Novel Imatinib Derivatives with Altered Specificity between Bcr–Abl and FMS,KIT, and PDGF Receptors

Imatinib is a clinically important ATP analogue inhibitor that targets the tyrosine kinase domain of the intracellular Abl kinase and the PDGF receptor family. Imatinib has revolutionised the treatment of chronic myeloid leukaemia, which is caused by the oncogene Bcr–Abl and certain solid tumours that harbor oncogenic mutations of the PDGF receptor family. As a leading kinase inhibitor, imatinib also provides an excellent model system to investigate how changes in drug design impact biological activity, which is an important consideration for rational drug design. Herein we report a new series of imatinib derivatives that in general have greater activity against the family of PDGF receptors and poorer activity against Abl, as a result of modifications of the phenyl and N‐methylpiperazine rings. These new compounds provide a platform for further drug development against the therapeutically important PDGF receptor family and they also provide insight into the engineering of drugs with altered biological activity.     

Simultaneous prediction of acetic acid/ethanol concentrations in their binary mixtures using metalloporphyrin based opto-electronic nose for meat safety applications

This paper presents a comprehensive study on the simultaneous prediction of volatile organic compound (VOC) concentrations in their binary mixtures (acetic acid and ethanol) using partial least square regression (PLSR) and multilayer perceptron neural network (MLP-NN). A metalloporphyrin based opto-electronic nose was developed to record the reflectance from metalloporphyrin sensing film. A ruthenium based metalloporphyrin, 2,3,7,8,12,13,17,18-octaethyl-21H, 23H-porphine ruthenium(II) carbonyl (RuOEPCO), was used as sensing material. The percent change in the reflectance (%ΔR) before and after exposure to different combinations of analyte concentrations were used as the input to the prediction models. The relative standard error of prediction (RSEP, %) of the PLS model was found to be 18.51 and 21.77% for acetic acid and ethanol prediction validated using independent test set, respectively. On the other hand, neural network (multilayer perceptron) produced an average RSEP of 7.27 and 9.13% for acetic acid and ethanol prediction validated using independent test set, respectively. Neural networks produced comparatively lower prediction errors using independent test set validation method and shows potential for further investigation and validation on larger dataset.     

Fourier transform infrared spectroscopy (FTIR) as a tool for discriminating <Emphasis Type="Italic">Salmonella typhimurium</Emphasis> contaminated beef

Detection of beef contamination from harmful pathogens will be helpful in protecting the consumer safety and controlling the outbreaks. In this paper, the potential of Fourier transform infrared spectroscopy (FTIR) was investigated to discriminate the Salmonella contaminated packed beef. A suitable headspace sampling system was designed and used to collect the headspace volatiles from the packed meat to the FTIR gas cell. Spectral signatures of headspace volatiles of meat packages were used to classify the packed meat samples as contaminated or not. FTIR spectrum was divided into several regions in order to reduce the dimensionality as well as to select the regions based on the absorbance properties of various volatiles present in headspace of meat package. Principal component analysis was performed on the entire spectrum (4000–500 cm⁻¹) as well as on the selected sub-regions of entire spectrum. Two statistical classification techniques (linear and quadratic discriminate analysis) were used to develop classification models. The statistical models were validated using bootstrap cross validation technique. The total average classification accuracies were evaluated in terms of coefficient of variance (% CV). Based on the mean of total average classification accuracies and its % CV calculated from five similarly conducted experiments, it was found that the statistical models developed on a part of the spectra (500–850 cm⁻¹) and full spectra (4000–500 cm⁻¹) can be used as potential classification models for non-destructive discrimination of Salmonella contaminated packed beef samples from uncontaminated ones. These results need to be further validated on dataset with larger sample size.     

Reduced Complexity Dynamic Systems Using Approximate Control Moments

This paper deals with reduction of computational complexities in dynamic systems. This paper develops a novel method of reducing complexities with use of control moments of the system. Though the proposed method is validated through channel estimation in this paper, the same can be equally applied to any other dynamic systems. Encouraging results given in this paper prove that the computational complexities can be reduced up to 10⁴ with a marginal affordable loss of performance.     

