Digital Commons Network^™

Ngasp - The Nematode Genome Annotation Assessment Project, Avril Coghlan, Tristan J. Fiedler, Sheldon J. Mckay, Paul R. Flicek, Todd W. Harris, Darin Blasiar, Lincoln D. Stein

Biomedical Engineering and Sciences Faculty Publications

While the C. elegans genome is extensively annotated, relatively little information is available for other Caenorhabditis species. The nematode genome annotation assessment project (nGASP) was launched to objectively assess the accuracy of protein-coding gene prediction software in C. elegans, and to apply this knowledge to the annotation of the genomes of four additional Caenorhabditis species and other nematodes. Seventeen groups worldwide participated in nGASP, and submitted 47 prediction sets across 10 Mb of the C. elegans genome. Predictions were compared to reference gene sets consisting of confirmed or manually curated gene models from WormBase. Results: The most accurate gene-finders were …

The Effect Of Deleting Anchor On The Classification Of Examinees, Tia Sukin, Lisa Keller

NERA Conference Proceedings 2008

The presence of outlying anchor items is an issue faced by many testing agencies. This study examines the effect of removing or retaining one aberrant anchor item. The degree of aberrancy was manipulated as well as the ability distribution of examinees, and four IRT scaling methods were investigated (Mean-sigma, mean-mean, Stocking & Lord, and Haebara). The results indicate that the percent of correctly classified students was not affected by either retaining or removing the aberrant item, although the over- and under- classification of examinees was. There was no difference among the methods.

Cerebellar Pathology Does Not Impair Performance On Identification Or Categorization Tasks, Shawn Ell, Richard B. Ivry

Psychology Faculty Scholarship

In comparison to the basal ganglia, prefrontal cortex, and medial temporal lobes, the cerebellum has been absent from recent research on the neural substrates of categorization and identification, two prominent tasks in the learning and memory literature. To investigate the contribution of the cerebellum to these tasks, we tested patients with cerebellar pathology (seven with bilateral degeneration, six with unilateral lesions, and two with midline damage) on rule-based and information-integration categorization tasks and an identification task. In rule-based tasks, it is assumed that participants learn the categories through an explicit reasoning process. In information-integration tasks, optimal performance requires the integration …

Semiparametric And Nonparametric Methods For Evaluating Risk Prediction Markers In Case-Control Studies, Ying Huang, Margaret Pepe

UW Biostatistics Working Paper Series

The performance of a well calibrated risk model, Risk(Y)=P(D=1|Y), can be characterized by the population distribution of Risk(Y) and displayed with the predictiveness curve. Better performance is characterized by a wider distribution of Risk(Y), since this corresponds to better risk stratification in the sense that more subjects are identified at low and high risk for the outcome D=1. Although methods have been developed to estimate predictiveness curves from cohort studies, most studies to evaluate novel risk prediction markers employ case-control designs. Here we develop semiparametric and nonparametric methods that accommodate case-control data and assume apriori knowledge of P(D=1). Large and …

The Varieties Of Pathways To Dysfluent Reading Comparing Subtypes Of Children With Dyslexia At Letter, Word, And Connected Text Levels Of Reading, Maryanne Wolf, Robin Morris, Maureen Lovett, Tami Katzir, Young-Suk Kim

Psychology Faculty Publications

The majority of work on the double-deficit hypothesis (DDH) of dyslexia has been done at the letter and word levels of reading. Key research questions addressed in this study are (a) do readers with different subtypes of dyslexia display differences in fluency at particular reading levels (e.g., letter, word, and connected text)? and (b) do children with dyslexia identified by either low-achievement or ability–achievement discrepancy criteria show similar differences when classified by the DDH? To address these questions, the authors assessed a sample of 158 children with severe reading impairments in second and third grades on an extensive battery and …

An Automatic Bridge Detection Technique For Multispectral Images, D. Chaudhuri, Ashok Samal

CSE Conference and Workshop Papers

Extraction of features from images has been a goal of researchers since the early days of remote sensing. While significant progress has been made in several applications, much remains to be done in the area of accurate identification of high-level features such as buildings and roads. This paper presents an approach for detecting bridges over water bodies from multispectral imagery. The multispectral image is first classified into eight land-cover types using a majority-must-be-granted logic based on the multiseed supervised classification technique. The classified image is then categorized into a trilevel image: water, concrete, and background. Bridges are then recognized in …

Illustrated Identification Keys To Strongylid Parasites Strongyllidae Nematoda Of Horses Zebras And Asses Equidae, J. Ralph Lichtenfels, Vitaliy A. Kharchenko, Grigory M. Dvojnos

Harold W. Manter Laboratory of Parasitology: Faculty and Staff Publications

The Equidae (the horse, Equus caballus, the ass, Equus asinus, zebras and their hybrids) are hosts to a great variety of nematode parasites, some of which can cause significant morbidity or mortality if individual hosts are untreated. Worldwide the nematode parasites of horses belong to 7 suborders, 12 families, 29 genera and 83 species. The great majority (19 of 29 genera and 64 of 83 species) are members of the family Strongylidae, which includes the most common and pathogenic nematode parasites of horses. Only the Strongylidae are included in this treatise.

The Strongylidae (common name strongylids) of horses …

Mapping Selective Logging In Mixed Deciduous Forest: A Comparison Of Machine Learning Algorithms, Christopher D. Lippitt, John Rogan, Zhe Li, J. Ronald Eastman, Trevor G. Jones

Geography

This study assesses the performance of five Machine Learning Algorithms (MLAS) in a chronically modified mixed deciduous forest in Massachusetts (USA) in terms of their ability to detect selective timber logging and to cope with deficient reference datasets. Multitemporal Landsat Enhanced Thematic Mapper-plus (ETM+) imagery is used to assess the performance of three Anificial Neural Networks - Multi-Layer Perceptron, ARTMAP, Self-Organizing Map, and two Classification Tree splitting algorithms: gini and entropy rules, MLA performance evaluations are based on susceptibility to reduced training set size, noise, and variations in the training set, as well as the operability/transparency of the classification process. …

Vegetation Identification Based On Satellite Imagery, Vamsi K.R. Mantena, Ramu Pedada, Srinivas Jakkula, Yuzhong Shen, Jiang Li, Hamid R. Arabnia (Ed.)

Electrical & Computer Engineering Faculty Publications

Automatic vegetation identification plays an important role in many applications including remote sensing and high performance flight simulations. This paper presents a method to automatically identify vegetation based upon satellite imagery. First, we utilize the ISODATA algorithm to cluster pixels in the images where the number of clusters is determined by the algorithm. We then apply morphological operations to the clustered images to smooth the boundaries between clusters and to fill holes inside clusters. After that, we compute six features for each cluster. These six features then go through a feature selection algorithm and three of them are determined to …

Sentiment Regression: Using Real-Valued Scores To Summarize Overall Document Sentiment, Adam Drake, Eric K. Ringger, Dan A. Ventura

Faculty Publications

In this paper, we consider a sentiment regression problem: summarizing the overall sentiment of a review with a real-valued score. Empirical results on a set of labeled reviews show that real-valued sentiment modeling is feasible, as several algorithms improve upon baseline performance. We also analyze performance as the granularity of the classification problem moves from two-class (positive vs. negative) towards infinite-class (real-valued).