Lowering the Risk of Rectal Cancer among Habitual Beer Drinkers by Dietary Means

Whole-life beer consumption and a quantitative measurement of several dietary micronutrients consumed in adult life were obtained from the dietary and alcohol data of the case-control arm of the population-based Melbourne Colorectal Cancer Study. There was a statistically significant risk, adjusted for other established risk factors, among habitual beer drinkers (AOR 1.75, 95% CI 1.28–2.41) with a significant positive dose-response effect (AOR trend 1.34, 95% CI 1.16–1.55). Among beer consumers the data were interpreted as showing an attenuation of this risk with consumption of the four micronutrients involved in methylation: folate, methionine, vitamins B6 and B12, and the four micronutrients examined with antioxidant properties: selenium, vitamins E, C, and lycopene. The strongest effects were noted with vitamins E, C, and lycopene, and the weakest with methionine and selenium. Whilst not condoning excessive beer drinking, the regular consumption of foods rich in these micronutrients may provide a simple and harmless preventative strategy among persistent habitual beer drinkers and deserves further study with larger study numbers

A Laboratory Analysis of Faunal Artifact Water Retention and Diagenesis

Submerged artifacts can be altered by water and made more fragile. Due to climate change, groundwater tables have been slowly rising submerging artifacts, altering their makeup, and changing the information that can be acquired from them. This investigation was performed on bone artifacts excavated in 2018 in the Southeastern Florida. After identification, bones were initially weighed, submerged in water for 48 hours, weighed, dried for 48 hours, and finally weighed. The results of statistical analyses indicate that some types of bone artifacts can absorb far greater quantities of water, which alter their weight, while others are less receptive

Parental Behavior Control as a Moderator Between Close Friend Support and Conduct Problems

Objective: Potential moderating effects between parental control and close friend support on youth conduct problems have not been investigated. Goals of this study were to investigate for a possible curvilinear relationship between parental behavioral control and child conduct issues, as well as the potential that parental control will moderate the relationship between friend support and those behaviors. Design: Surveyed 101 youth and their parents in a small Midwestern United States metropolitan area. Results: Through hierarchical regression, the current study shows a curvilinear main effect for parent control on conduct problems. Additionally, close friend support is a protective agent only for those youth in families with high and medium levels of parental control, and a risk factor for children from families with low parental control. Conclusion: Moderate levels of parental control are ideal and low levels of parental control are particularly problematic for youth who are highly engaged with peers

Parental Behavior Control as a Moderator Between Close Friend Support and Conduct Problems

Objective: Potential moderating effects between parental control and close friend support on youth conduct problems have not been investigated. Goals of this study were to investigate for a possible curvilinear relationship between parental behavioral control and child conduct issues, as well as the potential that parental control will moderate the relationship between friend support and those behaviors. Design: Surveyed 101 youth and their parents in a small Midwestern United States metropolitan area. Results: Through hierarchical regression, the current study shows a curvilinear main effect for parent control on conduct problems. Additionally, close friend support is a protective agent only for those youth in families with high and medium levels of parental control, and a risk factor for children from families with low parental control. Conclusion: Moderate levels of parental control are ideal and low levels of parental control are particularly problematic for youth who are highly engaged with peers

Footprints and Free Space from a Single Color Image

Understanding the shape of a scene from a single color image is a formidable computer vision task. However, most methods aim to predict the geometry of surfaces that are visible to the camera, which is of limited use when planning paths for robots or augmented reality agents. Such agents can only move when grounded on a traversable surface, which we define as the set of classes which humans can also walk over, such as grass, footpaths and pavement. Models which predict beyond the line of sight often parameterize the scene with voxels or meshes, which can be expensive to use in machine learning frameworks. We introduce a model to predict the geometry of both visible and occluded traversable surfaces, given a single RGB image as input. We learn from stereo video sequences, using camera poses, per-frame depth and semantic segmentation to form training data, which is used to supervise an image-to-image network. We train models from the KITTI driving dataset, the indoor Matterport dataset, and from our own casually captured stereo footage. We find that a surprisingly low bar for spatial coverage of training scenes is required. We validate our algorithm against a range of strong baselines, and include an assessment of our predictions for a path-planning task.Comment: Accepted to CVPR 2020 as an oral presentatio

Self-Supervised Monocular Depth Hints

Monocular depth estimators can be trained with various forms of self-supervision from binocular-stereo data to circumvent the need for high-quality laser scans or other ground-truth data. The disadvantage, however, is that the photometric reprojection losses used with self-supervised learning typically have multiple local minima. These plausible-looking alternatives to ground truth can restrict what a regression network learns, causing it to predict depth maps of limited quality. As one prominent example, depth discontinuities around thin structures are often incorrectly estimated by current state-of-the-art methods. Here, we study the problem of ambiguous reprojections in depth prediction from stereo-based self-supervision, and introduce Depth Hints to alleviate their effects. Depth Hints are complementary depth suggestions obtained from simple off-the-shelf stereo algorithms. These hints enhance an existing photometric loss function, and are used to guide a network to learn better weights. They require no additional data, and are assumed to be right only sometimes. We show that using our Depth Hints gives a substantial boost when training several leading self-supervised-from-stereo models, not just our own. Further, combined with other good practices, we produce state-of-the-art depth predictions on the KITTI benchmark.Comment: Accepted to ICCV 201

Policies for capacity development

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Center for Urban Health: Enhancing the health of cities by focusing on communities and the environment

Urban sustainability is a new philosophy of developing healthy, productive communities that (1) promote and use locally-produced foods and products, (2) ensure safe access to natural spaces, and (3) establish low-carbon transportation systems. Urban living is arguably the most sustainable form of community given the concentration of resources, protection of arable land, and vertical structure of housing. In fact, urbanization is becoming the global norm; the percentage of global population living in urban settings has increased from less than 30% in 1950 to 47% in 2000; the percentage of urban dwellers is expected to increase to 60% by 2025. The promise of a healthy and sustainable urban future is clouded, however, by the reality of environmental insults, economic disparities, and behavioral pressures that exist in modern cities. The challenge is not how to build a shiny carbon-neutral city from scratch, but rather how to transition our current urban state toward one that is healthier, has less environmental impact, and is more prepared to respond and adjust to variety of environmental, social, and health changes in the future. Several groups at IUPUI and in the community are collaborating to explore connections between environment, behavior, health, and climate as related to urban environments. These translational efforts are inter- and trans-disciplinary, as evidenced by earth scientists publishing with pediatricians, and geographers publishing with epidemiologists. These efforts are largely undertaken with a geospatial and geotemporal research template. This template allows environmental, health, and behavioral data to be collected individually but with reference to space and time, which become important metadata components for analysis. The Center for Urban Health promotes discovery by building research collaborations among Center Investigators, conducting workshops on cutting-edge developments in urban health, and bridging campus and community efforts in public health, including the Reconnecting to Our Waterways (RWO) initiative