Dataset Information

Spectral and temporal integration of brief tones.

ABSTRACT: Both spectral and temporal integration of tones have been explored in detail, but integration of tones varying across both dimensions has received little attention. This study explores temporal integration of tone pulses that vary over a range of frequencies. Baseline thresholds were obtained for both spectral and temporal integration with the same signals and compared with prior research. The signals were then varied on both dimensions in several ways: with equivalent spectral and temporal step sizes, different spectral and temporal step sizes, and a random pattern of frequency presentation. The data were also analyzed by spectral step size, temporal step size, frequency range, direction and slope of frequency change, and predictability. The spectral and temporal integration conditions showed that the current procedures and signals yielded the same improvement in detection thresholds as prior studies. The spectrotemporal integration conditions showed the improvement for overall detection of the signals to be limited by spectral integration, with improvement related primarily to the number of tones, regardless of timing and frequency. Surprisingly, trial-by-trial random presentation of signal frequencies did not negatively influence detection. These results support the multiple looks hypothesis [Viemeister, N. F. and Wakefield, G. H. (1991). "Temporal integration and multiple looks," J. Acoust. Soc. Am. 90, 858-865] as applied to spectrotemporal integration.

SUBMITTER: Hoglund EM

PROVIDER: S-EPMC2677269 | biostudies-other | 2009 Jan

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:Micro-CT is widely used for small animal imaging in preclinical studies of cardiopulmonary disease, but further development is needed to improve spatial resolution, temporal resolution, and material contrast. We present a technique for visualizing the changing distribution of iodine in the cardiac cycle with dual source micro-CT.The approach entails a retrospectively gated dual energy scan with optimized filters and voltages, and a series of computational operations to reconstruct the data. Projection interpolation and five-dimensional bilateral filtration (three spatial dimensions + time + energy) are used to reduce noise and artifacts associated with retrospective gating. We reconstruct separate volumes corresponding to different cardiac phases and apply a linear transformation to decompose these volumes into components representing concentrations of water and iodine. Since the resulting material images are still compromised by noise, we improve their quality in an iterative process that minimizes the discrepancy between the original acquired projections and the projections predicted by the reconstructed volumes. The values in the voxels of each of the reconstructed volumes represent the coefficients of linear combinations of basis functions over time and energy. We have implemented the reconstruction algorithm on a graphics processing unit (GPU) with CUDA. We tested the utility of the technique in simulations and applied the technique in an in vivo scan of a C57BL∕6 mouse injected with blood pool contrast agent at a dose of 0.01 ml∕g body weight. Postreconstruction, at each cardiac phase in the iodine images, we segmented the left ventricle and computed its volume. Using the maximum and minimum volumes in the left ventricle, we calculated the stroke volume, the ejection fraction, and the cardiac output.Our proposed method produces five-dimensional volumetric images that distinguish different materials at different points in time, and can be used to segment regions containing iodinated blood and compute measures of cardiac function.We believe this combined spectral and temporal imaging technique will be useful for future studies of cardiopulmonary disease in small animals.

Project description:As increasingly more genomes are sequenced, the vast majority of proteins may only be annotated computationally, given experimental investigation is extremely costly. This highlights the need for computational methods to determine protein functions quickly and reliably. We believe dividing a protein family into subtypes which share specific functions uncommon to the whole family reduces the function annotation problem's complexity. Hence, this work's purpose is to detect isofunctional subfamilies inside a family of unknown function, while identifying differentiating residues. Similarity between protein pairs according to various properties is interpreted as functional similarity evidence. Data are integrated using genetic programming and provided to a spectral clustering algorithm, which creates clusters of similar proteins. The proposed framework was applied to well-known protein families and to a family of unknown function, then compared to ASMC. Results showed our fully automated technique obtained better clusters than ASMC for two families, besides equivalent results for other two, including one whose clusters were manually defined. Clusters produced by our framework showed great correspondence with the known subfamilies, besides being more contrasting than those produced by ASMC. Additionally, for the families whose specificity determining positions are known, such residues were among those our technique considered most important to differentiate a given group. When run with the crotonase and enolase SFLD superfamilies, the results showed great agreement with this gold-standard. Best results consistently involved multiple data types, thus confirming our hypothesis that similarities according to different knowledge domains may be used as functional similarity evidence. Our main contributions are the proposed strategy for selecting and integrating data types, along with the ability to work with noisy and incomplete data; domain knowledge usage for detecting subfamilies in a family with different specificities, thus reducing the complexity of the experimental function characterization problem; and the identification of residues responsible for specificity.

Dataset Information

Spectral and temporal integration of brief tones.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure