Search

Contribution: This article presents a Mars Exploration Control Virtual Simulation Experiment Platform (MEC-vslab), which aims to address the current challenge of limited integration between control engineering education and engineering practice. MEC-vslab is oriented toward the practical needs of Mars exploration engineering. It offers unparalleled advantages by transcending temporal and spatial restrictions and has been applied to several basic control engineering curriculums. Background: Owing to the limited applicability of conventional control engineering education in practical engineering, students encounter difficulties in connecting theoretical knowledge with real-world application scenarios. Due to the impact of environmental complexity and conditional limitations, teaching laboratories cannot realistically reproduce aerospace engineering application environments and problems, making it difficult to develop students' ability to solve unknown engineering problems. Intended Outcomes: MEC-vslab facilitates students in acquiring comprehensive control knowledge, encompassing Mars rovers and drones' dynamics modeling, controller design, and parameter configuration for strongly coupled systems. By engaging with this virtual simulation platform, students develop a profound understanding of cutting-edge control engineering principles, augmenting their proficiency in employing control theory to address practical engineering challenges. Application Design: MEC-vslab as a part of the control-related theoretical curriculum, it encompasses three stages. By completing these stages in succession, students are able to apply their theoretical knowledge toward solving practical engineering problems in a virtual setting. Findings: The analysis based on positive student feedback as well as their learning behavior and questionnaire research that the MEC-vslab is an effective learning tool to integrate control engineering education with high-precision engineering practice needs.

To investigated the effects of spray application of zinc sulfate (ZnSO4) solution on the accumulation of crucial bioactive components and antioxidant activity, and explored the regulatory mechanism of zinc sulfate on glucosinolates in broccoli flower buds and seedlings at the molecular level, seven-day-old broccoli sprouts were treated with one of four concentrations (1, 2, 4, and 8 mmol/L) of aqueous ZnSO4 solutions or water (0 mmol/L ZnSO4 as the control). The effects of each ZnSO4 treatment on the bioactive components of broccoli sprouts were investigated by determining the contents of chlorophyll, ascorbic acid, total flavonoids, total phenols, and total glucosinolates, as well as myrosinase activity and DPPH free-radical scavenging rate. The expression levels of glucosinolate synthesis structural genes in the sprouts were analyzed using a real-time quantitative PCR detection system. The results showed that the chlorophyll content of all ZnSO4 treatments was lower than that of the control. The ascorbic acid content tended to be elevated with an increase in ZnSO4 concentration and was higher than that of the control for all ZnSO4 treatments. The total polyphenol content was the highest in response to 8 mmol/L ZnSO4 treatment and was significantly (P

Abstract Background Cancer development is intricately linked with metabolic dysregulation, including lactic acid metabolism, which plays a pivotal role in tumor progression and immune evasion. However, its specific implications in gastric adenocarcinoma (STAD) remain unclear. This study introduces a novel methodology to evaluate lactic acid metabolism comprehensively in STAD, aiming to elucidate its prognostic significance and impact on immunotherapy efficacy. Targeted therapies directed at key lactic acid metabolism genes (LMGs) identified within the tumor microenvironment (TME) hold promise for personalized treatment strategies. Methods Lactic acid metabolism patterns were assessed in 415 STAD patients using a panel of 21 LMGs. Cox regression and Lasso regression analyses were employed to develop a predictive risk model based on differentially expressed genes (DEGs). Validation of the model was conducted using independent cohorts from the GEO and TCGA databases, as well as additional datasets focused on immunotherapy responses. Further investigations into TME dynamics of lactic acid metabolism included functional assays targeting SLC16A3, a pivotal gene identified through our analyses. Results Patients were stratified into distinct risk groups based on their lactic acid metabolism profiles. Low-risk patients exhibited attenuated lactic acid metabolism, correlating with favorable clinical outcomes characterized by prolonged survival and enhanced responsiveness to immunotherapy. Notably, tumor cells within the TME demonstrated heightened levels of active lactic acid metabolism, particularly impacting tumor-infiltrating lymphocytes such as CD8 + T cells and regulatory T cells. Mechanistically, SLC16A3 emerged as a critical regulator promoting STAD cell proliferation, invasion, and migration while modulating the metabolic landscape. Conclusion This study underscores the prognostic value of a lactic acid metabolism-based model in STAD, providing insights into its potential as a predictive biomarker for patient stratification and therapeutic targeting. The findings highlight SLC16A3 as a promising candidate for therapeutic intervention aimed at modulating lactic acid metabolism in the TME, thereby advancing personalized treatment strategies in gastric cancer management.

