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- Creators: Arizona State University
Description
Alzheimer's Disease (AD) is a debilitating neurodegenerative disease. The disease leads to dementia and loss of cognitive functions and affects about 4.5 million people in the United States. It is the 7th leading cause of death and is a huge financial burden on the healthcare industry. There are no means of diagnosing the disease before neurodegeneration is significant and sadly there is no cure that controls its progression. The protein beta-amyloid or Aâ plays an important role in the progression of the disease. It is formed from the cleavage of the Amyloid Precursor Protein by two enzymes - â and ã-secretases and is found in the plaques that are deposits found in Alzheimer brains. This work describes the generation of therapeutics based on inhibition of the cleavage by â-secretase. Using in-vitro recombinant antibody display libraries to screen for single chain variable fragment (scFv) antibodies; this work describes the isolation and characterization of scFv that target the â-secretase cleavage site on APP. This approach is especially relevant since non-specific inhibition of the enzyme may have undesirable effects since the enzyme has been shown to have other important substrates. The scFv iBSEC1 successfully recognized APP, reduced â-secretase cleavage of APP and reduced Aâ levels in a cell model of Alzheimer's Disease. This work then describes the first application of bispecific antibody therapeutics to Alzheimer's Disease. iBSEC1 scFv was combined with a proteolytic scFv that enhances the "good" pathway (á-secretase cleavage) that results in alternative cleavage of APP to generate the bispecific tandem scFv - DIA10D. DIA10D reduced APP cleavage by â-secretase and steered it towards the "good" pathway thus increasing the generation of the fragment sAPPá which is neuroprotective. Finally, treatment with iBSEC1 is evaluated for reduced oxidative stress, which is observed in cells over expressing APP when they are exposed to stress. Recombinant antibody based therapeutics like scFv have several advantages since they retain the high specificity of the antibodies but are safer since they lack the constant region and are smaller, potentially facilitating easier delivery to the brain
ContributorsBoddapati, Shanta (Author) / Sierks, Michael (Thesis advisor) / Arizona State University (Publisher)
Created2011
Description
Patients with Alzheimer's disease (AD) exhibit a significantly higher incidence of unprovoked seizures compared to age-matched non-AD controls, and animal models of AD (i.e., transgenic human amyloid precursor protein, hAPP mice) display neural hyper-excitation and epileptic seizures. Hyperexcitation is particularly important because it contributes to the high incidence of epilepsy in AD patients as well as AD-related synaptic deficits and neurodegeneration. Given that there is significant amyloid-β (Aβ) accumulation and deposition in AD brain, Aβ exposure ultimately may be responsible for neural hyper-excitation in both AD patients and animal models. Emerging evidence indicates that α7 nicotinic acetylcholine receptors (α7-nAChR) are involved in AD pathology, because synaptic impairment and learning and memory deficits in a hAPPα7-/- mouse model are decreased by nAChR α7 subunit gene deletion. Given that Aβ potently modulates α7-nAChR function, that α7-nAChR expression is significantly enhanced in both AD patients and animal models, and that α7-nAChR play an important role in regulating neuronal excitability, it is reasonable that α7-nAChRs may contribute to Aβ-induced neural hyperexcitation. We hypothesize that increased α7-nAChR expression and function as a consequence of Aβ exposure is important in Aβ-induced neural hyperexcitation. In this project, we found that exposure of Aβ aggregates at a nanomolar range induces neuronal hyperexcitation and toxicity via an upregulation of α7-nAChR in cultured hippocampus pyramidal neurons. Aβ up-regulates α7-nAChRs function and expression through a post translational mechanism. α7-nAChR up-regulation occurs prior to Aβ-induced neuronal hyperexcitation and toxicity. Moreover, inhibition of α7-nAChR or deletion of α7-nAChR prevented Aβ induced neuronal hyperexcitation and toxicity, which suggests that α7-nAChRs are required for Aβ induced neuronal hyperexcitation and toxicity. These results reveal a profound role for α7-nAChR in mediating Aβ-induced neuronal hyperexcitation and toxicity and predict that Aβ-induced up-regulation of α7-nAChR could be an early and critical event in AD etiopathogenesis. Drugs targeting α7-nAChR or seizure activity could be viable therapies for AD treatment.
