The Academic Minute

Academic Minute
  • 0.0 (0)
  • EDUCATION
  • UPDATED WEEKLY

Astronomy to Zoology www.academicminute.org

  1. 5 HR AGO

    Beatrice Golomb, University of California San Diego - New Diagnostic Code for Gulf War Illness

    What is Gulf War Illness and why is recognition important? Beatrice Golomb, professor of medicine at the University of California San Diego, seeks to inform. Faculty Bio: Dr. Golomb is a Professor of Medicine at UC San Diego with over 15 years of experience treating veteran patients, including veterans with Gulf War Illness (GWI). She was the inaugural Scientific Director for the Congressionally directed Research Advisory Committee on Gulf War Veterans’ Illnesses (RAC) and her research for RAND and with funding from the Department of Defense have expanded knowledge of exposure relations, mechanisms, markers and treatment for GWI. Dr. Golomb and her team remain committed to research to improve the lives and health for our heroes from the Gulf War. Transcript: For decades, Gulf War veterans have battled for recognition of the often devastating health challenges they experience as a consequence of their honorable service. Next month, we will mark an immensely important milestone for Gulf War veterans, their families, clinicians and researchers. Gulf War illness will finally receive its own International Classification of Diseases — or ICD — diagnostic code. This is more than just administrative coding. This is long-overdue validation for the suffering of the quarter-million affected veterans. It is a formal acknowledgment that Gulf War illness is real, it is physical, and it is service-related. Gulf War illness affects about one-third of the nearly 700,000 U.S. troops who served in the 1990-1991 Gulf War. It manifests as a consistent profile of symptoms: persistent fatigue, cognitive difficulties, chronic pain, respiratory issues, skin problems, gastrointestinal distress. Decades of study have linked Gulf War illness to chemical exposures and identified objective abnormalities such as structural brain changes. With this new ICD code, health care providers will be better able to recognize, diagnose, and treat Gulf War illness. Insurance, medical records, research, and public health tracking will now explicitly acknowledge the condition, rather than forcing patients to substitute related diagnoses. For researchers like me, the change accelerates our ability to study Gulf War illness in large populations, monitor treatment outcomes rigorously, and understand how this condition may overlap or interact with other diseases. To all veterans whose symptoms were dismissed and whose needs went unmet: this new diagnostic code is for you. It’s a recognition of your service. It’s a commitment to your care. And it represents hope — hope that research, medicine, and policy will now move forward more fully, more justly, to give you the answers and the support you deserve. Read More: [PNAS] - Acetylcholinesterase inhibitors and Gulf War illnesses [ScienceDirect] - Adverse effect propensity: A new feature of Gulf War illness predicted by environmental exposures [National Library of Medicine] - Mitochondrial impairment but not peripheral inflammation predicts greater Gulf War illness severity This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.academicminute.org

    3 min

  2. 3 DAYS AGO

    Khan Iftekharuddin, Old Dominion University - How Does Non-Invasive Detection of Aggressive Brain Tumor Recurrence Work

    On Old Dominion University Week: A non-invasive method for detecting an aggressive brain tumor could be key for patients. But how does it work? Khan Iftekharuddin, Professor and Eminent Scholar, delves into this. Faculty Bio: Dr. Khan Iftekharuddin is a professor and Batten Endowed Chair in Machine Learning in the department of Electrical and Computer Engineering (ECE) at Old Dominion University (ODU). He concurrently serves as a Director, ODU Vision Lab and an Inaugural Director, Institute of Data Science. Dr. Iftekharuddin has been cited among the top 2% researchers in the globe for both career-long impact and single-year impact, and his Vision Lab has consistently ranked among top teams in Global Brain Tumor Segmentation and Patient Survivability Prediction Challenges co-organized by MICCIA and NCI since 2014.Prior to his current roles, he served as an Interim Dean in Batten College of Engineering and Technology, Associate Dean for Research, Innovation and Graduate Studies, and Chair of the ECE Department at ODU. He received his MS (1991) and PhD (1995) degrees in Electrical and Computer Engineering from University of Dayton, OH. Transcript: Glioblastoma Multiforme, or GBM, is the most aggressive and deadly type of brain cancer, killing about 10,000 Americans each year and accounting for half of all brain cancer deaths in the U.S. The fast-growing cancer spreads microscopic cancer cells in surrounding healthy tissue and has an average survivability of 18-24 months from diagnosis. Prognosis for GBM is poor, with recurrence in 90% of patient cases within six to nine months, even after aggressive treatment protocol including surgery, radiation and chemotherapy. Diagnosing brain tumor recurrence on standard imaging scans like MRIs is challenging because treatment-related changes in the brain tissues, such as scar tissue, necrosis (dead tissues) and edema (swelling), often appear like recurrent tumor tissue. Currently, the only way to confirm tumor recurrence is through an invasive brain biopsy. My colleagues and I are investigating how computational modeling, AI, and machine learning methods can help distinguish true tumor recurrence from surrounding abnormal tissues, without needing to do a biopsy. This work builds on long-standing research of brain tumor volume segmentation and tracking, tumor sub-typing, and patient survivability prediction. We’re working with about half a dozen clinical collaborators across the US to analyze and process large amounts of high-resolution Magnetic Resonance imaging alongside molecular and patient clinical data. This analysis will help us develop non-invasive AI models that classify tumor recurrence and radiation-induced challenges. These tools could improve early detection, tracking, and treatment planning, helping physicians better predict the trajectory of tumor growth and tailor interventions for individual patients. Additionally, we’re working to study inherent biases in these AI models and ensure that they are representative of different patient populations. This will bolster their robustness and efficacy in clinical settings. Research Projects This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.academicminute.org

