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Abstract The accurate detection of tunnel lining cracks and prompt identification of their primary causes are critical for maintaining tunnel availability. The advancement of deep learning, particularly in the domain of convolutional neural network (CNN) for image segmentation, has made tunnel lining crack detection more feasible. However, the CNN-based technique for tunnel lining crack detection commonly prioritizes increasing algorithmic complexity to enhance detection accuracy, posing a challenge in balancing the accuracy of detection and the efficiency of the algorithm. Motivated by the superior performance of Unet in image segmentation, this paper proposes a lightweight tunnel lining crack detection model named Mini-Unet, which refined the Unet architecture and utilized depthwise separable convolutions (DSConv) to replace some standard convolution layers. In the optimization of the proposed model parameters, applying a hybrid loss function that integrated dice loss and cross-entropy loss effectively tackled the imbalance between crack and background categories. Several models were set up to contrast with Mini-Unet and the experimental results were analyzed. Mini-Unet achieves a mean intersection over union (MIoU) of 60.76%, a mean precision of 84.18%, and a frame per second (FPS) of 5.635, respectively. Mini-Unet outperforms several mainstream models, enabling rapid detection while maintaining identified accuracy and facilitating the practical application of AI power for real-time tunnel lining crack detection.

Abstract The ability to heal from wounds is perhaps the most important biological function that ensures our survival and perpetuation. Cutaneous wound healing typically consists of four characteristic stages, namely hemostasis, inflammation, proliferation, and remodeling, which are carefully carried out by coordinated actions of various cells, cytokines, and hormones. Incoordination of these steps may impede complete and efficient reconstruction and functional recovery of wounds or even lead to worsened outcomes. Hormones, as powerful modulators of organ functions, participate in multiple steps of the wound healing process and play a pivotal role by choreographing the complex interplay of cellular and molecular events. Leveraging the regulatory effects of hormones to enhance the healing process, hormonal therapy has emerged as a promising approach in the clinical treatment of wounds. Current research has focused on determination of the optimal dosages, delivery methods, and combinations of hormonal therapies to maximize their therapeutic benefits while minimizing potential side effects. This review highlights the molecular mechanisms, clinical benefits and side effects of the most commonly used hormones in clinical treatment of wounds.

While laser-assisted milling machining is promising for promoting the machinability of Inconel 718, the geometrical parameters of laser beam relative to milling tool significantly influence its machining performance. In this work, we investigate the effects of various parameters of laser incidence geometry on the laser-assisted milling process of Inconel 718 by comprehensively employing analytical investigations, finite element simulations and experiments. Firstly, a laser-assisted milling experimental platform is constructed, and a fully coupled thermal-mechanical 3D finite element model of laser-assisted milling of Inconel 718 is established. Secondly, experiments and finite element simulations of laser-assisted milling of Inconel 718 are conducted to investigate the impact of laser assistance on cutting force and machined surface morphology. Results indicate that laser-assisted milling can moderately reduce cutting force and enhance surface quality compared to conventional milling. Thirdly, a single-factor experiment is carried out to explore the impact of laser incidence geometry parameters (laser power, laser incidence angle, and laser spot-tool edge distance) on the cutting force in laser-assisted milling of Inconel 718. Furthermore, the parametric investigation based on the response surface methodology is carried out to assess the coupled effects of the three parameters for deriving their optimal values. The experimental results show that the optimized parameters of laser-assisted milling can lead to a 47.4% reduction in surface roughness and a 25% reduction in cutting force, accompanied by significantly reduced tool wear, as compared to conventional milling. The findings demonstrate that the rational selection of laser incidence geometrical parameters is crucial for enhancing the laser-assisted milling capability for Inconel 718.

