Surveillance of HIV type 1 drug resistance among naive patients from Venezuela.

We have studied 65 HIV-1-infected untreated patients recruited in Caracas, Venezuela with TCD4 counts > or =350/microl. The reverse transcriptase and protease sequences of the virus were sequenced, aligned with reference HIV-1 group M strains, and analyzed for drug resistance mutations. Most of the viruses were subtype B genotype in both the protease and RT genomic regions. Five of the 62 virus isolates successfully amplified showed evidence of recombination between protease and RT, with their protease region being non-B while their RT region was derived from subtype B. Four strains were found bearing resistance mutations either to NRTIs, NNRTIs, or PIs. The prevalence of HIV-1 isolates bearing resistance mutations was therefore above the 5% threshold of WHO.

Spontaneous Speech and Emotional Response modeling based on One-class classifier oriented to Alzheimer Disease diagnosis

The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Sponta-neous Speech and Emotional Response Analysis. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis by One-class classifier. One-class classifi-cation problem differs from multi-class classifier in one essen-tial aspect. In one-class classification it is assumed that only information of one of the classes, the target class, is available. In this work we explore the problem of imbalanced datasets that is particularly crucial in applications where the goal is to maximize recognition of the minority class as in medical diag-nosis. The use of information about outlier and Fractal Dimen-sion features improves the system performance.

Once-daily dolutegravir versus darunavir plus ritonavir for treatment-naive adults with HIV-1 infection (FLAMINGO): 96 week results from a randomised, open-label, phase 3b study.

BACKGROUND: The primary analysis of the FLAMINGO study at 48 weeks showed that patients taking dolutegravir once daily had a significantly higher virological response rate than did those taking ritonavir-boosted darunavir once daily, with similar tolerability. We present secondary efficacy and safety results analysed at 96 weeks. METHODS: FLAMINGO was a multicentre, open-label, phase 3b, non-inferiority study of HIV-1-infected treatment-naive adults. Patients were randomly assigned (1:1) to dolutegravir 50 mg or darunavir 800 mg plus ritonavir 100 mg, with investigator-selected combination tenofovir and emtricitabine or combination abacavir and lamivudine background treatment. The main endpoints were plasma HIV-1 RNA less than 50 copies per mL and safety. The non-inferiority margin was -12%. If the lower end of the 95% CI was greater than 0%, then we concluded that dolutegravir was superior to ritonavir-boosted darunavir. This trial is registered with ClinicalTrials.gov, number NCT01449929. FINDINGS: Of 595 patients screened, 488 were randomly assigned and 484 included in the analysis (242 assigned to receive dolutegravir and 242 assigned to receive ritonavir-boosted darunavir). At 96 weeks, 194 (80%) of 242 patients in the dolutegravir group and 164 (68%) of 242 in the ritonavir-boosted darunavir group had HIV-1 RNA less than 50 copies per mL (adjusted difference 12·4, 95% CI 4·7-20·2; p=0·002), with the greatest difference in patients with high viral load at baseline (50/61 [82%] vs 32/61 [52%], homogeneity test p=0·014). Six participants (three since 48 weeks) in the dolutegravir group and 13 (four) in the darunavir plus ritonavir group discontinued because of adverse events. The most common drug-related adverse events were diarrhoea (23/242 [10%] in the dolutegravir group vs 57/242 [24%] in the darunavir plus ritonavir group), nausea (31/242 [13%] vs 34/242 [14%]), and headache (17/242 [7%] vs 12/242 [5%]). INTERPRETATION: Once-daily dolutegravir is associated with a higher virological response rate than is once-daily ritonavir-boosted darunavir. Dolutegravir compares favourably in efficacy and safety to a boosted darunavir regimen with nucleoside reverse transcriptase inhibitor background treatment for HIV-1-infected treatment-naive patients. FUNDING: ViiV Healthcare and Shionogi & Co.

