Genome-Wide Detection of Serpentine Receptor-Like Proteins in Malaria Parasites

Serpentine receptors comprise a large family of membrane receptors distributed over diverse organisms, such as bacteria, fungi, plants and all metazoans. However, the presence of serpentine receptors in protozoan parasites is largely unknown so far. In the present study we performed a genome-wide search for proteins containing seven transmembrane domains (7TM) in the human malaria parasite Plasmodium falciparum and identified four serpentine receptor-like proteins. These proteins, denoted PfSR1, PfSR10, PfSR12 and PfSR25, show membrane topologies that resemble those exhibited by members belonging to different families of serpentine receptors. Expression of the pfsrs genes was detected by Real Time PCR in P. falciparum intraerythrocytic stages, indicating that they potentially code for functional proteins. We also found corresponding homologues for the PfSRs in five other Plasmodium species, two primate and three rodent parasites. PfSR10 and 25 are the most conserved receptors among the different species, while PfSR1 and 12 are more divergent. Interestingly, we found that PfSR10 and PfSR12 possess similarity to orphan serpentine receptors of other organisms. The identification of potential parasite membrane receptors raises a new perspective for essential aspects of malaria parasite host cell infection.

LATENT RANK CHANGE DETECTION FOR ANALYSIS OF SPLICE-JUNCTION MICROARRAYS WITH NONLINEAR EFFECTS

Alternative splicing of gene transcripts greatly expands the functional capacity of the genome, and certain splice isoforms may indicate specific disease states such as cancer. Splice junction microarrays interrogate thousands of splice junctions, but data analysis is difficult and error prone because of the increased complexity compared to differential gene expression analysis. We present Rank Change Detection (RCD) as a method to identify differential splicing events based upon a straightforward probabilistic model comparing the over-or underrepresentation of two or more competing isoforms. RCD has advantages over commonly used methods because it is robust to false positive errors due to nonlinear trends in microarray measurements. Further, RCD does not depend on prior knowledge of splice isoforms, yet it takes advantage of the inherent structure of mutually exclusive junctions, and it is conceptually generalizable to other types of splicing arrays or RNA-Seq. RCD specifically identifies the biologically important cases when a splice junction becomes more or less prevalent compared to other mutually exclusive junctions. The example data is from different cell lines of glioblastoma tumors assayed with Agilent microarrays.

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.

Dual contactless conductivity and amperometric detection on hybrid PDMS/glass electrophoresis microchips

A new approach for the integration of dual contactless conductivity and amperometric detection with an electrophoresis microchip system is presented. The PDMS layer with the embedded channels was reversibly sealed to a thin glass substrate (400 mu m), on top of which a palladium electrode had been previously fabricated enabling end-channel amperometric detection. The thin glass substrate served also as a physical wall between the separation channel and the sensing copper electrodes for contactless conductivity detection. The latter were not integrated in the microfluidic device, but fabricated on an independent plastic substrate allowing a simpler and more cost-effective fabrication of the chip. PDMS/glass chips with merely contactless conductivity detection were first characterized in terms of sensitivity, efficiency and reproducibility. The separation efficiency of this system was found to be similar or slightly superior to other systems reported in the literature. The simultaneous determination of ionic and electroactive species was illustrated by the separation of peroxynitrite degradation products, i.e. NO(3)(-) (non-electroactive) and NO(2)(-) (electroactive), using hybrid PDMS/glass chips with dual contactless conductivity and amperometric detection. While both ions were detected by contactless conductivity detection with good efficiency, NO(2)(-) was also simultaneously detected amperometrically with a significant enhancement in sensitivity compared to contactless conductivity detection.

Sequence-specific electrochemical detection of Alicyclobacillus acidoterrestris DNA using electroconductive polymer-modified fluorine tin oxide electrodes

This study outlines the quantification of low levels of Alicyclobacillus acidoterrestris in pure cultures, since this bacterium is not inactivated by pasteurization and may remain in industrialized foods and beverages. Electroconductive polymer-modified fluorine tin oxide (FTO) electrodes and multiple nanoparticle labels were used for biosensing. The detection of A. acidoterrestris in pure cultures was performed by reverse transcription polymerase chain reaction (RT-PCR) and the sensitivity was further increased by asymmetric nested RT-PCR using electrochemical detection for quantification of the amplicon. The quantification of nested RT-PCR products by Ag/Au-based electrochemical detection was able to detect 2 colony forming units per mL (CFU mL(-1)) of spores in pure culture and low detection and quantification limits (7.07 and 23.6 nM, respectively) were obtained for the target A. acidoterrestris on the electrochemical detection bioassay.