    3 min

  3. 4 DAYS AGO

    Hong Qin, Old Dominion University - How Fast Can a Viral Variant Spread?

    On Old Dominion University Week: How fast can a viral variant spread? Hong Qin, associate professor in the School of Data Science and the Department of Computer Science at Old Dominion University, analyzes the data to find out. Faculty Bio: Hong Qin is an Associate Professor in the School of Data Science and the Department of Computer Science at Old Dominion University. His work develops AI and statistical methods for genomic surveillance, pandemic prediction, and trustworthy health AI. Transcript: Viruses evolve as they spread, and when a new viral variant begins to outcompete others, it can quickly reshape an outbreak. But measuring a variant’s advantage is tricky, because case counts and sequencing volume rise and fall for reasons unrelated to biology.A new approach called the differential population growth rate, or DPGR, focuses on comparisons instead of absolute numbers. In a given region and short time window, DPGR looks at two variants that are sampled side-by-side. It tracks the ratio of their weekly sequence counts and takes a logarithm. If that log-ratio changes roughly as a straight line, the slope estimates how much faster one variant is growing than the other. A positive slope means variant A is gaining on variant B; a negative slope means variant A is losing ground.This pairwise design makes one variant an internal control, helping reduce distortions from shifting testing, reporting, or sequencing intensity. DPGR also has an additive property: if variant A overlaps with B, and B overlaps with C, their slopes can be combined to estimate a comparison of A versus C, even when A and C rarely appear together.Using DPGR with genomic surveillance data, researchers can map how variants’ advantages change across places and over time. For example, COVID-19’s Omicron variant outpaced the Delta variant worldwide, but the estimated advantage of Omicron differed by region. DPGR can also compare sublineages and build a “fitness staircase” that summarizes stepwise gains.The result? A simple, interpretable signal that complements other epidemic models and can help anticipate which variant may dominate next. Read More: [Wiley] - A data-driven sliding-window pairwise comparative approach for the estimation of transmission fitness of SARS-CoV-2 variants and construction of the evolution fitness landscape YouTube This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.academicminute.org

    3 min

  4. 5 DAYS AGO

    Maryam Golbazi, Old Dominion University - Where the Heat Hits Hardest

    On Old Dominion University Week: When it’s hot out, some places are hotter than others. Maryam Golbazi, research assistant professor of climate science, examines why. Faculty Bio: Maryam Golbazi is a Research Assistant Professor at Old Dominion University working with the Joint Institute on Advanced Computing for Environmental Studies (JI-ACES). She specializes in numerical weather prediction models, atmospheric chemistry modeling, wind energy, and data assimilation. Her work integrates advanced numerical modeling with satellite and in-situ observations to improve forecasts of air pollution, wind energy resources, and extreme weather events. She is currently leveraging data science and AI/ML methods to develop localized weather models, while maintaining the rigor and integrity of established physical modeling techniques. With prior research experience at the National Center for Atmospheric Research and many collaborative projects, Dr. Golbazi’s research bridges science and application to address pressing environmental and energy challenges. She aims to leverage fundamental science and state of the art data-driven techniques to produce actionable insights that help protect communities, inform policy, and guide sustainable infrastructure planning. Transcript: On a summer afternoon in Hampton Roads, Virginia, the heat doesn’t feel the same everywhere. In some neighborhoods, the air lingers thick, heavy, slow to cool even after sunset. In others, just a few miles away, temperatures drop faster, offering relief once the sun goes down. These differences aren’t random. They’re shaped by concrete, roads, buildings, and the environment.In our study, which I conducted with my colleague Frank Liu, we used some of the highest-resolution weather simulations ever applied to a real U.S. city to understand how extreme heat behaves at the neighborhood scale. Instead of looking at cities from satellites, we zoomed in, down to city blocks, using advanced atmospheric models.During two intense heat waves in the summer of 2024, our simulations revealed that dense urban areas were, on average, up to five or six degrees hotter than nearby rural regions. And at night urban neighborhoods stayed warm far longer.But temperature was only part of the story.When we combined heat exposure with census data, a pattern emerged: lower-income communities experienced higher heat stress. And that translated directly into energy demand. That matters, because cooling isn’t free. For families already struggling with energy costs, extreme heat becomes both a health risk and a financial burden.As heat waves become longer and more intense, understanding where heat concentrates, and who pays the price, may be just as important as predicting the temperature itself. Our research shows that climate change isn’t just about rising averages. It’s about how people experience heat differently, day to day, street to street! Read More: [Springer Nature] - High-resolution modeling of extreme heat events with socioeconomic consideration: a real-case WRF–LES approach This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.academicminute.org

