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Objectives: With the improvement of production technology, the traditional diamond particle cleanliness detection method can no longer meet the requirements of high precision, high quality and high automation in the diamond industry due to its low efficiency and poor accuracy. The rapid development of computer technology, optical, and electronic technologies has led to the widespread application of visual inspection and deep learning in image classification and detection, providing new methods for diamond clarity detection. Therefore, based on transfer learning and combined with the convolutional block attention module (CBAM) attention mechanism and the feature pyramid network (FPN) structure, an improved ResNet50 diamond particle clarity detection algorithm, CBAM-ResNet50, is proposed. Methods: The CBAM-RESnet50 clarity detection algorithm uses ResNet50 as the backbone network and adds CBAM to each layer of the backbone network to improve the feature extraction ability of the model. In addition, the FPN structure is integrated into Layer 3 and Layer 4 of the backbone network, where part of the extracted features are aggregated to address the issues of losing features of small and medium-sized targets during the sampling process. At the same time, the transfer learning method is introduced to optimize the model's initial parameters with a cross-entropy loss function, thereby improving the generalization ability and robustness of the model. Moreover, multi-angle diamond images are collected on a diamond clarity detection device, a diamond particle clarity dataset is established, and the improved CBAM-ResNet50 network model is experimentally compared and verified using the data set. Results: Firstly, when compared with other classic mainstream network models, the accuracy of the CBAM-ResNet50 model during training is 99.2%, and the precision is 99.7%, ourperforming the classification results of other network models and significantly improving the identification ability for diamond particle clarity detection. The average detection time of the CBAM-ResNet50 model is 0.01629s, which meets the real-time requirements for industrial detection. Secondly, the CBAM-ResNet50 model is evaluated and ablated on diamond particles of various grades. The results show that the CBAM-ResNet50 model achieves an accuracy of over 99.2%, a classification recall rate of over 98.7%, specificity of over 99.7%, and an F1 score of over 99.2% for classifying diamonds of different grades. The ablation experiment results show that adding the CBAM attention mechanism and FPN feature fusion module significantly improves the classification performance of different grades of diamond particles. The ResNet50+CBAM model achieves a classification accuracy and recall rate of 100.0% for A and E grade diamonds, indicating that the CBAM module helps focus the network's attention on the black impurity features inside the diamond particles, reduces attention to irrelevant information, and improves classification accuracy. The CBAM-ResNet50, with the addition of the FPN feature fusion module, further enhances the classification accuracy and recall rate for B, C, and D grade diamonds. This improvement suggests that the FPN fuses both high-level and low-level feature information, enriching the small target features in the feature map, and enhances classification performance for B, C, and D grade diamonds with similar characteristics. Conclusions: Deep learning technology has been applied to the cleanliness detection of diamond particles, with the ResNet50 network, known for its strong feature extraction ability, serving as the backbone model. Based on the cleanliness features in diamond particle images, the CBAM attention mechanism, the FPN feature fusion module, transfer learning, and the entropy loss function are respectively integrated to address the challenges of insufficient feature extraction, the loss of small target features, and limited generalization in network models. By comparing experiments with other mainstream networks and conducting network ablation experiments, the impact of various improvements on the performance of the diamond particle cleanliness classification network is studied, confirming the effectiveness of the improved network model.

Corporate sustainability development has emerged as a significant concern globally. However, there is an inherent conflict between the corporate’s goals of maximization of profit and achievement of sustainable development. The increasing environmental responsibility offers a solution to reconcile this contradiction. Therefore, this paper aims to empirically investigate the influence of corporate environmental responsibility on corporate sustainable development and the mediator effect of corporate environmental strategy based on the nature of strategy that it can transform temporary and one-sided-focused activities into chronical and holistic actions. The study conducted two stages of data collection, gathering a total of 324 data sets from China in 2022, which were subsequently analyzed. The findings demonstrate that corporate environmental responsibility has a significant positive impact on corporate sustainable development, with corporate environmental strategy playing a crucial intermediary role. And this research offers theoretical contribution by extending literature on sustainable development and provides significant managerial implications.

Multi-source fusion constitutes a research focus in the navigation domain. This article focuses on the online estimation of the mounting angles between the body frame and vehicle frame within low-cost embedded vehicle navigation modules and the lever arm between the global satellite navigation system (GNSS) antenna/odometer and the inertial measurement unit (IMU). An online mounting angle error estimation algorithm, using odometers and IMU speeds, has been developed to estimate the angle errors while vehicles are in motion. At the same time, an online estimation algorithm model for the GNSS antenna lever arm and odometer lever arm was constructed. These two types of lever arms are used as the estimated states, and then Kalman filters are used to estimate them. The algorithm can simultaneously estimate the IMU mounting angle error, GNSS antenna arm, and odometer arm online. The experimental outcomes demonstrate that the lever arm estimation algorithm presented herein is effective for tactical and MEMS-level inertial navigation, with an estimation error of less than 2 cm. Meanwhile, the proposed online estimation of the mounting angle algorithm has an accuracy comparable to that of the post-processing algorithm. After making up the mounting angle and lever arm, we found that the position and speed precision of the multi-source fusion navigation systems were significantly improved. The results indicate that the proposed online estimation of mounting angle error and lever arm algorithm are effective and may promote the practical and widespread application of integrated navigation systems in vehicles. It solves the shortcomings of traditional methods, including the cumbersome and inaccurate manual measurement of the lever arm. It provides a technical solution for developing a more accurate and convenient low-cost vehicle navigation module.

