Search for: All records

Abstract Localized tropical rainfall changes commonly occur on 500–1,000 km scales under various climate forcings, but understanding their causality remains challenging. One helpful process‐oriented diagnostic (POD) decomposes the effects of undilute buoyancy and lower free‐tropospheric moisture through a precipitation‐buoyancy relationship, but its applicability at subregional scales is uncertain. We examine month‐to‐month rainfall changes in five South Asian monsoon subregions. The POD accurately characterizes the precipitation‐buoyancy relationship across all subregions and successfully predicts the sign of rainfall changes in four out of five subregions. However, the POD's ability to predict rainfall change magnitudes and identify causal mechanisms varies, providing confident explanations in only two subregions, where lower free‐tropospheric moisture emerges as the dominant driver of change. While these findings demonstrate the POD's utility in specific contexts, they also reveal limitations. We caution against using the POD as a standalone tool at these scales for predicting rainfall changes or decomposing their drivers. 

The measurement of neutralizing immune responses to viral infection is essential, given the heterogeneity of human immunity and the emergence of new virus strains. However, neutralizing antibody (nAb) assays often require high-level biosafety containment, sophisticated instrumentation, and long detection times. Here, as a proof-of-principle, we designed a nanoparticle-supported, rapid, electronic detection (NasRED) assay to assess the neutralizing potency of monoclonal antibodies (mAbs) against SARS-CoV-2. The gold nanoparticles (AuNPs) coated with human angiotensin-converting enzyme 2 (ACE2) protein as nAb potency reporters were mixed with the mAbs to be tested, as well as streptavidin-conjugated multivalent spike (S) protein or their receptor binding domains (RBD). High-affinity and ACE2-competitive nAbs alter the S (or RBD)-to-ACE2 binding level and modulate AuNP cluster formation and precipitation. The amount of free-floating AuNP reporters is quantified by a semiconductor-based readout system that measures the AuNPs' optical extinction, producing nAb signals that can differentiate SARS-CoV-2 variants (Wuhan-Hu-1, Gamma, and Omicron). The modular design nature, short assay time (less than 30 minutes), and portable and inexpensive readout system make this NasRED-nAb assay applicable to measuring vaccine potency, immune responses to infection, and the efficacy of antibody-based therapies. 

Online marketplaces use rating systems to promote the discovery of high-quality products. However, these systems also lead to high variance in producers' economic outcomes: a new producer who sells high-quality items, may unluckily receive a low rating early, severely impacting their future popularity. We investigate the design of rating systems that balance the goals of identifying high-quality products (``efficiency'') and minimizing the variance in outcomes of producers of similar quality (individual ``producer fairness'').We show that there is a trade-off between these two goals: rating systems that promote efficiency are necessarily less individually fair to producers. We introduce prior-weighted rating systems as an approach to managing this trade-off. Informally, the system we propose sets a system-wide prior for the quality of an incoming product; subsequently, the system updates that prior to a posterior for each product's quality based on user-generated ratings over time. We show theoretically that in markets where products accrue reviews at an equal rate, the strength of the rating system's prior determines the operating point on the identified trade-off: the stronger the prior, the more the marketplace discounts early ratings data (increasing individual fairness), but the slower the platform is in learning about true item quality (so efficiency suffers). We further analyze this trade-off in a responsive market where customers make decisions based on historical ratings. Through calibrated simulations in 19 different real-world datasets sourced from large online platforms, we show that the choice of prior strength mediates the same efficiency-consistency trade-off in this setting. Overall, we demonstrate that by tuning the prior as a design choice in a prior-weighted rating system, platforms can be intentional about the balance between efficiency and producer fairness. 

Introductory computer science courses for non-majors (CS0) aim to increase diversity and highlight the relevance of computing across disciplines. To enhance the accessibility and engagement of CS0, researchers have explored contextualized computing, where computing is integrated with another subject, to teach course content. While research has explored various designs for contextualized courses, we know less about how contextualized computing tasks impact students’ learning experiences. Through the lens of metacognition and affect, we conducted a secondary qualitative analysis on daily diary and retrospective interview data from 20 students in a CS0 course that applied coding to different contexts. Our findings demonstrate that students’ feeling of knowing and their perception of the task are two central themes that shape their affect and interest in the course. We conclude with design suggestions for contextualized computing in CS0 to better support students. 

The bacterial orderMagnimaribacterales, previously known as the SAR86 lineage, is among the most abundant groups of planktonic bacteria inhabiting the global surface ocean. Despite their prevalence, our understanding of how this genetically diverse lineage partitions into units with coherent ecology and evolution remains limited. Here we surveyed multiple stations in the tropical Pacific Ocean using shotgun metagenomes and 16S rRNA gene amplicons to resolve distinct habitat preferences forMagnimaribacteraleslineages across nearshore, offshore, and open-ocean environments. The comprehensive collection of genomes that captured a large fraction of the known evolutionary breadth ofMagnimaribacterales, revealed patterns of ecotypic differentiation manifested primarily among genus-level clusters with specific clear preferences for distinct marine habitats. Enrichment analyses identified several functional genes associated with genomes from genera abundant in the nearshore environment, including those associated with sugar metabolism, peptide transport, and glycerophospholipid biosynthesis. Such metabolic adaptations likely facilitate the predominance of specificMagnimaribacteralesgenera in nearshore environments, promoting ecological partitioning across marine habitats. 

Social media feed ranking algorithms fail when they too narrowly focus on engagement as their objective. The literature has asserted a wide variety of values that these algorithms should account for as well -- ranging from well-being to productive discourse -- far more than can be encapsulated by a single topic or theory. In response, we present a library of values for social media algorithms: a pluralistic set of 78 values as articulated across the literature, implemented into LLM-powered content classifiers that can be installed individually or in combination for real-time re-ranking of social media feeds. We investigate this approach by developing a browser extension, Alexandria, that re-ranks the X/Twitter feed in real time based on the user's desired values. Through two user studies, both qualitative (N=12) and quantitative (N=257), we found that diverse user needs require a large library of values, enabling more nuanced preferences and greater user control. With this work, we argue that the values criticized as missing from social media ranking algorithms can be operationalized and deployed today through end-user tools. 

While there is a growing body of research that explores the integration of music and coding in learning environments, much of this work has either emphasized the technical aspects of computer language design or music as a motivational context within which to learn computer science concepts. In this paper, we report on a study in which five undergraduate students with experience in both music and coding completed two creative musical tasks: one using conventional instruments and tools and one using Python code in an online music + coding environment. Inspired by the work of Christopher Small (1998. Musicking: The Meanings of Performing and Listening. University Press of New England), we describe music + coding as a set of interlocking processes which we call computational musicking and explore how practices from both domains are reimagined in this new hybrid context. We introduce semiotic theories of translation and transcription to make sense of the computational musicking process and describe strategies that participants devised in their creative process.