Deep learning methods for protein sequence and structure generation have shown remarkable success in many design scenarios when combined with structure prediction networks such as AlphaFold2. Despite this advance, many design challenges such as de novo binder design still haven’t been fully solved. Diffusion-based models have demonstrated considerable success in image and language generation yet their application in protein design has not yet been fully explored. Recently, the development of a protein diffusion model called RoseTTAFold Diffusion (RFdiffusion) has shown significant success in protein design and enabled us to explore the challenging problem of designing protein binders. Here I demonstrate utilization of RFdiffusion towards generation of de novo binders to disordered major histocompatibility complex (MHC) peptides. Specifically, we took an MHC peptide from KrasG12D and used RFdiffusion to generate a diverse range of structures that can bind this peptide. To optimize the sequence of these structures we used ProteinMPNN. We used AlphaFold2 to predict the structures of these optimized binders in complex with the peptide and saw promising interaction metrics. Further, structure prediction of the designs in complex with Kras wild type (WT) peptide resulted in lower AlphaFold2 confidence metrics of the interaction occurring. This is a promising preliminary result that RFdiffusion can generate fully de novo MHC-mimics, which can differentiate between neoantigens and WT peptide. Many cancers are caused by a single point mutation such as KrasG12D, thus, designing protein binders with point mutant specificity is exciting as it allows for targeting of disease causing proteins over healthy WT proteins. 

Although social Virtual Reality (VR) has attracted increasing attention as a new way for people to interact, it faces challenges with harassment, a problem other social platforms face as well, online gaming communities in particular. The embodied environment social VR provides also brings new forms of harassment compared to social media, requiring effective responses from social VR platforms. We examined the safety features of four popular social VR games: VRChat, Horizon World, Altspace, and RecRoom to learn the standard safety practices. To understand how social VR communities share and respond to harassment experiences, we collected 134 posts and comments from online communities for these games on Reddit, Twitter, and Oculus Forum. We used inductive coding to identify themes and trends. We found that the four social VR games have common safety features such as Personal Bubble, Block, and Report, but these features differ in name, effect, and ease of access. This can pose an increased learning curve for players and make them less aware of these functionalities. From the online posts, we found the most common harassment experiences include hate, unwanted sexual attention, and embodied sexual harassment. The most common response to harassment experiences is suggesting strategies or resources. However, these responses include a mix of positive (e.g., empathetic, supportive), neutral, and negative (e.g., gaslighting) tones. We also found a difference between the most commonly adopted safety feature and the most recommended feature, where the former is Personal Bubble and the latter is Block. Based on the findings, we provide design implications to improve safety features and build easier-to-access and informed safety systems for social VR games. This research contributes to developing a more inclusive environment for players from diverse backgrounds and identities by identifying opportunities to provide better safety features and improve safety norms in virtual worlds.

Remote collaboration today rarely involves a single communication channel. Instead, teams frequently juggle a myriad of communication tools, such as video conferencing, group chat, and email. Each of these platforms provides different mechanisms for relaying information and media to ultimately meet the needs and goals of the team. While discussions occurring on different platforms are often related, existing tools used to support each type of communication are disconnected. To research how to bridge this gap and support seamless collaboration and communication across different platforms, we developed a toolkit that connects conversations between three of the most commonly used remote collaboration platforms: Slack, Google Docs, and Zoom, covering both synchronous and asynchronous modes of communication. We iteratively designed and implemented features such as adding information from Slack chat directly to Google Docs notes to build up meeting agendas and selecting specific snippets of Zoom meetings to be embedded into notes or sent to chat. We also plan to evaluate the effectiveness of our toolkit in helping streamline the transfer of information across different team communication sites and enhancing the remote collaboration experience for teams via subsequent qualitative user studies. Specifically, we will be conducting a week-long field study with existing teams, such as teams from industry, teams working on school projects, research groups, committees, etc. We will use a combination of experience sampling, diary study and post-study interviews to understand their experience. The results we expect to get from these exploratory user studies will help us answer the following questions: Which aspects of the tools work best for the users? Does the current UI and design make sense for how the user interacts with the toolkit? In which scenarios is the toolkit being used most effectively? These results will also guide us in designing additional features for the toolkit in the future.
 

