HIV-1 subtypes and drug resistance in children during antiretroviral therapy in Brazil.

We evaluate the genetic characterization of 132 HIV-1 pol sequences from children and adolescents undergoing antiretroviral therapy in Northeast Brazil. Phylogenetic and recombination analyses were performed using the maximum likelihood method using SeaView version 4 and SIMPLOT software. Most individuals harbored HIV-1 B (84.8%) and BF recombinants (9.8%), although other non-B subtypes were detected: HIV-1 C (1.5%), HIV-1 F (2.4%), and BC recombinants (1.5%). Antiretroviral resistance was 47% (95% confidence interval [CI]: 38.7%-55.4%). Non-nucleoside reverse transcriptase inhibitors (NNRTIs) showed higher frequencies of primary mutations, with 40.9% (95% CI: 32.9%-49.4%), followed by nucleoside reverse transcriptase inhibitors (NRTI) and protease inhibitors (PIs) with 34.8% (95% CI: 27.3-43.3) and 6.1% (95% CI: 3.1%-11.5%), respectively. Among NRTIs, higher resistance levels were observed for abacavir, emtricitabine, and lamivudine; for NNRTI, nevirapine and efavirenz. The most common primary mutations found were M184V (29.5%), K103N (25%), M41L (9.8%), T215Y (8.3%), and G190A (8.3%). Our findings highlight the importance of surveillance of resistance mutations, which contributes to the continuous updating and implementation of preventive measures to decrease mother-to-child-transmission and transmitted drug resistance.

Classification of the interictal state with hypsarrhythmia from Zika Virus Congenital Syndrome and of the ictal state from epilepsy in childhood without hypsarrhythmia in EEGs using entropy measures.

This paper intends to classify the interictal state with hypsarrhythmia in patients with Zika Virus Congenital Syndrome (ZVCS) and of the ictal state in patients with epilepsy in childhood without the presence of hypsarrhythmia. Hypsarrhythmia is a specific interictal chaotic morphology, and the correct distinction between these two EEG states is crucial to improving the cognitive development of these epileptic patients. The proposed approach was assessed using the proprietary database of Casa Ninar, which contains data regarding children from northeastern Brazil born with microcephaly caused by the Zika virus. We also used data from the CHB-MIT database. Fundamental rhythms of the EEG signal δ, θ, α, and ß were analyzed, and then decomposed by Discrete Wavelet Transform, in which 45 mother wavelet functions were tested to determine the most appropriate function to represent the EEG signals in the hypsarrhythmia interictal and ictal states. We extracted Shannon, Log Energy, Norm, and Sure entropy measures of the subbands as relevant features, and the combinations among them were applied in the state-of-the-art machine learning methods. The combination of Sure entropy with Shannon entropy, or with Log Energy and Norm, extracted from the δ rhythm, allowed for the best linear separability between the classes in most of the classifiers, obtaining 100% accuracy, sensitivity, and specificity.

Classification of breast tissue in mammograms using efficient coding.

BACKGROUND: Female breast cancer is the major cause of death by cancer in western countries. Efforts in Computer Vision have been made in order to improve the diagnostic accuracy by radiologists. Some methods of lesion diagnosis in mammogram images were developed based in the technique of principal component analysis which has been used in efficient coding of signals and 2D Gabor wavelets used for computer vision applications and modeling biological vision. METHODS: In this work, we present a methodology that uses efficient coding along with linear discriminant analysis to distinguish between mass and non-mass from 5090 region of interest from mammograms. RESULTS: The results show that the best rates of success reached with Gabor wavelets and principal component analysis were 85.28% and 87.28%, respectively. In comparison, the model of efficient coding presented here reached up to 90.07%. CONCLUSIONS: Altogether, the results presented demonstrate that independent component analysis performed successfully the efficient coding in order to discriminate mass from non-mass tissues. In addition, we have observed that LDA with ICA bases showed high predictive performance for some datasets and thus provide significant support for a more detailed clinical investigation.

Detection of auditory cortex activity by fMRI using a dependent component analysis.

Functional MRI (fMRI) data often have low signal-to-noise-ratio (SNR) and are contaminated by strong interference from other physiological sources. A promising tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). BSS is based on the assumption that the detected signals are a mixture of a number of independent source signals that are linearly combined via an unknown mixing matrix. BSS seeks to determine the mixing matrix to recover the source signals based on principles of statistical independence. In most cases, extraction of all sources is unnecessary; instead, a priori information can be applied to extract only the signal of interest. Herein we propose an algorithm based on a variation of ICA, called Dependent Component Analysis (DCA), where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We applied such method to inspect functional Magnetic Resonance Imaging (fMRI) data, aiming to find the hemodynamic response that follows neuronal activation from an auditory stimulation, in human subjects. The method localized a significant signal modulation in cortical regions corresponding to the primary auditory cortex. The results obtained by DCA were also compared to those of the General Linear Model (GLM), which is the most widely used method to analyze fMRI datasets.