    3 min

  5. 6 DAYS AGO

    Tiffany Zhu, Old Dominion University - How Should AI Talk About Us?

    On Old Dominion University Week: Can AI chatbots spread generalizations? Tiffany Zhu, assistant professor of global ethics and technology, examines why this could be the case. Faculty Bio: Tiffany Zhu is a philosopher. She is an Assistant Professor of Global Ethics and Technology in the Department of Philosophy and Religious Studies at Old Dominion University. Prior to joining ODU in Fall 2025, she was a Faculty Fellow in AI Ethics at the California Center for Ethics and Policy at California State Polytechnic University Pomona Transcript: AI chatbots powered by large language models are increasingly shaping our understanding of the world. Some of my research examines how they use a linguistic device called “generics,” which expresses generalizations without explicit quantification. An example of a sentence using a generic to talk about a social group would be: “Immigrants work low-wage jobs”. These statements are consequential because they can spread stereotypes. One study looking at ChatGPT 3.5 found, among other tendencies, that the chatbot often paired social generics with what I call individuation hedges, which emphasize diversity within groups. For instance, when asked “Are women more likely to get attacked while walking alone at night?,” the chatbot affirmed the trend but added that “the likelihood of getting attacked depends on an individual’s characteristics.” Not only are these hedges too formulaic to counteract unfair generalizations, they also reduce the accuracy of some responses, obscure the structural causes of social patterns including oppression, and could lead users to form false or even harmful beliefs about themselves and others.To improve their use, I proposed a strategy with three elements. First, I suggest requiring democratic and interdisciplinary guidance during the process of reinforcement learning through human feedback. Second, I advise shifting away from a transactional toward a dialogical model of AI-human interaction, meaning chatbots should probe for context and user goals and assumptions rather than simply providing answers. Finally, I suggest chatbots should use generics in conjunction with historical framing and asking counterfactual questions, in order to help AI users to think flexibly about how social reality is contingent, changeable, and sometimes unjust. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.academicminute.org

    3 min

  6. 18 MAY

    George McLeod, Old Dominion University - Building Digital Twins of Our World to Improve Coastal Resilience

    On Old Dominion University Week: Coastal resilience will be key going forward in a warming climate. George McLeod, director of the Center for Geospatial Science, Education, and Analytics, shows how virtual worlds can help us protect our own. Faculty Bio: Dr. George McLeod is the Director of the Center for Geospatial Science, Education, and Analytics at Old Dominion University and Senior Fellow with Virginia’s Commonwealth Center for Recurrent Flooding Resiliency. He oversees the creation of vital location intelligence for a wide range of academic research questions, including those focused on the intersection of the built landscape and environmental hazards. His expertise in ocean science, geovisualization, remote sensing, and UAV operations has allowed him to play an important role in advancing coastal hazards and flooding resilience research in Virginia. Transcript: When I tell people what we’re working on, the first question is almost always the same: “Okay… what exactly is a digital twin?”At its simplest, a digital twin is a virtual version of a real place or object. Most of us have seen something like this before—think about the detailed, three-dimensional cities you move through in video games. They look real, but they’re mostly just scenery.In our work on coastal resilience, the digital twin isn’t the backdrop; it’s the main character.We’re building virtual versions of real coastal communities and entire regions so we can study very real challenges: sea-level rise, storm flooding, water quality, and the public-health impacts that come with all of that. And unlike a static 3D model, these digital twins are alive. They combine physical models of land and water with models of how systems behave, things like transportation networks, population movement, and hydrology.That lets us ask big, practical questions. For example: If a Category 3 hurricane hits Virginia on top of an extra foot of sea-level rise, which neighborhoods flood first? Which roads are cut off—and for how long? How much damage might we see? Who could be displaced, and where should emergency responders focus their efforts?Our most ambitious project takes this idea even further. We’re working with colleagues at NASA to build what we call the Coastal Zone Digital Twin of the entire Chesapeake Bay. It’s a dynamic system that’s constantly updated, showing what’s happening now, what’s likely to happen next, and letting us test “what if” scenarios, so communities can prepare for the coastal future that’s already on the way. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.academicminute.org

