Hyperuricemia is a common metabolic disorder and has become a global health concern. This study investigated the association between DNA methylation (DNAm) and serum uric acid (SUA) by conducting an epigenomewide association study (EWAS) in Chinese monozygotic (MZ) twins. Genomewide DNAm of 50 MZ twin pairs was profiled using the Infinium MethylationEPIC v2.0 BeadChip (935K). Generalized estimating equations (GEE) were used to examine the association between DNAm and SUA. Causal relationships between DNAm and SUA were assessed using ICE FALCON approach. Associations between mRNA expression and SUA were further assessed. Finally, candidate genes identified through epigenomewide association study (EWAS), causal inference, and gene expression analyses were validated in a longitudinal twin study. We identified 70 CpGs, mapping to genes such as DOK6 and NGLY1, significantly associated with SUA (Bonferroni correction p < 5.8 × 10−8). Causal analyses revealed one CpG with a causal effect of DNAm on SUA, 22 CpGs with causal effects of SUA on DNAm, and 33 CpGs showing bidirectional causality. Eleven genes displayed expression levels associated with SUA. DOK6, NGLY1, PKM, and SLC44A1 were selected as candidate genes, all of which showed unidirectional causal effect of SUA on DNAm. In the longitudinal analysis, baseline SUA levels (2012–13) were associated with subsequent DNAm levels in DOK6 and NGLY1 genes (2023–24). In conclusion, we found that SUA levels may influence DNAm variations, particularly at CpG loci within the DOK6 and NGLY1 genes. These findings provide key clues for future investigations into the mechanisms linking SUA with its epigenetic regulatory pathways.

Nutrition has long been investigated with respect to its influence on human health. With the availability of various omics data, such as metagenomics and metabolomics, novel insights have been obtained into the influence of nutrition, particularly concerning the gut microbiome. The gut microbiome plays an important role in the breakdown of food-derived compounds and in producing essential bioactive metabolites required for human health. However, this wealth of information made the interactions between nutrition and human health increasingly intricate, and unravelling these links is complex. This review covers the concepts of genome-scale metabolic modelling as a tool to understand the links between nutrition, the gut microbiome and human metabolism and its applications. Genome-scale metabolic modelling treats metabolism as a mathematical problem which was used to develop models of human metabolism that incorporate physiology and organ-specific metabolism, known as whole-body metabolic models (WBMs). WBMs can incorporate physiological data, such as sex, weight, and body fat percentage, as well as nutrition in the form of its metabolite constituents. Finally, the gut microbiome can also be incorporated through a mathematical representation of the species present, based on stool metagenomics. WBMs have already been applied to understand gut microbiome–host co-metabolism in various non-communicable diseases. However, challenges remain, as metabolites measured in food items in public databases typically cover only common metabolites, and engagement with end-users such as nutritionists and policymakers is limited. Nevertheless, WBMs represent a promising step towards digital metabolic twins and thus personalised nutrition and medicine.

Previous research shows that speakers use prosody to disambiguate between string-identical canonical information questions and noncanonical questions conveying surprise. In this study, we investigated whether the prosodic cues produced by speakers are enough for naïve listeners to distinguish between the two readings of two distinct question structures in French that can express either a request for information or an emotive reaction of surprise: ‘qu’est-ce que’ and ‘c’est quoi ce NP’ questions. This article provides experimental evidence that listeners can in fact disambiguate between the two interpretations based on prosodic cues alone, in particular the contour and the duration of the question word.

The genus Canoparmelia Elix & Hale contains c. 40 species, predominantly occurring in tropical and subtropical regions. The genus is characterized by having relatively narrow, subirregular lobes with rotund or subrotund eciliate margins, a pored epicortex, the presence of isolichenan in the cell walls, bifusiform conidia, and simple rhizines (Elix 1993; Crespo et al. 2010b). Phylogenetically, it belongs to the Parmotrema clade of parmelioid lichens (Crespo et al. 2010b). Since its original circumscription (Elix et al. 1986), species have been transferred to other genera, including Austroparmelina A. Crespo et al. (Crespo et al. 2010a), Crespoa (D. Hawksw.) Lendemer & B. P. Hodk. and Parmotrema A. Massal. (Crespo et al. 2010b; Hawksworth 2011; Lendemer & Hodkinson 2012; Kirika et al. 2016). In addition, some species of the genus Crespoa are morphologically similar to certain Canoparmelia species, which leaves uncertainty regarding the identification of the samples.

Synthetic control methods are widely used for causal inference in case studies and panel data settings, often applied to model counterfactuals for proportional outcomes. However, conventional synthetic control methods are designed for univariate outcomes, leading researchers to model counterfactuals for each proportion separately. We make the case for jointly estimating synthetic controls across multiple compositional outcomes. Using the same weights for each proportion establishes a constant control comparison, improving comparability while adhering to compositional constraints on treatment effects. We illustrate the benefits of the method through a simulation and two applications to recent empirical studies. This implementation integrates naturally with a wide range of synthetic control approaches, providing interpretable estimates for compositional panel data common in political science.

China impressed the world with its success in containing the COVID-19 pandemic in 2020 and 2021. Many researchers have attributed China’s success to its hybrid mode of urban neighborhood governance, which could effectively mobilize residents to implement strict surveillance and quarantine measures for the state. However, despite their growing attention to urban neighborhood governance, they tend to privilege the government’s role while neglecting residents’ agency, thereby perpetuating the myth of an omnipresent and omnipotent Chinese state and reducing residents to passive objects of governance. This article challenges earlier researchers’ assumptions by exploring a crisis scenario – the lockdown of Shanghai from March to June 2022 – in which the local government’s capacity to surveil and micro-manage individual residents and deliver public services was significantly hampered. Through semi-structured interviews with eighteen individuals who experienced the lockdown in Shanghai, it examines the self-preserving efforts made by Shanghai residents to address the most serious challenge confronting them – supply shortages. It demonstrates that spontaneous organization and autonomous action among Shanghai residents were crucial to their survival during the crisis.

Model organisms play a central role in biological research, yet the conceptual questions they raise continue to provoke debate among philosophers. This article examines one such question: What are the distinctive features that qualify model organisms as scientific models? Building on and extending previous work, we propose a refined conceptual framework for understanding model organisms in the context of contemporary scientific practice. Our framework highlights six key features that, collectively, uniquely characterize model organisms. We argue that model organisms should not be viewed merely as components within experimental systems but rather as autonomous experimental systems in their own right.