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Anti-mitochondrial antibody (AMA) is not always present in patients with primary biliary cholangitis (PBC). We aimed to determine the additional value of anti-hexokinase 1 (anti-HK1) and anti-kelch-like 12 (anti-KLHL12) antibody in PBC and analyzed the biochemical and immunological parameters of 212 subjects, including PBC patients and healthy controls.

ObjectiveThe accurate estimation of terraced field areas is crucial for addressing issues such as slope erosion control, water retention, soil conservation, and increasing food production. The use of high-resolution remote sensing imagery for terraced field information extraction holds significant importance in these aspects. However, as imaging sensor technologies continue to advance, traditional methods focusing on shallow features may no longer be sufficient for precise and efficient extraction in complex terrains and environments. Deep learning techniques offer a promising solution for accurately extracting terraced field areas from high-resolution remote sensing imagery. By utilizing these advanced algorithms, detailed terraced field characteristics with higher levels of automation can be better identified and analyzed. The aim of this research is to explore a proper deep learning algorithm for accurate terraced field area extraction in high-resolution remote sensing imagery.MethodsFirstly, a terraced dataset was created using high-resolution remote sensing images captured by the Gaofen-6 satellite during fallow periods. The dataset construction process involved data preprocessing, sample annotation, sample cropping, and dataset partitioning with training set augmentation. To ensure a comprehensive representation of terraced field morphologies, 14 typical regions were selected as training areas based on the topographical distribution characteristics of Yuanyang county. To address misclassifications near image edges caused by limited contextual information, a sliding window approach with a size of 256 pixels and a stride of 192 pixels in each direction was utilized to vary the positions of terraced fields in the images. Additionally, geometric augmentation techniques were applied to both images and labels to enhance data diversity, resulting in a high-resolution terraced remote sensing dataset. Secondly, an improved DeepLab v3+ model was proposed. In the encoder section, a lightweight MobileNet v2 was utilized instead of Xception as the backbone network for the semantic segmentation model. Two shallow features from the 4th and 7th layers of the MobileNet v2 network were extracted to capture relevant information. To address the need for local details and global context simultaneously, the multi-scale feature fusion (MSFF) module was employed to replace the atrous spatial pyramid pooling (ASPP) module. The MSFF module utilized a series of dilated convolutions with increasing dilation rates to handle information loss. Furthermore, a coordinate attention mechanism was applied to both shallow and deep features to enhance the network's understanding of targets. This design aimed to lightweight the DeepLab v3+ model while maintaining segmentation accuracy, thus improving its efficiency for practical applications.Results and DiscussionsThe research findings reveal the following key points: (1) The model trained using a combination of near-infrared, red, and green (NirRG) bands demonstrated the optimal overall performance, achieving precision, recall, F1-Score, and intersection over union (IoU) values of 90.11%, 90.22%, 90.17% and 82.10%, respectively. The classification results indicated higher accuracy and fewer discrepancies, with an error in reference area of only 12 hm2. (2) Spatial distribution patterns of terraced fields in Yuanyang county were identified through the deep learning model. The majority of terraced fields were found within the slope range of 8º to 25º, covering 84.97% of the total terraced area. Additionally, there was a noticeable concentration of terraced fields within the altitude range of 1 000 m to 2 000 m, accounting for 95.02% of the total terraced area. (3) A comparison with the original DeepLab v3+ network showed that the improved DeepLab v3+ model exhibited enhancements in terms of precision, recall, F1-Score, and IoU by 4.62%, 2.61%, 3.81% and 2.81%, respectively. Furthermore, the improved DeepLab v3+ outperformed UNet and the original DeepLab v3+ in terms of parameter count and floating-point operations. Its parameter count was only 28.6% of UNet and 19.5% of the original DeepLab v3+, while the floating-point operations were only 1/5 of UNet and DeepLab v3+. This not only improved computational efficiency but also made the enhanced model more suitable for resource-limited or computationally less powerful environments. The lightweighting of the DeepLab v3+ network led to improvements in accuracy and speed. However, the slection of the NirGB band combination during fallow periods significantly impacted the model's generalization ability.ConclusionsThe research findings highlights the significant contribution of the near-infrared (NIR) band in enhancing the model's ability to learn terraced field features. Comparing different band combinations, it was evident that the NirRG combination resulted in the highest overall recognition performance and precision metrics for terraced fields. In contrast to PSPNet, UNet, and the original DeepLab v3+, the proposed model showcased superior accuracy and performance on the terraced field dataset. Noteworthy improvements were observed in the total parameter count, floating-point operations, and the Epoch that led to optimal model performance, outperforming UNet and DeepLab v3+. This study underscores the heightened accuracy of deep learning in identifying terraced fields from high-resolution remote sensing imagery, providing valuable insights for enhanced monitoring and management of terraced landscapes.

Objective To analyze the risk factors for dismal prognosis in patients with cryptococcal meningitis and construct a prediction scoring system Nomogram model. Methods A total of 100 patients with cryptococcal meningitis who treated with anticryptococcal therapy admitted to Affiliated Hospital of Zunyi Medical University from January 2010 to August 2022 were selected. The patients were divided into favorable prognosis group (n = 19) and dismal prognosis group (n = 81) according to the results of cerebrospinal fluid (CSF) cryptococcal culture during hospitalization and clinical symptoms and signs at the time of discharge. Risk factors were screened by using univariate and multivariate stepwise Logistic regression analyses. A Nomogram model was constructed based on the risk factors, the receiver operating characteristic (ROC) curve and calibration curves of the model were plotted, and Hosmer - Lemeshow goodness-of-fit test was performed. Results The proportion of patients in the dismal prognosis group with Nutritional Risk Screening 2002 (NRS 2002) score (Z = - 3.898, P = 0.000), CSF pressure > 250 mm H2O (χ2 = 9.512，P = 0.002) and duration of antifungal treatment < 14 d (χ2 = 17.847，P = 0.000) on admission were higher than those in the favorable prognosis group, and the blood routine red blood cell count (t = - 2.802, P = 0.006) and lymphocyte count (Z = - 2.878, P = 0.004), plasma albumin (t = - 4.332, P = 0.000), and the proportion of amphotericin B application (χ2 = 4.597, P = 0.032) were lower than those in the favorable prognosis group. Logistic regression analysis showed the admission high NRS 2002 score (OR = 3.258, 95%CI: 1.337-7.940; P = 0.009), CBF pressure > 250 mm H2O (OR = 0.108, 95%CI: 0.018-0.659; P = 0.016), and the duration of antifungal treatment < 14 d (OR = 0.092, 95%CI: 0.011-0.742; P = 0.025) were risk factors for dismal prognosis of cryptococcal meningitis. A Nomogram model was constructed based on the above 3 risk factors, and the area under the ROC curve was 0.927 (95%CI: 0.873-0.980, P = 0.000), which predicted a cut - off value of 53.50 points for dismal prognosis in cryptococcal meningitis; the calibration curve (with good consistency), and the Hosmer-Lemeshow goodness-of-fit test (χ2 = 2.694, P = 0.912) indicated that the model had good discrimination, calibration and stability. Conclusions Patients with cryptococcal meningitis with a high NRS 2002 score, CSF pressure > 250 mm H2O, and antifungal treatment < 14 d had a dismal prognosis, and the Nomogram model constructed accordingly has a high predictive value of dismal prognostic risk.

