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Abstract Metabolic reprogramming is a pivotal biological process in which cellular metabolic patterns change to meet the energy demands of increased cell growth and proliferation. In this review, we explore metabolic reprogramming and its impact on fibrotic diseases, providing a detailed overview of the key processes involved in the metabolic reprogramming of renal fibrosis, including fatty acid decomposition and synthesis, glycolysis, and amino acid catabolism. In addition, we report that Chinese medicine ameliorates renal inflammation, oxidative stress, and apoptosis in chronic kidney disease by regulating metabolic processes, thereby inhibiting renal fibrosis. Furthermore, we reveal that multiple targets and signaling pathways contribute to the metabolic regulatory effects of Chinese medicine. In summary, this review aims to elucidate the mechanisms by which Chinese medicine inhibits renal fibrosis through the remodeling of renal cell metabolic processes, with the goal of discovering new therapeutic drugs for treating renal fibrosis.

Abstract Necroptosis is intimately associated with the initiation and progression of colon adenocarcinoma (COAD). However, studies on necroptosis-related genes (NRGs) and the regulating long non-coding RNAs (NRGlncRNAs) in the context of COAD are limited. We retrieved the cancer genome atlas (TCGA) to collect datasets of NRGs and NRGlncRNAs on COAD patients. The risk model constructed using Cox and least absolute shrinkage and selection operator (LASSO) regression was then employed to identify NRGs and NRGlncRNAs with prognostic significance. Subsequently, we validated the results using gene expression omnibus (GEO) datasets from different populations, conducted Mendelian randomization (MR) analysis to explore the potential causal relationships between prognostic NRGs and COAD, and conducted cell experiments to verify the expression of prognostic NRGlncRNAs in COAD. Furthermore, we explored potential pathways and regulatory mechanisms of these prognostic NRGlncRNAs and NRGs in COAD through enrichment analysis, immune cell correlation analysis, tumor microenvironment analysis, immune checkpoint analysis, tumor sample clustering, and so on. We identified eight NRGlncRNAs (AC245100.5, AP001619.1, LINC01614, AC010463.3, AL162595.1, ITGB1-DT, LINC01857, and LINC00513) used for constructing the prognostic model and nine prognostic NRGs (AXL, BACH2, CFLAR, CYLD, IPMK, MAP3K7, ATRX, BRAF, and OTULIN) with regulatory relationships with them, and their validation was performed using GEO and GWAS datasets, as well as cell experiments, which showed largely consistent results. These prognostic NRGlncRNAs and NRGs modulate various biological functions, including immune inflammatory response, oxidative stress, immune escape, telomere regulation, and cytokine response, influencing the development of COAD. Additionally, stratified analysis of the high-risk and low-risk groups based on the prognostic model revealed elevated expression of immune cells, increased expression of tumor microenvironment cells, and upregulation of immune checkpoint gene expression in the high-risk group. Finally, through cluster analysis, we identified tumor subtypes, and the results of cluster analysis were essentially consistent with the analysis between risk groups. The prognostic NGRlncRNAs and NRGs identified in our study serve as prognostic indicators and potential therapeutic targets for COAD, providing a theoretical basis for the clinical diagnosis and treatment of COAD and offering guidance for further research.

Abstract There is no reliable causal evidence for the effect of statins on diabetic nephropathy (DN) and diabetic retinopathy (DR), and the results of previous observational studies are contradictory. Genetic variants linked to low-density lipoprotein cholesterol (LDL-C) from a UK biobank genome-wide association study and located within a 100kb window around HMGCR were used to proxy statins, comparing with PCSK9 inhibitors (control). DN and DR genome-wide association study summary statistics were obtained from the FinnGen study. Secondary MR analyses and NHANES cross-sectional data were used for validation. Drug-target Mendelian randomization (MR) was applied to investigate the association between the genetically proxied inhibition of HMGCR and PCSK9 with DN and DR, p