Abstract Miscanthus spp. show excellent application prospects due to its bioenergy potential and multiple ecological services. Annual N export with biomass harvest from Miscanthus, even without fertilizer supplement, do not reduce soil N levels. The question arises regarding how Miscanthus can maintain stable soil N levels. Metagenomic strategies were used to reveal soil N‐cycling‐related microbiome and their functional contributions to processes of soil N‐cycling based on the comparison among the bare land, cropland, 10‐year Miscanthus × giganteus, and 15‐year Miscanthus sacchariflorus fields. The results showed that, after long‐term cropland‐to‐Miscanthus conversion (LCMC), 16 of 21 bacterial phyla and all the archaeal phyla exhibited significant changes. Soil microbial denitrification and nitrification functions were significantly weakened, and N fixation (NF) was significantly enhanced. The biosynthesis of amino acids, especially alanine, aspartate, and glutamate metabolism, in soil N‐cycling‐related microbiome was dramatically promoted. The genus Anaeromyxobacter contributed largely to the NF process after LCMC. Variations in the soil available potassium, available N, organic C, and total N contents drove a functional shift of soil microbiome from cropland to Miscanthus pattern. We conclude that Miscanthus can recruit Anaeromyxobacter communities to enhance NF benefiting its biomass sustainability and soil N balance.

With the increasing importance of motorized spindles in high-end machining, the accuracy requirements for motorized spindles have been increasing. Therefore, the accuracy problem caused by thermal deformation of the motor spindle during machining has become a prominent and current topic of interest. The problem of thermal deformation of the spindle is due to the fact that a large amount of heat is accumulated inside the spindle during the high-speed rotation of the electric spindle, which has no timely temperature compensation measures and can only rely on the conditions of heat conduction, heat convection and heat radiation to transfer the heat to the outside world, which leads to the thermal expansion of the spindle. Aiming at this problem, this paper takes the A02 electric spindle as the research object, and collects and analyzes the temperature information of the current temperature rise of the key parts of the electric spindle and the thermal elongation of the spindle by constructing the experimental platform for thermal simulation and analysis; and then through the COMSOL steady-state simulation cloud diagram combined with the K-means clustering and gray correlation analysis, it filters out the four temperature points with the highest correlation degree of the temperature data, and constructs the POA-LSTM thermal error model to predict the thermal elongation of the spindle at different rotational speeds, and compared the accuracy of SSA-LSTM and LSTM thermal error models, at a high rotational speed of 10,000 r/min, the accuracies of Early LSTM, SSA-LSTM, and LSTM were 97.44 %, 90.27 %, and 86.66 %, respectively, and the model accuracy of POA-LSTM was about 98.257 %; thus the POA-LSTM thermal error model has high prediction accuracy and robustness.

Due to constraints in launch platforms and cost, the maneuverability of gliding-guided projectiles is limited, necessitating a rational design of their trajectory schemes. To reduce the sensitivity of trajectory schemes to uncertainties while ensuring compatibility between flight schemes and guidance control systems and fully exploiting the control capability of the projectile, a closed-loop robust trajectory planning method is proposed. Models of major uncertain factors and state deviation at the control start point are established. Based on the NIPCE method, the stochastic dynamic model is transformed into a high-dimensional deterministic model with PCE coefficients as state variables, and the uncertainty propagation law is obtained. A PID algorithm is employed to design a tracking guidance law based on position error feedback, and open-loop and closed-loop robust trajectory planning models are established accordingly. The optimal control problem is solved by transforming it into a nonlinear programming problem using the direct shooting method. Our simulation results indicate that the NIPCE method can significantly improve the computational efficiency of uncertainty propagation while ensuring accuracy; compared with parallel MCS, the computation time is reduced by 96.8%. Open-loop robust planning can effectively mitigate the sensitivity of gliding trajectories to uncertainties (the standard deviations of terminal altitude and lateral deviations are reduced by 23.6% and 35.3%, respectively, compared to deterministic planning) but cannot completely eliminate terminal dispersion. Closed-loop robust planning effectively improves control effort consumption on the basis of open-loop planning.

