Emily Griffith

Dates: 07/01/2021 – 06/30/2026

Title: Using 3D Nonwovens Fabrication to Engineer Region-Specific Extracellular Matrix Structure and Bioactivity of the Knee Meniscus

Sponsor: National Institutes of Health (NIH)

Role: Co-Investigator

Description: “Meniscal tears are the most commonly reported knee injuries, and approximately 1 million surgeries involving the meniscus are performed annually in the US. Tissue engineering and regenerative medicine approaches are being actively pursued as potential alternatives to overcome limitations of current clinical treatments. Yet, the translation of these approaches to clinical application has been hampered by their limited ability to efficiently and reproducibly create physiologic-sized scaffolds featuring anisotropic structural and mechanical properties on the order of native meniscus and zone-specific biological cues provided by the ECM. The overall goals of this proposal are to 1) develop a scaffold that recapitulates the complex structural and mechanical characteristics of the meniscus at multiple scales and incorporates zone-specific ECM cues and 2) assess the long-term function of such scaffolds and their ability to prevent joint degeneration in-vivo. We will use a new high-throughput hybrid approach of 3D Melt Blowing (3DMB) in conjunction with Solution Blowing (SB) that synergistically integrates attributes of traditional nonwovens techniques and 3D printing to create a scaffold featuring macro-geometry, fibrous microarchitecture, and zonal biological cues (meniscus-derived ECM (mECM)) to match the native meniscus. We hypothesize that both biomechanics and mECM cues need to be similar to the meniscus to achieve superior in-vivo outcomes, primarily, reduced cartilage degeneration. Aim 1 is to determine how primary 3DMB and SB process variables influence the structural architecture and biomechanical properties of anatomically-sized meniscus scaffolds made of selected biopolymers and mECM. Aim 2 is to determine whether the incorporation of zone-specific mECM improves infiltration and tissue formation by cells as well as integration with the surrounding meniscus tissue. Aim 3 is to determine whether cartilage degeneration following partial meniscectomy is reduced through the addition of an appropriate mECM formulation within scaffolds with meniscus-matched mechanics. On completion, this project will provide fundamental knowledge about the micro- and macro-level process-structure-function relationships in meniscus-relevant bioactive scaffolds fabricated using our new nonwovens approach, and will serve as a base technology of great significance allowing advances in the treatment of orthopaedic fibrous soft tissue injuries.”

Reference: https://app.dimensions.ai/details/grant/grant.9733162

Spencer Muse

Dates: 03/15/2024 – 02/29/2028

Title: Hypothesis Testing using Phylogenies for the 21st Century

Sponsor: National Institutes of Health (NIH)

Role: MPI; Lead of NC State component

Description: “Between 2000, when the first version of Hypothesis Testing Using Phylogenies (HyPhy) was released, and 2022, the number of bases in GenBank increased ~250 fold, the number of sequenced genomes from a handful to >3,500, and the number of PubMed papers studying molecular evolution ~12 fold. Data generation has ceased to be the bottleneck for biological and biomedical discoveries, and the new bottleneck is methods, tools, and software for data analysis and interpretation. Comparative evolutionary analyses remain an essential and powerful method for extracting meaning and insight from ever-expanding genomic sequence data. But, the lack of emphasis and incentive structure for developing, maintaining, benchmarking, and improving software and analysis tools, despite their essential and critical role in modern biology, biotechnology, and medicine remains a major concern. Analytical, infrastructure, and incentive challenges exposed by the genomic data deluge during the COVID-19 pandemic were aptly summarized. Over the last quarter century HyPhy has established itself as a useful, popular, and durable platform for studying diverse evolutionary processes, such as natural selection and recombination, across different taxonomic scales. Datamonkey, a web application providing free access to “one-click” popular HyPhy analyses, has seen increasing use by researchers worldwide over the last two decades. Through continued methodological innovation, improvements in performance and scalability, accessible web services, focus on data visualization, and user support, HyPhy developers were able to sustain and increase the reach and impact of the program. This proposal seeks to: improve the software; enhance biological realism, relevance, accessibility, and interpretability of the methods; design novel approaches to address outstanding problems in evolutionary data analysis; and further lower access barriers to evolutionary comparative analyses through integration with the Galaxy ecosystem.”

Reference: https://pure.psu.edu/en/projects/hypothesis-testing-using-phylogenies-for-the-21st-century-equipme/

Len Stefanski 

Title: Fractional Ridge Regression.

Funding agency: National Science Foundation.

Dates: September 2023 — August 2026. 

Role: PI. 

Description: “Ridge regression was introduced by Hoerl and Kennard (1970a,b) and twenty-six years later was followed by the introduction of the lasso Tibshirani (1996). The body of research ensuing from these seminal papers is staggering, and has contributed immensely to our understanding of shrinkage and selection methodology and to the practice of regression modeling in many areas of science. In some applications of regression modeling the goal is simply to achieve the best possible predictions of future response values. In other applications, interpretation is important as a way to guide understanding of the process under investigation. Ridge regression is very good at prediction, although is often eclipsed by the lasso in terms of both prediction and interpretation because the lasso also allows for selection. The method to be studied with this grant, fractional ridge regression, has the potential to improve both prediction (as measured by mean square error) and interpretability (as measured by the specificity of variable selection) relative to the lasso.”

Reference: Research Roundup | Department of Statistics