Glioma represents a disparate group of tumors characterized by high invasion ability, and therefore it is of clinical significance to identify molecular markers and therapeutic targets for better clinical management. Previously, metastasis-associated protein family (MTA) is considered to promote tumor cell invasion and metastasis of human malignancies. Recently, the newly identified MTA3 has been shown to play conflicting roles in human malignancies, while the expression pattern and potential clinical significance of MTA3 in human glioma have not been addressed yet. In the present study, we investigated the protein expression of MTA3 by immunohistochemistry assay and analyzed its association with glioma prognosis in 186 cases of patients. Results showed that MTA3 expression was decreased in glioma compared with that in normal brain (P < 0.05). In addition, tumors with high MTA3 expression were more likely to be of low WHO grade (P = 0.005) and reserve of body function (P = 0.014). Survival analysis showed that decreased MTA3 expression was independently associated with unfavorable overall survival of patients (P < 0.001). These results provide the first evidence that MTA3 expression was decreased in human glioma and negatively associated with prognosis of patients, suggesting that MTA3 may play a tumor suppressor role in glioma.
In this study, near-infrared reflectance spectroscopy and radial basis function (RBF) neural network algorithm were used to measure the protein content of wheat owing to their nondestructiveness and quick speed as well as better performance compared to the traditional measuring method (semimicro-Kjeldahl) in actual practice. To simplify the complex structure of the RBF network caused by the excessive wave points of samples obtained by near-infrared reflectance spectroscopy, we proposed the particle swarm optimization (PSO) algorithm to optimize the cluster center in the hidden layers of the RBF neural network. In addition, a series of improvements for the PSO algorithm was also made to deal with its drawbacks in premature convergence and mechanical inertia weight setting. The experimental analysis demonstrated that the improved PSO algorithm greatly reduced the complexity of the network structure and improved the training speed of the RBF network. Meanwhile, the research result also proved the high performance of the model with its root-mean-square error of prediction (RMSEP) and prediction correlation coefficient ( R ) at 0.26576 and 0.975, respectively, thereby fulfilling the modern agricultural testing requirements featuring nondestructiveness, real-timing, and abundance in the number of samples.
Abstracts Azodicarbonamide is wildly used as a flour gluten fortifier in many countries, but according to the research results of toxicology of azodicarbonamide, its acute toxicity is slightly toxic. A dosage of 10 g/kg is lethal to mice, and it was proved by some researches to be dangerous or unhealthy for people and not suitable to be added in flour; hence, there is a need to identify the concentration of azodicarbonamide in flour quickly. Compared to traditional methods like highperformance liquid chromatography, the core advantage of near-infrared reflectance spectroscopy is rapid and economical. Spectral data in a range of 850 to 1050 nm were obtained by scanning 101 samples with different concentrations. The Mahalanobis distance method was used to distinguish abnormal spectral data, and the correlation coefficient method was used to choose characteristic wave bands. Radial basis function in combination with near-infrared reflectance spectroscopy was used to establish models in accordance. The limit of quantitation and the limit of detection of the first model were 72 and 15 mg/kg, respectively. Through analyzing the relative tolerances of predictive values and true values, the method of secondary modeling was proposed for low-concentration (72 mg/kg) samples. The predictions showed that nearinfrared reflectance spectroscopy could be used for detecting the content of azodicarbonamide added to flour.
This research proposed to design a prediction model based on Radial Basis Function (RBF) neural network and Near Infrared Reflectance Spectroscopy (NIRS) in detecting concentration of Benzoyl Peroxide (BPO) in flour. Near Infrared Reflectance (NIR) spectra acquired from 100 different concentration samples were pre-processed by Standard Normal Variate (SNV) method, detection of leverage and student residual. NIRS models were designed to predict BPO in the 36 samples by means of Partial Least Squares (PLS), BP neural network and RBF, respectively. The Downloaded by [Deakin University Library] at 13:53 12 August 2015A c c e p t e d M a n u s c r i p t 2 results demonstrated that the RBF model, with prediction correlation coefficient (R), root mean squared error of prediction (RMSEP) and ratio of performance to standard deviate (RPD) reaching 0.9937, 15.5095 and 8.8216, respectively, had optimal prediction accuracy and feasibility providing quality evaluation and dynamic monitoring service for quality inspection department and consumers.
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