Irradiation-induced cascade collisions produce numerous point defects within materials, which can severely deteriorate their thermo-mechanical properties and overall performance. We propose a computational scheme that combines molecular dynamic (MD) simulations with a denoising diffusion probabilistic model (DDPM) to rapidly and accurately predict the spatial coordinates of point defects at any given primary knock atom (PKA) energy, ranging from 0 to 100.0 keV. Importantly, this capability extends to PKA energies that are exclusive from the training data set, demonstrating the robustness and generalizability of the model. The proposed scheme has been thoroughly validated by several designed indicators, including the Fréchet inception distance, the number of point defects, the distance from vacancies and self-interstitial atoms (SIAs) to their respective centroids, the inter-centroid distance between the vacancies and SIAs, the probability density of clustered defect sizes, and the sub-cascade number. Compared to MD simulations, the DDPM can generate point defects at a specific PKA energy at least ten thousand times faster. By offering a rapid and reliable means to model defect distributions across various energy levels, the proposed scheme benefits the comprehension of the cascade process and provides a valuable database for both experimental investigations and large-scale simulations.

Inter-turn short circuit fault (ISCF) is the main fault type of dry-type air-core reactors (DARs), and the reactor burnout event caused by winding ISCF seriously threatens the safe and stable operation of the electric power system. In order to effectively curb the further worsening of ISCF in DARs, a fault detection method based on frequency response analysis was proposed. First, the effect of ISCF on the frequency response curve of the windings was investigated based on the distributed parameter model of DARs. Then, four sets of eigenvalues were proposed for ISCF diagnosis based on the influence law and curve data analysis theory. Finally, based on the eigenvalues, the support vector machine model optimized by the sparrow search algorithm was used to effectively classify the degree of ISCF of the reactor, and the accuracy reached 96%. Furthermore, a test platform was built based on the theoretical analysis for experimental verification, and the test results are consistent with the simulation results. Hence, the method proposed in this paper could effectively classify the ISCF and its severity in DARs. The research in this paper could provide effective guidance for the diagnosis of ISCF in DARs.

Employing the smooth overlap of atomic position (SOAP) descriptors, we established an artificial neural network (ANN) model with the ability to effectively and accurately predict the segregation energy Eseg distributions of hydrogen (H) atoms in various strained regions of tungsten (W). The model is verified to have comparable accuracy with the molecular statics (MS) relaxations, as quantitively evidenced by the root mean square error (RMSE) values of 0.018 eV for the homogeneously strained regions, averagely 0.025 eV for regions near different dislocations, and 0.032 eV for regions surrounding an interstitial H cluster. Besides, the predictions yield no systematic bias at both high and low Eseg extremes, which is a common issue in the elastic theory (ET) calculations. Moreover, Eseg values are precisely predicted with the RMSE value of 0.041 eV surrounding an interstitial H cluster that is not encountered during the learning phase of the model, demonstrating the model’s robust generalization capability. Considering its markedly enhanced computation speed, this accurate ANN model holds promising prospects in circumstances that require massive Eseg calculations.

Abstract Background Downstaging of hepatocellular carcinoma (HCC) makes it possible for patients beyond the criteria to have the chance of liver transplantation (LT) and improved outcomes. Thus, a procedure to predict the prognosis of the treatment is an urgent requisite. The present study aimed to construct a comprehensive framework with clinical information and radiomics features to accurately predict the prognosis of downstaging treatment. Methods Specifically, three-dimensional (3D) tumor segmentation from contrast-enhanced computed tomography (CT) is employed to extract spatial information of the lesions. Then, the radiomics features within the segmented region are calculated. Combining radiomics features and clinical data prompts the development of feature selection to enhance the robustness and generalizability of the model. Finally, we adopt the support vector machine (SVM) algorithm to establish a classification model for predicting HCC downstaging outcomes. Results Herein, a comparative study was conducted on three different models: a radiomics features-based model (R model), a clinical features-based model (C model), and a joint radiomics clinical features-based model (R-C model). The average accuracy of the three models was 0.712, 0.792, and 0.844, and the average area under the receiver-operating characteristic (AUROC) of the three models was 0.775, 0.804, and 0.877, respectively. Conclusions The novel and practical R-C model accurately predicted the downstaging outcomes, which could be utilized to guide the HCC downstaging toward LT treatment.

