Interference with Hemozoin Formation Represents an Important Mechanism of Schistosomicidal Action of Antimalarial Quinoline Methanols

Background: The parasitic trematode Schistosoma mansoni is one of the major causative agents of human schistosomiasis, which afflicts 200 million people worldwide. Praziquantel remains the main drug used for schistosomiasis treatment, and reliance on the single therapy has been prompting the search for new therapeutic compounds against this disease. Our group has demonstrated that heme crystallization into hemozoin (Hz) within the S. mansoni gut is a major heme detoxification route with lipid droplets involved in this process and acting as a potential chemotherapeutical target. In the present work, we investigated the effects of three antimalarial compounds, quinine (QN), quinidine (QND) and quinacrine (QCR) in a murine schistosomiasis model by using a combination of biochemical, cell biology and molecular biology approaches. Methodology/Principal Findings: Treatment of S. mansoni-infected female Swiss mice with daily intraperitoneal injections of QN, and QND (75 mg/kg/day) from the 11(th) to 17(th) day after infection caused significant decreases in worm burden (39%-61%) and egg production (42%-98%). Hz formation was significantly inhibited (40%-65%) in female worms recovered from QN- and QND-treated mice and correlated with reduction in the female worm burden. We also observed that QN treatment promoted remarkable ultrastructural changes in male and female worms, particularly in the gut epithelium and reduced the granulomatous reaction to parasite eggs trapped in the liver. Microarray gene expression analysis indicated that QN treatment increased the expression of transcripts related to musculature, protein synthesis and repair mechanisms. Conclusions: The overall significant reduction in several disease burden parameters by the antimalarial quinoline methanols indicates that interference with Hz formation in S. mansoni represents an important mechanism of schistosomicidal action of these compounds and points out the heme crystallization process as a valid chemotherapeutic target to treat schistosomiasis.

On the template synthesis of nanostructured inorganic/organic hybrid films

Prussian Blue has been introduced as a mediator to achieve stable, sensitive, reproducible, and interference-free biosensors. However, Na(+), Li(+), H(+), and all group II cations are capable to block the activity of Prussian Blue and, because Na(+) can be found in most human fluids, Prussian Blue analogs have already been developed to overcome this problem. These analogs, such as copper hexacyanoferrate, have also been introduced in a conducting polypyrrole matrix to create hybrid materials (copper hexacyanoferrate/polypyrrole, CuHCNFe/Ppy) with improved mechanical and electrochemical characteristics. Nowadays, the challenges in amperometric enzymatic biosensors consist of improving the enzyme immobilization and in making the chemical signal transduction more efficient. The incorporation of nanostructured materials in biosensors can optimize both steps and a nanostructured hybrid CuHCNFe/Ppy mediator has been developed using a template of colloidal polystyrene particles. The nanostructured material has achieved sensitivities 7.6 times higher than the bulk film during H(2)O(2) detection and it has also presented better results in other analytical parameters such as time response and detection limit. Besides, the nanostructured mediator was successfully applied at glucose biosensing in electrolytes containing Prussian Blue blocking cations. (C) 2008 The Electrochemical Society.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

Dual-task interference: Attentional and neurophysiological influences

Performing two tasks simultaneously often degrades performance of one or both tasks. While this dual-task interference is classically interpreted in terms of shared attentional resources, where two motor tasks are performed simultaneously interactions within primary motor cortex (i.e., activity-dependent coupling) may also be a contributing factor. In the present study TMS (transcranial magnetic stimulation) was used to examine the contribution of activity-dependent coupling to dual-task interference during concurrent performance of a bimanual coordination task and a discrete probe reaction time (RT) task involving the foot. Experiments 1 and 2 revealed that activity-dependent coupling within the leg corticomotor pathway was greater during dual-task performance than single-task performance, and this was associated with interference on the probe RT task (i.e., increased RT). Experiment 3 revealed that dual-task interference occurred regardless of whether the dual-task involved two motor tasks or a motor and cognitive task, however activity-dependent coupling was present only when a dual motor task was performed. This suggests that activity-dependent coupling is less detrimental to performance than attentional processes operating upstream of the corticomotor system. Finally, while prioritising the RT task reduced, but did not eliminate, dual-task interference the contribution of activity-dependent coupling to dual-task interference was not affected by task prioritisation. This suggests that although activity-dependent coupling may contribute to dual motor-task interference, attentional processes appear to be more important. It also suggests that activity-dependent coupling may not be subject to modulation by attentional processes. (C) 2009 Elsevier B.V. All rights reserved.

