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Abstract The ability to lubricate and resist wear at temperatures above 600 °C in an oxidative environment remains a significant challenge for metals due to their high-temperature softening, oxidation, and rapid degradation of traditional solid lubricants. Herein, we demonstrate that high-temperature lubricity can be achieved with coefficients of friction (COF) as low as 0.10-0.32 at 600-900 °C by tailoring surface oxidation in additively-manufactured Inconel superalloy. By integrating high-temperature tribological testing, advanced materials characterization, and computations, we show that the formation of spinel-based oxide layers on superalloy promotes sustained self-lubrication due to their lower shear strength and more negative formation and cohesive energy compared to other surface oxides. A reversible phase transformation between the cubic and tetragonal/monoclinic spinel was driven by stress and temperature during high temperature wear. To span Ni- and Cr-based ternary oxide compositional spaces for which little high-temperature COF data exist, we develop a computational design method to predict the lubricity of oxides, incorporating thermodynamics and density functional theory computations. Our finding demonstrates that spinel oxide can exhibit low COF values at temperatures much higher than conventional solid lubricants with 2D layered or Magnéli structures, suggesting a promising design strategy for self-lubricating high-temperature alloys.

Abstract In response to westward subduction of the Paleo-Pacific, the North China Craton experienced the uplift of an eastern coastal plateau followed by subsidence in the Early Cretaceous, which potentially drove a shift in climatic patterns. Here we use the oxygen isotope ratios of garnets from magmatic-hydrothermal ore systems to infer the origins and signatures of mineralization fluids during this tectonic transition. The garnet oxygen isotope values range from approximately –11.4 to +13.5‰, with extremely depleted oxygen isotope ratios exclusively found in the northern margin, indicating extensive involvement of meteoric and lacustrine fluid in the back-arc hinterland. This geological record aligns with climate modeling exhibiting that the coastal plateau amplified northeastward transport of moisture from tropical Tethyan Ocean. The long-distance transport strongly depleted 18O and 2H (D). As the cratonic lithosphere thinned and the plateau subsided, the Pacific influences began to dominate the climatic pattern of East Asia.

Abstract A hybrid power station comprising storage pump units and conventional hydropower components holds the potential to enhance the operational flexibility of basin hydroelectric regulation. The storage pumps must possess significant power capacity and operate with high efficiency to ensure viable energy storage. This study investigates the energy dissipation within a two‐stage storage pump using entropy generation theory. The numerical solution of flow energy dissipation (FED) components was obtained for various flow rates using the steady‐state single‐phase shear stress transport k–ω turbulence model. Results indicate that the return channel contributes the most to FED generation within the entire passage, with the FED proportion decreasing from 66.7% to 41.3% as the flow rate increases from 0.5QBEP to 1.2QBEP. The FED generation percentage from the runners increases from 10.4% to 46.9% with increasing flow rate, ranking second. The FED generation percentage attributed from the spiral case ranges from 10.3% to 16.7%, ranking third. Losses from the draft tube are found to be negligible. Flow pattern analysis reveals that FED generation primarily occurs at the junction of inferior flow (flow separation and vortex flow) and the main flow, where significant velocity gradients exist.

Abstract Background This paper investigated the link between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and diabetic kidney disease (DKD) in adult diabetic patients and identified the optimal NHHR value for impacting DKD. Methods This cross-sectional research made use of records from the National Health and Nutrition Examination Survey (NHANES) executed between 2005 and 2016. The link of NHHR to DKD risk was analyzed by logistic regression and restricted cubic spline (RCS) models. The stability and reliability of the results were assessed by subgroup analysis and sensitivity analysis. Results In total, 4,177 participants were involved. As a continuous variable, NHHR was markedly connected to an increased risk of DKD (OR 1.07, 95% CI 1.02, 1.12, P

Abstract Background Breast cancer (BC) poses significant burdens on women globally. While past research suggests a potential link between bone mineral density (BMD) and BC risk, findings remain inconsistent. Our study aims to elucidate the causal relationship between BMD and BC in East Asians using bidirectional Mendelian randomization (MR). Methods Genetic association data for bone mineral density T-scores (BMD-T) and Z-scores (BMD-Z) (Sample size = 92,615) and BC from two different sources (Sample size1 = 98,283; Sample size2 = 79,550) were collected from publicly available genome-wide association studies (GWAS). Single-nucleotide polymorphisms (SNPs) associated with BMD-T and BMD-Z as phenotype-related instrumental variables (IVs) were used, with BC as the outcome. As the primary means of causal inference, the inverse variance weighted (IVW) approach was employed. Heterogeneity analysis was conducted using Cochran’s Q test, while MR-Egger regression analysis was implemented to assess the pleiotropic effects of the IVs. Sensitivity analyses were performed using methods such as MR-Egger, weighted median, and weighted mode to analyze the robustness and reliability of the results. The MR-PRESSO method and the RadialMR were used to detect and remove outliers. The PhenoScanner V2 website was utilized to exclude confounding factors shared between BMD and BC. Besides, the Bonferroni correction was also used to adjust the significance threshold. Then, the meta-analysis method was applied to combine the MR analysis results from the two BC sources. Finally, a reverse MR analysis was conducted. Results The results of the IVW method were consolidated through meta-analysis, revealing a positive correlation between genetically predicted BMD-T ( $$\:OR\:=\:1.22$$ , $$\:95\%\:CI:\:1.13-1.33$$ , $$\:P

Abstract Background Plant-associated microorganisms can be found in various plant niches and collectively comprise the plant microbiome. The plant microbiome assemblages have been extensively studied, primarily in model species. However, a deep understanding of the microbiome assembly associated with plant health is still needed. Ginger rhizome rot has been variously attributed to multiple individual causal agents. Due to its global relevance, we used ginger and rhizome rot as a model to elucidate the metabolome-driven microbiome assembly associated with plant health. Results Our study thoroughly examined the biodiversity of soilborne and endophytic microbiota in healthy and diseased ginger plants, highlighting the impact of bacterial and fungal microbes on plant health and the specific metabolites contributing to a healthy microbial community. Metabarcoding allowed for an in-depth analysis of the associated microbial community. Dominant genera represented each microbial taxon at the niche level. According to linear discriminant analysis effect size, bacterial species belonging to Sphingomonas, Quadrisphaera, Methylobacterium-Methylorubrum, Bacillus, as well as the fungal genera Pseudaleuria, Lophotrichus, Pseudogymnoascus, Gymnoascus, Mortierella, and Eleutherascus were associated with plant health. Bacterial dysbiosis related to rhizome rot was due to the relative enrichment of Pectobacterium, Alcaligenes, Klebsiella, and Enterobacter. Similarly, an imbalance in the fungal community was caused by the enrichment of Gibellulopsis, Pyxidiophorales, and Plectosphaerella. Untargeted metabolomics analysis revealed several metabolites that drive microbiome assembly closely related to plant health in diverse microbial niches. At the same time, 6-({[3,4-dihydroxy-4-(hydroxymethyl)oxolan-2-yl]oxy}methyl)oxane-2,3,4,5-tetrol was present at the level of the entire healthy ginger plant. Lipids and lipid-like molecules were the most significant proportion of highly abundant metabolites associated with ginger plant health versus rhizome rot disease. Conclusions Our research significantly improves our understanding of metabolome-driven microbiome structure to address crop protection impacts. The microbiome assembly rather than a particular microbe’s occurrence drove ginger plant health. Most microbial species and metabolites have yet to be previously identified in ginger plants. The indigenous microbial communities and metabolites described can support future strategies to induce plant disease resistance. They provide a foundation for further exploring pathogens, biocontrol agents, and plant growth promoters associated with economically important crops. Video Abstract