Aniline is a widely used chemical. Chronic or high-dose exposure to aniline can lead to hepatocellular damage. Although the hepatic pathogenicity of aniline has been established in previous studies, studies involving pathogenic genes during aniline-induced liver injury are limited. Our study first discovered and identified the role and mechanism underlying a new circRNA mmu_circ_26984 in aniline-induced chemical liver injury. Further, we discuss the protective effect of N-acetylcysteine (NAC) in this pathway. After constructing in vitro and in vivo models of aniline treatment, we screened the circRNA with significant differences in expression in AML12 cells from control and aniline-treated groups by circRNA microarray analysis. Next, using RNA pulldown, liquid chromatography-mass spectrometry (LC-MS), and RNA immunoprecipitation, we analyzed the relationship between mmu_circ_26984 and myosin heavy chain 9 (Myh9). Subsequently, we determined the specific mechanism of action of mmu_circ_26984 and Myh9 in aniline-induced liver injury and the protective effect of NAC against aniline-induced liver injury process using Cell Counting Kit-8, Western blot, RNA extraction, a reverse transcription quantitative polymerase chain reaction (RT-qPCR), fluorescence in situ hybridization, immunohistochemistry, and immunofluorescence. The expression of mmu_circ_26984 was significantly increased in liver tissues and AML12 cells of aniline-treated mice compared with the control group. This high expression of mmu_circ_26984 increased the expression of injury-related inflammatory factors, such as NLRP3, Caspase-1, IL-18, and IL-1β in vivo and ex vivo, which exacerbated the level of liver injury. The interaction of mmu_circ_26984 with Myh9 also affected the course of liver injury. Mmu_circ_26984 overexpression and reduced treatment affected the levels of Myh9 expression in AML12 cells, as well as downstream inflammatory factors associated with injury, such as NLRP3. In addition, NAC reduced the process of liver injury mediated by the mmu_circ_26984/Myh9/NLRP3 axis. In conclusion, mmu_circ_26984 is a potential molecular marker and therapeutic target in the process of aniline-induced liver injury that can mediate aniline-exposure-induced liver injury via modulation of the mmu_circ_26984/Myh9/NLRP3 axis, and NAC can effectively attenuate the effect of this liver injury.

Previous experimental studies have demonstrated that the recrystallization in nuclear materials is very sensitive to the annealing temperature, dislocation density, and original grain morphology. However, the synergistic effect of these intrinsic and extrinsic factors on recrystallization has been rarely studied due to the elevated temperatures of recrystallization and the costliness of experiments, especially in tungsten (W). In the present work, we have developed an approach that combines a phase-field model with the physics-based classical nucleation theory to study the synergistic impact of these factors on the recrystallization process. We systematically investigate the synergistic effect of annealing temperature, dislocation density, and original grain morphology on the recrystallization rate and the average recrystallized grain size. The simulation results show that increasing the dislocation density and the annealing temperature can effectively reduce the average grain size after full recrystallization. For an annealing temperature above 1523 K, the recrystallization rates have minor changes with increasing the dislocation density and annealing temperature. Furthermore, we employ an empirical model to quantitatively calculate the Vickers hardness of deformed W during the recrystallization process based on the phase-field microstructures. Notably, columnar grain crystals are found to be more effective in reducing irradiation hardness than isometric grain crystals. We believe that these simulations can provide a valuable reference for the preparation and design of radiation-resistant W materials.

The regular operation of transformers is significantly impacted by the insulation effectiveness of the transformer insulation oil–paper. In order to explore the mechanism of the influence of an electric field on the thermal decomposition performance of insulating oil–paper, this paper simulated the process of electrothermal coupling decomposition of insulating oil–paper from the micro-level based on molecular dynamics. It was determined that the insulating oil is made up of three 16-carbon hydrocarbon molecules, while the insulating paper is made up of 30 fibrous disaccharide molecules. Using the molecular dynamics simulation approach, the pyrolysis of the insulating oil and insulating paper under various electric field strengths was simulated, and the lysis of reactants and the distribution of products were statistically examined. This paper also studied how the electric field affected the microscopic process of the insulating oil–paper pyrolysis. The findings demonstrate that under the influence of electrothermal coupling, the big molecules of the insulating oil and insulating paper are pyrolyzed to produce a variety of tiny molecules. For the insulating oil, it is easily subject to electron displacement polarization under the influence of an external electric field since it contains non-polar molecules, especially impacted by an electric field of 100 V/m. For the insulating paper, its polar nature, on the other hand, makes itself a good candidate for guiding polarization when exposed to an external electric field. So, the greater the electric field strength is, the greater the impact on the thermal decomposition of the insulating paper is.