ContributorsLiu, Qiang (Author) / Wu, Jie (Thesis advisor) / Lukas, Ronald J (Committee member) / Chang, Yongchang (Committee member) / Sierks, Michael (Committee member) / Smith, Brian (Committee member) / Vu, Eric (Committee member) / Arizona State University (Publisher)
Created2011
Description
Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and over- lapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
ContributorsThulasiram, Ramesh (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
Alzheimer's Disease (AD) is the sixth leading cause of death in the United States and the most common form of dementia. Its cause remains unknown, but it is known to involve two hallmark pathologies: Amyloid Beta plaques and neurofibrillary tangles (NFTs). Several proteins have been implicated in the formation of neurofibrillary tangles, including Tau and S100B. S100B is a dimeric protein that is typically found bound to Ca(II) or Zn(II). These experiments relate to the involvement of S100B in Alzheimer's Disease-related processes and the results suggest that future research of S100B is warranted. Zn(II)-S100B was found to increase the rate at which tau assembled into paired helical filaments, as well as affect the rate at which tubulin polymerized into microtubules and the morphology of SH-SY5Y neuroblastoma cells after 72 hours of incubation. Zn(II)-S100B also increased the firing rate of hippocampal neurons after 36 hours of incubation. Together, these results suggest several possibilities: Zn(II)-S100B may be a key part of the formation of paired helical filaments (PHFs) that subsequently form NFTs. Zn(II)-S100B may also be competing with tau to bind tubulin, which could lead to an instability of microtubules and subsequent cell death. This finding aligns with the neurodegeneration that is commonly seen in AD and which could be a result of this microtubule instability. Ultimately, these results suggest that S100B is likely involved in several AD-related processes, and if the goal is to find an efficient and effective therapeutic target for AD, the relationship between S100B, particularly Zn(II)-S100B, and tau needs to be further studied.
ContributorsNaegele, Hayley (Author) / Mcgregor, Wade C (Thesis advisor) / Baluch, Debra (Committee member) / Francisco, Wilson (Committee member) / Arizona State University (Publisher)
Created2013
Description
Cognitive function declines with normal age and disease states, such as Alzheimer's disease (AD). Loss of ovarian hormones at menopause has been shown to exacerbate age-related memory decline and may be related to the increased risk of AD in women versus men. Some studies show that hormone therapy (HT) can have beneficial effects on cognition in normal aging and AD, but increasing evidence suggests that the most commonly used HT formulation is not ideal. Work in this dissertation used the surgically menopausal rat to evaluate the cognitive effects and mechanisms of progestogens proscribed to women. I also translated these questions to the clinic, evaluating whether history of HT use impacts hippocampal and entorhinal cortex volumes assessed via imaging, and cognition, in menopausal women. Further, this dissertation investigates how sex impacts responsiveness to dietary interventions in a mouse model of AD. Results indicate that the most commonly used progestogen component of HT, medroxyprogesterone acetate (MPA), impairs cognition in the middle-aged and aged surgically menopausal rat. Further, MPA is the sole hormone component of the contraceptive Depo Provera, and my research indicates that MPA administered to young-adult rats leads to long lasting cognitive impairments, evident at middle age. Natural progesterone has been gaining increasing popularity as an alternate option to MPA for HT; however, my findings suggest that progesterone also impairs cognition in the middle-aged and aged surgically menopausal rat, and that the mechanism may be through increased GABAergic activation. This dissertation identified two less commonly used progestogens, norethindrone acetate and levonorgestrel, as potential HTs that could improve cognition in the surgically menopausal rat. Parameters guiding divergent effects on cognition were discovered. In women, prior HT use was associated with larger hippocampal and entorhinal cortex volumes, as well as a modest verbal memory enhancement. Finally, in a model of AD, sex impacts responsiveness to a dietary cognitive intervention, with benefits seen in male, but not female, transgenic mice. These findings have clinical implications, especially since women are at higher risk for AD diagnosis. Together, it is my hope that this information adds to the overarching goal of optimizing cognitive aging in women.
ContributorsBraden, Brittany Blair (Author) / Bimonte-Nelson, Heather A. (Thesis advisor) / Neisewander, Janet L (Committee member) / Conrad, Cheryl D. (Committee member) / Baxter, Leslie C (Committee member) / Arizona State University (Publisher)
Created2012
Description
I show that firms' ability to adjust variable capital in response to productivity shocks has important implications for the interpretation of the widely documented investment-cash flow sensitivities. The variable capital adjustment is sufficient for firms to capture small variations in profitability, but when the revision in profitability is relatively large, limited substitutability between the factors of production may call for fixed capital investment. Hence, firms with lower substitutability are more likely to invest in both factors together and have larger sensitivities of fixed capital investment to cash flow. By building a frictionless capital markets model that allows firms to optimize over fixed capital and inventories as substitutable factors, I establish the significance of the substitutability channel in explaining cross-sectional differences in cash flow sensitivities. Moreover, incorporating variable capital into firms' investment decisions helps explain the sharp decrease in cash flow sensitivities over the past decades. Empirical evidence confirms the model's predictions.
ContributorsKim, Kirak (Author) / Bates, Thomas (Thesis advisor) / Babenko, Ilona (Thesis advisor) / Hertzel, Michael (Committee member) / Tserlukevich, Yuri (Committee member) / Arizona State University (Publisher)
Created2013
Description
Public Private Partnerships (PPP) have been in use for years in the United Kingdom, Europe, Australia and for a shorter time here in the United States. Typical PPP infrastructure projects include a multi-year term of operation in addition to constructing the structural features to be used. Early studies are proving PPP delivery methods to be effective at construction cost containment. An examination of the key elements that constitute the early stage negotiation reveal that there is room for negotiation created by the governing documentation while maintaining a competitive environment that brings the best value available to the Public entity. This paper will examine why PPP's are effective during this critical construction period of the facilities life cycle. It is the intent of this study to examine why the features and outcomes of more or less negotiation and the degree of rigor associated with it.
