Date: Tuesday, March 5th, 2013
Speaker: Steven Hanes, Ph.D., Professor of Biochemistry and Molecular Biology at SUNY-Upstate Medical University
Title: Use of Model Organisms in Biomedical Research
Abstract: Most breakthrough medical treatments are based on basic research discoveries made using simple “model” organisms. A model organism is one that offers significant advantages for laboratory research. Powerful technologies available for model organism research can be applied to identify the biochemical, genetic, and cellular pathways that control embryo development, response to the environment, and the dysfunction that occurs in disease. Although it was suspected over the last century that fundamental biological mechanisms are similar in all organisms, studies over the last few decades including genome-wide DNA sequencing have revealed the extraordinary evolutionary conservation of genes, proteins, and even entire biochemical pathways from unicellular organisms all the way to humans. The conservation of function makes it possible to study the biology of model organisms such as yeast, C. elegans (worms), D. melanogaster (fruit flies), Zebrafish, and mice, and to apply the knowledge gained to understand human biology and disease. Only in this way can we hope to make the advances necessary to develop targeted therapies for diseases such as Alzheimer’s, AIDS and cancer. An introduction to model organisms will be presented, and include examples of research that has impacted our understanding and/or treatment of human disease.
Date: Tuesday, January 22nd, 2012
Speaker: Chris Jordan
Title: How Vitamin B12 Works: A Chemist’s Perspective
Abstract: Several molecular variations of vitamin B12 are essential nutrients that help catalyze a variety of reactions for most organisms. These molecules contain the only known metal-carbon bonds in biology, bonds that are broken and reformed during the catalytic process. Even after decades of research, it is unclear how the interactions between B12, the enzymes it binds to, and the molecules it induces a reaction in all contribute to enhance reaction rates by many orders of magnitude. In this presentation, we’ll discuss current experimental and theoretical approaches to teasing out this mystery.
Date: Tuesday, December 11th, 2012
Speaker: Matthew Pelletier, Associate Professor of Biology at Houghton
Title: Surface-Mediated Chondrogenesis in the Absence of Transforming Growth Factor Beta
Abstract: In my sabbatical research at Corning, the interactions between adult stem cells and controlling the differentiation of those cells based upon the growth surface were explored. This talk will focus on adult stem cells in general, the lineages into which they may differentiate, as well as our current results for triggering mesenchymal cells to differentiate into chondrocytes.
Date: Tuesday, December 4th, 2012
Speaker: Jason D. Smith, Department of Entomology at PSU
Title: Chemistry in action: the strategies insects and plants employ in their struggle for life
Abstract: Outbreaks of leaf-eating insects can strip forests bare and consume entire fields of crop plants—and yet the world remains mostly full of plants. How do plants avoid succumbing to their enemies? The answer lies largely in the chemistry of this age-old battle. Plants utilize an arsenal of chemical defenses that are often deployed only in response to insect feeding damage. These responses include the release of volatile plant compounds (odors) that can repel herbivores, attract predator allies, and warn nearby plants of the dangers at hand. While volatile signals recruit helpful predators, insect herbivores and even parasitic plants exploit these cues to find optimal food sources. In this seminar I will expound upon these common themes in plant-insect ecology, share some of my research on the chemical ecology of a parasitic plant (Cuscuta spp.), and highlight ways this discipline can be used to develop sustainable pest control in agriculture.
Date: Tuesday, November 27th, 2012
Speaker: Todd D. Krauss, Professor of Chemistry and Optics, Director of Materials Science Program, University of Rochester
Title: Nanoscience and Nanotechnology: Why Size Matters
Abstract: Nanocience and nanotechnology is the study and application of materials and integrated materials systems that have sizes approximately one billionth of a meter, or one nanometer. Nanomaterials exhibit the remarkable property that their basic physical properties such as color are often highly tunable by simply controlling the size and shape of the material, an effect fundamentally due to quantum mechanics. For macroscopic objects, tuning the color (say of a piece of metal) by changing its size or shape is just simply not possible. However, at the nanometer scale, the rules governing the physical properties of materials and how materials interact are different than for larger objects, thus enabling extraordinary and often unexpected behavior. Of interest to the scientific community for over three decades, the unique characteristics of nanomaterials and assemblies containing these materials has led to many inside and outside the field to see the potential for truly groundbreaking applications in numerous areas from biotechnology to renewable and sustainable energy.