Abstract Background and intention Erectile dysfunction (ED) is an underappreciated clinical condition in men. This study aims to compare the dynamic changes in the distribution of ED among male kidney transplant recipients (mKTRs) in four epochs: end-stage renal disease period (ESRDp), early post-transplant period (EPTP), pre-COVID-19, and post-COVID-19. Methods General information was gathered through interviews, follow-ups, and medical records. The International Index of Erectile Function Questionnaire-5 was used to assess erectile function. The Mann–Whitney U test and chi-square test were used to analyze differences in ED strength. Univariate and logistic regression analyses were conducted to identify risk factors for ED. Results The database contains 230 mKTRs. In the ESRDp, 17.0% had normal erectile function, 53.5% had mild ED, 18.3% had moderate ED, and 11.3% had severe ED. In the EPTP, the distribution was 38.2% normal, 42.6% mild, 10.8% moderate, and 8.2% severe. In the pre-COVID-19 period, it was 34.3%, 47.3%, 10.4%, and 7.8%, and in the post-COVID-19 period, it was 23.0%, 45.6%, 21.3%, and 10.0%. Overall, erectile function improved after kidney transplant (KT). However, post-COVID-19, the proportion of erectile function significantly decreased compared to EPTP and pre-COVID-19 periods. Risk factors for post-pandemic ED included degree, Generalized Anxiexy Disorder-7, kidney donor type, postoperative time, hypertension and hemoglobin concentration. Conclusion KT improves erectile function in mKTRs within 5 years, but post-SARS-CoV-2 viral infection, ED worsens due to altered risk factors. These findings inform future research for comprehensive ED prevention and management strategies in this population.

Abstract Nowadays, the malicious MS-Office document has already become one of the most effective attacking vectors in APT attacks. Though many protection mechanisms are provided, they have been proved easy to bypass, and the existed detection methods show poor performance when facing malicious documents with unknown vulnerabilities or with few malicious behaviors. In this paper, we first introduce the definition of im-documents, to describe those vulnerable documents which show implicitly malicious behaviors and escape most of public antivirus engines. Then we present GLDOC—a GCN based framework that is aimed at effectively detecting im-documents with dynamic analysis, and improving the possible blind spots of past detection methods. Besides the system call which is the only focus in most researches, we capture all dynamic behaviors in sandbox, take the process tree into consideration and reconstruct both of them into graphs. Using each line to learn each graph, GLDOC trains a 2-channel network as well as a classifier to formulate the malicious document detection problem into a graph learning and classification problem. Experiments show that GLDOC has a comprehensive balance of accuracy rate and false alarm rate − 95.33% and 4.33% respectively, outperforming other detection methods. When further testing in a simulated 5-day attacking scenario, our proposed framework still maintains a stable and high detection accuracy on the unknown vulnerabilities.

Abstract Cross‐site scripting (XSS) attack has been one of the most dangerous attacks in cyberspace security. Traditional methods essentially discover XSS attack by detecting malicious payloads in requests, which is unable to distinguish attacking attempts with the attacking reality. The authors collect responses from a web server and train a bagging‐based PU learning model to determine whether the XSS vulnerability is truly triggered. To validate the authors’ proposed framework, experiments are performed on 5 popular web applications with 11 specified CVE recorded vulnerabilities and 32 vulnerable inputs. Results show that the authors’ approach outperforms existing research studies, effectively identifies the attacking reality from attacking attempts, and meanwhile reduces the number of worthless security alarms.

Abstract Background This study aimed to investigate the relationships between accelerometer-measured physical activity (PA) and sedentary behaviour (SB) with physical function (PF) among older Chinese women in the community. Methods The present study comprised 1,113 community-dwelling older females, with an average age of 65 ± 2 years. We employed a linear regression analysis to investigate the relationship between patterns of PA and SB with PF. PA variables consisted of total PA time, bouted PA time (a continuous PA that lasts equal to or more than 10 min), and sporadic PA time (a continuous PA that lasts less than 10 min). SB variables included total SB time, 30-min bout of SB (a continuous SB that lasts equal to or more than 30 min), and 60-min bout of SB (a continuous SB that lasts equal to or more than 60 min). PF variables comprised handgrip strength (HGS), one-legged stance test with eyes closed (OLSTEC), usual walking speed (UWS), maximum walking speed (MWS) and chair-stand time (CT). To explore the joint effects of moderate-to-vigorous-intensity PA (MVPA) and SB on PF, we divided the duration of SB and MVPA participation in older women into different combinations: low MVPA & high SB, low MVPA & low SB, high MVPA & high SB, high MVPA & low SB. Results The study revealed a significant association between 30-min bout of SB and CT, which remained after adjusting for total MVPA time (P = 0.021). Both total MVPA and bouted MVPA were found to be positively associated with better UWS, MWS, CT, and PF Z-score. When the combination of low MVPA & high SB was used as a reference, the regression coefficients for PF ascended by 1.32 (P

Abstract Al‐Si alloys are widely used in automotive casting components while microporosity has always been a detrimental defect that leads to property degradation. In this study, a coupled three‐dimensional cellular automata (CA) model has been used to predict the hydrogen porosity as functions of cooling rate and initial hydrogen concentration. By quantifying the pore characteristics, it has been found that the average equivalent pore diameter decreases from 40.43 to 23.98 μm and the pore number density increases from 10.3 to 26.6 mm−3 as the cooling rate changes from 2.6 to 19.4°C/s at the initial hydrogen concentration of 0.25 mL/100 g. It is also notable that the pore size increases as the initial hydrogen concentration changes from 0.15 to 0.25 mL/100 g while the pore number remains stable. In addition, the linear regression between secondary dendrite arm spacing and the equivalent pore diameter has been studied for the first time, matching well with experiments. This work exhibits the application of CA model in future process optimization and robust condition design for advanced automotive parts made of Al‐Si alloys.

Metal tolerance protein (MTP) is a cation transporter that plays an important role in tolerance to heavy metal stress. However, thus far, there has been no genome-wide investigation of the MTP gene family in Quercus plants. Quercus dentata is one of the main constructive species of forest in northern China. It has strong tolerance to a variety of heavy metal stresses. In this study, 25 MTPs were identified from the Q. dentata genome and classified into three subfamilies and seven groups according to their sequence characteristics and phylogenetic relationships. Both tandem and segmental duplication events contributed to the expansion of the QdMTP gene family. Interestingly, all 10 tandem duplication events contributed to the expansion of the Mn-CDF subfamily. The expression of Mn-CDF subfamily members in different organs and tissues of Q. dentata was different, and they responded differently to manganese, iron, zinc and cadmium stress treatments. QdMTP10.7, a member of the Mn-CDF subfamily, enhanced yeast growth under manganese, zinc and iron stresses. The subcellular localization in tobacco leaf epidermis cells showed that QdMTP10.7 was located in vacuoles. These data generated from this study provide an important foundation to elucidate the biological roles of QdMTP genes related to heavy metal tolerance in Q. dentata.