ObjectiveWheat lodging is one of the key isuess threatening stable and high yields. Lodging detection technology based on deep learning generally limited to identifying lodging at a single growth stage of wheat, while lodging may occur at various stages of the growth cycle. Moreover, the morphological characteristics of lodging vary significantly as the growth period progresses, posing a challenge to the feature capturing ability of deep learning models. The aim is exploring a deep learning-based method for detecting wheat lodging boundaries across multiple growth stages to achieve automatic and accurate monitoring of wheat lodging.MethodsA model called Lodging2Former was proposed, which integrates the innovative hierarchical interactive feature pyramid network (HI-FPN ) on top of the advanced segmentation model Mask2Former. The key focus of this network design lies in enhancing the fusion and interaction between feature maps at adjacent hierarchical levels, enabling the model to effectively integrate feature information at different scales. Building upon this, even in complex field backgrounds, the Lodging2Former model significantly enhances the recognition and capturing capabilities of wheat lodging features at multiple growth stages.Results and DiscussionsThe Lodging2Former model demonstrated superiority in mean average precision (mAP) compared to several mainstream algorithms such as mask region-based convolutional neural network (Mask R-CNN), segmenting objects by locations (SOLOv2), and Mask2Former. When applied to the scenario of detecting lodging in mixed growth stage wheat, the model achieved mAP values of 79.5%, 40.2%, and 43.4% at thresholds of 0.5, 0.75, and 0.5 to 0.95, respectively. Compared to Mask2Former, the performance of the improved model was enhanced by 1.3% to 4.3%. Compared to SOLOv2, a growth of 9.9% to 30.7% in mAP was achieved; and compared to the classic Mask R-CNN, a significant improvement of 24.2% to 26.4% was obtained. Furthermore, regardless of the IoU threshold standard, the Lodging2Former exhibited the best detection performance, demonstrating good robustness and adaptability in the face of potential influencing factors such as field environment changes.ConclusionsThe experimental results indicated that the proposed HI-FPN network could effectively utilize contextual semantics and detailed information in images. By extracting rich multi-scale features, it enabled the Lodging2Former model to more accurately detect lodging areas of wheat across different growth stages, confirming the potential and value of HI-FPN in detecting lodging in multi-growth-stage wheat.

To enhance the efficiency and sustainability, technical preparations were made for eliminating the Temperature, Sample, Oxygen test of basic oxygen furnace (BOF) steelmaking process in this work. Utilizing data from 13,528 heats and state-of-the-art (SOTA) machine learning (ML) and deep learning algorithms, data-driven models with different types of inputs were developed, marking the first use of time series data (off-gas profiles and blowing practice related curves) for BOF steelmaking’s endpoint prediction, and the tabular features were expanded to 45. The prediction targets are molten steel’s concentrations of phosphorus (Endpoint [P], %) and carbon (Endpoint [C], %), and temperature (Endpoint-Temp, °C). The optimal models for each target were implemented at a Hesteel Group’s BOF steelmaking facility. Initially, SOTA ML models (XGBoost, LightGBM, Catboost, TabNet) were employed to predict Endpoint [P]/[C]/Temp with tabular data. The best mean absolute errors (MAE) achieved were 2.276 × 10−3% (Catboost), 6.916 × 10−3% (Catboost), and 7.955°C (LightGBM), respectively, which surpassed the conventional models’ performance. The prediction MAEs of the conventional models with the same inputs for Endpoint [P]/[C]/Temp were 3.158 × 10−3%, 7.534 × 10−3%, and 9.150°C (Back Propagation neural network) and 2.710 × 10−3%, 7.316 × 10−3%, and 8.310°C (Support Vector Regression). Subsequently, predictions were explored to be made using SOTA time series analysis models (1D ResCNN, TCN, OmniScaleCNN, eXplainable Convolutional neural network (XCM), Time-Series Transformer, LSTM-FCN, D-linear) with the original time series data and SOTA image analysis models (Pre-activation ResNet, DenseNet, DLA, Dual path networks (DPN), GoogleNet, Vision Transformer) with resized time series data. Finally, the concat-model and the paral-model architectures were designed for making predictions with both tabular data and time series data. It was determined that the concat-Model with TCN and ResCNN as the backbone exhibited the highest accuracy. It’s MAE for predicting Endpoint [P]/[C]/Temp reaches 2.153 × 10−3%, 6.413 × 10−3%, and 5.780°C, respectively, with field test’s MAE at 2.394 × 10−3%, 6.231 × 10−3%, and 7.679°C. Detailed results of the importance analysis for tabular data and time series are provided.

Infection following inguinal hernia repair (IHR) is uncommon. Rational use of antibiotics can significantly improve the prognosis of patients. However, accurately identifying the pathogen involved is usually challenging. This case report describes a patient who developed intermittent fever after undergoing open preperitoneal tension-free repair of a bilateral inguinal hernia. The scrotal fluid specimen was cultured and subjected to metagenomic next-generation sequencing (mNGS). Culture revealed the presence of Enterococcus faecalis (a facultative anaerobe). However, mNGS detected E. faecalis along with multiple anaerobic bacteria including Bacteroides thetaiotaomicron, Parabacteroides distasonis, and Levyella massiliensis. The patient was finally diagnosed with a mixed infection of E. faecalis and multiple anaerobes, and his condition was effectively controlled after timely adjustment of the antibiotic regimen. Treating postoperative infections with multiple concurrent conditions can be challenging. mNGS is valuable for the accurate diagnosis and treatment of infections, as it not only can further verify the culture results, but also assist clinicians in ruling out pulmonary infection caused by hematogenous dissemination after IHR in patients.

In this article, we develop an efficient Legendre-Galerkin approximation based on a reduced-dimension scheme for the fourth-order equation with singular potential and simply supported plate (SSP) boundary conditions in a circular domain. First, we deduce the equivalent reduced-dimension scheme and essential pole condition associated with the original problem, based on which a class of weighted Sobolev spaces are defined and a weak formulation and its discrete scheme are also established for each reduced one-dimensional problem. Second, the existence and uniqueness of the weak solution and the approximation solutions are given using the Lax-Milgram theorem. Then, we construct a class of projection operators, give their approximation properties, and then prove the error estimates of the approximation solutions. In addition, we construct a set of effective basis functions in approximate space using orthogonal property of Legendre polynomials and derive the equivalent matrix form of the discrete scheme. Finally, a large number of numerical examples are performed, and the numerical results illustrate the validity and high accuracy of our algorithm.

Abstract Background Breast cancer (BC) patients tend to suffer from distant metastasis, especially bone metastasis. Methods All the analysis based on open-accessed data was performed in R software, dependent on multiple algorithms and packages. The RNA levels of specific genes were detected using quantitative Real-time PCR as a method of detecting the RNA levels. To assess the ability of BC cells to proliferate, we utilized the CCK8 test, colony formation, and the 5-Ethynyl-20-deoxyuridine assay. BC cells were evaluated for invasion and migration by using Transwell assays and wound healing assays. Results In our study, we identified the molecules involved in BC bone metastasis based on the data from multiple BC cohorts. Then, we comprehensively investigated the effect pattern and underlying biological role of these molecules. We found that in the identified molecules, the EMP1, ACKR3, ITGA10, MMP13, COL11A1, and THY1 were significantly correlated with patient prognosis and mainly expressed in CAFs. Therefore, we explored the CAFs in the BC microenvironment. Results showed that CAFs could activate multiple carcinogenic pathways and most of these pathways play an important role in cancer metastasis. Meanwhile, we noticed the interaction between CAFs and malignant, endothelial, and M2 macrophage cells. Moreover, we found that CAFs could induce the remodeling of the BC microenvironment and promote the malignant behavior of BC cells. Then, we identified MMP13 for further analysis. It was found that MMP13 can enhance the malignant phenotype of BC cells. Meanwhile, biological enrichment and immune infiltration analysis were conducted to present the effect pattern of MMP13 in BC. Conclusions Our result can improve the understanding of researchers on the underlying mechanisms of BC bone metastasis.

