Parkinson's disease identification through Optimum-Path Forest

Artificial intelligence techniques have been extensively used for the identification of several disorders related with the voice signal analysis, such as Parkinson's disease (PD). However, some of these techniques flaw by assuming some separability in the original feature space or even so in the one induced by a kernel mapping. In this paper we propose the PD automatic recognition by means of Optimum-Path Forest (OPF), which is a new recently developed pattern recognition technique that does not assume any shape/separability of the classes/feature space. The experiments showed that OPF outperformed Support Vector Machines, Artificial Neural Networks and other commonly used supervised classification techniques for PD identification. © 2010 IEEE.

The Use of Smell Identification Tests in the Diagnosis of Parkinson`s Disease in Brazil

Smell identification tests may be of routine clinical value in the differential diagnosis of PD but are subject to cultural variation and have not been systematically evaluated in the Brazilian population. We have applied culturally adapted translations of the University of Pennsylvania 40-item smell identification test (UPSIT-40) and the 16-item identification test from Sniffin` Sticks (SS-16) to nondemented Brazilian PD patients and controls. Pearson`s correlation coefficient between the test scores was 0.76 (95% CI 0.70-0.81, n = 204, P < 0.001). To calculate reliability measures for each test we used the diagnosis (either PD or control) as outcome variable for separate logistic regression analyses using the score in the UPSIT-40 or the SS-16 as a covariate. The SS-16 specificity was 89.0% with a sensitivity of 81.1% (106 PD and 118 controls). The UPSIT-40 specificity was 83.5% and its sensitivity 82.1% (95 PD and 109 controls). Regression curves were used to associate an individual`s smell test score with the probability of belonging to the PD, as opposed to the control group. Our data provide support for the use of the UPSIT-40 and SS-16 to help distinguish early PD from controls. (c) 2008 Movement Disorder Society

Identification and mutational analyses of phosphorylation sites of the calcineurin-binding protein CbpA and the identification of domains required for calcineurin binding in Aspergillus fumigatus.

Calcineurin is a key protein phosphatase required for hyphal growth and virulence in Aspergillus fumigatus, making it an attractive antifungal target. However, currently available calcineurin inhibitors, FK506 and cyclosporine A, are immunosuppressive, limiting usage in the treatment of patients with invasive aspergillosis. Therefore, the identification of endogenous inhibitors of calcineurin belonging to the calcipressin family is an important parallel strategy. We previously identified the gene cbpA as the A. fumigatus calcipressin member and showed its involvement in hyphal growth and calcium homeostasis. However, the mechanism of its activation/inhibition through phosphorylation and its interaction with calcineurin remains unknown. Here we show that A. fumigatus CbpA is phosphorylated at three distinct domains, including the conserved SP repeat motif (phosphorylated domain-I; PD-I), a filamentous fungal-specific domain (PD-II), and the C-terminal CIC motif (Calcipressin Inhibitor of Calcineurin; PD-III). While mutation of three phosphorylated residues (Ser208, Ser217, Ser223) in the PD-II did not affect CbpA function in vivo, mutation of the two phosphorylated serines (Ser156, Ser160) in the SP repeat motif caused reduced hyphal growth and sensitivity to oxidative stress. Mutational analysis in the key domains in calcineurin A (CnaA) and proteomic interaction studies confirmed the requirement of PxIxIT motif-binding residues (352-NIR-354) and the calcineurin B (CnaB)-binding helix residue (V371) for the binding of CbpA to CnaA. Additionally, while the calmodulin-binding residues (442-RVF-444) did not affect CbpA binding to CnaA, three mutations (T359P, H361L, and L365S) clustered between the CnaA catalytic and the CnaB-binding helix were also required for CbpA binding. This is the first study to analyze the phosphorylation status of calcipressin in filamentous fungi and identify the domains required for binding to calcineurin.

Immunocytochemical identification of adenohypophyseal cells in the pirarucu (Arapaima gigas), an Amazonian basal teleost

The adenohypophysis (AH) of juvenile pirarucu (Arapaima gigas), a representative species of the Osteoglossomorpha (bonytongue fishes, one of the oldest living groups of the teleosts), was studied using histochemical and immunocytochemical methods. The AH is comprised of the pars distalis (PD), without a clear distinction between rostral pars distalis (RPD) and proximal pars distalis (PPD), and the pars intermedia (PI). The neurohypophysis (NH) is positioned on top of the PD and penetrates and branches into the PI. In the most rostral dorsal portion of the PD, adrenocorticotropic cells and fusiform gonadotropic cells were found. In the central PD, scarce prolactin-producing cells and growth-hormone-producing cells were located mainly in the dorsal part, whereas round gonadotropic cells were abundant in the ventral portion of this region. Human thyrotropin immunoreactive cells were not found in the entire AH. In the PI, melanotropic, some adrenocorticotropic, and somatolactin-producing cells were located intermingled surrounding the neurohypophyseal branches. Our results showed that the A. gigas pituitary has some basal characteristics between the ancient Actinopterygii and the more derived teleosts.

