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UF team develops AI tool to make genetic research more comprehensive

University of Florida researchers are addressing a critical gap in medical genetic research — ensuring it better represents and benefits people of all backgrounds. Their work, led by Kiley Graim, Ph.D., an assistant professor in the Department of Computer & Information Science & Engineering, focuses on improving human health by addressing "ancestral bias" in genetic data, a problem that arises when most research is based on data from a single ancestral group. This bias limits advancements in precision medicine, Graim said, and leaves large portions of the global population underserved when it comes to disease treatment and prevention. To solve this, the team developed PhyloFrame, a machine-learning tool that uses artificial intelligence to account for ancestral diversity in genetic data. With funding support from the National Institutes of Health, the goal is to improve how diseases are predicted, diagnosed, and treated for everyone, regardless of their ancestry. A paper describing the PhyloFrame method and how it showed marked improvements in precision medicine outcomes was published Monday in Nature Communications. Graim, a member of the UF Health Cancer Center, said her inspiration to focus on ancestral bias in genomic data evolved from a conversation with a doctor who was frustrated by a study's limited relevance to his diverse patient population. This encounter led her to explore how AI could help bridge the gap in genetic research. “If our training data doesn’t match our real-world data, we have ways to deal with that using machine learning. They’re not perfect, but they can do a lot to address the issue.” —Kiley Graim, Ph.D., an assistant professor in the Department of Computer & Information Science & Engineering and a member of the UF Health Cancer Center “I thought to myself, ‘I can fix that problem,’” said Graim, whose research centers around machine learning and precision medicine and who is trained in population genomics. “If our training data doesn’t match our real-world data, we have ways to deal with that using machine learning. They’re not perfect, but they can do a lot to address the issue.” By leveraging data from population genomics database gnomAD, PhyloFrame integrates massive databases of healthy human genomes with the smaller datasets specific to diseases used to train precision medicine models. The models it creates are better equipped to handle diverse genetic backgrounds. For example, it can predict the differences between subtypes of diseases like breast cancer and suggest the best treatment for each patient, regardless of patient ancestry. Processing such massive amounts of data is no small feat. The team uses UF’s HiPerGator, one of the most powerful supercomputers in the country, to analyze genomic information from millions of people. For each person, that means processing 3 billion base pairs of DNA. “I didn’t think it would work as well as it did,” said Graim, noting that her doctoral student, Leslie Smith, contributed significantly to the study. “What started as a small project using a simple model to demonstrate the impact of incorporating population genomics data has evolved into securing funds to develop more sophisticated models and to refine how populations are defined.” What sets PhyloFrame apart is its ability to ensure predictions remain accurate across populations by considering genetic differences linked to ancestry. This is crucial because most current models are built using data that does not fully represent the world’s population. Much of the existing data comes from research hospitals and patients who trust the health care system. This means populations in small towns or those who distrust medical systems are often left out, making it harder to develop treatments that work well for everyone. She also estimated 97% of the sequenced samples are from people of European ancestry, due, largely, to national and state level funding and priorities, but also due to socioeconomic factors that snowball at different levels – insurance impacts whether people get treated, for example, which impacts how likely they are to be sequenced. “Some other countries, notably China and Japan, have recently been trying to close this gap, and so there is more data from these countries than there had been previously but still nothing like the European data," she said. “Poorer populations are generally excluded entirely.” Thus, diversity in training data is essential, Graim said. "We want these models to work for any patient, not just the ones in our studies," she said. “Having diverse training data makes models better for Europeans, too. Having the population genomics data helps prevent models from overfitting, which means that they'll work better for everyone, including Europeans.” Graim believes tools like PhyloFrame will eventually be used in the clinical setting, replacing traditional models to develop treatment plans tailored to individuals based on their genetic makeup. The team’s next steps include refining PhyloFrame and expanding its applications to more diseases. “My dream is to help advance precision medicine through this kind of machine learning method, so people can get diagnosed early and are treated with what works specifically for them and with the fewest side effects,” she said. “Getting the right treatment to the right person at the right time is what we’re striving for.” Graim’s project received funding from the UF College of Medicine Office of Research’s AI2 Datathon grant award, which is designed to help researchers and clinicians harness AI tools to improve human health.

