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Nuclear magnetic resonance (NMR)-based plasma fatty acids are objective biomarkers of many diseases. Herein, we aim to explore the associations of NMR-based plasma fatty acids with the risk of hepatocellular carcinoma (HCC) and chronic liver disease (CLD) mortality in 252,398 UK Biobank participants. Here we show plasma levels of n-3 poly-unsaturated fatty acids (PUFA) and n-6 PUFA are negatively associated with the risk of incident HCC [HR Q4vsQ1 : 0.48 (95% CI: 0.33–0.69) and 0.48 (95% CI: 0.28–0.81), respectively] and CLD mortality [HR Q4vsQ1 : 0.21 (95% CI: 0.13–0.33) and 0.15 (95% CI: 0.08–0.30), respectively], whereas plasma levels of saturated fatty acids are positively associated with these outcomes [HR Q4vsQ1 : 3.55 (95% CI: 2.25–5.61) for HCC and 6.34 (95% CI: 3.68–10.92) for CLD mortality]. Furthermore, fibrosis stage significantly modifies the associations between PUFA and CLD mortality. This study contributes to the limited prospective evidence on the associations between plasma-specific fatty acids and end-stage liver outcomes. Different fatty acids have been associated to liver diseases. Here, the authors show that plasma levels of different circulating fatty acids are either negatively or positively associated with the risk of hepatocellular carcinoma and chronic liver disease mortality in the UK Biobank cohort.

The use of gaseous and air-captured CO 2 for technical biosynthesis is highly desired, but elusive so far due to several obstacles including high energy (ATP, NADPH) demand, low thermodynamic driving force and limited biosynthesis rate. Here, we present an ATP and NAD(P)H-free chemoenzymatic system for amino acid and pyruvate biosynthesis by coupling methanol with CO 2 . It relies on a re-engineered glycine cleavage system with the NAD(P)H-dependent L protein replaced by biocompatible chemical reduction of protein H with dithiothreitol. The latter provides a higher thermodynamic driving force, determines the reaction direction, and avoids protein polymerization of the rate-limiting enzyme carboxylase. Engineering of H protein to effectively release the lipoamide arm from a protected state further enhanced the system performance, achieving the synthesis of glycine, serine and pyruvate at g/L level from methanol and air-captured CO 2 . This work opens up the door for biosynthesis of amino acids and derived products from air. The use of gaseous and air-captured CO 2 for technical biosynthesis is highly desired but challenging due to high energy demands. Here, the authors present an ATP and NAD(P)H-free chemoenzymatic system for glycine, serine, and pyruvate biosynthesis by coupling methanol with gaseous and air-captured CO 2 .

This study presents a pedagogically motivated inquiry into the cross-cultural rhetorical patterns of academic writing, focusing on the use of general nouns (GNs). The research was initiated in response to persistent difficulties observed among advanced L2 writers, who struggle to use GNs with appropriate nuance to establish an authoritative stance. Employing a corpus-driven methodology, the study analyzes two purpose-built corpora: the Chinese Academic Written English Corpus (CAWEC) and the Inner-Circle Affiliated Written English Corpus (ICAWEC). The findings reveal divergent rhetorical tendencies. Writers in the CAWEC show a statistically significant preference for “Research-group” (e.g., study , research ) and “Result-group” nouns (e.g., difference , results ). Their collocational patterns, marked by temporality ( current study ) and subjectivity ( our study ), are consistent with a conceptual metaphor of ACADEMIC PROGRESS IS A JOURNEY. In contrast, writers in the ICAWEC use “Example-group” (e.g., case , fact ) and certain “Discussion-group” nouns (e.g., argument ) more frequently, dominated by objectifying collocations ( the study ). These patterns suggest a spatialized argumentative strategy consistent with a conceptual metaphor of THE STUDY IS A KNOWLEDGE CONTAINER. By making these frameworks explicit, the study proposes a pedagogical model to expand L2 learners’ rhetorical repertoires and metacognitive awareness, equipping them to navigate Anglophone academic discourse.