As a long-established fermented beverage, beer is rich in many essential amino acids, vitamins, trace elements, and bioactive substances that are involved in the regulation of many human physiological functions. The polyphenols in the malt and hops of beer are also important active compounds that interact in both directions with the gut microbiome. This review summarizes the mechanisms by which polyphenols, fiber, and other beneficial components of beer are fermentatively broken down by the intestinal microbiome to initiate the mucosal immune barrier and thus participate in immune regulation. Beer degradation products have anti-inflammatory, anticoagulant, antioxidant, and glucolipid metabolism-modulating potential. We have categorized and summarized reported data on changes in disease indicators and in vivo gut microbiota abundance following alcoholic and non-alcoholic beer consumption. The positive effects of bioactive substances in beer in cancer prevention, reduction of cardiovascular events, and modulation of metabolic syndrome make it one of the candidates for microecological modulators.

The surveying of forestry resources has recently shifted toward precision and real-time monitoring. This study utilized the BlendMask algorithm for accurately outlining tree crowns and introduced a Bayesian neural network to create a model linking individual tree crown size with diameter at breast height (DBH). BlendMask accurately outlines tree crown shapes and contours, outperforming traditional watershed algorithms in segmentation accuracy while preserving edge details across different scales. Subsequently, the Bayesian neural network constructs a model predicting DBH from the measured crown area, providing essential data for managing forest resources and conducting biodiversity research. Evaluation metrics like precision rate, recall rate, F1-score, and mAP index comprehensively assess the method’s performance regarding tree density. BlendMask demonstrated higher accuracy at 0.893 compared to the traditional watershed algorithm’s 0.721 accuracy based on experimental results. Importantly, BlendMask effectively handles over-segmentation problems while preserving edge details across different scales. Moreover, adjusting parameters during execution allows for flexibility in achieving diverse image segmentation effects. This study addresses image segmentation challenges and builds a model linking crown area to DBH using the BlendMask algorithm and a Bayesian neural network. The average discrepancies between calculated and measured DBH for Ginkgo biloba, Pinus tabuliformis, and Populus nigra varitalica were 0.15 cm, 0.29 cm, and 0.49cm, respectively, all within the acceptable forestry error margin of 1 cm. BlendMask, besides its effectiveness in crown segmentation, proves useful for various vegetation classification tasks like broad-leaved forests, coniferous forests, and grasslands. With abundant training data and ongoing parameter adjustments, BlendMask attains improved classification accuracy. This new approach shows great potential for real-world use, offering crucial data for managing forest resources, biodiversity research, and related fields, aiding decision-making processes.

Aqueous zinc batteries attract interest because of their potential for cost-effective and safe electricity storage. Here, the authors develop an in situ formed ion-oligomer nanometric interphase strategy to enable fast-charge aqueous Zn cells.

BackgroundChinese medicinal retention enemas have gradually attracted the attention of clinicians as an alternative approach for tubal obstructive infertility. The purpose of this study was to investigate the efficacy and safety of conventional surgery combined with traditional Chinese medicinal retention enemas for the treatment of tubal obstructive infertility.Materials and methodsEight electronic databases were searched from their inception to November 30, 2022. To assess the efficacy and safety of different treatments, following outcomes were measured: clinical pregnancy rate, clinical total effective rate, incidence of ectopic pregnancy, the improvement of Traditional Chinese Medicinal (TCM) symptoms, the improvement of the signs of obstructive tubal infertility and side effects.ResultsA total of 23 Randomized Controlled Trials (RCTs) with 1909 patients met the inclusion criteria. The pooled results showed a higher pregnancy rate in the experimental group than in the control group (RR 1.75, 95% CI [1.58, 1.94], Z = 10.55, PConclusionBased on current evidence, we concluded that conventional surgery combined with traditional Chinese medicinal retention enema for tubal obstructive infertility was superior to conventional surgery alone in improving the clinical pregnancy rate, improving clinical total effective rate, improving TCM symptoms, improving the signs of obstructive tubal infertility and lowering the incidence of ectopic pregnancy. However, further clinical trials with high-quality methodologies need to be conducted.

