Investigating the role of non-ionic surfactants as electrolyte additives for improved zinc anode performance in aqueous batteries.

Zinc metal anodes encounter significant challenges, including dendrite growth, hydrogen evolution, and corrosion, all of which impede the rate capability and longevity of aqueous zinc-ion batteries (AZIBs). To effectively tackle these issues, we introduced Tween-80 into the traditional ZnSO4 electrolyte as an additive. Tween-80 possesses electronegative oxygen atoms that enable it to adsorb onto the zinc (Zn) anode surface, facilitating the directional deposition of Zn metal along the (002) orientation. The hydroxyl and ether groups within Tween-80 can displace some of the coordinated water molecules in the Zn2+ inner solvation shell. This disruption of the hydrogen bond network regulates the solvation structure of Zn2+ ions and suppresses the possibility of hydrogen evolution. Moreover, the long hydrocarbon chain present in Tween-80 exhibits excellent hydrophobic properties, aiding in the resistance against corrosion of the Zn anode by water molecules and reducing hydrogen evolution. Consequently, a symmetric cell equipped with the Tween-80 additive can cycle stably for over 4000 h at 1 mA cm-2 and 1 mA h cm-2. When paired with the V2O5 cathode, the full cell demonstrates a high-capacity retention rate exceeding 80 % over 1000 cycles at a current density of 2 A g-1. This study underscores the advantages of utilizing non-ionic surfactants for achieving high-performance aqueous zinc-ion batteries.

Inhibition of the FOXO1-ROCK1 axis mitigates cardiomyocyte injury under chronic hypoxia in Tetralogy of Fallot by maintaining mitochondrial quality control.

INTRODUCTION: Persistent chronic myocardial hypoxia causes disturbances in mitochondrial quality control (MQC), ultimately leading to increased cardiomyocyte injury in patients with Tetralogy of Fallot (TOF). The present study aimed to identify the key effector molecules of cardiomyocyte injury under chronic hypoxia in TOF. METHODS: Clinical data from TOF patients were collected and whole transcriptome sequencing was performed on myocardial samples. Chronic hypoxia models were established in cardiac-specific knockout mice and cardiomyocytes, and a series of molecular experiments were used to determine the specific mechanisms involved. RESULTS: Clinical cohort data and whole-transcriptome sequencing analysis of myocardial samples from TOF patients revealed that forkhead box O1 (FOXO1) plays an important role in chronic hypoxic cardiomyocyte injury. In a model of chronic hypoxia established in FOXO1 cardiac-specific knockout mice and FOXO1 gene-deficient cardiomyocytes, the AMPK signaling pathway regulates the expression of FOXO1, which in turn disrupts MQC by regulating the transcriptional activation of Rho-associated protein kinase 1 (ROCK1), and increasing the production of mitochondrial ROS, thereby exacerbating damage to cardiomyocytes. Excessive reactive oxygen species (ROS) production during MQC dysfunction further activates Cox7a2L to increase the assembly of the respiratory chain supercomplex. In addition, we found that miR-27b-3p partially binds to the 3' untranslated region of FOXO1 to exert a protective effect. CONCLUSIONS: Maintenance of MQC under chronic hypoxia is achieved through a series of injury-protection mechanisms, suggesting that FOXO1 inhibition may be crucial for future mitigation of chronic hypoxic cardiomyocyte injury in TOF.

Multi-omics integration analysis reveals the role of N6-methyladenosine in lncRNA translation during glioma stem cell differentiation.