Noise reduction in biomedical speech signal processing based on time and frequency Kalman filtering combined with spectral subtraction

The goal of this paper is to study and propose a new technique for noise reduction used during the reconstruction of speech signals, particularly for biomedical applications. The proposed method is based on Kalman filtering in the time domain combined with spectral subtraction. Comparison with discrete Kalman filter in the frequency domain shows better performance of the proposed technique. The performance is evaluated by using the segmental signal-to-noise ratio and the Itakura-Saito`s distance. Results have shown that Kalman`s filter in time combined with spectral subtraction is more robust and efficient, improving the Itakura-Saito`s distance by up to four times. (C) 2007 Elsevier Ltd. All rights reserved.

Void fraction measurement and signal analysis from multiple-electrode impedance sensors

Void fraction sensors are important instruments not only for monitoring two-phase flow, but for furnishing an important parameter for obtaining flow map pattern and two-phase flow heat transfer coefficient as well. This work presents the experimental results obtained with the analysis of two axially spaced multiple-electrode impedance sensors tested in an upward air-water two-phase flow in a vertical tube for void fraction measurements. An electronic circuit was developed for signal generation and post-treatment of each sensor signal. By phase shifting the electrodes supplying the signal, it was possible to establish a rotating electric field sweeping across the test section. The fundamental principle of using a multiple-electrode configuration is based on reducing signal sensitivity to the non-uniform cross-section void fraction distribution problem. Static calibration curves were obtained for both sensors, and dynamic signal analyses for bubbly, slug, and turbulent churn flows were carried out. Flow parameters such as Taylor bubble velocity and length were obtained by using cross-correlation techniques. As an application of the void fraction tested, vertical flow pattern identification could be established by using the probability density function technique for void fractions ranging from 0% to nearly 70%.

Ultrasonic Viscosity Measurement Using the Shear-Wave Reflection Coefficient with a Novel Signal Processing Technique

Real-time viscosity measurement remains a necessity for highly automated industry. To resolve this problem, many studies have been carried out using an ultrasonic shear wave reflectance method. This method is based on the determination of the complex reflection coefficient`s magnitude and phase at the solid-liquid interface. Although magnitude is a stable quantity and its measurement is relatively simple and precise, phase measurement is a difficult task because of strong temperature dependence. A simplified method that uses only the magnitude of the reflection coefficient and that is valid under the Newtonian regimen has been proposed by some authors, but the obtained viscosity values do not match conventional viscometry measurements. In this work, a mode conversion measurement cell was used to measure glycerin viscosity as a function of temperature (15 to 25 degrees C) and corn syrup-water mixtures as a function of concentration (70 to 100 wt% of corn syrup). Tests were carried out at 1 MHz. A novel signal processing technique that calculates the reflection coefficient magnitude in a frequency band, instead of a single frequency, was studied. The effects of the bandwidth on magnitude and viscosity were analyzed and the results were compared with the values predicted by the Newtonian liquid model. The frequency band technique improved the magnitude results. The obtained viscosity values came close to those measured by the rotational viscometer with percentage errors up to 14%, whereas errors up to 96% were found for the single frequency method.

