Wireless instantaneous neurotransmitter concentration system-based amperometric detection of dopamine, adenosine, and glutamate for intraoperative neurochemical monitoring - laboratory investigation

Object  In a companion study, the authors describe the development of a new instrument named the Wireless Instantaneous Neurotransmitter Concentration System (WINCS), which couples digital telemetry with fast-scan cyclic voltammetry (FSCV) to measure extracellular concentrations of dopamine. In the present study, the authors describe the extended capability of the WINCS to use fixed potential amperometry (FPA) to measure extracellular concentrations of dopamine, as well as glutamate and adenosine. Compared with other electrochemical techniques such as FSCV or high-speed chronoamperometry, FPA offers superior temporal resolution and, in combination with enzyme-linked biosensors, the potential to monitor nonelectroactive analytes in real time.

Methods  The WINCS design incorporated a transimpedance amplifier with associated analog circuitry for FPA; a microprocessor; a Bluetooth transceiver; and a single, battery-powered, multilayer, printed circuit board. The WINCS was tested with 3 distinct recording electrodes: 1) a carbon-fiber microelectrode (CFM) to measure dopamine; 2) a glutamate oxidase enzyme–linked electrode to measure glutamate; and 3) a multiple enzyme–linked electrode (adenosine deaminase, nucleoside phosphorylase, and xanthine oxidase) to measure adenosine. Proof-of-principle analyses included noise assessments and in vitro and in vivo measurements that were compared with similar analyses by using a commercial hardwired electrochemical system (EA161 Picostat, eDAQ; Pty Ltd). In urethane-anesthetized rats, dopamine release was monitored in the striatum following deep brain stimulation (DBS) of ascending dopaminergic fibers in the medial forebrain bundle (MFB). In separate rat experiments, DBS-evoked adenosine release was monitored in the ventrolateral thalamus. To test the WINCS in an operating room setting resembling human neurosurgery, cortical glutamate release in response to motor cortex stimulation (MCS) was monitored using a large-mammal animal model, the pig.

Results   The WINCS, which is designed in compliance with FDA-recognized consensus standards for medical electrical device safety, successfully measured dopamine, glutamate, and adenosine, both in vitro and in vivo. The WINCS detected striatal dopamine release at the implanted CFM during DBS of the MFB. The DBS-evoked adenosine release in the rat thalamus and MCS-evoked glutamate release in the pig cortex were also successfully measured. Overall, in vitro and in vivo testing demonstrated signals comparable to a commercial hardwired electrochemical system for FPA.

Conclusions  By incorporating FPA, the chemical repertoire of WINCS-measurable neurotransmitters is expanded to include glutamate and other nonelectroactive species for which the evolving field of enzyme-linked biosensors exists. Because many neurotransmitters are not electrochemically active, FPA in combination with enzyme-linked microelectrodes represents a powerful intraoperative tool for rapid and selective neurochemical sampling in important anatomical targets during functional neurosurgery.

Detection of brain conditions from evoked responses using artificial neural networks

[No abstract available]

Online motor fault detection and diagnosis using a hybrid FMM-CART model

In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks.

Approach to fault detection of time-delay systems using functional observers

A new methodology is reported for designing functional observers to detect actuator faults of a class of time-delay systems where the matrix pair (A, C) is not observable. First, a generalised state transformation is used to transform the system into new coordinates where the delay term associated with the state vector is injected into the system's output and input. Then, a minimum-order functional observer is designed to construct a residual function that can trigger system faults. The finding is significant as it is now possible to detect faults of time-delay systems where the pair (A, C) is not required to be observable. A numerical example is given to illustrate the effectiveness of the proposed design approach. © The Institution of Engineering and Technology 2014.

Efficacy comparison of clustering systems for limb detection

This paper presents a comparison of applying different clustering algorithms on a point cloud constructed from the depth maps captured by a RGBD camera such as Microsoft Kinect. The depth sensor is capable of returning images, where each pixel represents the distance to its corresponding point not the RGB data. This is considered as the real novelty of the RGBD camera in computer vision compared to the common video-based and stereo-based products. Depth sensors captures depth data without using markers, 2D to 3D-transition or determining feature points. The captured depth map then cluster the 3D depth points into different clusters to determine the different limbs of the human-body. The 3D points clustering is achieved by different clustering techniques. Our Experiments show good performance and results in using clustering to determine different human-body limbs.

