Stochastic Variational Inference

We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet process topic model. Using stochastic variational inference, we analyze several large collections of documents: 300K articles from Nature, 1.8M articles from The New York Times, and 3.8M articles from Wikipedia. Stochastic inference can easily handle data sets of this size and outperforms traditional variational inference, which can only handle a smaller subset. (We also show that the Bayesian nonparametric topic model outperforms its parametric counterpart.) Stochastic variational inference lets us apply complex Bayesian models to massive data sets

Effects of riparian tree management on flood conveyance study of Manning's Roughness in vegetated floodplains with an application on the Embarras River in Illinois

"February 2002.

Autonomous Tennis Ball Collector

Practicing tennis often involves hitting many tennis balls from one side of the court to the other without an opponent to hit the balls back. In training sessions like these, the task of collecting the balls is laborious when performed manually. The objective of this project is to develop a robotic tennis ball collector that can automatically collect the balls from one side of the court so that the player can rest rather than collect the balls manually. This document outlines the process of designing such a robot. Included in this report is background research, prototype, and concept modeling, along with a finalized design, and a complete timeline of our process. We will also detail the manufacturing process and the design verification. In the conclusion we will provide you with recommendations for future projects. Throughout our research, we discovered many similar products, but none met all of the customer’s requirements, thus opening a window for our product. After copious design consideration, we selected the strongest idea that satisfied our customers’ needs and are moving forward with structural modeling and preliminary analysis on it. After the structural prototype revealed issues in the design we went back to work and finalized a design that we felt confident with and still satisfied all the requirements. As seen in this report the final design utilizes structural framing materials to build the robot and allows for ease of attachment for all the electrical components. The final step in the design process was to test the verification prototype to ensure that it met all our specifications. Unfortunately, our design did not pass as many of the tests as we would have liked, and this is detailed in that section. While at the conclusion of this project, we did not complete as much as we hoped, there is a good foundation in place for the project to continue as our sponsor so desires

Prescription and Other Medication Use in Pregnancy

OBJECTIVE: To characterize prescription and other medication use in a geographically and ethnically diverse cohort of women in their first pregnancy. METHODS: In a prospective, longitudinal cohort study of nulliparous women followed through pregnancy from the first trimester, medication use was chronicled longitudinally throughout pregnancy. Structured questions and aids were used to capture all medications taken as well as reasons they were taken. Total counts of all medications taken including number in each category and class were captured. Additionally, reasons the medications were taken were recorded. Trends in medications taken across pregnancy and in the first trimester were determined. RESULTS: Of the 9,546 study participants, 9,272 (97.1%) women took at least one medication during pregnancy with 9,139 (95.7%) taking a medication in the first trimester. Polypharmacy, defined as taking at least five medications, occurred in 2,915 (30.5%) women. Excluding vitamins, supplements, and vaccines, 73.4% of women took a medication during pregnancy with 55.1% taking one in the first trimester. The categories of drugs taken in pregnancy and in the first trimester include the following: gastrointestinal or antiemetic agents (34.3%, 19.5%), antibiotics (25.5%, 12.6%), and analgesics (23.7%, 15.6%, which includes 3.6%; 1.4% taking an opioid pain medication). CONCLUSION: In this geographically and ethnically diverse cohort of nulliparous pregnant women, medication use was nearly universal and polypharmacy was common

Discovery and characterization of small molecules that target the Ral GTPase

The Ras-like GTPases RalA and B are important drivers of tumor growth and metastasis. Chemicals that block Ral function would be valuable as research tools and for cancer therapeutics. Here, we used protein structure analysis and virtual screening to identify drug-like molecules that bind a site on the GDP-form of Ral. Compounds RBC6, RBC8 and RBC10 inhibited Ral binding to its effector RalBP1, Ral-mediated cell spreading in murine fibroblasts and anchorage-independent growth of human cancer cell lines. Binding of RBC8 derivative BQU57 to RalB was confirmed by isothermal titration calorimetry, surface plasma resonance and 15N-HSQC NMR. RBC8 and BQU57 show selectivity for Ral relative to Ras or Rho and inhibit xenograft tumor growth similar to depletion of Ral by siRNA. Our results show the utility of structure-based discovery for development of therapeutics for Ral-dependent cancers