Rapid and sensitive measurements of nitrate ester explosives using microchip electrophoresis with electrochemical detection

This article describes an effective microchip protocol based on electrophoretic-separation and electrochemical detection for highly sensitive and rapid measurements of nitrate ester explosives, including ethylene glycol dinitrate (EGDN), pentaerythritol tetranitrate (PETN), propylene glycol dinitrate (PGDN) and glyceryl trinitrate (nitroglycerin, NG). Factors influencing the separation and detection processes were examined and optimized. Under the optimal separation conditions obtained using a 15 mM borate buffer (pH 9.2) containing 20 mM SDS, and applying a separation voltage of 1500 V, the four nitrate ester explosives were separated within less than 3 min. The glassy-carbon amperometric detector (operated at -0.9 V vs. Ag/AgCl) offers convenient cathodic detection down to the picogram level, with detection limits of 0.5 ppm and 0.3 ppm for PGDN and for NG, respectively, along with good repeatability (RSD of 1.8-2.3%; n = 6) and linearity (over the 10-60 ppm range). Such effective microchip operation offers great promise for field screening of nitrate ester explosives and for supporting various counter-terrorism surveillance activities.

Rapid prototyping of polymeric electrophoresis microchips with integrated copper electrodes for contactless conductivity detection

A simple and easy approach to produce polymeric microchips with integrated copper electrodes for capacitively coupled contactless conductivity detection (CD) is described. Copper electrodes were fabricated using a printed circuit board (PCB) as an inexpensive thin-layer of metal. The electrode layout was first drawn and laser printed on a wax paper sheet. The toner layer deposited on the paper sheet was thermally transferred to the PCB surface working as a mask for wet chemical etching of the copper layer. After the etching step, the toner was removed with an acetonitrile-dampened cotton. A poly(ethylene terephthalate) (PET) film coated with a thin thermo-sensitive adhesive layer was used to laminate the PCB plate providing an insulator layer of the electrodes to perform CID measurements. Electrophoresis microchannels were fabricated in poly(dimethylsiloxane) (PDMS) by soft lithography and reversibly sealed against the PET film. These hybrid PDMS/PET chips exhibited a stable electroosmotic mobility of 4.25 +/- 0.04 x 10(-4) V cm(-2) s(-1), at pH 6.1, over fifty runs. Efficiencies ranging from 1127 to 1690 theoretical plates were obtained for inorganic cations.

Evaluation of the use of multiple lines for determination of metals in water by inductively coupled plasma optical emission spectrometry with axial viewing

Inductively coupled plasma optical emission spectrometers (ICP DES) allow fast simultaneous measurements of several spectral lines for multiple elements. The combination of signal intensities of two or more emission lines for each element may bring such advantages as improvement of the precision, the minimization of systematic errors caused by spectral interferences and matrix effects. In this work, signal intensities for several spectral lines were combined for the determination of Al, Cd, Co, Cr, Mn, Pb, and Zn in water. Afterwards, parameters for evaluation of the calibration model were calculated to select the combination of emission lines leading to the best accuracy (lowest values of PRESS-Predicted error sum of squares and RMSEP-Root means square error of prediction). Limits of detection (LOD) obtained using multiple lines were 7.1, 0.5, 4.4, 0.042, 3.3, 28 and 6.7 mu g L(-1) (n = 10) for Al, Cd. Co, Cr, Mn, Pb and Zn, respectively, in the presence of concomitants. On the other hand, the LOD established for the most intense emission line were 16. 0.7, 8.4, 0.074. 23, 26 and 9.6 mu g L(-1) (n = 10) for these same elements in the presence of concomitants. The accuracy of the developed procedure was demonstrated using water certified reference material. The use of multiple lines improved the sensitivity making feasible the determination of these analytes according to the target values required for the current environmental legislation for water samples and it was also demonstrated that measurements in multiple lines can also be employed as a tool to verify the accuracy of an analytical procedure in ICP DES. (C) 2009 Elsevier B.V. All rights reserved.

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.