Epithelial-mesenchymal transformation (EMT) plays an important role in the progression of diabetic nephropathy. Dexmedetomidine (DEX) has shown renoprotective effects against ischemic reperfusion injury; however, whether and how DEX prevents high glucose-induced EMT in renal tubular epithelial cells is incompletely known. Here, we conduct in vitro experiments using HK-2 cells, a human tubular epithelial cell line. Our results demonstrate that high glucose increases the expressions of EMT-related proteins, including Vimentin, Slug, Snail and Twist, while decreasing the expression of E-cadherin and increasing Cdk5 expression in HK-2 cells. Both Cdk5 knockdown and inhibition by roscovitine increase the expressions of E-cadherin while decreasing the expressions of other EMT-related markers. DEX inhibits Cdk5 expression without affecting cell viability and changes the expressions of EMT-related markers, similar to effects of Cdk5 inhibition. Furthermore, Cdk5 is found to interact with Drp1 at the protein level and mediate the phosphorylation of Drp1. In addition, Drp1 inhibition with mdivi-1 could also restrain the high glucose-induced EMT process in HK-2 cells. Immunofluorescence results show that roscovitine, Mdivi-1 and DEX inhibit high glucose-induced intracellular ROS accumulation, while the oxidant H2O2 eliminates the protective effect of DEX on the EMT process. These results indicate that DEX mitigates high glucose-induced EMT progression in HK-2 cells via inhibition of the Cdk5/Drp1/ROS pathway.

The new power system at the station level is the phased product of the large-scale and high-proportion penetration development of wind and solar new energy. The source network load storage system with the station area as the smallest adjustable unit is constructed by using energy storage +. Different from the traditional substation area system, the architecture of the new power system at the substation area level becomes extremely complex due to the change of access terminal attributes, interaction relationship and various business types. In order to systematically describe the new power system architecture at the station district level, this paper first combs and summarizes the relevant literature at home and abroad, and summarizes the literature related to the current substation District, wind and new energy access and energy storage system regulation. Secondly, from the perspectives of demand differences, common functional requirements and non functional requirements, the requirements of station area level new power system under different application scenarios are classified and analyzed in detail. Then, a new power system for different scenarios is designed, which is divided into local equipment layer, edge control layer, station management layer and centralized control layer from bottom to top, and the functions of each layer are described in detail. Taking Zhangjiajie scenery storage and charging comprehensive energy demonstration project as an example, this paper expounds the ways of terminal data access, transmission and visualization under the framework of Taiwan district level new power system, so as to provide reference for the networking of other similar projects. Finally, the research contents of this paper are summarized, and the contents that need further research in the future are prospected.

To facilitate the application of the subsumption sorting algorithm within vocabulary instruction, this study develops a novel Learning Management System (LMS). This system adopts the ESP vocabulary teaching model as its primary instructional framework delves into the requisites of English vocabulary acquisition, the system’s architecture, and its instructional content. It employs an enhanced DTW pattern-matching algorithm to dynamically refine English vocabulary resources. Furthermore, it integrates the merging and sorting algorithm to augment the efficiency of the LMS operations, enabling precise management of student learning progression and vocabulary retention. Comparative analyses of various sorting algorithms were conducted alongside assessments of the impacts of the LMS and ESP vocabulary teaching models on vocabulary learning outcomes. The findings from a semester-long study of vocabulary instruction indicate significant improvements within the experimental group: increases of 6.3, 5.32, and 9.33 points in word form, meaning, and usage accuracy, respectively. Notably, the usage of vocabulary saw the most substantial enhancement. The ESP model-based English vocabulary LMS effectively ameliorates students’ vocabulary usage errors and enhances accuracy in vocabulary application.

In the “Internet+” era, Civic Party building programs at universities face the challenge of modernizing their pedagogical approach to meet students’ evolving needs. This paper examines the hurdles in updating course content and diversifying teaching methods, utilizing the Logit model to determine factors crucial to innovating these courses. Analysis reveals that political cognition, emotion, and participation play pivotal roles in the success of course innovation, especially political cognition with a coefficient of 0.784. Recommendations for course improvement include adopting internet technologies, updating content, varying teaching techniques, and enhancing student involvement. Ultimately, the study advocates for a strategic overhaul of Civic Party building courses to fulfill their educational goals.

Multi-exposure image fusion as a technical means to bridge the dynamic range gap between real scenes and image acquisition devices, which makes the fused images better quality and more realistic and vivid simulation of real scenes, has been widely concerned by scholars from various countries. In order to improve the adaptive fusion effect of multi-exposure images, this paper proposes a fusion algorithm based on multilayer perceptron (MLP) based on the perceptron model and verifies the feasibility of the algorithm by the peak signal-to-noise ratio (PSNR), correlation coefficient (PCC), structural similarity (SSMI) and HDR-VDR-2, an evaluation index of HDR image quality. Comparison with other algorithms revealed that the average PSNR of the MLP algorithm improved by 4.43% over the Ma algorithm, 7.88% over the Vanmail algorithm, 10.30% over the FMMR algorithm, 11.19% over the PMF algorithm, and 11.19% over the PMF algorithm. For PCC, the MLP algorithm improves by 20.14%, 17.46%, 2.31%, 11.24%, and 15.36% over the other algorithms in that order. For SSMI, the MLP algorithm improved by 16.99%, 8.96%, 17.17%, 14.41%, and 4.85% over the other algorithms, in that order. For HDR-VDR-2, the MLP algorithm improved by 3.02%, 2.79%, 6.84%, 4.90%, and 6.55% over the other algorithms, in that order. The results show that the MLP algorithm can avoid image artifacts while retaining more details. The MLP-based adaptive fusion method is a step further in the theoretical study of multi-exposure image fusion, which is of great significance for subsequent research and practical application by related technology vendors.

Biodegradable metals and their alloys have gained significant attention as promising candidates for biomedical applications. Notably, Magnesium, Zinc, and Iron stand out as the most prominent biodegradable materials, prompting extensive research into novel alloy development. These metals exhibit inherent biodegradability, acceptable biocompatibility, and commendable mechanical integrity, rendering them suitable for implant use. In this comprehensive review, we delve into recent advancements in biodegradable implants within the biomedical domain, with a primary focus on orthopedic and cardiovascular applications. The paper extensively explores the in vivo and in vitro degradation behavior, mechanical properties, and corrosion mechanism of these materials. Additionally, we discuss progress in surface modification and fabrication techniques, vital in optimizing implant performance. However, certain limitations associated with alloying materials, especially Zinc and Iron, have hindered their widespread implementation. We underscore the importance of overcoming these challenges to unlock the full potential of these materials. Furthermore, we present the promise of new metals, such as Molybdenum, which exhibit remarkable properties, opening doors to implants with superior mechanical characteristics. Overall, this review emphasizes the growing significance of biodegradable metallic implants, showcasing their potential to address diverse clinical needs. By addressing material limitations and harnessing innovative metals, we pave the way for the development of implants with superior mechanical properties and desirable degradation rates, thus revolutionizing the field of biomedical applications.

