PNAS nexus最新文献

Over the past several decades, genetically encoded fluorescent indicators have revolutionized neuroscience by enabling cell-type-specific optical recording of neural activity. While most applications have focused on brain regions where stimulus-evoked activity correlates with behavior on the scale of seconds to minutes, many fundamental behavioral and physiological processes such as feeding, thermoregulation, and circadian timekeeping occur over hours to weeks. However, adapting optical recording techniques to these longer timescales presents unique challenges, particularly in accurately measuring and interpreting neural activity across extended recording durations. As a result, even studies using similar data have reached divergent conclusions, largely due to differences in data analysis and interpretation. This lack of standardization risks misinterpretation, miscommunication, and reduced reproducibility. In this article, we focus on in vivo fiber photometry calcium imaging in circadian neuroscience research as a case study. We review the current literature, outline theoretical, and practical challenges, and offer perspectives for optimizing experimental approaches and standardizing data interpretation. Importantly, the fundamental principles of long-term optical recording extend beyond circadian research and apply broadly to brain circuits that govern behavior and physiology over days to weeks.

While the COVID-19 pandemic is over, the road ahead is still clouded by concern about new variants and other similar infectious diseases. Human society, as an inherently complex system, is inextricably linked to the dynamics of respiratory infectious diseases from the interplay of individual behaviors, social interactions, and public policies. However, comprehending and predicting large-scale pandemic evolution based on fundamental individual behavior models remains a big challenge. In this study, we analogize the spread of respiratory infectious diseases to the nonequilibrium chemical reaction in a molecular gas, another complex system. Concepts and methodologies from molecular gas dynamics are extended to elucidate the pandemic. Individuals at distinct infection stages are treated as moving molecules of different species that undergo collisions and reactions. The velocity and collision cross-section are set according to real-world scenarios. Additionally, the viral load in human body is analogized to molecular vibrational energy level which affects the chemical reaction rate. Consequently, we introduce a specific nonequilibrium compartmental model incorporating a time-varying transmission rate, drawing upon the nonequilibrium gas dynamics. By employing the Direct Simulation Monte Carlo method, we directly derive key epidemiological metrics, including the secondary infection number, generation interval, and reproduction number. Furthermore, an initial exploration of the interplay between infection and individual behavior displays how the disease spread mitigates when the mobility of patients is reduced. This novel analogy highlights the generalized similarity between distinct complex systems and opens a new avenue for applying advanced concepts and methods from molecular gas dynamics to the pandemic study.

With the upcoming sample return missions to the Solar System where traces of past, extinct, or present life may be found, there is an urgent need to develop unbiased methods that can distinguish molecular distributions of organic compounds synthesized abiotically from those produced biotically but were subsequently altered through diagenetic processes. We conducted untargeted analyses on a collection of meteorite and terrestrial geologic samples using 2D gas chromatography coupled with high-resolution time-of-flight mass spectrometry and compared their soluble nonpolar and semipolar organic species. To deconvolute the resulting large dataset, we developed LifeTracer, a computational framework for processing and downstream machine learning analysis of mass spectrometry data. LifeTracer identified predictive molecular features that distinguish abiotic from biotic origins and enabled a robust classification of meteorites from terrestrial samples based on the composition of their nonpolar soluble organics.

In the digital age, hate speech poses a threat to the functioning of social media platforms as spaces for public discourse. Top-down approaches to moderate hate speech encounter difficulties due to conflicts with freedom of expression and issues of scalability. Counter speech, a form of collective moderation by citizens, has emerged as a potential remedy. Here, we aim to investigate which counter speech strategies are most effective in reducing the prevalence of hate, toxicity, and extremity on online platforms. We analyze more than 130,000 discussions on German Twitter starting at the peak of the migrant crisis in 2015 and extending over 4 years. We use human annotation and machine learning classifiers to identify argumentation strategies, ingroup and outgroup references, emotional tone, and different measures of discourse quality. Using matching and time-series analyses we discern the effectiveness of naturally observed counter speech strategies on the microlevel (individual tweet pairs), mesolevel (entire discussions) and macrolevel (over days). We find that expressing straightforward opinions, even if not factual but devoid of insults, results in the least subsequent hate, toxicity, and extremity over all levels of analyses. This strategy complements currently recommended counter speech strategies and is easy for citizens to engage in. Sarcasm can also be effective in improving discourse quality, especially in the presence of organized extreme groups. Going beyond one-shot analyses on smaller samples prevalent in most prior studies, our findings have implications for the successful management of public online spaces through collective civic moderation.

