Biomedical Engineering - OGI
Introduction to Computational Neurophysiology - BME 565/665
Instructor:
Credits: 3.0
Content: In this course students will explore how neurons communicate through electrical signals, how information transmission between neurons occurs, and how connectivity between neurons determines activity patterns and results in specialized behavior. Topics to be covered include Hodgkin-Huxley models of simple and complex morphologies; central pattern generators; models of simple invertebrate circuits; integrate-and-fire and spike-response neuron models for use in network models; models of neural development, ocular dominance and orientation columns; and rate versus spike-timing dependent plasticity.
Prerequisite: A solid math background is needed; some programming (in MATLAB) will be required.
Offering:
*
Mathematic and Computational Modeling of Biological Systems - BME 563/663 *
Instructor: Credits: Content: This course introduces mathematical and computational techniques for modeling the dynamics of biological systems. Topics will include analytical, numerical, and agent-based simulations using ordinary and partial differential equations, stochastic dynamics, and discrete methods. Applications will be drawn from topics including neural dynamics at the cellular and circuit level, circulation, respiration, and immune system response, molecular evolution, enzyme kinetics, and development. Discussion will include techniques for validating models and for incorporating experimental data, and the roles of simulation and theory in the effort to understand biological systems. Prerequisite: Offering: Computer Science - OGI
Artificial Intelligence - CS 560/660
Instructor:
Credits: 3.0
Content: This course surveys the foundations and applications of symbolic approaches to artificial intelligence. The approach emphasizes the formal basis of automated reasoning and includes an introduction to programming in Prolog. Fundamentals covered include search, knowledge representation, automated inference, planning, nonmonotonic reasoning and reasoning about belief. Applications include expert systems, natural language processing and agent architectures
Prerequisite:
Offering:
Introduction to Human-Computer Interaction - CS 564/664
Instructor:
Credits: 3.0
Content: This course emphasizes the user experience of computing, which centers on an understanding of real users and the specific tasks they need to accomplish when computing. In the pursuit of optimal user support, a multidisciplinary approach to system design and evaluation is stressed. The course reviews basic methods, terminology, viewpoints and activities in the broad field of human-computer interaction. It includes user interface principles, design guidelines and practical issues in user interface design as well as user interface evaluation criteria and metrics. Students gain hands-on experience by implementing and evaluating graphical, verbal and multimodal user interfaces. An introduction to this topic is essential for everyone working in the field of computer science.
Prerequisite:
Offering:
Natural Language Processing - CS 562/662
Instructor:
Credits: 3.0
Content: This course covers key algorithms and modeling techniques for processing human language sequences, which are needed for applications such as Automatic Speech Recognition and Machine Translation. Both statistical and symbolic approaches to modeling natural language phonology, morphology, and syntax are presented, along with widely used algorithms for efficiently learning and applying different kinds of natural language grammars. There is an emphasis on algorithms and data structures that scale up to handle very large real-world data sets, such as newswire text. The course includes several challenging hands-on programming assignments.
Prerequisite: CSE 560 or equivalent. C/C++ programming experience is highly recommended, as is familiarity with regular expressions.
Offering:
Spoken Dialogue Systems - CS 550/650
Instructor: Credits: 3.0
Content: Spoken language systems will revolutionize how people interact with machines, replacing the keyboard and mouse with natural conversation. These systems will act like helpful human assistants and teachers for information access, commercial transactions, and learning. You'll review the state of the art in building spoken language systems. You will gain hands-on experience using toolkits for building such systems, as well as learn the technologies needed for next-generation systems, such as robust parsing, semantic processing, dialogue management, and agent architectures. Class projects will be done using the CSLU toolkit, Tcl/Tk, and VoiceXML.
Statistical Pattern Recognition - CS 547/647
Instructor:
Credits: 3.0
Content: Students will learn fundamental theory and practices that are common to a broad range of pattern recognition applications and technologies, and apply principles to real-world examples. The emphasis is on developing theoretical and practical tools that provide grounding in pattern recognition problems and methods, rather than on showcasing particular technologies. The course will benefit those whose work may use any of a variety of recognition technologies in broad-ranging applications such as speech and image processing, data mining, finance. Topics include: random vectors, detection problems (binary decision problems), likelihood ratio tests, ROC curves, parametric and non-parametric density estimation, classification models, theoretical error bounds and practical error estimation through cross-validation. Maximum likelihood and Bayesian parameter estimation, model-based clustering, feature extraction for dimensionality reduction and for classification.
Prerequisite:
Offerings:
Conjoint - OHSU
Bioregulation - CON 663
Instructor: Drs. Matt Thayer and Matt Sachs
Credits: 3.0
Content: This course aims to develop a deeper understanding of gene regulation in eukaryotes and prokaryotes. Lectures will be based on textbook material and selected papers from the current literature, and will cover all aspects of gene regulation including: genome organization, chromatin structure, transcriptional regulation, RNA and protein metabolism, DNA synthesis, and cell cycle regulation. An important goal of this course is to provide insight into how research methods have been applied to achieve our current understanding of these processes.