Cadmium (Cd) is a naturally occurring toxic heavy metal that adversely affects plant germination, growth, and development. While the effects of Cd have been described on many crop species including rice, maize, wheat and barley, few studies are available on cadmium’s effect on Tartary buckwheat which is a traditional grain in China. We examined nine genotypes and found that 30 µM of Cd reduced the root length in seedlings by between 4 and 44% and decreased the total biomass by 7 to 31%, compared with Cd-free controls. We identified a significant genotypic variation in sensitivity to Cd stress. Cd treatment decreased the total root length and the emergence and growth of lateral roots, and these changes were significantly greater in the Cd-sensitive genotypes than in tolerant genotypes. Cd resulted in greater wilting and discoloration in sensitive genotypes than in tolerant genotypes and caused more damage to the structure of root and leaf cells. Cd accumulated in the roots and shoots, but the concentrations in the sensitive genotypes were significantly greater than in the more tolerant genotypes. Cd treatment affected nutrient uptake, and the changes in the sensitive genotypes were greater than those in the tolerant genotypes, which could maintain their concentrations closer to the control levels. The induction of SOD, POD, and CAT activities in the roots and shoots was significantly greater in the tolerant genotypes than in the sensitive genotypes. We demonstrated that Cd stress reduced root and shoot growth, decreased plant biomass, disrupted nutrient uptake, altered cell structure, and managed Cd-induced oxidative stress differently in the sensitive and tolerant genotypes of Tartary buckwheat.

Background. Diabetic nephropathy (DN), one of the most frequent complications of diabetes mellitus, is a leading cause of end-stage renal disease. However, the current treatment methods still cannot effectively halt the progression of DN. Jian-Pi-Gu-Shen-Hua-Yu (JPGS) decoction can be used for the treatment of chronic kidney diseases such as DN, but the specific mechanism of action has not been fully elucidated yet. Purpose. The aim of this study is to clarify whether JPGS alleviates the progression of diabetic nephropathy by inhibiting ferroptosis. Materials and Methods. We established a DN mouse model to investigate the therapeutic effect of JPGS in a DN mouse model. Subsequently, we examined the effects of JPGS on ferroptosis- and glutathione peroxidase 4 (GPX4) pathway-related indices. Finally, we validated whether JPGS inhibited ferroptosis in DN mice via the GPX4 pathway using GPX4 inhibitor and ferroptosis inhibitors. Results. The results indicate that JPGS has a therapeutic effect on DN mice by improving kidney function and reducing inflammation. Additionally, JPGS treatment decreased iron overload and oxidative stress levels while upregulating the expression of GPX4 pathway-related proteins. Moreover, JPGS demonstrated a similar therapeutic effect as Fer-1 in the context of DN treatment, and RSL3 was able to counteract the therapeutic effect of JPGS and antiferroptotic effect. Conclusion. JPGS has significant therapeutic and anti-inflammatory effects on DN mice, and its mechanism is mainly achieved by upregulating the expression of GPX4 pathway-related proteins, thereby alleviating iron overload and ultimately reducing ferroptosis.

Abstract The coronavirus disease 2019 (COVID-19), caused by a novel virus of the beta-coronavirus genus (SARS-CoV-2), has spread rapidly, posing a significant threat to global health. There are currently no drugs available for effective treatment. Severe cases of COVID-19 are associated with hyperinflammation, also known as cytokine storm syndrome. The reduce inflammation are considered promising treatments for COVID-19. Necroptosis is a type of programmed necrosis involved in immune response to viral infection, and severe inflammatory injury. Inhibition of necroptosis is pivotal in preventing associated inflammatory responses. The expression of key regulators of the necroptosis pathway is generally up-regulated in COVID-19, indicating that the necroptosis pathway is activated. Thus, necroptosis inhibitors are expected to be novel therapeutic candidates for the treatment of COVID-19. Better knowledge of the necroptosis pathway mechanism is urgently required to solve the remaining mysteries surrounding the role of necroptosis in COVID-19. In this review, we briefly introduce the pathogenesis of necroptosis, the relationship between necroptosis, cytokine storm, and COVID-19 also summarizes the progress of inhibitors of necroptosis. This research provides a timely and necessary suggest of the development of necroptosis inhibitors to treat COVID-19 and clinical transformation of inhibitors of necroptosis.

In high-precision machining machine tools, the high-speed electric spindle is the fundamental transmission component. Nonetheless, the electric spindle's small and tightly sealed internal structure may result in insufficient heat dissipation, resulting in significant heat accumulation during continuous operation. This might induce thermal displacement of the spindle and reduce machine tool machining precision. A response surface was established by a 2-factor 3-level interaction experiment utilizing Design-expert software to reduce the heat displacement of the electric spindle. The electric spindle cooling water jacket was optimized and front and rear bearing cooling water channels were added based on the response surface results. The thermal-solid coupling simulation was carried out using ANSYS, and the optimized data for the front and rear bearing temperature, as well as spindle thermal displacement, were obtained through simulation analysis. Comparing the simulation with the experimental data, it was found that the front and rear bearing temperature decreased by 4.34 °C and 8.97 °C, respectively, and the spindle thermal displacement was reduced by 16.57 μm, resulting in improved machining accuracy. The service life of the bearings was further prolonged by the decreased thermal stress between the inner and outer rings and the balls of the front and rear bearings.

