Regularized logistic regression and multi-objective variable selection for classifying MEG data

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori.

Collaborative data stream mining in ubiquitous environments using dynamic classifier selection

In ubiquitous data stream mining applications, different devices often aim to learn concepts that are similar to some extent. In these applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy. Coll-Stream integrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluate Coll-Stream classification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show that Coll-Stream resultant model achieves stability and accuracy in a variety of situations using both synthetic and real world datasets.

Towards a fuzzy-based multi-classifier selection module for activity recognition applications

Performing activity recognition using the information provided by the different sensors embedded in a smartphone face limitations due to the capabilities of those devices when the computations are carried out in the terminal. In this work a fuzzy inference module is implemented in order to decide which classifier is the most appropriate to be used at a specific moment regarding the application requirements and the device context characterized by its battery level, available memory and CPU load. The set of classifiers that is considered is composed of Decision Tables and Trees that have been trained using different number of sensors and features. In addition, some classifiers perform activity recognition regardless of the on-body device position and others rely on the previous recognition of that position to use a classifier that is trained with measurements gathered with the mobile placed on that specific position. The modules implemented show that an evaluation of the classifiers allows sorting them so the fuzzy inference module can choose periodically the one that best suits the device context and application requirements.

Selection of CSI-based precoding techniques in the HF channel

Multi-carrier modulations are widely employed in ionospheric communications to mitigate the adverse effects of the HF channel. In this paper we show how performance achieved by these modulations can be further increased by means of CSIbased precoding techniques in the context of our research on interactive digital voice communications. Depending on communication constraints and channel parameters, we will show which of the studied modulations and precoding techniques to select so that to maximise performance.

Selection of TDOA Parameters for MDM Speaker Diarization

Several methods to improve multiple distant microphone (MDM) speaker diarization based on Time Delay of Arrival (TDOA) features are evaluated in this paper. All of them avoid the use of a single reference channel to calculate the TDOA values and, based on different criteria, select among all possible pairs of microphones a set of pairs that will be used to estimate the TDOA's. The evaluated methods have been named the "Dynamic Margin" (DM), the "Extreme Regions" (ER), the "Most Common" (MC), the "Cross Correlation" (XCorr) and the "Principle Component Analysis" (PCA). It is shown that all methods improve the baseline results for the development set and four of them improve also the results for the evaluation set. Improvements of 3.49% and 10.77% DER relative are obtained for DM and ER respectively for the test set. The XCorr and PCA methods achieve an improvement of 36.72% and 30.82% DER relative for the test set. Moreover, the computational cost for the XCorr method is 20% less than the baseline.

Brain structures identification based on feature descriptor

Traumatic Brain Injury -TBI- -1- is defined as an acute event that causes certain damage to areas of the brain. TBI may result in a significant impairment of an individuals physical, cognitive and psychosocial functioning. The main consequence of TBI is a dramatic change in the individuals daily life involving a profound disruption of the family, a loss of future income capacity and an increase of lifetime cost. One of the main challenges of TBI Neuroimaging is to develop robust automated image analysis methods to detect signatures of TBI, such as: hyper-intensity areas, changes in image contrast and in brain shape. The final goal of this research is to develop a method to identify the altered brain structures by automatically detecting landmarks on the image where signal changes and to provide comprehensive information to the clinician about them. These landmarks identify injured structures by co-registering the patient?s image with an atlas where landmarks have been previously detected. The research work has been initiated by identifying brain structures on healthy subjects to validate the proposed method. Later, this method will be used to identify modified structures on TBI imaging studies.

Finite element code-based modeling of a multi-feature isolation system and passive alleviation of possible inner pounding