We will present a general overview of nanoscience and nanotechnology, and why at the nanometer scale size and shape are so important in determining the physical characteristics of a particular material. Several examples of commercial products containing nanometer scale materials already in the marketplace will be discussed, as well as areas where the potential for truly groundbreaking new technology from nanomaterials could be exploited. In particular, nanomaterials science and technology may someday be critical for powering the planet with clean, renewable, energy sources.
Date: Tuesday, November 20th, 2012
Speaker: Clara L. Kielkopf, Associate Professor of Biochemistry and Biophysics, University of Rochester Medical Center
Abstract: The basis for accurate selection of the splice sites in precursor gene transcripts is a fundamental question in gene regulation. A complex consisting of the essential U2AF65, U2AF35, and SF1 proteins accurately targets the 3’ splice sites and nucleates spliceosome assembly for the process of intron removal. It remains to be determined how U2AF65, a single protein, can accurately initiate splicing at a remarkably broad range of sites. Our structural and biochemical results demonstrate that U2AF65 universally recognizes splice sites through fly-casting of promiscuous and stringent protein domains. This means for versatile splice site selection by U2AF65 is likely to serve as a prototype for other essential gene regulatory proteins, many of which - like U2AF65 – are multi-domain proteins that can recognize diverse sequences.
Date: Tuesday, November 13th, 2012
Speaker: Tyler Reynolds and Keith Mann
Abstract: Inertial confinement fusion (ICF) is a process in which nuclear fusion reactions are triggered by exposing a nuclear fuel pellet to intense heat and pressure. ICF is a noteworthy topic under current study by the research community, with applications in alternative energy, astrophysics, and military technology. The 12C(n,2n)11C reaction can be used to measure tertiary neutron production, an important ICF diagnostic. Previous measurements of this cross section are inconsistent, disagreeing by up to a factor of two. A new experiment to measure this cross section has been performed using the Tandem Van de Graaff accelerator at Ohio University.
Date: Tuesday, November 6th, 2012
Speaker: Dr. Ryan Gantner, St. John Fisher College
Abstract: In this talk we'll learn about the stochastic voter model. In order to analyze its behavior, we'll look at some results concerning random walks and discrete probability. We'll then discuss how the model can be applied to diverse areas such as disease, zombies, Facebook --- and elections! The conclusion will feature an attempt to predict the outcome of today's election.
The tools we'll use in this talk include basic probability and a little calculus. Students who have had Calculus XXX (series) should be equipped to follow everything, others will understand most of the presentation.
Date: Tuesday, October 30th, 2012
Speaker: Dr. Jeffrey P. Bigham, University of Rochester Computer Science
Abstract: Over the past few years, I have been developing and deploying interactive crowd-powered systems. For instance, VizWiz has the crowd answer visual questions for blind people in less than a minute, Legion allows outsourcing of desktop tasks to the crowd, and Scribe allows the crowd to caption audio in real-time. Overall, thousands of people have engaged with these systems, providing an interesting look at how end users want to interact with
crowd work in their everyday lives.
Collectively, these systems illustrate a new approach to human computation in which the diverse and changing crowd is provided the computational support necessary to act as a single, high-quality actor. The classic advantage of the crowd has been its wisdom, but our systems are beginning to show how crowd agents can surpass even expert individuals on difficult real-time motor and cognitive performance tasks.
Abstract: This past summer of 2012 I had at internship at a biopharmaceutical company called Regeneron Pharmaceuticals, Inc. Regeneron works to develop proteins and monoclonal antibodies as medicine. They have become a leader in human antibody technologies and have built a pipeline of antibody drug candidates against both novel and validated drug targets in diseases ranging from cancer to rheumatoid arthritis and hypercholesterolemia. I worked in the Formulation Department at the company. The formulation is defined as all the components in the solution except for the protein or antibody itself. My project was focused on characterizing a curious pattern observed by the research group I was working with. For a specific monoclonal antibody, an initial reduction of aggregate was observed when the sample was exposed to thermal stress. This was contrary to the expected increase in aggregate which typically occurs with an increase in temperature. Using chromatography techniques, I studied this pattern in order to better understand the implications and potential impact it might have on the development of the antibody as a drug.
Date: Tuesday, October 2nd, 2012
Speaker: Dr. Carol Schumacher, Kenyon College
Title: Fast forward, slow motion: A graphical link between fast and slow time scales
Abstract: The world is shaped by interactions between things that develop slowly over time and things that happen very rapidly. Picture a garden. A bud takes hours to open up into a flower. A bee takes seconds to fly in, pollinate the flower and then depart. It can be difficult to fully consider both fast and slow time scales at the same time---yet it is the interaction between these events that makes the garden work. Mathematicians have developed a number of techniques for analyzing systems that include both fast and slow time scales. We will consider a graphical method for predicting what happens when fast and slow interact.