Glioblastoma is one of the most lethal brain diseases in humans. Although recent studies have shown reciprocal interactions between N6-methyladenosine (m6A) modifications and long noncoding RNAs (lncRNAs) in gliomagenesis and malignant progression, the mechanism of m6A-mediated lncRNA translational regulation in glioblastoma remains unclear. Herein, we profiled the transcriptomes, translatomes, and epitranscriptomics of glioma stem cells and differentiated glioma cells to investigate the role of m6A in lncRNA translation comprehensively. We found that lncRNAs with numerous m6A peaks exhibit reduced translation efficiency. Transcript-level expression analysis demonstrates an enrichment of m6A around short open reading frames (sORFs) of translatable lncRNA transcripts. Further comparison analysis of m6A modifications in different RNA regions indicates that m6A peaks downstream of sORFs inhibit lncRNA translation more than those upstream. Observations in glioma-associated lncRNAs H19, LINC00467, and GAS5 further confirm the negative effect of m6A methylation on lncRNA translation. Overall, these findings elucidate the dynamic profiles of the m6A methylome and enhance the understanding of the complexity of lncRNA translational regulation.

Achromatic Full Stokes Polarimetry Metasurface for Full-Color Polarization Imaging in the Visible Range.

Metasurfaces provide an ultrathin platform for compact, real-time polarimetry. However, their applications in polychromatic scenes are restricted by narrow operating bandwidths that causes spectral information loss. Here, we demonstrate full-color polarization imaging using an achromatic polarimeter consisting of four polarization-dependent metalenses. Leveraging an intelligent design scheme, we achieve effective arbitrary phase compensation and multiobjective matching with a limited database. This system provides broadband achromaticity across wavelengths from 450 to 650 nm, resulting in a relative bandwidth of approximately 0.364 for full Stokes imaging. Experimental reconstruction errors for wavelengths of 450, 550, and 650 nm are 7.5%, 5.9%, and 3.8%, respectively. Performance is evaluated based on both achromatic bandwidth and crosstalk, with our design achieving three times the performance of the current state-of-the-art. The full-color, full-polarization imaging capability of the device is further validated with a customized object. The proposed scheme advances polarization imaging for practical applications.

Low serum Metrnl levels are associated with increased risk of sarcopenia in the older adults.

PURPOSE: Sarcopenia is a geriatric syndrome characterized by progressive loss of muscle mass and function. Meteorin-like (Metrnl) is a secretory protein that has protective effects on skeletal muscle injury. However, the association of Metrnl level with sarcopenia remains unclear. METHODS: A total of 772 community-dwelling older adults (median age = 76 years), comprising 409 males and 363 females, from both urban and rural areas were enrolled. Serum Metrnl was measured by enzyme-linked immunosorbent assay. Appendicular skeletal muscle mass index (ASMI), grip strength, and gait speed were measured for the assessment of sarcopenia. RESULTS: We found that serum Metrnl levels were lower in patients with sarcopenia [median (IQR) = 180.1 (151.3-220.3) pg/mL] than older adults without sarcopenia [211.9 (163.2-270.0) pg/mL, P < 0.001]. Receiver-operating characteristic curve analysis showed that the optimal cut-off value of serum Metrnl level that predicted sarcopenia was 197.2 pg/mL with a sensitivity of 59.2% and a specificity of 63.8% (AUC = 0.63, 95% CI = 0.59-0.67, P < 0.001). Multivariate logistic regression analyses showed that lower serum Metrnl level (< 197.2 pg/mL) was significantly associated with increased risk of sarcopenia (adjusted OR = 2.358, 2.36, 95% CI = 1.528-3.685, P < 0.001). Moreover, serum Metrnl concentration was positively correlated with the components of sarcopenia including ASMI (r = 0.135, P < 0.001), grip strength (r = 0.102, P = 0.005), and gait speed (r = 0.106, P = 0.003). CONCLUSIONS: Taken together, our findings demonstrate that low serum Metrnl level is correlated with increased risk of sarcopenia in the older adults.

Exploring Mitochondrial Autophagy Dysregulation in Osteosarcoma: Its Implications for Prognosis and Targeted Therapy.

