Alumina-titanium diboride (Al2O3-TiB2) composites have high temperature, wear, and impact resistance that could be useful in high performance applications. Determining the effect of processing parameters on relative bond strength and effective plasticity may contribute to optimization and predictability of performance in the Al2O3-TiB2 system. Al2O3-TiB2 composites were obtained from a collection of samples that were created during a separate ongoing research program being conducted by Dr. Kathryn V. Logan. The Logan samples were initially formed by hot pressing powders produced using Self-Propagating High Temperature synthesis (SHS) of Al, TiO2, and B2O3 powders or manual mixing (MM) of Al2O3 and TiB2 powders. Samples were then fractured using standard single edge notched beam (SENB) fracture toughness testing. The obtained fractured surfaces were examined using Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS). Relative amounts of transgranular and intergranular fracture of Al2O3 and TiB2 grains were determined. Transgranular fracture was used as a measure of relative bond strength. Other samples were obtained from the Logan collection to conduct nano-indentation measurements on polished sample surfaces in Al2O3 grains and in TiB2 grains. Indent locations were verified using SEM. Reduced modulus, final displacements, and fracture toughness for indents in Al2O3 grains and in TiB2 grains were determined from nano-indentation curves. Reduced modulus was used as a measure of relative bond strength. Final displacement and fracture toughness were used as measures of relative effective plasticity. Analysis of Variance (ANOVA) using Taguchi arrays was conducted using the powder processing factor (SHS vs. MM) and the predominant microstructure factor (TiB2 grains surrounding Al2O3 grains vs. TiB2 grains distributed amongst Al2O3 grains) when examining the effect of processing parameters on relative bond strength as measured by amount of transgranular fracture. Analysis of Variance (ANOVA) using Taguchi arrays was conducted using the powder processing factor (SHS vs. MM), the predominant microstructure factor (TiB2 grains surrounding Al2O3 grains vs. TiB2 grains distributed amongst Al2O3 grains), and the indented phase factor (Al2O3 vs. TiB2) when examining the effect of processing parameters on relative bond strength as measured by nano-indentation reduced modulus and both measures of relative effective plasticity. Powder processing was significant for the relative bond strength measures, but was not significant for the relative effective plasticity measures. Predominant microstructure was significant for all measures except relative effective plasticity as measured by fracture toughness, for which none of the factors and interactions were significant. The interaction between powder processing and predominant microstructure was significant for most of the relative bond strength measures and for relative effective plasticity as measured by final displacements. Indented phase was significant for the nano-indentation measures except nano-indentation fracture toughness, although the significance for nano-indentation fracture toughness was just below the critical level. The interaction between powder processing and indented phase and the interaction between predominant microstructure and indented phase were only significant for the relative bond strength measure using nano-indentation reduced modulus. The interaction between powder processing, predominant microstructure, and indented phase was significant for the nano-indentation measures except nano-indentation fracture toughness. The optimum level for powder processing was predominantly manual mixing. The optimum level for predominant microstructure was predominantly TiB2 grains surrounding Al2O3 grains. The optimum level for indented phase was predominantly TiB2. / Master of Science

Phenotypic plasticity is the ability of a genotype to express different phenotypes in different environments. Despite its common occurrence, few have investigated differences in plasticity between populations, the selection pressures responsible for it, and costs and constraints associated with it. In this thesis, I investigated this by studying local adaptation and phenotypic plasticity in populations of the common frog Rana temporaria, inhibiting islands with different pool types (temporary, permanent or both). The tadpoles develop in these pools, and have to finish metamorphosis before the pool dries out. I found that the tadpoles were locally adapted both in development time and in phenotypic plasticity of development time. Tadpoles from islands with temporary pools had a genetically shorter development time than tadpoles from islands with permanent pools. The population differentiation in development time, estimated as QST, was larger than the population differentiation in neutral molecular markers (FST), which suggest that divergent selection among the populations is responsible for the differentiation. Moreover, tadpoles from islands with more variation in pool drying regimes had higher phenotypic plasticity in development time than tadpoles from islands with only one pool type present. Interestingly, increased migration among populations did not select for increased plasticity, rather it was the local environmental variation that was important. This adaptation has occurred over a short time scale, as the islands are less than 300 generations old. In temporary pools, it is adaptive to finish development before the pool dries out. This could be achieved by entering the metamorphosis at a smaller size, as a smaller size takes shorter time to reach. However, I found that there is a minimum threshold size below which tadpoles’ cannot enter metamorphosis, and that there had been no evolution of this threshold size in populations living in temporary environments. That suggests that this developmental threshold is tightly linked to physiological constraints in the developmental process. Despite their expected importance as constrains on the evolution of plasticity, costs of plasticity are often not found in nature.  However, theories of why they are absent have not been tested empirically. In this thesis, I show that fitness costs of phenotypic plasticity are only found in populations with genotypes expressing high levels of phenotypic plasticity, while in populations with low-plastic genotypes, I find costs of not being plastic. This suggests that costs of plasticity increase with increased level of plasticity in the population, and that might be a reason why costs of plasticity are hard to detect.

