Epstein-Barr virus encephalitis presenting with brain mass lesions in a patient with human immunodeficiency virus infection

Epstein-Barr virus (EBV) disease of central nervous system (CNS) in human immunodeficiency virus (HIV) patients is mostly associated with primary CNS lymphoma (PCNSL). In patients who cannot undergo biopsy and have typical clinical and radiographic findings, detection of EBV deoxyribonucleic acid in CSF may provide enough evidence to start treatment for PCNSL. Here, we described a case of EBV encephalitis presenting with fever, memory, and psycho-motor deficits in a patient with HIV infection and severe immunosuppression who started antiretroviral therapy (ART) one month earlier. Brain magnetic resonance imaging showed periventricular lesions with nodular enhancing pattern and restricted diffusion, and CSF was positive for EBV. Brain biopsy revealed inflammatory lesions and lymphoid infiltrate without signs of malignancy. After three months of ART, patient improved significantly and MRI showed a marked reduction of lesions. Two years later, patient’s condition remains stable. PCNSL is the leading diagnosis in HIV patients with CNS mass lesions and positive CSF for EBV. In the case described, starting treatment for PCNSL could have been considered if the patient could not undergo biopsy, or if there was no improvement under ART. However, EBV encephalitis can be a differential diagnosis in patients with compatible histopathology and clinical course. © 2023 Termedia Publishing House Ltd.. All rights reserved

Spectral normalization MFCC derived features for robust speech recognition

This paper presents a method for extracting MFCC parameters from a normalised power spectrum density. The underlined spectral normalisation method is based on the fact that the speech regions with less energy need more robustness, since in these regions the noise is more dominant, thus the speech is more corrupted. Less energy speech regions contain usually sounds of unvoiced nature where are included nearly half of the consonants, and are by nature the least reliable ones due to the effective noise presence even when the speech is acquired under controlled conditions. This spectral normalisation was tested under additive artificial white noise in an Isolated Speech Recogniser and showed very promising results [1]. It is well known that concerned to speech representation, MFCC parameters appear to be more effective than power spectrum based features. This paper shows how the cepstral speech representation can take advantage of the above-referred spectral normalisation and shows some results in the continuous speech recognition paradigm in clean and artificial noise conditions

On Separating Environmental and Speaker Adaptation

This paper presents a maximum likelihood (ML) approach, concerned to the background model estimation, in noisy acoustic non-stationary environments. The external noise source is characterised by a time constant convolutional and a time varying additive components. The HMM composition technique, provides a mechanism for integrating parametric models of acoustic background with the signal model, so that noise compensation is tightly coupled with the background model estimation. However, the existing continuous adaptation algorithms usually do not take advantage of this approach, being essentially based on the MLLR algorithm. Consequently, a model for environmental mismatch is not available and, even under constrained conditions a significant number of model parameters have to be updated. From a theoretical point of view only the noise model parameters need to be updated, being the clean speech ones unchanged by the environment. So, it can be advantageous to have a model for environmental mismatch. Additionally separating the additive and convolutional components means a separation between the environmental mismatch and speaker mismatch when the channel does not change for long periods. This approach was followed in the development of the algorithm proposed in this paper. One drawback sometimes attributed to the continuous adaptation approach is that recognition failures originate poor background estimates. This paper also proposes a MAP-like method to deal with this situation

Competitive biosorption of ortho-cresol, phenol, chlorophenol and chromium(VI) from aqueous solution by a bacterial biofilm supported on granular activated carbon

A biofilm of Arthrobacter viscosus supported on granular activated carbon was used to remove chromium and organic compounds (chlorophenol, phenol and o-cresol) from aqueous solutions. The compounds were studied as single solutes and in different combinations between them and Cr(VI). Optimum Cr(VI) adsorption was observed at a phenol concentration of 100 mg/l and at an initial concentration of the metal of 60 mg/l. The maximum values of biosorption of organic compounds were 9.94 mg/g for phenol, 9.70 mg/g for chlorophenol and 13.99 mg/g for o-cresol. In terms of removal percentage, after 15 h of experiment, the affinity order was as follows: phenol > chlorophenol > o-cresol > chromium(VI).Fundação para a Ciência e Tecnologia (FCT

Blind source separation by independent component analysis applied to electroencephalographic signals

Independent Component Analysis (ICA) is a statistical based method, which goal is to find a linear transformation to apply to an observed multidimensional random vector such that its components become as statistically independent from each other as possible. Usually the Electroencephalographic (EEG) signal is hard to interpret and analyse since it is corrupted by some artifacts which originates the rejection of contaminated segments and perhaps in an unacceptable loss of data. The ICA filters trained on data collected during EEG sessions can identify statistically independent source channels which could then be further processed by using event-related potential (ERP), event-related spectral perturbation (ERSP) or other signal processing techniques. This paper describes, as a preliminary work, the application of ICA to EEG recordings of the human brain activity, showing its applicability

Methodology evaluation of pin microrelief meter

The effects of natural weathering and different managements performed in agriculture may best be understood by studying the soil roughness. The aim of this study was to evaluate the optimization of the use of pin microrelief meter, an instrument used to determine the soil surface roughness, as the number of readings collected over traditional methodology proposed in the bibliography. The study was conducted in Rio Paranaiba (MG), in a Haplustox soil. The experimental design was completely randomized in a 2×3 factorial design with four replications. There were combined two types of primary tillage: conventional tillage with disc plow (PCAD) and harrow (PCGA), and three amounts of readings (100, 200, and 300 reading points) sampled in each experimental unit. Independently of the soil tillage, disc plow and harrow, the collection of 100 readings using a pin microrelief meter of a square meter, was sufficient to determine the surface roughness before and after soil preparation, without accuracy loss compared with the traditional method

Avaliação de fungicidas para o controle da ferrugem da videira.

Suplemento, ref. 257. Edição dos Resumos do XXXV Congresso Paulista de Fitopatologia, Jaguariúna, fev. 2012

Efeito do óleo essencial de Piper hispidinervum sobre Moniliophthora perniciosa.

O objetivo deste trabalho foi avaliar o efeito do óleo essencial de Piper hispidinervum sobre a germinação de esporos e do crescimento micelial de M. perniciosa in vitro.Resumo:94-1