ContributorsMaddex, William E (Author) / Chasey, Allan (Thesis advisor) / El Asmar, Mounir (Committee member) / Pendyala, Ram (Committee member) / Arizona State University (Publisher)
Created2012
Description
Alzheimer's Disease (AD) is a progressive neurodegenerative disease accounting for 50-80% of dementia cases in the country. This disease is characterized by the deposition of extracellular plaques occurring in regions of the brain important for cognitive function. A primary component of these plaques is the amyloid-beta protein. While a natively unfolded protein, amyloid-beta can misfold and aggregate generating a variety of different species including numerous different soluble oligomeric species some of which are precursors to the neurofibrillary plaques. Various of the soluble amyloid-beta oligomeric species have been shown to be toxic to cells and their presence may correlate with progression of AD. Current treatment options target the dementia symptoms, but there is no effective cure or alternative to delay the progression of the disease once it occurs. Amyloid-beta aggregates show up many years before symptoms develop, so detection of various amyloid-beta aggregate species has great promise as an early biomarker for AD. Therefore reagents that can selectively identify key early oligomeric amyloid-beta species have value both as potential diagnostics for early detection of AD and as well as therapeutics that selectively target only the toxic amyloid-beta aggregate species. Earlier work in the lab includes development of several different single chain antibody fragments (scFvs) against different oligomeric amyloid-beta species. This includes isolation of C6 scFv against human AD brain derived oligomeric amyloid-beta (Kasturirangan et al., 2013). This thesis furthers research in this direction by improving the yields and investigating the specificity of modified C6 scFv as a diagnostic for AD. It is motivated by experiments reporting low yields of the C6 scFv. We also used the C6T scFv to characterize the variation in concentration of this particular oligomeric amyloid-beta species with age in a triple transgenic AD mouse model. We also show that C6T can be used to differentiate between post-mortem human AD, Parkinson's disease (PD) and healthy human brain samples. These results indicate that C6T has potential value as a diagnostic tool for early detection of AD.
ContributorsVenkataraman, Lalitha (Author) / Sierks, Michael (Thesis advisor) / Rege, Kaushal (Committee member) / Pauken, Christine (Committee member) / Arizona State University (Publisher)
Created2013
Description
I study the performance of hedge fund managers, using quarterly stock holdings from 1995 to 2010. I use the holdings-based measure built on Ferson and Mo (2012) to decompose a manager's overall performance into stock selection and three components of timing ability: market return, volatility, and liquidity. At the aggregate level, I find that hedge fund managers have stock picking skills but no timing skills, and overall I do not find strong evidence to support their superiority. I show that the lack of abilities is driven by the large fluctuations of timing performance with market conditions. I find that conditioning information, equity capital constraints, and priority in stocks to liquidate can partly explain the weak evidence. At the individual fund level, bootstrap analysis results suggest that even top managers' abilities cannot be separated from luck. Also, I find that hedge fund managers exhibit short-horizon persistence in selectivity skill.
ContributorsKang, MinJeong (Author) / Aragon, George O. (Thesis advisor) / Hertzel, Michael G (Committee member) / Boguth, Oliver (Committee member) / Arizona State University (Publisher)
Created2013
Description
I examine the degree to which stockholders' aggregate gain/loss frame of reference in the equity of a given firm affects their response to the firm's quarterly earnings announcements. Contrary to predictions from rational expectations models of trade (Shackelford and Verrecchia 2002), I find that abnormal trading volume around earnings announcements is larger (smaller) when stockholders are in an aggregate unrealized capital gain (loss) position. This relation is stronger among seller-initiated trades and weaker in December, consistent with the cognitive bias referred to as the disposition effect (Shefrin and Statman 1985). Sensitivity analysis reveals that the relation is stronger among less sophisticated investors and for firms with weaker information environments, consistent with the behavioral explanation. I also present evidence on the consequences of this disposition effect. First, stockholders' aggregate unrealized capital gain position moderates the degree to which information-related determinants of trade (e.g. unexpected earnings, firm size, and forecast dispersion) affect abnormal announcement-window trading volume. Second, stockholders' aggregate unrealized capital gains position is associated with announcement-window abnormal returns, consistent with the disposition effect reducing the market's ability to efficiently incorporate earnings news into price.
ContributorsWeisbrod, Eric (Author) / Hillegeist, Stephen (Thesis advisor) / Kaplan, Steven (Committee member) / Mikhail, Michael (Committee member) / Arizona State University (Publisher)
Created2012