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Iron-based nanozymes, recognized for their biocompatibility and peroxidase-like activities, hold promise as catalysts in tumor therapy. However, their concurrent catalase-like activity undermines therapeutic efficacy by converting hydrogen peroxide in tumor tissues into oxygen, thus diminishing hydroxyl radical production. Addressing this challenge, this study introduces the hemin–cysteine–Fe (HCFe) nanozyme, which exhibits exclusive peroxidase-like activity. Constructed through a supramolecular assembly approach involving Fmoc- l -cysteine, heme, and Fe²⁺ coordination, HCFe distinctly incorporates heme and [Fe–S] within its active center. Sulfur coordination to the central Fe atom of Hemin is crucial in modulating the catalytic preference of the HCFe nanozyme towards peroxidase-like activity. This unique mechanism distinguishes HCFe from other bifunctional iron-based nanozymes, enhancing its catalytic selectivity even beyond that of natural peroxidases. This selective activity allows HCFe to significantly elevate ROS production and exert cytotoxic effects, especially against cisplatin-resistant esophageal squamous cell carcinoma (ESCC) cells and their xenografts in female mice when combined with cisplatin. These findings underscore HCFe’s potential as a crucial component in multimodal cancer therapy, notably in augmenting chemotherapy efficacy. Iron-based nanozymes are promising for tumor catalytic therapy owing to their biocompatibility and peroxidase-like activity, but the concurrent catalase-like activity undermines the therapeutic efficacy. Here, the authors address this issue by developing a hemin–cysteine–Fe nanozyme, which exhibits catalytic selectivity and exclusive peroxidase-like activity.

Natural antimicrobial peptides (AMPs) and enzymes (AMEs) are promising non-antibiotic candidates against antimicrobial resistance but suffer from low efficiency and poor stability. Here, we develop peptide nanozymes which mimic the mode of action of AMPs and AMEs through de novo design and peptide assembly. Through modelling a minimal building block of IHIHICI is proposed by combining critical amino acids in AMPs and AMEs and hydrophobic isoleucine to conduct assembly. Experimental validations reveal that IHIHICI assemble into helical β-sheet nanotubes with acetate modulation and perform phospholipase C-like and peroxidase-like activities with Ni coordination, demonstrating high thermostability and resistance to enzymatic degradation. The assembled nanotubes demonstrate cascade antifungal actions including outer mannan docking, wall disruption, lipid peroxidation and subsequent ferroptotic death, synergistically killing >90% Candida albicans within 10 min on disinfection pad. These findings demonstrate an effective de novo design strategy for developing materials with multi-antimicrobial mode of actions. Natural antimicrobial peptides and enzymes are good candidates for application but suffer from low stability. Here, the authors report on biomimetic self-assembling peptides which mimic both antimicrobial peptide and enzyme functionality, demonstrating application against fungal infection.

Due to climate effects and human influences, wildfire regimes in boreal forests are changing, leading to profound ecological consequences, including shortened fire return intervals and elevated tree mortality. However, a critical knowledge gap exists concerning the spatiotemporal dynamics of fire-induced tree mortality specifically within the vast North American boreal forest, as previous studies have predominantly focused on Mediterranean and tropical forests. Therefore, in this study, we used satellite observation data obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra MCD64A1 and related database data to study the spatial and temporal variability in burned area and forest mortality due to wildfires in North America (Alaska and Canada) over an 18-year period (2003 to 2020). By calculating the satellite reflectance data before and after the fire, fire-driven forest mortality is defined as the ratio of the area of forest loss in a given period relative to the total forest area in that period, i.e., the area of forest loss divided by the total forest area. Our findings have shown average values of burned area and forest mortality close to 8000 km2/yr and 40%, respectively. Burning and tree loss are mainly concentrated between May and September, with a corresponding temporal trend in the occurrence of forest fires and high mortality. In addition, large-scale forest fires were primarily concentrated in Central Canada, which, however, did not show the highest forest mortality (in contrast to the results recorded in Northern Canada). Critically, based on generalized linear models (GLMs), the results showed that fire size and duration, but not the burned area, had significant effects on post-fire forest mortality. Overall, this study shed light on the most sensitive forest areas and time periods to the detrimental effects of forest wildfire in boreal forests of North America, highlighting distinct spatial and temporal vulnerabilities within the boreal forest and demonstrating that fire regimes (size and duration) are primary drivers of ecological impact. These insights are crucial for refining models of boreal forest carbon dynamics, assessing ecosystem resilience under changing fire regimes, and informing targeted forest management and conservation strategies to mitigate wildfire impacts in this globally significant biome.

