Methods to Measure Importance of Data Attributes to Consumers of Information Products

Errors in data sources of information product (IP) manufacturing systems can degrade overall IP quality as perceived by consumers. Data defects from inputs propagate throughout the IP manufacturing process. Information Quality (IQ) research has focused on improving the quality of inputs to mitigate error propagation and ensure an IP will be fit for use by consumers. However, the feedback loop from IP consumers to IP producers is often incomplete since the overall quality of the IP is not based solely on quality of inputs but rather by the IP’s fitness for use as a whole. It remains uncertain that high quality inputs directly correlate to a high quality IP. The methods proposed in this paper investigate the effects of intentionally decreasing, or disrupting, quality of inputs, measuring the consumers\u27 evaluations as compared to an undisrupted IP, and proposes scenarios illustrating the advantage of these methods over traditional survey methods. Fitness for use may then be increased using those attributes deemed “important” by consumers in future IP revisions

Rethinking Data Collection and Signal Processing. 1. Real-Time Oversampling Filter for Chemical Measurements

Minimizing noise in chemical measurements is critical to achieve low limits of detection and accurate measurements. We describe a real-time oversampling filter that offers a method to reduce stochastic noise in a time-dependent chemical measurement. The power of this technique is demonstrated in its application to the separation of dopamine and serotonin by micellar electrokinetic chromatography with amperometric detection. Signal-to-noise ratios were increased by almost an order of magnitude, allowing for limits of detection of 100 and 120 amol, respectively. Real-time oversampling filters can be implemented using simple software algorithms and require no change to existing experimental apparatus. The application is not limited to analytical separations, and this technique can be used to improve the signal-to-noise ratio in any experiment where the necessary sampling rate is less than the maximum sampling rate of the analog-to-digital converter. Theory, implementation, and the performance of this filter are described. We propose that this technique should be the default mode of operation for an analog-to-digital converter

Differential release of dopamine in the nucleus accumbens evoked by low-versus high-frequency medial prefrontal cortex stimulation

The medial prefrontal cortex (mPFC) coordinates goal-directed behaviors, which may be mediated through mPFC regulation of dopamine release in the nucleus accumbens (NAc). Furthermore, frequency-specific oscillatory activity between the frontal cortex and downstream structures may facilitate interregion communication. Although high-frequency (e.g., 60 Hz) mPFC stimulation is known to increase basal dopamine levels in the NAc, little is known about how phasic dopamine release is affected by mPFC stimulation. Understanding the frequency-specific control of phasic dopamine release by mPFC stimulation could elucidate mechanisms by which the mPFC modulates other regions. It could also inform optimization of deep brain stimulation for treatment of neurological disorders. Objective: The goal of this work was to characterize the frequency response of NAc dopamine release resultant from mPFC stimulation. We hypothesized that the magnitude of dopamine release in the NAc would increase with increasing stimulation frequency. Methods: Electrical stimulation of the mPFC of anesthetized rats was delivered at 4-60 Hz and at varying durations while measuring NAc dopamine release with fast-scan cyclic voltammetry. Results: mPFC stimulation resulted in phasic dopamine release in the NAc. Furthermore, 20 Hz stimulation evoked the largest peak response for stimulation intervals > 5 s when compared to higher or lower frequencies. Conclusions: Activation of the mPFC drives dopamine release in the NAc in a complex frequency- and duration-dependent manner. This has implications for the use of deep brain stimulation treatment of disorders marked by dopaminergic dysregulation, and suggest that mPFC may exert more specialized control over neuromodulator release than previously understood. (C) 2017 Published by Elsevier Inc.University of Arizona; National Science Foundation [1450767]; National Institutes of Health [T32-GM00804]12 month embargo; published online: 15 November 2017This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Fast-Scan Controlled-Adsorption Voltammetry for the Quantification of Absolute Concentrations and Adsorption Dynamics

Fast-scan cyclic voltammetry has depended on background subtraction to quantify small changes in neurotransmitter concentration. Because of this requirement, measurements of absolute concentrations using fast-scan cyclic voltammetry have been limited. Here we develop and characterize fast-scan controlled-adsorption voltammetry (FSCAV), which enables direct measurements of absolute concentrations in vitro without the use of flow injection to change the concentration. This enables probing the diffusion-controlled adsorption dynamics of biogenic amines and other adsorbing species. An implicit finite-difference model of mass-transport-limited adsorption was developed and is in agreement with experimental results. Optimization of FSCAV yielded a sensitivity of 81 ± 11 nA/μM for dopamine, corresponding to a limit of detection of 3.7 ± 0.5 nM. Through the combination of novel instrumentation and validated computer simulations, we show that FSCAV is an important measurement tool that can be used to determine absolute concentrations and study mass-transport-limited adsorption

Rethinking Data Collection and Signal Processing. 2. Preserving the Temporal Fidelity of Electrochemical Measurements

Direct electrochemical measurements of biological events are often challenging because of the low signal relative to the magnitude of the background and noise. When choosing a data processing approach, the frequency and phase content of the data must be considered. Here, we employ a zero-phase (infinite impulse response (IIR)) filter to remove the noise from the analytical signal, while preserving the phase content. In fast-scan cyclic voltammetry, the frequency content of the signal is a function of the scan rate of the applied waveform. Fourier analysis was used to develop a relationship between scan rate and the filter cutoff frequency to maximize the reduction in noise, while not altering the true nature of the analytical signal. The zero-phase filter has the same effect as traditional filters with regards to increasing the signal-to-noise ratio. Because the zero-phase filter does not introduce a change to Δ<i>E</i>_peak, the heterogeneous electron rate transfer constant (0.10 cm/s) for ferrocene is calculated accurately. The zero-phase filter also improves electrochemical analysis of signaling molecules that have their oxidation potential close to the switching potential. Lastly, a quantitative approach to filtering amperometric traces of exocytosis based on the rise time was developed

In Vivo Ambient Serotonin Measurements at Carbon-Fiber Microelectrodes

The mechanisms that control extracellular serotonin levels in vivo are not well-defined. This shortcoming makes it very challenging to diagnose and treat the many psychiatric disorders in which serotonin is implicated. Fast-scan cyclic voltammetry (FSCV) can measure rapid serotonin release and reuptake events but cannot report critically important ambient serotonin levels. In this Article, we use fast-scan controlled adsorption voltammetry (FSCAV), to measure serotonin’s steady-state, extracellular chemistry. We characterize the “Jackson” voltammetric waveform for FSCAV and show highly stable, selective, and sensitive ambient serotonin measurements in vitro. In vivo, we report basal serotonin levels in the CA2 region of the hippocampus as 64.9 ± 2.3 nM (<i>n</i> = 15 mice, weighted average ± standard error). We electrochemically and pharmacologically verify the selectivity of the serotonin signal. Finally, we develop a statistical model that incorporates the uncertainty in in vivo measurements, in addition to electrode variability, to more critically analyze the time course of pharmacological data. Our novel method is a uniquely powerful analysis tool that can provide deeper insights into the mechanisms that control serotonin’s extracellular levels