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Corrosion fatigue behavior and mechanism of the weld joint of Q690qE high‐strength bridge steel was investigated in a simulated marine environment. It reveals the sub‐critical heat‐affected zone (SCHAZ) and coarse‐grained heat‐affected zone (CGHAZ) are the most vulnerable sites to corrosion fatigue cracking. The CGHAZ of the Q690qE steel weld joint was prone to corrosion fatigue initiation mainly because of micro‐galvanic corrosion between CGHAZ and weld metal (WM). The corrosion fatigue failure in SCHAZ mainly resulted from local stress/strain concentration due to weld softening. The final fracture location was determined by the competition between these two effects. Highlights: Corrosion fatigue mechanism of the weld joint of high‐strength bridge steel was proposed.The final corrosion fatigue fracture of the Q690qE steel weld joint mostly occurred in SCHAZ.SCHAZ was vulnerable to corrosion fatigue cracking mainly due to local softening. [ABSTRACT FROM AUTHOR]

The corrosion behaviour of X65 steel in Shijiazhuang soil-simulated solution was investigated under different alternating current (AC) interference. Results show X65 pipeline steel presented weak passivation characteristics in this weakly alkaline solution, in which case AC interference could activate the anodic dissolution reaction and greatly accelerate uniform corrosion and pitting corrosion. AC interference could easily induce corrosion pits initiation and propagation in this weakly alkaline solution because of unstable passive film on steel surface and low pitting potential. Both the average corrosion rate and corrosion pits depth presented a power function with the increase of AC density. [ABSTRACT FROM AUTHOR]

Breast cancer (BCA) is one of the most prevalent cancers among women. Emerging evidence has revealed that Annexin A‐9 (ANXA9) plays a crucial function in the development of some cancers. Notably, ANXA9 has been reported to be a new prognostic biomarker for gastric and colorectal cancers. However, its expression and biological function in BCA have not yet been investigated. Using online bioinformatics tools such as TIMER, GEPIA, HPA, and UALCAN, we predicted ANXA9 expression and its correlation with the clinicopathological characteristics of BCA patients. RT‐qPCR and western blot were utilized to measure ANXA9 mRNA and ANXA9 protein expression in BCA patient tissues and cells. BCA‐derived exosomes were identified by transmission electron microscopy. Functional assays were employed to evaluate the biological role of ANXA9 in BCA cell proliferation, migration, invasion, and apoptosis. A tumor xenograft in vivo model was utilized to assess the role of ANXA9 in tumor growth in mice. Bioinformatics and functional screening analysis revealed that ANXA9 was highly expressed in BCA patient tissues, with median ANXA9 expression 1.5‐ to 2‐fold higher than in normal tissues (p < 0.05). RT‐qPCR confirmed that ANXA9 expression in BCA tissues was around 1.5‐fold higher than the adjacent normal tissues (p < 0.001). ANXA9 expression in different subtypes of BCA also showed a difference, and ANXA9 was found to be mostly significantly upregulated in luminal BCA relative to normal tissues or other histological subtypes (p < 0.001). Moreover, ANXA9 expression was elevated in different races, ages, clinical stages, node metastasis status, and menopause status groups relative to the normal group (p < 0.001). Furthermore, ANXA9 was found to be secreted by BCA tissue‐derived exosomes and its expression was upregulated 1‐ to 7‐fold in BCA cells treated with exosomes (p < 0.001), while its expression in MCF10A cells was not significantly altered by treatment with exosomes (p > 0.05). ANXA9 silencing induced a significant decrease of around 30% in the colony number of BCA cells (p < 0.01). The number of migrated and invaded BCA cells also decreased by around 65 and 68%, respectively, after silencing ANXA9 (p < 0.01). Tumor size was significantly reduced (nearly half) in the LV‐sh‐ANXA9 group relative to the LV‐NC group in the xenograft model (p < 0.01), suggesting that ANXA9 silencing repressed tumor progression in BCA progression in vitro and in vivo. In conclusion, exosome‐derived ANXA9 functions as an oncogene that facilitates the proliferation, migration, and invasiveness of BCA cells and enhances tumor growth in BCA development, which may provide a new prognostic and therapeutic biomarker for BCA patients. [ABSTRACT FROM AUTHOR]

Soil nutrient prediction has been eliciting increasing attention in agricultural production. Backpropagation (BP) neural networks have demonstrated remarkable ability in many prediction scenarios. However, directly utilizing BP neural networks in soil nutrient prediction may not yield promising results due to the random assignment of initial weights and thresholds and the tendency to fall into local extreme points. In this study, a BP neural network model optimized by an improved genetic algorithm (IGA) was proposed to predict soil nutrient time series with high accuracy. First, the crossover and mutation operations of the genetic algorithm (GA) were improved. Next, the IGA was used to optimize the BP model. The symmetric nature of the model lies in its feedforward and feedback connections, i.e., the same weights must be used for the forward and backward passes. An empirical evaluation was performed using annual soil nutrient data from China. Soil pH, total nitrogen, organic matter, fast-acting potassium, and effective phosphorus were selected as evaluation indicators. The prediction results of the IGA–BP, GA–BP, and BP neural network models were compared and analyzed. For the IGA–BP prediction model, the coefficient of determination for soil pH was 0.8, while those for total nitrogen, organic matter, fast-acting potassium, and effective phosphorus were all greater than 0.98, exhibiting a strong generalization ability. The root-mean-square errors of the IGA–BP prediction models were reduced to 50% of the BP models. The results indicated that the IGA–BP method can accurately predict soil nutrient content for future time series. [ABSTRACT FROM AUTHOR]

