Neural precursor cells self-renew and differentiate throughout development and in response to neural injury in the adult brain. The DNA damage response in NPCs has yet to be characterized. Patients with defective nucleotide excision repair (NER) demonstrate neurodegeneration dismyelination, and microcephaly, suggesting a potential link to defective NPC function with accumulating DNA damage. We observed reduced self-renewal in Csbm/m and Xpam/m NPCs in response to UV damage. Serial passaging resulted in exhaustion of Csbm/m NPCs in the absence of exogenous DNA damage. In vitro neuronal differentiation resulted in abnormal neuritigenesis after UV DNA damage in Csbm/m NPCs, suggesting defects in the terminal differentiation process. Taken together, the results indicate that DNA damage can modulate the apoptotic, self-renewal and differentiation fates of NPCs.

A new and unique directional weighted error function was introduced into the backpropagation algorithm used in Artificial Neural Networks (ANNs) for applications where yes or no decisions are made on the output. A continuous error function based on a weighted curve is suggested for use in the backpropagation algorithm in an effort to increase the number of correct decisions. Results were compared to the standard and weighted error methods. A higher number of correct decisions were made with the new method. / Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical and Computer Engineering. / "July 2006." / Includes bibliographic references (leaves 36-37).

Neural precursor cells self-renew and differentiate throughout development and in response to neural injury in the adult brain. The DNA damage response in NPCs has yet to be characterized. Patients with defective nucleotide excision repair (NER) demonstrate neurodegeneration dismyelination, and microcephaly, suggesting a potential link to defective NPC function with accumulating DNA damage. We observed reduced self-renewal in Csbm/m and Xpam/m NPCs in response to UV damage. Serial passaging resulted in exhaustion of Csbm/m NPCs in the absence of exogenous DNA damage. In vitro neuronal differentiation resulted in abnormal neuritigenesis after UV DNA damage in Csbm/m NPCs, suggesting defects in the terminal differentiation process. Taken together, the results indicate that DNA damage can modulate the apoptotic, self-renewal and differentiation fates of NPCs.

This dissertation presents a new type of fuel control algorithm for gaseous fuelled vehicles. Gaseous fuels such as hydrogen and natural gas have been shown to be less polluting than liquid fuels such as gasoline, both at the tailpipe and on a total cycle basis. Unfortunately, it can be expensive to convert vehicles to gaseous fuels, partially due to small production runs for these vehicles. One of major development costs for a new vehicle is the development and calibration of the fuel controller. The research presented here includes a fuel controller which does not require an expensive calibration phase.<p>The controller is based upon a two-part model, separating steady state and dynamic effects. This model is then used to estimate the optimum fuelling for the measured operating condition. The steady state model is calculated using an artificial neural network with an online learning scheme, allowing the model to continually update to improve the controller's performance. This is important during both the initial learning of the characteristics of a new engine, as well as tracking changes due to wear or damage.<p>The dynamic model of the system is concerned with the significant transport delay between the time the fuel is injected and when the exhaust gas oxygen sensor makes the reading. One significant result of this research is the realization that a previous commonly used model for this delay has become significantly less accurate due to the shift from carburettors or central point injection to port injection.<p>In addition to a description of the control scheme used, this dissertation includes a new method of algebraically inverting a neural network, avoiding computationally expensive iterative methods of optimizing the model. This can greatly speed up the control loop (or allow for less expensive, slower hardware).<p>An important feature of a fuel control scheme is that it produces a small, stable limit cycle between rich and lean fuel-air mixtures. This dissertation expands the currently available models for the limit cycle characteristics of a system with a linear controller as well as developing a similar model for the neural network controller by linearizing the learning scheme.<p>One of the most important aspects of this research is an experimental test, in which the controller was installed on a truck fuelled by natural gas. The tailpipe emissions of the truck with the new controller showed better results than the OEM controller on both carbon monoxide and nitrogen oxides, and the controller required no calibration and very little information about the properties of the engine.<p>The significant original contributions resulting from this research include:<br> -collection and summarization of previous work,<br> -development of a method of automatically determining the pure time delay between the fuel injection event and the feedback measurement,<br> -development of a more accurate model for the variability of the transport delay in modern port injection engines,<br> -developing a fuel-air controller requiring minimal knowledge of the engine's parameters,<br> -development of a method of algebraically inverting a neural network which is much faster than previous iterative methods,<br> -demonstrating how to initialize the neural model by taking advantage of some important characteristics of the system,<br> -expansion of the models available for the limit cycle produced by a system with a binary sensor and delay to include integral controllers with asymmetrical gains,<br> -development of a limit cycle model for the new neural controller, and<br> -experimental verification of the controller's tailpipe emissions performance, which compares favourably to the OEM controller.