Research for estimating direction of saccadic eye movements by single trial processing.

Electroencephalogram (EEG) related to fast eye movement (saccade), has been the subject of application oriented research by our group toward developing a brain-computer interface(BCI). Our goal is to develop novel BCI based on eye movements system employing EEG signals online. Most of the analysis of the saccade-related EEG data has been performed using ensemble averaging approaches. However, ensemble averaging is not suitable for BCI. In order to process raw EEG data in real time, we performed saccade-related EEG experiments and processed data by using the non-conventional Fast ICA with Reference signal(FICAR). Visually guided saccade tasks and auditorily guided saccade tasks were performed and the EEG signal generated in the saccade was recorded. Saccade-related EEG signals and saccade-related ICs in visually and Auditorily guided saccade task are compared in the point of the latency between starting time of a saccade and time when a saccade-related EEG signal or an IC has maximum value and in the point of the peak scale where a saccade-related EEG signal or an IC has maximum value. As results, peak time when saccade-related ICs have maximum amplitude is earlier than peak time when saccade-related EEG signals have maximum amplitude. This is very important advantage for developing our BCI. However, S/N ratio in being processed by FICAR is not improved comparing S/N ratio in being processed by ensemble averaging. In next step, we tried to estimate direction of saccade from raw EEG signals by FICAR.

Single trial method for brain-computer interface.

Electroencephalogram (EEG) related to fast eye movement (saccade), has been the subject of application oriented research by our group toward developing a brain-computer interface (BCI). Our goal is to develop novel BCI based on eye movements system employing EEG signals online. Most of the analysis of the saccade-related EEG data has been performed using ensemble averaging approaches. However, ensemble averaging is not suitable for BCI. In order to process raw EEG data in real time, we performed saccade-related EEG experiments and processed data by using the non-conventional fast ICA with reference signal (FICAR). The FICAR algorithm can extract desired independent components (IC) which have strong correlation against a reference signal. Visually guided saccade tasks and auditory guided saccade tasks were performed and the EEG signal generated in the saccade was recorded. The EEG processing was performed in three stages: PCA preprocessing and noise reduction, extraction of the desired IC using Wiener filter with reference signal, and post-processing using higher order statistics fast ICA based on maximization of kurtosis. Form the experimental results and analysis we found that using FICAR it is possible to extract form raw EEG data the saccade-related ICs and to predict saccade in advance by about 10 [ms] before real movements of eyes occurs. For single trail EEG data we have successfully extracted the desire ICs with recognition rate about 70%. In next steps, saccade-related EEG signals and saccade-related ICs in visually and auditory guided saccade task are compared in the point of the latency between starting time of a saccade and time when a saccade-related EEG signal or an IC has maximum value and in the point of the peak scale where a saccade-related EEG signal or an IC has maximum value. As results, peak time when saccade-related ICs have maximum amplitude is earlier than peak time when saccade-related EEG signals have maximum amplitude. This is very important advantage for developing our BCI. However, S/N ratio in being processed by FICAR is not improved comparing S/N ratio in being processed by ensemble averaging.

Validity of the adaptive filter for accurate measurement of cardiac output in impedance cardiography.

The purpose of this study was to assess the validity of an adaptive filter, the scaled Fourier linear combiner (SFLC), in the impedance cardiography (ICG). Eight healthy males underwent constant-load bicycle exercise at different intensities from unloaded to near maximal intensity. The stroke volume (SV) and cardiac output (Q) measured by ICG at each condition were compared with those by the CO2 rebreathing method. We found that the noises were greatly reduced in the impedance waveform and that the inflection points, so-called the B- and X-points, were clearly detected even during strenuous exercise using the SFLC. Although a high correlation was observed between Qs measured by the two methods, the mean values of Qs in each method differed significantly and the regression line also differed significantly from the identity line. Likewise, a significant correlation was observed between SVs obtained by the two methods, but a significant difference in the group mean values and a trend of the regression line were observed. These findings suggest that the use of SFLC in ICG improves the performance in eliminating the noises and in detecting the inflection points in the waveform, thereby contributing to the accurate and beat-to-beat measurements of SV and Q especially during exercise.