LSU Experts Break Down Artificial Intelligence Boom Behind Holiday Shopping Trends

Consumers are increasingly turning to artificial intelligence tools for holiday shopping—especially Gen Z shoppers, who are using platforms like ChatGPT and social media not only for gift inspiration but also to find the best prices. Andrew Schwarz, professor in the LSU Stephenson Department of Entrepreneurship & Information Systems, and Dan Rice, associate professor and Director of the E. J. Ourso College of Business Behavioral Research Lab, share their insights on this emerging trend. AI is the new front door for search: Schwarz: We’re seeing a fundamental change in how consumers find information. Instead of browsing multiple pages of results, users—especially Gen Z—are skipping to conversational AI for curated answers. That dramatically shortens the shopping journey. For years, companies optimized for SEO to appear on the first page of Google; now they’ll have to think about how their products surface in AI-generated recommendations. This may lead to a new form of “AIO”—AI Information Optimization—where retailers tailor product descriptions, metadata, and partnerships specifically for AI visibility. The companies that adapt early will have a distinct advantage in capturing consumer attention. Rice: This issue of people being satisfied with the AI results (like a summary at the top of the Google results) and then not clicking on any of the paid or organic links leads to a huge increase in what we call “zero click search” (for obvious reasons). For some providers, this is leading to significant drops in web traffic from search results, which can be disconcerting due to the potential loss of leads. However, to Andrew’s point of shortening the journey, it means that the consumers who do come through are much more likely to buy (quickly) because they are “better” leads. This translates to seemingly paradoxical situations for providers: they see drops in click-through rates and visitors/leads, yet revenue increases because the visitors are “better.”  There is a rise in personalized shopping journeys: Schwarz: AI essentially acts as a personal shopper—one that can instantly analyze preferences, budget, personality traits, or past behavior to produce tailored gift lists. This shifts power toward “delegated decision-making,” in which consumers allow AI to narrow their choices. Younger consumers are already comfortable outsourcing this cognitive load. However, as ads enter the picture, these personalized journeys could be shaped by incentives that aren’t always transparent. That creates a new responsibility for platforms to disclose when suggestions are sponsored and for users to develop a more critical lens when interacting with AI-driven recommendations. Rice: This is also a great point. The “tools” marketers use to attract customers are constantly evolving, but this seems in many ways to be the next iteration of the Amazon.com suggestions that you find at the bottom of the product page for something you click on when searching Amazon (“buy all x for $” or “consumers also looked at…,” etc.), based on past histories of search and purchase, etc. One of the main differences is that you can now create virtually limitless ways to compare products, making comparisons less taxing (reducing cognitive load and stress), which may, in some cases, increase the likelihood of purchase. These idiosyncratic comparisons and prompts lead to the truly unique journeys Andrew is discussing. You no longer have to be beholden to a retailer-specified price range. You could choose your own, or instead ask an AI to list the products representing the best “value” based on consumer reviews, perhaps by asking to list the top ten products by cost per star rating, etc.  Advertising is becoming more subtle and conversational: Schwarz: With ads woven directly into AI responses, the traditional boundary between content and advertising blurs. Instead of banner ads, pop-ups, or clearly labeled sponsored posts, recommendations in a conversational thread may feel more like advice than marketing. This has enormous implications for consumer trust. Retailers will likely see higher engagement through these context-aware ad placements, but regulatory scrutiny may also increase as policymakers evaluate how clearly sponsored content is identified. The risk is that advertising becomes invisible—something both platform designers and regulators will need to monitor carefully. Rice: This is definitely true. I was recently exploring an AI-based tool for choosing downhill skis, but the tool was subtly provided by a single ski brand. I’m not sure the distribution of ski brands covered was truly delivering the “best overall fit” for a potential buyer, rather than the best possible ski in that brand. At least in that case, it was somewhat disclosed. It does, however, become an issue if consumers feel misled, but they’d have to notice it first. Still, the advantages are big for retailers, and the numbers don't lie. According to some preliminary Black Friday data, shoppers using an AI assistant were 60% more likely to make a purchase.  Schwarz: This shift is going to reshape multiple layers of the retail ecosystem: Retailers will need to rethink how they show up in AI-driven environments. Traditional SEO, ad bids, and social media strategies won’t be enough. Partnerships with AI platforms may become as important as being carried by major retailers today. Because AI tools can instantly compare prices across dozens of retailers, consumers will become more price-sensitive. Retailers may face increasing pressure to offer competitive pricing or unique value propositions, as AI reduces friction in comparison shopping. Retailers who integrate AI into their own websites—chat-based shopping assistants, personalized gift advisors, automated bundling—will gain an edge. Consumers are increasingly expecting conversational interfaces, and companies that delay will quickly feel outdated. As AI tools influence purchasing decisions, consumers and regulators alike will demand clarity around how recommendations are generated. Retailers will need to navigate this carefully to maintain What I think we are going to see accelerate as we move forward: AI-powered concierge shopping will become mainstream. Within a couple of years, using AI to generate shopping lists, compare prices, and find deals will be as common as using Amazon today. Retailers will create AI-specific marketing strategies. Instead of optimizing for keywords, they’ll optimize for prompts: how consumers might ask for products and how an AI system interprets those requests. More platforms will introduce advertising into AI models. ChatGPT is simply the first mover. Once the revenue potential becomes clear, others will follow with their own ad integrations. Greater scrutiny from policymakers. As conversational advertising grows, transparency rules and labeling requirements will almost certainly. A new era of “conversational commerce.” Buying directly through AI—“ChatGPT, order this for me”—will become increasingly common, merging search, recommendation, and transaction into a single seamless experience. I can speak to this on a personal level.  My college-aged son is interested in college football, and I wanted to get him a streaming subscription to watch the games.  However, the football landscape is fragmented across multiple, expensive platforms. I asked ChatGPT to generate a series of options. Hulu is $100/month for Live TV, but ChatGPT recommended a combination of ESPN+, Peacock, and Paramount+ for $400/year and identified which conferences would not be covered.  What would have taken me hours only took me a few minutes! Rice: On the other hand, AI isn’t infallible, and it can lead to sub-optimal results, hallucinations, and questionable recommendations. From my recent ski shopping experience, I encountered several pitfalls. First, for very specific questions about a specific model, I sometimes received answers for a different ski model in the same brand, or for a different ski altogether, which was not particularly helpful, or specs I knew were just plain wrong. Secondly, regarding Andrew’s point about the conversational tone, I asked questions intended to push the limits of what could be considered reliable. For example, I asked the AI to describe the difference in “feel” of the ski for the skier among several models and brands. While the AI gave very detailed and plausible comparisons that were very much like an in-store discussion with a salesperson or area expert, I’m not sure I fully trust when an AI tells me that you can really feel the power of a ski push you out of a turn, this ski has great edge hold, etc. It sounds great, but where is the AI sourcing this information? I’m not convinced it’s fully accurate. It also seems we’re starting to see Google shift toward a more AI-centric approach (e.g., AI summaries and full AI Mode). At the same time, we’re also starting to see AI migrate closer to Google as people use it for product-related chats, and companies like Amazon and Walmart have developed their own AI that is specifically focused on the consumer experience. I can’t imagine it will be long before companies like OpenAI and their competitors start “selling influence” in AI discussions to monetize the influence their engines will have.  