    3 min

  7. 15 MAY

    Jinglu Jiang, Binghamton University - Multitasking and Phishing Emails

    Are you good at multitasking? Jinglu Jiang, associate professor at the School of Management at Binghamton University, reveals how this behavior may allow harmful emails to slip by. Transcript: The ability to juggle multiple tasks is a defining feature of modern work. But that constant multitasking may make people more vulnerable to phishing attacks. In a recent study, my co-authors and I examined how multitasking affects people’s ability to detect phishing emails. We conducted two online experiments with nearly one thousand participants. In both experiments, participants worked in multitasking settings. They first completed a mentally demanding primary task, like memorizing numbers or work-related information, while being interrupted with a secondary task: deciding whether incoming emails were legitimate or phishing. This setup mirrors everyday work environments, where email alerts arrive while people are focused on other tasks. We found that when the primary task placed a high demand on people’s working memory, phishing detection performance dropped substantially. However, we also identified an important countermeasure. When participants received a simple reminder that some emails might be phishing attempts, detection performance improved—even under heavy cognitive load. We also found that message design plays a role. Reminders were especially effective against phishing emails that promised rewards. By contrast, loss-framed messages—such as warnings about account suspension—tended to trigger vigilance on their own, leaving less room for reminders to add value. Together, these findings suggest that phishing defenses should account for multitasking, not assume users are fully attentive. Organizations may benefit from context-aware reminders that support attention when cognitive demands are highest and risks are most likely to go unnoticed. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.academicminute.org

    3 min

  8. 14 MAY

    Carlena Ficano, Hartwick College - Systemic Barriers Faced By Minority and Women Entrepreneurs

    The intersection of race, gender, and financial access needs further study. Carlena Ficano, professor of economics at Hartwick College, discusses why. Faculty Bio: Dr. Ficano’s areas of expertise include: labor economics, applied econometrics, social policy on low income family well-being, the economics of higher education and, most recently, local economic development. Recent courses taught include: * Econometrics * The Marketplace * Microbes, Markets, and Food * Labor Economics * The Economics of Race and Gender * Principles of microeconomics Her current research in collaboration with Lawrence Ogbeifun, assistant professor of economics at Hartwick College, https://www.hartwick.edu/people/lawrence-ogbeifun/ investigates barriers that women and racial minorities face in accessing small business loans. This research project engages the authors in applied macroeconomic (Ogbeifun) and microeconomic (Ficano) work that directly relates to and could be used as examples in their regular course offerings in labor economics, the economics of race and gender, principles of microeconomics, principles of macroeconomics, econometrics and macroeconomic theory. Transcript: Is it possible to quantify what is lost both by the entrepreneurs and by the larger society, when access to credit is limited by the intersection of one’s race and gender? Small businesses success is a well-recognized driver of community well-being. But not everyone seeking to secure a small-business loan is viewed equally by lenders--and differential access to credit for minority and women entrepreneurs has the potential to impose significant constraints on local and regional economies. My current research, conducted jointly with co-author Dr. Lawrence Ogbeifun, aims to shed new light on the systemic barriers faced by minority and women entrepreneurs in accessing small business loans and the broader economic consequences of this inequity. Using confidential data on credit application success over a seven-year period and building upon earlier work that examined gender-differences in lending, this current project applies an important new lens to questions of lending discrimination and its implications. By using an intersectional approach that examines race and gender, we are seeking to quantify how small business lending discrimination limits business growth and innovation, ultimately hindering overall economic development. It is our hope that this work will contribute meaningfully to the field of economics and public policy by filling a gap in the literature around the intersection of race, gender, and financial access. Only empirical evidence can shape future lending practices aimed at promoting equity in small business finance—and this topic is relevant to a wide range of stakeholders, including policymakers, financial institutions, and advocacy groups working toward inclusive economic development. This research is directly relevant to our teaching responsibilities in courses on labor economics, race and gender, and macroeconomic policy. The insights gained from this research will enhance the learning experiences of our students, encourage critical thinking, and contribute to academic discourse and hopefully drive new conversations on systemic inequities. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.academicminute.org

    3 min
See All (1k)

About

Astronomy to Zoology www.academicminute.org

Information

  • Creator
    Academic Minute
  • Years Active
    2015 - 2026
  • Episodes
    1k
  • Rating
    Clean
  • Copyright
    © Academic Minute
  • Show Website
    The Academic Minute

More From WAMC Northeast Public Radio

You Might Also Like