As the exploitation of marginal oil fields becomes increasingly critical, cost -effective and flexible offloading solutions are in high demand. This paper presents the design and performance analysis of a novel Mobile Offshore Output Terminal (MOOT) specifically engineered for the typhoon-prone South China Sea. Distinct from traditional permanent Single Point Mooring (SPM) systems, this terminal incorporates a retrievable suction pile foundation system to facilitate relocation and reuse across multiple field lifecycles. A coupled time- domain analysis is employed to evaluate the system’s global performance under extreme survival (100-year return period) and operational (1-year return period) conditions. The study focuses on the critical dynamics introduced by the shal- low water depth and the specific challenges of ensuring station-keeping integrity for a lightweight, mobile hull. Numerical simulations were conducted using Hy- droSTAR and OrcaFlex, complying with Bureau Veritas (BV) NR 493 standards. The results demonstrate that the proposed 3 × 2 mooring configuration maintains sufficient safety factors and acceptable offsets. The analysis highlights the robustness of the mobile design under complex coupled loads, verifying its feasibility as a resilient solution for marginal field development.

Floating liquefied natural gas (FLNG) facilities integrate natural gas liquefaction, storage, and offloading into a single vessel. During ship-to-ship (STS) side-by-side offloading, an LNG carrier (LNGC) moors alongside the FLNG to transfer liquefied cargo through a loading-arm system. The hydrodynamic interactions between the two vessels, combined with environmental loads, can lead to excessive relative motions that pose a risk of collision or damage to the loading arms and fenders. Accurate short-term prediction of vessel motions would provide operators with advance warning of potentially dangerous conditions, allowing preventive actions to be taken. This study presents a data-driven approach to short-term motion prediction using experimental data obtained from comprehensive basin model tests of an FLNG system. The model tests covered 15 environmental conditions, including survival conditions (100-year return period) and operating conditions (1-year return period), under both single-vessel and side-by-side configurations. Three prediction methods were evaluated: an autoregressive linear model, a single-degree-of-freedom multi-layer perceptron, and a multi-head attention cross-coupling network (MAC-Net) that leverages temporal attention, cross-DOF graph message passing, and multi-task learning with uncertainty-weighted loss. The results show that surge, sway, and yaw can be predicted with high skill scores at model-scale horizons of up to 4 s (32 s full-scale equivalent), while heave and pitch exhibit limited predictability beyond 2 s model scale. The MAC-Net model demonstrates particular advantages for roll prediction, achieving a skill score of 0.88 at a 4 s model-scale horizon compared to 0.76 for the conventional method, attributable to the physical coupling between roll and the horizontal-plane motions through the mooring system. These findings support a practical early warning concept in which horizontal-plane motions provide advance collision alerts and heave/pitch are treated as short-horizon monitoring quantities.

Background Spike is the grain-bearing organ in cereal crops, which is a key proxy indicator determining the grain yield and quality. Machine learning methods for image analysis of spike-related phenotypic traits not only hold the promise for high-throughput estimating grain production and quality, but also lay the foundation for better dissection of the genetic basis for spike development. Barley ( Hordeum vulgare L.) is one of the most important crops globally, ranking as the fourth largest cereal crop in terms of cultivated area and total yield. However, image analysis of spike-related traits in barley, especially based on CT-scanning, remains elusive at present. Results In this study, we developed a non-invasive, high-throughput approach to quantitatively measuring the multitude of spike architectural traits in barley through combining X-ray computed tomography (CT) and a deep learning model (UNet). Firstly, the spikes of 11 barley accessions, including 2 wild barley, 3 landraces and 6 cultivars were used for X-ray CT scanning to obtain the tomographic images. And then, an optimized 3D image processing method was used to point cloud data to generate the 3D point cloud images of spike, namely ‘virtual’ spike, which is then used to investigate internal structures and morphological traits of barley spikes. Furthermore, the virtual spike-related traits, such as spike length, grain number per spike, grain volume, grain surface area, grain length and grain width as well as grain thickness were efficiently and non-destructively quantified. The virtual values of these traits were highly consistent with the actual value using manual measurement, demonstrating the accuracy and reliability of the developed model. The reconstruction process took 15 min approximately, 10 min for CT scanning and 5 min for imaging and features extraction, respectively. Conclusions This study provides an efficient, non-invasive and useful tool for dissecting barley spike architecture, which will contribute to high-throughput phenotyping and breeding for high yield in barley and other crops.