The variability of the postsynaptic response following a presynaptic action potential arises from: i) the neurotransmitter release being probabilistic and ii) channels in the postsynaptic cell involved in the response to neurotransmitter release, having stochastic properties. Spike timing dependent plasticity (STDP) is a form of plasticity that exhibits LTP or LTD depending on the precise order and timing of the firing of the synaptic cells. STDP plays a role in fundamental tasks such as learning and memory, thus understanding and characterizing the effect variability in synaptic transmission has on STDP is essential. To that end a model incorporating both forms of variability was constructed. It was shown that ion channel stochasticity increased the magnitude of maximal potentiation, increased the window of potentiation and severely reduced the post-LTP associated LTD in the STDP curves. The variability due to short term plasticity decreased the magnitude of maximal potentiation.

The variability of the postsynaptic response following a presynaptic action potential arises from: i) the neurotransmitter release being probabilistic and ii) channels in the postsynaptic cell involved in the response to neurotransmitter release, having stochastic properties. Spike timing dependent plasticity (STDP) is a form of plasticity that exhibits LTP or LTD depending on the precise order and timing of the firing of the synaptic cells. STDP plays a role in fundamental tasks such as learning and memory, thus understanding and characterizing the effect variability in synaptic transmission has on STDP is essential. To that end a model incorporating both forms of variability was constructed. It was shown that ion channel stochasticity increased the magnitude of maximal potentiation, increased the window of potentiation and severely reduced the post-LTP associated LTD in the STDP curves. The variability due to short term plasticity decreased the magnitude of maximal potentiation.

Brain injury is a serious clinical problem. The success of recovery from brain injury involves functional compensation in the affected brain area. We are interested in general mechanisms that underlie compensatory plasticity after brain damage, particularly when multiple brain areas or multiple modalities are included. In this thesis, I studied the function of auditory cortex after recovery from neonatal midbrain damage as a model system that resembles patients with brain damage or sensory dysfunction. I addressed maladaptive changes of auditory cortex after invasion by ectopic visual inputs. I found that auditory cortex contained auditory, visual, and multisensory neurons after it recovered from neonatal midbrain damage (Mao et al. 2011). The distribution of these different neuronal responses did not show any clustering or segregation. As might be predicted from the fact that auditory neurons and visual neurons were intermingled throughout the entire auditory cortex, I found that residual auditory tuning and tonotopy in the rewired auditory cortex were compromised. Auditory tuning curves were broader and tonotopic maps were disrupted in the experimental animals. Because lateral inhibition is proposed to contribute to refinement of sensory maps and tuning of receptive fields, I tested whether loss of inhibition is responsible for the compromised auditory function in my experimental animals. I found an increase rather than a decrease of inhibition in the rewired auditory cortex, suggesting that broader tuning curves in the experimental animals are not caused by loss of lateral inhibition. These results suggest that compensatory plasticity can be maladaptive and thus impair the recovery of the original sensory cortical function. The reorganization of brain areas after recovery from brain damage may require stronger inhibition in order to process multiple sensory modalities simultaneously. These findings provide insight into compensatory plasticity after sensory dysfunction and brain damage and new information about the role of inhibition in cross-modal plasticity. This study can guide further research on design of therapeutic strategies to encourage adaptive changes and discourage maladaptive changes after brain damage, sensory/motor dysfunction, and deafferentation.

