University of Texas at Austin

About: University of Texas at Austin is a education organization based out in Austin, Texas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 94352 authors who have published 206297 publications receiving 9070052 citations. The organization is also known as: UT-Austin & UT Austin.

Social Relationships and Health Behavior Across the Life Course

Abstract: Sociological theory and research point to the importance of social relationships in affecting health behavior. This work tends to focus on specific stages of the life course, with a division between research on childhood/adolescent and adult populations. Yet recent advances demonstrate that early life course experiences shape health outcomes well into adulthood. We synthesize disparate bodies of research on social ties and health behavior throughout the life course, with attention to explaining how various social ties influence health behaviors at different life stages and how these processes accumulate and reverberate throughout the life course.

A scalable active framework for region annotation in 3D shape collections

Abstract: Large repositories of 3D shapes provide valuable input for data-driven analysis and modeling tools. They are especially powerful once annotated with semantic information such as salient regions and functional parts. We propose a novel active learning method capable of enriching massive geometric datasets with accurate semantic region annotations. Given a shape collection and a user-specified region label our goal is to correctly demarcate the corresponding regions with minimal manual work. Our active framework achieves this goal by cycling between manually annotating the regions, automatically propagating these annotations across the rest of the shapes, manually verifying both human and automatic annotations, and learning from the verification results to improve the automatic propagation algorithm. We use a unified utility function that explicitly models the time cost of human input across all steps of our method. This allows us to jointly optimize for the set of models to annotate and for the set of models to verify based on the predicted impact of these actions on the human efficiency. We demonstrate that incorporating verification of all produced labelings within this unified objective improves both accuracy and efficiency of the active learning procedure. We automatically propagate human labels across a dynamic shape network using a conditional random field (CRF) framework, taking advantage of global shape-to-shape similarities, local feature similarities, and point-to-point correspondences. By combining these diverse cues we achieve higher accuracy than existing alternatives. We validate our framework on existing benchmarks demonstrating it to be significantly more efficient at using human input compared to previous techniques. We further validate its efficiency and robustness by annotating a massive shape dataset, labeling over 93,000 shape parts, across multiple model classes, and providing a labeled part collection more than one order of magnitude larger than existing ones.

ATM Activation by Oxidative Stress

Abstract: The ataxia-telangiectasia mutated (ATM) protein kinase is activated by DNA double-strand breaks (DSBs) through the Mre11-Rad50-Nbs1 (MRN) DNA repair complex and orchestrates signaling cascades that initiate the DNA damage response. Cells lacking ATM are also hypersensitive to insults other than DSBs, particularly oxidative stress. We show that oxidation of ATM directly induces ATM activation in the absence of DNA DSBs and the MRN complex. The oxidized form of ATM is a disulfide-cross-linked dimer, and mutation of a critical cysteine residue involved in disulfide bond formation specifically blocked activation through the oxidation pathway. Identification of this pathway explains observations of ATM activation under conditions of oxidative stress and shows that ATM is an important sensor of reactive oxygen species in human cells.

Striate cortex of monkey and cat: contrast response function.

Abstract: 1. We measured the responses of 247 neurons recorded from the striate cortex of monkeys and cats as a function of the contrast intensity of luminance-modulated spatialtemporal sine-wave grating patterns to provide a qualitative description and a quantitative mathematical formulation of the contast response function (CRF). 2. Qualitatively, it is possible to provide a general description of the contrast response function for the majority of cells as follows: as the luminance contrast of a pattern increases, the response increases in a relatively linear fashion over approximately 50-607o of the response range (generally less than I log unit along the contrast range), the slope of the function then begins a rapid compression to an asymptotic maximum-saturation response level. There is, however, a great deal of variation. from cell to cell, in the exact shape and location of the CRF. 3. Quantitatively, the responses of each cell were analyzed in terms of the leastsquares (parameter optimized) best fit using four different mathematical functions: linear, logarithmic power, and hyperbolic ratio. The results of this procedure showed that, across the range of contrasts measured ( 1.4567o), the hypcrbolic ratio (H ratio) provided the best fit for the vast majority of striate cells: some 7O9o f the cells were best fitted by the H ratio and further, averaged across all cells, the H ratio produced the least average residual variance. 4. The contrast response function is an important factor when considering the spatial properties of cortical cells; nonlinearities in the CRF (compression and saturation) will necessarily influence the spatial tuning. We therefore measured the CRF at different spatial frequencies and used the parameters of the H ratio to test the predictions of two general classes of models. If the overall gain, compression, and saturation are set by the absolute response level (response-set gain), then the CRFs measured at different frequencies should shift horizontally along the contrast axis. Results show that the measured CRFs (tested on the same cell using different spatial frequencies) were shifted primarily vertically, suggesting that the gain, compression, and saturation were set by the absolute contrast level (contrast-set gain), relatively independent of spatial frequency; in terms of the H ratio, the semisaturation contrast and the exponent were relatively constant in comparison to the asymptotic saturation response. Thus, the spatial frequency response functions are relatively constant when measured at different stimulus contrasts. 5. There is a great deal of variation in the location of the dynamic response range, from cell to cell, along the contrast axis: some cells distribute their dynamic response range over the first lOVo of contrast, others the second, etc. (relatively independent of preferred spatial frequency). One might expect this range variation to be an important factor in behavioral contrast discrimination. To provide an indication of the average population response as a function of contrast, all cells were averaged together (percent response relative to each cell 's maximum): the slope of the bcst-fitt ing [nwer function (0.77) falls well within the range of estimates found for human psychophysical contrast discrimination functions.