The thermal error compensation process of high-speed motorized spindle needs to establish an effective prediction model. In this study, firstly, the thermal characteristics of a motorized spindle are tested, and the data at different speeds are obtained. Considering the disadvantage that the number of clusters in traditional clustering methods needs to be specified artificially, particle clustering and grey correlation analysis are selected to optimize the temperature measuring points. Aiming at the influence of data noise and the slow training speed, the depth limit learning machine is used to establish the thermal error prediction model. Considering that the output of deep extreme learning machine (DELM) is easily influenced by random input weights and biases, weighted mean of vectors algorithm (INFO) proposed in 2022 is chosen to optimize the two influencing factors. By establishing the INFO-DELM thermal error prediction model, and using the screened data to train the INDO-DELM model, the final prediction result is obtained. The results show that the accuracy of the INFO-DELM model is improved by 3%–26% compared with the basic DELM model. In addition, the INFO-DELM model shows excellent stability in both forward and backward forecasting, which proves that the model has strong adaptability. The optimization method of measuring points and INFO-DELM model in this paper provide a new idea for the field of thermal error compensation of motorized spindle.

Based on the air quality monitoring data and meteorological data, the pollution characteristics of PM2.5 in southern Beijing and relationships between PM2.5 levels and meteorological features were analyzed. The results showed that during the investigation period, daily air quality was characterized as “excellent” and “favorable” (−3) on 77% of days in southern Beijing, and there were only two days with serious air pollution (>250 µg m−3). The PM2.5 concentration displayed obvious monthly variations with the highest concentration in November and the lowest concentration in August. When PM2.5 pollution episodes occurred (>75 µg m−3) in this area, the wind direction was often south and southeast with low wind speed (−1), followed by southwest wind; in addition, the air temperature was low (75%). The air quality of the study area was affected by the pollution transmission form the surrounding areas, especially the southeastern and the southern transmission paths. The PM2.5 concentration was positively correlated with SO2, NO2 and CO, and negatively correlated with O3. Therefore, to further improve the air quality in the study area, it is necessary to coordinate the control of PM2.5 and O3.

Background. Pulmonary tuberculosis (PTB) is a global epidemic of infectious disease; the purpose of our study was to explore new potential biomarkers for the diagnosis of pulmonary tuberculosis and to use the biomarkers for further pan-cancer analysis. Methods. Four microarray gene expression sets were downloaded from the GEO public databases and conducted for further analysis. Healthy control (HC) samples and samples of pulmonary tuberculosis (PTB) were calculated with enrichment scores in folate biosynthesis pathways. The scores acted as a new phenotype combined with clinical information (control or PTB) for subsequent analysis. Weight gene coexpression network analysis (WGCNA) was used to seek the modules mostly related to PTB and folate biosynthesis in training sets. Twenty-nine coexistence genes were screened by intersecting the genes in the green-yellow module of GSE28623 and the brown module of GSE83456. We used the protein-protein interaction network analysis to narrow the gene range to search for hub genes. Then, we downloaded the unified and standardized pan-cancer data set from the UCSC database for correlations between biomarkers and prognosis and tumor stage differences. Results. Eventually, RTP4 was selected as a biomarker. To verify the reliability of this biomarker, an area under the ROC (AUC) was calculated in gene sets (GSE28623, GSE83456, and GSE34608). Lastly, to explore the difference in RTP4 expression before and after antituberculosis treatment, the GSE31348 gene set was enrolled to compare the expressions in weeks 0 and 26. The results showed significant differences between these two time points (p

In this article, to heighten the accuracy of thermal error forecast model of motorized spindle, a thermal error modeling method of kernel extreme learning machine based on snake optimization is advanced. ANSYS simulation software is used to simulate the thermal characteristics of the electric spindle, and the temperature field distribution is acquired. Then, according to the experimental requirements, a platform is built to obtain the axial temperature and thermal displacement data of the spindle. The FCM algorithm and grey relation analysis are utilized to optimize the survey points, and ten measuring points are reduced to four. The SO-KELM model is established based on the snake-optimized kernel extreme learning machine. Comparing the established model with the KELM model and PSO-KELM model, it is proved that SO-KELM model has good prediction accuracy and stability.