An In-Depth Study of the Barkhausen Emission Signal Properties of the Plastically Deformed Fe-2%Si Alloy

This paper presents the results of the in-depth study of the Barkhausen effect signal properties for the plastically deformed Fe-2%Si samples. The investigated samples have been deformed by cold rolling up to plastic strain epsilon(p) = 8%. The first approach consisted of time-domain-resolved pulse and frequency analysis of the Barkhausen noise signals whereas the complementary study consisted of the time-resolved pulse count analysis as well as a total pulse count. The latter included determination of time distribution of pulses for different threshold voltage levels as well as the total pulse count as a function of both the amplitude and the duration time of the pulses. The obtained results suggest that the observed increase in the Barkhausen noise signal intensity as a function of deformation level is mainly due to the increase in the number of bigger pulses.

A new technique to control brushless motor for blood pump application

This article presents a back-electromotive force (BEMF)-based technique of detection for sensorless brushless direct current motor (BLDCM) drivers. The BLDCM has been chosen as the energy converter in rotary or pulsatile blood pumps that use electrical motors for pumping. However, in order to operate properly, the BLDCM driver needs to know the shaft position. Usually, that information is obtained through a set of Hall sensors assembled close to the rotor and connected to the electronic controller by wires. Sometimes, a large distance between the motor and controller makes the system susceptible to interference on the sensor signal because of winding current switching. Thus, the goal of the sensorless technique presented in this study is to avoid this problem. First, the operation of BLDCM was evaluated on the electronic simulator PSpice. Then, a BEMF detector circuitry was assembled in our laboratories. For the tests, a sensor-dependent system was assembled where the direct comparison between the Hall sensors signals and the detected signals was performed. The obtained results showed that the output sensorless detector signals are very similar to the Hall signals at speeds of more than 2500 rpm. Therefore, the sensorless technique is recommended as a responsible or redundant system to be used in rotary blood pumps.

In silico analysis of candidate genes involved in light sensing and signal transduction pathways in soybean

Several aspects of photoperception and light signal transduction have been elucidated by studies with model plants. However, the information available for economically important crops, such as Fabaceae species, is scarce. In order to incorporate the existing genomic tools into a strategy to advance soybean research, we have investigated publicly available expressed sequence tag ( EST) sequence databases in order to identify Glycine max sequences related to genes involved in light-regulated developmental control in model plants. Approximately 38,000 sequences from open-access databases were investigated, and all bona fide and putative photoreceptor gene families were found in soybean sequence databases. We have identified G. max orthologs for several families of transcriptional regulators and cytoplasmic proteins mediating photoreceptor-induced responses, although some important Arabidopsis phytochrome-signaling components are absent. Moreover, soybean and Arabidopsis gene-family homologs appear to have undergone a distinct expansion process in some cases. We propose a working model of light perception, signal transduction and response-eliciting in G. max, based on the identified key components from Arabidopsis. These results demonstrate the power of comparative genomics between model systems and crop species to elucidate several aspects of plant physiology and metabolism.

SacRALF1, a peptide signal from the grass sugarcane (Saccharum spp.), is potentially involved in the regulation of tissue expansion

Rapid alkalinization factor (RALF) is part of a growing family of small peptides with hormone characteristics in plants. Initially isolated from leaves of tobacco plants, RALF peptides can be found throughout the plant kingdom and they are expressed ubiquitously in plants. We took advantage of the small gene family size of RALF genes in sugarcane and the ordered cellular growth of the grass sugarcane leaves to gain information about the function of RALF peptides in plants. Here we report the isolation of two RALF peptides from leaves of sugarcane plants using the alkalinization assay. SacRALF1 was the most abundant and, when added to culture media, inhibited growth of microcalli derived from cell suspension cultures at concentrations as low as 0.1 mu M. Microcalli exposed to exogenous SacRALF1 for 5 days showed a reduced number of elongated cells. Only four copies of SacRALF genes were found in sugarcane plants. All four SacRALF genes are highly expressed in young and expanding leaves and show a low or undetectable level of expression in expanded leaves. In half-emerged leaf blades, SacRALF transcripts were found at high levels at the basal portion of the leaf and at low levels at the apical portion. Gene expression analyzes localize SacRALF genes in elongation zones of roots and leaves. Mature leaves, which are devoid of expanding cells, do not show considerable expression of SacRALF genes. Our findings are consistent with SacRALF genes playing a role in plant development potentially regulating tissue expansion.