Quarantine and isolation measures urgently adopted to control the COVID-19 pandemic might potentially have negative psychological and social effects. We conducted this cross-sectional, nationwide study to ascertain the psychological effect of quarantine and identify factors associated with mental health outcomes among population quarantined to further inform interventions of mitigating mental health risk especially for vulnerable groups under pandemic conditions. Sociodemographic data, attitudes toward the COVID-19, and mental health measurements of 56,679 participants from 34 provinces in China were collected by an online survey from February 28 to March 11, 2020. Of the 56,679 participants included in the study (mean [SD] age, 36.0 [8.2] years), 27,149 (47.9%) were male and 16,454 (29.0%) ever experienced home confinement or centralized quarantine during COVID-19 outbreak. Compared those without quarantine and adjusted for potential confounders, quarantine measures were associated with increased risk of total psychological outcomes (prevalence, 34.1% vs 27.3%; odds ratio [OR], 1.34; 95% CI, 1.28-1.39; P < 0.001). Multivariable logistic regression analyses showed that vulnerable groups of the quarantined population included those with pre-existing mental disorders or chronic physical diseases, frontline workers, those in the most severely affected areas during outbreak, infected or suspected patients, and those who are less financially well-off. Complying with quarantine, being able to take part in usual work, and having adequate understanding of information related to the outbreak were associated with less mental health issues. These results suggest that quarantine measures during COVID-19 pandemic are associated with increased risk of experiencing mental health burden, especially for vulnerable groups. Further study is needed to establish interventions to reduce mental health consequences of quarantine and empower wellbeing especially in vulnerable groups under pandemic conditions.

Alzheimer's disease (AD), the most common form of dementia, is highly prevalent in older adults. The main clinical feature is the progressive decline of memory function, which eventually leads to the decline of cognitive function. At present, the pathogenesis of AD is unclear. In the disease process, synaptic changes are the key. Recent studies have shown that the dysregulation of RNA methylation is related to many biological processes, including neurodevelopment and neurodegenerative diseases. N6-methyladenosine (m6A) is the most abundant modification in eukaryotic RNA. In this study, RNA m6A methylation was quantified in APP/PS1 transgenic mice, which is an AD mouse model, and C57BL/6 control mice, and data showed that m6A methylation was elevated in the cortex and the hippocampus of APP/PS1 transgenic mice. Next, the alterations of m6A RNA methylation in AD and in C57BL/6 mice were investigated using high-throughput sequencing. Genome-wide maps of m6A mRNA showed that the degrees of m6A methylation were higher in many genes and lower in others in AD mice. Interestingly, the expression of the m6A methyltransferase METTL3 was elevated and that of the m6A demethylase FTO was decreased in AD mice. The data were analyzed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, and pathways that might be related to synaptic or neuron development and growth were constructed. The related pathways and genes predicted the potential roles of the differentially expressed m6A methylation RNA in AD. Collectively, our findings demonstrate that the m6A methylation of RNA promotes the development of AD.

Animal husbandry is a vital sector in China’s agriculture sector, contributing to over one-third of its agricultural output, and more than 40% of farmers’ income. However, this industry is vulnerable to risks arising from production and operation, such as disease outbreaks, natural disasters, and market fluctuations. Livestock insurance can help mitigate these risks, but the lack of reliable data on shed environments has hindered its effectiveness. The objective of this study is to propose a livestock shed environmental regulatory platform that utilizes blockchain and the Internet of Things to ensure data authenticity, real-time monitoring, and transparency in the regulatory process. The platform also automates the insurance process, reducing costs and improving efficiency. The proposed platform employs blockchain to ensure data authenticity and devices to monitor and collect real-time environmental data. It also utilizes smart contracts to automate the insurance process, from negotiating and signing contracts to making insurance claims. The system’s design rationale, architecture, and implementation are detailed. The proposed platform has been implemented and currently manages over 300,000 livestock animals with more than 350,000 insurance contracts signed. The use of blockchain and the Internet of Things has ensured data authenticity, real-time monitoring, and transparency in the regulatory process, while the automation of the insurance process has reduced costs and improved efficiency. The proposed livestock shed environmental regulatory platform has the potential to improve the effectiveness of livestock insurance in China by addressing the critical issue of data reliability. The use of blockchain and the Internet of Things has enabled real-time monitoring, data authenticity, and transparency in the regulatory process, while the automation of the insurance process has improved efficiency and reduced costs. This platform could serve as a model for other countries looking to improve the effectiveness of their livestock insurance programs.

To investigate the mechanism of action of rhubarb in the treatment of cervical cancer by computer simulation techniques such as network pharmacology and molecular docking technology. The active ingredients of rhubarb were identified using TCMSP and HERB databases, and active ingredient target prediction was performed using SEA and Swiss TargetPrediction; cervical cancer-related targets were collected through four databases, namely, OMIM, GeneCards, CTD, and GDA; common targets of drugs and diseases were obtained through Draw Venn diagram; STRING online platform was applied to build protein-protein interaction networks (PPI) and core targets and most important modules were screened by cytoscape 3.10.3; use DAVID and REACTOM databases to perform GO functional enrichment analysis and KEGG pathway enrichment analysis, and visualize the results; Finally, molecular docking of key active ingredients and targets was performed by PubChem database and Auto Dock software, and the results were visualized by PyMOL. 23 active ingredients and 106 common targets were obtained after screening. The results of GO and KEGG enrichment analysis indicated that rhubarb is involved in phosphorylation, ATP binding, EGFR, HIF-1, PI3K-AKT, ESR-mediated signaling, IL-4 and IL-13 signaling pathways in cervical cancer treatment. The molecular docking results showed that rhubarb key active ingredients 3,5,3’-trihydroxy-6,7,4’-trimethoxyflavone, eupatin,5-carboxy-7-hydroxy-2-methyl-benzopyran-γ-one, rhapontigenin, chrysophanol had good docking activities with the core targets EGFR, IGF1R, AKT1, MMP9, MET, and SRC, among which AKT1 had the lowest binding energy to chrysophanol, indicating the strongest affinity between them. A variety of active ingredients in rhubarb play a therapeutic role in cervical cancer by regulating multi-targets and multiple pathways, among which, AKT1 showed higher correlation with ESR-mediated signaling, but the relevant results have to be verified by further in vitro and in vivo experiments.

Early spring sowing of maize in semi-arid, wind-eroded regions is increasingly threatened by cold snaps due to climate change.These events, often coupled with uneven soil moisture distribution,compromise seedling emergence and early development. Identifying critical temperature and moisture thresholds is essential to ensure successful germination in these vulnerable environments.A factorial experiment was conducted in a controlled environment using maize seeds (Zea mays L.) exposed to diurnal temperature cycles.Treatments included five minimum temperatures (0,2,4,6,8°C), three chilling durations (2,4,6 hours),and four soil moisture levels (60,70,80,90% field capacity). Key germination metrics,including final germination rate, weighted germination time,synchrony,delay days,and seedling dry matter at day 30,were measured and analyzed using three-way ANOVA and Pearson correlations. Temperatures below 6°C significantly delayed germination and reduced final germination rates,particularly under low moisture conditions.Moisture levels ≥80% effectively mitigated chilling effects at moderate temperatures(4 ~ 6°C).Extended chilling durations further suppressed germination.The strongest interaction was observed between minimum temperature and soil moisture.Seedling dry matter accumulation was also significantly affected by all three factors and their interactions.Soil moisture serves as a critical buffer against chilling stress during maize germination. This study provides quantitative benchmarks for temperature and moisture combinations that optimize early maize emergence under extreme spring weather, offering practical insights for precision moisture management in semi-arid agriculture.

The issue of temperature rise and heat dissipation becomes crucial to enhancing motor performance as permanent magnet synchronous motors (PMSMs) for electric vehicles (EVs) advance toward high power densities and heat load densities. A unique frame-rotor dual-waterway cooling system is developed to address the heat dissipation problem of high-power-density permanent magnet synchronous machines (HPDPMSMs). The frame contains the outside water circuit, whilst the rotor support cylinder is fitted with an inner water circuit. The dual water circuits constitute a cooling circulation system via a water-cooled bearing chamber and the spinning shaft. Through thermodynamic calculations of the waterway, the structural characteristics are ascertained, and a fluid-structure interaction temperature field simulation model is developed. The efficiency of the dual waterway cooling structure is validated by comparing its cooling impact to that of the single waterway structure. The analysis of temperature distribution in the motor with a dual-waterway cooling structure, under different coolants, extreme operational conditions, and variable water velocities, validates the efficacy of the developed frame-rotor dual-waterway cooling system.

Knowledge distillation (KD) enables the training of lightweight yet effective models, particularly in the visual domain. Meanwhile, reinforcement learning (RL) facilitates adaptive learning through environment-driven interactions, addressing the limitations of KD in handling dynamic and complex tasks. We propose a novel two-stage framework integrating Knowledge Distillation with Reinforcement Learning (KDRL) to enhance model adaptability to complex data distributions, such as remote sensing and medical imaging. In the first stage, supervised fine-tuning guides the student model using logit and feature-based distillation. The second stage refines the model via RL, leveraging confidence-based and cluster alignment rewards while dynamically reducing reliance on task loss. By combining the strengths of supervised knowledge distillation and reinforcement learning, KDRL provides a comprehensive approach to address the dual challenges of model efficiency and domain heterogeneity. A key innovation is the introduction of auxiliary layers within the student encoder to evaluate and reward the alignment of the characteristics with the teacher’s cluster centers, promoting robust feature learning. Our framework demonstrates superior performance and computational efficiency across diverse tasks, establishing a scalable design for efficient model training. Across remote sensing benchmarks, KDRL boosts the lightweight CLIP/ViT-B-32 student to 69.51% zero-shot accuracy on AID and 80.08% on RESISC45; achieves state-of-the-art cross-modal retrieval on RSITMD with 67.44% (I→T) and 74.76% (T→I) at R@10; and improves DIOR-RSVG visual-grounding precision to 64.21% at Pr@0.9. These gains matter in real deployments by reducing missed targets and speeding analyst search on resource-constrained platforms.