Investigations on luminescence of rare earths doped CaTiO3:Pr3+ phosphor

Calcium titanate doped with praseodymium was prepared through solid state reaction and it exhibited intense red emission at 612 nm. Phosphors were characterized for photoluminescence, thermo-luminescence and afterglow emissions. Study of effects of rare earths on photoluminescence as well as afterglow intensity was reported. Presence of gadolinium enhanced luminescence. Presence of some monovalent ions enhanced both afterglow and photoluminescence emission. Thermo-luminescence glow curves were analyzed and ...     

A hybrid unscented filtering and particle swarm optimization technique for harmonic analysis of nonstationary signals

This paper presents a modified unscented Kalman filter for accurate estimation of frequency and harmonic components of a time-varying signal embedded in noise with low signal-to-noise ratio. Further, the model and measurement error covariances along with the unscented Kalman filter parameters are selected using a modified particle swarm optimization algorithm. To circumvent the problem of premature convergence and local minima, a dynamically varying inertia weight based on the variance of the population fitness is used. This results in a better local and global searching ability of the particles, which improves the convergence of the velocity and better accuracy of the unscented Kalman filter parameters. Various simulation results for nonstationary sinusoidal signals with time varying amplitude, phase and harmonic content corrupted with noise, reveal significant improvement in noise rejection and speed of convergence and accuracy in comparison to the well known extended Kalman filter.     

Olfactory receptor-based polypeptide sensor for acetic acid VOC detection

Rapid detection of food-borne pathogens in packaged food products can prevent the spread of infectious diseases. This study investigates the application of novel sensing material that is sensitive to specific indicator volatile organic compound (VOC) related to Salmonella contamination in packaged meat. Specifically, the objective was to develop an olfactory receptor-based synthetic polypeptide sensor for the detecting acetic acid in low concentrations and at room temperature. Synthetic polypeptide was deposited on a quartz crystal microbalance (QCM) electrode and was evaluated for detecting acetic acid at 10–100 ppm. Developed sensor exhibited repeatable response to a particular concentration of acetic acid and displayed reproducibility among multiple sensors during acetic acid detection. Mean estimated lower detection limits of these sensors were about 1–3 ppm and linear calibration models showed linear relationships. Thus, the QCM sensors exhibited a great potential for detecting low concentrations of acetic acid at room temperature and can be used in the sensor array for packaged meat spoilage and contamination detection.     

Electricity price classification using extreme learning machines

Forecasting electricity prices has been a widely investigated research issue in the deregulated power market scenario. High price volatilities, price spikes caused by a number of factors such as weather uncertainty, fluctuating fuel prices, transmission bottlenecks, etc., make the task of accurate price forecasting a formidable challenge for the market participants. A number of models have been proposed by researchers; however, achieving high accuracy is always not possible. In some specific applications such as self-scheduling by demand side participants, certain price thresholds are more useful than accurate price forecasts. In this paper, we have investigated the application of a novel neural network-based technique called extreme learning machine for the problem of classification of future electricity prices with respect to certain price thresholds. Different models corresponding to different lead times are developed and tested with data corresponding to Ontario and PJM markets. It is observed that classification with ELM is fast, less sensitive to user defined parameters and easily implementable.     

Investigation of luminescence spectroscopic characteristics in Eu3+-doped Zn2SiO4 by Judd–Ofelt parameters

Journal of Materials Science: Materials in Electronics - Optical properties of a phosphor material depend on the nature of the impurity and the site of occupancy of the dopant in the host matrix....     

Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning

We propose a novel approach for credit card fraud detection, which combines evidences from current as well as past behavior. The fraud detection system (FDS) consists of four components, namely, rule-based filter, Dempster–Shafer adder, transaction history database and Bayesian learner. In the rule-based component, we determine the suspicion level of each incoming transaction based on the extent of its deviation from good pattern. Dempster–Shafer’s theory is used to combine multiple such evidences and an initial belief is computed. The transaction is classified as normal, abnormal or suspicious depending on this initial belief. Once a transaction is found to be suspicious, belief is further strengthened or weakened according to its similarity with fraudulent or genuine transaction history using Bayesian learning. Extensive simulation with stochastic models shows that fusion of different evidences has a very high positive impact on the performance of a credit card fraud detection system as compared to other methods.