AI Can’t Replace Therapists – But It Can Help Them

For a young adult who is lonely or just needs someone to talk to, an artificial intelligence chatbot can feel like a nonjudgmental best friend, offering encouragement before an interview or consolation after a breakup. AI’s advice seems sincere, thoughtful and even empathic – in short, very human. But when a vulnerable person alludes to thoughts of suicide, AI is not the answer. Not by itself, at least. Recent stories have documented the heartbreak of people dying by suicide after seeking help from chatbots rather than fellow humans. In this way, the ethos of the digital world – sometimes characterized as “move fast and break things” – clashes with the health practitioners’ oath to “first, do no harm.” When humans are being harmed, things must change. As a researcher and licensed therapist with a background in computer science, I am interested in the intersection between technology and mental health, and I understand the technological foundations of AI. When I directed a counseling clinic, I sat with people in their most vulnerable moments. These experiences prompt me to consider the rise of therapy chatbots through both a technical and clinical lens. AI, no matter how advanced, lacks the morality, responsibility and duty of care that humans carry. When someone has suicidal thoughts, they need human professionals to help. With years of training before we are licensed, we have specific ethical protocols to follow when a person reveals thoughts of suicide. Read the full article from US News & World Report here

Confused About the Economy? We can Help!

In a recent piece on CNN ... eyebrows were raised about the state of America's economy. The article describes a growing disconnect in the U.S. economy: strong growth and rising corporate productivity driven by artificial intelligence, but weak job creation. Companies are investing heavily in AI and automation, which allows them to increase output without hiring more workers — especially in roles involving routine or administrative tasks. This dynamic has produced what economists call a “jobless boom”: profits and productivity rise, while new jobs lag behind. The Federal Reserve is increasingly concerned because this trend creates risks for both workers and the broader economy. Lower-skilled and entry-level workers face displacement, wage inequality may widen, and traditional unemployment metrics may understate how difficult it has become for people to re-enter the job market. Policymakers now face a harder balancing act as AI reshapes labor demand, raising questions about retraining, social supports, and how to manage an economy where growth no longer guarantees broad-based job creation. If you're confused - don't feel bad. In fact, if you're a journalist covering this topic - let us help. Dr. Jared Pincin is a nationally respected expert on economic issues facing the United States of America.  He's available to speak with media - simply click on his icon now to arrange an interview today.