Pedal : identification of models for duodenal administration of levodopa

Backgound and aims: The main purpose of the PEDAL study is to identify and estimate sample individual pharmacokinetic- pharmacodynamic (PK/PD) models for duodenal infusion of levodopa/carbidopa (Duodopa®) that can be used for in numero simulation of treatment strategies. Other objectives are to study the absorption of Duodopa® and to form a basis for power calculation for a future larger study. PK/PD based on oral levodopa is problematic because of irregular gastric emptying. Preliminary work with data from [Gundert-Remy U et al. Eur J Clin Pharmacol 1983;25:69-72] suggested that levodopa infusion pharmacokinetics can be described by a two-compartment model. Background research led to a hypothesis for an effect model incorporating concentration-unrelated fluctuations, more complex than standard E-max models. Methods: PEDAL involved a few patients already on Duodopa®. A bolus dose (normal morning dose plus 50%) was given after a washout during night. Data collection continued until the clinical effect was back at baseline. The procedure was repeated on two non-consecutive days per patient. The following data were collected in 5 to 15 minutes intervals: i) Accelerometer data. ii) Three e-diary questions about ability to walk, feelings of “off” and “dyskinesia”. iii) Clinical assessment of motor function by a physician. iv) Plasma concentrations of levodopa, carbidopa and the metabolite 3-O-methyldopa. The main effect variable will be the clinical assessment. Results: At date of abstract submission, lab analyses were currently being performed. Modelling results, simulation experiments and conclusions will be presented in our poster.

Computerized identification of motor complications in Parkinson's disease

Objective: To investigate whether spirography-based objective measures are able to effectively characterize the severity of unwanted symptom states (Off and dyskinesia) and discriminate them from motor state of healthy elderly subjects. Background: Sixty-five patients with advanced Parkinson’s disease (PD) and 10 healthy elderly (HE) subjects performed repeated assessments of spirography, using a touch screen telemetry device in their home environments. On inclusion, the patients were either treated with levodopa-carbidopa intestinal gel or were candidates for switching to this treatment. On each test occasion, the subjects were asked trace a pre-drawn Archimedes spiral shown on the screen, using an ergonomic pen stylus. The test was repeated three times and was performed using dominant hand. A clinician used a web interface which animated the spiral drawings, allowing him to observe different kinematic features, like accelerations and spatial changes, during the drawing process and to rate different motor impairments. Initially, the motor impairments of drawing speed, irregularity and hesitation were rated on a 0 (normal) to 4 (extremely severe) scales followed by marking the momentary motor state of the patient into 2 categories that is Off and Dyskinesia. A sample of spirals drawn by HE subjects was randomly selected and used in subsequent analysis. Methods: The raw spiral data, consisting of stylus position and timestamp, were processed using time series analysis techniques like discrete wavelet transform, approximate entropy and dynamic time warping in order to extract 13 quantitative measures for representing meaningful motor impairment information. A principal component analysis (PCA) was used to reduce the dimensions of the quantitative measures into 4 principal components (PC). In order to classify the motor states into 3 categories that is Off, HE and dyskinesia, a logistic regression model was used as a classifier to map the 4 PCs to the corresponding clinically assigned motor state categories. A stratified 10-fold cross-validation (also known as rotation estimation) was applied to assess the generalization ability of the logistic regression classifier to future independent data sets. To investigate mean differences of the 4 PCs across the three categories, a one-way ANOVA test followed by Tukey multiple comparisons was used. Results: The agreements between computed and clinician ratings were very good with a weighted area under the receiver operating characteristic curve (AUC) coefficient of 0.91. The mean PC scores were different across the three motor state categories, only at different levels. The first 2 PCs were good at discriminating between the motor states whereas the PC3 was good at discriminating between HE subjects and PD patients. The mean scores of PC4 showed a trend across the three states but without significant differences. The Spearman’s rank correlations between the first 2 PCs and clinically assessed motor impairments were as follows: drawing speed (PC1, 0.34; PC2, 0.83), irregularity (PC1, 0.17; PC2, 0.17), and hesitation (PC1, 0.27; PC2, 0.77). Conclusions: These findings suggest that spirography-based objective measures are valid measures of spatial- and time-dependent deficits and can be used to distinguish drug-related motor dysfunctions between Off and dyskinesia in PD. These measures can be potentially useful during clinical evaluation of individualized drug-related complications such as over- and under-medications thus maximizing the amount of time the patients spend in the On state.