Prerequisite:
Offering: On Campus, Winter
Development, Differentiation and Cancer - CON 665
Instructor: Drs. Marcel Wehrli and Molly Kulesz-Martin
Credits: 3.0
Content: Orchestration of development requires precise timing, spatial coordination, and reciprocal signaling between cells to result in proper tissue generation and remodeling. Disruption of these normal cellular homeostatic mechanisms occurs in cancer and in many cases has led to discoveries about the function of normal genes and interacting signaling pathways in development. In this class, mechanisms of growth and development of higher eukaryotes are covered, including important signaling events, pattern formation and cell movements resulting in the fully differentiated tissues and organisms. Consideration will be given to how stem cell population are positioned and maintained, as well as mechanisms that underlie the maintenance and function of individual tissues in the fully developed organism. Moreover, aberrations in these events are covered relative to their underlying contributions to the etiology and progression of specific cancers.
Prerequisite:
Offering: On Campus, Spring
Genetic Mechanisms - CON 662
Instructor: Drs. Michael Liskay and Joseph Weiss
Credits: 3.0
Content: This course is designed provide students with a deeper understanding of the mechanisms that underlie inheritance. The course will rely primarily on lectures and literature reading and a text will be suggested for any remediation the students might feel that they need. The lectures will cover prokaryotic transmission genetics and gene regulation emphasizing genetic approaches. They will also include discussions of mitosis and meiosis, DNA recombination (homologous, non-homologous and site specific mechanisms), mutagenesis, DNA repair, genetic dissection of biological processes (e.g., design of mutant screens, complementation and epistasis analysis, suppression, and synthetic enhancement in various model systems), developmental and cancer genetics, gene therapy, and population/quantitative genetics.
Prerequisite: Undergraduate genetics or equivalent.
Offering: On Campus, Fall
Molecular Cell Biology - CON 664
Instructor: Drs. William Skach and Svetlana Lutsenko
Credits: 3.0
Content: This course is designed to introduce students to key aspects of cell structure and function as well as. the macromolecular components and physiological mechanisms that underlie structure and function of cells. Lectures will focus on recent scientific discoveries involving: i) organelle biogenesis structure and function, ii) intracellular compartmentation and protein/vesicular transport, iii) cytoskeleton architecture, cell motility and adhesion, iv) mechanisms of membrane transport and excitability, v) molecular mechanisms of signal transduction. In addition to addressing current scientific questions in cell biology, efforts will be made to familiarize students with recent technical advances in molecular, biochemical, microscopic, spectroscopic and electrophysiological techniques that have led to the explosive growth of this field.
Prerequisite:
Offering: On Campus, Winter
Structure and Function of Biological Molecules - CON 661
Instructor: Dr. Jeffrey Karpen
Credits: 3.0
Content: This course is designed to provide students with an in-depth understanding of macromolecular structure/function including: 1) protein structure; 2) thermodynamic considerations of protein folding; 3) nucleic acid structure and topology; 4) the functions of proteins as enzymes and in macromolecular assembly, including quantitative analyses of ligand binding phenomena and enzyme kinetics; 5) structural and biochemical properties of lipids, membrane assembly and dynamics and characteristics of membrane proteins; and 6) the principles of bioenergetics and metabolism.
Prerequisite: Undergraduate organic chemistry and biochemistry
Offering: On Campus, Fall
Human Investigation Program - OHSU
Molecular Biology for Clinical Research - HIP 514
Public Health Classes - OHSU
Introduction to Biostatistics - PHPM 524
Instructors: On campus: Dr. Byung Park, Online: Lori Lambert, Jodi Lapidus
Credits: 4.0
Content: A basic course in data analysis including descriptive statistics, sampling, correlation and linear regression, tests of significance (parametric and nonparametric), and selected multivariate topics. This is a broad survey course designed for individuals who will not take another course in statistics. Course satisfies core statistics requirement for non Epidemiology and Biostatistics majors in MPH program.
Prerequisite: None
Offerings: On Campus: Fall, Spring; Online: Fall
Syllabus
Biostatistics 1 - PHPM 525
Instructor:
Dr. Dongseok Choi
Credits: 4.0
Content: This course is designed for students in the Epidemiology & Biostatistics track of the Oregon MPH program and others who will go on to take Biostatistics 2 and 3. A broad range of topics in probability, distribution, estimation and hypothesis testing will be covered. These will be followed by nonparametric methods and simple methods for categorical data. In addition, one-way analysis of variance (ANOVA) and correlation and simple linear regression will be covered. Most homework will require using statistical software (SPSS preferred).
Prerequisite: Admission to the Epi/Biostat track, undergraduate statistics course, or permission of the instructor.