Noncontact detection and analysis of respiratory function using microwave Doppler Radar

Real-time respiratory measurement with Doppler Radar has an important advantage in the monitoring of certain conditions such as sleep apnoea, sudden infant death syndrome (SIDS), and many other general clinical uses requiring fast nonwearable and non-contact measurement of the respiratory function. In this paper, we demonstrate the feasibility of using Doppler Radar in measuring the basic respiratory frequencies (via fast Fourier transform) for four different types of breathing scenarios: normal breathing, rapid breathing, slow inhalation-fast exhalation, and fast inhalation-slow exhalation conducted in a laboratory environment. A high correlation factor was achieved between the Doppler Radar-based measurements and the conventional measurement device, a respiration strap. We also extended this work from basic signal acquisition to extracting detailed features of breathing function (I: E ratio). This facilitated additional insights into breathing activity and is likely to trigger a number of new applications in respiratory medicine.

Efficient road detection and tracking for unmanned aerial vehicle

Abstract - An unmanned aerial vehicle (UAV) has many applications in a variety of fields. Detection and tracking of a specific road in UAV videos play an important role in automatic UAV navigation, traffic monitoring, and ground–vehicle tracking, and also is very helpful for constructing road networks for modeling and simulation. In this paper, an efficient road detection and tracking framework in UAV videos is proposed. In particular, a graph-cut–based detection approach is given to accurately extract a specified road region during the initialization stage and in the middle of tracking process, and a fast homography-based road-tracking scheme is developed to automatically track road areas. The high efficiency of our framework is attributed to two aspects: the road detection is performed only when it is necessary and most work in locating the road is rapidly done via very fast homography-based tracking. Experiments are conducted on UAV videos of real road scenes we captured and downloaded from the Internet. The promising results indicate the effectiveness of our proposed framework, with the precision of 98.4% and processing 34 frames per second for 1046 x 595 videos on average.

Detection of dynamic background due to swaying movements from motion features

Dynamically changing background (dynamic background) still presents a great challenge to many motion-based video surveillance systems. In the context of event detection, it is a major source of false alarms. There is a strong need from the security industry either to detect and suppress these false alarms, or dampen the effects of background changes, so as to increase the sensitivity to meaningful events of interest. In this paper, we restrict our focus to one of the most common causes of dynamic background changes: 1) that of swaying tree branches and 2) their shadows under windy conditions. Considering the ultimate goal in a video analytics pipeline, we formulate a new dynamic background detection problem as a signal processing alternative to the previously described but unreliable computer vision-based approaches. Within this new framework, we directly reduce the number of false alarms by testing if the detected events are due to characteristic background motions. In addition, we introduce a new data set suitable for the evaluation of dynamic background detection. It consists of real-world events detected by a commercial surveillance system from two static surveillance cameras. The research question we address is whether dynamic background can be detected reliably and efficiently using simple motion features and in the presence of similar but meaningful events, such as loitering. Inspired by the tree aerodynamics theory, we propose a novel method named local variation persistence (LVP), that captures the key characteristics of swaying motions. The method is posed as a convex optimization problem, whose variable is the local variation. We derive a computationally efficient algorithm for solving the optimization problem, the solution of which is then used to form a powerful detection statistic. On our newly collected data set, we demonstrate that the proposed LVP achieves excellent detection results and outperforms the best alternative adapted from existing art in the dynamic background literature.

Detection of glucose and triglycerides using information visualization methods to process impedance spectroscopy data

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A novel algorithm for feature selection using Harmony Search and its application for non-technical losses detection

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A New Approach for Nontechnical Losses Detection Based on Optimum-Path Forest

Nowadays, fraud detection is important to avoid nontechnical energy losses. Various electric companies around the world have been faced with such losses, mainly from industrial and commercial consumers. This problem has traditionally been dealt with using artificial intelligence techniques, although their use can result in difficulties such as a high computational burden in the training phase and problems with parameter optimization. A recently-developed pattern recognition technique called optimum-path forest (OPF), however, has been shown to be superior to state-of-the-art artificial intelligence techniques. In this paper, we proposed to use OPF for nontechnical losses detection, as well as to apply its learning and pruning algorithms to this purpose. Comparisons against neural networks and other techniques demonstrated the robustness of the OPF with respect to commercial losses automatic identification.

Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Pyroelectric composite film for X-ray intensity detection

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Effects of meperidine or saline on thermal, mechanical and electrical nociceptive thresholds in cats

Objective To measure cutaneous electrical nociceptive thresholds in relation to known thermal and mechanical stimulation for nociceptive threshold detection in cats.Study design Prospective, blinded, randomized cross-over study with 1-week washout interval.Animals Eight adult cats [bodyweight 5.1 +/- 1.8 kg (mean + SD)].Methods Mechanical nociceptive thresholds were tested using a step-wise manual inflation of a modified blood pressure bladder attached to the cat's thoracic limb. Thermal nociceptive thresholds were measured by increasing the temperature of a probe placed on the thorax. The electrical nociceptive threshold was tested using an escalating current from a constant current generator passed between electrodes placed on the thoracic region. A positive response (threshold) was recorded when cats displayed any or all of the following behaviors: leg shake, head turn, avoidance, or vocalization. Four baseline readings were performed before intramuscular injection of meperidine (5 mg kg(-1)) or an equal volume of saline. Threshold recordings with each modality were made at 15, 30, 45, 60, 90, and 120 minutes post-injection. Data were analyzed using ANOVA and paired t-tests (significance at p < 0.05).Results There were no significant changes in thermal, mechanical, or electrical thresholds after saline. Thermal thresholds increased at 15-60 minutes (p < 0.01) and mechanical threshold increased at 30 and 45 minutes after meperidine (p < 0.05). Maximum thermal threshold was +4.1 +/- 0.3 degrees C above baseline at 15 minutes while maximum mechanical threshold was 296 +/- 265 mmHg above baseline at 30 minutes after meperidine. Electrical thresholds following meperidine were not significantly different than baseline (p > 0.05). Thermal and electrical thresholds after meperidine were significantly higher than saline at 30 and 45 minutes (p < 0.05), and at 120 minutes (p < 0.05), respectively. Mechanical thresholds were significantly higher than saline treatment at 30 minutes (p <= 0.05).Conclusion and clinical relevance Electrical stimulation did not detect meperidine analgesia whereas both thermal and mechanical thresholds changed after meperidine administration in cats.

Analysis of the electrical conductivity in milking fractions as a mean for detecting and characterizing mastitis in goats

The aim of the work was to study the effect of milking fraction on electrical conductivity of milk (EC) to improve its use in dairy goat mastitis detection using automatic EC measurements during milking. The experiment was carried out on a group of 84 Murciano-Granadina goats (28 primiparous and 56 multiparous). Goats were in the fourth month of lactation. A linear mixed model was used to analyse the relationship between EC or somatic cell count (SCC) of gland milk and parity, mammary gland health status, analysed fraction (first 100 mL=F-1; machine milk=F-2; and stripping milk=F-3) and their first order interactions. Additionally, the mastitis detection characteristics (sensitivity, specificity, positive predictive value and negative predictive value) of SCC and EC were studied at different thresholds.All factors considered were significant for EC and SCC. EC decreased significantly as milking progressed (from F-1 to F-3) in both healthy and infected glands. EC was not significantly different between healthy and infected glands in F-1 and F-2 fractions, but EC of healthy glands (5.01 mS/cm) was significantly lower than in infected glands (5.03 mS/cm) at F-3.Mastitis detection characteristics of EC did not differ amongst studied fractions. The small significant difference of EC between healthy and infected glands obtained in F-3 fraction did not yield better sensitivity results compared to F-1 and F-2. The best EC mastitis detection characteristics were obtained at 5.20 mS/cm threshold (sensitivity of 70% and specificity of 50%). The best SCC mastitis detection characteristics were obtained at 300,000 cells/mL threshold and F-3 fraction (sensitivity of 85% and specificity of 65%).It was concluded that mastitis detection characteristics of EC were similar in the three milking fractions analysed, being slightly better for SCC in F-3 fraction. As shown in previous studies, there are no factors other than the mammary gland health status that affect milk EC and should be considered in the algorithms for mastitis detection to improve the results. (C) 2012 Elsevier B.V. All rights reserved.