Cells' ability to efficiently replicate their genomes is essential for regulating chromosomal division and maintaining chromosome integrity.  Defects in any of these cellular processes may cause genomic instability, potentially leading to cancer.  The Chaos3 allele in the yeast Saccharomyces cerevisiae is a single base pair change causing an amino acid substitution in the Mcm4 protein.  Mcm4, a component of the replicative helicase, is recruited to replication origins to unwind double stranded DNA and initiate replication.  Chaos3 is in a region of MCM4 that is highly conserved across eukaryotes; while mutations in conserved regions are generally non-viable, Chaos3 is a viable allele that causes genomic instability, leading to elevated cancer rates in mice.  In S. cerevisiae, Chaos3 decreases early firing of the autonomously replicating sequences (ARS) where DNA replication begins.  Chaos3 does not affect all early firing ARSs in the genome; rather, a large proportion of origins near centromeres, thereby delaying replication of those centromeres.  Essential for chromosome segregation, the centromere is the location where spindle fibers attach to pull apart sister chromatids during cell division.  I hypothesize that this delay in centromere replication results in chromosomal instability, including the loss of a chromosome.  I am using CRISPR guided cutting directed by a customizable guide RNA to replace centromeric adjacent ARS510, that has decreased firing levels in Chaos3, with unaffected, early firing ARS305, to see if firing levels in the mutants are affected based on ARS chromosome location (i.e., proximity to a centromere) or ARS sequence.  If replacing ARS510 with ARS305 restores early origin firing in this region this will confirm the Chaos3 mutation affects specific ARS sequences rather than ARS location on the chromosome.  Furthermore, if centromere replication delays are the cause of genomic instability in Chaos3, this ARS replacement should rescue chromosome loss.

Approaches to learning new languages and cultures, among other subjects, are ever-changing alongside advancements in technology. In this study, I examined the informal self-regulated English as a foreign language (L2) and intercultural learning of a native Japanese (L1) speaker through their interactions with social media platforms such as YouTube, Discord, and X (formerly known as Twitter). In an online semi-structured retrospective interview with "Yuki," a Japanese woman in her thirties, I queried three areas of informal online language and intercultural learning: 1) her language learning background and the various media she interacts with; 2) her perspective and attitude towards self-regulated learning, her motivations, and how this compares to formal learning; and 3) how her connections to multiple communities on various platforms have influenced her learning and outlook on life. This interview was conducted in Japanese, to allow Yuki to describe her thoughts and experiences as clearly as possible. Findings show the strong impact of online communities on Yuki's motivations and objectives. The global community she encountered offered plentiful opportunities to expand her outlook on life, from her views on pronunciation to the importance of identities. Through the internet, Yuki found others with similar learning goals and began hosting weekly online book club sessions, where individuals of varying English levels are able to develop skills in collaboration with one another despite never having met in person. Along with the possibility for self-directed informal learning, other forms of online content, such as podcasts, allow Yuki to freely teach and challenge herself to interact with everyday spoken English, such as slang and diverse dialects. Although this study only consists of one participant, findings show the tremendous developmental potential of online spaces for language learners. 

In the rapidly evolving landscape of remote work, the challenges associated with recalling important information from meetings and missing meetings have increased. One potential solution is to use large language models (LLMs) to summarize meetings to help participants catch up after meetings are over. To have a better understanding of this topic, we systematically reviewed 17 existing commercial tools and research prototypes for LLM-generated meeting summaries. The results show that existing solutions fell short of supporting users to verify and validate the comprehensiveness and accuracy of the generated summary, hindering users from trusting the summary. To address this, the project aims to design and build a more trustworthy LLM-generated meeting summary tool. Specifically, we propose that LLM-generated summary should progressively display relevant meeting information based on the importance of the information and the user’s goals, and include trustworthiness cues to aid users in making accurate trust judgments of the summary. Our preliminary interviews and a literature review showed that users are more hesitant to trust the AI summary when the information is consequential, such as when they missed the meeting or specific action items. While trustworthiness cues such as quotes or links to raw transcripts could increase users’ trust, irrelevant and redundant information erodes people’s trust. To further validate these observations, we will conduct a formative interview study. We will show participants mid-fidelity prototypes exemplifying the key design decisions and elicit their feedback on appropriate trustworthiness cues, desired ways to indicate their goals and intentions, and expectations on the importance of different portions of a summary. These empirically supported insights will inform the final design of a trustworthy LLM-generated meeting summary tool, which we plan to implement and evaluate in the next step.

Brain-computer interfaces (BCI) are systems that allow direct control of machines or computers by decoding neural signals from the motor cortex, particularly the signals associated with movement intention. Existing BCI designs tend to combine signals from the premotor and primary motor cortices, treating them as a unified source for processing despite their functional and anatomical differences. Preliminary data indicates that different depths within these motor cortical areas perform different computations. Based on this data, my hypothesis is that shared computations are performed in the output layers of the premotor cortex and the input layers of the primary motor cortex. Addressing this hypothesis is difficult due to technological limitations. Many sensors used to measure neural activity in the motor cortex do not provide layer-specific information. To counteract this, my project leverages Neuropixel probes, which are high-density microelectrode arrays that record the activity of individual neurons (spiking activity) and groups of neurons (local field potentials). I developed a fixture capable of holding multiple Neuropixel sensors to simultaneously capture neural signals at known depths from the primary and premotor cortices. After determining the depth of each neuron by analyzing the local field potentials, we will compare spiking activity patterns across depths as a non-human primate learns a motor task. I expect to see similar patterns of spiking between the output layers of the premotor cortex as the input layers of the primary motor cortex. The analysis of this data will reveal how movement-related information is transmitted through motor cortical areas, which will inform the design of future BCIs.