PNAS nexus最新文献

Itaconic acid belongs to the high-value precursors for the production of biomass-based industrial compounds. It originates from the tricarboxylic acid cycle, and depending on the organism, it is produced by different biosynthetic routes. The basidiomycete fungus Ustilago maydis synthesizes itaconic acid via isomerization of cis-aconitic acid to trans-aconitic acid, and subsequent decarboxylation catalyzed by the trans-aconitate decarboxylase Tad1, which belongs to the aspartase/fumarase superfamily. Since no other decarboxylase has been identified within this protein superfamily, Tad1 constitutes a novel type of decarboxylase. Here, we present high-resolution crystal structures of Tad1, which, together with mutational analysis and nuclear magnetic resonance spectroscopy measurements, provide insight into the molecular mechanism of Tad1-dependent decarboxylation. Specifically, our study shows that decarboxylation is favored in acidic conditions, requires protonation as well as migration of a double bond, and coincides with structural rearrangements in the catalytic center. In summary, our study elucidates the molecular mechanism underlying a novel type of enzymatic decarboxylation and provides a starting point for protein engineering aimed at optimizing the efficient production of itaconic acid.

Diverse schools can help address implicit biases and increase empathy, mutual respect, and reflective thought by fostering connections between students from different racial/ethnic, socioeconomic, and other backgrounds. Unfortunately, demographic segregation remains rampant in US public schools, despite over 70 years since the passing of federal legislation formally outlawing segregation by race. However, changing how students are assigned to schools can help foster more integrated learning environments. In this article, we explore "school mergers" as one such under-explored, yet promising, student assignment policy change. School mergers involve merging the school attendance boundaries, or catchment areas, of schools and subsequently changing the grades each school offers. We develop an algorithm to simulate elementary school mergers across 200 large school districts serving 4.5 million elementary school students and find that pairing or tripling schools in this way could reduce racial/ethnic segregation by a median relative 20%-and as much as nearly 60% in some districts-while increasing driving times to schools by an average of a few minutes each way. Districts with many interfaces between racially/ethnically disparate neighborhoods tend to be prime candidates for mergers. We also compare the expected results of school mergers to other typical integration policies, like redistricting, and find that different policies may be more or less suitable in different places. Finally, we make our results available through a public dashboard for policymakers and community members to explore further (https://mergers.schooldiversity.org). Together, our study offers new findings and tools to support integration policy-making across US public school districts.

Offshore wind can be a key contributor to energy system decarbonization, but its deployment in certain regions has been slow, partly due to opposition from disparate interests. Failure to sufficiently address the concerns of external stakeholders could continue to hamper deployment. Here, we use a multi criteria model to assess all possible sites in a 2 km × 2 km grid of all potential locations in continental US federal waters, contrasting the perspectives of developers and other stakeholders. Our model elucidates how developers and policymakers could better approach future deployment. First, while developers prefer larger plants, we find that these facilities are more fragile-they are sensitive to location, and their impacts are more uncertain than smaller plants. Second, there is 600 GW of capacity where both developer and stakeholder interests align-developing these locations should be prioritized. Third, there are few areas on the US West Coast where developer and stakeholder preferences align, suggesting a need to reduce stakeholder-plant interactions or locate facilities in deeper waters than current technology allows.

Recent studies have demonstrated that wearable devices, such as smartwatches, can accurately detect infections in presymptomatic and asymptomatic individuals. Yet, the extent to which smartwatches can contribute to prevention and control of infectious diseases through a subsequent reduction in social contacts is not fully understood. We developed a multiscale modeling framework that integrates within-host viral dynamics and between-host interactions to estimate the risk of viral disease outbreaks within a given population. We used the model to evaluate the population-level effectiveness of smartwatch detection in reducing the transmission of three COVID-19 variants and seasonal and pandemic influenza. With a 66% reduction in contacts after smartwatch-based disease detection, we estimate that the reproduction number R would drop from 2.55 (interquartile range [IQR]: 2.09-2.97) to 1.37 (IQR: 1.00-1.55) for the ancestral COVID-19 variant; from 1.54 (IQR: 1.41-1.69) to 0.82 (IQR: 0.68-0.85) for the delta variant; from 4.15 (IQR: 3.38-4.91) to 2.20 (IQR: 1.57-2.52) for the omicron variant; from 1.55 (IQR: 1.34-1.74) to 0.81 (IQR: 0.63-0.87) for pandemic influenza; and from 1.28 (IQR: 1.18-1.35) to 0.74 (IQR: 0.64-0.79) for seasonal influenza. With a 75% reduction in contacts, R decreases below 1 for the delta variant and for pandemic and seasonal influenza. Sensitivity analyses across a wide array of parameter values confirm that self-isolation initiated shortly after smartwatch detection could significantly reduce R under diverse epidemiological conditions, different levels of smartwatch detection accuracy, and realistic self-isolation levels. Our study underscores the revolutionary potential of smartwatches to manage seasonal diseases and alter the course of future pandemics.