Abstract Background This study aims to analyse the clinical characteristics of different types of odontogenic jawbone cysts (OJCs) and to provide a theoretical basis for prevention and clinical treatment. Methods Data from 1,038 patients with OJCs were collected, and relevant information, such as sex, age, clinical symptoms and signs, imaging data, number of lesions, lesion location, pathological diagnosis, clinical treatment and prognosis, was statistically analysed. Results According to the World Health Organization (WHO) classification of OJCs in 2022, the highest incidence rate was observed for radicular cysts (RCs), accounting for 58.96% of the total number of cases, followed by dentigerous cysts (DCs), accounting for approximately 23.22% of cases. lateral periodontal cysts (LPCs) and calcifying odontogenic cysts (COCs) comprised the lowest number of cases. The age distribution of patients was between 4 and 89 years, and the high incidence age group was youth and middle age, accounting for 66.67% of the total number of cases. The male-to-female ratio of patients was 1.51:1, and there was a statistically significant difference between the sexes(p

Abstract Background and Aim Remnant cholesterol (RC) is substantially related to negative outcomes in cardiac patients. Patients with coexisting hypertension and heart failure (HF) often develop left ventricular hypertrophy (LVH) and have poor prognoses. This study investigated baseline RC levels and LV remodelling and patients’ prognoses. Methods and results Six hundred thirty consecutive individuals with hypertension and HF participated in this prospective trial from October 2018 to August 2020. Based on left ventricular mass index (LVMI), 560 those eligible were separated into LVH and non-LVH groups. Multiple linear regression and receiver operating characteristic (ROC) curves examined the RC and LV relationship. A Cox regression analysis was conducted to examine the predictive value of RC for clinical outcomes. The LVH group presented significantly elevated values of RC, triglyceride, and cholesterol and decreased high-density lipoprotein cholesterol (HDLC). The optimal cutoff value for RC to predict LV remodelling was 0.49. The subjects were observed for a median of 58 months, and 104 participants met the primary endpoint. The risk models involving the two Cox models were adjusted to incorporate confounding factors, which revealed that those with elevated baseline levels of RC were more susceptible to cardiovascular mortality, as shown by an increased hazard ratio. (HR: 1.91, 95% CI: 1.62–2.26 vs. HR: 1.75, 95% CI: 1.43–2.16, P

Abstract Cophylogeny has been identified between gut bacteria and their animal host and is highly relevant to host health, but little research has extended to gut bacteriophages. Here we use bee model to investigate host specificity and cophylogeny in the “animal-gut bacteria-phage” tripartite system. Through metagenomic sequencing upon different bee species, the gut phageome revealed a more variable composition than the gut bacteriome. Nevertheless, the bacteriome and the phageome showed a significant association of their dissimilarity matrices, indicating a reciprocal interaction between the two kinds of communities. Most of the gut phages were host generalist at the viral cluster level but host specialist at the viral OTU level. While the dominant gut bacteria Gilliamella and Snodgrassella exhibited matched phylogeny with bee hosts, most of their phages showed a diminished level of cophylogeny. The evolutionary rates of the bee, the gut bacteria and the gut phages showed a remarkably increasing trend, including synonymous and non-synonymous substitution and gene content variation. For all of the three codiversified tripartite members, however, their genes under positive selection and genes involving gain/loss during evolution simultaneously enriched the functions into metabolism of nutrients, therefore highlighting the tripartite coevolution that results in an enhanced ecological fitness for the whole holobiont.

Abstract Background Helicobacter pylori (H. pylori) infection is critical in the development and occurrence of gastric cancer. H. pylori secretes gamma-glutamyl transferase (GGT), which affects energy metabolism and histone methylation in mesenchymal stem cells. However, its effect on human gastric epithelial cells remains unclear. This study aimed to investigate the effects of GGT on energy metabolism and histone methylation in gastric epithelial cells and determine its role in the development and progression of H. pylori-induced gastric cancer. Methods A GGT knockout H. pylori strain and mouse gastric cancer model were constructed, and alpha-ketoglutarate (α-KG) was added. The underlying mechanism was investigated using proteomics, immunohistochemistry, Western blotting, and other experimental assays. Results H. pylori can colonize the host’s stomach and destroy the gastric epithelium. GGT secreted by H. pylori decreased the concentration of glutamine in the stomach and increased H3K9me3 and H3K27me3 expression, which promoted the proliferation and migration of gastric epithelial cells. Additionally, α-KG reversed this effect. GGT increased the tumorigenic ability of nude mice. GGT, secreted by H. pylori, promoted the expression of ribosomal protein L15 (RPL15), while GGT knockout and supplementation with α-KG and trimethylation inhibitors reduced RPL15 expression and Wnt signaling pathway expression. Conclusions H. pylori secreted GGT decreased the expression of glutamine and α-KG in gastric epithelial cells, increased the expression of histones H3K9me3 and H3K27me3, and activated the Wnt signaling pathway through RPL15 expression, ultimately changing the biological characteristics of the gastric epithelium and promoting the occurrence of gastric cancer. Altered energy metabolism and histone hypermethylation are important factors involved in this process. Graphical abstract

Abstract Renal dysfunction (RD) often characterizes the worse course of patients with advanced heart failure (AHF). Many prognosis assessments are hindered by researcher biases, redundant predictors, and lack of clinical applicability. In this study, we enroll 1736 AHF/RD patients, including data from Henan Province Clinical Research Center for Cardiovascular Diseases (which encompasses 11 hospital subcenters), and Beth Israel Deaconess Medical Center. We developed an AI hybrid modeling framework, assembling 12 learners with different feature selection paradigms to expand modeling schemes. The optimized strategy is identified from 132 potential schemes to establish an explainable survival assessment system: AIHFLevel. The conditional inference survival tree determines a probability threshold for prognostic stratification. The evaluation confirmed the system’s robustness in discrimination, calibration, generalization, and clinical implications. AIHFLevel outperforms existing models, clinical features, and biomarkers. We also launch an open and user-friendly website www.hf-ai-survival.com , empowering healthcare professionals with enhanced tools for continuous risk monitoring and precise risk profiling.

