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Abstract The mitoribosome translates mitochondrial mRNAs and regulates energy conversion that is a signature of aerobic life forms. We present a 2.2 Å resolution structure of human mitoribosome together with validated mitoribosomal RNA (rRNA) modifications, including aminoacylated CP-tRNA^Val. The structure shows how mitoribosomal proteins stabilise binding of mRNA and tRNA helping to align it in the decoding center, whereas the GDP-bound mS29 stabilizes intersubunit communication. Comparison between different states, with respect to tRNA position, allowed us to characterize a non-canonical L1 stalk, and molecular dynamics simulations revealed how it facilitates tRNA transitions in a way that does not require interactions with rRNA. We also report functionally important polyamines that are depleted when cells are subjected to an antibiotic treatment. The structural, biochemical, and computational data illuminate the principal functional components of the translation mechanism in mitochondria and provide a description of the structure and function of the human mitoribosome. 

With the growing ubiquity of the Internet and access to media-based social media platforms, the risks associated with media content sharing on social media and the need for safety measures against such risks have grown paramount. At the same time, risk is highly contextualized, especially when it comes to media content youth share privately on social media. In this work, we conducted qualitative content analyses on risky media content flagged by youth participants and research assistants of similar ages to explore contextual dimensions of youth online risks. The contextual risk dimensions were then used to inform semi- and self-supervised state-of-the-art vision transformers to automate the process of identifying risky images shared by youth. We found that vision transformers are capable of learning complex image features for use in automated risk detection and classification. The results of our study serve as a foundation for designing contextualized and youth-centered machine-learning methods for automated online risk detection. 

Multiple recent efforts have used large-scale data and computational models to automatically detect misinformation in online news articles. Given the potential impact of misinformation on democracy, many of these efforts have also used the political ideology of these articles to better model misinformation and study political bias in such algorithms. However, almost all such efforts have used source level labels for credibility and political alignment, thereby assigning the same credibility and political alignment label to all articles from the same source (e.g., the New York Times or Breitbart). Here, we report on the impact of journalistic best practices to label individual news articles for their credibility and political alignment. We found that while source level labels are decent proxies for political alignment labeling, they are very poor proxies-almost the same as flipping a coin-for credibility ratings. Next, we study the implications of such source level labeling on downstream processes such as the development of automated misinformation detection algorithms and political fairness audits therein. We find that the automated misinformation detection and fairness algorithms can be suitably revised to support their intended goals but might require different assumptions and methods than those which are appropriate using source level labeling. The results suggest caution in generalizing recent results on misinformation detection and political bias therein. On a positive note, this work shares a new dataset of journalistic quality individually labeled articles and an approach for misinformation detection and fairness audits. 

The idealization of a static machine-learned model, trained once and deployed forever, is not practical. As input distributions change over time, the model will not only lose accuracy, any constraints to reduce bias against a protected class may fail to work as intended. Thus, researchers have begun to explore ways to maintain algorithmic fairness over time. One line of work focuses on dynamic learning: retraining after each batch, and the other on robust learning which tries to make algorithms robust against all possible future changes. Dynamic learning seeks to reduce biases soon after they have occurred and robust learning often yields (overly) conservative models. We propose an anticipatory dynamic learning approach for correcting the algorithm to mitigate bias before it occurs. Specifically, we make use of anticipations regarding the relative distributions of population subgroups (e.g., relative ratios of male and female applicants) in the next cycle to identify the right parameters for an importance weighing fairness approach. Results from experiments over multiple real-world datasets suggest that this approach has promise for anticipatory bias correction. 

Multiple symptom tracking applications (apps) were created during the early phase of the COVID-19 pandemic. While they provided crowdsourced information about the state of the pandemic in a scalable manner, they also posed significant privacy risks for individuals. The present study investigates the interplay between individual privacy attitudes and the adoption of symptom tracking apps. Using the communication privacy theory as a framework, it studies how users’ privacy attitudes changed during the public health emergency compared to the pre-COVID times. Based on focus-group interviews (N = 21), this paper reports significant changes in users’ privacy attitudes toward such apps. Research participants shared various reasons for both increased acceptability (e.g., disease uncertainty, public good) and decreased acceptability (e.g., reduced utility due to changed lifestyle) during COVID. The results of this study can assist health informatics researchers and policy designers in creating more socially acceptable health apps in the future. 

Intelligently responding to a pandemic like Covid-19 requires sophisticated models over accurate real-time data, which is typically lacking at the start, e.g., due to deficient population testing. In such times, crowdsensing of spatially tagged disease-related symptoms provides an alternative way of acquiring real-time insights about the pandemic. Existing crowdsensing systems aggregate and release data for pre-fixed regions, e.g., counties. However, the insights obtained from such aggregates do not provide useful information about smaller regions e.g., neighborhoods where outbreaks typically occur and the aggregate-and-release method is vulnerable to privacy attacks. Therefore, we propose a novel differentially private method to obtain accurate insights from crowdsensed data for any number of regions specified by the users (e.g., researchers and a policy makers) without compromising privacy of the data contributors. Our approach, which has been implemented and deployed, informs the development of the future privacy-preserving intelligent systems for longitudinal and spatial data analytics.