The Claude D. Pepper Older Americans Independence Center (OAIC) at UConn (P30 AG067988; Kuchel, Fortinsky - MPIs) is one of fifteen such centers throughout the United States funded by the National Institute on Aging at NIH. "Pepper Centers" honor Claude D. Pepper, the late Congressman who advocated for senior health and research to enable older adults to maintain or restore their independence. The overarching goal of the OAIC (Pepper) Program is to enhance function and independence in older adults through research. The UConn Pepper Center is pioneering a theme focused on Precision Gerontology, an approach to aging research and the care of older adults that seeks to render interventions enhancing function and independence more effective through an improved understanding of heterogeneity and more precise targeting (https://health.uconn.edu/pepper-center/). A key component of all Pepper Centers is the Research Education Component (REC), which provides financial support, education, and training to Pepper Scholars who Pepper Center leadership identify as showing particular promise as independent investigators. The REC at the UConn Pepper Center, led by Dr. David Steffens and Dr. George Kuchel, includes senior research leaders at UConn and Jackson Lab who could serve as mentors to Pepper Scholars (https://health.uconn.edu/pepper-center/research-and-education-component-rec/). A mentored research project is a primary focus of being a REC Scholar. We are issuing this solicitation to early stage faculty with an interest in aging-related research. Individuals with experience in this area are encouraged to apply, as are those with research backgrounds in non-aging related fields whose research might be enhanced with a new aging focus. Physician scientists and clinicians in other disciplines are especially encouraged to apply. We anticipate funding two new UConn Pepper Scholars through this competition. Funding for two Pepper Scholar positions would begin July 1, 2025. Pepper Scholar awards are typically awarded for one year, with a second year of funding based on evidence of progress and need. Since this award is taking place in year 5 of the current UConn Pepper Center funding cycle, a year 2 Pepper Scholar Award will also be contingent upon the parent grant's successful competitive renewal. The overall goals of the Pepper Center are focused on improving function and independence among older adults. As such the following criteria for Pepper Scholar Selection were developed to be consistent with these goals: • Trainee potential and commitment to an academic career • Demonstrated commitment to aging research • Alignment of proposed training and work with the broader mission of the NIA OAIC Pepper Center Program and the focus on UConn Pepper Center on Precision Gerontology • Availability and commitment of suitable mentorship • Ability to benefit from OAIC resources As an initial step prior to submitting a full Pepper Scholar application, we are requesting that potential candidates submit a Letter of Intent (LOI), due by 5 PM on January 17, 2025. The LOI should be one single-spaced page consisting of two paragraphs. The first paragraph should highlight aspects of the candidate's educational, training, and research background that are most relevant to the Pepper Scholar program, followed by a statement of how the candidate will benefit from the Pepper Scholars program. The second paragraph should include a brief description of the research project that will be supported by the Pepper Center REC. In addition to the LOI, candidates should submit either an NIH Biosketch (preferred) or a CV. The LOI and Biosketch/CV should be addressed to Dr. David Steffens, REC Core Leader and Dr. George Kuchel, REC Core Co-Leader, and emailed to Ms. Laura Masi at masi@uchc.edu Selected candidates will then be notified by January 31, 2025 and invited to submit a full application for the Pepper Scholar Program for a March 28, 2025 deadline.

Our latest exhibit in Celeste LeWitt Gallery features "Luminous Pastels" by Jane Penfield and "Reflective Moments" by Paul R. Berger.(Note new date for reception.)

A hybrid course where participants will complete 7 modules that include both virtual and in-person meetings . It is designed to develop and strengthen the business and technical skills that many beginning farmers with 0 - 3 years of experience, but is for anyone that feels like they could benefit from the course. Participate in this learning experience with farmer peers and develop new networks in a safe environment to foster informal interactions, knowledge sharing, and relationship building. It is never too early to acquire some of the fundamental skills and habits in farm business management, such as How to Write a Business Plan, Statements for Financial Management, Grant Opportunities, Marketing/Branding, Tax Filing and more. Online Sessions: January 2nd, 9th, 16th, 23rd 12:00pm-1:30pm In Person Sessions: January 10th and 24th, 9:00am-3:30pm at the Hartford County Extension Center

Submit your nomination today!https://outreach.engagement.uconn.edu/awards/ (https://outreach.engagement.uconn.edu/awards/)

Participate with farmer peers in a course designed to develop and strengthen the business and technical skills for beginning farmers with 0 - 3 years of experience.

Urology Grand Rounds

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).