The bacterial communities in rhizosphere soil interact with the roots of plants. This interaction is beneficial for both the bacteria and the plants, which makes it very important to identify the structure of these bacterial communities for plant growth and development. However, the composition characteristics of bacterial communities in rhizosphere soil of 2-year and 5-year Haloxylon ammodendron have not been clearly defined. The purpose of this study was to identify the diverse composition of 2-year and 5-year Haloxylon ammodendron in Turpan, Xinjiang. Thus, rhizosphere soil bacteria were analyzed by isolating, purifying, and identifying the species through high-throughput sequencing technology. The bacterial strains in the rhizosphere soil of Haloxylon ammodendron were isolated with the dilution coating method, resulting in 37 isolated strains. The selective media were used to screen the growth-promoting characteristics of the rhizosphere soil isolates of Haloxylon ammodendron. The results of high-throughput amplification sequencing showed that the rhizosphere bacteria in the 2-year rhizosphere soil belonged to 45 phyla, 109 classes, 288 orders, 451 families, 826 genera, and 404 species, and those in the 5-year rhizosphere soil belonged to 56 phyla, 148 classes, 369 orders, 601 families, 1062 genera, and 671 species. Among them, Firmicutes, Proteobacteria, Actinobacteriota, Bacteroidota, Crenarchaeota, and so on are the dominant bacteria. There were 12206 and 14,053 OTUs in the 2-year-old and 5-year-old rhizosphere soil bacteria, respectively, and 3329 OTUs in the 2-year- and 5-year-old rhizosphere soil, accounting for 16.98% of the total number of OTUs. The results showed that three strains, sg16, sg21, and ss4, had the highest inorganic phosphorus solubility index (1.58). The isolated strain did not have the ability to dissolve organophosphorus and potassium, while the screened strain sg16 had the ability to fix nitrogen. Two strains with a good iron-bearing capacity, Sg9F and Sg1, were screened, among which Sg9F had the highest D/d value and Sg9F had the strongest iron-bearing capacity. The results showed that 37 strains of rhizosphere soil bacteria belonged to six genera. They are Bacillus, Corynebacterium, Phyllobacterium, Lysinibacillus, Sinorhizobium meliloti, and Streptomyces levis. Among them are sg21 (Bacillus sp.), sg1 (Bacillus sp.), sg9F (Streptomyces levis), sg16 (Phyllobacterium phragmitis), and ss4 (Sinorhizobium meliloti). This study provides a particular research basis for the influence of Haloxylon ammodendron rhizosphere bacteria on soil nutrient release and depicts a solution for improving the yield and quality of cistanche deserticola indirectly through isolating, screening, and identifying rhizosphere soil bacteria, including screening strains with growth-promoting functions and analyzing the population structure of rhizosphere bacteria in 2- and 5-year soil in combination with high-throughput sequencing technology.

With the incredible growth of the scale and complexity of datasets, creating proper visualizations for users becomes more and more challenging in large datasets. Though several visualization recommendation systems have been proposed, so far, the lack of practical engineering inputs is still a major concern regarding the usage of visualization recommendations in the industry. In this paper, we proposed AVA, an open-sourced web-based framework for Automated Visual Analytics. AVA contains both empiric-driven and insight-driven visualization recommendation methods to meet the demands of creating aesthetic visualizations and understanding expressible insights respectively. The code is available at https://github.com/antvis/AVA.

AI painting, as a popular research direction in the field of computer vision, is expanding its application boundaries in the fields of art creation, film and media, industrial design, and art education through natural language processing, graphic pre-training models, and diffusion models. Two types of AI painting, namely, image-to-image and text-to-image, are taken as the main lines, and the representative models and their key technologies and methods are analyzed in depth. For the image-to-image, the development lineage, generation principle, and advantages and disadvantages of each model are explored from two types of models based on AE and GAN, and their effects on the public dataset are summarized. For the text-to-image, the structural differences of the three types of models based on diffusion model and other models, as well as the generation effects of various types of models on three datasets are summarized. It is pointed out that the text-to-image utilizing the diffusion model has become a hot topic nowadays, which predicts the diversified development of image generation in the future. And the current mainstream AI painting platforms are compared and summarized from the perspectives of usage and generation speed. Finally, on the basis of summarizing the problems and controversies faced by AI painting at the technical and social levels, future trends such as the complementary development of AI painting and human artists, the increased interactivity of the painting process, and the emergence of new professions and industries are envisioned.