The existing seismic isolation systems are based on well-known and accepted physical principles, but they are still having some functional drawbacks. As an attempt of improvement, the Roll-N-Cage (RNC) isolator has been recently proposed. It is designed to achieve a balance in controlling isolator displacement demands and structural accelerations. It provides in a single unit all the necessary functions of vertical rigid support, horizontal flexibility with enhanced stability, resistance to low service loads and minor vibration, and hysteretic energy dissipation characteristics. It is characterized by two unique features that are a self-braking (buffer) and a self-recentering mechanism. This paper presents an advanced representation of the main and unique features of the RNC isolator using an available finite element code called SAP2000. The validity of the obtained SAP2000 model is then checked using experimental, numerical and analytical results. Then, the paper investigates the merits and demerits of activating the built-in buffer mechanism on both structural pounding mitigation and isolation efficiency. The paper addresses the problem of passive alleviation of possible inner pounding within the RNC isolator, which may arise due to the activation of its self-braking mechanism under sever excitations such as near-fault earthquakes. The results show that the obtained finite element code-based model can closely match and accurately predict the overall behavior of the RNC isolator with effectively small errors. Moreover, the inherent buffer mechanism of the RNC isolator could mitigate or even eliminate direct structure-tostructure pounding under severe excitation considering limited septation gaps between adjacent structures. In addition, the increase of inherent hysteretic damping of the RNC isolator can efficiently limit its peak displacement together with the severity of the possibly developed inner pounding and, therefore, alleviate or even eliminate the possibly arising negative effects of the buffer mechanism on the overall RNC-isolated structural responses.

A study of feature combination for vehicle detection based on image processing

Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification.

Detection of HIV-1 RNA by nucleic acid sequence-based amplification combined with fluorescence correlation spectroscopy

Nucleic acid sequence-based amplification (NASBA) has proved to be an ultrasensitive method for HIV-1 diagnosis in plasma even in the primary HIV infection stage. This technique was combined with fluorescence correlation spectroscopy (FCS) which enables online detection of the HIV-1 RNA molecules amplified by NASBA. A fluorescently labeled DNA probe at nanomolar concentration was introduced into the NASBA reaction mixture and hybridizing to a distinct sequence of the amplified RNA molecule. The specific hybridization and extension of this probe during amplification reaction, resulting in an increase of its diffusion time, was monitored online by FCS. As a consequence, after having reached a critical concentration of 0.1–1 nM (threshold for unaided FCS detection), the number of amplified RNA molecules in the further course of reaction could be determined. Evaluation of the hybridization/extension kinetics allowed an estimation of the initial HIV-1 RNA concentration that was present at the beginning of amplification. The value of initial HIV-1 RNA number enables discrimination between positive and false-positive samples (caused for instance by carryover contamination)—this possibility of discrimination is an essential necessity for all diagnostic methods using amplification systems (PCR as well as NASBA). Quantitation of HIV-1 RNA in plasma by combination of NASBA with FCS may also be useful in assessing the efficacy of anti-HIV agents, especially in the early infection stage when standard ELISA antibody tests often display negative results.

Detection of protein fold similarity based on correlation of amino acid properties

An increasing number of proteins with weak sequence similarity have been found to assume similar three-dimensional fold and often have similar or related biochemical or biophysical functions. We propose a method for detecting the fold similarity between two proteins with low sequence similarity based on their amino acid properties alone. The method, the proximity correlation matrix (PCM) method, is built on the observation that the physical properties of neighboring amino acid residues in sequence at structurally equivalent positions of two proteins of similar fold are often correlated even when amino acid sequences are different. The hydrophobicity is shown to be the most strongly correlated property for all protein fold classes. The PCM method was tested on 420 proteins belonging to 64 different known folds, each having at least three proteins with little sequence similarity. The method was able to detect fold similarities for 40% of the 420 sequences. Compared with sequence comparison and several fold-recognition methods, the method demonstrates good performance in detecting fold similarities among the proteins with low sequence identity. Applied to the complete genome of Methanococcus jannaschii, the method recognized the folds for 22 hypothetical proteins.

Simultaneous A8344G heteroplasmy and mitochondrial DNA copy number quantification in Myoclonus Epilepsy and Ragged-Red Fibers (MERRF) syndrome by a multiplex Molecular Beacon based real-time fluorescence PCR

The association of a particular mitochondrial DNA (mtDNA) mutation with different clinical phenotypes is a well-known feature of mitochondrial diseases. A simple genotype–phenotype correlation has not been found between mutation load and disease expression. Tissue and intercellular mosaicism as well as mtDNA copy number are thought to be responsible for the different clinical phenotypes. As disease expression of mitochondrial tRNA mutations is mostly in postmitotic tissues, studies to elucidate disease mechanisms need to be performed on patient material. Heteroplasmy quantitation and copy number estimation using small patient biopsy samples has not been reported before, mainly due to technical restrictions. In order to resolve this problem, we have developed a robust assay that utilizes Molecular Beacons to accurately quantify heteroplasmy levels and determine mtDNA copy number in small samples carrying the A8344G tRNALys mutation. It provides the methodological basis to investigate the role of heteroplasmy and mtDNA copy number in determining the clinical phenotypes.