The objective of this dissertation is to further the understanding of inelastic behavior in metallic materials. Despite the increasing use of polymeric composites in aircraft structures, high specific strength metals continue to be used in key components such as airframe, fuselage, wings, landing gear and hot engine parts. Design of metallic structures subjected to thermomechanical extremes in aerospace, automotive and nuclear applications requires consideration of the plasticity, creep and fracture behavior of these materials. Consideration of inelasticity and damage processes is also important in the design of metallic components used in functional applications such as thin films, flexible electronics and micro electro mechanical systems. Fracture mechanics has been largely successful in modeling damage and failure phenomena in a host of engineering materials. In the context of ductile metals, the Gurson void growth model remains one of the most successful and widely used models. However, some well documented limitations of the model in quantitative prediction of the fracture strains and failure modes at low triaxialities may be traceable to the limited representation of the damage microstructure in the model. In the first part of this dissertation, we develop an extended continuum model of void growth that takes into account details of the material microstructure such as the texture of the plastically deforming matrix and the evolution of the void shape. The need for such an extension is motivated by a detailed investigation of the effects of the two types of anisotropy on the materials' effective response using finite element analysis. The model is derived using the Hill-Mandel homogenization theory and an approximate limit analysis of a porous representative volume element. Comparisons with several numerical studies are presented towards a partial validation of the analytical model. Inelastic phenomena such as plasticity and creep result from the collective behavior of a large number of nano and micro scale defects such as dislocations, vacancies and grain boundaries. Continuum models relate macroscopically observable quantities such as stress and strain by coarse graining the discrete defect microstructure. While continuum models provide a good approximation for the effective behavior of bulk materials, several deviations have been observed in experiments at small scales such as an intrinsic size dependence of the material strength. Discrete dislocation dynamics (DD) is a mesoscale method for obtaining the mechanical response of a material by direct simulation of the motion and interactions of dislocations. The model incorporates an intrinsic length scale in the dislocation Burgers vector and potentially allows for size dependent mechanical behavior to emerge naturally from the dynamics of the dislocation ensemble. In the second part of this dissertation, a simplified two dimensional DD model is employed to study several phenomena of practical interest such as strain hardening under homogeneous deformation, growth of microvoids in a crystalline matrix and creep of single crystals at elevated temperatures. These studies have been enabled by several recent enhancements to the existing two-dimensional DD framework described in Chapter V. The main contributions from this research are: (i) development of a fully anisotropic continuum model of void growth for use in ductile fracture simulations and (ii) enhancing the capabilities of an existing two-dimensional DD framework for large scale simulations in complex domains and at elevated temperatures.