One AI-based advancement at a time, UF leaders are transforming the sports industry

As emerging technologies like AI reshape sport industries and professional demands evolve, it is essential for students to graduate with the expertise to thrive in their future careers. To ensure that these students are set up for success, the UF College of Health & Human Performance has launched a new sports analytics program. Led by Scott Nestler, Ph.D., CAP, PStat, a professor of practice in the Department of Sport Management and a national analytics and data science expert, the program ties back to the UF & Sport Collaborative – a five-part project intended to elevate UF’s presence on the global stage in sports performance, healthcare and communication. “Tools and insights that previously were only available to professional sports teams are now coming to the college level, and it makes sense for universities to begin using these data, technologies and new analytic methods,” Nestler said. The sports analytics program fosters collaboration between academic units, such as the Warrington College of Business and the University Athletic Association, helping bridge the gap between sport research and innovation and empowering students to address real-world challenges through data and AI. For example, the program offers opportunities to leverage technology and analytics for strategic decision making in player acquisition, team formation and in-game decisions. Beyond performance metrics, the program also explores marketing strategies and revenue analytics, providing a well-rounded understanding of the field. “When you have enough data and a large enough sample of individuals, AI can help make predictions that otherwise would take prohibitively longer for a human to accomplish with traditional methods,” said Garrett Beatty, Ph.D., the assistant dean for innovation and entrepreneurship and an instructional associate professor in the College of Health & Human Performance’s Department of Applied Physiology and Kinesiology. “Because those data volumes are getting so large, AI models, machine learning, deep learning and other strategies can be leveraged to make sense and glean insights from sport and human performance data in ways that have never been done before.” The program seeks to offer several educational opportunities, such as individual courses, certificate programs and potentially a full degree program. In the long term, Nestler envisions the program evolving into a center or institute, beginning with establishing a research lab in the spring. Additionally, the program will leverage the university’s supercomputer, HiPerGator, to analyze larger data sets and use newer predictive modeling machine learning algorithms. “As faculty and staff move from working with box score and play-by-play data to using tracking data, which contains coordinates of all players and the ball on the field or court tens of times per second, the size of data files in sports analytics has grown tremendously,” Nestler said. “HiPerGator, with its large storage capacity and multiple central processing units/graphic processing units, is ideal for using in sports analytics work in 2025.” Nestler also aims to increase student involvement by enhancing UF’s Sport Analytics Club and hiring research assistants to work on projects for the University Athletic Association. “We need to take a broader view of what AI is and realize that it incorporates a lot of what we’ve been calling data science and analytics in the form of machine learning models, which came more out of statistics and computer science. Those are types of AI and those that I think will largely continue to be used in the coming years within the sports space,” Nestler said. “Also, we’re continuing to see growth in the number of people interested in working in this space, and I don’t foresee that changing. Fortunately, we are also seeing the number of opportunities available to those with the appropriate skills increase as well.”