C4 olefin is an important feedstock for the chemical industry. Designing an effective and stable industrial process for preparing C4 olefin from renewable ethanol is crucial for further sustainable chemical production. In this study, a comprehensive evaluation system of an experimental scheme was constructed based on the Analytic Hierarchy Process/Entropy Weight Method-Technique for Order Preference by Similarity to Ideal Solution (AHP/EWM-TOPSIS) and Chemical production indicators. Using this evaluation system, a Back Propagation Neural Network (BPNN) based on a Genetic Algorithm (GA) was constructed after simulating C4 olefin production conditions using the Improved Mixed Congruential method. Subsequently, the production scheme with the highest evaluation score was determined when the temperature was not limited and when the temperature was lower than 350°C through a series of mathematical models. Overall, our mathematical models provide guidance for the commercial production of ethanol to butene and effectively reduce the risk of scaling up the chemical process to pilot or industrial scale.

Nickel compounds, an international carcinogen in the industrial environment, increased the risk of lung inflammation even lung cancer in Ni refinery workers. Metformin has displayed the intense anti-inflammation and anti-cancer properties through regulating pyroptosis. This study was designed to explore whether Nickel-refining fumes (NiRF) can induce cell pyroptosis and how AMPK/CREB/Nrf2 mediated the protection afforded by metformin against Ni particles-induced lung impairment. Our results represented that Ni fumes exposure evoked pyroptosis via GOLPH3 and induced oxidative stress, while, metformin treatment alleviated Ni particles-mediated above changes. Moreover, nuclear factor erythroid 2-related factor 2 (Nrf2) involved in the protection of metformin, and the deficiency of Nrf2 attenuated the beneficial protection. We also determined that Nrf2 was a downstream molecule of AMPK/CREB pathway. Furthermore, male C57BL/6 mice were administered with Ni at a dose of 2 mg/kg by non-exposed endotracheal instillation and metformin (100, 200 and 300 mg/kg) via oral gavage for 4 weeks. The results indicated that NiRF promoted GOLPH3 and pyroptosis by stimulating NLRP3, caspase-1, N-GSDMD, IL-18 and IL-1β expression. However, various doses of metformin reduced GOLPH3 and the above protein levels of pyroptosis, also improved AMPK/CREB/Nrf2 expression. In summary, we found that metformin suppressed NiRF-connected GOLPH3-prompted pyroptosis via AMPK/CREB/Nrf2 signaling pathway to confer pulmonary protection.

The use of insect-specific odorants to control the behavior of insects has always been a hot spot in research on “green” control strategies of insects. However, it is generally time-consuming and laborious to explore insect-specific odorants with traditional reverse chemical ecology methods. Here, an insect odorant receptor (OR) and ligand database website (iORandLigandDB) was developed for the specific exploration of insect-specific odorants by using deep learning algorithms. The website provides a range of specific odorants before molecular biology experiments as well as the properties of ORs in closely related insects. At present, the existing three-dimensional structures of ORs in insects and the docking data with related odorants can be retrieved from the database and further analyzed.

In this work, we propose an efficient numerical method to study the effects of microstructures on the effective diffusion coefficient of the diffusion component in materials. We take the diffusion of hydrogen (H) atoms in porous polycrystalline tungsten (W) as an example. The grain structures and irradiated void microstructures are generated by using the phase-field model. The effective diffusion coefficients of H in these microstructures are obtained by solving the steady-state diffusion equation, using a spectral iterative algorithm. We first validate our simulation code for calculating the effective diffusion coefficient by using three simple examples. We then investigate the effects of the grain morphology and porosity on the effective diffusion coefficient of H in W. Regardless of whether the grain boundary is beneficial to the diffusion of H or not, it is found that the effective diffusion coefficient of H along the elongated grain direction in columnar crystals is always greater than that in isometric crystals. The increase of the porosity can significantly decrease the effective diffusion coefficient of H from the simulations of the porous W. A correlation of converting the two-dimensional (2D) effective diffusion coefficient into three-dimensional (3D) in the porous and polycrystalline W is fitted by using our simulation data, respectively. Two fitted correlations can be used to predict the synergistic effect of the porosity and grain boundary on the effective diffusion coefficient of H in W. Consequently, our simulation results provide a good reference for understanding the influence of the complex microstructures on H diffusion, and may help to design W-based materials for the fusion reactor.