OBJECTIVE: This study aimed to investigate the differential expression of mitophagyrelated genes in osteosarcoma patients with distinct prognostic outcomes and explore potential molecular regulatory mechanisms. METHODS: We analyzed microarray data from metastatic and nonmetastatic osteosarcoma patients using the UCSC dataset. Differential gene screening and intersection of mitophagy-related genes were performed using NetworkAnalyst. Random forest and LASSO regression were employed to screen selected genes and establish a risk prediction model. Functional enrichment analysis, protein- protein interaction (PPI) networks, immunoassays, and in vitro experiments were conducted to validate the findings. RESULTS: Seven differentially expressed genes were identified, and a robust risk prediction model was developed (AUC=0.886). PPI and functional enrichment analyses provided insights into relevant molecules and regulatory pathways. The immunoassay results revealed differences in the immune environment between the metastatic and nonmetastatic groups. Immunohistochemistry demonstrated significant downregulation of EPHA3 expression in the metastatic group, and in vitro experiments indicated that inhibiting EPHA3 increased the proliferative activity and migration ability of osteosarcoma cells. CONCLUSION: Our study suggests that the downregulation of EPHA3 may contribute to mitochondrial autophagy dysfunction, thereby increasing the risk of osteosarcoma metastasis.

[A Method for Fine Mapping of Carbon Emissions from Regional Land Use Change and Its Application].

Land use changes are always patchy and widespread within a region, making it a challenge to identify the point-scale pressure of reducing carbon emissions from land use/cover change （LUCC）. The carbon emission observation index （CEOI） was thus proposed to conduct the point-scale comparability analysis, which was based on the unique net C flux effects of conversions between two different land use types. Then, the spatial-temporal characteristics of land use changes and the resulting pressure of reducing carbon emissions were studied in the Weihe River Basin of China, which adopted the LUCC data from 2000 to 2020 and models of the Markov transition matrix （MTM）, compound carbon emission coefficients （CEC） of various types of land use changes, and the CEOI-based classification method on point-scale pressure of reducing carbon emissions. The results showed that： ① The net C flux was from 3.551 Tg C （2000-2010） to 7.031 Tg C （2010-2020）, and the pressure of reducing carbon emissions from LUCC had been continuously increasing, which was mainly driven by the significant increase in change-spots with the super-strong ability to reduce carbon emissions. ② Due to contributions from change spots with carbon uptake ability, the amount of carbon released to the atmosphere was eliminated by approximately 19.21% over the period 2000-2020 and approximately 37.4% during 2000-2010. ③ Change spots on various pressure levels for reducing carbon emissions were distributed unevenly in the basin, with their gravity points in the previous 10 years （2010-2020） far away from those during 2000-2010. Additionally, the gravity points of change-spots with a strong ability to reduce carbon emissions from conversions of grassland into forestland moved northeastward from Tianshui City to Pingliang City, whereas the gravity points of other change-spots with different abilities to reduce carbon emissions were mostly northwestward to the north-central region with higher elevations from the Middle and Lower Reaches of the Weihe River Basin with low elevations.

Development of innovative multi-epitope mRNA vaccine against central nervous system tuberculosis using in silico approaches.

Tuberculosis(TB) of the Central nervous system (CNS) is a rare and highly destructive disease. The emergence of drug resistance has increased treatment difficulty, leaving the Bacillus Calmette-Guérin (BCG) vaccine as the only licensed preventative immunization available. This study focused on identifying the epitopes of PknD (Rv0931c) and Rv0986 from Mycobacterium tuberculosis(Mtb) strain H37Rv using an in silico method. The goal was to develop a therapeutic mRNA vaccine for preventing CNS TB. The vaccine was designed to be non-allergenic, non-toxic, and highly antigenic. Codon optimization was performed to ensure effective translation in the human host. Additionally, the secondary and tertiary structures of the vaccine were predicted, and molecular docking with TLR-4 was carried out. A molecular dynamics simulation confirmed the stability of the complex. The results indicate that the vaccine structure shows effectiveness. Overall, the constructed vaccine exhibits ideal physicochemical properties, immune response, and stability, laying a theoretical foundation for future laboratory experiments.

Inherent symmetry and flexibility in hepatitis B virus subviral particles.