Why Are Canadian Banks Not Protecting Seniors? The $40 Billion Dollar Question

After an 89-year-old Victoria man lost $1.7 million to phone scammers despite bank red flags, retirement expert and authour, Susan Pimento, exposes a critical protection gap: while U.S. banks like Bank of America offer "Trusted Contacts" (designated people banks call to verify suspicious transactions) for all accounts, Canadian banks restrict this safeguard to investment accounts only—leaving everyday banking vulnerable where most fraud occurs. In Canada, senior fraud is vastly underreported (RCMP estimates only 5-10% surface), and banks are treating this as a cost issue rather than a moral crisis.  Susan Pimento is available for interviews to discuss practical solutions, industry insights from her decades of work within financial institutions, and why Canadian banks are failing to implement a simple fix that could save seniors' life savings. Connect with her directly through ExpertFile to schedule TV, radio, podcast, or print interviews.  As I was polishing this post for Canadian Financial Literacy Month, another senior fraud story flashed across my screen. This one stopped me cold. According to this CBC story, an 89-year-old man in Victoria, B.C., was tricked into handing over nearly $1.7 million of his life savings in a months-long phone scam. The caller claimed to be from the fraud department at CIBC and said he was helping with a national money-laundering case. (Spoiler: he wasn't.) Despite red flags and staff awareness, the bank still allowed large in-person withdrawals. He was told to buy gold bars — yes, actual gold bars — with drafts of up to $395,000, which couriers then collected like some twisted Uber Eats retirement fraud. Every week in Canada, we see another heartbreaking headline: a senior sends thousands, sometimes millions, to a scammer pretending to be their grandchild, the CRA, or — the ultimate irony — their bank.  These scams targeting seniors don't require fancy hacking. They rely on fear, isolation, and misplaced trust. Once the money's gone, it's gone—no refund policy. And here's the kicker: what we're reading about is just the tip of the iceberg. For seniors, fraud now ranks as the top crime, and most fraud goes unreported—especially in this demographic. In a previous post, I showed how the data suggests the real figures could be 10 to 20 times higher than what's officially reported.  The RCMP estimates that only 5-10% of fraud victims come forward. Many victims never speak out due to embarrassment, fear, or confusion. Translation? For every story that makes the news, countless others suffer in silence. How The Banking Industry Can Actually Fight Fraud I've worked within financial institutions for decades. Let's just say I understand how the process works. Banks have billion-dollar tech stacks, layers of compliance, and advanced fraud detection systems that can flag a suspicious $47 transaction in milliseconds. But the solution for this type of fraud isn't a multimillion-dollar algorithm or a new "AI-powered fraud prevention dashboard." Instead, it's a human-based approach called a Trusted Contact. What's a "Trusted Contact," Anyway? It's not an app, a chatbot, or some new gadget that requires a firmware update every Thursday. It's a person.  Someone you trust — a family member, attorney, accountant, or another third-party who you believe would respect your privacy and know how to handle the responsibility of communicating with your bank in your best interests if something suspicious occurs. They don't access your money or view your accounts. They can't see that you spent $47 at the LCBO last Tuesday (Your secret is safe). They're simply your human safety net — a fraud wing person, if you will. The Origins of the Trusted Contact The concept began in the U.S. in 2018, when FINRA mandated investment firms to request a Trusted Contact Person. Canada followed in 2022, when the Canadian Securities Administrators introduced similar guidance for investment accounts. What things can be discussed with a trusted contact? As its name implies, a Trusted Contact is a designated person who is inherently trusted by the individual (and has no authority to transact business on a client’s account), so there is little to no danger that any reasonable disclosure would violate a client’s trust or give rise to any material issue.” What Canadian Banks Are Doing...And Not Doing Here's the good news. If you invest through Wealthsimple, RBC Direct Investing, TD Direct, or BMO InvestorLine, you can already designate a Trusted Contact. But here's where it gets ridiculous: RBC Direct might have that security feature — but your regular RBC chequing account? Not so much. That protection vanishes the moment Mom or Dad logs into their everyday banking. And that's where most fraud actually occurs. It's like installing a state-of-the-art security system on your front door but leaving the back door wide open with a welcome mat that says "Scammers Enter Here!" Fraud in Canada for Banks is Still a Budget Item: Not a Moral Crisis Here's the uncomfortable truth: For banks, fraud is considered a "cost of doing business." And since most of those losses are borne by customers, not the bank, there isn't much urgency to innovate.  