The thermal error of electric spindle is an important factor affecting the machining accuracy of machine tools. To establish the thermal error prediction model, the thermal characteristics of A02 electric spindle were simulated and analyzed by ANSYS, to determine the best temperature measuring point. The temperature and thermal error of A02 electric spindle at different rotational speeds were obtained by thermal characteristics experiment. Through the combination of systematic clustering and grey relational analysis, four best temperature measuring points were selected from ten temperature measuring points. A prediction model of least squares support vector machine (LSSVM) optimized by Aquila Optimizer (AO) is established and compared with the model optimized by Particle Swarm Optimization (PSO), it is superior to the latter in prediction accuracy (η), goodness of fit (R2), root mean square error (RMSE) and mean absolute error (MAE). The AO-LSSVM prediction model based on experimental data has a prediction accuracy of more than 94% for the thermal error of motorized spindle and has good stability and generalization ability.

Long-distance transport cadmium (Cd) from roots to shoots is a key factor for Cd phytoremediation. Our previous study indicated that heavy metal P1B2-ATPases, IlHMA2, was involved in improving the accumulation of Cd via mediated long-distance transport Cd, contributing to the phytoremediation in Cd accumulator Iris lactea. However, whether the overexpression of IlHMA2 could enhance the accumulation and tolerance to Cd remains unclear in plants. Here, we generated transgenic tobacco overexpressing IlHMA2 and tested its effect on the translocation and accumulation of Cd and zinc (Zn), as well as the physio-biochemical characteristics under 50 mg/L Cd exposure. The overexpression of IlHMA2 significantly increased Cd concentrations in xylem saps, resulting in enhanced root-to-shoot Cd translocation compared with wild-type. Meanwhile, overexpressing IlHMA2 promoted Zn accumulations, accompanied by elevating proline contents and antioxidant enzyme activity (SOD, POD, and CAT) to diminish the overproduction of ROS in transgenic tobacco. These pieces of evidence suggested that higher Zn concentrations and lower ROS levels could tremendously alleviate Cd toxicity for transgenic tobacco, thereby improving the growth and tolerance. Overall, the overexpression of IlHMA2 could facilitate Cd accumulation and enhance its tolerance in tobacco exposed to Cd contaminations. This would provide a valuable reference for improving Cd phytoremediation efficiency.

High-speed electric spindle is an important part of computer numerical control (CNC) machining equipment, and the thermal displacement generated by the electric spindle during operation affects the electric spindle machining stability and machining accuracy. Error compensation for the high-speed motorized spindle can well compensate for the influence of thermal displacement on machine tool processing. Therefore, the prediction of thermal displacement of electric spindle is particularly important. In this paper, firstly, the temperature field information is obtained by simulating and analyzing the calculation of the heat generation and heat exchange inside the electric spindle. Set up the experimental platform with reasonable temperature measurement points according to the simulation results; secondly, the temperature sensitive points are screened by fuzzy C-mean clustering algorithm and Pearson correlation coefficient, which can effectively improve the covariance and correlation between temperature variables; finally, based on the screened data for thermal error modeling, an optimized extreme learning machine based on marine predator algorithm (MPA-ELM) is provided to predict the thermal displacement of electric spindles model. And comparing the model accuracy of extreme learning machine (ELM), MPA-ELM and extreme learning machine optimized by genetic algorithm (GA-ELM), the experimental data show that MPA-ELM has better prediction accuracy.

The halophyte Karelinia caspia has not only fodder and medical value but also can remediate saline-alkali soils. Our previous study showed that salt-secreting by salt glands is one of main adaptive strategies of K. caspia under high salinity. However, ROS scavenging, ion homeostasis, and photosynthetic characteristics responses to high salinity remain unclear in K. caspia. Here, physio-biochemical responses and gene expression associated with ROS scavenging and ions transport were tested in K. caspia subjected to 100–400 mM NaCl for 7 days. Results showed that both antioxidant enzymes (SOD, APX) activities and non-enzymatic antioxidants (chlorogenic acid, α-tocopherol, flavonoids, polyamines) contents were significantly enhanced, accompanied by up-regulating the related enzyme and non-enzymatic antioxidant synthesis gene (KcCu/Zn-SOD, KcAPX6, KcHCT, KcHPT1, Kcγ-TMT, KcF3H, KcSAMS and KcSMS) expression with increasing concentrations of NaCl. These responses are beneficial for removing excess ROS to maintain a stable level of H2O2 and O2− without lipid peroxidation in the K. caspia response to high salt. Meanwhile, up-regulating expression of KcSOS1/2/3, KcNHX1, and KcAVP was linked to Na+ compartmentalization into vacuoles or excretion through salt glands in K. caspia. Notably, salt can improve the function of PSII that facilitate net photosynthetic rates, which is helpful to growing normally in high saline. Overall, the findings suggested that ROS scavenging systems and Na+/K+ transport synergistically contributed to redox equilibrium, ion homeostasis, and the enhancement of PSII function, thereby conferring high salt tolerance.