Abstract Background The study aims to analyze the epidemiology of preservation fluid (PF) contamination and investigate the impact of PF contamination and possible donor-derived infections(p-DDI) on early postoperative prognosis in kidney transplant (KT) recipients. Methods A total of 256 PF samples were collected for microbiological evaluation from all KT recipients who received deceased donor donations in our hospital from June 2018 to August 2022. Data on the baseline and clinical characteristics of these PF corresponding to recipients and donors were extracted from the electronic medical record. It mainly included the early postoperative complications and prognosis of KT recipients. Results From June 2018 to August 2022, 597 kidney transplants were performed in our center, with 260 recipients receiving kidney transplantation from donation after citizens’ death. A total of 256 samples of PF were collected, of which 64.5% (165/256) were culture positive, and 24.6% (63/165) of the culture-positive PF were polymicrobial contamination. A total of 238 strains were isolated, of which coagulase-negative staphylococci (CoNS) had the highest proportion of 34.0% (81/238), followed by Klebsiella pneumoniae with 20.6% (49/238) and Escherichia coli with 8.8% (21/238). Recipients with culture-positive PF had a significantly higher incidence of postoperative infection (55.8% vs. 20.9%, P

Purpose: The causal relationship between the incidence and prognosis of chronic kidney disease (CKD) and serum testosterone levels in patients is not yet fully understood. This study aims to use the National Health and Nutrition Examination Survey (NHANES), a large-scale nationally representative sample, to investigate the relationship between CKD and testosterone. Materials and Methods: This study included six NHANES cycles for linear regression analysis, verified by multiple imputation methods. Stratified analysis and subgroup analysis were used to demonstrate the stability of CKD’s effect on testosterone. Furthermore, we used Kaplan-Meier plots and log-rank tests to evaluate differences in survival rates between CKD male patients with low and normal levels of testosterone. Results: From a total of 71,163 subjects, the cohort selected 28,663 eligible participants. Results showed that CKD patients had testosterone levels 28.423 ng/mL (24.762, 32.083) lower than non-CKD patients. The results of multiple imputations (β=27.700, 95% confidence interval: 23.427, 31.974) were consistent with those of linear regression analysis, and the numerical match was good. Stratified regression analysis, and subgroup analysis results showed that CKD had a significant impact on testosterone at different dimensions. Kaplan-Meier plots showed significantly reduced survival rates in low testosterone CKD male patients (p

Abstract Background This study focuses on college students as research subjects, exploring the relationship between fear of evaluation, ambivalence over emotional expression, and self-care, as well as the moderating effect of self-care on the prediction of ambivalence over emotional expression by fear of evaluation. Methods This study selected physical education college students in Sichuan Province as the research participants. Convenience sampling was used to recruit participants through public recruitment information and questionnaire links on online platforms commonly used by college students. The questionnaire was distributed at two different time points to reduce the effect of common method bias. A total of 858 questionnaires were distributed, after excluding duplicate responses, a total of 743 valid questionnaires were obtained. Results The result as following: (1) College students exhibit a moderate to high level of negative fear of evaluation, ambivalence over emotional expression, and self-care, with female students having higher negative fear of evaluation; (2) There are significant correlations between fear of evaluation, ambivalence over emotional expression, and self-care; (3) Fear of evaluation and self-care can predict ambivalence over emotional expression, but self-care does not have a moderating effect on the prediction of ambivalence over emotional expression by fear of evaluation; (4) Self-care and negative self-care both have a moderating effect on the prediction of emotion rumination by positive fear of evaluation. Conclusions In the past, rumination has often been considered a manifestation of excessive rumination and anxiety. However, this study found that self-compassion’s modulation on fear of positive evaluation intensifies its impact on emotional rumination. Research suggests that moderate emotional rumination may not necessarily be a negative outcome and reflects a positive emotional adjustment process to some extent. Therefore, future studies can further explore the growth and decline of internal rumination processes in self-compassion or counseling, and potentially gain a better understanding of key elements of personal growth.

Significant coupling exists between DC and AC lines erected on the same tower or sharing transmission corridors. Electromagnetic and electrostatic coupling between DC and AC lines are investigated based on ± 500 kV MMC-HVDC and 500 kV double-circuit AC lines on the same tower. The interactive coupling characteristics and their impacts on protective relaying are analyzed during normal operation and after DC or AC faults. Voltage coupling ratio and current coupling ratio are defined. The 50 Hz components in MMC-HVDC line may be higher than ripple criterion under the coupling of AC line and can be even more significant when a fault occurs on adjacent AC line. The DC reactor with larger inductance can reduce its impact on MMC-HVDC system. Zero-sequence overcurrent relaying and zero-sequence directional relaying for AC lines may mal-operate when a fault occurs on adjacent MMC-HVDC line. And there is also an interruption risk for AC systems. Improvement schemes are proposed to avoid the mal-operation of protective relaying. Simulation case studies verify the correctness of coupling characteristic analysis and the effectiveness of proposed improvement scheme.

This study aimed to depict the mineral deposition on the cellulose extracted from mineral-rich Lycium barbarum L. leaves by the basic base/acid treatment. Alkali treatment made the cellulose fluffy, and the leaf phytoliths changed to liquid silicon, depositing onto the fluffy cellulose in crystalline forms, with the mineral content (mainly Si, Ca, and Al) reaching 9.41 % in cellulose after 24 h alkali treatment. Acid treatment decreased the deposited minerals affecting the cellulose crystallinity. Sulfuric acid (SA) mainly eroded the cellulosic amorphous zone and the attached minerals, and crystallized minerals (mainly quartz-SiO2) remained in the crystalline zone, increasing the cellulose crystallinity (66.8 %). Hydrofluoric acid (HF) indiscriminately removed nearly all minerals regardless of crystalline and amorphous zones, thus lowering the crystallinity. Further application research showed that the ability of the SA-refining cellulose to stabilize emulsion (89.8 %) was much stronger than that of the HF counterpart (62.6 %), and the mineral-relevant cellulose crystallinity determined the emulsion stability. These findings might help in understanding the impact of the basic extraction on cellulosic mineralization and quality and shed light on the development of novel functional cellulose.

Intelligent apple-picking robots can significantly improve the efficiency of apple picking, and the realization of fast and accurate recognition and localization of apples is the prerequisite and foundation for the operation of picking robots. Existing apple recognition and localization methods primarily focus on object detection and semantic segmentation techniques. However, these methods often suffer from localization errors when facing occlusion and overlapping issues. Furthermore, the few instance segmentation methods are also inefficient and heavily dependent on detection results. Therefore, this paper proposes an apple recognition and localization method based on RGB-D and an improved SOLOv2 instance segmentation approach. To improve the efficiency of the instance segmentation network, the EfficientNetV2 is employed as the feature extraction network, known for its high parameter efficiency. To enhance segmentation accuracy when apples are occluded or overlapping, a lightweight spatial attention module is proposed. This module improves the model position sensitivity so that positional features can differentiate between overlapping objects when their semantic features are similar. To accurately determine the apple-picking points, an RGB-D-based apple localization method is introduced. Through comparative experimental analysis, the improved SOLOv2 instance segmentation method has demonstrated remarkable performance. Compared to SOLOv2, the F1 score, mAP, and mIoU on the apple instance segmentation dataset have increased by 2.4, 3.6, and 3.8%, respectively. Additionally, the model’s Params and FLOPs have decreased by 1.94M and 31 GFLOPs, respectively. A total of 60 samples were gathered for the analysis of localization errors. The findings indicate that the proposed method achieves high precision in localization, with errors in the X, Y, and Z axes ranging from 0 to 3.95 mm, 0 to 5.16 mm, and 0 to 1 mm, respectively.

