Molecular docking and molecular dynamics simulation studies of GPR40 receptor–agonist interactions

In order to explore the agonistic activity of small-molecule agonists to GPR40, AutoDock and GROMACS software were used for docking and molecular dynamics studies. A molecular docking of eight structurally diverse agonists (six carboxylic acids (CAs) agonist, and two non-carboxylic acids (non-CAs) agonist) was performed and the differences in their binding modes were investigated. Moreover, a good linear relationship based on the predicted binding affinities (pK_i) determined by using AutoDock and experimental activity values (pEC50) was obtained. Then, the 10 ns molecular dynamics (MD) simulations of three obtained ligand–receptor complexes embedded into the phospholipid bilayer were carried out. The position fluctuations of the ligands located inside the transmembrane domain were explored, and the stable binding modes of the three studied agonists were determined. Furthermore, the residue-based decomposition of interaction energies in three systems identified several critical residues for ligand binding.     

Antiviral drug acyclovir exhibits antitumor activity via targeting βTrCP1: Molecular docking and dynamics simulation study

The critical role of βTrCP1 in cancer development makes it a discerning target for the development of small drug like molecules. Currently, no inhibitor exists that is able to target its substrate binding site. Through molecular docking and dynamics simulation assays, we explored the comparative binding pattern of βTrCP1-WD40 domain with ACV and its phospho-derivatives (ACVMP, ACVDP and ACVTP). Consequently, through principal component analysis, βTrCP1-ACVTP was found to be more stable complex by obscuring a reduced conformational space than other systems. Thus based on the residual contribution and hydrogen bonding pattern, ACVTP was considered as a noteworthy inhibitor which demarcated binding in the cleft formed by βTrCP1-WD40 specific β-propeller. The outcomes of this study may provide a platform for rational design of specific and potent inhibitor against βTrCP1, with special emphasis on anticancer activity.     

SpinA-AT and DNMR-SIM — two new PC-programs for analysis and simulation of NMR spectra

Two novel PC programs for simulation and interpretation of the NMR spectra SpinA-AT and DNMR-SIM, are introduced and demonstrated with examples. SpinA-AT covers the classical literature on explicit solutions for transition frequencies and intensities given for 2–4 spins. DNMR-SIM is a simulation program for dynamic NMR spectra.     

New efficient method for dynamic modeling and simulation of flexible multibody systems moving in plane

Efficient, precise dynamic analysis for general flexible multibody systems has become a research focus in the field of flexible multibody dynamics. In this paper, the finite element method and component mode synthesis are introduced to describe the deformations of the flexible components, and the dynamic equations of flexible bodies moving in plane are deduced. By combining the discrete time transfer matrix method of multibody system with these dynamic equations of flexible component, the transfer equations and transfer matrices of flexible bodies moving in plane are developed. Finally, a high-efficient dynamic modeling method and its algorithm are presented for high-speed computation of general flexible multibody dynamics. Compared with the ordinary dynamics methods, the proposed method combines the strengths of the transfer matrix method and finite element method. It does not need the global dynamic equations of system and has the low order of system matrix and high computational efficiency. This method can be applied to solve the dynamics problems of flexible multibody systems containing irregularly shaped flexible components. It has advantages for dynamic design of complex flexible multibody systems. Formulations as well as a numerical example of a multi-rigid-flexible-body system containing irregularly shaped flexible components are given to validate the method.     