Breast cancer has attracted tremendous research interest in treatment development as one of the major threats to public health. The use of nanoparticle (NP) for therapeutic DNA delivery has shown promise in treating various cancer types, including breast cancer, due to their high DNA loading capacity, high cell transfection efficiency, and design versatility. However, cytotoxicity and large sizes of NPs often raise safety concerns and hinder their applications in the clinic. Here we report the development of a novel nanoparticle formulation (termed NP-Chi- xPEI) that can safely and effectively deliver DNA into breast cancer cells for successful transfection. The nanoparticle is composed of an iron oxide core coated with low molecular weight (800 Da) polyethyleneimine crosslinked with chitosan via biodegradable disulfide bonds. The NP-Chi-xPEI can condense DNA into a small nanoparticle with the overall size of less than 100 nm and offer full DNA protection. Its biodegradable coating of small-molecular weight xPEI and mildly positive surface charge confer extra biocompatibility. NP-Chi-xPEI-mediated DNA delivery was shown to achieve high transfection efficiency across multiple breast cancer cell lines with significantly lower cytotoxicity as compared to the commercial transfection agent Lipofectamine 3000. With demonstrated favorable physicochemical properties and functionality, NP-Chi-xPEI may serve as a reliable vehicle to deliver DNA to breast cancer cells.

Medical image segmentation modeling is a high-stakes task where direct communication and interpretation of uncertainty is crucial for addressing visual ambiguity. Prior work has developed segmentation models utilizing probabilistic or generative mechanisms to infer uncertainty from labels where annotators draw a singular boundary. However, as these annotations cannot directly represent an individual annotator's uncertainty, even specialized models trained on these standard representations produce uncertainty maps that are difficult to interpret. We propose a novel segmentation representation, Confidence Contours, which uses high- and low-confidence ``contours’’ to capture uncertainty directly, and develop a novel annotation system for collecting contours. We collect both standard and Confidence Contours annotations on the Lung Image Dataset Consortium (LIDC) and a synthetic dataset simulating the structural ambiguity of many medical segmentation problems, FoggyBlob. Our analysis show that Confidence Contours provide high representative capacity without requiring significantly higher annotator effort. Moreover, general segmentation models trained on Confidence Contours can produce significantly more interpretable uncertainty maps than models with specialized mechanisms for uncertainty, and they can learn Confidence Contours at the same performance level as singular annotations. We conclude with a discussion on how we can infer regions of high and low confidence from existing segmentation datasets. Our data-centric approach crucially brings attention to the importance of human factors in responsible and robust AI, which have often been overlooked in model-centric medical segmentation work. By troubling and rethinking the very way that the ground truth is represented, our work opens up new paths of inquiry towards more human-friendly models -- paths which begin from the data.

Electrochemical water splitting, a promising green energy solution, produces O2 and energetically rich H2 gas molecules as products on the anode and cathode, respectively. Attachment of O2 and H2 bubbles on the surface can decrease the overall efficiency of electrochemical water splitting.Therefore, continued investigation of nanobubble characteristics is of utmost importance to improve the technologically relevant electrochemical generation of H2 gas. Nanobubbles can interfere with the efficiency and productivity of industrial processes by blocking electrode surfaces, however, they could also potentially be utilized to catalyze reactions themselves. To better understand these processes, it is important we create consistent samples of nanobubbles that can be formed individually and reproducibly, and gain a deeper understanding of their properties. We have been working to create a procedure that efficiently and reliably produces carbon nanoelectrodes for single nanobubble analysis. To accomplish this, we pull quartz capillaries to a nano-sized tip to create a nanopore, and heat the nanopores while applying a flow of methane in an oxygen-free environment to deposit carbon inside the capillaries, producing nanoelectrodes. We test the electrochemical properties of the nanoelectrodes by measuring the observed current when applying a potential and evaluating whether the cyclic voltammetry graph generated suggests that the nanoelectrode is capable of generating a nanobubble. Certain elements of our current procedure may need to be adjusted to improve the reliability of the nanoelectrodes, but so far our experimentation in nanoelectrode fabrication has allowed for a more reliable process in generating ideal hydrogen nanobubbles. This procedure has helped us gain a better understanding of the impact of nanobubbles on an electrochemical system and provide a better physicochemical description of the bubble. In the future, we plan to apply the knowledge gained through these experiments on theta nanoelectrode fabrication, which is an electrode with a partition in the middle.