The Big Five earned over $40 billion in total last year. They have the means to care. They're not particularly motivated to actually do so. The Big Opportunity for Banks: Add a Little Humanity to the System Banks like to boast about their AI, blockchain, and next-gen fraud analytics. But most scams don't occur because of breached firewalls — they happen because of breached hearts. A Trusted Contact provides an additional simple, low-tech layer: human verification. Picture this: The bank spots an unusual transaction — a large new payee, an international wire transfer, or a sudden gold-bar purchase (it happens). Instead of sending another automated text alert, the system could ask: "This looks unusual. Would you like us to confirm with your Trusted Contact before proceeding?" or “Just a heads-up: scammers often use urgent or unusual requests. Prefer we run this by your Trusted Contact before we proceed?” That's it. One additional step. One extra set of eyes. One brief conversation could save someone's life savings. This isn't about limiting independence — it's about safeguarding autonomy. Ensuring your decisions are genuinely yours, not the scammer's. Banks could even call it "Senior Protection Mode." I'd sign up tomorrow. Heck, I'd pay extra for it. (Shhh, don't tell them that.) Here's the Proof Trusted Contacts Work: Bank of America Did It In 2022, Bank of America became the first major bank to extend Trusted Contacts beyond investment accounts to everyday banking clients. Customers can now add a trusted person the bank can call if something seems wrong, if they can't reach you, or if staff suspect undue influence. That person can't access your money — they're just the human speed bump before disaster: one simple form, one phone number, and much heartbreak avoided. If Bank of America can do it, why can't ours? Canadian banks already have the tech — and indeed the profits — to make it happen. What's Holding Canada's Banks Back? Cue the usual excuses: "Our legacy systems can't handle that." Sure — some of your code still thinks "Y2K" is an active threat. But if you can build an app that tracks my latte points and sends me notifications about my "spending insights,"  you can add one field for a Trusted Contact. "Privacy laws make it risky." Nope. FINRA and the CSA already provide safe-harbour protections. With consent, banks can legally contact a Trusted Person. Just add a checkbox. You love checkboxes. You make us check dozens of them every time we update our password. "Customers haven't asked for it." They're asking now. Loudly. With megaphones. And pointing at stories like the Victoria gentleman who lost $1.7 million in gold bars. The business case has historically been weak because most fraud losses affect customers, not the bank's balance sheet. But here's the catch: every fraud story damages trust. And in banking, trust is supposed to be the core of the business. For Canadian Banks There's a Competitive Advantage in Caring Rolling out a Trusted Contact feature isn't just good ethics; it's good business. Imagine the marketing campaign: "We don't just protect your password — we protect your peace of mind." Seniors would love this. So would their kids. That's multi-generational loyalty money can't buy. If EQ Bank or any challenger brand wanted a PR home run, this would be it. It's Time to Take Action on Fraud To the Banks: Stop waiting for regulators to force your hand. Lead. Be the first to offer Trusted Contacts for all customers — not just investors. You have the framework, the talent, and the budget. You absolutely do not need another consultant to tell you this is the right thing to do. To Policymakers: The Financial Consumer Agency of Canada should update its Code of Conduct to include a mandatory Trusted Contact option for all customers, safe-harbour rules allowing banks to pause suspicious transactions, and annual public reporting on outcomes. Because sunshine is the best disinfectant, even in banking. To Consumers: Don't wait for policy — be the policy. Ask your bank today if you can add a Trusted Contact. If they say no, ask why not — and post it. Loudly. Talk to your family. Choose your Trusted Person now. Write your MP or MPP and ask why U.S. banks protect seniors better than ours. Remember the $3 ATM Fee Rebellion?  Canadians once revolted over paying $3 to access their own money at ATM's. We later got no-fee accounts, digital challengers, and a whole new generation of more innovative banking.  If we can rally over an ATM fee, surely we can rally to protect our parents and grandparents from losing their life savings. Fraud isn't an inevitable part of aging — it's a solvable problem. And Trusted Contacts are one of the simplest, most human solutions we have. Don't Forget Two-Factor Authentication for the Soul Adding a Trusted Contact won't stop all fraud — let's be clear about that. But it will go a long way toward slowing it down, adding a common-sense pause, and potentially saving even one senior from losing any part of their hard-earned money. It's unfortunately too late for that gentleman and his family in BC, but it's not too late for countless others. This won't crash legacy systems or drain bank profits. It just adds a little humanity back into banking — right where it belongs. Because the best kind of security isn't just two-factor authentication. It's two people who care. And if we don't care about protecting our elders, who exactly do we care about? Sue Don’t Retire…Re-Wire! Want to become an expert on serving the senior demographic? Just message me to be notified about the next opportunity to become a "Certified Equity Advocate" — mastering solution-based advising that transforms how you work with Canada's fastest-growing client segment.