The stretch forming process is frequently used in the automotive industry (outer pan- els, inner panels, stiffeners etc.), the packaging industry and household appliances sector, to manufacture complicated shapes and curvatures. However it requires accurate prediction of tool geometries and manufacturing parameters to avoid the currently used trial and error approach. Metal forming is also associated with cer- tain defects like local thinning, wrinkling, tearing etc. Avoiding such defects and prediction of spring back presumably requires a thorough understanding of the de- formation mechanics and material behavior beyond the elastic range. In the stretch forming operation, material essentially passes through the elastic, yield point and plastic states. Elastic behavior can be explained based on classical theory of elasticity wherein linear trend of infinitesimal deformation is expressed by generalized Hooke’s law. In the plastic range, the theory is based on certain exper- imental observations of the macroscopic behavior of metals in the uniform state of combined stresses. Experimentally observed results are idealized into mathematical formulation to describe the complex behavior of metals under combined state of stress. These formulations are based on some assumptions like material behavior is time independent, strain rate effects could be neglected, hysteresis loop and Bauschinger effects which arise from the non-uniformity of the microscopic scale could be disregarded etc. The thermal effects are neglected and material is assumed to be isotropic. Supposedly because of these assumptions existing theory of plastic- ity does not accurately predict the phenomenon of stretch forming occurring during plastic deformation. Theories are being developed like that of Rao and Shrinivasa [2002], which consider stresses during deformation as resistance due to shape change, volume change, rate of shape change and rate of volume change. Such theories need variation of material parameters like bulk modulus (K), shear modulus (G), bulk viscosity (µ’) and shear viscosity (µ) as deformation progreses. Therefore uni-axial tension exper- iments have been conducted to find out the strains at the corresponding loads. Mild steel and aluminum have been chosen for the experiments. Chemical and physical properties of the materials are chosen such that they are very similar to those used in the automotive industry for stretch forming. A procedure is developed using uni-axial tension test results to calculate the material parameters for the entire range of material deformation. For mild steel, bulk modulus and shear modulus decrease and become almost zero as the material deforms from elastic to transition region. After transition zone, both moduli increase and then decrease as material deforms in the strain-hardening region. For aluminum both bulk and shear moduli decrease non-linearly as material deforms from elastic to plastic region. The behavior of bulk modulus and shear modulus are consistent with the stress-strain behavior of the materials. For mild steel as well as aluminum, the bulk and shear viscosities are positive in the elastic region and in the large deformation region the values are small compared to elastic region. We can separate the various stresses, hydrostatic, deviatoric and viscous stresses, associated with (µ) and (µ’) and contribution of each to the total stresses can be obtained. It is observed that contribution from the viscous stresses is as high as 5 % when the material is subjected to large strain rate tests. The strain rate in stretch forming operation may be different from the strain rate at which the material parameters are calculated. Knowing the material para- meters at one strain rate, the stress-strain curves at different strain rates can be predicted. The repeatability of computation of the material parameters and contributions from the viscous and non-viscous stresses for large deformation has been ascertained by using different test samples. The material parameters obtained from one set of samples have been applied to different samples and experimental versus predicted stresses have been found to match fairly well. A lot more work needs to be done to reach the goal of accurately predicting the behavior during stretch forming. Test data on different materials need to be generated and the new theories need to be validated for compression as well as loading and unloading cases.

<p>The late phase of long-term synaptic potentiation (LTP) at glutamatergic synapses, which is thought to underlie the long lasting memory (at least hours), requires gene transcription in the nucleus. However, it remains elusive how signaling initiated at synapses during induction of LTP is transmitted into the nucleus to commence transcription. Using a combination of two-photon glutamate uncaging and a genetically encoded FRET sensor, I found that induction of synapse-specific LTP at only a few (3-7) dendritic spines leads to pronounced activation of extracellular signal-regulated kinase (ERK) in the nucleus and downstream phosphorylation of transcription factors, cAMP-response element-binding protein (CREB) and E26-like protein-1 (Elk-1). The underlying molecular mechanism of this nuclear ERK activation was investigated: it seems to involve activation of NMDA receptors, metabotrophic glutamate receptors, and the classical Ras pathway. I also found that the spatial pattern of synaptic stimulation matters: spatially dispersed stimulation over multiple dendritic branches activated nuclear ERK much more efficiently than clustered stimulation within a single dendritic branch. In sum, these results suggest that biochemical signals could be transmitted from individual spines to the nucleus following LTP induction and that such synapse-to-nucleus signaling requires integration across multiple dendritic branches.</p> / Dissertation