This study conducted a questionnaire survey on the physical condition of high school students based on FIT. The correlation between sedentary behavior, exercise level and physical fitness was studied. In this paper, the fuzzy C-average clustering method of the BP neural network is used to make statistics and classification of students’ physical condition. This paper collates the biological quality data of college students. Then this paper makes a comprehensive classification, classification and quantitative assessment of the data. The study found that college students’ sedentary habits varied from individual and family backgrounds. Family factors influence students’ physical exercise. Physical exercise was positively correlated with sedentary behavior and body mass index in adolescents. The accuracy of clustering combined with the BP neural network can reach 94%.

The threats of nonrenewable energy consumption and environmental disruption caused by the extensive use of metals and polymers derived from petroleum have prompted the development of eco-friendly, high-performance, and long-lasting structural materials. After various treatments, cellulose materials exhibit exceptional properties such as high strength, fire resistance, hydrophobic properties, and thermal stability. Cellulose-based structural materials have excellent mechanical strength and the distinct advantages of being lightweight, inexpensive, and energy efficient. This review summarizes the recent progress in the preparation methods and properties of high-performance cellulose structural materials such as high-strength cellulose structural materials, thermal insulation cellulose structural materials, flame-retardant cellulose structural materials, hydrophobic cellulose structural materials, cellulose structural material with electrical properties, and other cellulose structural materials. The future of high-performance cellulosic structural materials and the prospective of their development are concluded.

Fatty acid oxidation (FAO) has been proven to be an accomplice in tumor progression. Carnitine palmitoyltransferase 1C (CPT1C), a rate-limiting enzyme in FAO, mainly functions to catalyze fatty acid carnitinylation and guarantee subsequent entry into the mitochondria for FAO in colorectal cancer (CRC). Gene expression data and clinical information extracted from The Cancer Genome Atlas (TCGA) database show significantly higher expression of CPT1C in patients with metastatic CRC ( P=0.005). Moreover, overexpression of CPT1C is correlated with worse relapse-free survival in CRC (HR 2.1, P=0.0006), while no statistical significance is indicated for CPT1A and CPT1B. Further experiments demonstrate that downregulation of CPT1C expression leads to a decrease in the FAO rate, suppression of cell proliferation, cell cycle arrest and repression of cell migration in CRC, whereas opposite results are obtained when CPT1C is overexpressed. Furthermore, an FAO inhibitor almost completely reverses the enhanced cell proliferation and migration induced by CPT1C overexpression. In addition, analysis of TCGA data illustrates a positive association between CPT1C expression and HIF1α level, suggesting that CPT1C is a transcriptional target of HIF1α. In conclusion, CPT1C overexpression indicates poor relapse-free survival of patients with CRC, and CPT1C is transcriptionally activated by HIF1α, thereby promoting the proliferation and migration of CRC cells.

In this paper a novel method for fabrication of TiB2 coatings on graphite substrates by electrophoretic deposition of TiB2 nanoparticles in NaF-AlF3 melt at 980°C has been explored. With an applied cell voltage of 1.2 V (i.e. electric field 0.6 V/cm), a flat and dense TiB2 coating with a thickness of around 50 μm has been prepared. It was found that the surface consisted of TiB2 flakes with a nanoscale size. In addition, the average value of hardness of the TiB2 coating was determined as 3585 ± 139 HV0.1 (36.6 ± 1.4GPa) measured by a Vickers micro-hardness tester.

The antimicrobial substances produced by Bacillus velezensis 1-3, a strain with antibacterial activity isolated from oral samples, were separated and purified by ammonium sulfate precipitation, gel filtration chromatography and thin-layer chromatography and were analyzed by matrix-assisted laser desorption ionization-time of flight-mass spectrometry (MALDI-TOF-MS). These substances were identified as surfactin consisting of three cyclic lipopeptides with 14–16 carbon atoms, whose molecular masses were 1 030.642, 1 044.660 and 1 058.677 Da, respectively. Surfactin could function as a surfactant and exhibited good antimicrobial activity against common pathogenic bacteria in the food, medical and livestock industries. Meanwhile, the minimal inhibitory concentration (MIC) of surfactin against Listeria monocytogenes ATCC 19115 was 4 μg/mL. Surfactin did not show hemolytic activity against mouse red blood cells within the effective antibacterial concentration range. It could endure temperatures at 20–100 ℃ and withstand a wide pH range of 2.0–10.0. Hence, surfactin produced by B. velezensis 1-3 has great potential for applications in the food, medical, and livestock fields .

BackgroundThis research endeavors to examine the potential effects of human and societal interactions on individuals’ post-traumatic growth in the aftermath of the Corona outbreak. To achieve the aforementioned objective, the current research investigates the correlations between post-traumatic growth and group identity, while also examining the potential mediating function of social–emotional competence.MethodsA cross-sectional design included a representative sample of 2,637 high school students located in the capital territory of Pakistan using convenience sampling method. To explore the associations, correlation and mediation analyzes utilizing the group identification scale, the social–emotional competence scale, and the post-traumatic growth scale was performed with SPSS PROCESS 4 macro and AMOS.ResultsThe findings demonstrated that group identification emerged as a substantial predictor substantially associated with post-traumatic growth. Moreover, the relationship linking group identification and post-traumatic growth was found to be partially moderated by social–emotional competence.ConclusionThe phenomenon of group identification can exert influence on post-traumatic growth through both direct and mediating pathways, with the latter being essentially mediated by social–emotional competence. The aforementioned outcomes possess significant academic and practical implications concerning the promotion of post-traumatic growth and the improvement of psychological well-being after the Corona outbreak.

Random Fourier features represent one of the most influential and wide-spread techniques in machine learning to scale up kernel algorithms. As the methods based on random Fourier approximation of the kernel function can overcome the shortcomings of machine learning methods that require a large number of labeled sample, it is effective to be applied to the practical areas where samples are difficult to obtain. Network traffic forwarding policy making is one such practical application, and it is widely concerned in the programmable networks. With the advantages of kernel techniques and random Fourier features, this paper proposes an application of network traffic forwarding policy making method based on random Fourier approximation of kernel function in programmable networks to realize traffic forwarding policy making to improve the security of networks. The core of the method is to map traffic forwarding features to Hilbert high-dimensional space through random Fourier transform, and then uses the principle of maximum interval to detect adversarial samples. Compared with the traditional kernel function method, it improves the algorithm efficiency from square efficiency to linear efficiency. The AUC on the data set from real-world network reached 0.9984, showing that the method proposed can realize traffic forwarding policy making effectively to improve the security of programmable networks.

Boosting is an ensemble learning method that combines a set of weak learners into a strong learner to minimize training errors. AdaBoost algorithm, as a typical boosting algorithm, transforms weak learners or predictors to strong predictors in order to solve problems of classification. With remarkable usability and effectiveness, AdaBoost algorithm has been widely used in many fields, such as face recognition, speech enhancement, natural language processing, and network intrusion detection. In the large-scale enterprise network environment, more and more companies have begun to build trustworthy networks to effectively defend against hacker attacks. However, since trustworthy networks use trusted flags to verify the legitimacy of network requests, it cannot effectively identify abnormal behaviors in network data packets. This paper applies Adaboost algorithm in trustworthy network for anomaly intrusion detection to improve the defense capability against network attacks. This method uses a simple decision tree as the base weak learner, and uses AdaBoost algorithm to combine multiple weak learners into a strong learner by re-weighting the samples. This paper uses the real data of trustworthy network for experimental verification. The experimental results show that the average precision of network anomaly detection method based on AdaBoost algorithm is more than 0.999, indicating that it has a significant detection effect on abnormal network attacks and normal network access. Therefore, the proposed method can effectively improve the security of trustworthy networks.

