NT was .87.     

A 3D-QSAR Study of Analogs of (Z)-5-Decenyl Acetate, A Pheromone Component of the Turnip Moth, Agrotis segetum

The methodology of three-dimensional quantitative structure-activity relationships (3D-QSAR) has been employed to develop a quantitative model for the relationship between single-cell activities for a large set of analogs of (Z)-5-decenyl acetate, a pheromone component of the turnip moth, Agrotis segetum, and the structural characteristics of the analogs. The method of partial least squares (PLS) projections to latent structures has been used to delineate the relationships between measured activities and chemical structures. The training set includes 42 compounds, and a test set of seven molecules has been employed to assess the predictivity of the model. The model derived is able to calculate the activities of the seven test compounds with an average unsigned error of 0.3, close to the experimental error. Contour maps of regression-like coefficients from transformed PLS components display electronic and nonbonded features of high importance for the observed variation in the activities of compounds in the training set. The contours for the nonbonded (van der Waals) field support previous conclusions that the interaction between the pheromone component and its receptor is characterized by a high steric complementarity.     

Optimization of boiler control to improve the load-following capability of power-plant units

The capability to perform fast load changes has been an important issue in the power market, and will become increasingly more so due to the increasing commercialization of the European power market. An optimizing control system for improving the loadfollowing capability of power-plant units has therefore been developed. The system is implemented as a complement, producing control signals to be added to those of the existing boiler control system, a concept which has various practical advantages in terms of implementation and commissioning. The optimizing control system takes account of the multivariable and load-dependent nonlinear characteristics of the boiler process, as a scheduled LQG controller with feedforward action is utilized. The LQG controller improves the control of critical process variables, making it possible to increase the load-following capability of a specific plant. Field tests on a 265 MW coal-fired power-plant unit reveals that the maximum allowable load gradient that can be imposed on the plant, can be increased from 4 to 8 MW/min.     

Prediction of the receptor conformation for iGluR2 agonist binding: QM/MM docking to an extensive conformational ensemble generated using normal mode analysis

Highly flexible proteins constitute a significant challenge in molecular docking within the field of drug design. Depending on the efficacy of the bound ligand, the ligand-binding domain (LBD) of the ionotropic glutamate receptor iGluR2 adopts markedly different degrees of domain closure due to large-scale domain movements. With the purpose of predicting the induced domain closure of five known iGluR2 partial to full agonists we performed a validation study in which normal mode analysis (NMA) was employed to generate a 25-membered ensemble of iGluR2 LBD structures with gradually changing domain closures, followed by accurate QM/MM docking to the ensemble. Based on the docking scores we were able to predict the correct optimal degree of closure for each ligand within 1–3° deviation from the experimental structures. We demonstrate that NMA is a useful tool for reliable ensemble generation and that we are able to predict the ligand induced conformational change of the receptor through docking to such an ensemble. The described protocol expands and improves the information that can be obtained from computational docking when dealing with a flexible receptor.     

Identification of a putative binding site for 5-alkyl-benzothiadiazides in the AMPA receptor dimer interface

Crystal structures of three different allosteric modulators co-crystallized with the iGluR2 ligand-binding domain are currently available. The modulators, cyclothiazide, aniracetam and CX614, bind at overlapping binding sites in the dimer interface between two iGluR2 subunits. However, pharmacological data indicate that there are one or more additional binding sites for this class of compounds. Based on differences in structure-activity relationship data we show that 5-alkyl-benzothiadiazide (5ABTD) modulators and a series of close analogs of cyclothiazide, despite having a common core structure, do not have the same binding site. In the present work, a new potential binding site for allosteric modulators has been identified in the dimer interface of the iGluR2 ligand-binding domain. By comparing different iGluR2 crystal structures including different co-crystallized agonists, this cavity is shown to be a structurally conserved part of the dimer interface. The cavity is characterized with respect to shape and potential favorable interactions with ligands and docking is used to find a reasonable binding mode for the core structure of the 5ABTDs. The extensive structure-activity data available for this series of compounds are in agreement with the proposed binding mode, supporting the conclusion that the identified cavity most likely is the binding site for the 5ABTDs.     

Alkyl substitution in terminal chain of (Z)-5-decenyl acetate,a pheromone component of turnip moth,Agrotis segetum. Synthesis,single-sensillum recordings,and structure-activity relationships

Structure-activity relationships for 6-, 7-, 8-, and 9-alkyl substituted analogs of (Z)-5-decenyl acetate, a pheromone component of the turnip moth,Agrotis segetum, have been studied by electrophysiological single-sensillum recordings, and interpreted in terms of a receptor-interaction model. The compounds were prepared by alkenyl cuprate reactions with,-unsaturated carbonyl derivatives or alkyl halides. The electrophysiological results indicate steric repulsive interactions between the alkyl groups and the receptor in all the positions studied. This demonstrates a high complementarity between the receptor and the terminal alkyl chain.Schiff., Lepidoptera: Noctuidae.     

Maximum Margin Multisurface Support Tensor Machines with application to image classification and segmentation

Virtually all previous classifier models take vectors as inputs, performing directly based on the vector patterns. But it is highly necessary to consider images as matrices in real applications. In this paper, we represent images as second order tensors or matrices. We then propose two novel tensor algorithms, which are referred to as Maximum Margin Multisurface Proximal Support Tensor Machine (M³PSTM) and Maximum Margin Multi-weight Vector Projection Support Tensor Machine (M³VSTM), for classifying and segmenting the images. M³PSTM and M³VSTM operate in tensor space and aim at computing two proximal tensor planes for multisurface learning. To avoid the singularity problem, maximum margin criterion is used for formulating the optimization problems. Thus the proposed tensor classifiers have an analytic form of projection axes and can achieve the maximum margin representations for classification. With tensor representation, the number of estimated parameters is significantly reduced, which makes M³PSTM and M³VSTM more computationally efficient when handing the high-dimensional datasets than applying the vector representations based methods. Thorough image classification and segmentation simulations on the benchmark UCI and real datasets verify the efficiency and validity of our approaches. The visual and numerical results show M³PSTM and M³VSTM deliver comparable or even better performance than some state-of-the-art classification algorithms.