SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples.

The work presented in this paper is focused on the use of spectroscopy to identify the type of tissue of human brain samples employing support vector machine classifiers. Two different spectrometers were used to acquire infrared spectroscopic signatures in the wavenumber range between 1200⁻3500 cm-1. An extensive analysis was performed to find the optimal configuration for a support vector machine classifier and determine the most relevant regions of the spectra for this particular application. The results demonstrate that the developed algorithm is robust enough to classify the infrared spectroscopic data of human brain tissue at three different discrimination levels.This work has been supported in part by the European Commission through the FP7 FET Open Programme ICT-2011.9.2, European Project HELICoiD “HypErspectral Imaging Cancer Detection” under Grant Agreement 618080. This work has been also supported in part by the Canary Islands Government through the ACIISI (Canarian Agency for Research, Innovation and the Information Society), ITHACA project “Hyperespectral identification of Brain tumors” under Grant Agreement ProID2017010164. Additionally, this work has been supported in part by the 2016 PhD Training Program for Research Staff of the University of Las Palmas de Gran Canaria. Finally, this work was completed while Samuel Ortega was beneficiary of a pre-doctoral grant given by the “Agencia Canaria de Investigacion, Innovacion y Sociedad de la Información (ACIISI)” of the “Conserjería de Economía, Industria, Comercio y Conocimiento” of the “Gobierno de Canarias”, which is part-financed by the European Social Fund (FSE) (POC 2014-2020, Eje 3 Tema Prioritario 74 (85%))

Diagnosis of Breast Cancer by Modular Evolutionary Neural Networks

Abstract Machine learning and pattern recognition play a vital role in the field of biomedical engineering, where the task is to identify or classify a disease based on a set of observations. The inability of a single method to effectively solve the problem gives rise to the use multiple models for solving the same problem in a 'Mixture of Experts' mode. Further the data may be too large for any system to effectively solve the problem. This motivates the use of computational modularity in the system where a number of modules independently solve part of the problem. In this paper we construct a Mixture of Experts model where a number of different techniques are applied to solve the same problem. The individual decision by each of these experts is fused by an integrator that gives the final output. Each of the units is a complex modular neural network. The first modularity clusters the entire input space into a set of modules. The second modularity divides the number of attributes. Each cluster is a neural network that solves the problem. The individual neural networks are evolved using Genetic Algorithms which optimizes both the architecture and the parameters. The complete system is used for the diagnosis of Breast Cancer. Experimental results show that the proposed system outperforms the traditional simple and hybrid approaches. The system on the whole is highly scalable to both number of attributes and data items

Computational Biology: Insights into Hemagglutinin and Polycomb Repressive Complex 2 Function

Influenza B virus hemagglutinin (HA) is a major surface glycoprotein with frequent amino-acid substitutions. However, the roles of antibody selection in the amino-acid substitutions of HA were still poorly understood. An analysis was conducted on a total of 271 HA 1 sequences of influenza B virus strains isolated during 1940∼2007 finding positively selected sites all located in the four major epitopes (120-loop, 150-loop, 160-loop and 190-helix) supporting a predominant role of antibody selection in HA evolution. Of particular significance is the involvement of the 120-loop in positive selection. Influenza B virus HA continues to evolve into new sublineages, within which the four major epitopes were targeted selectively in positive selection. Thus, any newly emerging strains need to be placed in the context of their evolutionary history in order to understand and predict their epidemic potential. As key epigenetic regulators, polycomb group (PcG) proteins are responsible for the control of cell proliferation and differentiation as well as stem cell pluripotency and self-renewal. To facilitate experimental identification of PcG target genes, which are poorly understood, we propose a novel computational method, EpiPredictor , which models transcription factor interaction using a non-linear kernel. The resulting targets suggests that multiple transcription factor networking at the cis -regulatory elements is critical for PcG recruitment, while high GC content and high conservation level are also important features of PcG target genes. To try to translate the EpiPredictor into human data, we performed a computational study utilizing 22 human genome-wide CHIP data to identify DNA motifs and genome features that would potentially specify PRC2 using five motif discovery algorithms, Jaspar known transcription binding motifs, and other whole genome data. We have found multiple motifs within the various subgroups of experimental categories that have much higher enrichment against CHIP identified gene promoter than among random gene promoters. Specifically, we have identified Low CpG content CpG Islands (LeG's) as being critical in the separation of Cancer cell line identified targets from Embryonic Stem cell line identified targets. Additionally, there are differences between human and mouse ES cell predictions using the same motifs and features suggesting relevant evolutionary divergence

Brain and Human Body Modeling

This open access book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest developments in computational modelling and human phantom development to assess a given technology’s safety and efficacy in a timely manner. Describes construction and application of computational human models including anatomically detailed and subject specific models; Explains new practices in computational human modeling for neuroelectromagnetics, electromagnetic safety, and exposure evaluations; Includes a survey of modern applications for which computational human models are critical; Describes cellular-level interactions between the human body and electromagnetic fields

Design and Application of Electrical Machines

Electrical machines are one of the most important components of the industrial world. They are at the heart of the new industrial revolution, brought forth by the development of electromobility and renewable energy systems. Electric motors must meet the most stringent requirements of reliability, availability, and high efficiency in order, among other things, to match the useful lifetime of power electronics in complex system applications and compete in the market under ever-increasing pressure to deliver the highest performance criteria. Today, thanks to the application of highly efficient numerical algorithms running on high-performance computers, it is possible to design electric machines and very complex drive systems faster and at a lower cost. At the same time, progress in the field of material science and technology enables the development of increasingly complex motor designs and topologies. The purpose of this Special Issue is to contribute to this development of electric machines. The publication of this collection of scientific articles, dedicated to the topic of electric machine design and application, contributes to the dissemination of the above information among professionals dealing with electrical machines

Brain and Human Body Modeling

This open access book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest developments in computational modelling and human phantom development to assess a given technology’s safety and efficacy in a timely manner. Describes construction and application of computational human models including anatomically detailed and subject specific models; Explains new practices in computational human modeling for neuroelectromagnetics, electromagnetic safety, and exposure evaluations; Includes a survey of modern applications for which computational human models are critical; Describes cellular-level interactions between the human body and electromagnetic fields