A pancreatic stem cell could provide the tissue necessary for widespread β-cell transplantation therapy for diabetes. It is disputed whether pancreatic stem cells or β-cell replication are responsible for maintenance and regeneration of endocrine cells. Evidence presented here shows that pancreatic stem cells express insulin and produce multiple endocrine, exocrine and neural cells in vitro and in vivo. The human pancreas also contains stem cells that produce functional β-cells capable of reducing blood sugar levels in a diabetic mouse. Initial studies of pancreatic stem cells grown clonally in vitro indicated that they produced large numbers of neurons, suggesting they may be derived from the neural crest. Evidence shows that there are at least two distinct developmental origins for stem cells in the pancreas; one from the pancreatic lineage that produces endocrine and exocrine cells and one from the neural crest lineage that produces neurons and Schwann cells. Furthermore, pancreatic stem cells require the developmental transcription factor, Pax6, for endocrine cell formation suggesting they are using expected differentiation pathways. There is an interesting evolutionary connection between pancreatic β-cells and neurons which was applied to the derivation of pancreatic stem cells from human embryonic stem cells by using a clonal neural stem cell assay. These pancreatic stem cells express pancreatic and neural markers, self-renew and differentiate into insulin-expressing cells. The overexpression of SOX17 in these cells increases stem cell formation and self-renewal but inhibits differentiation. Overall I will show that there is a genuine stem cell in the adult mammalian pancreas capable of producing functional β-cells, that this stem cell is derived from the pancreatic developmental lineage but the pancreas also contains stem cells from the neural crest lineage, and that the neural stem cell assays that have identified these adult stem cells can be applied to the derivation of a pancreatic stem cell from hESCs.

A new methodology was developed for flow regime identification in pipes. The method utilizes the pattern recognition abilities of Artificial Neural Networks and the unprocessed time series of a system-monitoring-signal. The methodology was tested with synthetic data from a conceptual system, liquid level indicating Capacitance signals from a Horizontal flow system and with a pressure difference signal from a S-shape riser. The results showed that the signals that were generated for the conceptual system had all their patterns identified correctly with no errors what so ever. The patterns for the Horizontal flow system were also classified very well with a few errors recorded due to original misclassifications of the data. The misclassifications were mainly due to subjectivity and due to signals that belonged to transition regions, hence a single label for them was not adequate. Finally the results for the S-shape riser showed also good agreement with the visual observations and the few errors that were identified were again due to original misclassifications but also to the lack of long enough time series for some flow cases and the availability of less flow cases for some flow regimes than others. In general the methodology proved to be successful and there were a number of advantages identified for this neural network methodology in comparison to other ones and especially the feature extraction methods. These advantages were: Faster identification of changes to the condition of the system, inexpensive suitable for a variety of pipeline geometries and more powerful on the flow regime identification, even for transitional cases.