Interference of raw milk autochthonous microbiota on the performance of conventional methodologies for Listeria monocytogenes and Salmonella spp. detection

Pathogen detection in foods by reliable methodologies is very important to guarantee microbilogical safety. However, peculiar characteristics of certain foods, such as autochthonous microbiota, can directly influence pathogen development and detection. With the objective of verifying the performance of the official analytical methodologies for the isolation of Listeria monocytogenes and Salmonella in milk, different concentrations of these pathogens were inoculated in raw milk treatments with different levels of mesophilic aerobes, and then submitted to the traditional isolation procedures for the inoculated pathogens. Listeria monocytogenes was inoculated at the range of 0.2-5.2 log CFU/mL in treatments with 1.8-8.2 log CFU/mL. Salmonella Enteritidis was inoculated at 0.9-3.9 log CFU/mL in treatments with 3.0-8.2 log CFU/mL. The results indicated that recovery was not possible or was more difficult in the treatments with high counts of mesophilic aerobes and low levels of the pathogens, indicating interference of raw milk autochthonous microbiota. This interference was more evident for L. monocytogenes, once the pathogen recovery was not possible in treatments with mesophilic aerobes up to 4.0 log CFU/mL and inoculum under 2.0 log CFU/mL. For S. Enteritidis the interference appeared to be more non-specific. (C) 2007 Elsevier GmbH. All rights reserved.

Listeria monocytogenes and Salmonella spp. in raw milk produced in Brazil: Occurrence and interference of indigenous microbiota in their isolation and development

This study aimed to verify the occurrence of Listeria monocytogenes and Salmonella spp. in raw milk produced in Brazil. On account of the poor microbiological quality of this product, possible interference from the indigenous microbiota in these pathogens was also evaluated. Two-hundred and ten raw milk samples were collected in four important milk-producing areas in Brazil, tested for L. monocytogenes and Salmonella spp. presence, and for enumeration of indicator microorganisms: mesophilic aerobes, total coliforms and Escherichia coli. The interference of the indigenous microbiota in the isolation procedures was also tested, as well the frequency of naturally occurring raw milk strains with antagonistic activity against both pathogens. The pathogens were not isolated in any raw milk sample, but poor microbiological quality was confirmed by the high levels of indicator microorganisms. When present at high levels, the indigenous microbiota generated an evident interference in the methodologies of L. monocytogenes and Salmonella spp. isolation, mainly when the pathogens appeared at low levels. Three-hundred and sixty raw milk strains were tested for antagonistic activity against both pathogens, and 91 (25.3%) showed inhibitory activity against L. monocytogenes and 33 (9.2%) against Salmonella spp. The majority of the antagonistic strains were identified as Lactic Acid Bacteria species, mainly Lactococcus lactis subsp. lactis and Enterococcus faecium, known by antimicrobial substance production.

Interference of dexamethasone in the pulmonary cycle of Strongyloides venezuelensis in rats

The aim of this study was to investigate the interference of a daily treatment of dexamethasone in the pulmonary cycle of Strongyloides venezuelensis infection in rats. Three principal effects were found: 1) increased alveolar hemorrhagic inflammation provoked by the passage of larvae into alveolar spaces; 2) significant decrease of eosinophil and mast cell migration to the axial septum of the lungs; and 3) impaired formation of the reticular fiber network, interfering with granuloma organization. This study showed that the use of drugs with immunomodulatory actions, such as dexamethasone, in addition to interfering with the morbidity from the pulmonary cycle of S. venezuelensis infection, may contribute to showing the mechanisms involved in its pathogenesis.

A matching pursuit-based signal complexity measure for the analysis of newborn EEG

This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity, which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed relative structural complexity measure is used in the analysis of newborn EEG. To do this, firstly, a time-frequency (TF) decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).