Spectral analysis and resolving spatial ambiguities in human sound localization

This dissertation provides an overview of my research over the last five years into the spectral analysis involved in human sound localization. The work involved conducting psychophysical tests of human auditory localization performance and then applying analytical techniques to analyze and explain the data. It is a fundamental thesis of this work that human auditory localization response directions are primarily driven by the auditory localization cues associated with the acoustic filtering properties of the external auditory periphery, i.e., the head, torso, shoulder, neck, and external ears. This work can be considered as composed of three parts. In the first part of this work, I compared the auditory localization performance of a human subject and a time-delay neural network model under three sound conditions: broadband, high-pass, and low-pass. A “black-box” modeling paradigm was applied. The modeling results indicated that training the network to localize sounds of varying center-frequency and bandwidth could degrade localization performance results in a manner demonstrating some similarity to human auditory localization performance. As the data collected during the network modeling showed that humans demonstrate striking localization errors when tested using bandlimited sound stimuli, the second part of this work focused on human sound localization of bandpass filtered noise stimuli. Localization data was collected from 5 subjects and for 7 sound conditions: 300 Hz to 5 kHz, 300 Hz to 7 kHz, 300 Hz to 10 kHz, 300 Hz to 14 kHz, 3 to 8 kHz, 4 to 9 kHz, and 7 to 14 kHz. The localization results were analyzed using the method of cue similarity indices developed by Middlebrooks (1992). The data indicated that the energy level in relatively wide frequency bands could be driving the localization response directions, just as in Butler’s covert peak area model (see Butler and Musicant, 1993). The question was then raised as to whether the energy levels in the various frequency bands, as described above, are most likely analyzed by the human auditory localization system on a monaural or an interaural basis. In the third part of this work, an experiment was conducted using virtual auditory space sound stimuli in which the monaural spectral cues for auditory localization were disrupted, but the interaural spectral difference cue was preserved. The results from this work showed that the human auditory localization system relies primarily on a monaural analysis of spectral shape information for its discrimination of directions on the cone of confusion. The work described in the three parts lead to the suggestion that a spectral contrast model based on overlapping frequency bands of varying bandwidth and perhaps multiple frequency scales can provide a reasonable algorithm for explaining much of the current psychophysical and neurophysiological data related to human auditory localization

Schematic illustration of the conjugation of the cell-penetrating peptide (CPP) TAT to lyophilisomes.

<p>(1) Primary amine groups of lyophilisomes react with Sulfo-GMBS introducing reactive maleimide groups. (2) CPPs (cysteine-functionalized TAT-peptides; C-Ahx-YGRKKRRQRRR) are conjugated to maleimide-conjugated lyophilisomes, resulting in stable CPP-conjugated lyophilisomes. Sulfo-GMBS  =  sulfo-<i>N</i>-[γ-maleimidobutyryloxy]sulfo succinimide ester; Ahx  =  aminohexanoic acid; TAT  =  trans-activating transcriptional activator.</p

Quenching of FITC fluorescent lyophilisomes by trypan blue in the absence of cells.

<p>Results show decreased fluorescence (a) and efficient quenching (b) up to three washings steps of FITC fluorescent lyophilisomes by trypan blue.</p

Cellular binding and internalization of unmodified lyophilisomes and TAT-conjugated lyophilisomes.

<p>HeLa, OVCAR-3, Caco-2 and SKOV-3 cells incubated with TAT-conjugated and unmodified lyophilisomes resulted in 86±3% and 12±4%, 87±3% and 16±8%, 97±3% and 19±3%, and 95±10% and 67±20% lyophilisome-positive cells, respectively. TAT  =  trans-activating transcriptional activator. *p<0.01 ***p<0.0001.</p

Cellular uptake of TAT-conjugated lyophilisomes as analyzed by transmission electron microscopy.

<p>HeLa cells were incubated with unmodified (a) and TAT-conjugated lyophilisomes (b-d) for 4 h. a) No attachment or uptake was observed using unmodified lyophilisomes. b-d) TAT-conjugated lyophilisomes (white arrows) showed various processes required for effective drug delivery systems, such as attachment (b) and uptake (c). Additionally, signs of degradation of the capsule inside the cell were visualized (black arrows, d). Scale bar represents 1.0 µm. TAT  =  trans-activating transcriptional activator.</p

Internalization of lyophilisomes with and without TAT peptide into HeLa cells.

<p>FACS showed a large number of lyophilisome-positive cells for TAT-conjugated lyophilisomes after 1 h (67±3%) without trypan blue and a cellular uptake of 25±1% with trypan blue. Values for lyophilisomes without TAT peptide were low. When lyophilisomes were incubated for 4 h, TAT-conjugated lyophilisomes conserved the large number of lyophilisome-positive cells (79±8%) with an increased internalization of 59±14%, while unmodified lyophilisomes still showed few lyophilisome-positive cells and little cellular uptake. *p<0.01 **p<0.001. CPP  =  cell penetrating peptide; TAT  =  trans-activating transcriptional activator.</p

Sorting of lyophilisomes by fluorescence-activated cell sorting.

<p>a/b) A representative size distribution of the initial lyophilisome population (a) and sorted lyophilisomes (b) is depicted, showing smaller lyophilisomes after sorting. Note the difference in x and y axes. c) Initial lyophilisome population depicted in a FACS dot plot with forward (size)/FITC-positive lyophilisome (FL1 channel) scatter where gated FITC-positive lyophilisomes were sorted. d) After sorting, the scatter showed merely small lyophilisomes, as large lyophilisomes were removed.</p