Chronic hepatitis B virus (HBV) infection poses a major global health challenge with massive morbidity and mortality. Despite a preventive vaccine, current treatments provide limited virus clearance, necessitating lifelong commitment. The HBV surface antigen (HBsAg) is crucial for diagnosis and prognosis, yet its high-resolution structure and assembly on the virus envelope remain elusive. Utilizing extensive datasets and advanced cryo-electron microscopy analysis, we present structural insights into HBsAg at a near-atomic resolution of 3.7 angstroms. HBsAg homodimers assemble into subviral particles with D2- and D4-like quasisymmetry, elucidating the dense-packing rules and structural adaptability of HBsAg. These findings provide insights into how HBsAg assembles into higher-order filaments and interacts with the capsid to form virions.

Research progress on macrophage polarization during osteoarthritis disease progression: a review.

Primary osteoarthritis (OA) is a prevalent degenerative joint disease that mostly affects the knee joint. It is a condition that occurs around the world. Because of the aging population and the increase in obesity prevalence, the incidence of primary OA is increasing each year. Joint replacement can completely subside the pain and minimize movement disorders caused by advanced OA, while nonsteroidal drugs and injection of sodium hyaluronate into the joint cavity can only partially relieve the pain; hence, it is critical to search for new methods to treat OA. Increasing lines of evidence show that primary OA is a chronic inflammatory disorder, with synovial inflammation as the main characteristic. Macrophages, as one of the immune cells, can be polarized to produce M1 (proinflammatory) and M2 (anti-inflammatory) types during synovial inflammation in OA. Following polarization, macrophages do not come in direct contact with chondrocytes; however, they affect chondrocyte metabolism through paracrine production of a significant quantity of inflammatory cytokines, matrix metalloproteinases, and growth factors and thus participate in inducing joint pain, cartilage injury, angiogenesis, and osteophyte formation. The main pathways that influence the polarization of macrophages are the Toll-like receptor and NF-κB pathways. The study of how macrophage polarization affects OA disease progression has gradually become one of the approaches to prevent and treat OA. Experimental studies have found that the treatment of macrophage polarization in primary OA can effectively relieve synovial inflammation and reduce cartilage damage. The present article summarizes the influence of inflammatory factors secreted by macrophages after polarization on OA disease progression, the main signaling pathways that induce macrophage differentiation, and the role of different polarized types of macrophages in OA; thus, providing a reference for preventing and treating primary OA.

Unveiling the development trends of environmental and human health concerns for pesticides: Integrating an intelligent approach and data mining across diverse databases.

The impact of pesticide use on environmental and human health has been a persistent global concern. In the era of big data, the scientific literature concerning big data is a significant source of information; however, it is difficult to construct an optimal policy based on traditional insight using keyword searches or a single static-specialized database. In this study, we constructed a new path for data mining across multiple databases to provide a comprehensive picture of the major issues concerning environmental pollution and human health as a result of pesticide use at the global scale. This approach uses a classic unsupervised learning algorithm, Latent Dirichlet Allocation (LDA), in combination with a newly developed dataset of pesticide-associated human health outcomes (PAHHO), including 618 health outcomes classified into 14 types of toxic effects. Our data visualization revealed a shift in the scientific center for pesticide research over the past five decades. The major issues concerning environmental pollutants and health outcomes varied among different countries and in different periods, which was verified in our analysis of several organochlorine pesticides (OCPs) about which people are particularly concerned. A cooccurrence network of adverse health outcomes has gradually increased, suggesting that the impact of pesticides on human health is persistent and cumulative. Our work not only provides a promising research direction related to the most concerning issues in a systematic and visualized way but also provides valuable references to formulate optimal strategies for the goal of the global "One Health" objective in pesticide regulation.

DeepIRES: a hybrid deep learning model for accurate identification of internal ribosome entry sites in cellular and viral mRNAs.

