Phosphate Contaminant Detection in Water Through a Paper-based Microfluidic Device

This report describes a project aimed at developing a low-cost, portable, on-site, user-friendly system for detecting different concentrations of phosphate in drinking water. Phosphate is a natural chemical, but toxic in large concentrations; detection is therefore important to avoid drinking contaminated water. Despite this fact, no cheap, and/or nontoxic system for phosphate detection is yet on the market. The detection system utilizes a paper-based microfluidic device to automate the electrochemical detection process, which normally requires expert use of lab equipment. When combined with a portable potentiostat that works with a mobile app, the device will allow untrained users to determine if any source of drinking water contains unsafe levels of phosphate without equipment or training, and to communicate that information to a central database for further analysis. Those of any background, particularly in developing countries, will be able to maintain health and raise awareness about clean water. Microfluidic devices are useful tools for the detection of water contaminants, but there is a gap in technology for the detection of phosphate. Our phosphate detection system is a paper-based microfluidic device with an already-developed voltammetry device that automates the detection process so that any user can safely find phosphate in water. The system will provide a binary analysis about whether the water is safe to consume or not. Completion of the project provides a valuable tool to both average customers in developing countries and scientific researchers in determining the safety of drinking water

Riparian reference condition: Using regional plant composition to guide functional improvements in the City of Austin

This technical report focuses on ways to restore riparian habitat within specific Austin creeks. This study is designed to serve as a template for restoration efforts with other urbanized creeks (Waller).As a result of an expanding and rapidly urbanizing metropolitan area, the riparian vegetation communities of Austin-area streams continue to diverge further from their natural state. In an effort to maintain the ecological function and the natural character of Austin watersheds, the City of Austin Watershed Protection Department has identified a need to characterize an archetype, or background condition of Edwards Plateau and Blackland Prairie riparian communities for use as a template for both benchmarking and target for stream restoration projects. Species composition, spatial arrangement and physical attributes of vegetation communities for 12 sites located in both smaller and larger watersheds were characterized using multiple belt-transects. Multivariate analyses including detrended correspondence analysis (DCA), analysis of similarity (ANOSIM), and similarity percentage (SIMPER) were performed by Community Analysis Package software (Seaby and Henderson 2007). Results show that there was a significant difference in plant community composition in all compared drainage areas and ecoregions for both ground cover and overstory communities (p<0.05). The analysis of similarity showed that the samples should be grouped by ecoregion and location within the watershed for overstory and ground cover communities. Recommended vegetation templates are presented as a guide for comparison to other riparian communities in the Austin area, and also a reference point for restoration of degraded systems. These quantitative species distribution lists are an important resource for riparian ecologists in this region.Waller Creek Working Grou

Searching for Dark Matter Annihilation in the Smith High-Velocity Cloud

Recent observations suggest that some high-velocity clouds may be confined by massive dark matter halos. In particular, the proximity and proposed dark matter content of the Smith Cloud make it a tempting target for the indirect detection of dark matter annihilation. We argue that the Smith Cloud may be a better target than some Milky Way dwarf spheroidal satellite galaxies and use gamma-ray observations from the Fermi Large Area Telescope to search for a dark matter annihilation signal. No significant gamma-ray excess is found coincident with the Smith Cloud, and we set strong limits on the dark matter annihilation cross section assuming a spatially-extended dark matter profile consistent with dynamical modeling of the Smith Cloud. Notably, these limits exclude the canonical thermal relic cross section ($\sim 3\times10^{-26}{\rm cm}^{3}{\rm s}^{-1}$) for dark matter masses $\lesssim 30$ GeV annihilating via the $b \bar b$ or $\tau^{+}\tau^{-}$ channels for certain assumptions of the dark matter density profile; however, uncertainties in the dark matter content of the Smith Cloud may significantly weaken these constraints.Comment: 7 pages, 5 figures. Published in Ap

Protocols for Disease Classification from Mass Spectrometry Data

We report our results in classifying protein matrix-assisted laser desorption/ionizationtime of flight mass spectra obtained from serum samples into diseased and healthy groups. We discuss in detail five of the steps in preprocessing the mass spectral data for biomarker discovery, as well as our criterion for choosing a small set of peaks for classifying the samples. Cross-validation studies with four selected proteins yielded misclassification rates in the 10-15% range for all the classification methods. Three of these proteins or protein fragments are down-regulated and one up-regulated in lung cancer, the disease under consideration in this data set. When cross-validation studies are performed, care must be taken to ensure that the test set does not influence the choice of the peaks used in the classification. Misclassification rates are lower when both the training and test sets are used to select the peaks used in classification versus when only the training set is used. This expectation was validated for various statistical discrimination methods when thirteen peaks were used in cross-validation studies. One particular classification method, a linear support vector machine, exhibited especially robust performance when the number of peaks was varied from four to thirteen, and when the peaks were selected from the training set alone. Experiments with the samples randomly assigned to the two classes confirmed that misclassification rates were significantly higher in such cases than those observed with the true data. This indicates that our findings are indeed significant. We found closely matching masses in a database for protein expression in lung cancer for three of the four proteins we used to classify lung cancer. Data from additional samples, increased experience with the performance of various preprocessing techniques, and affirmation of the biological roles of the proteins that help in classification, will strengthen our conclusions in the future