Wheat, being a crucial global food crop, holds immense significance for food safety and agricultural economic stability, as the quality and condition of its grains are critical factors. Traditional methods of wheat grain detection are inefficient, and the advancements in deep learning offer a novel solution for fast and accurate grain recognition. This study proposes an improved deep learning model based on YOLOv8n, referred to as YOLO-SDL, aiming to achieve efficient wheat grain detection. A high-quality wheat grain dataset was first constructed, including images of perfect, germinated, diseased, and damaged grains. Multiple data augmentation techniques were employed to enhance the dataset’s complexity and diversity. The YOLO-SDL model incorporates the ShuffleNetV2 architecture in its backbone and combines depthwise separable convolutions (DWConv) with the large separable kernel attention (LSKA) mechanism in its neck structure, significantly improving detection speed and accuracy while ensuring the model remains lightweight. The results indicate that YOLO-SDL achieves superior performance in wheat grain detection, balancing lightweight design and performance optimization. The model achieved a P of 0.942, R of 0.903, mAP50 of 0.965, and mAP50-95 of 0.859, with low computational complexity, making it suitable for resource-constrained environments. These findings demonstrate the efficiency of the ShuffleNetV2, DWConv, and LSKA structures. The proposed YOLO-SDL model provides a new technical solution for agricultural automation and serves as a reliable reference for detecting other crops.

Abstract Existential state of solutes substantially affects the efficiency and direction of various chemical and biological processes, about which current consensus is still limited at macro and micro levels. At the trace level, solutes assume a pivotal role across a spectrum of critical fields. However, their existential states, especially at interfaces, remain largely elusive. Herein, an exceptional evolution of solute molecules is unveiled from micro to trace, solution to interface, with the aid of surface‐enhanced Raman spectroscopy, extinction, DLS and theoretical simulations. Given predominant existence of monomers within the solution, these aggregates dominate the interfacial behavior of solute molecules. Moreover, a universal, aggregate‐controlled mechanism is demonstrated that aggregates triggered by cosolvent, which can dramatically promote efficiency of catalytic reactions. The results provide novel insights into the interaction mechanisms between reactants and catalysts, potentially offering fresh perspectives for the manipulation of multiphase catalysis and related biological processes.

Copper and copper alloy are commonly used to fabricate ship propellers and sealing valves that encounter harsh corrosion and wear. To improve their service life, Cu coating and Cu–AlN composite coatings with different volume content of AlN (10%, 20%, 30%) were prepared by cold spraying. The investigation focused on analyzing the coatings’ microstructure, microhardness, resistance to wear, and corrosion. Results show that the composite coatings with AlN have fewer pores and denser microstructure than the Cu coating. The coating demonstrates a relatively low deposition efficiency, resulting in the attainment of only 3.8 vol% of AlN when the feedstock AlN powder content was 30 vol%. The microhardness of Cu-3.8AlN coating is 159.7 HV, about 53.1% higher than that of Cu coating. The specific wear rate of Cu-3.8AlN coating is reduced by about 73.5% compared to that of Cu coating, measuring at 1.69 × 10−3 mm3/(N·m). Compared with other coatings, Cu-3.8AlN has the highest corrosion resistance (Ecorr = −0.199 V, Icorr = 1.2 μA/cm2, Rct = 3200 Ω·cm2). The Cu-3.8AlN coating exhibits superior wear and corrosion resistance among the four coatings, attributed to its highest level of densification and the presence of AlN particles.

A novel (Co40Cr25(FeNi)31Mo4)99·5C0.5 HEA (high entropy alloy) was prepared by vacuum arc melting furnace and hot rolled with a reduction of 20 % at 1100 °C, cold rolled with a reduction of ∼50 %, and then annealed at 800, 850, 900, 950, 1000, and 1100 °C for 6 min, respectively. The effect of annealed on the microstructure evolution and mechanical properties of HEAs were systematically analyzed. The results showed that the HEAs still maintains single FCC(Face-centered cubic) crystal structure after different annealing temperatures. With increasing annealed temperature, the area of abnormal grain gradually decreases and forming equiaxed grain finally. Annealing at lower temperature (800 °C) gives rise to the formation of nanoscale and submicron M23C6 carbide, which hindered the recrystallization of grains and resulting to the higher tensile strength of ∼1352 MPa and lower elongation of ∼37 % when comparing with other annealed HEAs. However, the amount of M23C6 precipitates decreases when the annealing temperature was increased to 1100 °C. At this time, the alloy structure was consisting of recrystallized grains and a large number of annealing twins, and the tensile strength decreased to ∼1062 MPa and the elongation increased to ∼85 %. Due to annealing twins effect and grain refinement, the 1100 °C annealed HEA achieved superior strength-ductility balance (TS × EL) of ∼90.3 GPa%, while the strength-ductility balance (TS × EL) of 800 °C annealed HEA was only ∼50 GPa%. The 1100 °C annealed HEA possesses excellent comprehensive mechanical properties, which is superior to a majority of reported HEAs and conventional metal alloys.