This thesis is devoted to the numerical investigation of mechanical behavior of Dual phase (DP) steels. Such grade of advanced high strength steels (AHSS) is favorable to the automotive industry due the unique properties such as high strength and ductility with low finished cost. Many experimental and numerical studies have been done to achieve the optimized behavior of DP steels by controlling their microstructure. Experiments are costly and time consuming so in recent years numerical tools are utilized to help the metallurgist before doing experiments. Most of the numerical studies are based on classical (local) constitutive models where no material length scale parameters are incorporated in the model. Although these models are proved to be very effective in modeling the material behavior in the large scales but they fail to address some critical phenomena which are important for our goals. First, they fail to address the size effect phenomena which materials show at microstructural scale. This means that materials show stronger behavior at small scales compared to large scales. Another issue with classical models is the mesh size dependency in modeling the softening behavior of materials. This means that in the finite element context (FEM) the results will be mesh size dependent and no converged solution exist upon mesh refinement. Thereby by applying the classical (local) models one my loose the accuracy on measuring the strength and ductility of DP steels. Among the non-classical (nonlocal) models, gradient-enhanced plasticity models which consider the effect of neighboring point on the behavior of one specific point are proved to be numerically effective and versatile tools to accomplish the two concerns mentioned above. So in this thesis a gradient-enhanced plasticity model which incorporates both the energetic and dissipative material length scales is derived based on the laws of thermodynamics. This model also has a consistent yield-like function for the interface which is an essential part of the higher-order gradient theories. The main issue with utilizing these theories is the implementation which limits the application of these theories for modeling the real problems. Here a straightforward implementation method based on the classical FEM and Meshless method will be proposed which due to its simplicity it can be applied for many problems. The application of the developed model and implementation will be shown on removing the mesh size dependency and capturing the size effect in microstructure level of dual phase steels.

<p>It has long been appreciated that the process of learning might invoke a physical change in the brain, establishing a lasting trace of experience. Recent evidence has revealed that this change manifests, at least in part, by the formation of new connections between neurons, as well as the modification of preexisting ones. This so-called structural plasticity of neural circuits – their ability to physically change in response to experience – has remained fixed as a primary point of focus in the field of neuroscience. </p><p>A large portion of this effort has been directed towards the study of dendritic spines, small protrusions emanating from neuronal dendrites that constitute the majority of recipient sites of excitatory neuronal connections. The unique, mushroom-like morphology of these tiny structures has earned them considerable attention, with even the earliest observers suggesting that their unique shape affords important functional advantages that would not be possible if synapses were to directly contact dendrites. Importantly, dendritic spines can be formed, eliminated, or structurally modified in response to both neural activity as well as learning, suggesting that their organization reflects the experience of the neural network. As such, elucidating how these structures undergo such rearrangements is of critical importance to understanding both learning and memory. </p><p>As dendritic spines are principally composed of the cytoskeletal protein actin, their formation, elimination, and modification requires biochemical signaling networks that can remodel the actin cytoskeleton. As a result, significant effort has been placed into identifying and characterizing such signaling networks and how they are controlled during synaptic activity and learning. Such efforts have highlighted Rho family GTPases – binary signaling proteins central in controlling the dynamics of the actin cytoskeleton – as attractive targets for understanding how the structural modification of spines might be controlled by synaptic activity. While much has been revealed regarding the importance of the Rho GTPases for these processes, the specific spatial and temporal features of their signals that impart such structural changes remains unclear. </p><p>The central hypotheses of the following research dissertation are as follows: first, that synaptic activity rapidly initiates Rho GTPase signaling within single dendritic spines, serving as the core mechanism of dendritic spine structural plasticity. Next, that each of the Rho GTPases subsequently expresses a spatially distinct pattern of activation, with some signals remaining highly localized, and some becoming diffuse across a region of the