Development of a bioluminescence resonance energy-transfer assay for estrogen-like compound in vivo monitoring

A new bioluminescence resonance energy transfer (BRET) homogeneous assay to evaluate the presence of estrogen-like compounds has been developed and optimized. The assay is based on the direct evaluation of estrogen alphareceptor (ERalpha) homodimerization as a result of estrogen-like compound binding. ERalpha monomer was genetically fused either to Renilla luciferase (Rluc) or to enhanced yellow fluorescent protein (EYFP). In the presence of estrogens, ERalpha dimerization brings Rluc and EYFP molecules close enough for an energy transfer. An in vitro BRET assay was first developed using purified fusion proteins (ERalpha-Rluc and ERalpha-EYFP) expressed in Escherichia coli to evaluate and optimize the analytical performances of the assay in the presence of 17-beta estradiol. The "in vivo" BRET quantitative assay was then developed by coexpressing the two fusion proteins in live HepG2 cells. The assay can be performed in 96-well microplate format with a 30-min incubation and allows detection with adequate accuracy and precision of as low as 1 nM of 17-beta estradiol. This new "in vivo" BRET assay allows evaluating the estrogen-like activity and synthetic xenoestrogens from biological and environmental samples.     

Enhancement of nonlinear effects using photonic crystals

The quest for all-optical signal processing is generally deemed to be impractical because optical nonlinearities are usually weak. The emerging field of nonlinear photonic crystals seems destined to change this view dramatically. Theoretical considerations show that all-optical devices using photonic crystal designs promise to be smaller than the wavelength of light, and to operate with bandwidths that are very difficult to achieve electronically. When created in commonly used materials, these devices could operate at powers of only a few milliwatts. Moreover, if these designs are combined with materials and systems that support electromagnetically induced transparency, operation at single-photon power levels could be feasible.     

Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces

It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult tо analyze with conventional analytical methods. Here, a novel design of an adaptive neuro fuzzy inference system (ANFIS) for estimation contact forces of a new adaptive gripper is presented. Since the conventional analytical methods is a very challenging task, fuzzy logic based systems are considered as potential candidates for such an application. The main points of this paper are in explanation of kinetostatic analyzing of the new gripper structure using rigid body model with added compliance in every single joint. The experimental results can be used as training data for ANFIS network for estimation of gripping forces. An adaptive neuro-fuzzy network is used to approximate correlation between contact point locations and contact forces magnitudes. The simulation results presented in this paper show the effectiveness of the developed method. This system is capable to find any change in ratio of positions of the gripper contacts and magnitudes of the contact forces and thus indicates state of both finger phalanges.     

Modelling effects of S3D visual discomfort in human emotional state using data mining techniques

Multimedia Tools and Applications - In recent years, the rapid development of diverse media has been evident in disparate fields such as consumer electronics, automotive infotainment and healthcare...     

This is not classified: everyday information seeking and encountering in smart urban spaces

We present a multipronged comparative study of citizens’ self-proclaimed information needs and actual information seeking behavior in smart urban spaces. We first conducted several user studies to identify the types of information services that citizens believed to be useful in urban setting utilizing methods ranging from contextual inquiry with lo-fi prototypes to “card sorting” exercise with a separate set of participants, and finally to implementing selected services. We then made a sizeable constructive intervention into the urban space by deploying in a city center 12 large, interactive public displays called “hotspots” to offer a wide range of previously identified information services. We collected comprehensive qualitative and quantitative data on the usage of the hotspots and their services by the general public during 13 months. Our study reveals discrepancies between a priori and a posteriori information seeking strategies extracted from the self-proclaimed information needs and the actual usage of the hotspots.     

Finding best algorithmic components for clustering microarray data

The analysis of microarray data is fundamental to microbiology. Although clustering has long been realized as central to the discovery of gene functions and disease diagnostic, researchers have found the construction of good algorithms a surprisingly difficult task. In this paper, we address this problem by using a component-based approach for clustering algorithm design, for class retrieval from microarray data. The idea is to break up existing algorithms into independent building blocks for typical sub-problems, which are in turn reassembled in new ways to generate yet unexplored methods. As a test, 432 algorithms were generated and evaluated on published microarray data sets. We found their top performers to be better than the original, component-providing ancestors and also competitive with a set of new algorithms recently proposed. Finally, we identified components that showed consistently good performance for clustering microarray data and that should be considered in further development of clustering algorithms.     

Discrete-time chattering free exponentially stabilizing sliding mode scalar control via Lyapunov’s method

Linear time-invariant discrete-time plant, with no restrictions on the form of the state equation and with scalar control, is considered. The exponentially stabilizing state feedback control algorithm is developed by Lyapunov’s second method leading to the variable structure system with chattering free sliding modes. Essentially, the control algorithm drives the system from an arbitrary initial state to a prescribed so-called sliding subspace S, in finite time and with prescribed velocity estimate. Inside the sliding subspace S, the system is switched to the sliding mode regime and stays in it forever. The proposed algorithm is tested on the real system in practice, direct current (DC) servo motor, and simulation and experimental results are given. Also, it is compared with another already known algorithm from literature.     

Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult make decision strategies using conventional techniques. Here, an adaptive neuro fuzzy inference system (ANFIS) for controlling input displacement and object recognition of a new adaptive compliant gripper is presented. The grasping function of the proposed adaptive multi-fingered gripper relies on the physical contact of the finger with an object. This design of the each finger has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Fuzzy based controllers develop a control signal according to grasping object shape which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS strategy, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.