The number of wheat spikes has an important influence on wheat yield, and the rapid and accurate detection of wheat spike numbers is of great significance for wheat yield estimation and food security. Computer vision and machine learning have been widely studied as potential alternatives to human detection. However, models with high accuracy are computationally intensive and time consuming, and lightweight models tend to have lower precision. To address these concerns, YOLO-FastestV2 was selected as the base model for the comprehensive study and analysis of wheat sheaf detection. In this study, we constructed a wheat target detection dataset comprising 11,451 images and 496,974 bounding boxes. The dataset for this study was constructed based on the Global Wheat Detection Dataset and the Wheat Sheaf Detection Dataset, which was published by PP Flying Paddle. We selected three attention mechanisms, Large Separable Kernel Attention (LSKA), Efficient Channel Attention (ECA), and Efficient Multi-Scale Attention (EMA), to enhance the feature extraction capability of the backbone network and improve the accuracy of the underlying model. First, the attention mechanism was added after the base and output phases of the backbone network. Second, the attention mechanism that further improved the model accuracy after the base and output phases was selected to construct the model with a two-phase added attention mechanism. On the other hand, we constructed SimLightFPN to improve the model accuracy by introducing SimConv to improve the LightFPN module. The results of the study showed that the YOLO-FastestV2-SimLightFPN-ECA-EMA hybrid model, which incorporates the ECA attention mechanism in the base stage and introduces the EMA attention mechanism and the combination of SimLightFPN modules in the output stage, has the best overall performance. The accuracy of the model was P=83.91%, R=78.35%, AP= 81.52%, and F1 = 81.03%, and it ranked first in the GPI (0.84) in the overall evaluation. The research examines the deployment of wheat ear detection and counting models on devices with constrained resources, delivering novel solutions for the evolution of agricultural automation and precision agriculture.

Objectives: Fungal bloodstream infection (fBSI) following pediatric liver transplantation presents a significant challenge; however, there remains a paucity of guidance regarding antifungal prophylaxis in this population. This study aimed to evaluate the effectiveness of universal antifungal prophylaxis and propose a desirable strategy. Methods: We enrolled 604 pediatric patients who underwent liver transplantation between 2020 and 2023, including 242 patients with empirical prophylaxis and 362 patients with universal prophylaxis. Univariate and multivariate logistic regression analyses were performed to identify independent factors for fBSI. Results: Eight (2.2%) pediatric recipients in the universal prophylaxis group and 13 (5.4%) in the empirical group developed fBSI (P = 0.038). Universal prophylaxis was a protective factor (P = 0.044), while high-volume intraoperative plasma transfusion and deceased donor liver transplantation were independent risk factors for fBSI (P = 0.035 and 0.008, respectively). Universal antifungal strategy showed an increased overall survival trend after liver transplantation although without significant statistical difference (P = 0.217). Patients with fBSI had poorer survival than those without fBSI (P

Fabricating SiC ceramics via the digital light processing (DLP) technology is of great challenge due to strong light absorption and high refractive index of deep-colored SiC powders, which highly differ from those of resin, and thus significantly affect the curing performance of the photosensitive SiC slurry. In this paper, a thin silicon oxide (SiO2) layer was in-situ formed on the surface of SiC powders by pre-oxidation treatment. This method was proven to effectively improve the curing ability of SiC slurry. The SiC photosensitive slurry was fabricated with solid content of 55 vol% and viscosity of 7.77 Pa·s (shear rate of 30 s−1). The curing thickness was 50 μm with exposure time of only 5 s. Then, a well-designed sintering additive was added to completely convert low-strength SiO2 into mullite reinforcement during sintering. Complex-shaped mullite-bond SiC ceramics were successfully fabricated. The flexural strength of SiC ceramics sintered at 1550 ℃ in air reached 97.6 MPa with porosity of 39.2 vol%, as high as those prepared by spark plasma sintering (SPS) techniques.