Abstract Background Hypertension influences the inflammatory pathological changes in the retina. The function of the inflammasomes is significant. To see if Sirtuin 1 (SIRT1) regulates angiotensin II (Ang II)-induced hypertensive retinopathy and inflammation by modulating NOD-like receptor thermal protein domain associated protein 3 (NLRP3) inflammasome activation and the potential protective effects of fucoidan (FO) in mouse retinal vascular endothelial cells (mRECs) and mice retina. Methods The diagnosis of hypertensive retinopathy was made after three weeks of Ang II infusion (3000 ng/kg/min). One day prior to the commencement of Ang II infusion, the mice were treatment with NLRP3 inhibitor MCC950 (10 mg/kg/day, intraperitoneal injections) or FO (300 mg/kg/day, oral gavage). A blood pressure was recorded. Hematoxylin and eosin (H&E) staining was used to conduct pathological alterations, dihydroethidium bromide (DHE) was utilized to assess oxidative stress damage in the retina, and fluorescence angiography was used to identify vascular disorders in the eye. Using immunohistochemical labeling, NLRP3 expression was found. Reactive protein and mRNA expression levels in mouse retina and cells were assessed using Western blot and real-time quantitative polymerase chain reaction (RT-qPCR). Results NLRP3 inflammasome activation and SIRT1 decrease were brought about by Ang II infusion. Retinopathy and dysfunction were lessened by MCC950 target-induced NLRP3 inflammasome activation, while overexpression of SIRT1 had the opposite impact on NLRP3 inflammasome activation, indicating that SIRT1 functions as an upstream regulator of NLRP3 activity. FO may improve SIRT1 expression and decrease NLRP3 activation in retinopathy and dysfunction brought on by Ang II, and the effects were consistent across both in vivo and in vitro models. Conclusions SIRT1 adversely regulates the NLRP3 inflammasome pathway, which in turn increases Ang II-induced inflammation and hypertensive retinopathy. FO may mitigate Ang II-induced retinopathy and dysfunction via modulating the expression of SIRT1/NLRP3. This implies practical approaches to the management of hypertensive retinopathy.

Abstract In this paper, we are concerned with the Kirchhoff-type variable-order fractional Laplacian problems involving critical exponents and logarithmic nonlinearity. By using the constraint variational method, we show the existence of one least energy sign-changing solution. Moreover, we show that this energy is strictly larger than twice the ground energy.

The combination of mobile edge computing (MEC) and radio frequency-based wireless power transfer (WPT) presents a promising technique for providing sustainable energy supply and computing services at the network edge. This study considers a wireless-powered mobile edge computing system that includes a hybrid access point (HAP) equipped with a computing unit and multiple Internet of Things (IoT) devices. In particular, we propose a novel multi-user cooperation scheme to improve computation performance, where collaborative clusters are dynamically formed. Each collaborative cluster comprises a source device (SD) and an auxiliary device (AD), where the SD can partition the computation task into various segments for local processing, offloading to the HAP, and remote execution by the AD with the assistance of the HAP. Specifically, we aims to maximize the weighted sum computation rate (WSCR) of all the IoT devices in the network. This involves jointly optimizing collaboration, time and data allocation among multiple IoT devices and the HAP, while considering the energy causality property and the minimum data processing requirement of each device. Initially, an optimization algorithm based on the interior-point method is designed for time and data allocation. Subsequently, a priority-based iterative algorithm is developed to search for a near-optimal solution to the multi-user collaboration scheme. Finally, a deep learning-based approach is devised to further accelerate the algorithm’s operation, building upon the initial two algorithms. Simulation results show that the performance of the proposed algorithms is comparable to that of the exhaustive search method, and the deep learning-based algorithm significantly reduces the execution time of the algorithm.

Abstract Our previous work demonstrated the tendon-derived extracellular matrix (ECM) extracts as vital niches to specifically direct mesenchymal stem cells towards tenogenic differentiation. This study aims to further define the effective ECM molecules capable of teno-lineage induction on human adipose-derived stem cells (hASCs) and test their function for tendon engineering. By detecting the teno-markers expression levels in hASCs exposed to various substrate coatings, collagen I (COL1) and fibromodulin (FMOD) were identified to be the key molecules as a combination and further employed to the modification of poly(L-lactide-co-ε-caprolactone) electrospun nanoyarns, which showed advantages in inducting seeded hASCs for teno-lineage specific differentiation. Under dynamic mechanical loading, modified scaffold seeded with hASCs formed neo-tendon in vitro at the histological level and formed better tendon tissue in vivo with mature histology and enhanced mechanical properties. Primary mechanistic investigation with RNA sequencing demonstrated that the inductive mechanism of these two molecules for hASCs tenogenic differentiation was directly correlated with positive regulation of peptidase activity, regulation of cell-substrate adhesion and regulation of cytoskeletal organization. These biological processes were potentially affected by LOC101929398/has-miR-197-3p/TENM4 ceRNA regulation axis. In summary, COL1 and FMOD in combination are the major bioactive molecules in tendon ECM for likely directing tenogenic phenotype of hASCs and certainly valuable for hASCs-based tendon engineering.

Global ocean surface currents estimated from satellite derived data based on a regular global grid are affected by the grid’s shape and placement. Due to different neighbourhood relationships, the rectangular lat/lon grids lose accuracy when interpolating and fitting elevation data. Hexagonal grids have shown to be advantageous due to their isotropic, uniform neighbourhood. Considering these merits, this paper aims to estimate global ocean surface current using a global isotropic hexagonal grid from satellite remote sensing data. First, gridded satellite altimeter data and wind data with different resolutions are interpolated into the centre of the global isotropic hexagonal grid. Then, geostrophic and Ekman currents components are estimated according to the Lagerlof Ocean currents theory. Finally, the inversion results are verified. By analyzing the results, we conclude that the ocean surface currents estimated based on the global isotropic hexagonal grid have considerable accuracy, with improvement over rectangular lat/lon grids.

Ion-adsorbing rare earth tailings (IRETs) contain a large amount of clay minerals, which are a potential source of silicon and aluminum for the preparation of zeolite materials. The complexity of the tailings’ composition and the impurity composition are the main difficulties in the controllable preparation of zeolite. Herein, IRETs were treated by classification activation technology for the preparation of IRET-ZEO, which was used for the removal of heavy metal Pb2+ in water. A new method of resource utilization of ion-type rare earth tailings is realized by “treating waste with waste”. The results showed that the IRETs were classified and then thermally activated, and the optimal activation parameter was calcination at 850 °C for 1 h. The optimal NaOH concentration used in the crystallization process was 5 mol/L, with a crystallization time of 3 h and a crystallization temperature of 85 °C, and the crystallization product was zeolite A. The removal rate of the Pb2+ solution with an initial concentration of 100 mg/L was as high as 96.7% in an acidic solution with a pH value from 2 to 5.5. In particular, when the solution pH was higher than 4.2, the adsorption rate of Pb2+ was close to 100%. The IRET-ZEO showed a fast adsorption rate (5 min to reach adsorption equilibrium), a large adsorption capacity (378.35 mg/g), excellent acid resistance, and selectivity and regenerability for Pb2+. This work provides a new strategy for the green resource utilization of IRETs and the treatment of lead-containing wastewater.