Electrical switching of nanophotonic structural color elements is a promising approach towards addressable color switching pixels for next generation reflective displays. However, electrical switching between the primary colors to colorless near-white state remains a challenge. Here, we present a reversible electrical switching approach, relying on the electrocoagulation of Ag nanoparticles between silicon nanostructures that support Mie resonances. The electrodeposited Ag nanoparticles enable the excitation of the hybrid plasmon-Mie resonance as supported on Ag-silicon nanostructures, resulting in a large spectral transformation. Importantly, this process is reversible. This device design outperforms other designs in terms of electrotonic color control since it is highly stable and reliable for use in high-resolution reflective displays, such as colored electronic papers and smart display glass, where the combination is scalable to other nanostructure designs and electrolytic solutions.

Objective To investigate the influencing factors of postoperative meningioma recurrence. Methods A total of 176 patients with meningioma treated in Beijing Tiantan Hospital, Capital Medical University from May 2010 to January 2011 were included. The general information, imaging findings, extent of tumor resection and postoperative pathological results were analyzed. Kaplan⁃Meier survival curve comparison and Log⁃rank test were used to compare the related influencing factors of meningioma recurrence. Multifactor Cox's proportional hazards regression model was used for survival analysis. Results The median progression free survival (PFS) of patients with preoperative Karnofsky Performance Status (KPS)＜70 was lower than that with KPS≥70 [(8.04±1.98) years vs. (8.08±1.18) years; χ2=5.370, P=0.020]. The median PFS of patients with peritumoral edema grade 2-3 was lower than that with peritumoral edema grade 0-1 [(8.05±1.79) years vs. (8.06±1.09) years; χ2=9.805, P=0.002]. The median PFS of patients with dural mater invasion was lower than that without dural mater invasion [(7.97±1.70) years vs. (8.06±1.09) years; χ2=12.357, P=0.000]. The median PFS of patients with WHO grade 2-3 was lower than that with WHO grade 1 [(5.14±2.07) years vs. (8.12±0.87) years; χ2=113.774, P=0.000]. Multivariate Cox's proportional hazards regression model showed that WHO grade 2-3 could increase the risk of postoperative meningioma recurrence (RR=15.693, 95%CI: 5.905-41.707; P=0.000). Conclusions The preoperative KPS score, peritumoral edema, dura mater invasion and WHO grade may affect the recurrence of meningioma, and WHO grade 2-3 can increase the risk of meningioma recurrence.

Objective To evaluate brain plasticity by resting⁃state fMRI (rs⁃fMRI), and to explore the potential neuroendocrine mechanism of repetitive transcranial magnetic stimulation (rTMS) in improving post⁃stroke cognitive impairment (PSCI) in patients with low thyroid hormone (TH) level. Methods A total of 28 patients with PSCI with low TH level who received rehabilitation treatment in Shijiazhuang People's Hospital from August 2020 to December 2021 were randomly divided into rTMS group (n=15) and control group (n=13). Cognitive function was assessed by Montreal Cognitive Scale (MoCA) before and 4 weeks after rTMS, and serum levels of tri⁃iodothyronine (T3), thyroxine (T4) and thyroid stimulating hormone (TSH) were measured by double antibody sandwich method. rs⁃fMRI was used to calculate the fractional amplitude of low⁃frequency fluctuation (fALFF) and observe the activated brain regions after stimulation. Pearson correlation analysis was used to obtain the distribution of synchronous changes of activated brain areas based on whole⁃brain functional connection strength of hypothalamus and serum TH level in 2 groups. Results After 4 weeks of intervention, compared with control group, MoCA score (F=1163.054, P=0.000), and serum T3 (F=1111.685, P=0.000), T4 (F=847.333, P=0.000), TSH (F=251.316, P=0.000) in rTMS group was increased. In rTMS group, fALFF value increased mainly in posterior cerebellar lobe (t=4.699, P＜0.05), middle frontal gyrus (t=4.356, P＜0.05) and superior frontal gyrus (t=5.675, P＜0.05), while fALFF value decreased in superior temporal gyrus (t=⁃7.874, P＜0.05) and middle temporal gyrus (t=⁃5.776, P＜0.05). The enhanced functional connectivity of the whole brain with bilateral hypothalamus as the region of interest (ROI) and the simultaneous increase of serum T3 and TSH levels were mainly concentrated in the cerebellum, frontal lobe and parahippocampal gyrus. Conclusions rTMS can indirectly affect hypothalamic function through cerebral cortex, increase serum TH level and improve cognitive function in PSCI patients.

Natural gas hydrates (NGH) are widely found in seafloor sediments. In engineering, it is usually necessary to inject 60% of thermodynamic inhibitors, which makes hydrate extraction costly and polluting. Currently, kinetic inhibitors have attracted much attention due to their low injection dose and environmental friendliness, but the research is costly and time-consuming. In this study, we investigated the interaction between winter flounder antifreeze proteins (AFPs) and methane hydrate growth using molecular dynamics simulations. AFPs adsorbed on the hydrate surface and hindered the mass transfer of methane molecules. At the same time, the water molecules around the AFP adsorption surface are in a quasi-liquid state, a structure that facilitates the binding of AFPs to the hydrate surface. Analysis of the probability of amino acid adsorption showed that AFP was adsorbed to the hydrate surface through a combination of hydrophobic and hydrogen bonding interactions. Subsequent directional mutagenesis experiments showed that increasing the hydrophobicity of AFP rather weakens its adsorption capacity. This suggests that excessive hydrophobicity of AFP may be counterproductive to its adsorption on the hydrate surface. These findings deepen the understanding of the AFP mechanism and its potential for the development of novel hydrate inhalants.

25 models are designed according to the orthogonal table, to study the comprehensive relationship between heat transfer and sealing characteristics in straight-through labyrinth seals with different geometrical parameters. The experimental investigations are carried out at different pressure ratios of 1.2, 1.3, 1.4, 1.6, 1.8. The results show that different geometry parameters brought obvious different influence on both leakage efficiency and heat transfer characteristics, and in which the parameters (H, c,β) have the most significant influence on leakage efficiency, and heat transfer characteristics under different pressure ratios, whose small changes in size can lead to performance optimization in leakage and heat transfer. The geometrical parameters always show consistent influence on leakage efficiency and heat transfer characteristics, while heat transfer characteristics show larger vibration with the increase of pressure ratio.

