Engineering topics
Rogers, David
Publications and source records attributed to Rogers, David.
Mechanism of fusion of pathogenic enveloped viruses with the endosomal membrane
Dengue virus is a devastating human pathogen responsible for millions of infections each year. No antiviral therapies for Dengue currently exist, making effective treatment of the virus challenging. Dengue is taken into the cell through endocytosis. Low-pH mediated structural rearrangements of the envelope protein E leads to the formation of fusogenic E trimers that facilitate membrane fusion with late endosomes. The fusion mechanism is not fully understood, but is a key target for inhibiting the viral infection pathway. An important aspect of fusion is the dependence on endosomal membrane composition, and in particular, the requirement of anionic lipids. This study aims to characterize the biophysical reasons for this dependence. The work includes experimental studies and molecular simulations of the interactions of E with lipid membranes. These approaches revealed the structure of E bound to lipid membranes including the depth of its insertion into the membrane and the average angle with respect to the membrane, the fundamental interactions involved, the dependence of adsorption and anchoring energy on membrane composition, the membrane curvature induced upon insertion, and the correlation of the above with fusion efficiency of virus like particles (VLPs) with liposomes. As a part of this work we developed a new biophysical technique to measure the energy for pulling E out of a membrane, and distinguished anchoring (pull-out) and binding energies for this nonequilibrium system. We also developed a modeling approach combining molecular and continuum approaches to provide the first theoretical estimate of the binding energy. Taken together, this work lays the foundation for developing a systematic fundamental understanding of fusion in enveloped viruses that has been elusive to date.
Atomic Layer Deposition of L-Alanine Polypeptide
L-Alanine polypeptide thin films were synthesized via atomic layer deposition (ALD). Rather, instead of using an amino acid monomer as the precursor, an L-alanine amino acid derivatized with a protecting group was used to prevent self-polymerization, increase the vapor pressure, and allow linear cycle-by-cycle growth emblematic of ALD. Moreover, the successful deposition of a conformal polypeptide film has been confirmed by FTIR, TEM, and Mass Spectrometry, and the ALD process has been extended to polyvaline.
Evaluation of Methods to Integrate Analysis into a Large-Scale Shock Physics Code.
Abstract not provided.
Biophysical study of the dependence of fusion of dengue virus with host membranes on lipid composition.
Abstract not provided.
Productivity Challenges for Code Coupling in an HPC Environment.
Abstract not provided.
Convective Distribution of Tropospheric Ozone and Tracers in the Central American ITCZ Region: Evidence from Observations During TC4
During the Tropical Composition, Clouds and Climate Coupling (TC4) experiment that occurred in July and August of 2007, extensive sampling of active convection in the ITCZ region near Central America was performed from multiple aircraft and satellite sensors. As part of a sampling strategy designed to study cloud processes, the NASA ER-2, WB-57 and DC-8 flew in stacked "racetrack patterns" in convective cells. On July 24, 2007, the ER-2 and DC-8 probed an actively developing storm and the DC-8 was hit by lightning. Case studies of this flight, and of convective outflow on August 5, 2007 reveal a significant anti-correlation between ozone and condensed cloud water content. With little variability in the boundary layer and a vertical gradient, low ozone in the upper troposphere indicates convective transport. Because of the large spatial and temporal variability in surface CO and other pollutants in this region, low ozone is a better convective indicator. Lower tropospheric tracers methyl hydrogen peroxide, total organic bromine and calcium substantiate the ozone results. OMI measurements of mean upper tropospheric ozone near convection show lower ozone in convective outflow. A mass balance estimation of the amount of convective turnover below the tropical tropopause transition layer (TTL) is 50%, with an altitude of maximum convective outflow located between 10 and 11 km, 4 km below the cirrus anvil tops. It appears that convective lofting in this region of the ITCZ is either a two-stage or a rapid mixing process, because undiluted boundary layer air is never sampled in the convective outflow.
Intercontinental Transport of Air Pollution
The development of the global economy goes beyond raising our standards of living. We are in an ear of increasing environmental as well as economic interdependence. Long-range transport of anthropogenic atmospheric pollutants such as ozone, ozone precursors, airborne particles, heavy metals (such as mercury) and persistent organic pollutants are the four major types of pollution that are transported over intercontinental distances and have global environmental effects. The talk includes: 1) an overview of the international agreements related to intercontinental transport of air pollutants, 2) information needed for decision making, 3) overview of the past research on intercontinental transport of air pollutants - a North American's perspective, and 4) future research needs.
G/SPLINES: A hybrid of Friedman's Multivariate Adaptive Regression Splines (MARS) algorithm with Holland's genetic algorithm
G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.