The internal ribosome entry site (IRES) is a cis-regulatory element that can initiate translation in a cap-independent manner. It is often related to cellular processes and many diseases. Thus, identifying the IRES is important for understanding its mechanism and finding potential therapeutic strategies for relevant diseases since identifying IRES elements by experimental method is time-consuming and laborious. Many bioinformatics tools have been developed to predict IRES, but all these tools are based on structure similarity or machine learning algorithms. Here, we introduced a deep learning model named DeepIRES for precisely identifying IRES elements in messenger RNA (mRNA) sequences. DeepIRES is a hybrid model incorporating dilated 1D convolutional neural network blocks, bidirectional gated recurrent units, and self-attention module. Tenfold cross-validation results suggest that DeepIRES can capture deeper relationships between sequence features and prediction results than other baseline models. Further comparison on independent test sets illustrates that DeepIRES has superior and robust prediction capability than other existing methods. Moreover, DeepIRES achieves high accuracy in predicting experimental validated IRESs that are collected in recent studies. With the application of a deep learning interpretable analysis, we discover some potential consensus motifs that are related to IRES activities. In summary, DeepIRES is a reliable tool for IRES prediction and gives insights into the mechanism of IRES elements.

Effect of microwave treatment and water-bath heating treatment on the performance of glutenin from Tiger nut seed meal: Insights into changes in structural characteristics, functional properties, and in vitro gastrointestinal digestibility.

In this study, the structural characteristics, functional properties, and in vitro gastrointestinal digestibility of glutenin from Tiger nut seed meal (TNSMG) treated by microwave (140-700 W, 20-60 s) and water-bath heating (40-100 °C, 10-30 min) were investigated. Analysis of the surface hydrophobicity, intrinsic fluorescence spectroscopy and Fourier transform infrared spectroscopy indicated that both microwave and water-bath heating treatments caused structure changes of TNSMG. The results showed an increase in the exposure of sulfhydryl groups and the content of ß-sheet, coupled with a decrease in the content of α-helix and ß-turn. These structural changes contributed to the improved solubility, foamability, emulsification properties, and digestibility of TNSMG under proper thermal treatment conditions. TNSMG exhibited the best solubility (68.48%) and foamability (85.56%) after water-bath heating treatment for 20 min at 80 °C. Furthermore, TNSMG showed the best emulsification property (9.61 m2/g) and digestibility (78.58%) when treated by microwave treatment at 560 W for 40 s.

Moderate alternate wetting and drying irrigation enhances drought-resistance abilities by improving structural mesophyll conductance of water-saving and drought-resistant rice under severe drought.

Water-saving and drought-resistant rice (WDR) coupled with alternate wetting and drying irrigation (AWDI) possesses a high photosynthetic potential due to higher mesophyll conductance (gm) under drought conditions. However, the physiological and structural contributions to the gm of leaves and their mechanisms in WDR under AWDI are still unclear. In this study, WDR (Hanyou 73) and drought-sensitive rice (Huiliangyou 898) were selected as materials. Three irrigation patterns were established from transplanting to the heading stage, including conventional flooding irrigation (W1), moderate AWDI (W2), and severe AWDI (W3). A severe drought with a soil water potential of -50 kPa was applied for a week at the heading stage across all treatments and cultivars. The results revealed that severe drought reduced gas exchange parameters and gm but enhanced antioxidant enzyme activities and malondialdehyde content in the three treatments and both cultivars. The maximal photosynthetic rate (Amax) of HY73 in the W2 treatment was greater than that in the other combinations of cultivars and irrigation patterns. The contribution of leaf structure (54%) to gm (gm-S, structural gm) was higher than that of leaf physiology (46%) to gm (gm-P, physiological gm) in the W2 treatment of Hanyou 73. Additionally, gm-S was significantly and linearly positively correlated with gm under severe drought. Moreover, both the initial and apparent quantum efficiencies were significantly and positively with gm in rice plants (p < 0.05). These results suggest that the improvements in photosynthesis and yield in the WDR combined with moderate AWDI can mainly be attributed to the enhancement of gm-S under severe drought conditions. Quantum efficiency may be a potential factor in regulating photosynthesis by cooperating with the gm of rice plants under severe drought conditions.