This research studied the effect of cordyceps militaris noodles on alleviating physical fatigue and concentration of serum lipids in mouse. The mouse were divided into feed group, blank noodle group, low dose of C.militaris noodles group and high dose of C.militaris noodles group, and the animal model of swimming fatigue was established. The exhausted swimming time of mouse in each group was recorded, and the related indexes of alleviating physical fatigue and affecting lipid concentration in mouse were measured. The results show that there was no significant difference in body weight of mouse in each group. Compared with feed group and blank noodle group, cordyceps militaris noodles could effectively prolong the exhausted swimming time of mouse (P

A data enhanced iterative few-sample (DEIFS) algorithm is proposed to achieve the accurate and efficient inverse design of multi-shaped 2D chiral metamaterials. Specifically, three categories of 2D diffractive chiral structures with different geometrical parameters, including widths, separation spaces, bridge lengths, and gold lengths are studied utilising both the conventional rigorous coupled wave analysis (RCWA) approach and DEIFS algorithm, with the former approach assisting the training process for the latter. The DEIFS algorithm can be divided into two main stages, namely data enhancement and iterations. Firstly, some “pseudo data” are generated by a forward prediction network that can efficiently predict the circular dichroism (CD) response of 2D diffractive chiral metamaterials to reinforce the dataset after necessary denoising. Then, the algorithm uses the CD spectra and the predictions of parameters with smaller errors iteratively to achieve accurate values of the remaining parameters. Meanwhile, according to the impact of geometric parameters on the chiroptical response, a new functionality is added to interpret the experimental results of DEIFS algorithm from the perspective of data, improving the interpretability of the DEIFS. In this way, the DEIFS algorithm replaces the time-consuming iterative optimization process with a faster and simpler approach that achieves accurate inverse design with dataset whose amount is at least one to two orders of magnitude less than most previous deep learning methods, reducing the dependence on simulated spectra. Furthermore, the fast inverse design of multiple shaped metamaterials allows for different light manipulation, demonstrating excellent potentials in applications of optical coding and information processing. This work belongs to one of the first attempts to thoroughly characterize the flexibility, interpretability, and generalization ability of DEIFS algorithm in studying various chiroptical effects in metamaterials and accelerating the inverse design of hypersensitive photonic devices.

Abstract Background Human milk banks (HMBs) are essential facilities for the selection, collection, testing, transportation,storage, and distribution of DHM for special medical needs. The aim of this analysis was to analyze the operation status and data over the last 8 years of operation of the first human milk bank (HMB) in East China. Methods Data related to the costs, donors, donation, pasteurization, and recipients were extracted from the web-based electronic monitoring system of the HMB for the period August 1, 2013 to July 31, 2021. Results Over the 8 years of operation, 1,555 qualified donors donated 7,396.5L of qualified milk at a cost of ¥1.94 million($306,051), with the average cost per liter of donor human milk being ¥262.3($41.4). The donors were between 25 and 30 years of age, and the majority (80.1%) were primipara. All the donated milk was pasteurized and subjected to bacteriological tests before and after pasteurization: 95.4% passed the pre-pasteurization tests, and 96.3% passed the post-pasteurization tests. A total of 9,207 newborns received 5,775.2L of pasteurized donor milk. The main reason for the prescription of donor human milk was preterm birth. As a result of continuous quality improvements, January 2016 witnessed a significant increase in the volume of qualified DHM and the number of qualified donors. However, in 2020, as a result of the restrictions related to the COVID-19 pandemic, the volume of qualified DHM and the number of qualified donors decreased. Conclusions Over its 8 years of operation, our HMB has made steady quality improvements in its screening and information processes. Continuous quality improvement is on ongoing need, along with recruiting more qualified donors and collecting donor human milk for vulnerable newborns.

The mu-opioid receptor (MOR), a membrane-bound G protein-coupled receptor, is implicated in progression and long-term outcome of several types of tumors. However, the expression and clinical significance of MOR in colorectal cancer (CRC) remain unclear. In this study, a total of 180 paraffin-embedded samples of paired tumors and normal tissues from CRC patients are used to explore expression levels of MOR by immunohistochemistry (IHC). Results show that MOR is highly expressed in tumors compared with that in paired normal tissues ( P

Abstrct: Polycaprolactone/hydroxyapatite (PCL/HA) composite coating was fabricated by a combination of hydrothermal and dipping methods to delay the degradation of Mg alloy AZ31 as bioresorbable materials. The PCL/HA coating was composed of nano rod-shape HA crystals and PCL filled in the space of HA crystals. Compared with the single HA coating, the binding strength between the PCL/HA composite coating and Mg alloy was obviously improved and the PCL/HA coating still adhered to the surface of AZ31 substrate even after 38 days of immersion. The electrochemical corrosion rate of HA coated sample was reduced by ten times after being filled by PCL. The electrochemical impedance spectroscopy (EIS) and immersion test results showed that the PCL/HA composite coating could provide a more effective barrier for Mg substrate than the HA coating alone. The cytocompatibility and the antibacterial property of HA coating and PCL/HA coating were evaluated by culturing with bone marrow-derived mesenchymal stem cells (BMSCs) and methicillin-resistant staphylococcus aureus (MRSA) for 24 h under direct culture conditions, respectively. The PCL/HA composite coating showed better BMSC cell compatibility, more suitable for BMSC adhesion than HA coating alone and showed a potential application prospect as a biological materials. However, from the perspective of clinical applications, the antibacterial property of PCL/HA composite coating needs to be further improved.

In order to explore the low-temperature oxidation characteristics and evolution law of oxidized coal, this paper adopted a temperature-programmed experiment system. Firstly, the low-temperature oxidation temperature-programmed experiment was carried out on the oxidized coal formed by N2 anaerobic cooling after the coal samples were pre-oxidized to 60 ℃, 90 ℃, 120 ℃, 150 ℃, 180 ℃ and 210 ℃ respectively. At the same time, in order to further reveal the characteristics of low-temperature oxidation behavior of oxidized coal formed under different fire extinguishing conditions, the low-temperature oxidation temperature programmed test was carried out on the oxidized coal formed after fire extinguishing with three different volume fractions of N2 when the coal sample was pre-oxidized to 120 ℃. This reveals the changes of oxygen consumption rate, production rate of signature gases (CO, CO2) and heat release intensity of these two kinds of oxidized coals during low temperature oxidation process. The results show that the oxygen consumption rate, signature gas generation rate and heat release intensity of oxidized coal are all lower than those of raw coal. Furthermore, the spontaneous combustion characteristic parameters of coal samples pre-oxidized to 90 ℃ are closer to that of raw coal, indicating that coals pre-oxidized to critical temperature are more likely to reignite. When pre-oxidized to 120 ℃, the higher the volume fraction of N2, the closer the spontaneous combustion characteristic parameters of this oxidized coal are to the original coal, indicating that the coal with higher volume fraction of N2 has stronger recrudescence ability. Therefore, it is necessary to prevent the recurrence of coal when mining the coal body in the near coal seam group, the recrudescence working face and the unsealing fire area.

In this article, an effective finite element method based on dimension reduction scheme is proposed for a fourth-order Steklov eigenvalue problem in a circular domain. By using the Fourier basis function expansion and variable separation technique, the original problem is transformed into a series of radial one-dimensional eigenvalue problems with boundary eigenvalue. Then we introduce essential polar conditions and establish the discrete variational form for each radial one-dimensional eigenvalue problem. Based on the minimax principle and the approximation property of the interpolation operator, we prove the error estimates of approximation eigenvalues. Finally, some numerical experiments are provided, and the numerical results show the efficiency of the proposed algorithm.