Developing environmentally friendly bulk materials capable of easily and thoroughly removing trace amounts of dye pollutants from water to rapidly obtain clean water has always been a goal pursued by researchers. Herein, a green material with a 3D architecture and with strong underwater rebounding and fatigue resistance ability was prepared by means of the assembly of biopolymer chitosan (CS) and natural caraganate fibers (CKFs) under freezing conditions. The CKFs can randomly and uniformly distribute in the lamellar structure formed during the freezing process of CS and CKFs, playing a role similar to that of “steel bars” in concrete, thus providing longitudinal support for the 3D-architecture material. The 2D layers formed by CS and CKFs as the main basic units can provide the material with a higher strength. The 3D-architecture material can bear the compressive force of a weight underwater for multiple cycles, meeting the requirements for water purification. The underwater compression test shows that the 3D-architecture material can quickly rebound to its original shape after removing the stress. This 3D-architecture material can be used to purify dye-containing water. When its dosage is 3 g/L, the material can remove 99.65% of the Congo Red (CR) in a 50 mg/L dye solution. The adsorption performance of the 3D architecture adsorbent for CR removal in actual water samples (i.e., tap water, seawater) is superior than that of commercial activated carbon. Due to its porous block characteristics, this material can be used for the continuous and efficient treatment of wastewater containing trace amounts of CR dye to obtain pure clean water, meaning that it has great potential for the effective purification of dye wastewater.

Abstract A recently emerging cell death pathway, known as copper‐induced cell death, has demonstrated significant potential for treating infections. Existing research suggests that cells utilizing aerobic respiration, as opposed to those reliant on glycolysis, exhibit greater sensitivity to copper‐induced death. Herein, a MnO2‐loaded copper metal–organic frameworks platform is developed denoted as MCM, to enhance bacterial cuproptosis‐like death via the remodeling of bacterial respiratory metabolism. The reversal of hypoxic microenvironments induced a cascade of responses, encompassing the reactivation of suppressed immune responses and the promotion of osteogenesis and angiogenesis. Initially, MCM catalyzed O2 production, alleviating hypoxia within the biofilm and inducing a transition in bacterial respiration mode from glycolysis to aerobic respiration. Subsequently, the sensitized bacteria, characterized by enhanced tricarboxylic acid cycle activity, underwent cuproptosis‐like death owing to increased copper concentrations and aggregated intracellular dihydrolipoamide S‐acetyltransferase (DLAT). The disruption of hypoxia also stimulated suppressed dendritic cells and macrophages, thereby strengthening their antimicrobial activity through chemotaxis and phagocytosis. Moreover, the nutritional effects of copper elements, coupled with hypoxia alleviation, synergistically facilitated the regeneration of bones and blood vessels. Overall, reshaping the infection microenvironment to enhance cuproptosis‐like cell death presents a promising avenue for eradicating biofilms.

Fetal chromosomal abnormalities are the main cause of adverse pregnancy outcomes and are the focus of invasive prenatal diagnosis. Recent studies have demonstrated that various techniques have distinct advantages. Achieving high-resolution and effective prenatal chromosomal abnormality diagnosis requires a multi-technology integration strategy. Based on retrospective samples from a single center, we propose that integrating CNV-seq and karyotype analysis is an effective strategy for prenatal diagnosis of chromosomal abnormalities. In this study, 13.80% of the pregnant women (347/2514) were found to have likely pathogenic or pathogenic fetal chromosomal abnormalities using this integrated approach. Among these cases, 53.89% (187/347) had consistent chromosomal abnormalities detected by both CNV-seq and karyotyping analysis, while 19.02% (66/347) and 27.09% (94/347) of cases were diagnosed solely by CNV-seq or karyotyping, respectively. Fetal chromosomal abnormalities were identified in 18.39% of samples with abnormal ultrasound, which was significantly higher than the percentage found in samples with normal ultrasound (p < 0.001). Samples with multiple ultrasound abnormalities and single-indicator ultrasound abnormalities such as nasal bone dysplasia, renal dysplasia, or echogenic fetal bowel also had higher rates of chromosomal abnormalities (p < 0.05) compared to normal samples. Analyzing samples with Trio family data (N = 521) revealed that about 94% of variants of uncertain significance were inherited from parents and were non-pathogenic. Overall, integrating CNV-seq and karyotype analysis is an effective strategy for prenatal diagnosis of chromosomal abnormalities. This study provides valuable insights for correlating prenatal screening indicators with chromosomal abnormalities.

BackgroundCervical cancer is primarily caused by HPV infection. The epidemiology of HPV infection in specific areas is of great meaning of guide cervical cancer screening and formulating HPV vaccination strategies. Here, we evaluated the epidemiological characteristics of HPV infection in Xiamen population.MethodsIn total, 159,049 cervical exfoliated cell samples collected from female outpatients in Women and Children’s Hospital, School of Medicine, Xiamen between January 2013 and July 2023 were analyzed. HPV DNA detection was performed using HPV genotyping kits (Hybribio Limited Corp, China). An analysis was conducted on the prevalence of HPV infection, taking into account factors such as age, year, and multiple patterns of HPV infection. The differences in prevalence among age groups and years were compared using χ2 test.ResultsThe overall prevalence of any 21 HPV genotypes was 18.4%, of which the high-risk HPV (HR-HPV) positive rate was 14.6%. The age-specific prevalence of HPV infection showed a bimodal distribution, with two distinct peaks, one at

Abstract Aiming at the problem of denoising chaotic signals with low signal-to-noise ratio and unknown dynamic system parameters, a new chaotic signal denoising algorithm is proposed, which combines adjustable Q-factor wavelet transform (TQWT) and adaptive singular value decomposition (ASVD). This method uses TQWT to decompose the noisy chaotic signal. According to the maximum wavelet entropy theory and energy threshold rule, the subband of TQWT is accurately divided into signal subband and noise subband. For noise subbands, adaptive SVD is used to denoise them, to achieve preliminary denoising. In ASVD, the standard deviation of the singular value subset is used to determine the effective reconstruction order to improve the noise suppression effect. To further remove noise in the signal subband, TQWT reconstruction is performed on the preliminarily denoised signal, and ASVD is used to denoise the reconstructed signal again to obtain the chaotic signal after secondary denoising. Chua's simulated signal and four kinds of underwater radiated noise measured by TQWT-ASVD were denoised, and compared with the SVD denoising method, TQWT denoising method, complete ensemble empirical mode decomposition with adaptive noise and threshold denoising method (CEEMDAN-WT) and modified ensemble empirical mode decomposition combined with least squares denoising method (MEEMD-LMS), The experimental results show that the TQWT-ASVD method can reduce the noise of chaotic signals more effectively. Compared with SVD, TQWT, CEEMDAN-WT, MEEMD-LMS, and Chua's signal denoising method, the signal-to-noise ratio (SNR) of this method increased by 23.22%, 26.46%, 18.79%, 16.11% the root mean square error (RMSE) decreased by 32.53%,39.48%, 30.96%, 27.94%, and the row entropy (PE) decreased by 40.44%, 41.96%, 22.78%, 20.59%; After reducing the radiation noise of cargo ships, the PE value of this method is reduced by 13.91%, 10.18%, 10.88%, 8.68% respectively, and the FE value is reduced by 33.66%, 31.42%, 26.98%, 21.32% respectively.

