Students who may benefit from attending a Let's Talk: Mental Health Office Hours session include:Students who want help connecting to resources but are unsure where to begin Students who are looking for advice on a non-clinical issue Students who are unsure about therapy and are curious about what it is like to talk to a therapist Students who may have concerns about the mental health of a friend and seek advice on how to support their friend If a student is not an imminent risk, and is refusing your support in contacting our office, you may also consider contacting the UConn Student CARE Team (https://studentcareteam.uconn.edu/). This session is held by Chelsea Morales, Psy.D (https://studenthealth.uconn.edu/person/chelsea-morales/)

Title: "Binding of Fusobacterium nucleatum autotransporter adhesin CbpF to human CEACAM1 and CEACAM5: A Velcro model for bacterium adhesion" Information Link: https://www.pnas.org/doi/10.1073/pnas.2516574122 (https://www.pnas.org/doi/10.1073/pnas.2516574122)

Stop by South Campus to connect with Student Health and Wellness's Sexperts & chat about sex and relationships! Sexpert Peer Health Educator Peer Support Drop-In Hours are a free service offered on the UConn Storrs campus. Peer Support Drop-In Hours are a great option for students who have questions about sex and sexual health, are looking for a non-judgmental, laid-back environment to discuss a sex related concern or issue, or are interested in improving their sexual health and personal well-being. The Sexperts are trained to provide education, support, and connection to resources on and off-campus on a wide variety of topics pertaining to sex, sexual health, and relationships.  Fall 2025 Drop In Hours: September 15th – December 5thMonday: 12pm-4pm Tuesday: 9am-6:30pm Wednesday: 11:15am-6pm Thursday: 11am-5:30pm Friday: 10:30am-5:30pm   Sexperts (and supervising staff) are designated confidential employees under UConn's Title IX Reporting Obligations. Peer support sessions are for educational and support purposes only. Peer support visits are not on-call or emergency services, and are not for individualized medical advice, nor are they counseling or therapy. If you can't make the times listed, or would prefer to schedule an appointment with a staff sex educator, please reach out to Program Manager for Sexual Health and Peer Education Initiatives, Cassy Setzler, at cassy@uconn.edu (mailto:cassy@uconn.edu) For more information, visit: studenthealth.uconn.edu/sexperts (https://studenthealth.uconn.edu/sexperts) or email cassy@uconn.edu (mailto:cassy@uconn.edu)

In recognition of Veterans Day, join us for a virtual presentation featuring Emily Lugo, Outreach Coordinator, and Rebekah Mizener, Veteran Services Coordinator, from Veterans and Military Programs at UConn. This session will highlight the university's ongoing commitment to supporting veterans, service members, and military-affiliated students.The Office of Veterans Affairs and Military Programs fosters a seamless, inclusive experience for military-affiliated students, their families, and alumni across all UConn campuses, uniting comprehensive services and support under one flagship university to ensure every student can thrive. Through community engagement, interdepartmental collaboration, and external partnerships, the office provides an individualized approach that strengthens connection and belonging within the UConn community.This event is part of a virtual workshop series highlighting programs and initiatives that foster collaboration and engagement across the university.

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

AbstractA unified framework of data-efficient and generalizable computer vision models is presented for scientific and industrial imaging, connecting various domains such as additive manufacturing, operando microscopy, and biomedical analysis. Extracting meaningful information from complex visual data is challenging due to limited annotations, heterogeneous imaging domains, fine-scale ambiguities, and inherently noisy conditions. To tackle these challenges, we introduce the concept of using paired images as a foundational approach for learning, reframing a wide range of imaging problems, including segmentation, defect detection, and dynamic analysis, as unified change detection tasks. This approach enables robust, data-efficient solutions that can be adapted to new challenges across diverse industrial and scientific imaging fields.The first problem focuses on domain adaptation for additive manufacturing vision tasks. In binder-jet 3D printing, traditional defect detection methods rely on rigid camera setups and handcrafted thresholds. We develop a Semi-Siamese neural network that directly compares a reference schematic to a camera image, allowing for pixel-level defect localization that remains effective under varying illumination and viewpoints without the need to predefine defect types.The second problem we address is the automated analysis of dynamic microscopy data. In operando environmental transmission electron microscopy, accurately segmenting nanoscale features is crucial for quantifying reaction kinetics. However, this process is often complicated by limited annotations and visually ambiguous structures. We present a change-detection-based framework that compares paired frames to capture subtle temporal and structural variations while simultaneously segmenting related regions of interest. This method transforms traditional in-situ imaging into spatially resolved operando characterization, allowing for the automated tracking of nanoscale material evolution and creating new opportunities for data-driven studies of reaction mechanisms and catalyst regeneration.The third problem considers label-efficient learning for biomedical imaging. Medical datasets tend to be small and heterogeneous, which limits the performance of conventional deep learning models. We propose a multi-fidelity framework that automatically generates low-fidelity versions of high-resolution images and trains a Semi-Siamese network to learn from the comparisons between different fidelity levels. This approach enhances the delineation of morphological boundaries while reducing reliance on extensive annotations.Together, these contributions create a cohesive framework that integrates change detection, domain adaptation, and multi-fidelity modeling for small-data, cross-domain scientific imaging. The resulting methods advance the objective of achieving autonomous, data-efficient visual understanding in complex experimental systems, paving the way toward self-driving laboratories and intelligent imaging platforms.