A comprehensive overview of diffuse correlation spectroscopy: Theoretical framework, recent advances in hardware, analysis, and applications.

Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by the already-complex DCS theoretical framework but also by the broad range of component options and system architectures. To facilitate new entry to this exciting field, we present a comprehensive review of DCS hardware architectures (continuous-wave, frequency-domain, and time-domain) and summarize corresponding theoretical models. Further, we discuss new applications of highly integrated silicon single-photon avalanche diode (SPAD) sensors in DCS, compare SPADs with existing sensors, and review other components (lasers, sensors, and correlators), as well as data analysis tools, including deep learning. Potential applications in medical diagnosis are discussed and an outlook for the future directions is provided, to offer effective guidance to embark on DCS research.

DiagSWin: A multi-scale vision transformer with diagonal-shaped windows for object detection and segmentation.

Recently, Vision Transformer and its variants have demonstrated remarkable performance on various computer vision tasks, thanks to its competence in capturing global visual dependencies through self-attention. However, global self-attention suffers from high computational cost due to quadratic computational overhead, especially for the high-resolution vision tasks (e.g., object detection and semantic segmentation). Many recent works have attempted to reduce the cost by applying fine-grained local attention, but these approaches cripple the long-range modeling power of the original self-attention mechanism. Furthermore, these approaches usually have similar receptive fields within each layer, thus limiting the ability of each self-attention layer to capture multi-scale features, resulting in performance degradation when handling images with objects of different scales. To address these issues, we develop the Diagonal-shaped Window (DiagSWin) attention mechanism for modeling attentions in diagonal regions at hybrid scales per attention layer. The key idea of DiagSWin attention is to inject multi-scale receptive field sizes into tokens: before computing the self-attention matrix, each token attends its closest surrounding tokens at fine granularity and the tokens far away at coarse granularity. This mechanism is able to effectively capture multi-scale context information while reducing computational complexity. With DiagSwin attention, we present a new variant of Vision Transformer models, called DiagSWin Transformers, and demonstrate their superiority in extensive experiments across various tasks. Specifically, the DiagSwin Transformer with a large size achieves 84.4% Top-1 accuracy and outperforms the SOTA CSWin Transformer on ImageNet with 40% fewer model size and computation cost. When employed as backbones, DiagSWin Transformers achieve significant improvements over the current SOTA modules. In addition, our DiagSWin-Base model yields 51.1 box mAP and 45.8 mask mAP on COCO for object detection and segmentation, and 52.3 mIoU on the ADE20K for semantic segmentation.

All-Calixarene-Protected Silver Nanocluster with All Silver Atoms in a Face-Centered Cubic Arrangement.

Tailoring the surface ligands of metal nanoclusters is important for engineering unique configurations of metal nanoclusters. Thiacalix[4]arene has found extensive applications in the construction of metal nanoclusters. In this investigation, we present the synthesis and characterization of the first all-calixarene-protected silver nanoclusters, [Ag(CH3CN)4]2[Ag44(BTCA)6] (Ag44, H4BTCA = p-tert-butylthiacalix[4]arene). Single-crystal X-ray structural analysis reveals that all silver atoms are in a face-centered cubic (fcc) arrangement. The formation of such an fcc structure is attributed to the selectively passivation on {100} facets by BTCA4-. Thiacalixarene substantially facilitates the stability of Ag44 due to its multiple coordination sites and bulkiness. Mass spectrometry and theoretical calculations reveal that Ag44 is a superatomic silver nanocluster with 22 free electrons in the following configuration: 1S21P61D61F22S21D4. This work not only elucidates the impact of macrocyclic ligands on the stabilization of silver clusters but also furnishes an approach for assembling atomically precise fcc nanoclusters.

Interplay of kernel shape and surface structure for NIR luminescence in atomically precise gold nanorods.

