The funding comes via through the UofL Office of Research and Innovation: the Jon Rieger Seed Grants and Programmatic Support programs.��

“This internal funding provides critical support for groundbreaking research and scholarship,” said Will Metcalf, associate vice president for research and innovation. “I’m excited for the strong and diverse projects funded in this round, and look forward to seeing what these researchers accomplish.”��

Jon Rieger Seed Grants provide up to $7,500 to assist full-time, active-status early career researchers in the initiation of new scholarship, creative activities and other research approaches. Winners this round were:��

• Collaborative��multimodal sensor fusion with edge intelligence for connected and autonomous vehicles (Sabur Hassan Baidya, J.B. Speed School of Engineering);
• Assessing and responding to psychosocial and health equity needs of immigrant and refugee communities through library partnerships (Rebecka Bloomer, Kent School of Social Work);
• Evaluation of the physicochemical properties of a new bioceramic endodontic sealer: an initial approach (Eduardo Antunes Bortoluzzi, School of Dentistry);
• Emotions, context and alcohol use (Konrad Bresin, College of ����ֱ�� and Human Development);
• Developing 3D-printed lattice nasopharyngeal swabs for COVID-19 tests (Yanyu Chen, J.B. Speed School of Engineering);
• The impacts of drought on hemp physiology, chemistry, and the microbiome (Natalie Christian, College of Arts and Sciences);
• Multi-pathogen wastewater surveillance system to improve health and stop pathogenic outbreaks within low- and middle-income country communities (Rochelle Holm, School of Medicine);
• Reactions to experiencing discrimination (RED) study (Yara Mekawi, College of Arts and Sciences);
• Quantifying the controls of streamflow permanence and sediment connectivity in urban headwater streams (Tyler Mahoney, J.B. Speed School of Engineering);
• A��physics-based machine learning framework for smart self-adaptable multi-stage manufacturing systems��(Luis Segura Sangucho, J.B. Speed School of Engineering);
• Homing��in: community engaged research on LGBTQ+ youth houselessness��in Louisville, Kentucky��(Cara Snyder, College of Arts and Sciences); and
• Eliciting��expert knowledge in empirical selection of machine learning methods��(Xiaomei Wang, J.B. Speed School of Engineering).

The Programmatic Support grant provides up to $3,000 of funding to assist full-time, active-status faculty with the completion of a project where other funding sources are not available. Winners this round were:��

• Human��mate-copying and the popularity of Halo in an online venue (Michael Cunningham, College of Arts and Sciences)��
• Development of a gastric reflux simulator for the analysis of teeth and dental materials (Grace DeSouza, School of Dentistry)
• Youth/young adults of color responding to racial inequities and COVID-19 in listening sessions��(Melanie Gast, College of Arts and Sciences)
• Validating techniques for collecting vocal and listening effort during remote and in-person speech-language intervention (Maria Kondaurova, College of Arts and Sciences)
• On the border, between empires: A bioarchaeological examination of health, diet, and biological relatedness in individuals from the cemetery of Oymaağaç during the Roman to Byzantine transition (Kathryn Marklein, College of Arts and Sciences)��
• Chance��designs recording��(John Ritz, School of Music)
• Development of expertise in perception of speech and music (Christian Stilp, College of Arts and Sciences)
• Automating emotional safety and post-traumatic growth: An exploratory study to investigate gender-based violence survivors’ user experiences on social media (Heather Storer, Kent School of Social Work)
• Campus sustainability, community context��(Angela Storey, College of Arts and Sciences)
• Antibiotic��bone cement intramedullary nails for treating orthopaedic infections��(Michael Voor, School of Medicine)��
• Exploring��the relationships between student behaviors and special education teachers’ physical well-being and instruction: a pilot study��(Jeremy Whitney, College of ����ֱ�� and Human Development)��
• Effect of powder feedstock on the material characteristics of small-size Ti6Al4V geometries fabricated by laser powder bed fusion additive manufacturing (Li Yang, J.B. Speed School of Engineering)
• Translation of the Chinese��fashion industry: an ethnographic approach (Jianhua Zhao, College of Arts and Sciences)

In addition to the programmatic and Rieger grants, two more internal grants programs accept applications annually in fall: Collaborative Mentoring Grants (up to $10,000) and Capacity Building Grants (up to $25,000). Open applications will be announced in September with application deadlines in late October. More information is available on the Office of Research and Innovation .��

“Hopefully they’ll catch it, but what if they miss?” said Dr. Harpal Sandhu, an assistant professor of ophthalmology at the University of Louisville.

That was the inspiration behind new research done by Sandhu and colleagues at UofL, which aims to make the process of identifying diabetic retinopathy more objective by teaching software to diagnose. The results were published in the British Journal of Ophthalmology and the .

The team used a technique called machine learning to teach the software how to identify the condition, which occurs when high blood sugar levels cause damage to the blood vessels in the eye.

They showed the software pictures of eyes — tons of them, both healthy and unhealthy — until it “learned” what to look for when making a diagnosis. It learned to look for swollen vessels or leaking of blood into the eye, which could cause impaired or even lost vision.

When tested on more than 100 cases, the software got 94.3 percent of the diagnoses correct.

Sandhu said diagnosis of diabetic retinopathy is especially important, given the “epidemic proportions” of diabetes. According to the , the number of people with diabetes rose from 108 million in 1980, to 422 million in 2014.

The software combines two systems developed by the UofL team for optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA). Together, they allow the machine to, non-invasively, get a clear view of the blood vessels in the eye and make a diagnosis.

The software works with a machine used by many optometrists and ophthalmologists to examine patients’ eyes. The automated technology could supplement care in areas where medical staffing is low and optical specialists are sometimes unavailable.

“We think this machine could be set up in the family doctor’s clinic,” said Dr. Ayman El-Baz, professor and chair of bioengineering, who was part of the development team.

He said the technology is part of a “new trend for using artificial intelligence in medicine.” Similar research is being done at UofL for diagnosis of other medical conditions, such as lung cancer and organ rejection.

The development was backed by from the Wallace H. Coulter Translational Partnership, which promotes collaborative translational research, and is currently available for through the UofL Office of Technology Transfer.

This project is “a great example of collaboration between engineers and clinicians to address unmet clinical needs,” said Jessica Sharon, director of the Coulter Translational Partnership at UofL, adding the resulting technology “has the potential to make a significant impact through commercialization to diagnose disease and improve patient outcomes.”