nearby dendrite. The diffusive signals modify the plasticity induction threshold of nearby dendritic spines, and the spatially restricted signals serve to keep the expression of plasticity specific to those spines that receive synaptic input. This combination of differentially spatially regulated signals thus equips the neuronal dendrite with the ability to perform local biochemical computations, potentially establishing an organizational preference for the arrangement of dendritic spines along a dendrite. Finally, the consequences of the differential signal patterns also help to explain several seemingly disparate properties of one of the primary upstream activators of these proteins: brain-derived neurotrophic factor (BDNF). </p><p>The first section of this dissertation describes the characterization of the activity patterns of one of the Rho family GTPases, Rac1. Using a novel Förster Resonance Energy Transfer (FRET)- based biosensor in combination with two-photon fluorescence lifetime imaging (2pFLIM) and single-spine stimulation by two-photon glutamate uncaging, the activation profile and kinetics of Rac1 during synaptic stimulation were characterized. These experiments revealed that Rac1 conveys signals to both activated spines as well as nearby, unstimulated spines that are in close proximity to the target spine. Despite the diffusion of this structural signal, however, the structural modification associated with synaptic stimulation remained restricted to the stimulated spine. Thus, Rac1 activation is not sufficient to enlarge spines, but nonetheless likely confers some heretofore-unknown function to nearby synapses. </p><p>The next set of experiments set out to detail the upstream molecular mechanisms controlling Rac1 activation. First, it was found that Rac1 activation during sLTP depends on calcium through NMDA receptors and subsequent activation of CaMKII, suggesting that Rac1 activation in this context agrees with substantial evidence linking NMDAR-CaMKII signaling to LTP in the hippocampus. Next, in light of recent evidence linking structural plasticity to another potential upstream signaling complex, BDNF-TrkB, we explored the possibility that BDNF-TrkB signaling functioned in structural plasticity via Rac1 activation. To this end, we first explored the release kinetics of BDNF and the activation kinetics of TrkB using novel biosensors in conjunction with 2p glutamate uncaging. It was found that release of BDNF from single dendritic spines during sLTP induction activates TrkB on that same spine in an autocrine manner, and that this autocrine system was necessary for both sLTP and Rac1 activation. It was also found that BDNF-TrkB signaling controls the activity of another Rho GTPase, Cdc42, suggesting that this autocrine loop conveys both synapse-specific signals (through Cdc42) and heterosynaptic signals (through Rac1). </p><p>The next set of experiments detail one the potential consequences of heterosynaptic Rac1 signaling. The spread of Rac1 activity out of the stimulated spine was found to be necessary for lowering the plasticity threshold at nearby spines, a process known as synaptic crosstalk. This was also true for the Rho family GTPase, RhoA, which shows a similar diffusive activity pattern. Conversely, the activity of Cdc42, a Rho GTPase protein whose activity is highly restricted to stimulated spines, was required only for input-specific plasticity induction. Thus, the spreading of a subset of Rho GTPase signaling into nearby spines modifies the plasticity induction threshold of these spines, increasing the likelihood that synaptic activity at these sites will induce structural plasticity. Importantly, these data suggest that the autocrine BDNF-TrkB loop described above simultaneously exerts control over both homo- and heterosynaptic structural plasticity. </p><p>The final set of experiments reveals that the spreading of GTPase activity from stimulated spines helps to overcome the high activation thresholds of these proteins to facilitate nearby plasticity. Both Rac1 and RhoA, the activity of which spread into nearby spines, showed high activation thresholds, making weak stimuli incapable of activating them. Thus, signal spreading from a strongly stimulated spine can lower the plasticity threshold at nearby spines in part by supplementing the activation of high-threshold Rho GTPases at these sites. In contrast, the highly compartmentalized Rho GTPase Cdc42 showed a very low activation threshold, and thus did not require signal spreading to achieve high levels of activity to even a weak stimulus. As a result, synaptic crosstalk elicits cooperativity of nearby synaptic events by first priming a local region of the dendrite with several (but not all) of the factors required for structural plasticity, which then allows even weak inputs to achieve plasticity by means of localized Cdc42 activation. </p><p>Taken together, these data reveal a molecular pattern whereby BDNF-dependent structural plasticity can simultaneously maintain input-specificity while also relaying heterosynaptic signals along a local stretch of dendrite via coordination of differential spatial signaling profiles of the Rho GTPase proteins. The combination of this division of spatial signaling patterns and different activation thresholds reveals a unique heterosynaptic coincidence detection mechanism that allows for cooperative expression of structural plasticity when spines are close together, which in turn provides a putative mechanism for how neurons arrange structural modifications during learning.</p> / Dissertation