It is challenging to attain strong near-infrared (NIR) emissive gold nanoclusters. Here we show a rod-shaped cluster with the composition of [Au28(p-MBT)14(Hdppa)3](SO3CF3)2 (1 for short, Hdppa is N,N-bis(diphenylphosphino)amine, p-MBT is 4-methylbenzenethiolate) has been synthesized. Single crystal X-ray structural analysis reveals that it has a rod-like face-centered cubic (fcc) Au22 kernel built from two interpenetrating bicapped cuboctahedral Au15 units. 1 features NIR luminescence with an emission maximum at 920 nm, and the photoluminescence quantum yield (PLQY) is 12%, which is 30-fold of [Au21(m-MBT)12(Hdppa)2]SO3CF3 (2, m-MBT is 3-methylbenzenethiolate) with a similar composition and 60-fold of Au30S(S­t­Bu)18 with a similar structure. time-dependent DFT(TDDFT)calculations reveal that the luminescence of 1 is associated with the Au22 kernel. The small Stokes shift of 1 indicates that it has a very small excited state structural distortion, leading to high radiative decay rate (kr) probability. The emission of cluster 1 is a mixture of phosphorescence and thermally activated delayed fluorescence(TADF), and the enhancement of the NIR emission is mainly due to the promotion of kr rather than the inhibition of knr. This work demonstrates that the metal kernel and the surface structure are both very important for cluster-based NIR luminescence materials.

A shock-tube experimental and kinetic simulation study on the autoignition of methane at ultra-lean and lean conditions.

Coalbed methane represents an important kind of natural gas resource in many countries. However, the low-concentration property of coalbed methane limits its applications. To gain insight into the combustion kinetics of coalbed methane and facilitate its combustion utilization, this work reports an experimental and kinetic simulation study on the autoignition properties of methane at ultra-lean and lean conditions. A shock-tube (ST) facility is used for ignition delay time (IDT) measurements with equivalence ratios at 0.5, 0.1, and 0.05 with pressure at 2 and 10 bar under the temperature ranging from 1320 to 1850 K. The measured IDTs can be correlated into a general Arrhenius expression, and the equivalence ratio effect on IDTs is then analyzed. Seven detailed chemical kinetic mechanisms are employed to predict the IDTs and statistical error indicators are used to evaluate their performance. Detailed kinetic analysis via sensitivity and reaction path analysis is performed to uncover the kinetic differences among the seven mechanisms. It is shown that some of the reaction paths only exist in the NUIGMech1.3 mechanism, while the other detailed mechanisms do not consider them. Reaction path analysis indicates that the reactions related to O2, OH and O species become more important compared to the reactions involving CH3 and H radicals as the equivalence ratio decreases from lean to ultra-lean conditions. Detailed chemical kinetics analysis is also conducted to demonstrate the uncertainty of key reactions. The present work should be valuable to gain insight into the methane ignition characteristics and to facilitate kinetic mechanism optimization of methane combustion.

Within-canopy variation in chlorophyll fluorescence parameters can be well captured by vertical patterns of leaf biophysical and chemical traits.

The strong correlation between chlorophyll fluorescence (ChlF) parameters and photosynthesis highlights the need for a comprehensive spatial representation of ChlF parameters within the canopy. Such an approach is essential to advance our understanding and to improve the representation and modeling of water and carbon fluxes at scales ranging from the leaf to the canopy level. However, the challenge remains in determining how to effectively describe and track the variability of ChlF parameters within the canopy. In this study, we determined the variation in leaf biophysical and chemical traits and ChlF parameters along the vertical height of the canopy for several species in a temperate deciduous forest. We observed general associations of height with leaf biophysical and chemical traits and ChlF parameters, although these relationships were species-dependent. In addition, leaf biophysical and chemical traits, particularly light-harvesting pigments, showed significant effects on ChlF parameters. To effectively track variation in ChlF parameters within the canopy, we used gradient-boosted regression (GBR) models driven by leaf traits and species, which explained more than 80% of the variation in all ChlF parameters. Our study demonstrates the feasibility of utilizing leaf biophysical and chemical traits to predict vertical variation in ChlF parameters and provide supportive data for modeling canopy photosynthesis.