At present, the natural gas pipeline transportation plan is mainly prepared based on the artificial experience, which requires considerable time and energy, unfavourable for the overall optimization of the plan. In order to enhance the preparation efficiency and scientificity of the natural gas pipeline transportation plan, the artificial experience for preparation of the natural gas pipeline transportation plan was encoded by summarizing the existing preparation methods, and auxiliary software was developed to seek a feasible solution for the pipeline transportation plan. With the constraints of supply and sales balance and the design throughput of the natural gas pipeline network, and with the minimum energy consumption of the gas pipeline network as the optimization target, a natural gas pipeline network optimization model was established based on the statistical and fitting analysis on the massive historical data of energy consumption and throughput, and the optimal feasible solution was sought with the optimization method. Furthermore, the fitting process between unit consumption and turnover volume was elaborated by taking two trunk lines as the examples. The new optimization method could provide an efficient and scientific optimized preparation method of pipeline transportation plan for complex natural gas pipeline network system.

Nickel (Ni) compounds is recognized industrial carcinogen, which could increase the risk of lung cancer in Ni refineries workers. However, the underlying carcinogenic mechanism still remains to elucidate. Metformin has shown the anticancer properties through suppressing aerobic glycolysis. In the present study, we evaluated the effect of Ni-refining fumes exposure on aerobic glycolysis and the role of AMPK/GOLPH3, as well as how metformin alleviated nickel-induced aerobic glycolysis in vitro and vivo. Firstly, Beas-2B cells were exposed to different concentrations of Ni-refining fumes and pretreated with metformin (activation of AMPK), compound C (AMPK inhibitor) in vitro. Our findings indicated that Ni fumes expose evoked aerobic glycolysis by AMPK/GOLPH3, while metformin attenuated Ni particles-promoted GOLPH3-mediated aerobic glycolysis by p-AMPK expression increase. Then Mito-TEMPT (a mitochondria-targeted antioxidant) and lipopolysaccharide (LPS, ROS activator) were pretreated to affect ROS production in Beas-2B cells. Ni-induced ROS prevented AMPK activation. Moreover, C57BL/6 mice were exposed to 2 mg/kg Ni by non-exposed endotracheal instillation and metformin (100, 200 and 300 mg/kg) via oral gavage for 4 weeks. The effects of AMPK/GOLPH3 axis on Ni-induced aerobic glycolysis were assessed. The results indicated that metformin decreased the protein levels of GOLPH3, LDHA, HK2, MCT-4 and improved p-AMPK expression. Thus, our findings demonstrated metformin antagonized Ni-refining fumes-caused aerobic glycolysis via AMPK/GOLPH3.

Background & ObjectivePerineural invasion is an important biological feature of hilar cholangiocarcinoma (HCCA). We developed a whole-mount histologic large sections (WHLS) of the liver to evaluate peripheral nerve invasion (PNI) of HCCA.MethodsUsing sampling, fixation, dehydration, embedding, sectioning, hematoxylin and eosin (H&E) and immunohistochemical (IHC) staining, and scanning, the characteristics of intrahepatic and extrahepatic PNI in 20 patients with Bismuth type III and type IV HCCA were analyzed with WHLS. Correlation between the characteristics of nerve invasion and tumor size, vascular invasion (artery, portal vein), degree of differentiation, microvascular invasion (MVI), carbohydrate antigen19-9 (CA19-9), and differentiation degree of HCCA was statistically evaluated.ResultsThe WHLS of the liver was successfully established, which enabled us to observe intrahepatic and extrahepatic distribution of HCCA and whether surrounding tissues including nervous, blood, and lymph vessels were infiltrated. Extrahepatic and intrahepatic PNI were identified in 20 (100%) patients and 1 (5.0%) patient, respectively. Vessel density decreased in most invaded nerves presented by CD-34, which correlated with 100% of poorly differentiated and 83% of moderately differentiated tumors (P