In the realm of precision cattle health monitoring, this paper introduces the development and evaluation of a novel wearable continuous health monitoring device designed for cattle. The device integrates a sustainable solar-powered module, real-time signal acquisition and processing, and a storage module within an animal ergonomically designed curved casing for non-invasive cattle health monitoring. The curvature of the casing is tailored to better fit the contours of the cattle’s neck, significantly enhancing signal accuracy, particularly in temperature signal acquisition. The core module is equipped with precision temperature sensors and inertial measurement units, utilizing the Arduino MKR ZERO board for data acquisition and processing. Field tests conducted on a cohort of ten cattle not only validated the accuracy of temperature sensing but also demonstrated the potential of machine learning, particularly the Support Vector Machine algorithm, for precise behavior classification and step counting, with an average accuracy of 97.27%. This study innovatively combines real-time temperature recognition, behavior classification, and step counting organically within a self-powered device. The results underscore the feasibility of this technology in enhancing cattle welfare and farm management efficiency, providing clear direction for future research to further enhance these devices for large-scale applications.

The receptive field (RF) plays a crucial role in convolutional neural networks (CNNs) because it determines the amount of input information that each neuron in a CNN can perceive, which directly affects the feature extraction ability. As the number of convolutional layers in CNNs increases, there is a decay of the RF according to the two-dimensional Gaussian distribution. Thus, an effective receptive field (ERF) can be used to characterize the available part of the RF. The ERF is calculated by the kernel size and layer number within the neural network architecture. Currently, ERF calculation methods are typically applied to single-channel input data that are both independent and identically distributed. However, such methods may result in a loss of effective information if they are applied to more general (i.e., multi-channel) datasets. Therefore, we proposed a multi-channel ERF calculation method. By conducting a series of numerical experiments, we determined the relationship between the ERF and the convolutional kernel size in conjunction with the layer number. To validate the new method, we used the recently published global wave surrogate model for climate simulation (GWSM4C) and its accompanying dataset. According to the newly established relationship, we refined the kernel size and layer number in each neural network of the GWSM4C to produce the same ERF but lower RF attenuation rates than those of the original version. By visualizing the gradient map at several points in West African and East Pacific areas, the high gradient value regions confirmed the known swell sources, which indicated effective feature extraction in these areas. Furthermore, the new version of the GWSM4C yielded better prediction accuracy for significant wave height in global swell pools. The root mean square errors in the West African and East Pacific regions reduced from approximately 0.3 m, in the original model to about 0.15 m, in the new model. Moreover, these improvements were attributed to the higher efficiency of the newly modified neural network structure that allows the inclusion of more historical winds while maintaining acceptable computational consumption.

Tumor metastasis is responsible for 90 % of cancer-associated deaths, and its early detection may decrease the likelihood of mortality. Studies have demonstrated that metastasis results from the interaction between “seeds” (tumor cells) and “soil” (pre-metastatic niche, PMN). As the first and most abundant immune cells to be recruited to PMN, neutrophils play a key role in the ultimate formation of metastatic foci through mechanisms such as supporting tumor cell growth, promoting angiogenesis, and shaping an immune-suppressive microenvironment. In this study, two distinct types of sialic acid (SA)-modified liposomes were prepared to target and regulate pro-metastatic neutrophils through the l-selectin receptor. One of these liposomes, named ICG@SAL, was used to encapsulate indocyanine green (ICG) and was specifically designed for the early detection of cancer metastasis. The other liposome, referred to as ABE/Cur@SAL, co-loaded abemaciclib (ABE) and curcumin (Cur), with the intention of suppressing the progression of metastatic tumor. Fluorescence imaging results from the mouse spontaneous metastasis model indicated that ICG@SAL demonstrated faster targeting and stronger accumulation in the metastatic organs than unmodified ICG liposomes (ICG@CL). This suggested that ICG@SAL could detect tumor metastasis at an early stage. The therapy with co-loaded liposomes in the mouse experimental lung metastasis model indicated that ABE/Cur@SAL could inhibit regulatory T (Treg) cell proliferation, enhance effector T cell activity and reduce tumorigenic factor release, implying that ABE/Cur@SAL could inhibit tumor metastasis. Overall, our work provided a sensitive and convenient approach to early diagnosis and treatment of tumor metastasis. ICG@SAL could be employed for the early detection of tumor metastasis, while ABE/Cur@SAL could be used to inhibit the development of tumor metastasis when early metastasis was identified.

BackgroundImage-guided surgical navigation systems are widely regarded as the benchmark for computer-assisted surgical robotic platforms, yet a persistent challenge remains in addressing intraoperative image drift and mismatch. It can significantly impact the accuracy and precision of surgical procedures. Therefore, further research and development are necessary to mitigate this issue and enhance the overall performance of these advanced surgical platforms.ObjectiveThe primary objective is to improve the precision of image guided puncture navigation systems by developing a computed tomography (CT) and structured light imaging (SLI) based navigation system. Furthermore, we also aim to quantifying and visualize intraoperative image drift and mismatch in real time and provide feedback to surgeons, ensuring that surgical procedures are executed with accuracy and reliability.MethodsA CT-SLI guided orthopedic navigation puncture system was developed. Polymer bandages are employed to pressurize, plasticize, immobilize and toughen the surface of a specimen for surgical operations. Preoperative CT images of the specimen are acquired, a 3D navigation map is reconstructed and a puncture path planned accordingly. During surgery, an SLI module captures and reconstructs the 3D surfaces of both the specimen and a guiding tube for the puncture needle. The SLI reconstructed 3D surface of the specimen is matched to the CT navigation map via two-step point cloud registrations, while the SLI reconstructed 3D surface of the guiding tube is fitted by a cylindrical model, which is in turn aligned with the planned puncture path. The proposed system has been tested and evaluated using 20 formalin-soaked lower limb cadaver specimens preserved at a local hospital.ResultsThe proposed method achieved image registration RMS errors of 0.576 ± 0.146 mm and 0.407 ± 0.234 mm between preoperative CT and intraoperative SLI surface models and between preoperative and postoperative CT surface models. In addition, preoperative and postoperative specimen surface and skeletal drifts were 0.033 ± 0.272 mm and 0.235 ± 0.197 mm respectively.ConclusionThe results indicate that the proposed method is effective in reducing intraoperative image drift and mismatch. The system also visualizes intraoperative image drift and mismatch, and provides real time visual feedback to surgeons.

