Data management plan for the grant, "Collaborative Research: CIF: Medium: Fundamental Limits of Cache-aided Multi-user Private Function Retrieval." This project is motivated by the need to efficiently execute complex queries on massive databases in a way that minimizes the use of communication resources while preserving the privacy of the entity that initiated the query. Such queries are functions of the data points that are stored at remote servers. This project develops a principled and holistic framework for the problem of privately retrieving, at distributed cache-aided nodes, the output of functions based on both data that is locally computed and data that is received from multiple servers.

Data management plan for the grant, "New approach based on enzyme stimulating of peptides for targeting drug resistance breast cancers." In this project, we propose the development of selective self-assembling peptides that form nanofibers via a self-assembling process upon the action of Eyes absent (EYA) enzyme, specific to drug resistance Triple Negative Breast cancer cells (TNBC). The objectives are: Aim 1: Design and synthesize self-assembling peptide substrates for EYA enzymes; Aim 2:Determining the efficacy of peptide substrates for inhibiting TNBC in spheroid 3D cell cultures. The correlation between the enzyme kinetic and the activity of nanostructures for targeting EYA will be evaluated. Aim 3: The apoptosis response of the TNBC cells will be determined. This study will lead to finding the potent peptide substrate and the effective dose for inhibiting TNBC cells with apoptosis cell death.

Data management plan for the grant, "Collaborative Research: SHF: Small: LEGAS: Learning Evolving Graphs At Scale." This project aims to improve the computation efficiency of graph neural networks (GNNs), which are an emerging class of deep learning models on graphs, with many successful applications, such as, recommendation systems, drug discovery, social network analysis, and code vulnerability detection. This project aims to to design an efficient GNN framework via algorithm and system co-design for both static and dynamic graphs.

Data management plan for the grant, "Child Health and Human Development Extramural Research." This study will use the genome-edited human induced pluripotent stem cell (hiPSC) with NOTCH1 knockout to recapitulate the genetic variants of NOTCH1 mutation in the Hypoplastic left heart syndrome (HLHS). It will use advances in the vascularized cardiac organoid directly differentiated from hiPSCs to replay the development and function of cardiomyocytes, endothelial cells, smooth muscle cells, and other cardiac cells in a defined 3D cell culture model by stencil-based micropatterning. It will elucidate the pathogenesis of cardiovascular underdevelopment and dysfunction found in HLHS with NOTCH1 mutation via the NOTCHDELTA/JAG ligand-receptor binding and multicellular crosstalk by single-cell RNA-seq and proteomics analysis.

Data management plan for the grant, "Optic-nerve-head (ONH) Chips for Glaucomatous Neurodegeneration." The most prominent causative risk factor of glaucoma, the leading cause of irreversible blindness worldwide, is elevated intraocular pressure (IOP), which could deform the optic nerve head (ONH) and cause glaucomatous neurodegeneration. However, current glaucoma therapies that focus on lowering IOP do not stop vision loss effectively, and thus there is a pressing need to understand the mechanisms underlying glaucoma pathogenesis. In this project, we will develop a biomimetic 3-D ONH-on-a-chip system that closely mimics the key anatomical and pathophysiological characteristics of the native ONH to study astrocytic mechanisms of glaucoma pathogenesis, a missing link to develop efficacious therapies.

Data management plan for the grant, "Collaborative Research: DMREF: AI-enabled Automated design of ultrastrong and ultraelastic metallic alloys"

Data management plan for the grant, "CAREER: Developing a Flexible Serverless Multimedia Streaming Cloud Platform."

Data management plan for the grant, "Collaborative Research: IRES Track I: Wireless Federated Fog Computing for Remote Industry 4.0 Applications."

Data management plan for a NSF grant with a name that has not been determined.

Data management plan for the grant, "Collaborative Research: Engaging Blind and Visually Impaired Youth in Computer Science through Music Programming."

Data management plan for the grant, "Collaborative Research: Topological Defects and Dynamic Motion of Symmetry-breaking Tadpole Particles in Liquid Crystal Medium."

Data management plan for the grant, "Understanding the Role of Residual Stress Gradients on Plastic Strain Recovery in Nanocrystalline Thin Films."