Weifeng Xu - US grants

Activity-dependent ciianges in synaptic strengtli at glutamatergic synapses are tiiougiit to contribute to tiie development of neural circuitry and many forms of experience-dependent plasticity, including learning and memory. The hippocampus, a major site of synaptic plasticity, plays a fundamental role in some forms of learning and memory, and has been implicated in a number of neurological and psychiatric disorders, including depression, epilepsy, Alzheimer's disease, and schizophrenia. In this proposal, I outline a series of experiments that will test the functional significance of key synaptic scaffolding proteins in regulating glutamate receptor function and synaptic plasticity at hippocampal Schaffer collateral-CAl synapses. This will involve making simultaneous whole cell patch clamp recordings from neurons in organotypic hippocampal slice cultures that haven been molecularly modified using lentiviral-mediated gene knockdown via shRNA and simultaneous lentiviral-mediated gene transfer. The bicistronic lentiviral vector I will use allows expression of mutant forms of a protein on the background of acute knockdown of the endogenous protein. I will specifically focus on the function of the postsynaptic scaffolding proteins of the family of the disc-large (DLG) membrane-associated guanylate kinases (MAGUKs) and their interacting partners. My previous results demonstrate that two family members of DLG-MAGUKs, PSD-95 and SAP97, regulate synaptic AMPAR function differently in terms of their activity-dependence. During the K99 training period, I found that the effects of PSD-95 on basal transmission and long-term depression are dissociable. The N-terminal domain of PSD-95 is required for dimerization and appropriate synaptic enrichment of PSD-95 but alone does not influence synaptic function. The C-terminal portion of PSD-95 serves a dual function. It is required to localize PSD-95 at the synapse and as a scaffold for critical downstream signaling proteins that are required for LTD. The specific objectives of my independent research are: (1) to analyze the signaling scaffold that is important for long-term depression (LTD), in particular, the role of the A-kinase anchoring protein 79/150 (AKAP79/150), and (2) to examine the interaction of PSD-95 with transmembrane AMPA receptor regulatory proteins (TARPs) in mediating long-term potentiation (LTP), (3) to examine the role of SAP97 in mediating LTP. Results from these experiments will begin to elucidate how dynamic interactions among different components of the postsynaptic density influence synaptic function and will address fundamental questions about how signaling specificity is achieved during different forms of synaptic plasticity

The Historically Black Colleges and Universities Undergraduate Program (HBCU-UP) through Targeted Infusion Projects supports the development, implementation, and study of evidence-based innovative models and approaches for improving the preparation and success of HBCU undergraduate students so that they may pursue science, technology, engineering or mathematics (STEM) graduate programs and/or careers. The project at Bowie State University seeks to enhance cybersecurity undergraduate education by strengthening students' learning experiences through cryptography. The activities and strategies are evidence-based and a strong plan for formative and summative evaluation is part of the project.

The objectives of the project are to: (1) develop an interactive cryptographic simulator with real-world scenarios and deploy the cryptographic simulator to a cloud-based platform; (2) infuse the cloud-based cryptographic simulation modules for academic training in the cybersecurity courses; and (3) build a cloud-based platform to share the instructional materials online. Additionally, workshops are being held to build capacity in cryptography education for high school teachers and college faculty. This project will have an impact on enhancing cybersecurity education by providing a platform for interactive real life scenario simulations in the context of cryptography. It is expected that over 200 undergraduate students will be impacted by this project annually.

The Internet of Things (IoT) integrates software applications, physical devices, and algorithms to interact with the physical world and humans. The economic and societal potential of such systems is vastly greater than has been realized, and major investments are being made worldwide to develop the technology. The technology for building IoT is based on embedded systems, scientific computations, and software embedded in devices. Because the physical components of IoT are directly interactive with humans, the security and reliability requirements are qualitatively different from those in general purpose computing. Failure to meet the security and reliability requirements exposes IoT and humans to malignant attacks. The goal of this project is to conduct interdisciplinary research that utilizes artificial intelligence methodologies against cybercriminals who initiate attacks or target internet connected devices and users.

This project aims to explore applications of Deep Learning in cybersecurity research to detect security vulnerabilities in the Internet of Things through automated digital forensic evidence analytics. The project will actively engage a team of researchers in the investigation of deep learning, which includes a broader family of Artificial Intelligence that has produced results comparable and in some cases superior to human experts, to conduct the following research activities: (1) Assessing potential data vulnerabilities related to personal data privacy violations by analyzing the extracted hidden contents evidence and encrypted messages from IoT devices in a forensically sound manner; (2) Evaluating IoT software forensic evidence. Analyzing software vulnerabilities in IoT application source code to better mitigate the risk to software systems. Typical source code vulnerability evidence in applications includes buffer overflow, integer overflow, and Carriage Return and Line Feed injection; (3) Reconstructing attack scenes based on forensic evidence to find existing system vulnerabilities of IoT; (4) Increase research capacity and collaborations to generate new research opportunities for undergraduates from underrepresented communities to pursue advanced degrees in computer science.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.