The primary method for harvesting Lycium barbarum L. (L. barbarum) is manual labor, making it one of the most labor-intensive fruit crops in the Northwest region of China. Due to the decrease of labor supply and the increase of labor cost, the cost of harvesting has become a major hindrance to the development of the L. barbarum industry. Therefore, it is important to achieving mechanized harvesting of L. barbarum. In this study, a vibration harvesting machine was designed. Plackett-Burman experiment was conducted to assess the correlation between the picking rate of ripe fruit and various parameters. It was found that the significant factors were vibration amplitude, vibration frequency, and spacing of the vibrating rods. Based on the response surface methodology (RSM), parameter experiment was conducted to analyze the impact of these factors on picking rate of ripe fruit, picking rate of unripe fruit, and damage rate of ripe fruit. The optimal harvesting parameters were determined to be: vibration amplitude of 44 mm, spacing of the vibrating rods of 24 mm, and vibration frequency of 9Hz. The verification experiment showed that the picking rate of ripe fruit was 86.44%, the picking rate of unripe fruit was 6.81%, and the damage rate of ripe fruit was 5.54%. This study provides a design basis for realizing mechanized harvesting.

BackgroundPulmonary fibrosis (PF) emerges as a significant pulmonary sequelae in the convalescent phase of coronavirus disease 2019 (COVID-19), with current strategies neither specifically preventive nor therapeutic. Licoricesaponin G2 (LG2) displays a spectrum of natural activities, including antibacterial, anti-inflammatory, and antioxidant properties, and has been effectively used in treating various respiratory conditions. However, the potential protective effects of LG2 against PF remain underexplored.MethodsNetwork analysis and molecular docking were conducted in combination to identify the core targets and pathways through which LG2 acts against PF. In the model of bleomycin (BLM)-induced C57 mice and transforming growth factor-β1 (TGF-β1)-induced A549 and MRC5 cells, techniques such as western blot (WB), quantitative Real-Time PCR (qPCR), Immunohistochemistry (IHC), Immunofluorescence (IF), and Transwell migration assays were utilized to analyze the expression of Epithelial-mesenchymal transition (EMT) and inflammation proteins. Based on the analysis above, we identified targets and potential mechanisms underlying LG2’s effects against PF.ResultsNetwork analysis has suggested that the mechanism by which LG2 combats PF may involve the TNF-α pathway. Molecular docking studies have demonstrated a high binding affinity of LG2 to TNF-α and MMP9. Observations from the study indicated that LG2 may mitigate PF by modulating EMT and extracellular matrix (ECM) remodeling. It is proposed that the therapeutic effect is likely arises from the inhibition of inflammatory expression through regulation of the TNF-α pathway.ConclusionLG2 mitigates PF by suppressing TNF-α signaling pathway activation, modulating EMT, and remodeling the ECM. These results provide compelling evidence supporting the use of LG2 as a potential natural therapeutic agent for PF in clinical trials.

Background The disorder of serum uric acid metabolism during pregnancy is associated with adverse pregnancy outcomes, while few studies have analyzed and compared the correlation of serum uric acid and serum uric acid/creatinine ratio levels with adverse pregnancy outcomes. Objective To explore the correlation of serum uric acid and serum uric acid/creatinine ratio levels with adverse pregnancy outcomes in late pregnancy. Methods A total of 743 pregnant women with singleton live births who were routinely examined and delivered at Nanjing Drum Tower Hospital from 2015 to 2022 were selected . Based on the occurrence of adverse pregnancy outcomes, pregnant women were divided into normal group (344 cases) and adverse outcome group (399 cases) . Serum uric acid and serum uric acid/creatinine ratio were divided into three levels by quartiles, respectively, including Q1 (serum uric acid359 μmol/L) and q1 (serum uric acid/creatinine ratio7.94) . According to the median age of pregnant women, they were divided into the age subgroup

Abstract Respiratory diseases, marked by structural changes in the airways and lung tissues, can lead to reduced respiratory function and, in severe cases, respiratory failure. The side effects of current treatments, such as hormone therapy, drugs, and radiotherapy, highlight the need for new therapeutic strategies. Traditional Chinese Medicine (TCM) offers a promising alternative, leveraging its ability to target multiple pathways and mechanisms. Active compounds from Chinese herbs and other natural sources exhibit anti-inflammatory, antioxidant, antitumor, and immunomodulatory effects, making them valuable in preventing and treating respiratory conditions. Ferroptosis, a unique form of programmed cell death (PCD) distinct from apoptosis, necrosis, and others, has emerged as a key area of interest. However, comprehensive reviews on how natural products influence ferroptosis in respiratory diseases are lacking. This review will explore the therapeutic potential and mechanisms of natural products from TCM in modulating ferroptosis for respiratory diseases like acute lung injury (ALI), asthma, pulmonary fibrosis (PF), chronic obstructive pulmonary disease (COPD), lung ischemia–reperfusion injury (LIRI), pulmonary hypertension (PH), and lung cancer, aiming to provide new insights for research and clinical application in TCM for respiratory health.