ObjectiveTo explore the relationship between the clinical onset locations and the electrophysiological characteristics of different spinal segments in amyotrophic lateral sclerosis (ALS) patients. To develop a rapid examination method using electromyographs (EMGs) for the diagnosis of ALS.MethodsThe clinical symptoms and electrodiagnostic examination results of 150 patients with definite or probable ALS were retrospectively analyzed. The patients were divided into four groups according to the primary onset locations (arms and legs onset, arms onset, legs onset, and bulbar onset groups). The differences between the onset locations and the electrophysiological characteristics revealed the lower motor neuron dysfunction in EMGs.ResultsThe most affected onset location was the lower limbs (36.7%), particularly in the distal muscles. Nerve conduction showed that the sensory system was damaged in 22 patients (14.7%). The positive diagnostic rate of EMGs varied due to different onset locations. EMG abnormalities were seen in approximately 40% of asymptomatic limb muscles. Distal limb muscles showed higher electrodiagnostic sensitivity (78.4%) than proximal limb muscles. Cervical muscles showed the highest electrodiagnostic sensitivity (86.3%).ConclusionsThe sensory system in ALS patients was commonly impaired. Cervical muscles showed the highest electrodiagnostic sensitivity. The highest positive rate was generated from detecting the spinal segment onset and the special distal muscles onset ALS in our optimized test method. Through this improved examination based on the most affected individual muscles, physicians can greatly optimize the test duration and significantly reduce patient discomfort.

This study theoretically explores the underlying mechanisms through which China's AI-specific policies influence the allocation of labor resources and empirically examines this relationship using panel data from 224 prefecture-level cities in China from 2010 to 2020. The results indicate that AI policies have significantly improved labor resource allocation efficiency in these cities, a conclusion that remains robust across various sensitivity tests. Moreover, the policy effects exhibit notable heterogeneity across regions. Due to differences in economic foundations and resource endowments, the effectiveness of policy implementation varies significantly across different areas. Additionally, the role of policy tools, particularly in the areas of policy goals, research and development (R&D) support, and development environments, has proven crucial in enhancing labor resource allocation efficiency. Finally, the study also finds that AI policies have had a profound indirect impact on labor resource allocation by promoting the synchronous upgrading of industrial structures and optimizing the innovation element structure. The findings affirm the positive role of government intervention in improving labor resource allocation, enriching the research on labor economics and the use of policy tools, and providing important empirical evidence for optimizing AI policy delivery and enhancing its implementation effectiveness in developing countries. This study offers valuable insights for policy practice in labor markets.

Recurrent aphthous ulcer (RAU), one of the most common diseases in humans, has an unknown etiology and is difficult to treat. Thalidomide is an important immunomodulatory and antitumor drug and its effects on the gut microbiota still remain unclear. We conducted a metagenomic sequencing study of fecal samples from a cohort of individuals with RAU, performed biochemical assays of cytokines, immunoglobulins and antimicrobial peptides in serum and saliva, and investigated the regulation effects of thalidomide administration and withdrawal. Meanwhile we constructed the corresponding prediction models. Our metagenome-wide association results indicated that gut dysbacteriosis, microbial dysfunction and immune imbalance occurred in RAU patients. Thalidomide regulated gut dysbacteriosis in a species-specific manner and had different sustainable effects on various probiotics and pathogens. A previously unknown association between gut microbiota alterations and RAU was found, and the specific roles of thalidomide in modulating the gut microbiota and immunity were determined, suggesting that RAU may be affected by targeting gut dysbacteriosis and modifying immune imbalance. In-depth insights into sophisticated networks consisting of the gut microbiota and host cells may lead to the development of emerging treatments, including prebiotics, probiotics, synbiotics, and postbiotics.