Identification and antimicrobial susceptibility of Staphylococcus from home-treated peritoneal dialysis patients

Staphylococcus aureus is the main agent of infections during peritoneal dialysis (PD). The presence of S. aureus in the nasal cavity has been extensively studied and suggested as a risk factor of dialysis-related infections, whereas coagulase-negative Staphylococcus (CNS) species are frequently considered part of the normal human microbiota. The aim of this study was to identify Staphylococcus in the nasal cavity, pericatheter skin and peritoneal effluent from PD patients, as well as to evaluate the antimicrobial activity evolution in vitro. Thirty-two chronic PD patients were observed during 12 months and had nasal and pericatheter skin samples collected for culture. When peritonitis was detected, samples were also collected from the peritoneal effluent for culture. The activity of several antimicrobial drugs (penicillin G, oxacillin, cephalothin, ofloxacin, netilmicin and vancomycin) against different Staphylococcus species was measured by using the agar drug diffusion assay (Kirby-Bauer method). Staphylococcus was separated into S. aureus, S. epidermidis and other CNS species in order to determine the in vitro resistance level. S. epidermidis resistance to oxacillin progressively increased during the study period (p < 0.05). Resistance to ofloxacin was inexpressive, whereas resistance to netilmicin and vancomycin was not detected. of the oxacillin-resistant species (n = 74), 83% were S. epidermidis, 13% other CNS and 4% S. aureus (p < 0.05). Regarding multidrug resistant strains (n = 45), 82% were S. epidermidis, 13% other CNS, and 5% S. aureus (p < 0.05). This study shows the relevance of resistance to oxacillin and CNS multi-drug resistance, particularly concerning S. epidermidis, in PD patients.

Improving Parkinson's disease identification through evolutionary-based feature selection

Parkinson's disease (PD) automatic identification has been actively pursued over several works in the literature. In this paper, we deal with this problem by applying evolutionary-based techniques in order to find the subset of features that maximize the accuracy of the Optimum-Path Forest (OPF) classifier. The reason for the choice of this classifier relies on its fast training phase, given that each possible solution to be optimized is guided by the OPF accuracy. We also show results that improved other ones recently obtained in the context of PD automatic identification. © 2011 IEEE.

Eletro-oxidação de metanol e etanol sobre catalisadores suportados de 'PD''AU'

Pós-graduação em Química - IQ

Partial Discharge Phenomena in Converter and Traction Transformers: Identification and Reliability

After the development of power electronics converters, the number of transformers subjected to non-sinusoidal stresses (including DC) has increased in applications such as HVDC links and traction (electric train power cars). The effects of non-sinusoidal voltages on transformer insulation have been investigated by many researchers, but still now, there are some issues that must be understood. Some of those issues are tackled in this Thesis, studying PD phenomena behavior in Kraft paper, pressboard and mineral oil at different voltage conditions like AC, DC, AC+DC, notched AC and square waveforms. From the point of view of converter transformers, it was found that the combined effect of AC and DC voltages produces higher stresses in the pressboard that those that are present under pure DC voltages. The electrical conductivity of the dielectric systems in DC and AC+DC conditions has demonstrated to be a critical parameter, so, its measurement and analysis was also taken into account during all the experiments. Regarding notched voltages, the RMS reduction caused by notches (depending on firing and overlap angles) seems to increase the PDIV. However, the experimental results show that once PD activity has incepted, the notches increase PD repetition rate and magnitude, producing a higher degradation rate of paper. On the other hand, the reduction of mineral oil stocks, their relatively low flash point as well as environmental issues, are factors that are pushing towards the use of esters as transformer insulating fluids. This PhD Thesis also covers the study of two different esters with the scope to validate their use in traction transformers. Mineral oil was used as benchmark. The complete set of dielectric tests performed in the three fluids, show that esters behave better than mineral oil in practically all the investigated conditions, so, their application in traction transformers is possible and encouraged.