Temporal learning in the cerebellum: The microcircuit model
The cerebellum is that part of the brain which coordinates motor reflex behavior. To perform effectively, it must learn to generate specific motor commands at the proper times. We propose a fundamental circuit, called the MicroCircuit, which is the minimal ensemble of neurons both necessary and sufficient to learn timing. We describe how learning takes place in the MicroCircuit, which then explains the global behavior of the cerebellum as coordinated MicroCircuit behavior.
Weather prediction using a genetic memory
Kanaerva's sparse distributed memory (SDM) is an associative memory model based on the mathematical properties of high dimensional binary address spaces. Holland's genetic algorithms are a search technique for high dimensional spaces inspired by evolutional processes of DNA. Genetic Memory is a hybrid of the above two systems, in which the memory uses a genetic algorithm to dynamically reconfigure its physical storage locations to reflect correlations between the stored addresses and data. This architecture is designed to maximize the ability of the system to scale-up to handle real world problems.
BIRD: A general interface for sparse distributed memory simulators
Kanerva's sparse distributed memory (SDM) has now been implemented for at least six different computers, including SUN3 workstations, the Apple Macintosh, and the Connection Machine. A common interface for input of commands would both aid testing of programs on a broad range of computer architectures and assist users in transferring results from research environments to applications. A common interface also allows secondary programs to generate command sequences for a sparse distributed memory, which may then be executed on the appropriate hardware. The BIRD program is an attempt to create such an interface. Simplifying access to different simulators should assist developers in finding appropriate uses for SDM.
A CCIR aeronautical mobile satellite report
Propagation effects in the aeronautical mobile-satellite service differ from those in the fixed-satellite service and other mobile-satellite services because: small antennas are used on aircraft, and the aircraft body may affect the performance of the antenna; high aircraft speeds cause large Doppler spreads; aircraft terminals must accommodate a large dynamic range in transmission and reception; and due to their high speeds, banking maneuvers, and three-dimensional operation, aircraft routinely require exceptionally high integrity of communications, making even short-term propagation effects very important. Data and models specifically required to characterize the path impairments are discussed, which include: tropospheric effects, including gaseous attenuation, cloud and rain attenuation, fog attenuation, refraction and scintillation; surface reflection (multipath) effects; ionospheric effects such as scintillation; and environmental effects (aircraft motion, sea state, land surface type). Aeronautical mobile-satellite systems may operate on a worldwide basis, including propagation paths at low elevation angles. Several measurements of multipath parameters over land and sea were conducted. In some cases, laboratory simulations are used to compare measured data and verify model parameters. The received signals is considered in terms of its possible components: a direct wave subject to atmospheric effects, and a reflected wave, which generally contains mostly a diffuse component.
Statistical prediction with Kanerva's sparse distributed memory
A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presented. In conditions of near- or over-capacity, where the associative-memory behavior of the model breaks down, the processing performed by the model can be interpreted as that of a statistical predictor. Mathematical results are presented which serve as the framework for a new statistical viewpoint of sparse distributed memory and for which the standard formulation of SDM is a special case. This viewpoint suggests possible enhancements to the SDM model, including a procedure for improving the predictiveness of the system based on Holland's work with genetic algorithms, and a method for improving the capacity of SDM even when used as an associative memory.
Kanerva's sparse distributed memory: An associative memory algorithm well-suited to the Connection Machine
The advent of the Connection Machine profoundly changes the world of supercomputers. The highly nontraditional architecture makes possible the exploration of algorithms that were impractical for standard Von Neumann architectures. Sparse distributed memory (SDM) is an example of such an algorithm. Sparse distributed memory is a particularly simple and elegant formulation for an associative memory. The foundations for sparse distributed memory are described, and some simple examples of using the memory are presented. The relationship of sparse distributed memory to three important computational systems is shown: random-access memory, neural networks, and the cerebellum of the brain. Finally, the implementation of the algorithm for sparse distributed memory on the Connection Machine is discussed.
Using data tagging to improve the performance of Kanerva's sparse distributed memory
The standard formulation of Kanerva's sparse distributed memory (SDM) involves the selection of a large number of data storage locations, followed by averaging the data contained in those locations to reconstruct the stored data. A variant of this model is discussed, in which the predominant pattern is the focus of reconstruction. First, one architecture is proposed which returns the predominant pattern rather than the average pattern. However, this model will require too much storage for most uses. Next, a hybrid model is proposed, called tagged SDM, which approximates the results of the predominant pattern machine, but is nearly as efficient as Kanerva's original formulation. Finally, some experimental results are shown which confirm that significant improvements in the recall capability of SDM can be achieved using the tagged architecture.