Teaching about the electricity supply industry—The development and evaluation of a computer based teaching package for schools

This paper describes the development of a computer based teaching package for use at upper secondary school level. The project was funded by Understanding Electricity, which is the educational service of the Electricity Council. The principles of electricity supply are integral to several subject areas in the school curriculum. The purpose of this package is firstly to provide well-resourced teaching material to cover these curriculum needs and secondly to provide an industrial context in which to apply the technical skills acquired in individual subject disciplines. The package is based on a computer simulation that enables the user to experience the problems of running an electricity supply system. The use of the computer in this way enables a powerful investigative approach to be adopted in the classroom.The paper begins by discussing the background to joint industry/education projects and the procedural models that have emerged. It continues by presenting the project history, the development team model used and the educational concepts of electricity supply that provide the background to the computer simulation. Attention is drawn to the ways in which the finished package matches the industrial resource material to curriculum needs.Teachers were involved from the beginning, both in the development of the computer software and in the production of curriculum material. They also undertook the design of investigations in specific subject areas—economics, physics, mathematics and geography. This paper presents the results of field trials and discusses the problems of evaluating and marketing educational material.Although the detail of this paper deals specifically with educational material developed for the Electricity Council, the principles discussed have significance for approaches to the production of educational software generally.     

Computational performance of basic state reduction based dynamic programming algorithms for bi-objective 0–1 knapsack problems

This paper studies a group of basic state reduction based dynamic programming (DP) algorithms for the multi-objective 0–1 knapsack problem (MKP), which are related to the backward reduced-state DP space (BRDS) and forward reduced-state DP space (FRDS). The BRDS is widely ignored in the literature because it imposes disadvantage for the single objective knapsack problem (KP) in terms of memory requirements. The FRDS based DP algorithm in a general sense is related to state dominance checking, which can be time consuming for the MKP while it can be done efficiently for the KP. Consequently, no algorithm purely based on the FRDS with state dominance checking has ever been developed for the MKP. In this paper, we attempt to get some insights into the state reduction techniques efficient to the MKP. We first propose an FRDS based algorithm with a local state dominance checking for the MKP. Then we evaluate the relative advantage of the BRDS and FRDS based algorithms by analyzing their computational time and memory requirements for the MKP. Finally different combinations of the BRDS and FRDS based algorithms are developed on this basis. Numerical experiments based on the bi-objective KP instances are conducted to compare systematically between these algorithms and the recently developed BRDS based DP algorithm as well as the existing FRDS based DP algorithm without state dominance checking.     

Identification and in vitro evaluation of new leads as selective and competitive glycogen synthase kinase-3β inhibitors through ligand and structure based drug design

Glycogen synthase kinase-3β elicits multi-functional effects on intracellular signaling pathways, thereby making the kinase a therapeutic target in multiple pathologies. Hence, it is important to selectively inhibit GSK-3β over structurally and biologically similar targets, such as CDK5. The current study was designed to identify and evaluate novel ATP-competitive GSK-3β inhibitors. The study was designed to identify new leads by ligand based drug design, structure based drug design and in vitro evaluation. The best validated pharmacophore model (AADRRR) identified using LBDD was derived from a dataset of 135 molecules. There were 357 primary hits within the SPECS database using this pharmacophore model. A SBDD approach to the GSK-3β and CDK5 proteins was applied to all primary hits, and 5 selective inhibitors were identified for GSK-3β. GSK-3β and CDK5 in vitro kinase inhibition assays were performed with these molecules to confirm their selectivity for GSK-3β. The molecules showed IC₅₀ values ranging from 0.825 μM to 1.116 μM and were 23- to 57-fold selective for GSK-3β. Of all the molecules, molecule 3 had the lowest IC₅₀ value of 0.825 μM. Our research identified molecules possessing benzothiophene, isoquinoline, thiazolidinedione imidazo-isoquinoline and quinazolinone scaffolds. Potency of these molecules may be due to H-bond interaction with backbone residues of Val135, Asp133 and side chain interaction with Tyr134. Selectivity over CDK5 may be due to side chain interactions with Asp200, backbone of Val61, ionic interaction with Lys60 and π-cationic interaction with Arg141. These selective molecules were also exhibited small atom hydrophobicity and H-bond interaction with water molecule.     

