Zinc oxide nanoparticles (ZnO NPs) have shown adverse health impact on the human male reproductive system, with evidence of inducing apoptosis. However, whether or not ZnO NPs could promote autophagy, and the possible role of autophagy in the progress of apoptosis, remain unclear. In the current study, in vitro and in vivo toxicological responses of ZnO NPs were explored by using a mouse model and mouse Leydig cell line. It was found that intragastrical exposure of ZnO NPs to mice for 28 days at the concentrations of 100, 200, and 400 mg/kg/day disrupted the seminiferous epithelium of the testis and decreased the sperm density in the epididymis. Furthermore, serum testosterone levels were markedly reduced. The induction of apoptosis and autophagy in the testis tissues was disclosed by up-regulating the protein levels of cleaved Caspase-8, cleaved Caspase-3, Bax, LC3-II, Atg 5, and Beclin 1, accompanied by down-regulation of Bcl 2. In vitro tests showed that ZnO NPs could induce apoptosis and autophagy with the generation of oxidative stress. Specific inhibition of autophagy pathway significantly decreased the cell viability and up-regulated the apoptosis level in mouse Leydig TM3 cells. In summary, ZnO NPs can induce apoptosis and autophagy via oxidative stress, and autophagy might play a protective role in ZnO NPs-induced apoptosis of mouse Leydig cells.

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All rights reservedCorn distillers dried grains with solubles (DDGS) is the main byproduct of the bioethanol industry 1 . It has high nutritional content, especially protein. Using corn DDGS as protein feed material not only increases animal productivity but also saves cost. 2,3 However, the protein content in corn DDGS can vary due to different sources of corn grains and different fermentation conditions. 1,4,5 The protein content in corn DDGS is essential for livestock diet formulation and a major determinant of price. Conventional laboratory analysis methods for determining protein content are time-consuming and costly. Near infrared (NIR) reflectance spectroscopy is a rapid, noninvasive, reliable and green detection technology, which has Corn distillers dried grains with solubles (DDGS), a byproduct of the bioethanol industry, is commonly used as animal feed. This paper evaluates the use of backward variable selection partial least square (BVSPLS) and genetic algorithm (GA) methods to select the spectral variables of near infrared (NIR) reflectance spectroscopy and construct high-performance calibration models of protein content in corn DDGS. The BVSPLS analysis utilised 16% of the spectral variables. Compared to the full spectrum model, the model constructed from the variables selected by the BVSPLS analysis significantly improved the accuracy of the model fit and achieved a 19% decrease in the standard error of validation (SEP) and a 23% increase in the residual validation deviation (RPD). The GA analysis selected 8% of the total NIR spectral variables and the model constructed from these selected variables had a fitted accuracy comparable to that of the full spectrum model. The spectral variables selected by both the BVSPLS analysis and GA analysis significantly simplified the NIR calibration model and provided better correlation between the selected spectral variables and protein content of corn DDGS. These results also have important implications for the development of a rapid, non-invasive, online analysis system to detect protein content of corn DDGS in-situ.

Near-infrared spectroscopy combined with chemometrics was applied to construct a hybrid model for the non-invasive detection of protein content in different types of plant feed materials. In total, 829 samples of plant feed materials, which included corn distillers’ dried grains with solubles (DDGS), corn germ meal, corn gluten meal, distillers’ dried grains (DDG) and rapeseed meal, were collected from markets in China. Based on the different preprocessed spectral data, specific models for each type of plant feed material and a hybrid model for all the materials were built. Performances of specific model and hybrid model constructed with full spectrum (full spectrum model) and selected wavenumbers with VIP (variable importance in the projection) scores value bigger than 1.00 (VIP scores model) were also compared. The best spectral preprocessing method for this study was found to be the standard normal variate transformation combined with the first derivative. For both full spectrum and VIP scores model, the prediction performance of the hybrid model was slightly worse than those of the specific models but was nevertheless satisfactory. Moreover, the VIP scores model obtained generally better performances than corresponding full spectrum model. Wavenumbers around 4500 cm-1, 4664 cm-1 and 4836 cm-1 were found to be the key wavenumbers in modeling protein content in these plant feed materials. The values for the root mean square error of prediction (RMSEP) and the relative prediction deviation (RPD) obtained with the VIP scores hybrid model were 1.05% and 2.53 for corn DDGS, 0.98% and 4.17 for corn germ meal, 0.75% and 6.99 for corn gluten meal, 1.54% and 4.59 for DDG, and 0.90% and 3.33 for rapeseed meal, respectively. The results of this study demonstrate that the protein content in several types of plant feed materials can be determined using a hybrid near-infrared spectroscopy model. And VIP scores method can be used to improve the general predictability of hybrid model.