Factory-like rearing of silkworm (Bombyx mori) using artificial diet for all instars is a brand-new rearing mode of silkworm. Accurate feeding is one of the core technologies to save cost and increase efficiency in factory silkworm rearing. Automatic identification and counting of silkworm play a key role to realize accurate feeding. In this study, a machine vision system was used to obtain digital images of silkworms during main instars, and an improved Mask R-CNN model was proposed to detect the silkworms and residual artificial diet. The original Mask R-CNN was improved using the noise data of annotations by adding a pixel reweighting strategy and a bounding box fine-tuning strategy to the model frame. A more robust model was trained to improve the detection and segmentation abilities of silkworm and residual feed. Three different data augmentation methods were used to expand the training dataset. The influences of silkworm instars, data augmentation, and the overlap between silkworms on the model performance were evaluated. Then the improved Mask R-CNN was used to detect silkworms and residual feed. The AP50 (Average Precision at IoU=0.5) of the model for silkworm detection and segmentation were 0.790 and 0.795, respectively, and the detection accuracy was 96.83%. The detection and segmentation AP50 of residual feed were 0.641 and 0.653, respectively, and the detection accuracy was 87.71%. The model was deployed on the NVIDIA Jetson AGX Xavier development board with an average detection time of 1.32 s and a maximum detection time of 2.05 s for a image. The computational speed of the improved Mask R-CNN can meet the requirement of real-time detection of the moving unit of the silkworm box on the production line. The model trained by the fifth instar data showed a better performance on test data than the fourth instar model. The brightness enhancement method had the greatest contribution to the model performance as compared to the other data augmentation methods. The overlap between silkworms also negatively affected the performance of the model. This study can provide a core algorithm for the research and development of the accurate feeding information system and feeding device for factory silkworm rearing, which can improve the utilization rate of artificial diet and improve the production and management level of factory silkworm rearing.

Aims: The purpose of this study was to explore a simple and effective method of preparing human acellular amniotic membrane (HAAM) scaffolds, and explore the effect of HAAM scaffolds with juvenile cartilage fragments (JCFs) on osteochondral defects. Methods: HAAM scaffolds were constructed via trypsinization from fresh human amniotic membrane (HAM). The characteristics of the HAAM scaffolds were evaluated by haematoxylin and eosin (H&E) staining, picrosirius red staining, type II collagen immunostaining, Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). Human amniotic mesenchymal stem cells (hAMSCs) were isolated, and stemness was verified by multilineage differentiation. Then, third-generation (P3) hAMSCs were seeded on the HAAM scaffolds, and phalloidin staining and SEM were used to detect the growth of hAMSCs on the HAAM scaffolds. Osteochondral defects (diameter: 3.5 mm; depth: 3 mm) were created in the right patellar grooves of 20 New Zealand White rabbits. The rabbits were randomly divided into four groups: the control group (n = 5), the HAAM scaffolds group (n = 5), the JCFs group (n = 5), and the HAAM + JCFs group (n = 5). Macroscopic and histological assessments of the regenerated tissue were evaluated to validate the treatment results at 12 weeks. Results: In vitro, the HAAM scaffolds had a network structure and possessed abundant collagen. The HAAM scaffolds had good cytocompatibility, and hAMSCs grew well on the HAAM scaffolds. In vivo, the macroscopic scores of the HAAM + JCFs group were significantly higher than those of the other groups. In addition, histological assessments demonstrated that large amounts of hyaline-like cartilage formed in the osteochondral defects in the HAAM + JCFs group. Integration with surrounding normal cartilage and regeneration of subchondral bone in the HAAM + JCFs group were better than those in the other groups. Conclusion: HAAM scaffolds combined with JCFs promote the regenerative repair of osteochondral defects. Cite this article: Bone Joint Res 2022;11(6):349–361.

Objective The clinical characteristics and imaging information of insular glioma patients were collected to predict mutation status of isocitrate dehydrogenase 1 (IDH1). Methods A total of 596 patients with gliomas confirmed by postoperative pathology in The First Affiliated Hospital with Nanjing Medical University from January 2011 to June 2021 were enrolled, including 72 insular gliomas, 213 frontal gliomas, 165 temporal gliomas, 76 parietal gliomas, 28 and 29 midline gliomas. All patients were examined by MRI, fifteen glioma⁃related features were selected from the visually accessible rembrandt images (VASARI), including enhancement quality, enhancement proportion, non⁃enhancement proportion, necrosis proportion, edema proportion, cyst, thickness of enhanced margin, definition of the enhanced margin, hemorrhage, diffusion, deep white matter involvement, deep ventricle involvement, midline cross, T2⁃FLAIR mismatch, maximum diameter of tumor. Univariate and multivariate Logistic regression analysis were used to screen the predictive factors related to IDH1⁃mutant in insular glioma. The receiver operating characteristic (ROC) curve was plotted and area under the curve (AUC), sensitivity and specificity were calculated to evaluate the predictive power of MRI features for IDH1⁃mutant glioma. Results IDH1 mutation rate was higher in insular and frontal gliomas (P＜0.01, for all). WHO grade Ⅱ had the highest IDH1 mutation rate (P=0.008, 0.000), and grade Ⅳ had the lowest mutation rate (P=0.000). The IDH1 mutation rate in low⁃expression Ki⁃67 gliomas was higher than that in high⁃expression gliomas (P=0.000). Logistic regression analysis showed that weak enhancement (OR=35.671, 95%CI: 2.805-453.600; P=0.006), non⁃enhancement (OR=75.453, 95%CI: 2.881-1872.759; P=0.009), unlimited diffusion (OR=10.573, 95%CI: 1.043-107.175; P=0.046), no deep ventricle involvement (OR=187.601, 95%CI: 2.269-15507.607; P=0.020), T2⁃FLAIR mismatch (OR=47.536, 95%CI: 2.838-796.097; P=0.007) were independent predictive factors for IDH1 mutation in insular glioma. The AUC of enhancement degree, diffusion, deep ventricle involvement and T2⁃FLAIR mismatch for the diagnosis of IDH1⁃mutant glioma were 0.846 (95%CI: 0.748-0.944, P=0.000), 0.730 (95%CI: 0.609-0.850, P=0.001), 0.708 (95%CI: 0.584-0.833, P=0.003) and 0.745 (95%CI: 0.627-0.864, P=0.000). The combination of the 4 groups had the highest diagnostic efficacy, and the AUC was 0.961 (95%CI: 0.923-0.999, P=0.000). Conclusions Low grade insular glioma has a high IDH1 mutation rate. MRI features of weak enhancement and non⁃enhancement, unlimited diffusion, non deep ventricle involvement and T2⁃FLAIR mismatch contribute to noninvasive prediction of IDH1⁃mutant insular glioma.

Optimizing the pigment volume concentration of zinc phosphate pigments can protect Al alloy 6101 from alkaline media. Additionally, zinc phosphate pigments form a shielding film on the substrate and facilitate stopping the penetration of aggressive corrosion ions. The efficiency of eco-friendly zinc phosphate pigments is almost 98% during the corrosion analysis. A comparative study of physical aging of neat epoxy and Zinc Phosphate (ZP) pigment-modified epoxy coatings on Al alloy 6101 was conducted in Xi’an, China, for one year in all four seasons, where in summer for 3 months, results degraded more due to high UV radiation and humidity; it is found that peeling force of ZP pigments modified epoxy coatings is 50% higher of than that of the neat epoxy coatings though both peel-off adhesion strength and scratch test visibility decreased in both coatings; The electrochemical resistance of ZP pigments modified epoxy coatings is about 30% higher of than that of neat epoxy coatings, the corrosion rate of ZP pigments modified epoxy coatings is about 70% lower of than that of neat epoxy coatings, moreover the gloss retention is 20% higher in the modified epoxy; Optical surface observation of the coatings showed that the ZP modified epoxy coating could effectively restrict the crack and shrinkage in coatings after aging experimentation in the natural environment.