Erratum to: New efficient method for dynamic modeling and simulation of flexible multibody systems moving in plane

Efficient, precise dynamic analysis for general flexible multibody systems has become a research focus in the field of flexible multibody dynamics. In this paper, the finite element method and component mode synthesis are introduced to describe the deformations of the flexible components, and the dynamic equations of flexible bodies moving in plane are deduced. By combining the discrete time transfer matrix method of multibody system with these dynamic equations of flexible component, the transfer equations and transfer matrices of flexible bodies moving in plane are developed. Finally, a high-efficient dynamic modeling method and its algorithm are presented for high-speed computation of general flexible multibody dynamics. Compared with the ordinary dynamics methods, the proposed method combines the strengths of the transfer matrix method and finite element method. It does not need the global dynamic equations of system and has the low order of system matrix and high computational efficiency. This method can be applied to solve the dynamics problems of flexible multibody systems containing irregularly shaped flexible components. It has advantages for dynamic design of complex flexible multibody systems. Formulations as well as a numerical example of a multi-rigid-flexible-body system containing irregularly shaped flexible components are given to validate the method.     

A framework for development and evaluation of a dynamic subchannel allocation scheme in an OFDMA system

This paper presents a framework for allocating radio resources to the Access Points (APs) introducing an Access Point Controller (APC). Radio resources can be either time slots or subchannels. The APC assigns subchannels to the APs using a dynamic subchannel allocation scheme. The developed framework evaluates the dynamic subchannel allocation scheme for a downlink multicellular Orthogonal Frequency Division Multiple Access (OFDMA) system. In the considered system, each AP and the associated Mobile Terminals (MTs) are not operating on a frequency channel with fixed bandwidth, rather the channel bandwidth for each AP is dynamically adapted according to the traffic load. The subchannels assignment procedure is based on quality estimations due to the interference measurements and the current traffic load. The traffic load estimation is realized with the measurement of the utilization of the assigned radio resources. The reuse partitioning for the radio resources is done by estimating mutual Signal to Interference Ratio (SIR) of the APs. The developed dynamic subchannel allocation ensures Quality of Service (QoS), better traffic adaptability, and higher spectrum efficiency with less computational complexity.

Designing efficient text presentation of multimedia CAI — the evaluation of dynamic text patterns and the negative repetition effect on memory

The present study investigated the effects of presenting the same material with dynamic text patterns in multimedia CAI. Four commonly used dynamic text patterns were utilized to present 10 basic concepts of mechanics. Two hundred college students selected at random from two universities were randomly assigned to one of 10 experimental conditions created by combining five text patterns (i.e. four dynamic patterns and a static presentation) and two types of repetition conditions (i.e. text presented once or three times). Their working memory capacity was assessed via a reading span test to ensure that the subjects were able to process and retain information at about the same level. Differences were found on the affective and perceptual evaluation of the dynamic text pattern. After partialing out the effect of working memory capacity, we found that, on the memory test, participants performed worse in conditions where learning material was presented three times. Interpretations and implications in designing CAI are discussed.     

Agent–cellular automata model for the dynamic fluctuation of EV traffic and charging demands based on machine learning algorithm

Neural Computing and Applications - Electric vehicles (EV) comprise one of the foremost components of the smart grid and tightly link the power system with the road network. Spatial and temporal...     

Merging of IRS LISS III and PAN data—evaluation of various methods for a predominantly agricultural area

The present study evaluates six different methods of data merging using Indian Remote Sensing Satellite (IRS) panchromatic (PAN; high spatial resolution) and Linear Imaging Self-scanning Sensor (LISS) III (high spectral resolution) data for a predominantly agricultural area including a potato research farm in Jalandhar, Punjab, India. The methods used were intensity–hue–saturation (IHS), principal component substitution (PCS), high pass filter (HPF), Brovey, synthetic variable ratio (SVR) and cubic spline wavelet (CSW) technique. The LISS III data, which were of the same date as the PAN data, were registered to the PAN data by identifying ground control points (GCPs) and using the cubic convolution resampling method. Merged data products were generated using the above-mentioned six techniques. The merged products were evaluated on three aspects, i.e. visually, statistically and by comparing classification accuracy. The study could help to rank the suitability of various merging methods for agricultural land-use applications. The HPF, SVR and CSW merging methods were more accurate than the commonly used IHS, PCS and Brovey methods. The PCS was found to be least accurate among all.     

