PNAS nexus最新文献

Atomically precise metal nanoclusters (MNCs) are metallic kernels wrapped by ligand shells whose chemistry dictates solubility, stability, and interfacial reactivity. Focusing on water-soluble MNCs, this review treats the interface as the bridge from synthesis to function. We map out both direct aqueous syntheses and gentle postsynthetic routes that install hydrophilic motifs while preserving atomic structures. Design rules are then codified: interfacial structure controls stability and mobility, recognition at the interface dictates selectivity, and the local environment sets charge transfer and photophysical responses. Furthermore, ligand shells can define nanoscopic reaction pockets that steer substrate approach and govern electron or energy transfer. These principles illuminate unique advantages in bioimaging and labeling, enzyme-compatible sensing, and controlled charge and mass transport for electrocatalysis. By consolidating synthesis, interfacial physics, and use cases within one framework, this review provides actionable guidelines for linking molecular structure to macroscopic performance and for rationally engineering aqueous MNCs for biomedical and catalytic applications.

Tissue culture in 96-well microplates is conventionally a tedious, highly manual process sensitive to individual technique and experimenter error. Here, we describe the Automated Cell Culture Splitter, a system for passaging plates of adherent or suspension cells, for routine culture maintenance or specialized applications such as seeding plates for microscopy. The system is built around the Opentrons OT-2 liquid handling robot and incorporates a novel on-deck imaging-based cell counter which allows it to compensate for density disparities across a source plate and control the number of cells seeded on a per-well basis. We find this solution can cut hands-on time by 61% and the results compare favorably to our existing manual cell culture processes in terms of both seeding density precision and biological outcomes, achieving a control of seeding density with a well-to-well coefficient of variation under 11%. The system is designed to be adaptable and an accessible entry point into automation for high-throughput cell culture; to that end, all of the source code and hardware designs are released under open source licenses.

Digital trace data hold tremendous potential for measuring policy-relevant outcomes in real-time, yet its reliability is often questioned. Here, we propose a principled yet simple approach: capturing individual disclosures of unemployment using a fine-tuned AI model and post-stratification adjustment using inferred user demographics. We show that our methodology consistently outperforms the industry's forecasting average and can improve the predictions of US unemployment insurance claims, up to 2 weeks in advance, at the national, state, and city levels at both turbulent and stable times. The results demonstrate the potential of combining AI models with statistical modeling to complement traditional survey methodology, and contribute to better-informed policymaking, especially at turbulent times.

There is a growing proportion of people with several disease conditions ("multimorbidity"), placing increasing demands on healthcare systems. One hypothesis is that clusters of diseases may arise from shared underlying disease processes (shared "pathogenesis"), whereby the presence of one disease indicates the state of disease progression to several related disease types. This article explains how this hypothesis can be tested using observational data for disease incidence. Specifically, a multistage model is used to test whether two diseases can have a "shared stage" or "step," before either disease can occur, and how the unobserved rate of this step can be determined. The approach offers a simple method for studying multiple diseases and identifying shared underlying causes of multiple conditions and is illustrated with published data and numerical examples. The fundamental mathematical model is analyzed to compare key statistical properties such as the expectation and variance with those of independent diseases. The main results do not need an understanding of the underlying mathematics and can be appreciated by a nonexpert.

We investigate how information-spreading mechanisms affect opinion dynamics and vice versa via an agent-based simulation on adaptive social networks. First, we characterize the impact of reposting on user behavior with limited memory, a feature that introduces novel system states. Then, we build an experiment mimicking information-limiting environments seen on social media platforms and study how the model parameters can determine the configuration of opinions. In this scenario, different posting behaviors may sustain polarization or reverse it. We further show the adaptability of the model by calibrating it to reproduce the statistical organization of information cascades as seen empirically in a microblogging social media platform. Our model combines mechanisms for platform content recommendation, connection rewiring, and limited-attention user behavior, paving the way for a robust understanding of echo chambers as a specialized phenomenon of opinion polarization.