Platelets play a crucial role in the recruitment of neutrophils, mediated by P-selectin, CCL5, and ICAM-2. In this study, we prepared platelet membrane nanovesicles from activated platelets. Whether activated platelet membrane nanovesicles can recruit neutrophils has not been reported, nor has their role in antitumor immunity. The results of SDS-PAGE showed that the platelet membrane nanovesicles retained almost all the proteins of platelets. Western blotting showed that both the activated platelets and the platelet membrane nanovesicles expressed P-selectin, ICAM-2, and CCL5. In vivo results of a mouse model of breast cancer-transplanted tumor showed that tumor volume reduced significantly, Ki-67-positive tumor cells decreased, and TUNEL-positive tumor cells increased in tumors after treatment with activated platelet membrane nanovesicles (aPNs). After treatment with aPNs, not only the number of neutrophils, CD8+, CD4+ T cells, and B cells increased, but also IL-12, TNF-α, and IFN-γ levels elevated significantly in tumor tissues.

The extensive applications of graphene oxide (GO) inevitably lead to entry into the natural aquatic environment. However, information on its toxicity to emergent plants is still lacking. In this study, an emergent plant, Iris pseudacorus, was exposed to GO (1, 20, 80, and 140 mg·L−1) under hydroponic conditions for 15 weeks. Changes in plant growth were assessed by analyzing plant biomass and photosynthetic pigment contents; the photosynthesis response was verified by measuring chlorophyll a fluorescence; and the nutrient levels of the plant were evaluated. Results showed that GO at 20–140 mg·L−1 significantly increased plant dry weight by 37–84% and photosynthetic pigment contents by 26–178%, and 80 mg·L−1 was the optimal concentration. PSII activity, adjustment capacities of electron transport in PSII, the grouping or energetic connectivity between PSII units, light energy conversion efficiency, photosynthesis performance indexes (by 11–51%), and contents of several nutrient elements (N, Fe, and Cu) were increased by 49–69%, 34–84%, and 11–38%, respectively. These findings indicate that GO can enhance plant growth by promoting plant photosynthesis performance and improving plant nutrient levels, and has great application potential in promoting the growth and development of this emergent plant as a phytoremediation agent.

San-Huang-Yi-Shen capsule (SHYS) has been used in the treatment of diabetic kidney disease (DKD) in clinics. However, the mechanism of SHYS on DKD remains unclear. In this study, we used a high-fat diet combined with streptozocin (STZ) injection to establish a rat model of DKD, and different doses of SHYS were given by oral gavage to determine the therapeutic effects of SHYS on DKD. Then, we studied the effects of SHYS on PINK1/Parkin-mediated mitophagy and the activation of NLRP3 inflammasome to study the possible mechanisms of SHYS on DKD. Our result showed that SHYS could alleviate DKD through reducing the body weight loss, decreasing the levels of fasting blood glucose (FBG), and improving the renal function, insulin resistance (IR), and inhibiting inflammatory response and oxidative stress in the kidney. Moreover, transmission electron microscopy showed SHYS treatment improved the morphology of mitochondria in the kidney. In addition, western blot and immunoflourescence staining showed that SHYS treatment induced the PINK1/Parkin-mediated mitophagy and inhibited the activation of NLRP3 signaling pathway. In conclusion, our study demonstrated the therapeutic effects of SHYS on DKD. Additionally, our results indicated that SHYS promoted PINK1/Parkin-mediated mitophagy and inhibited NLRP3 inflammasome activation to improve mitochondrial injury and inflammatory responses.

The bioenergy crop switchgrass (Panicum virgatum L.) has been recognized as friendly to the soil of cultivated land depending on the previous land use types and management practices. However, the effects of switchgrass establishment on soil properties at a broader depth when it is harvested annually without any fertilization in northern China largely remain unknown. To explore the impacts of unfertilized switchgrass on soil physical and chemical properties, 0–100 cm soil samples were collected from 7-year cropland-to-switchgrass conversion and the bare land (control). The results showed that switchgrass establishment increased soil total and capillary porosity, CFU numbers of the microbial communities (fungi, bacteria, and actinomycetes), contents of microbial biomass (carbon, nitrogen, and phosphorus), and water-soluble organic carbon, and decreased soil bulk density, mostly at 0–60 cm depths, compared to the control values. Notably, the annual harvest of switchgrass insignificantly increased soil total and available nitrogen contents and slightly reduced available phosphorus and potassium contents. In conclusion, long-term cropland conversion to unfertilized switchgrass could ameliorate soil properties and does not cause soil depletion. The output of this study could inspire governments and farmers to make large-scale use of switchgrass in the ecological restoration of abandoned cropland in north China.

