Facial expression (mood) recognition from facial images using committee neural networks

<p>Abstract</p> <p>Background</p> <p>Facial expressions are important in facilitating human communication and interactions. Also, they are used as an important tool in behavioural studies and in medical rehabilitation. Facial image based mood detection techniques may provide a fast and practical approach for non-invasive mood detection. The purpose of the present study was to develop an intelligent system for facial image based expression classification using committee neural networks.</p> <p>Methods</p> <p>Several facial parameters were extracted from a facial image and were used to train several generalized and specialized neural networks. Based on initial testing, the best performing generalized and specialized neural networks were recruited into decision making committees which formed an integrated committee neural network system. The integrated committee neural network system was then evaluated using data obtained from subjects not used in training or in initial testing.</p> <p>Results and conclusion</p> <p>The system correctly identified the correct facial expression in 255 of the 282 images (90.43% of the cases), from 62 subjects not used in training or in initial testing. Committee neural networks offer a potential tool for image based mood detection.</p

Gene Expression Based Leukemia Sub-Classification Using Committee Neural Networks

Analysis of gene expression data provides an objective and efficient technique for sub-classification of leukemia. The purpose of the present study was to design a committee neural networks based classification systems to subcategorize leukemia gene expression data. In the study, a binary classification system was considered to differentiate acute lymphoblastic leukemia from acute myeloid leukemia. A ternary classification system which classifies leukemia expression data into three subclasses including B-cell acute lymphoblastic leukemia, T-cell acute lymphoblastic leukemia and acute myeloid leukemia was also developed. In each classification system gene expression profiles of leukemia patients were first subjected to a sequence of simple preprocessing steps. This resulted in filtering out approximately 95 percent of the non-informative genes. The remaining 5 percent of the informative genes were used to train a set of artificial neural networks with different parameters and architectures. The networks that gave the best results during initial testing were recruited into a committee. The committee decision was by majority voting. The committee neural network system was later evaluated using data not used in training. The binary classification system classified microarray gene expression profiles into two categories with 100 percent accuracy and the ternary system correctly predicted the three subclasses of leukemia in over 97 percent of the cases

Neural network committees for finger joint angle estimation from surface EMG signals

<p>Abstract</p> <p>Background</p> <p>In virtual reality (VR) systems, the user's finger and hand positions are sensed and used to control the virtual environments. Direct biocontrol of VR environments using surface electromyography (SEMG) signals may be more synergistic and unconstraining to the user. The purpose of the present investigation was to develop a technique to predict the finger joint angle from the surface EMG measurements of the extensor muscle using neural network models.</p> <p>Methodology</p> <p>SEMG together with the actual joint angle measurements were obtained while the subject was performing flexion-extension rotation of the index finger at three speeds. Several neural networks were trained to predict the joint angle from the parameters extracted from the SEMG signals. The best networks were selected to form six committees. The neural network committees were evaluated using data from new subjects.</p> <p>Results</p> <p>There was hysteresis in the measured SMEG signals during the flexion-extension cycle. However, neural network committees were able to predict the joint angle with reasonable accuracy. RMS errors ranged from 0.085 ± 0.036 for fast speed finger-extension to 0.147 ± 0.026 for slow speed finger extension, and from 0.098 ± 0.023 for the fast speed finger flexion to 0.163 ± 0.054 for slow speed finger flexion.</p> <p>Conclusion</p> <p>Although hysteresis was observed in the measured SEMG signals, the committees of neural networks were able to predict the finger joint angle from SEMG signals.</p

Contrasting selective patterns across the segmented genome of bluetongue virus in a global reassortment hotspot

For segmented viruses, rapid genomic and phenotypic changes can occur through the process of reassortment, whereby co-infecting strains exchange entire segments creating novel progeny virus genotypes. However, for many viruses with segmented genomes, this process and its effect on transmission dynamics remain poorly understood. Here, we assessed the consequences of reassortment for selection on viral diversity through time using bluetongue virus (BTV), a segmented arbovirus that is the causative agent of a major disease of ruminants. We analysed ninety-two BTV genomes isolated across four decades from India, where BTV diversity, and thus opportunities for reassortment, are among the highest in the world. Our results point to frequent reassortment and segment turnover, some of which appear to be driven by selective sweeps and serial hitchhiking. Particularly, we found evidence for a recent selective sweep affecting segment 5 and its encoded NS1 protein that has allowed a single variant to essentially invade the full range of BTV genomic backgrounds and serotypes currently circulating in India. In contrast, diversifying selection was found to play an important role in maintaining genetic diversity in genes encoding outer surface proteins involved in virus interactions (VP2 and VP5, encoded by segments 2 and 6, respectively). Our results support the role of reassortment in driving rapid phenotypic change in segmented viruses and generate testable hypotheses for in vitro experiments aiming at understanding the specific mechanisms underlying differences in fitness and selection across viral genomes

Full-genome sequencing as a basis for molecular epidemiology studies of bluetongue virus in India

Since 1998 there have been significant changes in the global distribution of bluetongue virus (BTV). Ten previously exotic BTV serotypes have been detected in Europe, causing severe disease outbreaks in naïve ruminant populations. Previously exotic BTV serotypes were also identified in the USA, Israel, Australia and India. BTV is transmitted by biting midges (Culicoides spp.) and changes in the distribution of vector species, climate change, increased international travel and trade are thought to have contributed to these events. Thirteen BTV serotypes have been isolated in India since first reports of the disease in the country during 1964. Efficient methods for preparation of viral dsRNA and cDNA synthesis, have facilitated full-genome sequencing of BTV strains from the region. These studies introduce a new approach for BTV characterization, based on full-genome sequencing and phylogenetic analyses, facilitating the identification of BTV serotype, topotype and reassortant strains. Phylogenetic analyses show that most of the equivalent genome-segments of Indian BTV strains are closely related, clustering within a major eastern BTV ‘topotype’. However, genome-segment 5 (Seg-5) encoding NS1, from multiple post 1982 Indian isolates, originated from a western BTV topotype. All ten genome-segments of BTV-2 isolates (IND2003/01, IND2003/02 and IND2003/03) are closely related (&gt;99% identity) to a South African BTV-2 vaccine-strain (western topotype). Similarly BTV-10 isolates (IND2003/06; IND2005/04) show &gt;99% identity in all genome segments, to the prototype BTV-10 (CA-8) strain from the USA. These data suggest repeated introductions of western BTV field and/or vaccine-strains into India, potentially linked to animal or vector-insect movements, or unauthorised use of ‘live’ South African or American BTV-vaccines in the country. The data presented will help improve nucleic acid based diagnostics for Indian serotypes/topotypes, as part of control strategies