Georgia Southern University computer science professor awarded NSF grant to advance protein imaging research

Proteins, often called the building blocks of life, play a central role in drug development. When scientists develop new treatments, they must understand how drugs interact with proteins involved in disease mechanisms and with proteins in the human body that influence drug response. Scientists commonly use cryo-electron microscopy (cryo-EM) 3D imaging data to study proteins. While recent advances have enabled higher-resolution images that are easier to analyze, medium-resolution images—which are more difficult to interpret—are still the most common for larger protein complexes. Salim Sazzed, Ph.D., an assistant professor in the computer science department of Georgia Southern University’s Allen E. Paulson College of Engineering and Computing, has been awarded a two-year National Science Foundation grant of about $175,000 to lead a groundbreaking project to develop novel Artificial Intelligence (AI) techniques for determining protein secondary structures from medium-resolution cryo-electron microscopy (cryo-EM) images. Improved modeling from medium-resolution images will help researchers study more proteins efficiently, giving new insights into diseases and potentially guiding the development of new treatments and future drugs. At its core, this research will combine biology and machine learning to study protein structures. The multidisciplinary approach and potential impacts on public health are what most excite Sazzed. “The impetus behind this research is the positive impact on public health and possibly contributing to the biomedical workforce,” he said. “Seeing biology and computer science combine for that kind of impact is incredibly moving.” As the Principal Investigator (PI) for the project, Sazzed will use his expertise in deep learning computer models to focus on a major challenge in structural biology: identifying the two main secondary structures of proteins—the alpha helix and the beta sheet. These structures are critical for a protein’s overall shape and function, but in medium-resolution cryo-EM images they often appear indistinct or lack clear detail, making them particularly difficult to analyze. Sazzed’s research will focus on two main goals. First, he will quantify the variability of alpha helices and beta sheets in medium-resolution images, comparing them to idealized structures. Second, by integrating this structural variability with the image data in a deep learning model, he will aim to generate more precise and accurate representations of protein secondary structures. “When we feed this information into a deep learning model along with the image data, the model should be able to determine protein secondary structures more precisely,” Sazzed elaborated. Sazzed believes students will greatly benefit from this multi-disciplinary approach. In addition to a Ph.D. student, several undergraduate students will be directly engaged in the research. A full-day workshop will also be organized, allowing Georgia Southern students from diverse disciplines to participate. This initiative will build on Georgia Southern’s strong tradition of involving undergraduates in research and will support the University’s recent focus on biomedical and health sciences. “There are many different knowledge areas coming together in this work,” Sazzed said. “It involves computer science, biology, chemistry, and even public health. I look forward to students following the research and exploring these different fields themselves.” Allen E. Paulson College of Engineering & Computing Interim Associate Dean of Research, Masoud Davari, Ph.D., echoes this sentiment and emphasizes its importance to the University’s research profile. “Sazzed’s interdisciplinary research, which bridges the gap between biology and computer science, will foster multidisciplinary research in our college—as it is cutting-edge and potentially groundbreaking in drug development to impact people’s lives nationally and globally,” Davari said. “It’s also well aligned with the college’s strategic research plan—as we make the move to R1 status to be aligned with ‘Soaring to R1,’ which is among the transformational initiatives for the University.” Looking to know more about Georgia Southern University or connect with Salim Sazzed — simply contact Georgia Southern's Director of Communications Jennifer Wise at jwise@georgiasouthern.edu to arrange an interview today.

The University of Florida’s ‘AI Queen’ is using AI technology to help prevent dementia

