Theranostic Interpolation of Genomic Instability in Breast Cancer

Breast cancer is a diverse disease caused by mutations in multiple genes accompanying epigenetic aberrations of hazardous genes and protein pathways, which distress tumor-suppressor genes and the expression of oncogenes. Alteration in any of the several physiological mechanisms such as cell cycle checkpoints, DNA repair machinery, mitotic checkpoints, and telomere maintenance results in genomic instability. Theranostic has the potential to foretell and estimate therapy response, contributing a valuable opportunity to modify the ongoing treatments and has developed new treatment strategies in a personalized manner. “Omics” technologies play a key role while studying genomic instability in breast cancer, and broadly include various aspects of proteomics, genomics, metabolomics, and tumor grading. Certain computational techniques have been designed to facilitate the early diagnosis of cancer and predict disease-specific therapies, which can produce many effective results. Several diverse tools are used to investigate genomic instability and underlying mechanisms. The current review aimed to explore the genomic landscape, tumor heterogeneity, and possible mechanisms of genomic instability involved in initiating breast cancer. We also discuss the implications of computational biology regarding mutational and pathway analyses, identification of prognostic markers, and the development of strategies for precision medicine. We also review different technologies required for the investigation of genomic instability in breast cancer cells, including recent therapeutic and preventive advances in breast cancer.     

Correction to: Convolutional neural networks based time–frequency image enhancement for the analysis of EEG signals

Multidimensional Systems and Signal Processing -     

Designing end-to-end resource reservations in predictable distributed embedded systems

Contemporary distributed embedded systems in many domains have become highly complex due to ever-increasing demand on advanced computer controlled functionality. The resource reservation techniques can be effective in lowering the software complexity, ensuring predictability and allowing flexibility during the development and execution of these systems. This paper proposes a novel end-to-end resource reservation model for distributed embedded systems. In order to support the development of predictable systems using the proposed model, the paper provides a method to design resource reservations and an end-to-end timing analysis. The reservation design can be subjected to different optimization criteria with respect to runtime footprint, overhead or performance. The paper also presents and evaluates a case study to show the usability of the proposed model, reservation design method and end-to-end timing analysis.     

Simulation and Optimization of Wiped-Film Poly-Ethylene Terephthalate (PET) Reactor Using Multiobjective Differential Evolution (MODE)

The multiobjective differential evolution (MODE), which is an extension of the Differential Evolution (DE), is applied to solve the multiobjective optimization problem (MOOP) of wet film Poly-Ethylene Terephthalate (PET) reactor considering minimization of acid end group and vinyl end group as the main objectives. The objective function is modified to solve five different possible cases. The results show that a Pareto set (a set of equally good solutions) is obtained for the cases when two of the parameters (residence time of the polymeric reaction mass, θ, and the speed of the wiped-film agitator, N) are considered as decision variables, unlike a unique solution obtained using Nondominated Sorting Genetic Algorithm (NSGA). The Pareto optimal front provides wide-ranging optimal sets of operating conditions. And an appropriate set of operating conditions can be selected based on the requirements of the user.     

Convolutional Neural Networks Based Time-Frequency Image Enhancement For the Analysis of EEG Signals

Multidimensional Systems and Signal Processing - Quadratic time-frequency (TF) methods are commonly used for the analysis, modeling, and classification of time-varying non-stationary...