Chromium-free materials as eco-friendly coatings with higher corrosion resistance are crucial in various industrial processes. Herein, we report the deposition of cerium-based conversion, a chromium-free, eco-friendly chemical conversion coating for aluminum alloy 6101, by the dip coating method. Immersion in cerium salt precursors assisted with hydrogen peroxide was performed for the deposition of cerium-based conversion coatings on aluminum alloy 6101 at different bathing temperatures. The electrochemical corrosion behavior was assessed in an alkaline solution of sodium hydroxide (pH 11), including mass loss measurements, free corrosion risk, polarization, and electrochemical impedance spectroscopy. X-ray diffraction and photoelectron spectroscopy analysis showed that the coatings were composed of Ce (III) and Ce (IV) oxides. Surface modifications and surface degradation of the coating and substrate after immersion in corrosive media were analyzed by scanning electron microscopy. Additionally, energy dispersive scanning analysis demonstrated the elemental composition before and after corrosion of the cerium salt conversion-based coating. The results demonstrated that selectively deposited cerium-based conversion coatings improved the corrosion resistance by up to 96% in a strong corrosive alkaline media.

Corn grits were treated with 0.2% sulphurous acid solution. The purpose of this treatment is to protect pure bacteria (yeast, Lactobacillus) from fermentation. Based on the single factor test, the fermentation technique of instant corn noodle was optimized by orthogonal design. The optimum technological parameters were obtained as follows: 0.2% yeast, plus 0.1% lactobacillus bulgaricus, with fermentation time 78 hours, fermentation particle size 8 mm, and fermentation temperature 30 ℃. The product of fermentation was optimized as follows: 3.5% pregelatinized starch with 0.30% sodium hexametaphosphate, 1.0% xanthan gum, and 3% sunflower seed edible oil. The fermentation had the best comprehensive effect on the instant corn noodle with the broken strip rate of 4.5% and cooking loss rate of 4.5%. Through the mechanical hot extrusion of fermented dough, the broken rate of vermicelli was the lowest, which was 4.3%, and the sensory quality score reached 96.6%. The room temperature storage period of vacuum packaging is 14 days, and the cold storage period is 60 days.

We used brightfield and epifluorescence microscopy, as well as permeability tests, to investigate the apoplastic histochemical features of plant roots associated with ion hyperaccumulation, invasion, and tolerance of oligotrophic conditions. In hyperaccumulator species with a hypodermis (exodermis absent), ions penetrated the root apex, including the root cap. By contrast, in non-hyperaccumulator species possessing an exodermis, ions did not penetrate the root cap. In vivo, the lignified hypodermis blocked the entry of ions into the cortex, while root exodermis absorbed ions and restricted them to the cortex. The roots of the hyperaccumulators Pteris vittata and Cardamine hupingshanensis, as well as the aquatic invasives Alternanthera philoxeroides, Eichhornia crassipes, and Pistia stratiotes, contained lignin and pectins. These compounds may trap and store ions before hypodermis maturation, facilitating ion hyperaccumulation and retention in the apoplastic spaces of the roots. These apoplastic histochemical features were consistent with certain species-specific characters, including ion hyperaccumulation, invasive behaviors in aquatic environments, or tolerance of oligotrophic conditions. We suggest that apoplastic histochemical features of the root may act as invasion mechanisms, allowing these invasive aquatic plants to outcompete indigenous plants for ions.

With China's strong support for innovation and development, the policies of government guidance funds are an important driving force for leading innovation and economic transformation, and the effect of its related policies is of great significance. This paper selects 518 policies related to government guidance funds in the Beijing–Tianjin–Hebei region from 2005 to 2018 as the research object and constructs a policy efficiency evaluation indicator system from three dimensions of policy intensity, policy objectives and measures. The analytic hierarchy process (AHP) and entropy method are used to weight the output level of policy innovation performance and economic performance of government guidance funds, and grey relational analysis (GRA) is used to measure the relationship between policies of government guidance funds and innovation performance and economic performance, and corresponding policy recommendations based on the research results. The results of the research show that the policy efficiency of the government guidance funds in Tianjin and Hebei is relatively high, and the policy efficiency of the Beijing government guidance funds needs to be improved. In general, the policy efficiency of the government guidance funds needs to be further improved.

The aim of this study was to improve the low accuracy of equipment spare parts requirement predicting, which affects the quality and efficiency of maintenance support, based on the summary and analysis of the existing spare parts requirement predicting research. This article introduces the current latest popular long short-term memory (LSTM) algorithm which has the best effect on time series data processing to equipment spare parts requirement predicting, according to the time series characteristics of spare parts consumption data. A method for predicting the requirement for maintenance spare parts based on the LSTM recurrent neural network is proposed, and the network structure is designed in detail, the realization of network training and network prediction is given. The advantages of particle swarm algorithm are introduced to optimize the network parameters, and actual data of three types of equipment spare parts consumption are used for experiments. The performance comparison of predictive models such as BP neural network, generalized regression neural network, wavelet neural network, and squeeze-and-excitation network prove that the new method is effective and provides an effective method for scientifically predicting the requirement for maintenance spare parts and improving the quality of equipment maintenance.

V-set and transmembrane domain-containing protein 1 (VSTM1) is negatively correlated with inflammation. However, its effect on atherosclerosis (AS) remains largely unexplored. In this study, we aimed to assess the effect of VSTM1 on the biological function of human peripheral blood mononuclear cells /macrophages stimulated by oxidized low-density lipoprotein (ox-LDL).

To explore reasons and solutions for the rancidity of peanut nougat in storage, this paper investigates the oxidation stability of shelled peanuts and unshelled peanut stored in different temperatures and the quality of nougats prepared by adding peanuts treated in different ways. The results show that when raw peanuts are stored at temperatures of 25 ℃, 37 ℃ and 60 ℃, the oxidation rate of shelled raw peanuts is slower than that of unshelled raw peanuts. The peroxide value of shelled peanuts does not change significantly and is stable below 2 mmol/kg, while the peroxide value of roasted cooked peanuts changes faster when stored at 60 ℃, and the change of the shelled peanuts is slower than that of the unshelled peanuts. The oxidation rate of baked shelled peanuts is slower than that of unshelled, which could potentially prolong the shelf life of peanut nougats made of them.

MicroRNAs (miRNAs) operate as tumor suppressor or carcinogen to regulate cell proliferation, metastasis, invasion, differentiation, apoptosis, and metabolic process. In the present research, we investigated the effect and mechanism of miR-496 in human gastric cancer cells. miR-496 was downregulated in two gastric cancer cell lines, AGS and MKN45, compared with normal gastric epithelial cell line GES-1. miR-496 mimics inhibited the proliferation of AGS cells after the transfection for 48 and 72 h. The migration and invasion of AGS cells were also inhibited by the transfection of miR-496 mimics. miR-496 mimics induced the apoptosis through upregulating the levels of Bax and Active Caspase 3 and downregulating the levels of Bcl-2 and Total Caspase 3. Bioinformatics analysis showed that there was a binding site between miR-496 and Lyn kinase (LYN). miR-496 mimics could inhibit the expression of LYN in AGS cells. LYN overexpression blocked the inhibition of tumor cell growth, as well as the inhibition of AKT/mTOR signaling pathway induced by miR-496. In conclusion, miR-496 inhibited the proliferation through the AKT/mTOR signaling pathway via targeting LYN in gastric cancer cells. Our research provides a new potential target for clinical diagnosis and targeted treatment for gastric cancer.