Abstract Seawater electrolysis offers a renewable, scalable, and economic means for green hydrogen production. However, anode corrosion by Cl- pose great challenges for its commercialization. Herein, different from conventional catalysts designed to repel Cl- adsorption, we develop an atomic Ir catalyst on cobalt iron layered double hydroxide (Ir/CoFe-LDH) to tailor Cl- adsorption and modulate the electronic structure of the Ir active center, thereby establishing a unique Ir-OH/Cl coordination for alkaline seawater electrolysis. Operando characterizations and theoretical calculations unveil the pivotal role of this coordination state to lower OER activation energy by a factor of 1.93. The Ir/CoFe-LDH exhibits a remarkable oxygen evolution reaction activity (202 mV overpotential and TOF = 7.46 O2 s−1) in 6 M NaOH+2.8 M NaCl, superior over Cl--free 6 M NaOH electrolyte (236 mV overpotential and TOF = 1.05 O2 s−1), with 100% catalytic selectivity and stability at high current densities (400-800 mA cm−2) for more than 1,000 h.

Apple detection plays a critical role in enabling the functionality of harvesting robots within natural orchard environments. To address challenges related to low detection accuracy, slow inference speed, and high parameter count, we present PcMNet, a lightweight detection model based on an improved YOLOv8 network. Initially, we employed Partial Convolution (Pconv) to construct a PR module, forming the Pconv-block, which subsequently replaced the original C2f feature extraction module within the YOLOv8n backbone. This replacement led to improvements in both detection accuracy and speed, while simultaneously reducing computational complexity (FLOPs), parameter count, and model size. Furthermore, the Cross-Scale Feature Fusion (CCFF) module was refined into Faster-Cross-Scale Feature Fusion (Faster-CCFF) with the integration of Pconv-block, significantly enhancing the model's feature extraction and fusion capabilities. Additionally, we introduced Mixed Local Channel Attention (MLCA) to further strengthen the model's capacity to capture essential features while effectively suppressing background noise. Experimental results demonstrate that PcMNet achieved a detection accuracy of 92.8 % and an mAP@0.5 of 95.5 %, representing improvements of 1.4 and 0.7 percentage points, respectively, over YOLOv8n. Moreover, PcMNet successfully reduced FLOPs, parameter count, and model size to 5.1 G, 1.4 M, and 3.2 MB, respectively. The per-image detection time was reduced to 2.3 ms, indicating reductions of 37.80 %, 53.33 %, 49.21 %, and 56.60 % in FLOPs, parameters, model size, and detection time compared to YOLOv8n. When deployed on edge computing devices with TensorRT acceleration, PcMNet achieved a detection rate of 92 FPS. Field validation experiments conducted in natural orchard environments confirmed PcMNet's superior ability to detect apples under challenging conditions, such as occlusions and varying lighting conditions. Its lightweight design and rapid detection capabilities provide a valuable reference for achieving real-time apple detection in automated and intelligent harvesting robots, thereby contributing to advancements in smart agriculture.

Predicting nearshore significant wave heights (SWHs) with high accuracy is of great importance for coastal engineering activities, marine and coastal resource studies, and related operations. In recent years, the prediction of SWHs in two-dimensional fields based on deep learning has been gradually emerging. However, predictions for nearshore areas still suffer from insufficient resolution and poor accuracy. This paper develops a NS (NearShore) model based on the GWSM4C model (Global Wave Surrogate Model for Climate simulations). In the training area, the GWSM4C -NS model achieved a correlation coefficient (CC) of 0.977, with a spatial Root Mean Square Error (RMSE), annual mean spatial relative error (MAPE), and annual mean spatial absolute error (MAE) of 0.128 m, 10.7%, and 0.103 m, respectively. Compared to the GWSM4C model’s predictions, the RMSE and MAE decreased by 59% and 60% respectively, demonstrating the model’s effectiveness in enhancing nearshore SWH predictions. Additionally, applying this model to untrained sea areas to further validate its learning capability in wave energy propagation resulted in a CC of 0.951, with RMSE, MAPE, and MAE of 0.161m, 12.9%, and 0.137m, respectively. The RMSE and MAE were 43% and 39% lower than the GWSM4C model’s interpolated predictions. The results shown above suggest that the newly proposed model can effectively improve the performance of GWSM4C in nearshore areas.

