Belmont University is a private Christian university in Nashville, Tennessee. Descended from Belmont Women's College, founded in 1890 by schoolteachers Ida Hood and Susan Heron, the institution was incorporated in 1951 as Belmont College. It became Belmont University in 1991. Belmont's current enrollment consists of approximately 8,400 students representing every state and 28 nations. The university served as the host site for the final presidential debate in the 2020 election cycle. Although the university cut its ties with the Tennessee Baptist Convention in 2007, it continues to emphasize a Christian identity.
Interrupting glucagon signaling decreases gluconeogenesis and the fractional extraction of amino acids by liver from blood, resulting in lower glycemia. The resulting hyperaminoacidemia stimulates α cell proliferation and glucagon secretion via a liver/α cell axis. We hypothesized that α cells detect and respond to circulating amino acids’ levels via a unique amino acid transporter repertoire. We found that Slc7a2/SLC7A2 is the most highly expressed cationic amino acid transporter in α cells, with its expression being 3-fold greater in α than β cells in both mouse and human. Employing cell culture, zebrafish, and knockout mouse models, we found that the cationic amino acid arginine and SLC7A2 are required for α cell proliferation in response to interrupted glucagon signaling. Ex vivo and in vivo assessment of islet function in Slc7a2–/– mice showed decreased arginine-stimulated glucagon and insulin secretion. We found that arginine activation of mTOR signaling and induction of the glutamine transporter SLC38A5 was dependent on SLC7A2, showing that the role of both in α cell proliferation is dependent on arginine transport and SLC7A2. Finally, we identified single nucleotide polymorphisms in SLC7A2 associated with HbA1c. Together, these data indicate a central role for SLC7A2 in amino acid–stimulated α cell proliferation and islet hormone secretion.
Peer review is the main mechanism by which the software engineering community assesses the quality of scientific results. However, the rapid growth of paper submissions in software engineering venues has outpaced the availability of qualified reviewers, creating a growing imbalance that risks constraining and negatively impacting the long-term growth of the Software Engineering (SE) research community. Our vision of the Future of the SE research landscape involves a more scalable, inclusive, and resilient peer review process that incorporates additional mechanisms for: 1) attracting and training newcomers to serve as high-quality reviewers, 2) incentivizing more community members to serve as peer reviewers, and 3) cautiously integrating AI tools to support a high-quality review process.
LLM-based automated program repair (APR) agents generate patches to fix software bugs with minimal human intervention. These agents often produce long trajectories of reasoning, tool use, and feedback to produce candidate patches. Final patch outcomes show whether a repair attempt succeeded or failed, but they do not show how the agent reached that outcome, or where the process became repetitive or misaligned with the task. This makes agentic repair failures difficult to diagnose, reproduce, and prevent. To help developers address these challenges, we present TraceView, an interactive tool for labeling and visualizing repair trajectories from APR systems. TraceView organizes raw and pre-labeled agentic runs with Thought, Action, and Result components to support semantic relation labeling and diagnosis, and renders the resulting trajectory as graph views. Furthermore, TraceView provides relation filters, patch outcome summaries, metrics, and node-level evidence panels to help users inspect how reasoning, actions, and feedback connect across the various steps of an agentic repair attempt. We evaluate TraceView with five researchers through a survey-based user study. Participants reported that TraceView made trajectories easier to scan and that its overview-to-detail workflow helped them better understand repair behavior. The TraceView source code is available at https://github.com/SOAR-Lab/agent-traj-visualization. A screencast of TraceView is available at https://youtu.be/9ZCh7Ifj2AQ.
Rich internal representations of musical structure are essential for music understanding tasks such as machine-assisted music co-writing, yet self-supervised approaches for symbolic music representation remain underexplored, particularly those that encode the hierarchical multiscale nature of musical structures. We present MIDI-RAE-JEPA, combining a pitch- and time-shift equivariance objective with LeJEPA and a Swin Transformer V2 encoder to learn such hierarchical representations of symbolic music encoded as piano roll images. The time-shift equivariance objective encourages the model to internalize temporal musical relationships. The encoder is trained purely on self-supervised objectives – including a masked embedding predictor (MEP) – with collapse prevented via SIGReg. A separate decoder trained on the frozen encoder embeddings achieves reconstruction F1 of 0.995, and a flow matching generative model conditioned on those embeddings produces generations that closely match the pitch register and rhythmic density of the conditioning excerpt, while mismatched conditioning yields unrelated but musically plausible output. Learned representations outperform a Haar scattering transform baseline on a downstream emotion classification task, and embedding distances increase monotonically with pitch and time shift magnitude, confirming measurable equivariance. These results suggest that equivariance-based SSL objectives, combined with sufficient fine-level encoder capacity, provide a viable path toward semantically rich, generatively useful representations of symbolic music.
Asymmetry in white matter is believed to give rise to the brain's capacity for specialized processing and is involved in the lateralization of various cognitive processes, such as language and visuo-spatial reasoning. Although studies of white matter asymmetry have been previously documented, they have often been constrained by limited age ranges, sample sizes, or the scope of the tracts and structural features examined. While normative lifespan charts for brain structures are emerging, comprehensive charts detailing white matter asymmetries across numerous pathways and diverse structural measures have been notably absent. This study addresses this gap by leveraging a large-scale dataset of 35,120 typically developing and aging individuals, ranging from 0 to 100 years of age, from 50 primary neuroimaging studies. We generated comprehensive lifespan trajectories for 30 lateralized association and projection white matter tracts, examining six distinct microstructural and macrostructural features of these pathways. Our findings reveal that: (1) asymmetries are widespread across the brain's white matter and are present in all 30 pathways; (2) for a given pathway, the degree and direction of asymmetry differ between features of tissue microstructure and pathway macrostructure; (3) asymmetries vary across and within pathway types (association and projection tracts); and (4) these asymmetries are not static, following unique trajectories across the lifespan, with distinct changes during development, and a general trend of becoming more asymmetric with increasing age (particularly in later adulthood) across pathways. This study represents the most extensive characterization of white matter asymmetry across the lifespan to date, charting how lateralization patterns emerge, mature, and change throughout life. It provides a foundational resource for understanding the principles of white matter organization from early to late life, its relation to functional specialization and inter-individual variability, and offers a key reference for interpreting deviations during healthy development and aging as well as those associated with clinical populations.