Bioremediation of PCB-contaminated marine sediments: From identification of indigenous dehalorespirers to enhancement of microbial reductive dechlorination

Marine sediments are the main accumulation reservoir of organic recalcitrant pollutants such as polychlorinated biphenyls (PCBs). In the anoxic conditions typical of these sediments, anaerobic bacteria of the phylum Chloroflexi are able to attack these compounds in a process called microbial reductive dechlorination. Such activity and members of this phylum were detected in PCB-impacted sediments of the Venice Lagoon. The aim of this work was to investigate microbial reductive dechlorination and design bioremediation approaches for marine sediments of the area. Three out of six sediment cultures from different sampling areas exhibited dechlorination activities in the same conditions of the site and two phylotypes (VLD-1 and VLD-2) were detected and correlated to this metabolism. Biostimulation was tested on enriched dechlorinating sediment cultures from the same site using five different electron donors, of which lactate was the best biostimulating agent; complementation of microbial and chemical dechlorination catalyzed by biogenic zerovalent Pd nanoparticles was not effective due to sulfide poisoning of the catalyst. A new biosurfactant-producing strain of Shewanella frigidimarina was concomitantly obtained from hydrocarbon-degrading marine cultures and selected because of the low toxicity of its product. All these findings were then exploited to develop bioremediation lab-scale tests in shaken reactors and static microcosms on real sediments and water of the Venice lagoon, testing i) a bioaugmentation approach, with a selected enriched sediment culture from the same area, ii) a biostimulation approach with lactate as electron donor, iii) a bioavailability enhancement with the supplementation of the newly-discovered biosurfactant, and iv) all possible combinations of the afore-mentioned approaches. The best bioremediation approach resulted to be a combination of bioaugmentation and bioremediation and it could be a starting point to design bioremediation process for actual marine sediments of the Venice Lagoon area.

Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

This paper proposes a method for the identification of different partial discharges (PDs) sources through the analysis of a collection of PD signals acquired with a PD measurement system. This method, robust and sensitive enough to cope with noisy data and external interferences, combines the characterization of each signal from the collection, with a clustering procedure, the CLARA algorithm. Several features are proposed for the characterization of the signals, being the wavelet variances, the frequency estimated with the Prony method, and the energy, the most relevant for the performance of the clustering procedure. The result of the unsupervised classification is a set of clusters each containing those signals which are more similar to each other than to those in other clusters. The analysis of the classification results permits both the identification of different PD sources and the discrimination between original PD signals, reflections, noise and external interferences. The methods and graphical tools detailed in this paper have been coded and published as a contributed package of the R environment under a GNU/GPL license.

Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

This paper proposes a method for the identification of different partial discharges (PDs) sources through the analysis of a collection of PD signals acquired with a PD measurement system. This method, robust and sensitive enough to cope with noisy data and external interferences, combines the characterization of each signal from the collection, with a clustering procedure, the CLARA algorithm. Several features are proposed for the characterization of the signals, being the wavelet variances, the frequency estimated with the Prony method, and the energy, the most relevant for the performance of the clustering procedure. The result of the unsupervised classification is a set of clusters each containing those signals which are more similar to each other than to those in other clusters. The analysis of the classification results permits both the identification of different PD sources and the discrimination between original PD signals, reflections, noise and external interferences. The methods and graphical tools detailed in this paper have been coded and published as a contributed package of the R environment under a GNU/GPL license.

A clustering technique for partial discharge and noise sources identification in power cables by means of waveform parameters

On-line partial discharge (PD) measurements have become a common technique for assessing the insulation condition of installed high voltage (HV) insulated cables. When on-line tests are performed in noisy environments, or when more than one source of pulse-shaped signals are present in a cable system, it is difficult to perform accurate diagnoses. In these cases, an adequate selection of the non-conventional measuring technique and the implementation of effective signal processing tools are essential for a correct evaluation of the insulation degradation. Once a specific noise rejection filter is applied, many signals can be identified as potential PD pulses, therefore, a classification tool to discriminate the PD sources involved is required. This paper proposes an efficient method for the classification of PD signals and pulse-type noise interferences measured in power cables with HFCT sensors. By using a signal feature generation algorithm, representative parameters associated to the waveform of each pulse acquired are calculated so that they can be separated in different clusters. The efficiency of the clustering technique proposed is demonstrated through an example with three different PD sources and several pulse-shaped interferences measured simultaneously in a cable system with a high frequency current transformer (HFCT).

Identification, quantification and structuring of two-lane rural highway safety problems and solutions. Volume I - technical report. Final report.

Federal Highway Administration, Washington, D.C.