IntroductionTo enhance the precision of evaluating the impact of urban environments on resident health, this study introduces a novel fuzzy intelligent computing model designed to address health risk concerns using multi-media environmental monitoring data.MethodsThree cities were selected for the study: Beijing (B City), Kunming (K City), and Wuxi (W City), representing high, low, and moderate pollution levels, respectively. The study employs a Fuzzy Inference System (FIS) as the chosen fuzzy intelligent computing model, synthesizing multi-media environmental monitoring data for the purpose of urban health risk assessment.Results(1) The model reliably estimates health risks across diverse cities and environmental conditions. (2) There is a positive correlation between PM2.5 concentrations and health risks, though the impact of noise levels varies by city. In cities B, K, and W, the respective correlation coefficients are 0.65, 0.55, and 0.7. (3) The Root Mean Square Error (RMSE) values for cities B, K, and W, are 0.0132, 0.0125, and 0.0118, respectively, indicating that the model has high accuracy. The R2 values for the three cities are 0.8963, 0.9127, and 0.9254, respectively, demonstrating the model’s high explanatory power. The residual values for the three cities are 0.0087, 0.0075, and 0.0069, respectively, indicating small residuals and demonstrating robustness and adaptability. (4) The model’s p-values for the Indoor Air Quality Index (IAQI), Thermal Comfort Index (TCI), and Noise Pollution Index (NPI) all satisfy p < 0.05 for the three cities, affirming the model’s credibility in estimating health risks under varied urban environments.DiscussionThese results showcase the model’s ability to adapt to diverse geographical conditions and aid in the accurate assessment of existing risks in urban settings. This study significantly advances environmental health risk assessment by integrating multidimensional data, enhancing the formulation of comprehensive environmental protection and health management strategies, and providing scientific support for sustainable urban planning.

Abstract The aim of this study is to provide effective solutions to promote the transition of resource-based cities to low carbon and sustainable development. Firstly, this study investigates the background of low-carbon transformation of resource-based cities. Secondly, it introduces the application method of Chat Generative Pre-trained Transformer (ChatGPT) in detail. Finally, this study proposes a comprehensive application of ChatGPT and artificial bee colony (ABC) algorithm. The results show that the average energy utilization efficiency improvement index of the group using ChatGPT is 0.11. The average energy efficiency improvement index of the group using ABC algorithm is 0.02 higher than that of the control group. The integrated application of ChatGPT and ABC algorithm can further improve the low-carbon transformation effect of resource-based cities and achieve the goal of green development.

Abstract Extracellular vesicles (EVs) have been identified as promising biomarkers for the noninvasive diagnosis of various diseases. However, challenges in separating EVs from soluble proteins have resulted in variable EV recovery rates and low purities. Here, we report a high-yield ( > 90%) and rapid (

Homomorphic encryption (HE) is a promising method in privacy-preserving cloud computing. Applying HE on feedforwad neural networks has been frequently reported recently but the research on recurrent neural networks is still insufficient. In previous studies, HE-based GRU is built with bootstrapping due to the changeable input length and large number of required multiplications, which is not time-efficient. In this study, we give a guideline of building bootstrapping-free HE-based GRU for text classification tasks. We discuss the methods of pre-processing of texts to decrease the input sequence length but keep the accuracy in a comparable level as the original GRU. The architecture of GRU is designed with flexibility to process the input sequence with different lengths while fixing the number of recurrent steps. At last, the HE parameter selection is discussed. We analyze the noise raised from HE operations and select the parameters that ensure the results from encrypted data are the same as that on plaintexts. The proposed model is evaluated on 6 popular text datasets, and the results show that the accuracy is only lower than the original GRU by at most 4.2%. Despite the complicated calculations in GRU, the proposed model is light-weighted and the fastest inference among our implementation costs only 10 minutes. We show the potential of applying HE schemes on complex models without bootstrapping to achieve fast encrypted computations.

Abstract Transient receptor potential melastatin 8 (TRPM8) is a cold sensory receptor in primary sensory neurons that regulates various neuronal functions. Substance P (SP) is a pro-inflammatory neuropeptide secreted by the neurons, and it aggravates colitis. However, the regulatory role of TRPM8 in SP release is still unclear. Our study aimed to investigate TRPM8’s role in SP release from primary sensory neurons during colitis and clarify the effect of SP on colonic epithelium. We analyzed inflammatory bowel disease patients’ data from the Gene Expression Omnibus dataset. Dextran sulfate sodium (DSS, 2.5%)-induced colitis in mice, mouse dorsal root ganglion (DRG) neurons, ND7/23 cell line, and mouse or human colonic organoids were used for this experiment. Our study found that TRPM8, TAC1 and WNT3A expression were significantly correlated with the severity of ulcerative colitis in patients and DSS-induced colitis in mice. The TRPM8 agonist (menthol) and the SP receptor antagonist (Aprepitant) can attenuate colitis in mice, but the effects were not additive. Menthol promoted calcium ion influx in mouse DRG neurons and inhibited the combination and phosphorylation of PKAca from the cAMP signaling pathway and GSK-3β from the Wnt/β-catenin signaling pathway, thereby inhibiting the effect of Wnt3a-driven β-catenin on promoting SP release in ND7/23 cells. Long-term stimulation with SP inhibited proliferation and enhanced apoptosis in both mouse and human colonic organoids. Conclusively, TRPM8 inhibits SP release from primary sensory neurons by inhibiting the interaction between PKAca and GSK-3β, thereby inhibiting the role of SP in promoting colonic epithelial apoptosis and relieving colitis.