CASQUS: A new simulation tool for coupled 3D finite element modeling of tectonic and surface processes based on ABAQUS™ and CASCADE

CASQUS is a numerical simulation tool to model the feedback mechanism between surface and tectonic processes. It includes the surface processes model CASCADE into the finite element solver ABAQUS/Standard?. The finite element method allows for geomechanical simulations of the subsurface with geometrically complex structures in 3D. Additionally, in the commercial software ABAQUS? various types of rheological behavior are already implemented. CASCADE simulates erosion and sedimentation as the combination of fluvial transport and hillslope processes. For the integration of CASCADE into ABAQUS/Standard? an Arbitrary Lagrangian–Eulerian modeling technique is used, which makes a coupled and automated simulation possible. Two benchmark models that are easy to reproduce demonstrate the functionality of CASQUS. Our tool aims at a better understanding of the feedback between mass redistribution at the Earth's surface and processes within a heterogenous subsurface, and at a quantification of the involved processes.     

A novel offline programming approach of robot welding for multi-pipe intersection structures based on NSGA-Ⅱ and measured 3D point-clouds

The multi-pipe intersection structure in form of co-main pipe is widely used in various industries. To improve its welding quality and efficiency, this paper is devoted to proposing an offline programming approach to the robot trajectory based on NSGA-Ⅱ and measured 3D point-clouds. First, considering the existence of deviation between the actual workpiece and its ideal model, this paper selects the actual 3D point-cloud of weld seam as the research object and extracts its feature points by combining the characteristic of the spatial curve, which can reduce the data density while preserving their geometric characteristics. Second, to ensure the continuity of motion parameters while taking the calculation and fitting accuracy into account, the cubic NURBS is applied to fit the actual weld position, and a fast-adaptive fitting nodes configuration scheme is designed according to the variation characteristics of the spatial curve and the fitting error restriction. Third, this paper introduces a trajectory adaptive discretization method based on the chord error constraint for robot program generation, and establishes an optimization model of the robot welding trajectory based on NSGA-Ⅱ frame, which gives the robot joint's motion trajectories with optimal welding quality and cable twisting fluctuation. Finally, the experiments are designed to verify the correctness of the aforementioned approach.     

Message classification as a basis for studying command and control communications—an evaluation of machine learning approaches

In military command and control, success relies on being able to perform key functions such as communicating intent. Most staff functions are carried out using standard means of text communication. Exactly how members of staff perform their duties, who they communicate with and how, and how they could perform better, is an area of active research. In command and control research, there is not yet a single model which explains all actions undertaken by members of staff well enough to prescribe a set of procedures for how to perform functions in command and control. In this context, we have studied whether automated classification approaches can be applied to textual communication to assist researchers who study command teams and analyze their actions. Specifically, we report the results from evaluating machine leaning with respect to two metrics of classification performance: (1) the precision of finding a known transition between two activities in a work process, and (2) the precision of classifying messages similarly to human researchers that search for critical episodes in a workflow. The results indicate that classification based on text only provides higher precision results with respect to both metrics when compared to other machine learning approaches, and that the precision of classifying messages using text-based classification in already classified datasets was approximately 50%. We present the implications that these results have for the design of support systems based on machine learning, and outline how to practically use text classification for analyzing team communications by demonstrating a specific prototype support tool for workflow analysis.     

