Reverse and Forward Genetics Approaches Reveal the Gene Networks That Regulate Development of Inner Ear Neurons

Stato-Acoustic Ganglion (SAG) neurons originate from the floor of the otic vesicle during a brief developmental window. They subsequently leave the otic vesicle and undergo a phase of migration and proliferation (transit-amplification). Neuroblasts finally differentiate into mature SAG neurons and extend processes to connect sensory cells of the inner ear to the information processing centers in the brain. The goal of this dissertation has been to elucidate mechanisms controlling these diverse events, which have heretofore been only poorly understood. First we showed that a threshold level of Fgf signaling initially sets the neurogenic domain in the otic epithelium. However, the level of Fgf signaling increases during development and becomes inhibitory to otic neurogenesis. Specfically, fgf5 is expressed by accumulating SAG neurons, which serves to terminate specification of new neuroblasts and delay differentiation of transit-amplifying cells. Second, we tested the role of transcription factor tfap2a, which we found is expressed in the neurogenic domain in both zebrafish and chick. Gain and loss-of-function studies revealed that Tfap2a activates expression of bmp7a, which in turn partially inhibits Fgf and Notch signaling. By modulating the inhibitory functions of Fgf and Notch, Tfap2a regulates the duration, amount and speed of SAG development. Third, we investigated the mechanism by which SAG neuroblasts leave the otic epithelium. We showed that Goosecoid (Gsc) regulates epithelial-mesenchymal transition of the otic neuroblasts. Fgf signaling regulates expression of gsc in a region iii partially overlapping with the neurogenic otic domain. The medial marker Pax2a acts in opposition to Gsc and stabilizes otic epithelia in non-neurogenic parts of the otic vesicle. Lastly, we conducted a mutagenesis screen in zebrafish to identify ENU-induced mutations that affect SAG development. We recovered a SAG deficient mutation, termed sagd1 that strongly reduces a subset of SAG neurons required for vestibular (balance) functions. Whole genome sequencing revealed that sagd1 affects the glycolytic enzyme, Phosphoglycerate kinase-1 (Pgk1). Further analysis revealed that Pgk1 acts nonautonomously to augment Fgf signaling during early stages of otic neurogenesis. Together, these studies have uncovered a number of previously unknown mechanisms for dynamic regulation of Fgf to control specification, delamination, and maturation of SAG neurons

Common hepatic artery arising from the aorta – demonstration with multidetector CT angiography and its clinical importance

We present a case of a rare variation of the hepatic artery discovered during the routine examination of abdomen by computed tomography. This variation was showed by multidetector-row computed tomography (MDCT) in a 35-year-old man. There have been shown the common hepatic artery arising from the anterior surface of abdominal aorta, 4 mm inferior to the celiac trunk. We discussed clinical significances of this variation during radiological procedures and surgical operations

Adversarial Machine Learning-Based Anticipation of Threats Against Vehicle-to-Microgrid Services

In this paper, we study the expanding attack surface of Adversarial Machine Learning (AML) and the potential attacks against Vehicle-to-Microgrid (V2M) services. We present an anticipatory study of a multi-stage gray-box attack that can achieve a comparable result to a white-box attack. Adversaries aim to deceive the targeted Machine Learning (ML) classifier at the network edge to misclassify the incoming energy requests from microgrids. With an inference attack, an adversary can collect real-time data from the communication between smart microgrids and a 5G gNodeB to train a surrogate (i.e., shadow) model of the targeted classifier at the edge. To anticipate the associated impact of an adversary's capability to collect real-time data instances, we study five different cases, each representing different amounts of real-time data instances collected by an adversary. Out of six ML models trained on the complete dataset, K-Nearest Neighbour (K-NN) is selected as the surrogate model, and through simulations, we demonstrate that the multi-stage gray-box attack is able to mislead the ML classifier and cause an Evasion Increase Rate (EIR) up to 73.2% using 40% less data than what a white-box attack needs to achieve a similar EIR.Comment: IEEE Global Communications Conference (Globecom), 2022, 6 pages, 2 Figures, 4 Table

Sacrifice for nostalgia : the American small-town and the grotesque

Title from PDF of title page (University of Missouri--Columbia, viewed on September 12, 2012).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Thesis advisor: Dr. Andrew J. HoberekIncludes bibliographical references.M.A. University of Missouri--Columbia 2012."May 2012"The American small-town as a literary construction has been studied extensively in criticism. These studies mostly concentrate on the different manifestations of the small-town America during the 19th and 20th century. In these works, a main thread that surfaces is that the American small-town is either a preserver of traditional values against encroaching capitalism, or a dying human-settlement with no relevance in the modern day. In my thesis, I argue that the situation is more complicated than this binary opposition. I contend that the American small-town emerges as a site of tension that is torn between the onslaught of industrial capitalism and a nostalgic feeling towards a past that does not exist anymore. Moreover, this manifestation becomes much more palpable during social upheavals. I argue that in order to protect itself against the change, the small-town tries to act as a social body, as a unified whole against the transformation that comes with capitalism. This protection manifests itself as various acts of ritual sacrifice. In the three texts I examine in my thesis, I trace this occurrence using the grotesque as a literary mode. The grotesque, which is a harbor of incongruities such as the mundane and the dreadful, the familiar and the unfamiliar, and the regular and the irregular, provides me with the perspective to examine the American small-town not as an either/or construction, but a site where incompatible doubles play themselves out, and to show that the small-town is still a rich pool of signification from which we can detect the substantial changes the United States went through during the past century

Decentralized Microgrid Energy Management: A Multi-agent Correlated Q-learning Approach

Microgrids (MG) are anticipated to be important players in the future smart grid. For proper operation of MGs an Energy Management System (EMS) is essential. The EMS of an MG could be rather complicated when renewable energy resources (RER), energy storage system (ESS) and demand side management (DSM) need to be orchestrated. Furthermore, these systems may belong to different entities and competition may exist between them. Nash equilibrium is most commonly used for coordination of such entities however the convergence and existence of Nash equilibrium can not always be guaranteed. To this end, we use the correlated equilibrium to coordinate agents, whose convergence can be guaranteed. In this paper, we build an energy trading model based on mid-market rate, and propose a correlated Q-learning (CEQ) algorithm to maximize the revenue of each agent. Our results show that CEQ is able to balance the revenue of agents without harming total benefit. In addition, compared with Q-learning without correlation, CEQ could save 19.3% cost for the DSM agent and 44.2% more benefits for the ESS agent.Comment: Accepted by 2020 IEEE International Conference on SmartGridComm, 978-1-7281-6127-3/20/$31.00 copyright 2020 IEE

Correlated Deep Q-learning based Microgrid Energy Management

Microgrid (MG) energy management is an important part of MG operation. Various entities are generally involved in the energy management of an MG, e.g., energy storage system (ESS), renewable energy resources (RER) and the load of users, and it is crucial to coordinate these entities. Considering the significant potential of machine learning techniques, this paper proposes a correlated deep Q-learning (CDQN) based technique for the MG energy management. Each electrical entity is modeled as an agent which has a neural network to predict its own Q-values, after which the correlated Q-equilibrium is used to coordinate the operation among agents. In this paper, the Long Short Term Memory networks (LSTM) based deep Q-learning algorithm is introduced and the correlated equilibrium is proposed to coordinate agents. The simulation result shows 40.9% and 9.62% higher profit for ESS agent and photovoltaic (PV) agent, respectively.Comment: Accepted by 2020 IEEE 25th International Workshop on CAMAD, 978-1-7281-6339-0/20/$31.00 \copyright 2020 IEE