Interleukin-17 (IL-17) plays a central role in the pathogenesis of various autoimmune diseases. Soluble CD14 (sCD14), a marker of innate immune activation, is elevated in several inflammatory conditions. However, its influence on IL-17 production and the differentiation of Th17 cells remains poorly understood. We found that sCD14 enhances Th17-associated cytokine production and up-regulates critical transcription factors such as STAT3 and RORC. Notably, sCD14's effect on Th17 polarization was mediated indirectly through autologous sCD14-treated peripheral blood mononuclear cell (PBMC) supernatant (sCD14-PBMC-Sup). Additionally, we identified a distinct cytokine profile enriched for pro-inflammatory cytokines and chemokines in sCD14-treated T cells, further reinforcing the Th17-promoting role of sCD14. Interestingly, gamma-aminobutyric acid (GABA), a metabolite elevated in sCD14-treated monocytes, was identified as a potential contributor to Th17 polarization. GABA supplementation in T-cell cultures enhanced IL-17A secretion, indicating its role as a signaling molecule in T-cell differentiation. Our findings also revealed the expansion of innate lymphoid cell (ILC)2/3-like cells in T-cell cultures exposed to sCD14-PBMC-Sup and GABA, highlighting the potential role of monocytes in Th17-mediated immunity. Furthermore, while sCD14 promoted Th17 polarization, it simultaneously impaired T-cell activation and proliferation, suggesting an immunosuppressive effect mediated by soluble factors released from monocytes. These results underscore the dual role of sCD14 in modulating T-cell responses, promoting Th17 differentiation while suppressing T-cell effector functions. This study identifies a previously unrecognized role for sCD14 in promoting Th17 induction, highlighting its contribution to immune regulation and its potential as a therapeutic target in Th17-driven autoimmune conditions. Classification: Immunology.

[This corrects the article DOI: 10.1093/pnasnexus/pgaf210.].

Climate change is altering the timing and intensity of pollen seasons, increasing human exposure to allergenic pollen. Climate-driven changes in pollen seasons present a unique opportunity to craft messaging that communicates how climate change is affecting biological systems. However, it is unclear how pollen seasons are experienced and understood by the public, including how well we detect pollen seasons and what factors we view as responsible for changes in pollen seasons. Here, we use social media data (Twitter) in the United States from 2012 to 2022 to assess public perceptions of pollen seasons across the country. We find that pollen seasons detected by social media users are consistent with natural pollen seasons. Attribution of changing pollen seasons, however, varies based on political ideology: liberal users are more likely to attribute changing pollen seasons to climate change when compared with conservative users. Mass media and scientific experts shape communication about how climate change drives changes in pollen seasons. Our findings reveal how political ideology and scientific communication affect public perceptions of pollen seasons and climate change. Our findings are a key step towards improved communication of climate change impacts.

Sulfonation is one of the two main phase II detoxification pathways in eukaryotes which transforms nonpolar compounds into hydrophilic metabolites. Sulfotransferases catalyze these reactions by transferring a sulfo group from a donor to an acceptor molecule. Human cytosolic sulfotransferases use only 3'-phosphoadenosine 5'-phosphosulfate (PAPS) as a donor to sulfonate a variety of chemicals. Less understood are microbial aryl-sulfate sulfotransferases (ASSTs), which catalyze sulfo transfer reactions, without utilizing PAPS as a donor. Currently, the identity of physiological sulfo donor substrates remains unknown and sulfo acceptor substrates are underexplored. With this study, we aim to understand the potential contribution of a gut microbial enzyme to sulfonation chemistry by uncovering its substrate preferences. Here, we show that a sulfotransferase (Bacteroides vulgatus ASST) from the prevalent gut microbe B. vulgatus (now Phocaeicola vulgatus) is a versatile catalyst that utilizes a wide range of phenolic molecules as substrates that are commonly encountered by the host. With this action, it modulates concentrations of donor phenolic sulfates such as acetaminophen sulfate, dopamine sulfate, p-cresol sulfate, and related compounds in vitro and displays broad acceptor flexibility by sulfonating diverse phenolic compounds, including p-coumaric acid, p-cresol, 4-ethylphenol, tyramine, among others. These findings suggest that gut microbial enzymes like ASSTs may contribute to host detoxification of phenolics, a role previously attributed solely to human sulfotransferases. However, further in vivo studies are necessary to understand the potential contributions of ASSTs to host detoxification processes.

Decoding the connectivity patterns of complex networks from time series measurements is crucial for understanding and controlling their dynamics. Although network inference algorithms have advanced significantly in identifying both pairwise and higher-order interactions, they often rely on the availability of full-state measurements, an assumption that is difficult to satisfy in practice. In this article, we address this limitation by introducing Network Inference from Partial States (NIPS), a framework for network reconstruction from partial-state observations of network units. Focusing initially on networks coupled through observable states, we model coupling inputs as external forcing and utilize forced-delay embedding theory to establish a map that describes the evolution of the node observables as a function of observable state components. Specifically, the dynamics of the observable state of a node depends only on delayed observations of that node itself, not on delayed observations of other nodes. This enables accurate network reconstruction with limited data, which is demonstrated using both simulated and experimental data obtained from a wide range of networks. We evaluate the robustness of NIPS to noisy data and hidden network nodes and subsequently extend the framework to networks coupled through unobservable states.