The tumor microenvironment (TME)—the ecosystem surrounding a tumor—is an important factor that influences the growth of a tumor. Specifically, features of TME may be associated with patients’ survival time, which suggests that further study of TME structures is warranted. This study used the existing METABRIC breast cancer dataset which contains spatial data of cells from tissue samples. Using cox regression, a statistical method, we aimed to analyze general patterns in TME structures to model the relationship between TME and patient survival time. Log cell type proportion and Ripley’s K function, which quantifies cell clustering, were compared in cox regression. We found that log immune cell proportion was associated with decreased patient survival time. These findings suggest that further research to determine the exact relationship between TME structures in breast cancer and patient survival outcomes is important.

3D perovskites have enormous potential for optoelectronic applications such as light-emitting devices, photodetectors and lasers, due to tunable optical properties. Achieving precise control over their characteristics, specifically color purity, can be costly to discover because of their highly nonlinear behavior.  In this work, machine learning (ML) will be employed to explore the synthesis parameter space and target perovskite films with desired RGB values. By varying the annealing time and composition of the MAPbIBr₂ perovskite while fixing other synthesis parameters the film’s optical response can be adjusted. Using Bayesian Optimization, a data-driven approach will be established based on experimental feedback for precisely tuning the perovskite. This synthesis framework is designed for easy adaptation to other synthetic spaces requiring precise material control. This research aims to accelerate ML-driven design of perovskites while enhancing our understanding of their nonlinear synthesis space.

Laboratory automation has demonstrated great potential in accelerating the discovery and optimization of new materials. However, the lack of low cost high-throughput characterization has been a limiting factor in the development of autonomous self-driving labs. To address this, we developed an open-source 3D-printable robotic framework that can be integrated with an ocean optics spectrometer probe designed to measure materials properties in a high-throughput fashion. The device is low-cost, easy to construct and fully compatible with the Opentron (OT-2) automated liquid handler. The system operates on a printer-gantry system that moves the spectrometer probe across a laboratory plate as scanning progresses. We aim to achieve scanning speeds of 1 second per well, allowing a standard 48 well laboratory plate to be completed in under 1 minute – a significant improvement over current times achieved with human testing. Additionally, we outline potential applications for the system through the characterization of perovskite semiconductors for energy-efficient lighting and discuss the challenges of fully integrating this device into a completely autonomous workflow. Despite its current limitations, by facilitating high throughput characterization through affordable, open-source technologies, this device enables materials researchers in underserved regions to accelerate progress in key areas such as green technology development. 

Panãra is a Jê language spoken in the Panará Indigenous Land in the Brazilian Amazon by around 730 people. I am an undergraduate research assistant working as part of the larger Panãra Documentation Team at the University of Washington. I am in the process of transcribing, coding, and archiving field notes taken by team members during the summer of 2024. I have employed my experience with Panãra and Portuguese to resolve ambiguities in the notes and to code materials in a standardized, accessible manner. Many letters, such as ⟨b, d, g, z, l⟩, and sequences, such as ⟨-ät-⟩ or ⟨-me-⟩ are impossible due to Panãra’s phonology and orthography. However, these letters may occur in the notes due to transcriber error or Portuguese loans. When I identified suspect items, I had to use my knowledge of Panãra to determine their status. I typed the notes into text format before transferring items into a spreadsheet. In the spreadsheet, I coded part of speech and added lexical items to the ongoing dictionary. My work is a case study in longer-term, multi-researcher documentary efforts in linguistics. Not only will the body of data I code be valuable in further analysis of the language, but the processes developed will be useful in rethinking how documentary linguistics is carried out. In particular I emphasize the need for a coherent vision of data usage, from collection to coding. As the dictionary work moves forward, my next steps will be to give words that have not yet been checked in the field to the research team for the summer and to code the phonological, orthographic, and lexical information for each word into the FLEx database.