PNAS nexus最新文献

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.

Vascular hydraulic conductivity ( $L_p$ ) plays important roles in cancer metastasis, progression, and treatments. To date, there are a few noninvasive methods to image $L_p$ in cancers, and these methods rely on several assumptions. Common assumptions include that vascular conductivity is dominant over interstitial conductivity inside the tumor and that interstitial effects in the background are dominant over interstitial effects inside the tumor, limiting their applicability to cancers with a narrow range of $L_p$ . In this article, we propose a new method to image a wide range of $L_p$ in cancers using ultrasound spatiotemporal elastography data, without the limitations of existing methods. The method is based on the knowledge of Young's modulus, Poisson's ratio, and volumetric strain. This method shows superior performance with respect to the previous methods in terms of percent-relative-error in simulation studies. In vivo experimental results in an orthotopic mouse model of breast cancer show that $L_p$ estimated by ultrasound imaging using the proposed method is highly correlated with histological CD31 data. The proposed imaging methods can thus provide clinically significant information noninvasively and cost-effectively.

Humans are drawn to patterns and hierarchies in nature, mimicking them particularly in decoration and architecture. Natural patterns, however, are never purely esthetic and, since evolution works on a variety of factors simultaneously, natural structural systems are intrinsically multifunctional. In order to understand the roles that structural patterns play in biology (and therefore their potential capabilities and utilization in design, architecture and engineering), we need to catalog and encapsulate the diversity of examples and the materials involved. Here, we provide a first classification of biological "tilings," tessellated natural architectures that involve the repeated pattern of geometric, discrete elements bound by a joint material. By examining 100 examples across the Tree of Life, we reveal this natural structural motif is unexpectedly prevalent: we cover a huge taxonomic diversity, eight orders of magnitude in size scale, and myriad morphologies and functions ranging from optics to armor, allowing us to construct a hierarchical system of eight variables to classify form, function, and materiality in biological tilings. Using diverse means of data analysis (including multiple correspondence analysis), we show this database can be explored to reveal fundamental links among anatomical characteristics and functions as well as connections among and within taxonomic groups. Our resulting collection of "tessellated materials" and its companion website act therefore as a multidisciplinary meeting point (e.g. for biologists, designers, engineers, architects). In this way, our database offers windows for exploring selective pressures and trade-offs and a launchpad for future research and collaborative, cross-disciplinary, bioinspired projects.