PNC-27, a Chimeric p53-Penetratin Peptide Binds to HDM-2 in a p53 Peptide-like Structure, Induces Selective Membrane-Pore Formation and Leads to Cancer Cell Lysis

PNC-27, a 32-residue peptide that contains an HDM-2 binding domain and a cell-penetrating peptide (CPP) leader sequence kills cancer, but not normal, cells by binding to HDM-2 associated with the plasma membrane and induces the formation of pores causing tumor cell lysis and necrosis. Conformational energy calculations on the structure of PNC-27 bound to HDM-2 suggest that 1:1 complexes form between PNC-27 and HDM-2 with the leader sequence pointing away from the complex. Immuno-scanning electron microscopy was carried out with cancer cells treated with PNC-27 and decorated with an anti-PNC-27 antibody coupled to 6 nm gold particles and an anti-HDM-2 antibody linked to 15 nm gold particles. We found multiple 6 nm- and 15 nm-labeled gold particles in approximately 1:1 ratios in layered ring-shaped structures in the pores near the cell surface suggesting that these complexes are important to the pore structure. No pores formed in the control, PNC-27-treated untransformed fibroblasts. Based on the theoretical and immuno-EM studies, we propose that the pores are lined by PNC-27 bound to HDM-2 at the membrane surface with the PNC-27 leader sequence lining the pores or by PNC-27 bound to HDM-2

Fractional Fourier transform and its optical applications

A definition of fractional Fourier transform as the generalization of ordinary Fourier transform is given at the beginning. Then due to optical reasons the fractional transform of a so-called chirp functions is considered in both theory and practical simulations. Because of a quadratic phase factor which is common in the definition of the transform and some optical concepts, a comparison between these concepts such as Fresnel diffraction, spherical wave, thin lens and free space propagation and the transform has been done. Finally an optical setup for performing the fractional transform is introduced

REAL-TIME CLASSIFIER BASED ON ADAPTIVE COMPETITIVE SELF-ORGANIZING ALGORITHM

This research proposes a novel Adaptive Competitive Self-organizing model, shortly named ACS, with applicability for real-time clustering and vector quantization. The model is designed based on sets of Ordinary Differential Equations (ODE’s) and free of any external controls. These properties make it suitable for hardware implementation and real-time applications. This classifier considers as unsupervised Neural Network (NN) since it doesn’t have any prior knowledge on input pattern’s classes. The design of this classifier is based on developing an energy function, constructed based on the sum of Lorentzian functions, where they correspond to the clusters of similar input patterns. The defined energy function is a form of Lyapunov function, and it guarantees trajectories of weights, which are labeling a set of similar input pattern will finalize in a set of isolated equilibrium points. The stability of those equilibrium points is investigated. Valleys on the surface are the representation of clusters, and they are defined with their parameters such as depth, width and vigilance parameter. These control parameters are effective on convergence speed, the accuracy of labeling, etc. We are going to study the level of effectiveness of those parameters on clustering assignments with different illustrations. All these parameters need to be dynamically adjusted in the model, resulting in the highest level of self-adjustment. To comprehend the way clustering occurs in ACS, two main processes are utilized to pattern clustering: learning and recalling. In learning phase the surface of energy modifies as sets of weights are self-adjusting themselves in a competitive manner to label/cluster an exposed input pattern on the surface of energy function, while recalling phase is as a newly exposed input pattern accommodate itself into existence similar cluster, which has the shortest Euclidean distance from it. The effectiveness of ACS model is demonstrated with implementing it on both real and artificial data sets as well as comparing with other well-known clustering methods. ACS method showed a better clustering performance in some categories and an overall comparable rendition. System dynamics is simulated with two optimizers Gradient Descent (GD) and Adaptive Momentum Gradient Descent (AMGD), in cooperation with a competition mechanism based on the Lotka-Volterra competition exclusion. Simulation results indicate the effectiveness of Adaptive Momentum Gradient Descent (AMGD) in achieving the optimal convergence speed of ACS in doing clustering assignments, in compare with GD and classical Momentum method

A NOVEL DESIGN OF AN ALL-OPTICAL BISTABLE DEVICE USING A SINGLE ACTIVE ELEMENT

One of the main challenges in the area of all-optical information processing devices, is to develop low power and fast responding elements. Specically, in the eld of analog to digital converters (ADCs), higher modulation rates as well as enhanced power eciency are desired. The key component in such circuits, is the bistable switching device, also known as Schmitt trigger switch. In ch. 3, a mathematical model describing the bistable device that consists of two inverting ampliers, is developed. Then a full characterization of the bistability with regard to the individual inverting amplier parameters is given. From this characterization, device\u27s valid regions of operation is then investigated and tested with a more realistic optical simulation. Finally in ch. 4, a novel design of an all-optical bistable device using only a single gain medium is studied theoretically as well as experimentally implemented in two schemes, optical ber and free space optics. In both cases, the circuits are implemented by means of ring resonators and the aim is to conrm the bistability behavior predicted by theoretical analysis in the semiconductor active region