Anomaly detection for the Internet of Things (IoT) is a major intelligent service required by many fields, including intrusion detection, state monitoring, device-activity analysis, and security supervision. However, the heterogeneous distribution of data and resource-constrained end nodes in ubiquitous IoT systems present challenges for existing anomaly detection models. Due to the advantages of flexible deployment and multi-dimensional resources, high altitude platform stations (HAPSs) and unmanned aerial vehicles (UAVs), which are important components of vertical heterogeneous networks (VHetNets), have significant potential for sensing, computing, storage, and communication applications in ubiquitous IoT systems. In this paper, we propose a novel VHetNet-enabled asynchronous federated learning (AFL) framework by adopting the compound-action actor-critic (CA2C) algorithm for UAV selection, which enables decentralized UAVs to collaboratively train a global anomaly detection model based on their local sensory data from IoT devices. In the VHetNet-enabled AFL framework, the UAV selection process aims to prevent UAVs with low local model quality and large energy consumption from affecting the learning efficiency and model accuracy. Due to the wide coverage as well as strong storage and computation capabilities, a HAPS operates as a central aerial server for aggregating local models of UAVs asynchronously and making decisions intelligently. Moreover, we propose a CA2C-based joint device association, UAV selection, and UAV placement algorithm to further enhance the overall federated execution efficiency and detection model accuracy under UAV energy constraints. Extensive experimental evaluation on real-world datasets demonstrates that the proposed algorithm can achieve high detection accuracy with short federated execution time and low energy consumption.

Doping divalent metal cations into Ga2O3 films plays a key role in adjusting the conductive behavior of the film. N-type high-resistivity β-Ga2O3:Mg films were prepared using electron beam evaporation and subsequent postannealing processing. Various characterization methods (X-ray diffraction, X-ray photoelectron spectroscopy, photoluminescence, etc.) revealed that the Mg content plays an important role in affecting the film quality. Specifically, when the Mg content in the film is 3.6%, the S2 film’s resistivity, carrier content, and carrier mobility are 59655.5 Ω·cm, 1.95 × 1014 cm3/C, and 0.53682 cm2/Vs. Also, the film exhibits a smoother surface, more refined grains, and higher self-trapped exciton emission efficiency. The Mg cation mainly substitutes the Ga+ cation at a tetrahedral site, acting as a trap for self-trapped holes.

In this study, a novel algorithm to retrieve the current speed along the range direction under extreme sea states is developed from C-band synthetic aperture radar imagery. To this aim, a Sentinel-1 (S-1) dual-polarized synthetic aperture radar (SAR) dataset consisting of 2300 images is collected during 200 tropical cyclones (TCs). The dataset is complemented with collocated wave simulations from the Wavewatch-III (WW3) model and reanalysis currents from the HYbrid Coordinate Ocean Model (HYCOM). The corresponding TC winds are officially released by IFRMER, while the Stokes drift following the wave propagation direction is estimated from the waves simulated by WW3. In this study, first the dependence of wind, Stokes drift, and range current on the Doppler centroid anomaly is investigated, and then the extreme gradient boosting (XGBoost) machine learning model is trained on 87% of the S-1 dataset for range current retrieval purposes. The rest of the dataset is used for testing the retrieval algorithm, showing a root mean square error (RMSE) and a correlation coefficient (r) of 0.11 m/s and 0.97, respectively, with the HYCOM outputs. A validation against measurements collected from two high-frequency (HF) phased-array radars is also performed, resulting in an RMSE and r of 0.12 m/s and 0.75, respectively. Those validation results are better than the 0.22 m/s RMSE and 0.28 r achieved by the empirical CDOP model. Hence, the experimental results confirm the soundness of the XGBoost, exhibiting a certain improvement over the empirical model.

Mycoplasma pneumoniae (M. pneumoniae, Mp) is a cell wall-deficient microorganism known to cause chronic respiratory infections in both children and adults. Nucleotide-binding oligomerization domain-containing protein 2 (NOD2) is an intracellular pattern recognition receptor primarily responsible for identifying muramyl dipeptide (MDP) found in bacterial cell walls. Previous experiments have demonstrated that Mycoplasma ovipneumoniae induces macrophage autophagy through NOD2. In this study, we conducted RNA-seq analysis on macrophages infected with M. pneumoniae and observed an up-regulation in the expression of genes associated with the NOD2 signaling pathway. Mechanistic investigations further revealed the involvement of the NOD2 signaling pathway in the inflammatory response of macrophages activated by M. pneumoniae. We utilized GST pull-down technology in conjunction with liquid chromatography–tandem mass spectrometry (LC–MS/MS) to pinpoint the M. pneumoniae proteins that interact with NOD2. Additionally, co-immunoprecipitation (Co-IP) and immunofluorescence co-localization techniques were used to confirm the interaction between DUF16 protein and NOD2. We found that DUF16 protein can enter macrophages and induce macrophage inflammatory response through the NOD2/RIP2/NF-κB pathway. Notably, the region spanning amino acids 13–90 was identified as a critical region necessary for DUF16-induced inflammation. This research not only broadens our comprehension of the recognition process of the intracellular receptor NOD2, but also deepens our understanding of the development of M. pneumoniae infection.