As an essential part of electronic component assembly, it is crucial to rapidly and accurately detect electronic components. Therefore, a lightweight electronic component detection method based on knowledge distillation is proposed in this study. First, a lightweight student model was constructed. Then, we consider issues like the teacher and student's differing expressions. A knowledge distillation method based on the combination of feature and channel is proposed to learn the teacher's rich class-related and inter-class difference features. Finally, comparative experiments were analyzed for the dataset. The results show that the student model Params (13.32 M) are reduced by 55%, and FLOPs (28.7 GMac) are reduced by 35% compared to the teacher model. The knowledge distillation method based on the combination of feature and channel improves the student model's mAP by 3.91% and 1.13% on the Pascal VOC and electronic components detection datasets, respectively. As a result of the knowledge distillation, the constructed student model strikes a superior balance between model precision and complexity, allowing for fast and accurate detection of electronic components with a detection precision (mAP) of 97.81% and a speed of 79 FPS.

In this work, SiCp reinforced Al–Si–Mg matrix composites were prepared by stirring casting with different SiCp sizes. The effect of SiCp sizes on the microstructure and mechanical properties was investigated, and different strengthening effects were systematically calculated and analyzed. With the decrease of SiCp sizes, the microstructure of composites is refined and the plasticity is improved significantly. The refined SiCp also leads to a more intense grain refinement strengthening and thermal expansion mismatch strengthening. However, an abnormal decrease of yield strength is discovered with the refinement of SiCp, which is due to the influence of SiCp on GP zones strengthening in the matrix. As SiCp sizes decreasing, the increased SiCp/Al interfaces capture more Mg atoms, and then inhibits the formation of GP zones in the α-Al. Therefore, the refined SiCp weaken the precipitation strengthening in composites. These acicular Mg–Si GP zones exhibit a preferentially growing direction along Al and present a same structure as α-Al with coherent interfaces which pin dislocations and provide precipitation strengthening. The high density of GP zones also increases nano-hardness and elastic modulus of α-Al, which improves the wear resistance of SiCp/Al–Si–Mg composites. Therefore, it is suggested that the interaction between the SiCp and precipitates in matrix should also be considered to obtain a suitable strengthening effect in the design of SiCp/Al alloy composites.

Abstract Objective To investigate the relationship between suprasellar extension (SSE) and intracranial infection after endoscopic endonasal transsphenoidal approach (EETA) for pituitary adenoma resection. Methods We retrospectively analyzed 94 patients with suprasellar extended pituitary adenoma admitted to the Department of Neurosurgery of the Affiliated Hospital of Guilin Medical College from January 2018 to December 2021. We measured the preoperative magnetic resonance sagittal SSE and collected clinical data and divided the patients into groups according to the presence of postoperative intracranial infection. The critical value for the SSE was calculated by using a working characteristic curve for the subjects. The risk factors for intracranial infection after EETA resection of pituitary adenomas were analyzed by multivariate regression analysis. Results Among the 94 patients, 12 cases (12.8%) were placed in the infection group and 82 cases (87.2%) in the non-infection group. The cut-off value for the SSE in the sagittal position was 15.6 mm, the sensitivity was 75%, the specificity was 87.8%, and the area under the curve (AUC) was 0.801. The coronary cut-off value for the SSE was 15.8 mm, the sensitivity was 66.7%, the specificity was 79.3%, and the AUC was 0.787. The SSE values in the sagittal and coronal positions were correlated with postoperative intracranial infection (P

Abstract Background Epithelial–mesenchymal transition (EMT) plays a key role in tubulointerstitial fibrosis, which is a hallmark of diabetic kidney disease (DKD). Our previous studies showed that CRTC2 can simultaneously regulate glucose metabolism and lipid metabolism. However, it is still unclear whether CRTC2 participates in the EMT process in DKD. Methods We used protein‒protein network (PPI) analysis to identify genes that were differentially expressed during DKD and EMT. Then, we constructed a diabetic mouse model by administering STZ plus a high-fat diet, and we used HK-2 cells that were verified to confirm the bioinformatics research results. The effects that were exerted by CRTC2 on epithelial-mesenchymal transition in diabetic kidney disease through the CREB-Smad2/3 signaling pathway were investigated in vivo and in vitro by real-time PCR, WB, IHC and double luciferase reporter gene experiments. Results First, bioinformatics research showed that CRTC2 may promote EMT in diabetic renal tubules through the CREB-Smad2/3 signaling pathway. Furthermore, the Western blotting and real-time PCR results showed that CRTC2 overexpression reduced the expression of E-cadherin in HK-2 cells. The CRTC2 and α-SMA levels were increased in STZ-treated mouse kidneys, and the E-cadherin level was reduced. The luciferase activity of α-SMA, which is the key protein in EMT, was sharply increased in response to the overexpression of CRTC2 and decreased after the silencing of CREB and Smad2/3. However, the expression of E-cadherin showed the opposite trends. In the real-time PCR experiment, the mRNA expression of α-SMA increased significantly when CRTC2 was overexpressed but partially decreased when CREB and Smad2/3 were silenced. However, E-cadherin expression showed the opposite result. Conclusion This study demonstrated that CRTC2 activates the EMT process via the CREB-Smad2/3 signaling pathway in diabetic renal tubules.

Interfacial bonding has attracted extensive attention because it essentially affects the performance of SiC reinforced Al matrix composites. To investigate the impact of Ni and Cu inherent in the Al matrix on the interfacial bonding of the composite, Al–Si–Mg/oxidized SiC composite and Al–Si–Cu–Ni–Mg/unoxidized SiC composite were fabricated by semisolid stir casting. Our study demonstrated that several intermetallics can nucleate and grow on the surface of SiC in Al–Si–Cu–Ni–Mg/unoxidized SiC composite during solidification, forming the intermetallic/unoxidized SiC interface and Al-intermetallic-SiC transition structure. The bonding of the Al3CuNi/unoxidized SiC interface is stronger than the conventional Al/oxidized SiC interface. The Al3CuNi/unoxidized SiC interface is functional in impeding crack propagation and withstanding loading during deformation. Moreover, the Al-intermetallic-SiC transition structure can alleviate the agglomeration of SiC particles, improve the overall coherency and coordinate the deformation of the composite. The extraordinary interface and structure strengthen the composite to a certain extent both at room and high temperatures.