EFFECTS OF 14-WEEK SWIMMING TRAINING PROGRAM ON THE PSYCHOLOGICAL, HORMONAL, AND PHYSIOLOGICAL PARAMETERS OF ELITE WOMEN ATHLETES

Santhiago, V, da Silva, ASR, Papoti, M, and Gobatto, CA. Effects of 14-week swimming training program on the psychological, hormonal, and physiological parameters of elite women athletes. J Strength Cond Res 25(3): 825-832, 2011-The purpose of the study was to investigate the influence of a 14-week swimming training program on psychological, hormonal, and performance parameters of elite women swimmers. Ten Olympic and international-level elite women swimmers were evaluated 4 times along the experiment (i.e., in T1, T2, T3, and T4). On the first day at 8: 00 AM, before the blood collecting at rest for the determination of hormonal parameters, the athletes had their psychological parameters assessed by the profile of mood-state questionnaire. At 3: 00 AM, the swimmers had their anaerobic threshold assessed. On the second day at 3: 00 AM, the athletes had their alactic anaerobic performance measured. Vigor score and testosterone levels were lower (p <= 0.05) in T4 compared with T3. In addition, the rate between the peak blood lactate concentration and the median velocity obtained in the alactic anaerobic performance test increased in T4 compared with T3 (p < 0.05). For practical applications, the swimming coaches should not use a tapering with the present characteristics to avoid unexpected results.

Offline Detection, Identification, and Correction of Branch Parameter Errors Based on Several Measurement Snapshots

This paper proposes a three-stage offline approach to detect, identify, and correct series and shunt branch parameter errors. In Stage 1 the branches suspected of having parameter errors are identified through an Identification Index (II). The II of a branch is the ratio between the number of measurements adjacent to that branch, whose normalized residuals are higher than a specified threshold value, and the total number of measurements adjacent to that branch. Using several measurement snapshots, in Stage 2 the suspicious parameters are estimated, in a simultaneous multiple-state-and-parameter estimation, via an augmented state and parameter estimator which increases the V - theta state vector for the inclusion of suspicious parameters. Stage 3 enables the validation of the estimation obtained in Stage 2, and is performed via a conventional weighted least squares estimator. Several simulation results (with IEEE bus systems) have demonstrated the reliability of the proposed approach to deal with single and multiple parameter errors in adjacent and non-adjacent branches, as well as in parallel transmission lines with series compensation. Finally the proposed approach is confirmed on tests performed on the Hydro-Quebec TransEnergie network.

Fault Detection in Power Distribution Systems Using Automated Integration of Computational Intelligence Tools

The main purpose of this paper is to present architecture of automated system that allows monitoring and tracking in real time (online) the possible occurrence of faults and electromagnetic transients observed in primary power distribution networks. Through the interconnection of this automated system to the utility operation center, it will be possible to provide an efficient tool that will assist in decisionmaking by the Operation Center. In short, the desired purpose aims to have all tools necessary to identify, almost instantaneously, the occurrence of faults and transient disturbances in the primary power distribution system, as well as to determine its respective origin and probable location. The compilations of results from the application of this automated system show that the developed techniques provide accurate results, identifying and locating several occurrences of faults observed in the distribution system.

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.

Innovation concept for measurement gross error detection and identification in power system state estimation

In this study, the innovation approach is used to estimate the measurement total error associated with power system state estimation. This is required because the power system equations are very much correlated with each other and as a consequence part of the measurements errors is masked. For that purpose an index, innovation index (II), which provides the quantity of new information a measurement contains is proposed. A critical measurement is the limit case of a measurement with low II, it has a zero II index and its error is totally masked. In other words, that measurement does not bring any innovation for the gross error test. Using the II of a measurement, the masked gross error by the state estimation is recovered; then the total gross error of that measurement is composed. Instead of the classical normalised measurement residual amplitude, the corresponding normalised composed measurement residual amplitude is used in the gross error detection and identification test, but with m degrees of freedom. The gross error processing turns out to be very simple to implement, requiring only few adaptations to the existing state estimation software. The IEEE-14 bus system is used to validate the proposed gross error detection and identification test.

Assessment of the Performance of Acoustic and Mass Balance Methods for Leak Detection in Pipelines for Transporting Liquids

On-line leak detection is a main concern for the safe operation of pipelines. Acoustic and mass balance are the most important and extensively applied technologies in field problems. The objective of this work is to compare these leak detection methods with respect to a given reference situation, i.e., the same pipeline and monitoring signals acquired at the inlet and outlet ends. Experimental tests were conducted in a 749 m long laboratory pipeline transporting water as the working fluid. The instrumentation included pressure transducers and electromagnetic flowmeters. Leaks were simulated by opening solenoid valves placed at known positions and previously calibrated to produce known average leak flow rates. Results have clearly shown the limitations and advantages of each method. It is also quite clear that acoustics and mass balance technologies are, in fact, complementary. In general, an acoustic leak detection system sends out an alarm more rapidly and locates the leak more precisely, provided that the rupture of the pipeline occurs abruptly enough. On the other hand, a mass balance leak detection method is capable of quantifying the leak flow rate very accurately and of detecting progressive leaks.