Plastic recycling is the most effective way to solve plastic pollution. Color recognition of plastic is a key step in the plastic recycling process, which is very important for improving the utilization rate of waste plastic. At present, the manual recognition method is inefficient and cannot meet the requirements of large-scale production, while the existing color recognition based on machine vision has problems such as low recognition accuracy of similar colors, complicated algorithm and high system cost. According to the above problems, a color recognition method based on multi-wavelength transmission spectrum was proposed in this paper for the color recognition of colorless, light blue, light green and apple green transparent plastic. For each transparent plastic, the transmission spectra at 381 nm, 439 nm, 620 nm and white transmission light source were obtained, and the ratios of R, G and B wavebands of the transmission spectra were used to set up a color feature, which was then used for color recognition. A color recognition model was established based on the color features and naive Bayes model. The 10-fold cross-validation results showed that the recognition accuracy of the four colors of transparent plastic reached 100%. [ABSTRACT FROM AUTHOR]

Objective: This study aimed at comprehensively exploring the value applying positron emission tomography (PET) to predict the effect of molecularly targeted therapy in solid tumors. Materials and methods: A systematic search was performed for potentially relevant studies from the time of inception to February 2017. The primary endpoints were progression-free survival (PFS), overall survival (OS), and time to progression (TTP). The results were analyzed by Review Manager version 5.3 (RevMan 5.3) statistical software. Subgroup analyses were implemented based on the type of molecularly targeted agents (monoclonal antibodies arm and small molecular targeted agents arm), mechanism (erlotinib/gefitinib arm and bevacizumab arm), radioactive tracers, type of tumor, and reevaluated PET timing. Results: Twenty-six studies incorporating 865 individuals were eligible. Compared with PET nonresponse group, PET response group displayed a decrease in maximal standard uptake value (SUVmax), which was associated with a significantly prolonged PFS (HR =0.41, 95% CI [0.29, 0.59]; P<0.00001), OS (HR =0.52, 95% CI [0.40, 0.67]; P<0.00001), and TTP (HR =0.30, 95% CI [0.14, 0.66]; P=0.003). Similar results were obtained in the subgroup analyses of PFS in erlotinib/gefitinib arm and small molecular targeted agents arm; and OS in lung cancer arm, erlotinib/gefitinib arm, bevacizumab arm, small molecular targeted agents arm, monoclonal antibodies arm, 18F-fluorodeoxythymidine (18F-FLT) arm, 18F-fluorodeoxyglucose (18F-FDG) arm, and early PET timing arm. Conclusion: Our study demonstrated that PET was a favorable approach to predict the prognosis of molecularly targeted therapy for solid tumors. PET assessment within 2 weeks could be useful to predict clinical outcome. [ABSTRACT FROM AUTHOR]

In this study, the property of 3-aminopropyltriethoxysilane modified graphite nanosheets (GNS) decorated with Fe3O4nanoparticles (Fe3O4-GNS-NH2hybrids) for adsorption of Hg(II) in aqueous solution were evaluated by batch methods. The adsorption mechanism was also studied by X-ray photoelectron spectroscopy. The effect of factors, such as pH, contact time, initial concentration of Hg(II) and the GNS content (wt.%) for Fe3O4in Fe3O4-GNS-NH2hybrid materials on Hg(II) removal were investigated. The equilibrium data were analyzed using the Langmuir and Freundlich isotherm models. The pseudo-first-order adsorption and the pseudo-second-order adsorption models were used to fit kinetics experimental data. The results showed that the main adsorption mechanism for Hg(II) was the formation of N-metal ions chemical complex, the solution pH value had a major impact on Hg(II) adsorption with optimal removal observed around pH 4.0–6.0, the Langmuir isotherm model was found to be suitable for the Hg(II) adsorption and the adsorption kinetic followed the pseudo-second-order equation for Fe3O4-GNS-NH2hybrid materials. [ABSTRACT FROM PUBLISHER]

The rapid detection of the elements nitrogen (N), phosphorus (P), and potassium (K) is beneficial to the control of the compound fertilizer production process, and it is of great significance in the fertilizer industry. The aim of this work was to compare the detection ability of laser-induced breakdown spectroscopy (LIBS) coupled with support vector regression (SVR) and obtain an accurate and reliable method for the rapid detection of all three elements. A total of 58 fertilizer samples were provided by Anhui Huilong Group. The collection of samples was divided into a calibration set (43 samples) and a prediction set (15 samples) by the Kennard–Stone (KS) method. Four different parameter optimization methods were used to construct the SVR calibration models by element concentration and the intensity of characteristic line variables, namely the traditional grid search method (GSM), genetic algorithm (GA), particle swarm optimization (PSO), and least squares (LS). The training time, determination coefficient, and the root-mean-square error for all parameter optimization methods were analyzed. The results indicated that the LIBS technique coupled with the least squares–support vector regression (LS-SVR) method could be a reliable and accurate method in the quantitative determination of N, P, and K elements in complex matrix like compound fertilizers. [ABSTRACT FROM AUTHOR]