Classifier combination for in vivo magnetic resonance spectra of brain tumours

Peer-reviewed

Classifier combination for in vivo magnetic resonance spectra of brain tumours

In this paper we present a multi-stage classifier for magnetic resonance spectra of human brain tumours which is being developed as part of a decision support system for radiologists. The basic idea is to decompose a complex classification scheme into a sequence of classifiers, each specialising in different classes of tumours and trying to reproducepart of the WHO classification hierarchy. Each stage uses a particular set of classification features, which are selected using a combination of classical statistical analysis, splitting performance and previous knowledge.Classifiers with different behaviour are combined using a simple voting scheme in order to extract different error patterns: LDA, decision trees and the k-NN classifier. A special label named "unknown¿ is used when the outcomes of the different classifiers disagree. Cascading is alsoused to incorporate class distances computed using LDA into decision trees. Both cascading and voting are effective tools to improve classification accuracy. Experiments also show that it is possible to extract useful information from the classification process itself in order to helpusers (clinicians and radiologists) to make more accurate predictions and reduce the number of possible classification mistakes.

A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning

Peer-reviewed

Brain ischemia alters platelet ATP diphosphohydrolase and 5'-nucleotidase activities in naive and preconditioned rats

The effects of transient forebrain ischemia, reperfusion and ischemic preconditioning on rat blood platelet ATP diphosphohydrolase and 5'-nucleotidase activities were evaluated. Adult Wistar rats were submitted to 2 or 10 min of single ischemic episodes, or to 10 min of ischemia 1 day after a 2-min ischemic episode (ischemic preconditioning) by the four-vessel occlusion method. Rats submitted to single ischemic insults were reperfused for 60 min and for 1, 2, 5, 10 and 30 days after ischemia; preconditioned rats were reperfused for 60 min 1 and 2 days after the long ischemic episode. Brain ischemia (2 or 10 min) inhibited ATP and ADP hydrolysis by platelet ATP diphosphohydrolase. On the other hand, AMP hydrolysis by 5'-nucleotidase was increased after 2, but not 10, min of ischemia. Ischemic preconditioning followed by 10 min of ischemia caused activation of both enzymes. Variable periods of reperfusion distinctly affected each experimental group. Enzyme activities returned to control levels in the 2-min group. However, the decrease in ATP diphosphohydrolase activity was maintained up to 30 days of reperfusion after 10-min ischemia. 5'-Nucleotidase activity was decreased 60 min and 1 day following 10-min ischemia; interestingly, enzymatic activity was increased after 2 and 5 days of reperfusion, and returned to control levels after 10 days. Ischemic preconditioning cancelled the effects of 10-min ischemia on the enzymatic activities. These results indicate that brain ischemia and ischemic preconditioning induce peripheral effects on ecto-enzymes from rat platelets involved in nucleotide metabolism. Thus, ATP, ADP and AMP degradation and probably the generation of adenosine in the circulation may be altered, leading to regulation of microthrombus formation since ADP aggregates platelets and adenosine is an inhibitor of platelet aggregation.

Imbalance of naive and memory T lymphocytes with sustained high cellular activation during the first year of life from uninfected children born to HIV-1-infected mothers on HAART

The immune consequences of in utero HIV exposure to uninfected children whose mothers were submitted to highly active antiretroviral therapy (HAART) during gestation are not well defined. We evaluated 45 HIV-exposed uninfected (ENI) neonates and 45 healthy unexposed control (CT) neonates. All HIV-infected mothers received HAART during pregnancy, and the viral load at delivery was <50 copies/mL for 56.8%. Twenty-three ENI neonates were further evaluated after 12 months and compared to 23 unexposed healthy age-matched infants. Immunophenotyping was performed by flow cytometry in cord and peripheral blood. Cord blood lymphocyte numbers did not differ between groups. However, ENI neonates had a lower percentage of naive T cells than CT neonates (CD4+, 76.6 vs 83.1%, P < 0.001; CD8+, 70.9 vs 79.6%, P = 0.003) and higher percentages of central memory T cells than CT neonates (CD4+, 13.9 vs 8.7%, P < 0.001; CD8+, 8.6 vs 4.8%, P = 0.001). CD38 mean fluorescence intensity of T cells was higher in ENI neonates (CD4+, 62.2 vs 52.1, P = 0.007; CD8+, 47.7 vs 35.3, P < 0.001). At 12 months, ENI infants still had higher mean fluorescence intensity of CD38 on T cells (CD4+, 34.2 vs 23.3, P < 0.001; CD8+, 26.8 vs 19.4, P = 0.035). Despite effective maternal virologic control at delivery, HIV-exposed uninfected children were born with lower levels of naive T cells. Immune activation was present at birth and remained until at least 12 months of age, suggesting that in utero exposure to HIV causes subtle immune abnormalities.