Persistent oxygen-depleted zones in the ocean are known primarily from enclosed basins in temperate regions or the open ocean (including oxygen minimum and limiting zones) (1). However, little is known about the possibility of such zones forming in tropical coastal domains, even though the combination of warmer temperatures and complex geological features in some tropical regions makes their occurrence more likely (1, 2). Here, we report two subsurface oxygen-depleted zones within deep (>490 m) depressions of the Red Sea's Difaht Farasan-a carbonate platform hosting the world's third largest contiguous tropical coral reef system. One zone maintains suboxic oxygen levels (∼11-14 µmol O₂ kg⁻¹), while the other sustains oxygen levels below detection (<2 µmol O₂ kg⁻¹). The suboxic zone shows no fixed nitrogen loss, while the near-anoxic zone hosts anaerobic microbial populations and shows signs of nitrogen loss. We propose that the warm and saline environment of the Red Sea interacts with the semienclosed depressions to restrict vertical mixing, limiting oxygen resupply at depth. However, unlike most other oxygen-depleted zones, our deep-sea vehicle surveys demonstrate that these zones support resistant aerobically respiring taxa, indicating an unusual capacity to reduce aerobic oxygen demands at high temperatures (>21 °C). Targeted exploration of deep tropical coastal environments is crucial for determining whether similar zones exist beyond the Red Sea and understanding their potential responses to climate change.

Leveraging background fluid flows for propulsion has the potential to enhance the range and speed of autonomous aerial and underwater vehicles. In this work, we demonstrate experimentally a fully autonomous strategy for exploiting vortex rings for energy-efficient propulsion. First, an underwater robot used an onboard inertial measurement unit (IMU) to sense the motion induced by the passage of a vortex ring generated by a thruster in a 13,000-L water tank. In response to the sensed acceleration, an impulsive maneuver entrained the robot into the material boundary of the vortex ring. After entrainment, the robot was propelled across the tank without expending additional energy or control effort. By advecting with the vortex ring, the robot achieved a nearly fivefold reduction in the energy required to traverse the tank compared to swimming in quiescent flow. Using the controlled finite-time Lyapunov exponent field and corresponding Lagrangian coherent structures, we analyze and explain the initial entrainment process and the sensitivity to the starting time and position of the surfing maneuver. Additionally, linear acceleration as sensed by the onboard IMU was found to correspond with the pressure gradient of the background flow, and rotational acceleration is suggested as a method for measuring the vorticity of the vortex ring. This study serves as a proof-of-concept demonstration of the potential for onboard inertial measurements to enable efficient interaction with background fluid flows.

This paper scrutinizes the unintended consequences of employing large language models (LLMs) like ChatGPT for editing user-generated content (UGC), particularly focusing on alterations in sentiment. Through a detailed analysis of a climate change tweet dataset, we uncover that LLM-rephrased tweets tend to display a more neutral sentiment than their original counterparts. By replicating an established study on public opinions regarding climate change, we illustrate how such sentiment alterations can potentially skew the results of research relying on UGC. To counteract the biases introduced by LLMs, our research outlines two effective strategies. First, we employ predictive models capable of retroactively identifying the true human sentiment underlying the original communications, utilizing the altered sentiment expressed in LLM-rephrased tweets as a basis. While useful, this approach faces limitations when the origin of the text-whether directly crafted by a human or modified by an LLM-remains uncertain. To address such scenarios where the text's provenance is ambiguous, we develop a second approach based on the fine-tuning of LLMs. This fine-tuning process not only helps in aligning the sentiment of LLM-generated texts more closely with human sentiment but also offers a robust solution to the challenges posed by the indeterminate origins of digital content. This research highlights the impact of LLMs on the linguistic characteristics and sentiment of UGC, and more importantly, offers practical solutions to mitigate these biases, thereby ensuring the continued reliability of sentiment analysis in research and policy.

Accurate forecasts of population-level behavior critically inform institutional choices and public policy. While neuroforecasting research suggests that measurements of group brain activity can improve forecasting accuracy relative to behavior, less is known about how and when brain activity can effectively improve out-of-sample forecasts. We analyzed neural and behavioral data collected in two experiments to forecast choice in more vs. less demographically representative aggregate internet markets in order to test when forecasts based on brain activity generalize better than behavior. In both experiments, while the accuracy of market forecasts based on behavior varied as a function of sample representativeness, market forecasts based on brain activity remained significant regardless of sample representativeness. These findings are consistent with the notion that brain activity associated with early affective responses can generalize across individuals to index aggregate choice more broadly than downstream behavior. Thus, brain activity from limited samples may reveal generalizable components of choice that can improve market forecasts. These findings inform theory regarding which components of individual choice generalize to improve market forecasts and provide insights into mechanisms that underlie the effective application of neuroforecasting.

Heterogeneous oxidation of SO₂ by NO₂ on aerosols has recently been found to be one of the major formation pathways of sulfate in the polluted troposphere, but the chemical mechanisms and kinetics remain uncertain. By combining lab experiments, theoretical chemistry calculations, and field measurements, here we show that the SO₂ oxidation by NO₂ is critically dependent on anions at the air-aerosol aqueous interface. The reaction rate of NO₂ with ${\mathrm{HSO}}_3^{-}$ (1.1 × 10⁸-1.6 × 10⁹ M^-1 s^-1) is more than four orders of magnitude larger than the traditionally held value for the bulk phase due to the abundant occurrence of chloride, nitrate, and carboxylic anions at the air-aqueous interface, which remarkably accelerates sulfate formation during China haze periods by enhancing the uptake of NO₂ through interfacial electrostatic attraction. Atmospheric models not accounting for this aerosol interfacial process likely produce major misrepresentations of tropospheric sulfate aerosols under polluted conditions.

[This corrects the article DOI: 10.1093/pnasnexus/pgae577.].