In this study, a novel coarse-to-fine image registration algorithm based on feature matching using the Gabor filters is provided for repeat-pass SAR interferometry (InSAR). In the coarse registration, we propose a feature-based algorithm developed using the Gabor odd filter ratio-based operator (GOFRO), and the innovative feature based on the GOFRO (GOFRO-F) consists of the improved nonlinear scale space and the descriptor. The establishment of nonlinear scale spaces informed by the GOFRO enhances the stability of feature detection. Also, an improved 64-dimensional descriptor based on the GOFRO is presented to optimize the capability of feature matching. In the fine registration, we employ the normalized correlation coefficient algorithm (NCCA) and the random consensus sample (RANSAC) for determining offsets and the sub-pixel projection model calculation. The datasets of interferograms are derived from the 0.3 THz stepped-frequency radar. Compared to other algorithms, registration experiments of both simple and complex scatters show that GOFRO-F is more suitable for high precision image registration because of its enhanced robustness to the speckle noise. Furthermore, the three-dimensional (3D) reconstruction is conducted based on our proposed algorithm. The results demonstrate that our proposed algorithm facilitates the attainment of millimeter-level spatial resolutions for InSAR in THz band (THz InSAR).

Abstract Background Triangular fibrocartilage complex (TFCC) injury is a frequent soft tissue injury that has been observed to accompany distal radius fractures (DRFs) with concomitant changes in radiologic parameters. The aim of this study was to investigate the relevance of distal radial radiologic parameters associated with DRF and traumatic TFCC injury. Methods A total of 172 patients with distal radius fractures who underwent X-ray, CT, and MRI before undergoing volar locking plate or external splint fixation between October 2021 and December 2022 were included in this study. An analysis of various radiologic parameters and the classification of fracture type and TFCC injuries by CT and MRI was performed. All patients were divided into the TFCC uninjured group and the injured group. The incidence and relevant radiologic parameters were compared. Results This study included 76 males and 96 females with a mean age of 56.1 years. Among all patients, 33 (19.2%), 40 (23.2%), and 99 (57.6%) had DRF with A, B, and C fractures, respectively, according to the AO/OTA classification. In patients with fractures, the TFCC was found to be injured in 54.1% (93/172) of patients (type 1A in 21, 1B in 46, 1C in 39, and 1D in 35) but uninjured in 45.9% (79/172). There were significant differences between the TFCC injured and uninjured groups regarding the radius length (p = 0.044) and DRUJ distance (p = 0.040) of radiologic parameters that changed with DRF, although there were no differences between the two groups regarding gender, age, injured side, intra- and extra-articular, radius inclination and palmer tilt angle, or sagittal translation. Within the TFCC injured group, the radius length and DRUJ distance were 4.83 mm and 2.95 mm less or wider than 7.19 mm and 1.83 mm of the uninjured group. Moreover, shorter radius length was related to type lB TFCC injury (p = 0.041). Both radius length (AUC = 0.658) and DRUJ distance (AUC = 0.582) had no convincing predictive value for TFCC injury in DRF. Conclusion 1B TFCC injury is most common in patients with DRF and concomitant TFCC injury. Both radius length and DRUJ distance have a significant statistical correlation with TFCC injury, and patients with TFCC injury tend to have a shortened radius and wider DRUJ distance, although they have no predictive value for TFCC injury in DRF. In addition, a shorter radius length was related to type lB TFCC injury.

Aiming at the problems of poor passability of a tracked chassis due to small plots, complicated road conditions and steep slopes during mechanized potato harvesting in hilly and mountainous areas. To design a potato combine harvester and take the tracked chassis of the harvester as the research object to study its driving performance under typical road conditions in mountainous areas. Firstly, mechanical analysis and theoretical calculation are carried out on the tracked chassis to obtain the relevant parameters of key components, and secondly, its driving performance is analyzed to obtain the driving limit values of passing performance under different working conditions; RecurDyn software was used to establish the dynamics simulation and analysis model of the whole machine, and the driving limit values of the harvester were determined under five different road conditions through simulation. The results show the following: the driving limit gradient angle is 20° in cross-gradient conditions, 25° in longitudinal up-gradient conditions, 25° in longitudinal down-gradient conditions, the limiting height of the chassis that can be overpassed in obstacle-crossing conditions is 450 mm, and the limiting width of the chassis that can be spanned in trench-crossing conditions is 1200 mm. The simulation results were verified through field tests, and the results of the field tests showed that the harvester met the requirements of stable travelling on longitudinal slopes of 24°, climbed over a 450 mm straight wall and crossed over a 1200 mm trench, which were similar to the simulation results, indicating that the simulation results were accurate and feasible, and met the design requirements for the travelling passage of the crawler potato harvester. This study provides an in-depth understanding of the tracked chassis of the potato combine harvester in hilly mountainous areas, with a view to providing reference for the design of tracked harvesters for other crops in hilly mountainous areas.