Zirconia nanoparticles (ZrO2 NPs) are commonly used in the field of biomedical materials, but their antitumor activity and mechanism is unclear. Herein, we evaluated the anti-tumor activity of ZrO2 NPs and explored the anti-tumor mechanism. The results of in vitro and in vivo experiments showed that the level of intracellular reactive oxygen species (ROS) in HeLa cells was elevated after ZrO2 NPs treatment. Transmission electron microscopy (TEM) showed that after treatment with ZrO2 NPs, the mitochondria of HeLa cells were swollen, accompanied with the induction of autophagic vacuoles. In addition, flow cytometry analysis showed that the apoptotic rate of HeLa cells increased significantly by Annexin staining after treatment with ZrO2 NPs, and the mitochondrial membrane potential (MMP) was reduced significantly. The proliferation of HeLa cells decreased as indicated by reduced Ki-67 labeling. In contrast, TUNEL-positive cells in tumor tissues increased after treatment with ZrO2 NPs, which is accompanied by increased expression of mitochondrial apoptotic proteins including Bax, Caspase-3, Caspase-9, and Cytochrome C (Cyt C) and increased expression of autophagy-related proteins including Atg5, Atg12, Beclin-1, and LC3-II. Treating HeLa cells with N-acetyl-L-cysteine (NAC) significantly reduced ROS, rate of apoptosis, MMP, and in vivo anti-tumor activity. In addition, apoptosis- and autophagy-related protein expressions were also suppressed. Based on these observations, we conclude that ZrO2 NPs induce HeLa cell death through ROS mediated mitochondrial apoptosis and autophagy.

Background: Endometrial cancer (EC) is one of the most common gynecologic cancers of the female reproductive system. Its incidence and mortality are currently increasing. Patients with early-stage EC have a much better prognosis than those with late-stage EC. Therefore, early detection, diagnosis, and treatment are critical to improving the outcome for EC patients. The proposition of molecular classification promotes the individualization for diagnosis and treatment of EC. TCOF1 has been identified as an oncogenic gene in several tumors but has been seldom studied in EC. Methods: TCGA and immunohistochemistry (IHC) experiments were performed to verify the protein level of TCOF1 expressed in endometrial cancer while its prognostic ability in EC patients was assessed by the TCGA database. Linked Omics database, Web Gestalt gene enrichment, and string database were applied to analyze the possible biological functions of TCOF1 in EC. Mutation types of TCOF1 in EC and its mutation frequency were explored in c-BIOPORTAL. The Relationship between molecules was detected by utilizing the GEPIA database. Results: TCOF1 is up-regulated in endometrial cancer compared to para cancer and it was positively correlated with poor prognosis of patients. TCOF1 is mutated in endometrial cancer and is closely associated with microsatellite instability (MSI), this being one type of molecular classification in EC. Conclusions: TCOF1 may function as a potential biomarker and is associated with molecular classification in endometrial cancer.

Background: The association between the premorbid use of statin and the early outcomes of acute ischemic stroke (AIS) after intravenous thrombolysis (IVT) remains uncertain. We performed a meta-analysis of observational studies to evaluate the influence of the premorbid use of statin on functional outcome and symptomatic intracranial hemorrhage (SIH) in AIS after IVT.Methods: Relevant studies were identified by search of PubMed, Embase, and Cochrane's Library databases. Only studies with multivariate analyses were included. A random-effect model, incorporating inter-study heterogeneity, was used to pool the results.Results: Twenty observational studies with 20,752 AIS patients who were treated with IVT were included. The pooled results showed that the premorbid use of statin was not associated with improved 3-month favorable functional outcome [odds ratio (OR): 1.05, 95% confidence interval (CI): 0.87–1.26, p = 0.60, I2 = 52%), 3-month functional independence (OR: 1.13, 95% CI: 0.96–1.33, p = 0.15, I2 = 52%), or 3-month mortality (OR: 1.12, 95% CI: 0.94–1.34, p = 0.20, I2 = 20%). Moreover, the premorbid use of statin was associated with an increased risk of SIH in AIS after IVT (OR: 1.48, 95% CI: 1.12–1.95, p = 0.006, I2 = 60%). Subgroup analyses according to study design, adjustment of baseline low-density lipoprotein cholesterol, and definitions of SIH showed consistent results (p-values for subgroup difference all >0.05).Conclusions: The premorbid use of statin is not associated with improved functional outcomes or mortality but is associated with a higher risk of SIH in AIS patients after IVT.

