Isolation and characterization of a novel substrate for the pro-apoptotic Omi/HtrA2 protease

Omi, also known as HtrA2, is a mammalian pro-apoptotic mitochondrial protein and a member of the HtrA (high temperature requirement A) family of serine proteases. Omi promotes the caspase-dependent apoptotic pathway through cleavage of IAPs (inhibitor of apoptosis proteins); this cleavage inactivates IAPs and facilitates caspase activity. Omi\u27s proteolytic activity is necessary and essential for its pro-apoptotic function. This study is aimed to further understand the role of Omi in the cytoplasm by using the yeast two-hybrid system to identify novel Omi interactors/substrates. A HeLa (cervical carcinoma cell line) cDNA library was screened using Omi as a bait protein. One of the proteins indentified in this screen as a strong Omi interactor was the S5a protein and was selected for further analysis. S5a is a soluble cytosolic mammalian protein and a component of the proteasome\u27s 19S regulatory subunit. The proteasome is a large cytosolic protein complex responsible for the controlled degradation of damaged or denatured cellular proteins. Further characterization of the interaction through an in vitro proteolytic assay demonstrated that Omi can cleaves recombinant S5a protein. This data suggests that S5a is a bona fide substrate of Omi that is degraded upon induction of apoptosis. It also provides a new mechanism that leads to the inactivation of the proteasome during cell death

Social Ontology, Spectacle, and Hyperreality: A Critical Examination of Searle, Debord And Baudrillard

In this thesis I examine the philosophical views of John Searle, Guy Debord, and Jean Baudrillard. These thinkers have radically different methodologies and theoretical alignments, but they are not entirely dissimilar. John Searle argues that there are two types of facts—those independent of human observation and those whose meaning depends on agreement. Guy Debord posits that modern society has replaced authentic social life with mere representation. The “spectacle” has replaced real interactions with others so that meaning itself is no longer authentic; it is treated as a commodity or currency. Jean Baudrillard argues that society has replaced reality with signs and symbols. Thus, human experience consists only of simulations, not reality itself. Each of these figures maintains that meaning is socially constructed. After examining the key assumptions of their respective theories, I demonstrate that their accounts are compatible and argue that their accounts are most cohesive when considered together

Doctor of Philosophy

dissertationThis dissertation begins with an overview of skilled performance and how hierarchical control theory (HCT) has been successful in explaining skilled performance. Next, two novel premises of HCT are generated that provide evidence for distinct, hierarchical control systems (outer and inner loops). These control systems have unique properties and lead to very different predictions when applied to complex skills. By manipulating primary task predictability and secondary task workload of a complex skill, these properties can be dissociated. This is followed by an application of HCT to driving and driver distraction. I discuss how secondary task cognitive workload affects driving performance and how previous research has not explained paradoxical patterns of driving performance (i.e., lane maintenance). Then two premises of HCT are generated and used to make predictions about lane maintenance. Next, another influential theory of skilled performance (ACT-R) is discussed, and this theory is contrasted with HCT in terms of predictions regarding lane maintenance. Two experiments are designed to test HCT and differentiate it from ACT-R. The results support the predictions of HCT and suggest that ACT-R is somewhat limited in its ability to fully explain lane maintenance. HCT provides a framework for future driving research as well as future research on a variety of complex skills

Utilizing Ground-Based LIDAR Measurements to Aid Autonomous Airdrop Systems

Uncertainty in atmospheric winds represents one of the primary sources of landing error in airdrop systems. In this work, a ground-based LIDAR system samples the wind field at discrete points above the target and transmits real-time data to approaching autonomous airdrop systems. In simulation and experimentation, the inclusion of a light detection and ranging (LIDAR) system showed a maximum of 40% improvement over unaided autonomous airdrop systems. Wind information nearest ground level has the largest impact on improving accuracy

On the Benefits of In-Flight System Identification for Autonomous Airdrop Systems

A unique feature of airdrop systems is the inherent and large variability in flight dynamic characteristics. The same physical article dropped on two different occasions will exhibit significantly different dynamic response. The problem only becomes worse for different test articles. Control systems for autonomous airdrop systems explicitly or implicitly assume knowledge of the flight dynamic characteristics in some way, shape, or form. A question facing autonomous airdrop designers is whether to use precomputed dynamic characteristics inside the control law, or to compute the needed flight dynamic characteristics in-flight and subsequently employ them in the control law. This paper establishes conditions when in-flight identified characteristics, with a focus on the turn rate dynamics, should be used, and when it is better to use precomputed results. It is shown that with expected levels of system variability, sensor noise, and atmospheric wind, in-flight identification generally produces significantly more accurate dynamic behavior of the lateral dynamics than a precomputed model of the nominal system, even when the in-flight identification is performed with highly inaccurate sensor data. The only exception to this rule observed in this work is the situation where atmospheric winds are high and a direct heading measurement is not available. In this situation, a precomputed estimate of the time constant of the lateral dynamics is more accurate than an in-flight estimate. These conclusions are reached though a comprehensive simulation study using a validated airdrop flight dynamic model applied to both a small and large parafoil

Specialized System Identification for Parafoil and Payload Systems

There are a number of peculiar aspects to parafoil and payload systems that make it difficult to apply conventional system identification procedures used for aerospace systems. Parafoil and payload systems are unique because typically there is very little sensor information available, the sensors that are available are separated from the canopy by a complex network of flexible rigging, the systems are very sensitive to wind and turbulence, the systems exhibit a number of nonlinear behaviors, and the systems exhibit a high degree of variability from flight to flight. The current work describes a robust system identification procedure developed to address the specific difficulties posed by airdrop systems. By employing a two-phase approach that separately considers atmospheric winds estimation and aerodynamic coefficient estimation, a nonlinear, 6-degree-of-freedom dynamic simulation model is generated using only Global Positioning System data from the flight test. The key to this approach is the use of a simplified aerodynamic representation of the canopy, which requires identification of only the steady-state response to control input to completely define the dynamic model. The proposed procedure is demonstrated by creating a simulation model using Global Positioning System data from actual flight tests. To validate the procedure, the dynamic response of the simulation model is then compared to inertial measurement unit data that were not used in any way to develop the simulation model, with excellent results

Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz Algorithm

We obtain an improved finite-sample guarantee on the linear convergence of stochastic gradient descent for smooth and strongly convex objectives, improving from a quadratic dependence on the conditioning (L/µ) 2 (where L is a bound on the smoothness and µ on the strong convexity) to a linear dependence on L/µ. Furthermore, we show how reweighting the sampling distribution (i.e. importance sampling) is necessary in order to further improve convergence, and obtain a linear dependence in the average smoothness, dominating previous results. We also discuss importance sampling for SGD more broadly and show how it can improve convergence also in other scenarios. Our results are based on a connection we make between SGD and the randomized Kaczmarz algorithm, which allows us to transfer ideas between the separate bodies of literature studying each of the two methods. In particular, we recast the randomized Kaczmarz algorithm as an instance of SGD, and apply our results to prove its exponential convergence, but to the solution of a weighted least squares problem rather than the original least squares problem. We then present a modified Kaczmarz algorithm with partially biased sampling which does converge to the original least squares solution with the same exponential convergence rate