To meet the challenges of energy sustainability, the integrated energy system (IES) has become a key component in promoting the development of innovative energy systems. Accurate and reliable multivariate load prediction is a prerequisite for IES optimal scheduling and steady running, but the uncertainty of load fluctuation and many influencing factors increase the difficulty of forecasting. Therefore, this article puts forward a multi-energy load prediction approach of the IES, which combines the fennec fox optimization algorithm (FFA) and hybrid kernel extreme learning machine. Firstly, the comprehensive weight method is used to combine the entropy weight method and Pearson correlation coefficient, fully considering the information content and correlation, selecting the key factors affecting the prediction, and ensuring that the input features can effectively modify the prediction results. Secondly, the coupling relationship between the multi-energy load is learned and predicted using the hybrid kernel extreme learning machine. At the same time, the FFA is used for parameter optimization, which reduces the randomness of parameter setting. Finally, the approach is utilized for the measured data at Arizona State University to verify its effectiveness in multi-energy load forecasting. The results indicate that the mean absolute error (MAE) of the proposed method is 0.0959, 0.3103 and 0.0443, respectively. The root mean square error (RMSE) is 0.1378, 0.3848 and 0.0578, respectively. The weighted mean absolute percentage error (WMAPE) is only 1.915%. Compared to other models, this model has a higher accuracy, with the maximum reductions on MAE, RMSE and WMAPE of 0.3833, 0.491 and 2.8138%, respectively.

Connected and automated vehicles (CAVs) represent a significant development in the transport industry owing to their intelligent and interconnected features. Potential field theory has been extensively used to model CAV driving behaviour owing to its objectivity, universality, and measurability. However, existing car-following models do not consider the impact of time delays and the influence of information from multiple vehicles ahead and behind. This paper focuses on the driving-safety risks associated with CAVs, aiming to enhance vehicle safety and reliability during travelling. We developed a multi-vehicle car-following model based on safety potential fields (MIDM-SPF), taking into account the characteristics of multi-vehicle connected information and time delays. To enhance the model’s precision, real-world data from urban roads were employed, alongside an improved optimisation algorithm to fine-tune the car-following model. The simulation experiment revealed that MIDM-SPF significantly reduces stop-and-go traffic, thereby improving traffic flow stability in urban areas. Additionally, we validated the stability of our model under varying market penetration rates in large-scale mixed traffic. Our findings indicate that increasing the CAV proportion improves the stability of mixed traffic flows, which has important implications for alleviating traffic congestion and guiding the large-scale implementation of autonomous driving in the future.

Power system control is commonly based on linear controllers, where linear controllers are designed using a linearized model of the system at a specific operating point. However, when the system’s operating point is changed, the dynamic characteristics of the system shift significantly. At this point, linear controllers often fail to meet system stability requirements. Furthermore, the range of state variables in the power system is limited by the objective conditions. In addition, the power system has high-precision constraints on the deviation of the load frequency and so on. Therefore, it is worth designing a finite-time controller that satisfies the prescribed performance and full-state constraints based on the nonlinear model of the power systems. Firstly, the prescribed performance is incorporated into the barrier Lyapunov function to ensure that the tracking error is within the desired accuracy. Then, the tracking strategy is designed based on backstepping and incorporating a first-order filter to ensure that the controlled system’s signals and tracking errors remain bounded in finite time. Finally, two simulations are given to illustrate the effectiveness of the proposed control scheme, confirming that all states keep within the predefined range.

With the rapid development of new-generation cyber–physical system (CPS) technologies, the smart operation and management of the broad bean harvesting system are enabled. This paper proposed a smart broad bean harvesting system (SBHS) and a self-adaptive control method based on CPS technologies. Firstly, the overall architecture of the SBHS is designed, which consists of four main components, namely optimal intelligent perception environment configuration, digital twin model construction, virtual simulation and real-time optimization, self-adaptive adjustment and control. Then, three key enabling technologies are discussed in detail, namely, intelligent perception environment configuration for the SBHS, digital twin model construction for the SBHS, colored Petri net (CPN)-based self-adaptive analysis and control of the harvesting system. Lastly, a proof-of-concept experiment based on a cooperative company is presented to illustrate the main work logic and advantage of the proposed SBHS. After the edge–cloud cooperative intelligent harvesting environment is configured, the CPN model for the workflow of the SBHS is created to analyze and optimize the harvesting processes. In addition, a management and control platform are developed to further illustrate the implementation of the proposed SBHS and the self-adaptive control method.

Borehole strain gauges play a crucial role in geophysical, seismological, and crustal dynamics studies. While existing borehole strain gauges are proficient in measuring horizontal strains within vertical boreholes, their effectiveness in capturing vertical and oblique strains is limited due to technical constraints arising from the cylindrical probe’s characteristics. However, the accurate measurement of three-dimensional strain is essential for a comprehensive understanding of crustal tectonics, dynamics, and geophysics, particularly considering the diverse geological structures and force sources within the crustal medium. In this study, we present a novel approach to address this challenge by enhancing an existing horizontal-component borehole strain gauge with a bellows structure and line strain measurement technology to enable vertical and borehole oblique strain measurements. Integrating these enhancements with horizontal strain measurement capabilities enables comprehensive three-dimensional borehole strain measurements within the same hole section. The system was deployed and tested at the Gongxian seismic station in Sichuan Province. Clear observations of solid tides were recorded across horizontal, oblique, and vertical measurement units, with the tidal morphology and amplitude being consistent with the theoretical calculations. The achieved measurement sensitivity of 10-10 meets the requirements for borehole strain measurement, enabling the characterization of three-dimensional strain states within boreholes through association methods.

This work investigated the influence of cooling rate (0.04, 0.87, 15.2 and 156.2 K/s) on the microstructure and compressive properties of Al–4Cu–3Li-0.7 Mg–1Zn alloys fabricated by several casting molds. The experiment results revealed that the microstructure cooled at low cooling rates of 0.04, 0.87 and 15.2 K/s is composed of α-Al, α(Al)+T2, θ (Al2Cu), T1 phases and core-shell configuration (Al13Fe4/Al7Cu2Fe phase). This core-shell structure is observed for the first time in an Al–Cu–Li alloy. However, T1 and core-shell configuration vanish at the high cooling rate of 156.2 K/s due to the insufficient diffusion time of Cu, Li and Fe elements. As the cooling rate increases, the average secondary dendrite arm spacing (SDAS) decreases significantly from 110.3 to 7.5 μm and the relationship between SDAS and cooling rate has been established. The average diameter/thickness of the α(Al)+T2, Al2Cu and Al13Fe4/Al7Cu2Fe phases decreases dramatically from 16.9 to 1.0 μm, 8.1 to 0.8 μm and 7.5 to 0.7 μm with the increase of cooling rate, respectively. Fine spherical Al13Fe4/Al7Cu2Fe and Al2Cu phases are beneficial to the improvement of comprehensive compressive properties. Additionally, the solute concentration in the matrix decreases, while the hot-tear resistance and volume fraction of secondary phases increase with increasing cooling rate. The empirical equations are established between (compressive properties, SDAS) and cooling rate. The fracture failure is responsible for the initial hot tearing at low cooling rates, the α(Al)+T2 or Al2Cu or Al13Fe4/Al7Cu2Fe phases at the moderate cooling rate, and the α(Al)+T2 phase at the high cooling rate.