Zirconia nanoparticles (ZrO2 NPs) are widely applied in the field of biomedicine. In this study, we constructed a nanoplatform of ZrO2 NPs coated with a platelet membrane (PLTm), named PLT@ZrO2. PLTm nanovesicles camouflage ZrO2 NPs, prevent nanoparticles from being cleared by macrophage, and target tumor sites. Compared to ZrO2 alone, PLT@ZrO2 is better at inhibiting the invasion and metastasis of Hela cells in vitro and in vivo. In vitro, PLT@ZrO2 inhibited the growth and proliferation of Hela cells. Scratch-wound healing recovery assay demonstrated that PLT@ZrO2 inhibited Hela cells migration. Transwell migration and invasion assays showed that PLT@ZrO2 inhibited Hela cells migration and invasion. In vivo, PLT@ZrO2 inhibited the tumor growth of Xenograft mice and inhibited the lung and liver metastasis of Hela cells. Immunofluorescence and Western blotting results showed that anti-metastasis protein (E-cadherin) was upregulated and pro-metastasis proteins (N-cadherin, Smad4, Vimentin, E-cadherin,β-catenin, Fibronectin, Snail, Slug, MMP2, Smad2) were down-regulated. Our study indicated that PLT@ZrO2 significantly inhibits tumor growth, invasion, and metastasis.

3D printing, that is, additive manufacturing, has solved many major problems in general manufacturing, such as three-dimensional tissue structure, microenvironment control difficulty, product production efficiency and repeatability, etc., improved the manufacturing speed and precision of personalized bone implants, and provided a lot of support for curing patients with bone injuries. The application of 3D printing technology in the medical field is gradually extensive, especially in orthopedics. The purpose of this review is to provide a report on the related achievements of bone implants based on 3D printing technology in recent years, including materials, molding methods, optimization of implant structure and performance, etc., in order to point out the existing shortcomings of 3D printing bone implants, promote the development of all aspects of bone implants, and make a prospect of 4D printing, hoping to provide some reference for the subsequent research of 3D printing bone implants.

High-speed motorized spindle heating will produce thermal error, which is an important factor affecting the machining accuracy of machine tools. The thermal error model of high-speed motorized spindles can compensate for thermal error and improve machining accuracy effectively. In order to confirm the high precision thermal error model, Beetle antennae search algorithm (BAS) is proposed to optimize the thermal error prediction model of motorized spindle based on BP neural network. Through the thermal characteristic experiment, the A02 motorized spindle is used as the research object to obtain the temperature and axial thermal drift data of the motorized spindle at different speeds. Using fuzzy clustering and grey relational analysis to screen temperature-sensitive points. Beetle antennae search algorithm (BAS) is used to optimize the weights and thresholds of the BP neural network. Finally, the BAS-BP thermal error prediction model is established. Compared with BP and GA-BP models, the results show that BAS-BP has higher prediction accuracy than BP and GA-BP models at different speeds. Therefore, the BAS-BP model is suitable for prediction and compensation of spindle thermal error.

Graphene family nanomaterials (GFNs) have experienced significant development in recent years and have been used in many fields. Despite the benefits, they bring to society and the economy, their potential for posing environmental and health risks should also be considered. The increasing release of GFNs into the ecosystem is one of the key environmental problems that humanity is facing. Although most of these nanoparticles are present at low concentrations, many of them raise considerable toxicological concerns, particularly regarding their accumulation in plants and the consequent toxicity introduced at the bottom of the food chain. Here, we review the recent progress in the study of toxicity caused by GFNs to plants, as well as its influencing factors. The phytotoxicity of GFNs is mainly manifested as a delay in seed germination and a severe loss of morphology of the plant seedling. The potential mechanisms of phytotoxicity were summarized. Key mechanisms include physical effects (shading effect, mechanical injury, and physical blockage) and physiological and biochemical effects (enhancement of reactive oxygen species (ROS), generation and inhibition of antioxidant enzyme activities, metabolic disturbances, and inhibition of photosynthesis by reducing the biosynthesis of chlorophyll). In the future, it is necessary to establish a widely accepted phytotoxicity evaluation system for safe manufacture and use of GFNs.

We investigate here the thickness effect on microstructures and mechanical properties of a quenched and tempered 178 mm thickness ASTM A517 GrQ steel. The microstructures at sub-surface, 1/4 thickness (t/4), and 1/2 thickness (t/2) were characterized. A comparison of hardness, strength, and impact toughness of the different positions shows that the lowest strength and toughness occurred at t/2, where a mixture of coarse, tempered martensite and bainite were found, and their inter-lath boundaries were occupied with highly dense, film-like or coarse, spheroidized carbides. The cooling rate for transformation was measured to be 0.6 °C/s at t/2 from the industrial processing data. In addition, the alloy elements at t/2 were heavily segregated, as revealed by electron probe microanalysis (EPMA) and a microhardness test. The resulted coarse microstructures thus lowered both the yield strength and the impact energies significantly, e.g., the crack propagation energy was completely lost at −60 °C. This study correlates the variation of mechanical properties to varied transformed microstructures based on the industrial quenching condition, which shows promise for improving the designing of the hardenability and controlling the carbides for ultra-thick quenched and tempered steel.