The formation of neural connections in the mammalian nervous system is a complex process. During development, axons are initially overproduced and compete for limited quantities of target-derived growth factors. Axons which participate in functional circuits and secure appropriate amounts of growth factors are stabilized, while those axons that are either inappropriately connected or do not obtain sufficient concentrations of growth factors are eliminated in a process termed ‘axon pruning’. In this thesis, I examined the mechanisms that regulate pruning of peripheral, NGF-dependent sympathetic neurons that project to the eye. I determined that pruning of these projections in vivo requires the p75 neurotrophin receptor (p75NTR) and synthesis of brain-derived neurotrophic factor (BDNF) from the activity-dependent exon IV promoter. Furthermore, analysis of an in vitro model of axon competition, which is regulated by the interplay between nerve growth factor (NGF) and neuronal activity, revealed that p75NTR and BDNF are also essential for axon competition in culture. In this model, in the presence of NGF, neural activity confers a competitive growth advantage to stimulated, active axons by enhancing downstream TrkA (NGF receptor) signaling locally in axons. More interestingly, the unstimulated, inactive axons deriving from the same and neighboring neurons acquire a "growth disadvantage" due to secreted BDNF acting through p75NTR, which induces axon degeneration by suppressing TrkA signaling that is essential for axonal integrity. These data support a model where, during developmental axon competition, successful axons secrete BDNF in an activity-dependent fashion which activates p75NTR on unsuccessful neighboring axons, suppressing TrkA signaling, and ultimately promoting pruning by a degenerative mechanism.

Application of neural prostheses in today's medicine successfully helps patients to increase their activities of daily life and participate in social activities again. These implantable microsystems provide an interface to the nervous system, giving cellular resolution to physiological processes unattainable today with non-invasive methods. The latest developments in genetic engineering, nanotechnologies and materials science have paved the way for these complex systems to interface the human nervous system. The ideal system for neural signal recording would be a fully implantable device which is capable of amplifying the neural signals and transmitting them to the outside world while sustaining a long-term and accurate performance, therefore different sciences from neurosciences, biology, electrical engineering and computer science have to interact and discuss the synergies to develop a practical system which can be used in daily medicine practice. This work investigates the main building blocks necessary to improve the quality of acquired signal from the micro-electronics and MEMS perspectives. While all of these components will be ultimately embedded in a fully implantable recording probe, each of them addresses and deals with a specific obstacle in the neural signal recording path. Specifically we present a low-voltage low-noise low-power CMOS amplifier particularly designed for neural recording applications. This is done by surveying a number of designs and evaluating each design against the requirements for a neural recording system such as power dissipation and noise, and then choosing the most suitable topology for design and implementation of a fully implantable system. In addition a surface modification method is investigated to improve the sacrificial properties and biocompatibility of probe in order to extend the implant life and enhance the signal quality.

No description available.

The teratogenic drugs cytochalasin B and valproic acid have been shown to alter F-actin polymerization, an effect that is crucial in forming microfilaments. Microfilaments form important cytoskeletal structures that maintain the structural integrity of the cell, cause cell motility and cell migration. Microfilament alterations are known to cause neural tube defects such as spina bifida and anencephaly (Walmod et al., 1999). We here aim to show that disruption of microfilaments by cytochalasin B and valproic acid affects the tensile properties of the tissue. Biomechanics is an interdisciplinary field that allows mechanical concepts to help us understand embryo development. This project used a novel tissue stretching device that measures the tensile properties of neural and epidermal tissue. The instrument used a pair of cantilevered wires to which the specimen was glued. This device stretched the mid-neural and -lateral tissue anterior-posterior (AP) and medio-lateral (ML) unidirectionally. The tensile properties of the tissue were determined by Resultant Young’s Modulus that depends on the true stress and true strain in the tissue sample. The experiment was conducted at a strain rate of 50%. Axolotl embryos were treated with 5ug/mL and 2.5ug/mL cytochalasin B and 5mM valproic acid at stage 13 (early neurula) for an hour, washed, and allowed to develop to stage 15 before it was used in the uniaxial tissue stretcher. Changes in the F-actin filaments were analysed by phalloidin staining and viewed under a confocal microscope. The tests show that disruption of microfilaments by cytochalasin B increases the stiffness of the dorsal-tissue by as much as 101% for CB-treated tissues stretched in the AP direction and 298% when stretched in the ML direction. VA-treated neural plate tissue showed a stiffness increase of 278% when stretched in the AP direction and 319%, when stretched in the ML direction. Changes in the F-actin filaments are quantified by phalloidin staining viewed with confocal microscopy. These findings indicate that direction-dependent mechanical forces in the tissue are contributing factors in closure of the neural tube in axolotl embryos.