In navigation scenarios, integrity is a crucial metric for evaluating system availability, with improvements in integrity having implications for transportation, security, and surveillance. This article proposes a novel integrity monitoring strategy, termed the optimal advanced receiver autonomous integrity monitoring (Optimal-ARAIM), which is designed to optimize the vertical protection level (VPL) in the context of BeiDou/global navigation satellite systems (GNSS). Optimal-ARAIM employs a minimax estimator to minimize VPL by adjusting the full-set solution, optimally allocating integrity and continuity risks. To mitigate the combinatorial explosion caused by multiple heterogeneous satellite faults, we introduce a maximum monitoring order mechanism. All worst-case fault scenarios are formulated as a minimax optimization problem, which is approximated by a convex optimization to ensure the global convergence of VPL. To evaluate the performance of Optimal-ARAIM, we utilize BeiDou observation data for validation and almanac data for predictive analysis. The results indicate that the average VPL is consistently maintained below 8 m across five selected stations in the Asian region when using observation data. Additionally, VPL distributions predicted using BeiDou almanac data are predominantly below 10 m. Further validation using GNSS almanac data demonstrates that the proposed method achieves a global availability coverage rate exceeding 93%, meeting the CAT-I standard. These findings confirm that the proposed Optimal-ARAIM effectively reduces the VPL for BeiDou/GNSS, ensuring that navigation operations can be conducted with high robustness and reliability.

We have found that the overexpression of ASCL1, miR9/9 ${ }^{*}$ -124, nPTB shRNA, and p53 shRNA efficiently converted human skin fibroblasts to neurons. To identify key regulators of the transdifferentiation, we analyzed longitudinal RNA-seq data of human skin fibroblasts being converted with various combinations of these reprogramming factors, and constructed gene regulatory network (GRN) models capturing the high-order information important for neuronal conversion. Examination of gene communities and transcription factors (TFs) in the GRNs identified OTX2 and LMX1A as the key regulators of the conversion to neurons, as they had strongest connections to genes functionally associated with neuronal development and differentiation. Indeed, knocking down OTX2 or LMX1A significantly impaired the transdifferentiation of human skin fibroblasts to neurons. We also validated the approach in neuronal conversion of mouse embryonic stem cells. The study demonstrates the effectiveness of using GRN models to identify key regulators of neuronal conversion. The strategy will enhance mechanistic understanding of cellular reprogramming in general.

High-value decisions tend to be made more quickly. For instance, decision-makers are generally faster when choosing between two preferred options than when choosing between two less preferred options. Several theories have been developed to explain why people are faster for higher overall values, such as facilitation of information processing, reduced caution, or increased processing noise. Importantly, these theories make different predictions for how overall value should influence accuracy, though current results in the literature provide mixed conclusions. Here, we reanalyzed data from 40 previous studies to examine whether decision accuracy is consistently influenced by the overall value of the options. We find that, aside from low-level stimuli-driven effects, decision accuracy does not show a consistent pattern of increase or decrease based on overall value. Our results suggest that earlier claims of a systematic effect of overall value on decision accuracy may have been premature. We provide a mechanistic account of results, discuss why these results may challenge many prevailing theories of decision-making, and highlight open questions for future research.

