Fifteen proposals awarded funding in the President’s Research Excellence Accelerator Grant program

Fifteen teams have been awarded a total of nearly $750,000 in funding in the second year of the President’s Research Excellence (PRE) Accelerator Grant program. Each grant of up to $50,000 is designated for small teams formed to pursue curiosity-driven, novel, high-risk and high-reward research.

Administered through the Office of Knowledge Enterprise, the program provides seed support for cross- and interdisciplinary research projects that have the potential to attract external funding and supports the university’s goal to  grow its research and innovation enterprise by attracting more externally sponsored research funding, enabling curiosity-driven research and discoveries, and addressing large, complex societal challenges. 

“When we bring together different expertise and different points of view, we have the potential to solve some of society’s biggest problems,” said Dorota Grejner-Brzezinska, vice president for knowledge enterprise at Ohio State. “This funding provides a vital seed for Ohio State researchers to explore their ideas to solve complex problems and a mechanism to drive convergent research across the university.”

The PRE program also offers Catalyst Grants to support the efforts of large cross- and interdisciplinary teams to pursue large-scale, high-impact research that addresses emerging or existing challenges of national and international societal importance. These grants may be up to $200,000 and are expected to generate long-term, sustained, and significant impact while positioning Ohio State as THE research area leader. Concept papers for Catalyst Grant funding are due on June 24, 2022.

March 2022 Accelerator Grant Recipients

Targeting Ethylene Signaling to Condition Crop Resistance to Geminiviruses

• PI: David Bisaro, College of Arts and Sciences
• Co-investigators: Adriana Dawes, College of Arts and Sciences; David Mackey, College of Food, Agricultural, and Environmental Sciences
• Project description: Crop losses caused by geminiviruses exacerbate the global issue of food security. By elucidating mechanisms by which the plant hormone, ethylene, conditions geminivirus resistance, and interacts with other host defense pathways, this project will inform rational strategies to build resistant plants.

Determining primary splicing changes in Spinal Muscular Atrophy

• PI: Arthur Burghes, College of Medicine
• Co-investigators: Guramrit Singh, College of Arts and Sciences; Anton Blatnik, College of Medicine
• Project description: SMN-deficiency in Spinal Muscular Atrophy reduces assembly of spliceosome components. How this leads to motor neuron loss is unknown. This project proposes to use a spliceosome footprinting approach to zoom in on altered splicing events to identify genes sensitive to SMN-deficiency.

Development of nanobody-based protein degraders targeting misfolded proteins in neurodegenerative diseases

• PI: Nam Chu, College of Medicine
• Co-investigators: Nhat Le, College of Medicine; Dehua Pei, College of Arts and Sciences
• Project description: Neurodegenerative diseases are caused by protein misfolding. This proposal aims at developing a novel selective protein degrader targeting misfolded proteins in human iPSC-derived CNS cell models. This work is fundamental to identifying new therapeutic opportunities for neurological disorders.

Cis-inhibition of Notch pathway activity in development and disease: Identification and analysis of novel ligand receptor interactions

• PI: Susan Cole, College of Arts and Sciences
• Co-investigators: Tom Magliery, College of Arts and Sciences; David Carbone, College of Medicine; Christopher Lucas, College of Engineering
• Project description: The researchers have identified unrecognized roles for the Notch ligand DLL3 in modulating signaling in development and disease. This project will use developmental and cancer biology, biochemistry, and engineering approaches to study novel protein domains mediating receptor-ligand interactions in the Notch pathway.

Developing the ‘van der Waals’ vacuum as a host for quantum bits

• PI: Jay Gupta, College of Arts and Sciences
• Co-investigators: David McComb, College of Engineering; Shamsul Arafin, College of Engineering
• Project description: There is a broad search for new hosts that protect quantum bits from electromagnetic noise and can be incorporated into devices. This project proposes a novel approach based on rare gas solids that may offer the best possible protection, in a 2D materials platform that also allows device nanofabrication.

Small Molecule Inhibitors for Directed Targeting of BET/Brd4 Extra-Terminal Domain as a Novel Cancer Therapeutic

• PI: Ross Larue, College of Medicine
• Co-investigators: Tom Li and Mitch Phelps, College of Pharmacy; Mark Foster, College of Arts and Sciences
• Project description: Brd4, a transcriptional activator, is strongly associated with cancer and has been traditionally targeted with bromodomain inhibitors. The researchers have developed novel small molecule inhibitors which specifically target the ET domain, potentially overcoming dose limiting toxicities of bromodomain inhibitors.