BackgroundGolgi phosphoprotein 3 (GOLPH3) is an oncoprotein localized in the Golgi apparatus. Abnormal GOLPH3 expression is potentially related to carcinogenesis. However, the potential biological regulation network of GOLPH3 in lung adenocarcinoma (LUAD) remains to be determined.MethodsExpression of GOLPH3 was identified in LUAD via TIMER, Oncomine, Lung Cancer Explorer (LCE), Human Protein Atlas (HPA), and UALCAN database. Survival analysis was performed using the Kaplan–Meier plotter. GOLPH3 alterations were analyzed through cBioPortal. LinkedOmics was used to perform functional analysis and predict interacted targets. The protein–protein interaction network was constructed by GeneMANIA. In addition, candidate miRNAs and lncRNAs targeting GOLPH3 were generated to construct competing endogenous RNA (ceRNA) network, and survival analysis of ceRNA was performed using LnCeVar. The mRNA or protein expression of TUG1, miR-142-5p, and GOLPH3 in Beas-2B and LUAD cells was verified using qPCR or Western blotting. CCK-8 assay, wound healing assay, and transwell assay were used to detect the ability of cell proliferation, migration, and invasion.ResultsOverexpression of GOLPH3 was identified in LUAD. UALCAN analysis showed that upregulated GOLPH3 was linked to different pathological features of LUAD patients. Importantly, high GOLPH3 expression indicated a negative correlation with the first progression (FP) in LUAD patients. GOLPH3 alterations were also found. Moreover, co-expressed genes with GOLPH3 were analyzed; and they were involved in ribosome and oxidative phosphorylation pathways. Functional network analysis indicated GOLPH3 regulated T-cell receptor signaling pathway and interferon signaling pathway with kinase and transcription factor targets. Notably, TUG1/miR-142-5p/GOLPH3 affected overall survival of LUAD patients. GOLPH3 expression was decreased in the cells with overexpression of miR-142-5p and TUG1 knockdown. GOLPH3 reduction inhibited cell proliferation, migration, and invasion.ConclusionsUpregulation of GOLPH3 has a positive correlation with clinicopathological subtypes and poor FP in LUAD. GOLPH3 promoted LUAD progression. Moreover, TUG1 may act as ceRNA to regulate GOLPH3 expression by competitive binding miR-142-5p.

Introduction Ovarian cancer (OC) is one of the most common malignancies and the leading cause of cancer-related death among women. The long non-coding RNA Prostate cancer-associated transcript-1 (PCAT-1) has been reported to play important roles in multiple human cancers. However, the role of PCAT-1 in OC has never been investigated. The purpose of this study was to investigate the expression and roles of PCAT-1 in OC. Material and methods Expression of PCAT-1 and miR-129-5p in OC tissues and cell lines was determined by qRT-PCR. Cell proliferation and apoptosis were analyzed by MTT assay and flow cytometry, respectively. The interaction between PCAT-1 and miR-129-5p was demonstrated by luciferase reporter assay. Results PCAT-1 is significantly upregulated in OC tissues and cell lines (p < 0.05). Overexpression of PCAT-1 promotes proliferation of OC cells and inhibits their apoptosis (p < 0.05). In addition, miR-129-5p is markedly downregulated in OC and its level is inversely correlated with PCAT-1 expression in OC tumor tissues (p < 0.05). miR-129-5p inhibits proliferation and induces apoptosis in OC cell lines (p < 0.05). Furthermore, it has been demonstrated that miR-129-5p is directly targeted by PCAT-1 and miR-129-5p overexpression can effectively attenuate the effects of PCAT-1 on the proliferation and apoptosis of OC cells. Conclusions Our results suggest that PCAT-1 functions as an oncogene by inhibiting miR-129-5p in OC and silencing PCAT-1 may be a novel therapeutic strategy in the treatment of OC.