Analysis of genetic instability during mammary tumor progression using a novel selection-based assay for in vivo mutations in a bacteriophage lambda transgene target.

Genetic instability is thought to be responsible for the numerous genotypic changes that occur during neoplastic transformation and metastatic progression. To explore the role of genetic instability at the level of point mutations during mammary tumor development and malignant progression, we combined transgenic mouse models of mutagenesis detection and oncogenesis. Bitransgenic mice were generated that carried both a bacteriophage lambda transgene to assay mutagenesis and a polyomavirus middle T oncogene, mammary gland-targeted expression of which led to metastatic mammary adenocarcinomas. We developed a novel assay for the detection of mutations in the lambda transgene that selects for phage containing forward mutations only in the lambda cII gene, using an hfl- bacterial host. In addition to the relative ease of direct selection, the sensitivity of this assay for both spontaneous and chemically induced mutations was comparable to the widely used mutational target gene, lambda lacI, making the cII assay an attractive alternative for mutant phage recovery for any lambda-based mouse mutagenesis assay system. The frequencies of lambda cII- mutants were not significantly different in normal mammary epithelium, primary mammary adenocarcinomas, and pulmonary metastases. The cII mutational spectra in these tissues consisted mostly of G/C-->A/T transitions, a large fraction of which occurred at CpG dinucleotides. These data suggest that, in this middle T oncogene model of mammary tumor progression, a significant increase in mutagenesis is not required for tumor development or for metastatic progression.

Targeted gene inactivation in petunia by PCR-based selection of transposon insertion mutants.

Establishment of loss-of-function phenotypes is often a key step in determining the biological function of a gene. We describe a procedure to obtain mutant petunia plants in which a specific gene with known sequence is inactivated by the transposable element dTph1. Leaves are collected from batches of 1000 plants with highly active dTph1 elements, pooled according to a three-dimensional matrix, and screened by PCR using a transposon- and a gene-specific primer. In this way individual plants with a dTph1 insertion can be identified by analysis of about 30 PCRs. We found insertion alleles for various genes at a frequency of about 1 in 1000 plants. The plant population can be preserved by selfing all the plants, so that it can be screened for insertions in many genes over a prolonged period.

Preferential expansion and survival of B lymphocytes based on VH framework 1 and framework 3 expression: "positive" selection in appendix of normal and VH-mutant rabbits.

B cells with a rearranged heavy-chain variable region VHa allotype-encoding VH1 gene segment predominate throughout the life of normal rabbits and appear to be the source of the majority of serum immunoglobulins, which thus bear VHa allotypes. The functional role(s) of these VH framework region (FR) allotypic structures has not been defined. We show here that B cells expressing surface immunoglobulin with VHa2 allotypic specificities are preferentially expanded and positively selected in the appendix of young rabbits. By flow cytometry, a higher proportion of a2+ B cells were progressing through the cell cycle (S/G2/M) compared to a2- B cells, most of which were in the G1/G0 phase of the cell cycle. The majority of appendix B cells in dark zones of germinal centers of normal 6-week-old rabbits were proliferating and very little apoptosis were observed. In contrast, in 6-week-old VH-mutant ali/ali rabbits, little cell proliferation and extensive apoptosis were observed. Nonetheless even in the absence of VH1, B cells with a2-like surface immunoglobulin had developed and expanded in the appendix of 11-week-old mutants. The numbers and tissue localization of B cells undergoing apoptosis then appeared similar to those found in 6-week-old normal appendix. Thus, B cells with immunoglobulin receptors lacking the VHa2 allotypic structures were less likely to undergo clonal expansion and maturation. These data suggest that "positive" selection of B lymphocytes through FR1 and FR3 VHa allotypic structures occurs during their development in the appendix.