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DTSTART;TZID=America/Los_Angeles:20251211T090000
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DTSTAMP:20260417T063114
CREATED:20251202T162054Z
LAST-MODIFIED:20251209T161343Z
UID:10005717-1765443600-1765450800@events.ucsc.edu
SUMMARY:Tran\, L. (BMEB) -  Polysome Shadowing: A Long-Read Sequencing Approach to Study Translation
DESCRIPTION:Translation is a central and highly regulated step of gene expression\, yet there are few quantitative\, high-throughput tools to study translation. Existing methods such as sucrose gradients provide only bulk ribosome counts\, while Ribo-Seq offers positional information in the genome but destroys long-range structure and transcript expression information. Because of these limitations\, many fundamental questions about mRNA translation into protein remain difficult to assay. In this proposal\, I outline my plans to develop a novel technology\, deemed Polysome Shadowing\, that covalently marks ribosome-unprotected regions of RNA with hyperactive base editors. Because ribosomes protect ~21–30 nt regions of mRNAs\, ribosome “shadows” appear as tracts of unedited bases in long-read sequencing. In Aim 1\, I will identify ribosome shadows on single molecules by increasing editing efficiency through optimization of dual cytosine and adenosine base editors and statistical modeling. In Aim 2\, I will maximize the accuracy of information recovered from highly-edited RNAs by developing a multipass library preparation protocol to generate high-confidence reads. In Aim 3\, I will apply the tools I have already developed to examine previously difficult-to-assay paradigms of translational control in the form of viral frameshifting mechanisms. Together\, completion of these aims will build an information-rich sequencing technology capable of positioning ribosomes on intact mRNAs while preserving long-range information and establish feasibility to study nascent paradigms. \nHost: Liam Tran\, Ph.D. Student\, Biomolecular Engineering and Bioinformatics  \nAdvisor: Joshua Arribere 
URL:https://events.ucsc.edu/event/tran-l-bmeb-polysome-shadowing-a-long-read-sequencing-approach-to-study-translation/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20251211T120000
DTEND;TZID=America/Los_Angeles:20251211T140000
DTSTAMP:20260417T063114
CREATED:20251209T224244Z
LAST-MODIFIED:20251209T224244Z
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SUMMARY:Chambers\, K. (BMEB) - Using Genomics and Artificial Intelligence to improve prognosis for osteosarcoma patients
DESCRIPTION:Transcriptomic profiling has been transformative in pediatric oncology. Pediatric cancers arise from disrupted developmental programs. Their impaired transcriptional states reflect cell lineage infidelity\, aberrant differentiation\, and immune-microenvironment interactions distinct from those of adult tumors(Gröbner et al.\, 2018; X. Ma et al.\, 2018). Within the osteosarcoma (OS) landscape\, despite being the most common bone tumor of childhood\, it remains one of the least genomically characterized pediatric cancers. Advancements in survival for localized disease\, outcomes for metastatic or recurrent OS have remained stagnant for decades. Transcriptomics characterization of OS has facilitated the exposure of the unique chromothripsis patterns associated with the disease (Sayles et al.\, 2019; Schott et al.\, 2023). Largely\, progress in OS genomics is still limited by the lack of harmonized\, cross-study datasets accessible to researchers. I detail my contributions to OS research\, beginning with the curation of the largest publicly available and harmonized RNA-sequencing osteosarcoma dataset (Chapter 2). A continuous part of my research involved the systematic democratization\, aggregation\, harmonization\, and open sharing of pediatric cancer transcriptomic datasets within the Treehouse Childhood Cancer Initiative (Beale et al.\, 2025). This dataset provided a foundation for the analyses and discoveries presented in this dissertation. I utilize the multi-cohort and transcriptomic multi-omic public OS dataset to discover and define biologically meaningful subtypes that may explain differences in progression and treatment response (Chapter 3). Finally\, I expand these advanced computational approaches into the realm of diagnostic pathology by evaluating strategies for integrating generative AI into rare cancer classification. I leverage both general and domain-specific diffusion models alongside GPT-4o–generated pathology prompts to guide histologic image synthesis (Chapter 4). In summary\, my work advances transcriptional subtyping in OS by leveraging transcriptomic data to identify molecular subtypes of OS that could inform treatment strategies. \nHost: Krizia Chambers\, Ph.D. Candidate\, Biomolecular Engineering & Bioinformatics  \nAdvisor: Olena Vaske \nZoom- https://ucsc.zoom.us/j/93569812001?pwd=RWBuZUdQq2Yo1K4kQ75WRmP0uKjYAH.1&jst=3 \nPasscode- 915392
URL:https://events.ucsc.edu/event/chambers-k-bmeb-using-genomics-and-artificial-intelligence-to-improve-prognosis-for-osteosarcoma-patients/
LOCATION:
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251211T130000
DTEND;TZID=America/Los_Angeles:20251211T150000
DTSTAMP:20260417T063114
CREATED:20251202T232256Z
LAST-MODIFIED:20251202T232256Z
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SUMMARY:Laffan\, N. (CM) - Digital Memory Tools and Their Impact On Collective Remembering
DESCRIPTION:Today\, both individual and collective memories are increasingly mediated by digital platforms. Both are fundamentally enmeshed in platform ecosystems that orient around commercial imperatives very much at odds with community cohesion. The digital archive where our mediated memories are stored does not merely store information but actively inscribes it\, often privileging narratives aligned with commercial incentives rather than community cohesion. This invisibility is a problem: as we offload our personal memories onto commercial tools\, we unwittingly subject our shared past to algorithmic curation and “algo-time\,” which raises serious questions about how the use of our personal devices is quietly restructuring the way societies remember. \nDuring this presentation\, I will propose a three-pronged method of investigating and engaging in this conceptual space. All three prongs revolve around a shared question : how do the technologies that extend our personal memories affect what we remember collectively? The research first establishes a conceptual ecology around the question by tracing the lifecycle of a single image from individual capture to platform archive. Second\, it employs Research through Design (RtD) and speculative design methods to prototype tools explicitly built for collective remembrance rather than commercial extraction. Finally\, it utilizes artistic practice to “diffract” these concepts\, creating interactive installations that expose the distortions and contradictions inherent in digital memory. Together\, these projects aim to make visible the hidden dynamics that shape the memories we construct together. \nHost: Nate Laffan\, Ph.D. Student\, Computational Media  \nAdvisor: Nathan Altice  \nZoom- https://ucsc.zoom.us/j/93762016105?pwd=RBXDHnuleAECZdVghEaAz9L4KK4p1d.1 \nPasscode- 668969
URL:https://events.ucsc.edu/event/laffan-n-cm-digital-memory-tools-and-their-impact-on-collective-remembering/
LOCATION:
CATEGORIES:Ph.D. Presentations
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