Browsing by Subject "linear discriminant analysis"

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    Alcohol-Naïve USVs Distinguish Male HAD-1 from LAD-1 Rat Strains
    (Elsevier, 2017) Mittal, Nitish; Thakore, Neha; Reno, James M.; Bell, Richard L.; Maddox, W. Todd; Schallert, Timothy; Duvauchelle, Christine L.; Psychiatry, School of Medicine
    Ultrasonic vocalizations (USVs) are mediated through specific dopaminergic and cholinergic neural pathways and serve as real-time measures of positive and negative emotional status in rodents. Although most USV studies focus primarily on USV counts, each USV possesses a number of characteristics shown to reflect activity in the associated neurotransmitter system. In the present study, we recorded spontaneously emitted USVs from alcohol-naïve high alcohol drinking (HAD-1) and low alcohol drinking (LAD-1) rats. Using our recently developed WAAVES algorithm we quantified four acoustic characteristics (mean frequency, duration, power and bandwidth) from each 22 – 28 kHz and 50 – 55 kHz frequency modulated (FM) USV. This rich USV representation allowed us to apply advanced statistical techniques to identify the USV acoustic characteristics that distinguished HAD-1 from LAD-1 rats. Linear mixed models (LMM) examined the predictability of each USV characteristic in isolation and linear discriminant analysis (LDA) and binomial logistic regression examined the predictability of linear combinations of the USV characteristics as a group. Results revealed significant differences in acoustic characteristics between HAD-1 and LAD-1 rats in both 22 – 28 kHz and 50 – 55 kHz FM USVs. In other words, these rats selectively bred for high- and low-alcohol consumption can be identified as HAD-1 or LAD-1 rats with high classification accuracy (approx. 92-100%) exclusively on the basis of their emitted 22-28 kHz and 50-55 kHz FM USV acoustic characteristics. In addition, acoustic characteristics of 22 – 28 kHz and 50 – 55 kHz FM USVs emitted by alcohol-naïve HAD-1 and LAD-1 rats significantly correlate with their future alcohol consumption. Our current findings provide novel evidence that USV acoustic characteristics can be used to discriminate between alcohol-naïve HAD-1 and LAD-1 rats, and may serve as biomarkers in rodents with a predisposition for, or against, excessive alcohol intake.
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    Combining Multivariate Statistical Methods and Spatial Analysis to Characterize Water Quality Conditions in the White River Basin, Indiana, U.S.A.
    (2011-02-25) Gamble, Andrew Stephan; Babbar-Sebens, Meghna; Tedesco, Lenore P.; Peng, Hanxiang
    This research performs a comparative study of techniques for combining spatial data and multivariate statistical methods for characterizing water quality conditions in a river basin. The study has been performed on the White River basin in central Indiana, and uses sixteen physical and chemical water quality parameters collected from 44 different monitoring sites, along with various spatial data related to land use – land cover, soil characteristics, terrain characteristics, eco-regions, etc. Various parameters related to the spatial data were analyzed using ArcHydro tools and were included in the multivariate analysis methods for the purpose of creating classification equations that relate spatial and spatio-temporal attributes of the watershed to water quality data at monitoring stations. The study compares the use of various statistical estimates (mean, geometric mean, trimmed mean, and median) of monitored water quality variables to represent annual and seasonal water quality conditions. The relationship between these estimates and the spatial data is then modeled via linear and non-linear multivariate methods. The linear statistical multivariate method uses a combination of principal component analysis, cluster analysis, and discriminant analysis, whereas the non-linear multivariate method uses a combination of Kohonen Self-Organizing Maps, Cluster Analysis, and Support Vector Machines. The final models were tested with recent and independent data collected from stations in the Eagle Creek watershed, within the White River basin. In 6 out of 20 models the Support Vector Machine more accurately classified the Eagle Creek stations, and in 2 out of 20 models the Linear Discriminant Analysis model achieved better results. Neither the linear or non-linear models had an apparent advantage for the remaining 12 models. This research provides an insight into the variability and uncertainty in the interpretation of the various statistical estimates and statistical models, when water quality monitoring data is combined with spatial data for characterizing general spatial and spatio-temporal trends.
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    Towards Development of Smart Nanosensor System To Detect of Hypoglycemia From Breath
    (2020-05) Thakur, Sanskar S.; Agarwal, Mangilal; Tovar, Andres; Anwar, Sohel
    The link between volatile organic compounds (VOCs) from breath and various diseases and specific conditions has been identified since long by the researchers. Canine studies and breath sample analysis on Gas chromatography/ Mass Spectroscopy has proven that there are VOCs in the breath that can detect and potentially predict hypoglycemia. This project aims at developing a smart nanosensor system to detect hypoglycemia from human breath. The sensor system comprises of 1-Mercapto-(triethylene glycol) methyl ether functionalized goldnanoparticle (EGNPs) sensors coated with polyetherimide (PEI) and poly(vinylidene fluoride -hexafluoropropylene) (PVDF-HFP) and polymer composite sensor made from PVDF-HFP-Carbon Black (PVDF-HFP/CB), an interface circuit that performs signal conditioning and amplification, and a microcontroller with Bluetooth Low Energy (BLE) to control the interface circuit and communicate with an external personal digital assistant. The sensors were fabricated and tested with 5 VOCs in dry air and simulated breath (a mixture of air, small portion of acetone, ethanol at high humidity) to investigate sensitivity and selectivity. The name of the VOCs is not disclosed herein but these VOCs have been identified in-breath and are identified as potential biomarkers for other diseases as well. The sensor hydrophobicity has been studied using contact angle measurement. The GNPs size was verified using Ultra-Violent-Visible (UV-VIS) Spectroscopy. Field Emission Scanning Electron Microscope (FESEM) image is used to show GNPs embedded in the polymer film. The sensors sensitivity increases by more than 400\% in an environment with relative humidity (RH) of 93\% and the sensors show selectivity towards VOCs of interest. The interface circuit was designed on Eagle PCB and was fabricated using a two-layer PCB. The fabricated interface circuit was simulated with variable resistance and was verified with experiments. The system is also tested at different power source voltages and it was found that the system performance is optimum at more than 5 volts. The sensor fabrication, testing methods, and results are presented and discussed along with interface circuit design, fabrication, and characterization.