To help the 50 million people globally who live with dementia, the National Institute on Aging is finding researchers to develop tech-based breakthroughs that target the disease — researchers like the University of Florida’s “AI Queen.” It’s a fitting nickname for Aprinda Indahlastari Queen, Ph.D., who is applying artificial intelligence technology to study transcranial direct current stimulation, or tDCS — a technique that involves placing electrodes on the scalp to deliver a weak electrical current to the brain — as a possible way to prevent dementia. The assistant professor in the UF College of Public Health and Health Professions’ Department of Clinical and Health Psychology is using UF’s supercomputer, HiPerGator, to perform neuroimaging and machine learning analyses to study how anatomical differences may affect tDCS outcomes. “Investigating working memory in patients with mild cognitive impairment offers an opportunity to understand how cognitive processes are disrupted in the early stages of Alzheimer’s disease,” said Queen, whose study — funded by a National Institute on Aging research career development grant — integrates neuroimaging with information on brain structure that is unique to older adults and those with mild cognitive impairment. Refining the treatment with AI Using neuroimaging, Queen captures real-time changes during tDCS to the parts of the brain associated with working memory, which is the type of memory that allows humans to temporarily keep track of small amounts of information. Think of this as a mental “scratchpad.” Her study includes older adults with mild cognitive impairment as well as individuals who are cognitively healthy. In tDCS, a safe, weak electrical current passes through electrodes placed on a person’s head. The stimulation is being used in research and clinical settings for a variety of conditions and has shown partial success as a nonpharmaceutical intervention that can improve cognitive and mental health in older adults. But tDCS results can vary across individuals, and the suspected cause is both simple and complex: Everyone’s head is different. “One potential reason tDCS may not work for some individuals is the variation in head tissue anatomy, including differences in brain structure,” Queen said. “Since electrical stimulation must travel through multiple layers of tissue to reach the brain, and every individual’s anatomy is unique, these differences likely affect outcomes.” To address this further, Queen is using AI. “Artificial intelligence will play a major role in the modeling pipeline, including constructing individualized head models, conducting predictive analyses to identify which participants will respond to the stimulation, and disentangling multiple individual factors that may contribute to these outcomes,” Queen said. An estimated 10 to 20% of adults over age 65 have memory or thinking problems characterized as mild cognitive impairment. Their symptoms are not as severe as Alzheimer’s disease and other dementias, but they may be at increased risk for developing dementia. “The fact that not all individuals with mild cognitive impairment progress to Alzheimer’s disease emphasizes the need to identify effective interventions that can slow the progression to dementia,” Queen said. “This project presents an opportunity to differentiate between multiple types of mild cognitive impairment and investigate how tDCS affects the brain across these subtypes.” An AI visionary Queen, who joined the UF faculty under the university’s AI hiring initiative, is an instructor in the College of Public Health and Health Professions’ undergraduate certificate program in AI and public health and health care, and the co-chair of the college’s AI Workgroup. She is also the assistant director for computing and informatics at the UF Center for Cognitive Aging and Memory Clinical Translational Research and a member of UF’s McKnight Brain Institute. Queen received her Ph.D. training in engineering with a focus on building and running computational models to investigate medical devices. She experienced a career “a-ha” moment as a postdoc, when she was a co-investigator on a large clinical trial that paired brain stimulation with cognitive training to enhance cognition in older adults. “This experience was transformative for me. I had the chance to interact directly with participants, which was both fulfilling and eye-opening. These interactions allowed me to see the immediate, real-world implications of my work and sparked a passion for pursuing aging research,” Queen said. “I realized that, through this type of research, I could have a more direct impact on addressing age-related challenges, which prompted a shift in my career plans.” The new grant will help Queen further improve her understanding of the neurobiology and progression of Alzheimer’s disease and other dementias. “These experiences will ultimately prepare me to become a well-rounded aging investigator, capable of making meaningful contributions to the field of aging research,” Queen said. She also credits her mentors and collaborators — Ronald Cohen, Ph.D.; Adam Woods, Ph.D.; Steven DeKosky, M.D.; Ruogu Fang, Ph.D.; Joseph Gullett, Ph.D.; and Glenn Smith, Ph.D. — with supporting her as an early career scientist. “It really takes a village to get here!” Queen said.

New AI model predicts harmful videos before they go viral

Certain short-form videos on major social media platforms can trigger suicidal thoughts among vulnerable viewers, according to new research led by the University of Delaware's Jiaheng Xie. His team developed an AI model that can predict and flag these videos. In the study, published Nov. 17 in Information Systems Research, UD professor Jiaheng Xie and his co-authors showed how AI can assist with safety by predicting high-risk videos before they go viral by looking through both the content and what people write in the comments. • Distinguish what creators choose to post from what viewers think or feel after watching. • Separate known medical risk factors from emerging social media trends, such as viral heartbreak clips or challenges that may influence teens. • This system is especially novel because it predicts the high-risk videos before they reach large audiences. Xie is available for a Zoom interview to share how the model was developed and how it could potentially change the way platforms such as TikTok moderate.

U.S. News: AI Can’t Replace Therapists – But It Can Help Them

For a young adult who is lonely or just needs someone to talk to, an artificial intelligence chatbot can feel like a nonjudgmental best friend, offering encouragement before an interview or consolation after a breakup. AI’s advice seems sincere, thoughtful and even empathic – in short, very human. But when a vulnerable person alludes to thoughts of suicide, AI is not the answer. Not by itself, at least. Recent stories have documented the heartbreak of people dying by suicide after seeking help from chatbots rather than fellow humans. In this way, the ethos of the digital world – sometimes characterized as “move fast and break things” – clashes with the health practitioners’ oath to “first, do no harm.” When humans are being harmed, things must change. As a researcher and licensed therapist with a background in computer science, I am interested in the intersection between technology and mental health, and I understand the technological foundations of AI. When I directed a counseling clinic, I sat with people in their most vulnerable moments. These experiences prompt me to consider the rise of therapy chatbots through both a technical and clinical lens. AI, no matter how advanced, lacks the morality, responsibility and duty of care that humans carry. When someone has suicidal thoughts, they need human professionals to help. With years of training before we are licensed, we have specific ethical protocols to follow when a person reveals thoughts of suicide. Read the full article here: