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Sleep disorder significantly affects the life quality of a large number of people but is still an underrecognized disease. Dietary nutrition is believed to play a significant impact on sleeping wellness. Many nutritional supplements have been used trying to benefit sleep wellness. However, the relationship between nutritional components and sleep is complicated. Nutritional factors vary dramatically with different diet patterns and depend significantly on the digestive and metabiotic functions of each individual. Moreover, nutrition can profoundly affect the hormones and inflammation status which directly or indirectly contribute to insomnia. In this review, we summarized the role of major nutritional factors, carbohydrates, lipids, amino acids, and vitamins on sleep and sleep disorders and discussed the potential mechanisms.

Neutrophil extracellular traps (NETs), crucial in immune defense mechanisms, are renowned for their propensity to expel decondensed chromatin embedded with inflammatory proteins. Our comprehension of NETs in pathogen clearance, immune regulation and disease pathogenesis, has grown significantly in recent years. NETs are not only pivotal in the context of infections but also exhibit significant involvement in sterile inflammation. Evidence suggests that excessive accumulation of NETs can result in vessel occlusion, tissue damage, and prolonged inflammatory responses, thereby contributing to the progression and exacerbation of various pathological states. Nevertheless, NETs exhibit dual functionalities in certain pathological contexts. While NETs may act as autoantigens, aggregated NET complexes can function as inflammatory mediators by degrading proinflammatory cytokines and chemokines. The delineation of molecules and signaling pathways governing NET formation aids in refining our appreciation of NETs’ role in immune homeostasis, inflammation, autoimmune diseases, metabolic dysregulation, and cancer. In this comprehensive review, we delve into the multifaceted roles of NETs in both homeostasis and disease, whilst discussing their potential as therapeutic targets. Our aim is to enhance the understanding of the intricate functions of NETs across the spectrum from physiology to pathology.

Background Gene expression is a key determinant of cellular response. Natural variation in gene expression bridges genetic variation to phenotypic alteration. Identification of the regulatory variants controlling the gene expression in response to drought, a major environmental threat of crop production worldwide, is of great value for drought-tolerant gene identification. Results A total of 627 RNA-seq analyses are performed for 224 maize accessions which represent a wide genetic diversity under three water regimes; 73,573 eQTLs are detected for about 30,000 expressing genes with high-density genome-wide single nucleotide polymorphisms, reflecting a comprehensive and dynamic genetic architecture of gene expression in response to drought. The regulatory variants controlling the gene expression constitutively or drought-dynamically are unraveled. Focusing on dynamic regulatory variants resolved to genes encoding transcription factors, a drought-responsive network reflecting a hierarchy of transcription factors and their target genes is built. Moreover, 97 genes are prioritized to associate with drought tolerance due to their expression variations through the Mendelian randomization analysis. One of the candidate genes, Abscisic acid 8′-hydroxylase , is verified to play a negative role in plant drought tolerance. Conclusions This study unravels the effects of genetic variants on gene expression dynamics in drought response which allows us to better understand the role of distal and proximal genetic effects on gene expression and phenotypic plasticity. The prioritized drought-associated genes may serve as direct targets for functional investigation or allelic mining.

Recent advances in gene editing can produce large genotype–fitness maps for targeted genes, yet predicting the effects of mutations between genes remains challenging. Indeed, biochemical models require knowledge of underlying parameters and interactions, whereas machine learning methods typically lack interpretability, as they do not link model parameters to biological quantities. We introduce D-LIM, a neural network that infers low-dimensional fitness landscapes directly from mutation–fitness data. The distinctive feature of D-LIM is that it assumes genes act through independent gene-specific molecular phenotypes whose nonlinear interactions determine fitness. When this assumption holds, the model yields accurate predictions and interpretable effective phenotypes. Conversely, failure reveals that a low-dimensional model is insufficient. Applied to deep mutational scanning of metabolic pathways, protein–protein interactions, and yeast environmental adaptation, D-LIM achieves state-of-the-art predictive accuracy. The inferred phenotype–fitness landscapes reveal whether epistatic interactions can be captured by a low-dimensional continuous model and identify potential trade-offs. Moreover, D-LIM estimates mutational effects on the effective phenotypes, enabling weak extrapolation beyond the training domain. D-LIM demonstrates how simple structure constraints in a neural network can help inference and hypothesis generation in biology.

In the context of climate change, drought is one of the most limiting factors that influence crop production. Maize, as a major crop, is highly vulnerable to water deficit, which causes significant yield loss. Thus, identification and utilization of drought-resistant germplasm are crucial for the genetic improvement of the trait. Here we report on a high-quality genome assembly of a prominent drought-resistant genotype, CIMBL55. Genomic and genetic variation analyses revealed that 65 favorable alleles of 108 previously identified drought-resistant candidate genes were found in CIMBL55, which may constitute the genetic basis for its excellent drought resistance. Notably, ZmRtn16 , encoding a reticulon-like protein, was found to contribute to drought resistance by facilitating the vacuole H + -ATPase activity, which highlights the role of vacuole proton pumps in maize drought resistance. The assembled CIMBL55 genome provided a basis for genetic dissection and improvement of plant drought resistance, in support of global food security. High-quality genome assembly of a prominent drought-resistant maize germplasm CIMBL55 and genetic variation analyses provide a resource for genetic dissection and result in the improvement of maize drought resistance.

Synergistic drug combination screening is a promising strategy in drug discovery, but it involves navigating a costly and complex search space. While AI, particularly deep learning, has advanced synergy predictions, its effectiveness is limited by the low occurrence of synergistic drug pairs. Active learning, which integrates experimental testing into the learning process, has been proposed to address this challenge. In this work, we explore the key components of active learning to provide recommendations for its implementation. We find that molecular encoding has a limited impact on performance, while the cellular environment features significantly enhance predictions. Additionally, active learning can discover 60% of synergistic drug pairs with only exploring 10% of combinatorial space. The synergy yield ratio is observed to be even higher with smaller batch sizes, where dynamic tuning of the exploration-exploitation strategy can further enhance performance. The code can be found at https://github.com/LBiophyEvo/DrugSynergy

The homeostasis balance of copper, as an essential trace element for life activities, is crucial for maintaining the normal function of cells. Cuproptosis, discovered in recent years, is a novel type of programmed cell death triggered by the accumulation of excessive copper ions in mitochondria. The core mechanism lies in that copper ions, after being reduced by ferridoxin (FDX1), directly target and induce the oligomerization of the acylated tricarboxylic acid (TCA) cycle enzyme, thereby triggering fatal protein toxic stress. This distinctive mechanism operates independently of other recognized pathways of cell death, offering a novel perspective for elucidating the pathological processes underlying various diseases. A review of pertinent research conducted over the past four years reveals that cuproptosis is not only significantly implicated in the onset, progression, and treatment resistance of tumors but is also intricately associated with diverse pathological processes, including neurodegenerative diseases, cardiovascular diseases, metabolic disorders, and immune abnormalities. This article conducts a multi-level summary from molecular mechanisms to physiological and pathological significance; deeply explores the interaction between cuproptosis and various subcellular structures, as well as their complex signal regulatory network; and systematically expounds the cutting-edge strategies for treating cuproptosis, including traditional copper chelating agents, ion carriers, and copper-based nanomedicines, with a particular focus on the latest progress in the field of natural product research. This review has systematically summarized the therapeutic potential demonstrated by numerous natural active ingredients when precisely regulating the cuproptosis pathway to provide a theoretical reference for future research in this field.

Ammonia is a green zero-carbon fuel, yet its low reactivity poses challenges, including difficult ignition and slow combustion rates. Compared to diesel or biodiesel, dimethyl ether (DME) has no C-C bonds, which means it produces almost no soot and has a high cetane number that helps it ignite easily. So, using the highly reactive DME to help ignite liquid ammonia is a good way to make it burn better. This study uses computer simulations to look at how well liquid ammonia and DME work together as fuel with different injection setups. Results indicate optimal DME ignition enhancement at injector spacing L   = 6 cm, injection angle 180°, and ammonia energy share 70%, outperforming cases with spacings of 7–8 cm and angles of 150°, 120°, 90°, and 60°. At the same time, having shorter spacing during DME combustion leads to smaller areas for OH but more NH₂ formation, showing that ammonia is more effective at cooling the flame and that more ammonia is being used as fuel. Additionally, when DME is not burning, both OH and NH₂ areas grow larger at shorter spacings, showing that the fuel mixes sooner, the reaction areas get bigger, and the burning process is more complete. Regarding the bimodal NH₂ peaks, the initial peak reflects partial ammonia oxidation that is flame-entrained during DME combustion, while the secondary peak indicates the onset of autoignition, which is characterized by diminished reaction rates and reduced combustion intensity.

In order to develop microbial additives for rabbit feed, a spore-forming bacteria was isolated from the feces of Hyla rabbit using reinforced clostridium medium (RCM). The 16S rDNA sequence of the bacterium was subjected to pairwise sequence alignment using BLAST; the colony morphology, and physiological, biochemical, and stress resistance were studied. The results showed that the bacterium was Clostridium sartagoforme , a gram positive anaerobe, which can produce spores. The colony diameter was 0.5 mm—2.5 mm, the diameter of the bacteria was 0.5 μm—1.0 μm × 2.0 μm—6.3 μm, and the spore diameter was 1 μm—1.2 μm × 1 μm—1.2 μm. C . sartagoforme can utilize various sugars and alcohols such as fructose, galactose, sorbitol, and inositol. It secreted cellulase into the extracellular environment to form a transparent hydrolysis circle in Congo red medium, it could not liquify gelatin, and the lysine decarboxylase reaction was positive. In liquid medium it entered the stable growth period after 9 h of inoculation. Additionally, it had good stress resistance with a survival rate that exceeded 53% after gastric juice (pH 2.5) treatment for 3 h, it grew in a medium with a bile salt concentration of 0.3%, and the survival rate exceeded 85% after 10 minutes at 80°C. Moreover, animal testing indicated that this strain has no adverse effects on the morbidity and mortality of rabbits. In summary, C . sartagoforme XN-T4 was isolated from rabbit feces. This bacterium has good resistance to stress, can decompose a variety of monosaccharides and polysaccharides including cellulose, which is relatively harmless for animal health.

Bio-denitrification is vital in wastewater treatment plants (WWTPs), yet its integration with naturally abundant thermal energy remains unexplored. Here, we introduce a biohybrid-based pyroelectric bio-denitrification (BHPD) process that harnesses thermoelectric energy from ambient temperature fluctuations. By integrating Thiobacillus denitrificans with tungsten disulfide (WS 2 ), we develop a biohybrid system that achieves complete denitrification over three 5-day cycles under 5 °C temperature fluctuations. WS 2 either precipitates on the cellular surface or is internalized by cells, generating pyroelectric charges that serve as reducing equivalents to drive bio-denitrification. In real wastewater, the BHPD process enhances nitrate removal by up to 8.09-fold under natural temperature fluctuations compared to stable-temperature conditions. Life-cycle assessment demonstrates that the BHPD process has significantly lower environmental impacts than the conventional anaerobic-anoxic-oxic process, and cost analysis confirms its economic feasibility. Our findings highlight the potential of the pyroelectric effect in enhancing bio-denitrification, offering valuable insights for a paradigm shift in WWTPs. Bio-denitrification is vital in wastewater treatment, but its integration with naturally abundant thermal energy remains unexplored. Here, the authors develop a biohybrid system to enable complete pyroelectric denitrification over three successive 5-day cycles with room temperature fluctuations within 5 °C.