Modelling Energy Consumption of a Switch using Fuzzy-Rule Classifier

The energy consumption of IT equipments is becoming an issue of increasing importance. In particular, network equipments such as routers and switches are major contributors to the energy consumption of internet. Therefore it is important to understand how the relationship between input parameters such as bandwidth, number of active ports, traffic-load, hibernation-mode and their impact on energy consumption of a switch. In this paper, the energy consumption of a switch is analyzed in extensive experiments. A fuzzy rule-based model of energy consumption of a switch is proposed based on the result of experiments. The model can be used to predict the energy saving when deploying new switches by controlling the parameters to achieve desired energy consumption and subsequent performance. Furthermore, the model can also be used for further researches on energy saving techniques such as energy-efficient routing protocol, dynamic link shutdown, etc.

Toward a Reliable Classifier For Arrhythmia Diagnosis

Illnesses related to the heart are one of the major reasons for death all over the world causing many people to lose their lives in last decades. The good news is that many of those sicknesses are preventable if they are spotted in early stages. On the other hand, the number of the doctors are much lower than the number of patients. This will makes the auto diagnosing of diseases even more and more essential for humans today. Furthermore, when it comes to the diagnosing methods and algorithms, the current state of the art is lacking a comprehensive study on the comparison between different diagnosis solutions. Not having a single valid diagnosing solution has increased the confusion among scholars and made it harder for them to take further steps. This master thesis will address the issue of reliable diagnosing algorithm. We investigate ECG signals and the relation between different diseases and the heart’s electrical activity. Also, we will discuss the necessary steps needed for auto diagnosing the heart diseases including the literatures discussing the topic. The main goal of this master thesis is to find a single reliable diagnosing algorithm and quest for the best classifier to date for heart related sicknesses. Five most suited and most well-known classifiers, such as KNN, CART, MLP, Adaboost and SVM, have been investigated. To have a fair comparison, the ex-periment condition is kept the same for all classification methods. The UCI repository arrhythmia dataset will be used and the data will not be preprocessed. The experiment results indicates that AdaBoost noticeably classifies different diseases with a considera-bly better accuracy.

Realisation of a Target Classifier For Noise Sources in the Ocean

This thesis addresses one of the emerging topics in Sonar Signal Processing.,viz.the implementation of a target classifier for the noise sources in the ocean, as the operator assisted classification turns out to be tedious,laborious and time consuming.In the work reported in this thesis,various judiciously chosen components of the feature vector are used for realizing the newly proposed Hierarchical Target Trimming Model.The performance of the proposed classifier has been compared with the Euclidean distance and Fuzzy K-Nearest Neighbour Model classifiers and is found to have better success rates.The procedures for generating the Target Feature Record or the Feature vector from the spectral,cepstral and bispectral features have also been suggested.The Feature vector ,so generated from the noise data waveform is compared with the feature vectors available in the knowledge base and the most matching pattern is identified,for the purpose of target classification.In an attempt to improve the success rate of the Feature Vector based classifier,the proposed system has been augmented with the HMM based Classifier.Institutions where both the classifier decisions disagree,a contention resolving mechanism built around the DUET algorithm has been suggested.

Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier

This paper presents the application of wavelet processing in the domain of handwritten character recognition. To attain high recognition rate, robust feature extractors and powerful classifiers that are invariant to degree of variability of human writing are needed. The proposed scheme consists of two stages: a feature extraction stage, which is based on Haar wavelet transform and a classification stage that uses support vector machine classifier. Experimental results show that the proposed method is effective