Prescottella, a distinct genus separate from Rhodococcus, has garnered attention for its adaptability and ecological versatility. In this study, a Gram-stain positive and ovoid-rod shaped the actinobacterium strain R16 was isolated from deep-sea sediment (with a depth of 6,310 m) in the Western Pacific. On the basis of 16S rRNA gene sequence analysis, average nucleotide identity and phylogenomic analysis, strain R16 clearly represents a novel species within the genus Prescottella. Genomic analyses indicate Prescottella sp. R16 contains a circular chromosome of 4,531,251 bp with an average GC content of 68.9%, 4,208 protein-coding genes, 51 tRNA genes, and 12 rRNA operons. Additionally, four CRISPRs and 24 genomic islands are also identified. The presence of rich categories related to catalytic activity, membrane part and metabolic process highlights their involvement in cellular component, biological process, and molecular function. The genome sequence of strain R16 also revealed the presence of 13 putative biosynthetic gene clusters for secondary metabolites, including those for ε-Poly-L-lysine, ectoine, heterobactin, isorenieratene and corynecin, suggesting its potential for antibiotic production and warranting further exploration.

Ginseng (Panax ginseng C.A. Mey) is known for its rich saponin compounds and tonic effects. To better utilize the medicinal value of ginseng, this study investigated the extraction process, components, free radical scavenging ability, and immunomodulatory activity of total saponins of ginseng fibrous roots. The response surface methodology was employed to optimize the extraction process of total saponins, and Q-Orbitrap high-resolution liquid chromatography–mass spectrometry (LC-MS) was used to identify the chemical constituents in the total saponins extract of ginseng fibrous roots (GRS). The results showed that the optimal extraction process was achieved with an ethanol concentration of 68%, a material–solvent ratio of 1:25 mL/g, and an extraction time of 20 min, yielding a total saponin content of 6.34% under these conditions. The extract contained four terpenoid compounds and four polyphenolic compounds. GRS exhibited considerable scavenging activity against DPPH and ABTS radicals, with IC50 values of 0.893 and 0.210 mg/mL, respectively. Moreover, GRS restored immune suppression in mice by increasing white blood cell, red blood cell, and neutrophil counts, and improving the lymphocyte. It also promoted immune system recovery, as evidenced by elevated serum levels of IL-2, IFN-γ, TNF-α, and IL-1β in mice. GRS is a natural compound with promising potential for developing antioxidants and immunomodulatory foods.

For the air-to-air missile warhead, there is a cabin with a certain thickness at a distance around the fragments. At present, the influence of missile cabin has not yet been taken into account in the study of fragment velocity. In this paper, based on the law of conservation of energy, the theoretical equation of fragment velocity considering the kinetic energy of cabin debris was deduced. Then, the rationality of the theoretical formula is validated through the static explosion experiments of two prototype warheads, one with a titanium alloy cabin and the other without any cabin. It was found that after the warhead is equipped with the cabin, part of the energy is consumed to drive the cabin debris, resulting in a decrease in fragment velocity, but the velocity of cabin debris was greater than that of fragment of warheads without any cabin. Besides, through numerical simulation, the driving process of fragments and cabin debris during explosive detonation loading of the warhead with the cabin was studied, which can be divided into six stages, and the error between numerical result and experimental value is not more than 4.8%. Finally, the variety regulation of fragment velocity and cabin debris velocity at different interval distances was further studied by numerical simulation. The results indicate that fragment velocity of warheads with cabin at different interval distances is basically the same, but cabin debris velocity decreases with the increase of interval distance. This conclusion can provide a reference for the structural design and fragment velocity evaluation of warheads with cabin.