Offering: On Campus, Fall
Biostatistics 2 - PHPM 526
Instructor: Dr. Nichole Carlson
Credits: 4.0
Content: This course is the second course in the required sequence for the Epi/Biostat track in the Oregon MPH program. This course expands on the analyses techniques presented in Biostatistics 1 (PHPM 525). In particular, we focus on multiple regression analysis and various analysis of variance techniques ending with a conceptual overview of techniques for correlated continuous outcomes (i.e., random effects and repeated measures). Classes consist of lecture, examples of data analysis and SPSS computer application techniques. Written homework assignments and data analysis projects are used to assist in mastery of the analysis methods.
Prerequisites: PHPM 525 (Biostatistics 1) and familiarity with statistical software, or permission of the instructor.
Offering: On Campus, Winter
Biostatistics 3 - PHPM 527
Instructor: Dr. Rochelle Fu
Credits: 4.0
Content: Biostatistics 3 is the third course in the required sequence for the Epi/Biostat track in the Oregon MPH program. This course covers topics in categorical data analysis such as cross tabulation statistics, statistics for matched samples, and methods to assess confounding and interaction via stratified tables. It will also explore logistic regression in detail, and relate results back to those found with stratified analyses. Similar to linear regression in Biostatistics 2, topics for logistic regression will include parameter interpretation, statistical adjustment, variable selection techniques and model fit assessment. If time allows, students will have the opportunity to briefly explore other analysis methods. Most homework assignments for this course are to be completed using statistical software. This course is cross-listed for Mathematics/Statistics masters and doctoral students at PSU.
Prerequisites: PHPM 526 (Biostatistics 2) and familiarity with statistical software, or permission of the instructor.
Offering: On Campus, Spring
Statistical Analysis of Microarrays - PHPM 507
Instructor: Drs. Shannon McWeeney and Tomi Mori
Credits: 3.0
Content:Microarray technology is increasingly utilized in basic sciences and clinical research, and is especially playing a critical role in translational research. This technology generates a tremendous amount of biological information, posing great challenges to both bioinformaticists and statisticians. In this special topic course, we will study statistical issues particularly relevant to microarray studies, including normalization, multiple testing, dimension reduction, data visualization and gene annotation. We will also examine extensions of microarray technology - such as Chip-Chip related to transcriptional regulation. Once a month, we will have a clinically themed lecture where clinical and translational researchers are invited to participate. The lectures will combine a traditional lecture format with discussion or computer lab. The course is appropriate for graduate students in Public Health, Medical Informatics, Behavioral Neuroscience, Computer Science as well as basic science and clinical researchers with basic statistical background.
Prerequisite: PHPM 525 (Biostatistics 1) and PHPM 526 (Biostatistics 2), equivalent course work, or permission of the instructors.
Offering: On Campus, Fall
Portland State
Current Topics in Proteomics - EBS 598/698
Instructor: Credits: 2.0
Content: Proteomics is a new area of molecular biology which aims to identify and map the total protein complement of a genome. It expands the scope of biological investigation from studying single proteins to systematically studying all proteins. Proteomics has broad applications in disease diagnosis, drug discovery, and agriculture. The key technologies used in proteomics are 2-dimensional gel electrophoresis, mass spectrometry (ESI-MS, MALDI-TOF), imaging, and database. This course will focus on electrophoresis, mass spectrometry, and applications, using lectures, student seminars, and literature readings
Prerequisite: Offering: Introduction to Biomedical Imaging - EE 588/688
Instructor: Credits: 3.0
Content: This course introduces imaging and analysis methods in medicine and biology. It is intended for students and researchers in biomedical engineering, computer and electrical engineering, physics and other engineering disciplines as well as medical professionals and students who would like to attain a basic understanding of medical imaging. The course is intended to provide the basic understanding of both the image formation and relevant image processing techniques. The course will introduce the physics and phenomenology of image formation for a variety of image modalities that include X-ray, computer tomography (CT), magnetic resonance imaging (MRI), nuclear imaging (PET, SPECT) and ultra sound. Subsequently, students will be introduced to the techniques for image representation, processing and analysis. Specific topics include image reconstruction, image enhancement, segmentation, registration, characterization, pattern recognition interpretation and visualization. The course will also briefly address issues related to image-based diagnosis, intervention and therapy. This course includes weekly lectures, home work, journal article review sessions and a final project.
Prerequisite: Offerings:
Machine Learning - CSE 410/510
Instructor: Credits: 3.0
Content: Learning objectives for this course include: (1) an introduction to several prominent areas of machine learning, including feature extraction, decision trees, neural networks, genetic algorithms, Bayesian learning, and reinforcement learning, and illustrate what types of problems the different methods are suited for. (2) hands-on experience with these methods and tools for implementing and using them on real-world problems (3) experience with performing simulations and doing statistical data analysis of the results (4) experience in reading and writing summaries of research papers, giving presentations, and peer-reviewing.
Prerequisite: Offering: Note: The information included in this catalog was accurate at the time of publication. Information described in this catalog may change without notice. Not all courses are offered each academic year and faculty assignments may change. 9/13/2006.