The aging of the transformer oil-paper insulation distributes spatially, which results in changes in paper resistivity in different regions. This paper establishes an iterative inversion algorithm using the finite element method to calculate the oil-immersed paper resistivity in different regions of a transformer. This algorithm sets the transformer dielectric loss factor tan δ at low frequency, the dielectric parameters of the transformer oil as inputs and the oil-immersed paper resistivity as output. The resistivity obtained from inversion can be used as a reference to access the insulation state of the oil-paper insulation. This paper aims at achieving the nondestructive detection of the partial state of the oil-paper insulation.

Tungsten (W) is considered to be the most promising plasma-facing material in fusion reactors. During their service, severe irradiation conditions create plenty of point defects in W, which can significantly degrade their performance. In this work, we first employ the molecular static simulations to investigate the interaction between a 1/2[111] dislocation loop and a vacancy-type defect including a vacancy, di-vacancy, and vacancy cluster in W. The distributions of the binding energies of a 1/2[111] interstitial and vacancy dislocation loop to a vacancy along different directions at 0 K are obtained, which are validated by using the elasticity theory. The calculated distributions of the binding energies of a 1/2[111] interstitial dislocation loop to a di-vacancy and a vacancy cluster, showing a similar behavior to the case of a vacancy. Furthermore, we use the molecular dynamics simulation to study the effect of a vacancy cluster on the mobility of the 1/2[111] interstitial dislocation loop. The interaction is closely related to the temperature and their relative positions. A vacancy cluster can attract the 1/2[111] interstitial dislocation loop and pin it at low temperatures. At high temperatures, the 1/2[111] interstitial dislocation loop can move randomly. These results will help us to understand the essence of the interaction behaviors between the dislocation loop and a vacancy-type defect and provide necessary parameters for mesoscopic scale simulations.

Reconfigurable computing is that the logical resources in the system can be reconfigured according to the real-time changing data flow to achieve different calculation functions. The reconfigurable computing system has both high efficiency on hardware and universality of software. The field programmable gate array (FPGA)-based real-time simulator for active distribution networks (ADNs) requires a long compilation time for case modification, with low efficiency and low versatility, making it inconvenient and difficult for users. To solve the problem of long compile time with a new case, a universal design of the FPGA-based real-time simulator for ADNs based on reconfigurable computing is proposed in this paper. It includes the universal design of the simulation parameter configuration, the simulation initial value setting, the linear equations solving module and the simulation result output module. The proposed universal design of the simulator makes the modification and change of the cases and parameters without recompiling and further improves the simulation efficiency. Simulation results are conducted and compared with PSCAD/EMTDC to validate the correctness and effectiveness of the universal design.

The synthesis of the azoxybenzenes by the reduction of nitroarenes with reducing agent potassium borohydride in water was reported for the first time. PEG-400 was used as a phase transfer catalyst and could effectively catalyze the reduction. The electronic effects of substituent groups play an important role in determining the reduction efficiencies. Electron-withdrawing substituents promote the formation of the azoxybenzene products, while electron-releasing groups retard the reductions to various degrees depending on the extent of their electron-donating ability.

The resistivity of oil impregnated paper will decrease during its aging process. This paper takes paper resistivity as an assessment index to evaluate the insulation condition of oil impregnated paper in power transformer. The feasibility of this method are discussed in two aspects: reliability and sensitivity. Iterative inversion of paper resistivity was combined with finite element simulation. Both the bisection method and Newton’s method were used as iterative methods. After the analysis and comparison, Newton’s method was selected as the first option of paper resistivity iteration for its faster convergence. In order to consider the spatial distribution characteristic of paper aging and enhance the calculation accuracy, the resistivity calculation is expanded to a multivariate iteration based on Newton’s method, in order to consider the spatial distribution characteristic of paper aging and improve the calculation accuracy. This paper presents an exploratory research on condition assessment of oil impregnated paper insulation, and provides some reference to the security and economy operation of power transformers.

In the title compound, C29H25NO, the dihedral angles between the indolin-2-one ring system and the three benzene rings are 62.78 (9), 31.69 (9) and 80.94 (9)°.