The activation of human Vδ2 γδ T cells by phosphoantigens (pAg) strictly depends on transmembrane butyrophilin (BTN) molecules, specifically BTN3A isoforms and BTN2A1. Several bacteria, including M. tuberculosis, produce potent pAg and thus trigger a strong activation of Vδ2 T cells. The antigen-specific activation of CD4 and regulatory (Treg) T cells can be monitored by the rapid upregulation of CD154 and CD137, respectively. We have previously established that CD137 is also rapidly upregulated on Vδ2 T cells upon stimulation with pAg. In the present study, we have used antagonistic anti-BTN3A/2A1 antibodies to dissect the pAg-dependent and pAg-independent activation of Vδ2 T cells by various microbes. While the activation of Vδ2 T cells by pAg and aminobisphosphonate zoledronate was completely blocked by anti-BTN3A/2A1 antibodies, only partial inhibition was observed for activation with M. tuberculosis and other bacteria as analyzed by CD137/CD154 upregulation and intracellular interferon-γ expression. Similarly, anti-TCR antibody 7A5 and Lck inhibitor emodin had only a minimal inhibitory effect on activation by bacteria but strongly reduced pAg activation of Vδ2 T cells. Further studies revealed a crucial role of IL-18 in the BTN/TCR-independent early activation of Vδ2 T cells by bacteria. Neutralizing anti-IL-18 antibodies and inflammasome inhibition did not affect pAg activation of Vδ2 T cells but strongly reduced their activation by bacteria. Our results identify a BTN/TCR-independent but IL-18 and inflammasome-dependent activation pathway of Vδ2 T cells, which might be relevant for the role of Vδ2 T cells during bacterial infections.

Discussions around managing hunting and the consumption of wild animal meat increasingly emphasizes public health concerns and the risk of zoonotic spillover. In this article, we explore factors that may lead to under- or overestimating health risks from wild meat and break down key terminology for a multidisciplinary audience. We outline key principles of disease ecology and epidemiology that are often overlooked when quantifying spillover risk, and reflect on the importance of contextualizing health risks relative to food-health systems more broadly. We discuss how misrepresenting risks, intentionally or unintentionally, to justify conservation practices can have unintended negative conservation and public health consequences-despite the importance of conservation in protecting human health more broadly. We stress the importance of considering individual and local health outcomes (food security, neglected tropical diseases, etc.), not only those impacting global health (i.e. pandemic prevention). Finally, we advocate for evidence-informed, context-appropriate strategies for wild meat management.

The global demand for improved productivity, sustainability, welfare, and quality in livestock production presents significant challenges for breeders. Understanding trait correlations, often driven by pleiotropy, is essential for simultaneously improving traits of economic interest. Integrating multi-omics data and functional annotations can improve the disentangling of biological processes underlying the pleiotropic effect. Network-based machine learning (ML) models offer a robust solution for this integration. This study estimated gene-level P-values for pleiotropic effects using two phenotypic datasets: (i) Trait_GWAS, with phenotypic values of 12 traits covering milk production, composition, cheeseability, and mastitis resistance; and (ii) EBV_GWAS, with estimated breeding values for five similar traits, excluding cheeseability. Weighted gene co-expression networks (WGCNs) were constructed from milk somatic cell transcriptomics of Assaf ewes. Gene-term networks were built from gene ontology, metabolic pathways, and quantitative trait loci annotation for the genes in the WGCN. These networks were processed through a representative learning pipeline to create a latent vector representing gene importance. A hierarchical model integrated gene-level P-values and the latent vector, generating posterior probabilities of association for each gene. Significant results included 14 and 111 genes for Trait_GWAS and EBV_GWAS, respectively, with three shared genes (PHGDH, SLC1A4, and CSN3). Prioritized genes were linked to biological processes such as amino acid transport, lipid metabolism, mammary gland development, and immune regulation, often involving multiple biological functions. This reinforces the potential pleiotropic role of these genes. These findings highlight the utility of network-based ML models for prioritizing candidate genes with pleiotropic effects on milk, cheese, and health-related traits in dairy sheep.