by Chan Pak Kei, Bernard. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 125-127). / Chapter 1 --- Introduction --- p.10 / Chapter 1.1 --- Overview --- p.10 / Chapter 1.2 --- Classification Approaches --- p.11 / Chapter 1.3 --- The Use of Neural Network --- p.12 / Chapter 1.4 --- Motivations --- p.14 / Chapter 1.5 --- Organization of Thesis --- p.16 / Chapter 2 --- Related Work --- p.19 / Chapter 2.1 --- Overview --- p.19 / Chapter 2.2 --- Neural Network --- p.20 / Chapter 2.2.1 --- Backpropagation Feedforward Neural Network --- p.20 / Chapter 2.2.2 --- Training of a Backpropagation Feedforward Neural Network --- p.22 / Chapter 2.2.3 --- Single Hidden-layer Model --- p.27 / Chapter 2.2.4 --- Data Preprocessing --- p.27 / Chapter 2.3 --- Fuzzy Sets --- p.29 / Chapter 2.3.1 --- Fuzzy Linear Regression Analysis --- p.29 / Chapter 2.4 --- Network Architecture Altering Algorithms --- p.31 / Chapter 2.4.1 --- Pruning Algorithms --- p.32 / Chapter 2.4.2 --- Constructive/Growing Algorithms --- p.35 / Chapter 2.5 --- Summary --- p.38 / Chapter 3 --- Hybrid Classification Systems --- p.39 / Chapter 3.1 --- Overview --- p.39 / Chapter 3.2 --- Literature Review --- p.41 / Chapter 3.2.1 --- Fuzzy Linear Regression(FLR) with Fuzzy Interval Analysis --- p.41 / Chapter 3.3 --- Data Sample and Methodology --- p.44 / Chapter 3.4 --- Hybrid Model --- p.46 / Chapter 3.4.1 --- Construction of Model --- p.46 / Chapter 3.5 --- Experimental Results --- p.50 / Chapter 3.5.1 --- Experimental Results on Breast Cancer Database --- p.50 / Chapter 3.5.2 --- Experimental Results on Synthetic Data --- p.53 / Chapter 3.6 --- Conclusion --- p.55 / Chapter 4 --- Searching for Suitable Network Size Automatically --- p.59 / Chapter 4.1 --- Overview --- p.59 / Chapter 4.2 --- Literature Review --- p.61 / Chapter 4.2.1 --- Pruning Algorithm --- p.61 / Chapter 4.2.2 --- Constructive Algorithms (Growing) --- p.66 / Chapter 4.2.3 --- Integration of methods --- p.67 / Chapter 4.3 --- Methodology and Approaches --- p.68 / Chapter 4.3.1 --- Growing --- p.68 / Chapter 4.3.2 --- Combinations of Growing and Pruning --- p.69 / Chapter 4.4 --- Experimental Results --- p.75 / Chapter 4.4.1 --- Breast-Cancer Cytology Database --- p.76 / Chapter 4.4.2 --- Tic-Tac-Toe Database --- p.82 / Chapter 4.5 --- Conclusion --- p.89 / Chapter 5 --- Conclusion --- p.91 / Chapter 5.1 --- Recall of Thesis Objectives --- p.91 / Chapter 5.2 --- Summary of Achievements --- p.92 / Chapter 5.2.1 --- Data Preprocessing --- p.92 / Chapter 5.2.2 --- Network Size --- p.93 / Chapter 5.3 --- Future Works --- p.94 / Chapter A --- Experimental Results of Ch3 --- p.95 / Chapter B --- Experimental Results of Ch4 --- p.112 / Bibliography --- p.125

by Yung, Kim Ming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 135-153). / Abstract --- p.i / Acknowledgments --- p.iv / List of content --- p.v / Chapter Chapter 1 --- General Introduction --- p.1 / Chapter 1.1 --- Neural crest cells and cardiac neural crest cells --- p.1 / Chapter 1.2 --- The role of cardiac neural crest cells in the septation of the outflow tract --- p.5 / Chapter 1.3 --- Neural crest-related malformations --- p.8 / Chapter 1.4 --- Early changes in cardiovascular development induced by neural crest ablation --- p.11 / Chapter 1.5 --- Experimental strategies commonly employed in tracing the premigratory neural crest cells --- p.14 / Chapter 1.6 --- Objectives of the present study --- p.21 / Chapter Chapter 2 --- Location of the cardiac neural crest along the neural axis in the mouse