Can human allergy drug fexofenadine,an antagonist of histamine (H1) receptor,be used to treat dog and cat? Homology modeling,docking and molecular dynamic Simulation of three H1 receptors in complex with fexofenadine

Fexofenadine, a potent antagonist to human histamine 1 (H₁) receptor, is a non-sedative third generation antihistamine that is widely used to treat various human allergic conditions such as allergic rhinitis, conjunctivitis and atopic dermatitis. Encouragingly, it’s been successfully used to treat canine atopic dermatitis, this supports the notion that it might have a great potential for treating other canine allergic conditions and other mammal pets such as dog. Regrettably, while there is a myriad of studies conducted on the interactions of antihistamines with human H₁ receptor, the similar studies on non-human pet H₁ are considerably scarce. The published studies using the first and second generation antihistamines drugs have shown that the antihistamine response is varied and unpredictable. Thus, to probe its efficacy on pet, the homology models of dog and cat H₁ receptors were built based on the crystal structure of human H₁ receptor bound to antagonist doxepin (PDB 3RZE) and fexofenadine was subsequently docked to human, dog and cat H₁ receptors. The docked complexes are then subjected to 1000 ns molecular dynamics (MD) simulations with explicit membrane. Our calculated MM/GBSA binding energies indicated that fexofenadine binds comparably to the three receptors; and our MD data also showed the binding poses, structural and dynamic features among three receptors are very similar. Therefore, our data supported the application of fexofenadine to the H₁ related allergic conditions of dog and cat. Nonetheless, subtle systemic differences among human, dog and cat H₁ receptors were also identified. Clearly, there is still a space to develop a more selective, potent and safe antihistamine alternatives such as Fexofenadine for dog or cat based on these differences. Our computation approach might provide a fast and economic way to predict if human antihistamine drugs can also be safely and efficaciously administered to animals.     

Modulated T-complex protein 1 ζ and peptidyl-prolyl cis-trans isomerase B are two novel indicators for evaluating lymph node metastasis in colorectal cancer: Evidence from proteomics and bioinformatics

Lymph node metastasis (LNM) is an important indicator for systematic therapy, which could increase the survival of colorectal cancer (CRC) patients. However, effective clinical evaluation for LNM is still absent to date. In this study, protein expression profiles of CRC tissues were compared between patients with and without LNM. Based on average expression level, 12 proteins were found to be differentially expressed in the CRC tissues with LNM, whose discrimination reliability was confirmed by PCA. With stepwise linear discriminant analysis, T-complex protein 1 ζ subunit and peptidyl-prolyl cis-trans isomerase B (PPIB) were identified as two main contributors for separating CRC tissues with positive LNM from those negative ones in both original-grouped and cross-validated-grouped cases, which was also supported in subsequent linear support vector machine analysis. In addition, the expression alterations of the two proteins were verified by Western blot and immunohistochemistry. Functional studies also confirmed the role of PPIB in migration and invasion of cancer cells. Taken together, the down-regulated T-complex protein 1 ζ subunit and up-regulated PPIB were identified as two promising indicators for the clinical evaluation of LNM in CRC patients.     

DFT simulation towards evaluation the molecular structure and properties of the heterogeneous C16Mg8O8 nano–cage as selective nano–sensor for H2 and N2 gases

Adsorption of hydrogen (H₂) and nitrogen (N₂) molecules was analyzed on a new fullerene–like C₁₆Mg₈O₈ nano–cage, composed of magnesium, oxygen, and carbon, using density functional theory. A detailed analysis of the energy, geometry, and electronic structure of various H₂ and N₂ adsorptions on the cluster surface was performed. The adsorption energies of H₂ and N₂ were estimated to ranging from −0.16 to −0.52 eV, respectively. The most stable adsorption configurations were those in which the H or N atoms of the adsorbates were located near the Mg atom of the cluster surface at different sides. It was found that the heterogeneous C₁₆Mg₈O₈ nano–cluster selectively act against the H₂ and N₂ gaseous molecules. The electrical conductivity of the cluster, arising from HOMO/LUMO energy gap, was more sensitive to N₂ gaseous molecule rather than H₂ one, indicating that the heterogeneous C₁₆Mg₈O₈ nano–cage may be potential nano–sensor for N₂ molecule. These findings were specified by analyzing the characteristics in the electron density of states (DOS).