It's January and you might be redesigning your spring course. Are you curious about how to use tools like CoPilot or ChatGPT to support your teaching this semester? Some would argue that GenAI can free up course prep time that you can instead spend on enhancing feedback to students. Ideas and resources for using GenAI to edit assignment prompts for clarity, create case studies, generate problem sets, develop remedial modules, and make quizzes, etc., will be shared. In this interactive session, participants are encouraged to share examples, solicit feedback, or ask questions of other attendees. This session is suitable for participants with minimal experience using generative AI but will be most useful if you know how to access either Microsoft CoPilot through your UConn Microsoft 365 login, or ChatGPT (both the free "mini" and paid versions will be demonstrated). Register - https://fins.uconn.edu/secure_inst/workshops/workshop_view.php?ser=3336 (https://fins.uconn.edu/secure_inst/workshops/workshop_view.php?ser=3336)

Forest fires play a critical role in shaping ecosystems but also pose severe threats to biodiversity, carbon dynamics, and human safety. To address the current challenges, this research focuses on monitoring, characterizing, and predicting burn severity using remote sensing and a machine learning-based data-driven approach. We first demonstrated the feasibility of predicting burn severity using environmental variables through a case study in New South Wales, Australia. By integrating vegetation-specific thresholds, drought indices, and fire weather conditions into machine learning models, we achieved high prediction accuracy of annual burn severity. This foundational work highlighted the potential for scaling the predictive framework globally. Recognizing the lack of a high-resolution global forest burn severity dataset, we developed a 30-m resolution Global Forest Burn Severity (GFBS) dataset using Landsat imagery from 2003 to 2016. This dataset, validated against regional and global benchmarks, provides unprecedented detail in capturing burn severity patterns and ecological impacts. Expanding the dataset to include data through 2023, allowed us to analyze burn severity trends across global ecoregions using the Mann-Kendall test and Sen's slope estimator. Results revealed significant increases in burn severity in tropical and subtropical regions and decreases in boreal zones. For the ecoregions exhibiting significant trends, we developed predictive models using the XGBoost algorithm, incorporating 14 climate variables from the TerraClimate dataset. These models achieved high predictive performance and identified key drivers of burn severity including vegetation water stress and atmospheric dynamics. SHAP (Shapley Additive Explanations) analysis further revealed region-specific factor importance, enabling a detailed understanding of wildfire dynamics. The contributions of this research are threefold: (1) a robust framework for predicting burn severity at local to global scales; (2) the development of a comprehensive, high-resolution dataset of burn severity; and (3) insights into the drivers of wildfire severity, providing a basis for future scenario projections under climate change. By integrating trend analysis, predictive modeling, and climate-driven applications, this study offers critical tools for advancing fire management, enhancing resilience, and mitigating the impacts of wildfires on global carbon cycles and biodiversity.

Forest fires play a critical role in shaping ecosystems but also pose severe threats to biodiversity, carbon dynamics, and human safety. To address the current challenges, this research focuses on monitoring, characterizing, and predicting burn severity using remote sensing and a machine learning-based data-driven approach. We first demonstrated the feasibility of predicting burn severity using environmental variables through a case study in New South Wales, Australia. By integrating vegetation-specific thresholds, drought indices, and fire weather conditions into machine learning models, we achieved high prediction accuracy of annual burn severity. This foundational work highlighted the potential for scaling the predictive framework globally. Recognizing the lack of a high-resolution global forest burn severity dataset, we developed a 30-m resolution Global Forest Burn Severity (GFBS) dataset using Landsat imagery from 2003 to 2016. This dataset, validated against regional and global benchmarks, provides unprecedented detail in capturing burn severity patterns and ecological impacts. Expanding the dataset to include data through 2023, allowed us to analyze burn severity trends across global ecoregions using the Mann-Kendall test and Sen's slope estimator. Results revealed significant increases in burn severity in tropical and subtropical regions and decreases in boreal zones. For the ecoregions exhibiting significant trends, we developed predictive models using the XGBoost algorithm, incorporating 14 climate variables from the TerraClimate dataset. These models achieved high predictive performance and identified key drivers of burn severity including vegetation water stress and atmospheric dynamics. SHAP (Shapley Additive Explanations) analysis further revealed region-specific factor importance, enabling a detailed understanding of wildfire dynamics. The contributions of this research are threefold: (1) a robust framework for predicting burn severity at local to global scales; (2) the development of a comprehensive, high-resolution dataset of burn severity; and (3) insights into the drivers of wildfire severity, providing a basis for future scenario projections under climate change. By integrating trend analysis, predictive modeling, and climate-driven applications, this study offers critical tools for advancing fire management, enhancing resilience, and mitigating the impacts of wildfires on global carbon cycles and biodiversity.

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).