embryo --- p.24 / Chapter 2.1 --- Introduction --- p.24 / Chapter 2.2 --- Materials and Methods --- p.29 / Chapter 2.2.1 --- Preparation of DiI --- p.29 / Chapter 2.2.2 --- Embryo collection --- p.29 / Chapter 2.2.3 --- Microinjection of DiI --- p.30 / Chapter 2.2.4 --- Isolation of tissue fragments from the lateral neural epithelium --- p.31 / Chapter 2.2.5 --- Dil labelling of the donor fragment isolated from the lateral neural epithelium --- p.32 / Chapter 2.2.6 --- Grafting of DiI labelled fragments from the lateral neural epithelium --- p.32 / Chapter 2.2.7 --- Embryo culture --- p.33 / Chapter 2.2.8 --- Examination of cultured embryos --- p.34 / Chapter 2.2.9 --- Cryosection --- p.35 / Chapter 2.3 --- Results --- p.36 / Chapter 2.3.1 --- Development of the cultured embryos in control and experimental groups --- p.36 / Chapter 2.3.2 --- Location of the cardiac neural crest region along the neural axis --- p.38 / Chapter 2.4 --- Discussion --- p.44 / Chapter 2.4.1 --- Development of embryos in vitro --- p.44 / Chapter 2.4.2 --- Comparison of the two methods for tracing cell migration: focal labelling and orthotopic grafting --- p.49 / Chapter 2.4.3 --- Location of the cardiac neural crest region along the neural tube --- p.53 / Chapter Chapter 3 --- Initial and terminal stages of cardiac neural crest cell migration --- p.56 / Chapter 3.1 --- Introduction --- p.56 / Chapter 3.2 --- Materials and Methods --- p.62 / Chapter 3.2.1 --- Examination of the initial and terminal stages of migration of cardiac neural crest cells by haematoxylin and eosin (H&E) staining --- p.62 / Chapter 3.2.2 --- Preparation of WGA-Au --- p.62 / Chapter 3.2.3 --- Collection of embryos for microinjection of WGA-Au --- p.63 / Chapter 3.2.4 --- WGA-Au labelling of the presumptive cardiac neural crest region --- p.64 / Chapter 3.2.5 --- Embryo culture --- p.65 / Chapter 3.2.6 --- Examination of cultured embryos --- p.66 / Chapter 3.2.7 --- Silver enhancement staining --- p.66 / Chapter 3.3 --- Results --- p.67 / Chapter 3.3.1 --- Initial stage of cardiac neural crest migration studied by haematoxylin and eosin staining and silver enhancement staining --- p.67 / Chapter 3.3.2 --- Terminal stage of cardiac neural crest migration studied by haematoxylin and eosin staining and silver enhancement staining --- p.69 / Chapter 3.4 --- Discussion --- p.71 / Chapter 3.4.1 --- Wheat germ agglutinin-gold conjugate (WGA-Au) as a cell marker --- p.71 / Chapter 3.4.2 --- Initial stage for cardiac neural crest cell migration --- p.72 / Chapter 3.4.3 --- Terminal stage for cardiac neural crest cell migration --- p.74 / Chapter Chapter 4 --- Migration pathways of cardiac neural crest cells… --- p.77 / Chapter 4.1 --- Introduction --- p.77 / Chapter 4.2 --- Materials and Methods --- p.82 / Chapter 4.2.1 --- Preparation of DiI --- p.82 / Chapter 4.2.2 --- Preparation of WGA-Au --- p.82 / Chapter 4.2.3 --- Embryo collection --- p.82 / Chapter 4.2.4 --- Microinjection of WGA-Au and DiI --- p.82 / Chapter 4.2.5 --- Isolation of tissue fragments from the lateral neural epithelium --- p.83 / Chapter 4.2.6 --- WGA-Au labelling of the donor fragments from the lateral neural epithelium --- p.83 / Chapter 4.2.7 --- DiI labelling of the donor neural epithelium --- p.83 / Chapter 4.2.8 --- Grafting of WGA-Au or DiI-labelled donor tissues from the lateral neural epithelium --- p.83 / Chapter 4.2.9 --- Coating of latex beads by WGA-Au --- p.83 / Chapter 4.2.10 --- Microinjection of WGA-Au-coated latex beads --- p.84 / Chapter 4.2.11 --- Embryo culture --- p.84 / Chapter 4.2.12 --- Examination of cultured embryos --- p.85 / Chapter 4.2.13 --- Silver enhancement staining of the WGA-Au labelled sections --- p.85 / Chapter 4.2.14 --- Cryosection --- p.85 / Chapter 4.3 --- Results --- p.86 / Chapter 4.3.1 --- Distribution of labelled cells after WGA-Au labelling or orthotopic grafting --- p.86 / Chapter 4.3.2 --- Distribution of labelled cells after DiI labelling or orthotopic grafting --- p.88 / Chapter 4.3.3 --- Distribution of latex beads --- p.90 / Chapter 4.4 --- Discussion --- p.92 / Chapter 4.4.1 --- Methodology --- p.92 / Chapter 4.4.2 --- Migration pathways of the cardiac neural crest cells --- p.94 / Chapter 4.4.3 --- Migration of latex beads --- p.98 / Chapter Chapter 5 --- Derivatives of cardiac neural crest cells in the developing mouse heart --- p.101 / Chapter 5.1 --- Introduction --- p.101 / Chapter 5.2 --- Materials and Methods --- p.110 / Chapter 5.2.1 --- DiI labelling of the cardiac neural crest region of the mouse embryo --- p.110 / Chapter 5.2.2 --- Collection of the embryonic hearts --- p.111 / Chapter 5.2.3 --- Heart organ culture --- p.111 / Chapter 5.2.4 --- Cryosectioning --- p.112 / Chapter 5.2.5 --- Paraffin wax sectioning --- p.113 / Chapter 5.2.6 --- Immunohistochemical staining --- p.113 / Chapter 5.3 --- Results --- p.118 / Chapter 5.3.1 --- Distribution of 2H3 positive cells in the heart developedin vivo --- p.118 / Chapter 5.3.2 --- Development of the heart at 10.5 d.p.c. in organ culture --- p.119 / Chapter 5.3.3 --- Distribution of DiI labelled cells in the heart one day after organ culture --- p.119 / Chapter 5.3.4 --- Distribution of 2H3 positive cells in the hearts one day after organ culture --- p.120 / Chapter 5.4 --- Discussion --- p.121 / Chapter 5.4.1 --- Relationship between 2H3 positive cells and cardiac conduction system --- p.121 / Chapter 5.4.2 --- Development of the mouse embryonic hearts in vitro --- p.123 / Chapter 5.4.3 --- Distribution patterns of the 2H3 immunopositive cellsin the hearts developed in vitro and in vivo --- p.125 / Chapter 5.4.4 --- Relationship between the DiI labelled cells and2H3 immunopositive cells --- p.125 / Chapter 5.4.5 --- Genes that express in the cardiac neural crest cells --- p.127 / Chapter Chapter 6 --- Conclusion --- p.129 / References --- p.135 / Appendix --- p.154

Wong Hing-Kwok. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 34-35). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Radial Basis Function (RBF) of Neural Network --- p.5 / Chapter 2.1 --- Neural Network --- p.6 / Chapter 2.2 --- Radial Basis Function (RBF) Network --- p.8 / Chapter 2.3 --- Model Specification --- p.10 / Chapter 2.4 --- Estimation --- p.12 / Chapter 3 --- RBF in Performance Attribution --- p.17 / Chapter 3.1 --- Background of Data Set --- p.18 / Chapter 3.2 --- Portfolio Construction --- p.20 / Chapter 3.3 --- Portfolio Rebalance --- p.22 / Chapter 3.4 --- Result --- p.23 / Chapter 4 --- Comparison --- p.26 / Chapter 4.1 --- Standard Linear Model --- p.27 / Chapter 4.2 --- Fixed Additive Model --- p.28 / Chapter 4.3 --- Refined Additive Model --- p.29 / Chapter 4.4 --- Result --- p.30 / Chapter 5 --- Conclusion --- p.32 / Bibliography --- p.34

During nervous system development, thousands of distinct neuronal cell types are generated and assembled into highly precise circuits. The proper wiring of these circuits requires that developing neurons recognize their appropriate synaptic partners. Analysis of a vertebrate spinal circuit that controls motor behavior reveals distinct synaptic connections of two types of inhibitory interneurons, a ventral V1 class that synapses with motor neurons and a dorsal dI4 class that selectively synapses with proprioceptive sensory neuron terminals that are located on or in close proximity to motor neurons. What are the molecular and cellular programs that instruct this remarkable synaptic specificity? Are only subsets of these interneurons capable of integrating into this circuit, or do all neurons within the same class behave similarly? The ability to answer such questions, however, is hampered both by the complexity of the spinal cord, where many different neuronal cell types can be found synapsing in the same area; as well as by the challenge of obtaining enough neurons of a particular subtype for analysis. Meanwhile, pluripotent stem cells have emerged as powerful tools for studying neural development, particularly because they can be differentiated to produce large amounts of diverse neuronal populations. Mouse embryonic stem cell-derived neurons can thus be used in a simplified in vitro system to study the development of specific neuronal cell types as well the interactions between defined cell types in a controlled environment. Using stem cell-derived neurons, I investigated how the V1 and dI4 cardinal spinal classes differentiate into molecularly distinct subtypes and acquire cell type-specific functional properties, including synaptic connectivity. In Chapter Two, I describe the production of lineage-based reporter stem cell lines and optimized differentiation protocols for generating V1 and dI4 INs from mouse embryonic stem cells, including confirming that they have molecular and functional characteristics of their in vivo counterparts. In Chapter Three, I show that a well-known V1 interneuron subtype, the Renshaw cell, which mediates recurrent inhibition of motor neurons, can be efficiently generated from stem cell differentiation. Importantly, manipulation of the Notch signaling pathway in V1 progenitors impinges on V1 subtype differentiation and greatly enhances the generation of Renshaw cells. I further show that sustained retinoic acid signaling is critical for the specific development of the Renshaw cell subtype, suggesting that interneuron progenitor domain diversification may also be regulated by spatially-restricted cues during embryonic development. In Chapter Four, using a series of transplantation, rabies virus-based transsynaptic tracing, and optogenetics combined with whole-cell patch-clamp recording approaches, I demonstrate that stem cell-derived Renshaw cells exhibit significant differences in physiology and connectivity compared to other V1 subpopulations, suggesting that synaptic specificity of the Renshaw cell-motor neuron circuit can be modeled and studied in a simplified in vitro co-culture preparation. Finally, in Chapter Five, I describe ongoing investigations into molecular mechanisms of dI4 interneuron subtype diversification, as well as approaches to studying their synaptic specificity with proprioceptive sensory neurons. Overall, my results suggest that our stem cell-cell based system is well-positioned to probe the functional diversity of molecularly-defined cell types. This work represents a novel use of embryonic stem cell-derived neurons for studying inhibitory spinal circuit assembly and will contribute to further understanding of neural circuit formation and function during normal development and potentially in diseased states.

Rapid and efficient escape behaviors in response to noxious sensory stimuli are essential for protection and survival. In Drosophila larvae, the class III (cIII) and class IV (cIV) dendritic arborization (da) neurons detect low-threshold mechanosensory and noxious stimuli, respectively. Their axons project to modality-specific locations in the neuropil, reminiscent of vertebrate dorsal horn organization. Despite extensive characterization of nociceptors across organisms, how noxious stimuli are transformed to the coordinated behaviors that protect animals from harm remains poorly understood. In larvae, noxious mechanical and thermal stimuli trigger an escape behavior consisting of sequential C-shape body bending followed by corkscrew-like rolling, and finally an increase in forward locomotion (escape crawl). The downstream circuitry controlling the sequential coordination of escape responses is largely unknown. This work identifies a population of interneurons in the nerve cord, Down-and-Back (DnB) neurons, that are activated by noxious heat, promote nociceptive behavior, and are required for robust escape responses to noxious stimuli. Activation of DnB neurons can trigger both rolling, and the initial C-shape body bend independent of rolling, revealing modularity in the initial nociceptive responses. Electron microscopic circuit reconstruction shows that DnBs receive direct input from nociceptive and mechanosensory neurons, are presynaptic to pre-motor circuits, and link indirectly to a population of command-like neurons (Goro) that control rolling. DnB activation promotes activity in Goro neurons, and coincident inactivation of Goro neurons prevents the rolling sequence but leaves intact body bending motor responses. Thus, activity from nociceptors to DnB interneurons coordinates modular elements of nociceptive escape behavior. The impact of DnB neurons may not be restricted to synaptic partners, as DnB presynaptic sites accumulate dense-core vesicles, suggesting aminergic or peptidergic signaling. Anatomical analyses show that DnB neurons receive spatially segregated input from cIII mechanosensory and cIV nociceptive neurons. However, DnB neurons do not seem to promote or be required for gentle-touch responses, suggesting a modulatory role for cIII input. Behavioral experiments suggest that cIII input presented prior to cIV input can enhance nociceptive behavior. Moreover, weak co-activation of DnB and cIII neurons can also enhance nociceptive responses, particularly C-shape bending. These results indicate that timing and level of cIII activation might determine its modulatory role. Taken together, these studies describe a novel nociceptive circuit, which integrates nociceptive and mechanosensory inputs, and controls modular motor pathways to promote robust escape behavior. Future work on this circuit could reveal neural mechanisms for sequence transitions, peptidergic modulation of nociception, and developmental mechanisms that control convergence of sensory afferents onto common synaptic partners.

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Pan, Yunpeng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (p. 98-107). / Abstract also in Chinese. / Abstract --- p.i / p.iii / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.2 / Chapter 1.1 --- Model Predictive Control --- p.2 / Chapter 1.2 --- Neural Networks --- p.3 / Chapter 1.3 --- Existing studies --- p.6 / Chapter 1.4 --- Thesis structure --- p.7 / Chapter 2 --- Two Recurrent Neural Networks Approaches to Linear Model Predictive Control --- p.9 / Chapter 2.1 --- Problem Formulation --- p.9 / Chapter 2.1.1 --- Quadratic Programming Formulation --- p.10 / Chapter 2.1.2 --- Linear Programming Formulation --- p.13 / Chapter 2.2 --- Neural Network Approaches --- p.15 / Chapter 2.2.1 --- Neural Network Model 1 --- p.15 / Chapter 2.2.2 --- Neural Network Model 2 --- p.16 / Chapter 2.2.3 --- Control Scheme --- p.17 / Chapter 2.3 --- Simulation Results --- p.18 / Chapter 3 --- Model Predictive Control for Nonlinear Affine Systems Based on the Simplified Dual Neural Network --- p.22 / Chapter 3.1 --- Problem