Identification and validation of calmodulin as a binding protein of an anti-proliferative small molecule 3,4-dihydroisoquinolinium salt

IHY-153 (2-(2,5-difluorobenzyl)-3,4-dihydro-5-(10-hydroxydecyl)-6-methoxy-1-undecylisoquinolinium bromide) was recently discovered as a small molecule that potently inhibits proliferation of tumor cells by inducing cell-cycle arrest at G0-G1 phase. To investigate the basis of anti-proliferative activity of IHY-153, cellular binding proteins of biotinyl-IHY-153 were screened using T7 phage displayed human cDNA libraries. Calmodulin-expressing phage specifically bound to immobilized IHY-153 in a Ca(2+) -dependent manner. The interaction between IHY-153 and Ca(2+) /CaM was validated through phage competition binding assays, surface plasmon resonance analysis, and molecular modeling. IHY-153 induced sustained phosphorylation of extracellular signal-regulated kinase (ERK) 1/2 and subsequently increased p21(WAF1) expression in colon cancer cells. These results demonstrate that IHY-153, a novel small molecule, targets Ca(2+) /CaM and indicate that this compound functions as an anti-proliferative agent by influencing Ca(2+) /CaM-dependent signal transduction.     

Spline-based feature curves from point-sampled geometry

Defining sharp features in a 3D model facilitates a better understanding of the surface and aids geometric processing and graphics applications, such as reconstruction, filtering, simplification, reverse engineering, visualization, and non-photo realism. We present a robust method that identifies sharp features in a point-based model by returning a set of smooth spline curves aligned along the edges. Our feature extraction leverages the concepts of robust moving least squares to locally project points to potential features. The algorithm processes these points to construct arc-length parameterized spline curves fit using an iterative refinement method, aligning smooth and continuous curves through the feature points. We demonstrate the benefits of our method with three applications: surface segmentation, surface meshing and point-based compression.     

A personalized counseling system using case-based reasoning with neural symbolic feature weighting (CANSY)

In this article, we introduce a personalized counseling system based on context mining. As a technique for context mining, we have developed an algorithm called CANSY. It adopts trained neural networks for feature weighting and a value difference metric in order to measure distances between all possible values of symbolic features. CANSY plays a core role in classifying and presenting most similar cases from a case base. Experimental results show that CANSY along with a rule base can provide personalized information with a relatively high level of accuracy, and it is capable of recommending appropriate products or services. An erratum to this article can be found at     

A geodesic-active-contour-based variational model for short-axis cardiac MR image segmentation

We propose a novel geodesic-active-contour-based (GAC-based) variational model that uses two level-set functions to segment the right and left ventricles and the epicardium in short-axis magnetic resonance (MR) images. For the right ventricle, the myocardial wall is typically very thin and hard to identify using the resolution of existing MR scanners. We propose to use two level sets to identify both the endocardial wall by pushing away one level-set function from another, in the setting of the edge-driven GAC model with a new edge detection function. Existing edge detection functions have strict restrictions on the location of initial contours. We develop a new edge detection function that relaxes this restriction and propose an iterative method that uses a sequence of edge detection functions to minimize the energy of our model successively. Experimental results are presented to validate the effectiveness of the proposed model.     

Stabilising agent design for the control of interconnected systems

This article presents a new control design strategy for stabilising large-scale interconnected systems operating in semi-automatic control modes. The large-scale system is modelled by subsystems connected to each other in an arbitrary configuration. Each subsystem is regulated by a dedicated multivariable controller that also allows for a manual control mode. The notion of asymptotically positive realness constraint (APRC) is introduced and applied for deriving the interconnection stabilisability condition in the time domain. The interactions between subsystems are taken into consideration in the stability condition. The APRC is subsequently employed in the so-called stabilising agent to accommodate the closed-loop control and man-in-the-loop coexistence. The multipliers of the APRC quadratic supply rate are updated on-the-fly to ensure that the constraint satisfaction of stabilising agents is recursively feasible. The stabilising agents are developed independently from the control law under the same auspice controller. Due to this independence, operational errors from the manual control adjustments, that may destabilise the control systems, can be avoided. The decentralised agents render stabilising bounds for the manipulated variables in the automatic control mode, and at the same time, provide warning signals and manipulation guidance for the operators to prevent possible plant-wide destabilisation in the manual control mode. Our main results are illustrated through numerical simulations for an industrial modular system.     

Design and analysis of a multi-candidate selection scheme for greedy routing in wireless sensor networks

Sleep and wake-up scheduling of sensor nodes is an efficient solution to prolong the network lifetime. However, existing scheduling algorithms may significantly decrease the number of active nodes so that the network may be intermittently connected. In such networks, traditional geographic routing protocols are inappropriate to obtain low latency routes due to route discovery and data forwarding latency. In this paper, we propose a novel multi-candidate selection (MCS) scheme for greedy routing that makes the best effort to find minimum latency routes in the sensor networks. In MCS, each source node sends an RREQ to a list of first wake-up forwarder candidates and selects a route with minimum estimated delivery latency based on their replies. The route found by MCS may be longer than that of distance-based greedy forwarding (DGF) (Finn, 1987). Hence, we introduce a latency-adaptive distance-based multi-candidate selection scheme for greedy forwarding to find routes with a small number of hops and acceptable delivery latency. Probabilistic analysis and simulation results demonstrate that MCS increases the routing performance significantly compared with DGF and ODML (Su et al., 2008) in terms of delivery latency.     

Shape design optimization of SPH fluid–structure interactions considering geometrically exact interfaces

Fluid–structure interaction problems are solved by applying a smoothed particle hydrodynamics method to a weakly compressible Navier–Stokes equation as well as an equilibrium equation for geometrically nonlinear structures in updated Lagrangian formulation. The geometrically exact interface, consisting of B-spline basis functions and the corresponding control points, includes the high order geometric information such as tangent, normal, and curvature. The exactness of interface is kept by updating the control points according to the kinematics obtained from response analysis. Under the scheme of explicit time integration and updated Lagrangian formulation, the required shape design velocity should be updated at every single step. The update scheme of design velocity is developed using the sensitivity of physical velocity. The developed sensitivity analysis method is further utilized in gradient-based shape optimization problems and turns out to be very efficient since the interaction pairs of particles determined in the response analysis can be directly utilized.     

Numerical method for shape optimization using T-spline based isogeometric method

Numerical methods for shape design sensitivity analysis and optimization have been developed for several decades. However, the finite-element-based shape design sensitivity analysis and optimization have experienced some bottleneck problems such as design parameterization and design remodeling during optimization. In this paper, as a remedy for these problems, an isogeometric-based shape design sensitivity analysis and optimization methods are developed incorporating with T-spline basis. In the shape design sensitivity analysis and optimization procedure using a standard finite element approach, the design boundary should be parameterized for the smooth variation of the boundary using a separate geometric modeler, such as a CAD system. Otherwise, the optimal design usually tends to fall into an undesirable irregular shape. In an isogeometric approach, the NURBS basis function that is used in representing the geometric model in the CAD system is directly used in the response analysis, and the design boundary is expressed by the same NURBS function as used in the analysis. Moreover, the smoothness of the NURBS can allow the large perturbation of the design boundary without a severe mesh distortion. Thus, the isogeometric shape design sensitivity analysis is free from remeshing during the optimization process. In addition, the use of T-spline basis instead of NURBS can reduce the number of degrees of freedom, so that the optimal solution can be obtained more efficiently while yielding the same optimum design shape.     

Performance of an ultra-compact biofilm reactor treating domestic and synthetic wastewaters

The performance of an ultra-compact biofilm reactor (UCBR) treating domestic wastewater (DWW) collected from a local water reclamation plant; and gradually shifting to a mono-type carbon source synthetic wastewater (SWW) combined with DDW (CWW) and finally SWW; was investigated in this study. The total COD concentrations of influent DWW and CWW/SWW were 413.6 ± 80.8 mg/L and 454.9 ± 51.3 mg/L, respectively. The UCBR was able to achieve average total COD removal efficiencies of 70 ± 10% and 80 ± 4% for DWW and SWW respectively. The total COD concentrations of the effluent of DWW and CWW/SWW were 122.5 ± 44.4 mg/L and 89.7 ± 10.3 mg/L, respectively. These observations suggested that heterotrophs in the UCBR system were able to better assimilate and remove carbon of mono-type SWW compared to diverse carbon sources such as DWW; although the influent soluble COD concentrations of the SWW were higher than those of the DWW. However, the effluent NH(4)(+)-N concentrations for both types of wastewater were rather similar, <3.0 mg/L; although the influent NH(4)(+)-N concentrations of the DWW were 1.5 times those of the SWW.