Native-like endothelium regeneration is a prerequisite for material-guided small-diameter vascular regeneration. In this study, a novel strategy is proposed to achieve phase-adjusted endothelial healing by step-wise modification of parallel-microgroove-patterned (i.e., micropatterned) nanofibers with polydopamine-copper ion (PDA-Cu2+) complexes, polylysine (PLys) molecules, and Cys-Ala-Gly (CAG) peptides (CAG@PLys@PDA-Cu2+). Using electrospun poly(l-lactide-co-caprolactone) random nanofibers as the demonstrating biomaterial, step-wise modification of CAG@PLys@PDA-Cu2+ significantly enhanced substrate wettability and protein adsorption, exhibited an excellent antithrombotic surface and outstanding phase-adjusted capacity of endothelium regeneration involving cell adhesion, endothelial monolayer formation, and the regenerated endothelium maturation. Upon in vivo implantation for segmental replacement of rabbit carotid arteries, CAG@PLys@PDA-Cu2+ modified grafts (2 mm inner diameter) with micropatterns on inner surface effectively accelerated native-like endothelium regeneration within 1 week, with less platelet aggregates and inflammatory response compared to those on non-modified grafts. Prolonged observations at 6- and 12-weeks post-implantation demonstrated a positive vascular remodeling with almost fully covered endothelium and mature smooth muscle layer in the modified vascular grafts, accompanied with well-organized extracellular matrix. By contrast, non-modified vascular grafts induced a disorganized tissue formation with a high risk of thrombogenesis. In summary, step-wise modification of CAG@PLys@PDA-Cu2+ on micropatterned nanofibers can significantly promote endothelial healing without inflicting thrombosis, thus confirming a novel strategy for developing functional vascular grafts or other blood-contacting materials/devices.

Abstract In the treatment of spinal cord injury (SCI), the complex process of secondary injury is mainly responsible for preventing SCI repair or even exacerbating the injury. In this experiment, we constructed the 8-gingerol (8G)-loaded mesoporous polydopamine (M-PDA), M@8G, as the in vivo targeting nano-delivery platform, and investigated the therapeutic effects of M@8G in secondary SCI and its related mechanisms. The results indicated that M@8G could penetrate the blood-spinal cord barrier to enrich the spinal cord injury site. Mechanism research has shown that all of the M-PDA,8G and M@8G displayed the anti-lipid peroxidation effect, and then M@8G can inhibit the secondary SCI by suppressing the ferroptosis and inflammation. In vivo assays showed that M@8G significantly diminished the local injury area, reduced axonal and myelin loss, thus improving the neurological and motor recovery in rats. Based on the analysis of cerebrospinal fluid samples from patients, ferroptosis occurred locally in SCI and continued to progress in patients during the acute phase of SCI as well as the stage after their clinical surgery. This study showcases effective treatment of SCI through the aggregation and synergistic effect of M@8G in focal areas, providing a safe and promising strategy for the clinical treatment of SCI.

[This corrects the article DOI: 10.1371/journal.pone.0303525.].

The connected and automated car-following model can provide a model reference for the queue control algorithm of connected and automated driving and has become a hot research topic in the field of connected vehicles and intelligent transportation. A queue of fast-moving vehicles on urban roads can cause traffic congestion when forced to slow down and, in serious cases, can cause rear-impact accidents. Therefore, this paper introduces information on the time delay of information reception and processing, a collision risk quantification factor reflecting the speed characteristics of the front vehicle, and the speed limit and proposes an improved intelligent driver collision quantification model that considers drastic changes in the speed of the front vehicle. Additionally, the model parameters are calibrated using real vehicle data from urban roads combined with an improved salp swarm algorithm. Finally, the evolution rule of disturbance in the traffic flow under different states is analyzed using a time-space diagram, and the DIDM-CSCL model is compared with the classical IDM. The results show that the improved IDM can better describe the following behavior at the microscopic level, which provides a basis for research related to connected and automated driving.

Vegetable and fruit classification can help all links of agricultural product circulation to better carry out inventory management, logistics planning and supply chain coordination, and improve the efficiency and response speed of the supply chain. However, the current classification of vegetables and fruits mainly relies on manual classification, which inevitably introduces the influence of human subjective factors, resulting in errors and misjudgments in the classification of vegetables and fruits. In response to this serious problem, this research proposes an efficient and reproducible novel model to classify multiple vegetables and fruits using handcrafted features. In the proposed model, preprocessing operations such as Gaussian filtering, grayscale and binarization are performed on the pictures of vegetables and fruits to improve the quality of the pictures; statistical texture features representing vegetable and fruit categories, wavelet transform features and shape features are extracted from the preprocessed images; the feature dimension reduction method of diffusion maps is used to reduce the redundant information of the combined features composed of statistical texture features, wavelet transform features and shape features; five effective machine learning methods were used to classify the types of vegetables and fruits. In this research, the proposed method was rigorously verified experimentally and the results show that the SVM classifier achieves 96.25% classification accuracy of vegetables and fruits, which proves that the proposed method is helpful to improve the quality and management level of vegetables and fruits, and provide strong support for agricultural production and supply chain.

BackgroundPatients with T2DM often suffer from CVD-related complications, significantly impacting morbidity and mortality rates. The upsurge in CVD prevalence among them is partly linked to sd LDL particles. Understanding the mechanisms behind elevated sd LDL levels is critical for preventing and managing cardiovascular complications in diabetes.MethodsMR was employed to identify instrumental variables and establish causality, exploring underlying mechanisms.ResultsNotably, T2DM itself, insulin resistance, and fasting glucose seemingly do not directly impact sd LDL levels. Instead, the presence of T2DM or insulin resistance, leading to reduced HDL cholesterol or elevated TG levels, directly contributes to subsequent sd LDL increases, indicating a comprehensive mediating effect. While LDL cholesterol levels correlate positively with sd LDL, they appear unaffected by T2DM or insulin resistance. Importantly, hypertension induced by T2DM or insulin resistance exhibits a positive effect on sd LDL reversal. Unlike T2DM or insulin resistance, blood glucose levels show no significant impact on all processes.ConclusionsIt is hoped that these insights might influence the treatment of patients with diabetes and the management of blood parameters in clinical practice. Examining the effect of T2DM or insulin resistance on sd LDL within HDL cholesterol and triglycerides pathways might provide valuable insights for targeted cardiovascular treatments. Additionally, the study's exploration of the potential positive effects of elevated blood pressure on sd LDL reversal may introduce novel considerations for blood pressure management in patients with diabetes.

IntroductionFrequent epileptic seizures can cause irreversible damage to the brains of patients. A potential therapeutic approach is to detect epileptic seizures early and provide artificial intervention to the patient. Currently, extracting electroencephalogram (EEG) features to detect epileptic seizures often requires tedious methods or the repeated adjustment of neural network hyperparameters, which can be time- consuming and demanding for researchers.MethodsThis study proposes an automatic detection model for an EEG based on moth-flame optimization (MFO) optimized one-dimensional convolutional neural networks (1D-CNN). First, according to the characteristics and need for early epileptic seizure detection, a data augmentation method for dividing an EEG into small samples is proposed. Second, the hyperparameters are tuned based on MFO and trained for an EEG. Finally, the softmax classifier is used to output EEG classification from a small-sample and single channel.ResultsThe proposed model is evaluated with the Bonn EEG dataset, which verifies the feasibility of EEG classification problems that involve up to five classes, including healthy, preictal, and ictal EEG from various brain regions and individuals.DiscussionCompared with existing advanced optimization algorithms, such as particle swarm optimization, genetic algorithm, and grey wolf optimizer, the superiority of the proposed model is further verified. The proposed model can be implemented into an automatic epileptic seizure detection system to detect seizures in clinical applications.