Formulation --- p.22 / Chapter 3.2 --- A Neural Network Approach --- p.25 / Chapter 3.2.1 --- The Simplified Dual Network --- p.26 / Chapter 3.2.2 --- RNN-based MPC Scheme --- p.28 / Chapter 3.3 --- Simulation Results --- p.28 / Chapter 3.3.1 --- Example 1 --- p.28 / Chapter 3.3.2 --- Example 2 --- p.29 / Chapter 3.3.3 --- Example 3 --- p.33 / Chapter 4 --- Nonlinear Model Predictive Control Using a Recurrent Neural Network --- p.36 / Chapter 4.1 --- Problem Formulation --- p.36 / Chapter 4.2 --- A Recurrent Neural Network Approach --- p.40 / Chapter 4.2.1 --- Neural Network Model --- p.40 / Chapter 4.2.2 --- Learning Algorithm --- p.41 / Chapter 4.2.3 --- Control Scheme --- p.41 / Chapter 4.3 --- Application to Mobile Robot Tracking --- p.42 / Chapter 4.3.1 --- Example 1 --- p.44 / Chapter 4.3/2 --- Example 2 --- p.44 / Chapter 4.3.3 --- Example 3 --- p.46 / Chapter 4.3.4 --- Example 4 --- p.48 / Chapter 5 --- Model Predictive Control of Unknown Nonlinear Dynamic Sys- tems Based on Recurrent Neural Networks --- p.50 / Chapter 5.1 --- MPC System Description --- p.51 / Chapter 5.1.1 --- Model Predictive Control --- p.51 / Chapter 5.1.2 --- Dynamical System Identification --- p.52 / Chapter 5.2 --- Problem Formulation --- p.54 / Chapter 5.3 --- Dynamic Optimization --- p.58 / Chapter 5.3.1 --- The Simplified Dual Neural Network --- p.59 / Chapter 5.3.2 --- A Recursive Learning Algorithm --- p.60 / Chapter 5.3.3 --- Convergence Analysis --- p.61 / Chapter 5.4 --- RNN-based MPC Scheme --- p.65 / Chapter 5.5 --- Simulation Results --- p.67 / Chapter 5.5.1 --- Example 1 --- p.67 / Chapter 5.5.2 --- Example 2 --- p.68 / Chapter 5.5.3 --- Example 3 --- p.76 / Chapter 6 --- Model Predictive Control for Systems With Bounded Uncertainties Using a Discrete-Time Recurrent Neural Network --- p.81 / Chapter 6.1 --- Problem Formulation --- p.82 / Chapter 6.1.1 --- Process Model --- p.82 / Chapter 6.1.2 --- Robust. MPC Design --- p.82 / Chapter 6.2 --- Recurrent Neural Network Approach --- p.86 / Chapter 6.2.1 --- Neural Network Model --- p.86 / Chapter 6.2.2 --- Convergence Analysis --- p.88 / Chapter 6.2.3 --- Control Scheme --- p.90 / Chapter 6.3 --- Simulation Results --- p.91 / Chapter 7 --- Summary and future works --- p.95 / Chapter 7.1 --- Summary --- p.95 / Chapter 7.2 --- Future works --- p.96 / Bibliography --- p.97

With increasing demands on limited water resources and unavailability of suitable dam sites, it is essential that available storage works be carefully planned and efficiently operated to meet the present and future water needs.This research report presents an attempt to: i) use Artificial Neural Networks (ANN) for the simulation of the Crocodile water resource system located in the Mpumalanga province of South Africa and ii) use the model to assess to what extent Kwena dam, the only major dam in the system could meet the required 0.9m3/s cross border flow to Mozambique. The modelling was confined to the low flow periods when the Kwena dam releases are significant. The form of ANN model developed in this study is the standard error backpropagation run on a daily time scale. It is comprised of 32 inputs being four irrigation abstractions at Montrose, Tenbosch, Riverside and Karino; current and average daily rainfall totals for the previous 4 days at the respective rainfall stations; average daily temperature at Karino and Nelspruit; daily releases from Kwena dam; daily streamflow from the tributaries of Kaap, Elands and Sand rivers and the previous day’s flow at Tenbosch. The single output was the current day’s flow at Tenbosch. To investigate the extent to which the 0.9m3/s flow requirement into Mozambique could be met, data from a representative dry year and four release scenarios were used. The scenarios assumed that Kwena dam was 100%, 75%, 50% and 25% full at the beginning of the year. It was found as expected that increasing Kwena releases improved the cross border flows but the improvement in providing the 0.9m3/s cross border flow was minimal. For the scenario when the dam is initially full, the requirement was met with an improvement of 11% over the observed flows.