Date: Tuesday, September 18th, 2012
Speaker: Elizabeth Bailey ’13 and Laura Ballard ’13, Houghton College
Title: Graphs, Games and Polynomials
Abstract: This summer, Elizabeth Bailey and Laura Ballard were part of math research projects funded by the National Science Foundation at The University of North Carolina Asheville and Hope College, respectively. They will each present highlights from their research projects, both of which have to do with vertex-edge graphs.
Laura: Based on a puzzle created by Tiger Electronics, "Lights Out" can be modeled as a problem in graph theory and linear algebra. In the puzzle, each light can be either on or off and changing one light affects nearby lights. The goal of Lights Out is to turn off all of the lights. Our work involves generalized Lights Out puzzles in which each light is in one of several states, one of which is "off". We determine light configurations that that can always be won regardless of which set of lights are on at the start.
Elizabeth: Starting with a graph, we can determine a polynomial based on which vertices are not connected by edges. When the polynomial has coefficients that increase and then decrease, it is called unimodal and the largest coefficient is the mode. We investigate families of graphs with unimodal independence polynomials in order to efficiently find the location of the mode. For certain "path-like" graphs, we are able to find a closed form for the independence polynomial and further analyze its behavior. A fair amount of information about a graph and its behavior can be seen by studying the independence polynomial. These algebraic expressions have applications in physics and chemistry as well.
Date: Tuesday, September 11th, 2012
Speaker: Dr. Matthew Pelletier, Houghton College
Title: Screening a Local Amish Population for Propionic Acidemia
Abstract: Several children in a Houghton-area Amish population have been diagnosed with propionic acidemia (PA), a disease of amino acid metabolism that is treatable primarily through altering diet. This recessive disease is extremely rare in the general U.S. population, with an estimated incidence of about 1 in 50,000. Previous research has shown that a particular mutation in the PCCB gene is responsible for the Amish form of the disease. Because newborn screening for PA has only been performed in New York for about a decade, it is likely that undiagnosed individuals may be present in the population. We developed a simple polymerase chain reaction (PCR) and direct DNA sequencing assay to determine carrier status for this mutation. After obtaining informed consent and a cheek swab from among seventy-nine individuals of unknown genotype, twenty-eight carriers were found. Future work may involve expanding the screening of the population and possibly doing further molecular/cell studies involving cultured cells from PA patients.
Date: Tuesday, September 4th, 2012
Speaker: Liaoruo Wang, Oracle, Inc.
Title: The Structure and Dynamics of Large Social Networks
Abstract: What do social networks look like? What is a community? Is there any difference between the communities? How does information propagate in social networks? In this talk, we try to answer these questions from a computer science perspective.
In many social networks, there exist two types of users that exhibit different influence and different behavior. For instance, statistics have shown that less than 1% of the Twitter users (e.g. entertainers, politicians, writers) produce 50% of its content, while the others (e.g. fans, followers, readers) have much less influence and completely different social behavior. In this talk, we define and explore a novel problem called community kernel detection in order to uncover the hidden community structure in large social networks. We discover that influential users pay closer attention to those who are more similar to them, which leads to a natural partition into different community kernels. We propose two efficient algorithms for finding community kernels in large social networks. We conduct experiments on three large social networks: Twitter, Wikipedia, and Coauthor, which show that our algorithms achieve an average 15-50% performance improvement over the other state-of-the-art algorithms, and are 6-2,000 times faster on average in detecting community kernels.
Cascading processes, such as disease contagion, information diffusion, and viral marketing, are a pervasive phenomenon in many types of networks. The problem of devising intervention strategies to facilitate or inhibit such processes has recently received considerable attention. However, a major challenge is that the underlying network is often unknown. We revisit the problem of inferring latent network structure given observations from a diffusion process, such as the spread of trending topics in social media. We define a family of novel probabilistic models that can explain recurrent cascading behavior, and take into account not only the time differences between events but also a richer set of additional features. Further, we demonstrate the effectiveness of our approach by inferring the underlying network structure of a subset of the popular Twitter following network by analyzing the topics of a large number of messages posted by users over a 10-month period. Experimental results show that our models accurately recover the links of the Twitter network, and significantly improve the performance over previous models based entirely on time.