2.
Gene expression changes in tumor xenografts by Py-Im
polyamides - Raskatov, Jevgenij A.; Dervan, Peter B.
Many human diseases are caused by dysregulated gene expression. Py-Im polyamides are
synthetic mols. programmed to read the minor groove of the DNA double helix by a set of
simple chem. principles. Hairpin oligomers achieve affinities and specificities comparable
to transcription factors, alter the structure of the DNA and disrupt protein-DNA
interactions. Recent investigations demonstrate that cell permeable hairpin Py-Im
polyamides possess favorable pharmacokinetics and controllable toxicity in mice. Current
research efforts are focused on understanding how these mols. modulate gene expression
pathways in both cell culture and xenograft tumor models.
3.
New methods and strategies for the enantioselective
synthesis of polycyclic natural products - Reisman, Sarah E.
The overarching goal of the Reisman lab. is to discover, develop, and study new chem.
reactions within the context of natural product total synthesis. The chem. synthesis of
natural products enables the study of their biol. mechanisms, and can provide access to
synthetic derivs. with improved therapeutic properties. As importantly, these synthetic
undertakings serve to drive innovation in, and deepen our fundamental understanding of,
org. and organometallic chem. This seminar will describe our latest progress in both our
methodol. and target-directed synthesis endeavors.
4.
Capturing protein dynamics by time-resolved spectroscopy:
Folding and electron tunneling in cytochromes - Ford, Nicole B.; Yamada, Seiji; Gray, Harry B.; Winkler, Jay R.
We have resolved the folding kinetics of two c-type cytochromes, one that exhibits twostate
folding and one that has an on-pathway folding intermediate. We resolve
millisecond-timescale folding by coupling time-resolved fluorescence energy transfer
(FRET) with a continuous flow mixer. The efficiency of energy transfer between a dansyl
label, attached to single-cysteine mutants, and the cytochrome heme during the folding
process provides us with time-dependent distance distributions, which provide information
about the kinetics and mechanism of folding.We are also interested in characterizing the
pathway dependence of electron tunneling rates between metal sites in proteins. We have
converted a b-type cytochrome to a c-type cytochrome by covalently linking the porphyrin
to...
8.
Bayesian Updating and Model Class Selection of Deteriorating Hysteretic Structural Models using Seismic Response Data - Beck, James L.; Muto, Matthew
Identification of structural models from measured earthquake response can play a
key role in structural health monitoring, structural control and improving performance-based
design. System identification using data from strong seismic shaking is complicated by the
nonlinear hysteretic response of structures where the restoring forces depend on the previous
time history of the structural response rather than on an instantaneous finite-dimensional
state. Furthermore, this inverse problem is ill-conditioned because even if some components
in the structure show substantial yielding, others will exhibit nearly elastic response, producing
no information about their yielding behavior. Classical least-squares or maximum likelihood
estimation will not work with a realistic class of hysteretic models because...
10.
Bayesian Structural Model Updating and Model Selection with Modal Data using Gibbs Sampler - Ching, Jianye; Muto, Matthew; Beck, James
This paper presents a new Bayesian model updating approach for linear
structural models based on the Gibbs sampler, a stochastic simulation
method. We show that with incomplete modal data (modal
frequencies and incomplete modeshapes of some lower modes), and
with appropriate choices of conjugate priors, the uncertain stiffness
and mass parameters of the linear structural model can be decomposed
into three groups so that the sampling from any one group is
possible when conditional on the other groups and the modal data.
Such decomposition provides a major advantage for the Gibbs sampler:
even if the number of uncertain parameters is large, the effective
dimension for the Gibbs sampler is always...
11.
Robust Mass Damper Design using Stochastic Simulation - Taflanidis, Alexandros; Beck, James; Angelides, Demos
Mass dampers (for example, TMDs or TLCDs) are widely used for
suppression of structural vibrations. Their design is based on the adjustment
of their parameters, referred to herein as design variables, to
the dynamic characteristics of the coupled damper-structure system.
Uncertainty in the parameters of the model considered for the system
significantly influences the effectiveness of this design. Prior knowledge
about the system is quantified in this study by specifying probability
distributions for the uncertain model parameters. The objective
function for optimal design is chosen to be maximization of the systems
reliability against failure, an appropriate concept for applications
that involve uncertainty. Failure is defined to be exceedance of limit
states...
12.
Damage Detection in Hysteretic Structures using Measured Seismic Response - Muto, M; Beck, James
Damage in structures is often correlated with a loss of structural stiffness.
However, using dynamic response measurements from structures
subjected to earthquakes could show significant decreases in
stiffness as a result of yielding that is not necessarily an indicator of
permanent damage. The use of hysteretic models in system identification
may allow for distinctions between permanent losses in structural
stiffness and temporary decreases due to nonlinear yielding
response. While yielding parameters cannot be identified using small-amplitude
vibration data, such as ambient vibrations or weak earthquakes,
the information concerning the behavior of the structure in the
linear elastic range can serve as useful prior information for Bayesian
model updating of hysteretic models....
13.