Mental illness is currently diagnosed using subjective observational criteria as outlined in the 5th Edition of the Diagnostic and Statistical Manual (DSM-V), yet many have argued for the medicalization of the diagnosis of mental illness by incorporating biomedical and neuroanatomical criteria. The following literature review explores the neural circuit responsible for regulating emotion, as well as the structural and chemical alterations to this circuit that have been shown to correlate with aggressive and/or violent behaviors characteristic of certain types of mental illness. The neural circuit regulating emotion is comprised of the prefrontal cortex, the subcortical limbic system, the dopaminergic pathway, the serotonergic pathway, catecholaminergic neurons, and GABAergic neurons. Alterations to these structures or chemicals have been associated with major depressive disorder, suicidal ideations, substance use disorders, schizophrenia, and personality disorders. Medicalization of mental illness has the potential to serve two purposes – first, to standardize diagnosis and treatment of mental illness, and second, to decrease the stigma often associated with mental illness – and to improve outcomes for those patients living with mental illness.

The neural crest is a transient, multipotent, cell population that gives rise to several important tissues during embryonic development, including the craniofacial skeleton, peripheral nervous system, and melanocytes. The neural crest arises from the ectoderm, along with the skin and central nervous system. This process of specification is dependent on a gene regulatory network (GRN) which is made up of transcription factors that regulate each other. While we know many of the members of this GRN, the direct connections among the members are largely unsolved. Breakdown of this GRN can lead to birth defects, such as cleft lip and palate, and cancer of neural crest derivatives, such as melanoma, thus understanding the intricate details of this network is important. The transcription factor Tfap2a is an important member of the GRN, as loss of tfap2a and its paralog tfap2c leads to loss of pre-migratory neural crest and all neural crest derivatives. Despite its importance in this network little is known about how its expression is regulated. We hypothesized that, due to its importance in this network, it will have multiple enhancers that drive its expression in the neural crest. We have identified two neural crest enhancers of tfap2a. We found that one of these enhancers is responsive to WNT signals and is maintained by forming a positive feedback loop with Sox10. Our results suggest that this enhancer is important for both induction and maintenance of tfap2a expression in the neural crest. Tfap2 paralogs are important at several different stages throughout neural crest lineage specification. However, the only direct target of Tfap2a that has been identified is sox10. Thus, we wanted to determine the direct targets of Tfap2 in this network. Through the integration of several data sets, including ATAC-seq and expression profiling of tfap2a/c double mutants, we have identified several direct targets including sox9b and alx1. Melanoma is cancer of the melanocytes, a neural crest derivative. Recent studies have shown that melanoma and the neural crest share genetic similarities. TFAP2A expression is decreased in metastatic melanoma compared to primary tumors, thus we wanted to investigate the mechanism of TFAP2A in metastatic melanoma. We found that the promoter of TFAP2A is hypermethylated in some metastatic melanoma tumors. This was confirmed by samples in the TCGA database. Hypermethylation of the promoter contributes to the downregulation of TFAP2A in metastatic melanoma. In conclusion, we have further illuminated the connections among transcription factors in the GRN important for neural crest lineage specification. Further, we have identified a new mechanism regulating TFAP2A expression in metastatic melanoma. Together, these studies reveal regulatory mechanisms of TFAP2A gene expression.