Developing a Transformative Social-Emotional Learning Program for Adolescents

• PI: Tzu-Jung Lin, College of Education and Human Ecology
• Co-investigators: Thomas Bihari and Leon Madrid, College of Engineering; Jodi Ford, College of Nursing
• Project description: A novel social-emotional learning intervention called Mindfulness-based Collaborative Social Reasoning (MBCSR) for middle school students will be developed to establish its instructional, technological, and assessment frameworks. Researchers will field-test MBCSR for its short-term efficacy.

Discovery of Novel Therapeutic Targets to Improve Health span after Spinal Cord Injury

• PI: Dana McTigue, College of Medicine
• Co-investigators: Richard Bruno and Rachel Kopec, College of Education and Human Ecology; Jie Gao, College of Medicine
• Project description: The researcher’s work shows the liver’s response to spinal cord injury (SCI) impairs recovery in rodents. In this project, the researchers will use multi-omics approaches to determine pathological gene and lipid changes that are feasible candidates for impairing recovery, with the goal of discovering testable therapeutic targets.

Artificial intelligence-driven development of novel chemical tools for controlling mosquito disease vectors

• PI: Peter Piermarini, College of Food, Agricultural, and Environmental Sciences
• Co-investigators: Liva Rakotondraibe and Xiaolin Cheng, College of Pharmacy
• Project description: The proposed transdisciplinary research will use an innovative artificial intelligence (AI) approach to accelerate development of novel, plant-derived chemical tools for controlling mosquito vectors of devastating diseases (e.g., malaria, dengue fever, and Zika virus).

A Feasibility Pilot Study to Reduce the Intergenerational Transmission of Obesity to Children of Parents Undergoing Bariatric Surgery

• PI: Keeley Pratt, College of Education and Human Ecology
• Co-investigators: Alicia Bunger, College of Social Work; Chris Taylor and Bradley Needleman, College of Medicine
• Project description: Children of parents with severe obesity who have bariatric surgery are at high risk of developing obesity and disordered eating behaviors. This project will compare the early effects of two interventions (parent-only vs parent and child), integrated into routine preoperative care for parents and their children.

What lies beneath: Using microsporidian parasites to control mosquito breeding in stormwater catch basins.

• PI: Sarah Short, College of Food, Agricultural, and Environmental Sciences
• Co-investigators: Ryan Winston, College of Food, Agricultural, and Environmental Sciences; and Risa Pesapane, College of Veterinary Medicine
• Project description: Mosquitoes transmit multiple human pathogens which cause hundreds of millions of disease cases each year. This project will test whether microsporidian parasites could be used for effective mosquito control in storm water catch basins, which are prime breeding sites in cities around the world.

Engineering durable disease resistance in plants by exploiting a novel host protein

• PI: Guo-Liang Wang, College of Food, Agricultural, and Environmental Sciences
• Co-investigators: Venkat Gopalan, College of Arts and Sciences; Charles Bell, College of Medicine
• Project description: Reducing the use of chemical pesticides for plant disease control is a key challenge in sustainable agriculture. The researchers describe an innovative strategy that leverages a host protein to enhance immunity of a staple crop and confer protection to fungal/bacterial pathogens without application of chemicals

Leveraging Artificial Intelligence to Accelerate Life-Saving 911 Care

• PI: Henry Wang, College of Medicine
• Co-investigators: Ashish Panchal and Travis Sharkey-Toppen, College of Medicine; Rajiv Ramnath, College of Engineering
• Project description: Each year there are over 240M calls to 911 for emergency help. This project aims to transform the 911 process, applying a trained artificial intelligence system to accelerate the identification of life-threatening conditions, and set the stage for a prototype real-time decision support system.

Towards a Conversational Assistant for Patient Prep

• PI: Michael White, College of Arts and Sciences
• Co-investigators: Douglas Danforth, College of Medicine; William Schuler, College of Arts and Sciences
• Project description: In this proposal, the researchers aim to take initial steps towards developing an automated conversational assistant that can help patients properly prepare for procedures. The project will conduct a pilot study and evaluate baseline neural models to develop preliminary results for a planned NSF proposal.

Fair Machine Learning Adaptable to Deployment Environments in Healthcare

• PI: Xueru Zhang, College of Engineering
• Co-investigators: Ping Zhang and Jeffrey Caterino, College of Medicine
• Project description: This project develops a framework for learning fair machine learning models in healthcare. The trained models can improve health equity and are adaptable to various deployment environments and real-world clinical settings, further facilitating the practical use of machine learning in healthcare.