Sparse Bayesian Learning for Structural Health Monitoring - Oh, Chang; Beck, James
Recently-developed techniques for statistical pattern recognition have
been investigated for their applicability to Structural Health Monitoring
(SHM). One of the state-of-the-art pattern recognition techniques is
the Support Vector Machine (SVM) which determines decision boundaries
from the data corresponding to different damage features; it
does this by simultaneously maximizing the margin between data
from different damage states in the transformed feature space and
minimizing the misclassification error. However, the errors caused by
modeling and measurement result in inevitable misclassification and
so a probabilistic treatment of learning from data and making damage
predictions becomes important. In this paper, a recently-developed
technique called the Relevance Vector Machine (RVM), which can be
viewed as a probabilistic...
14.
Reliability-based Performance Objectives and Probabilistic Model Uncertainty in Optimal Structural Control Applications - Taflanidis, Alexandros; Scruggs, Jeffrey; Beck, James
A reliability-based structural control design approach is presented,
which optimizes a control system explicitly to minimize the probability of structural failure. Here, failure is interpreted as the probability that
the system state trajectory will exit a safe region, inside a given time
duration. This safe region is bounded by hyperplanes in the system
state space, each of which corresponds to an important dynamic
response variable. The failure threshold for each of these response
variables is designated as a bound on acceptable performance. Thus
defined, an accurate analytical approximation for the probability of
failure, and for its optimization through feedback control, are
discussed. Versions of the approach are described for...
15.
Real-time Reliability Estimation for Serviceability Limit States in Structures with Uncertain Dynamical Excitation and Incomplete Output Data - Ching, Jianye; Beck, James
We present a novel technique for indirectly monitoring threshold
exceedance in a partially instrumented structure represented by a
linear structural model class subject to uncertainn Gaussian dynamic
excitation. The goal of this technique is to answer the following question:
given incomplete output data from a structure excited by uncertain
dynamic loading, what Is the probability that any particular
unobserved response of the structure exceeds a prescribed
threshold? To apply this technique, it is assumed that a good probabilistic
linear model class of the target structure has already been
determined. The new technique is useful for monitoring the serviceability
limit states of a structure subject to unmeasured small-amplitude
excitation (e.g. wind excitation...
16.
A New Adaptive Importance Sampling Scheme for Reliability Calculations - Au, S.K.; Beck, J.L.
An adaptive importance sampling methodology is proposed to compute the multidimensional
integrals encountered in reliability analysis. In the proposed methodology,
samples are simulated as the states of a Markov chain and are distributed asymptotically
according to the optimal importance sampling density. A kernel sampling
density is then constructed from these samples which is used as the sampling density
in an importance sampling simulation. The Markov chain samples populate the region
of higher probability density in the failure domain and so the kernel sampling
density approximates the optimal importance sampling density for a large variety of
shapes of the failure domain. This adaptive feature is insensitive to the probability
level...
17.
Modelling Ca2+ -dependent proteins in the spine - challenges and solutions - Stefan, Melanie I.; Pepke, Shirley; Mihalas, Stefan; Bartol, Thomas; Sejnowski, Terrence; Kennedy, Mary B.
Background / Purpose:
Modelling post-synaptic proteins poses three technical problems: small absolute molecule numbers, large numbers of possible states, and the complex geometry of the spine, which is not a well-mixed compartment. Computational approaches are needed that solve all three of these problems.
Main conclusion:
Stochastic simulation methods can be used for systems with small molecule numbers, agent-based methods to represent multi-state molecules, and spatial methods to simulate events in complex geometries. We used the agent-based spatial stochastic simulator MCell to model the Ca2+-dependent activation of calmodulin and Ca2+/calmodulin-dependent kinase II (CaMKII) in the spine.
Next steps:
Next steps will include the extension of our...
18.
Statistical Methodology for Optimal Sensor Locations for Damage Detection in Structures - Beck, James L.; Chan, Eduardo; Papadimitriou, Costas
A Bayesian statistical methodology
is presented for optimally locating the sensors
in a structure for the purpose of extracting the most
information about the model parameters which can
be used in model updating and in damage detection
and localization. This statistical approach properly
handles the unavoidable uncertainties in the measured
data as well as the uncertainties in the mathematical
model used to represent the structural behavior.
The optimality criterion for the sensor locations
is based on information entropy which is a
measure of the uncertainty in the model parameters.
The uncertainty in these parameters is computed
by the Bayesian statistical methodology and
then the entropy measure is minimized over the set
of possible sensor configurations...
19.
A Bayesian Probabilistic Approach to Structural Health Monitoring - Vanik, M. W.; Beck, J. L.
Some general issues associated with on-line structural health
monitoring are discussed. In order to address the problem of
determining the existence and location of damage in the presence
of uncertainties, a global model-based structural health
monitoring method which utilizes Bayesian probabilistic inference
is developed. The results of tests using simulated data
are described.
20.
Using Information Theory Concepts to Compare Alternative Intensity Measures for Representing Ground Motion Uncertainty - Jalayer, F.; Beck, J. L.
The seismic risk assessment of a structure in performance-based design may be
significantly affected by the representation of ground motion uncertainty. The
uncertainty in the ground motion is commonly represented by adopting a
parameter or a vector of parameters known as the intensity measure (IM). In this
work, a new measure, called a sufficiency measure, is derived based on
information theory concepts, to quantify the suitability of one IM relative to
another in representing ground motion uncertainty. Based on this measure,
alternative IM’s can be compared in terms of the expected difference in
information they provide about a designated structural response parameter.
Several scalar IM’s are compared in terms...
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