Molecular Cloning and Expression Responses of Jarid2b to High-Temperature Treatment in Nile Tilapia (Oreochromis niloticus).

Nile tilapia is a GSD + TE (Genetic Sex Determination + Temperature Effect) fish, and high-temperature treatment during critical thermosensitive periods (TSP) can induce the sex reversal of Nile tilapia genetic females, and brain transcriptomes have revealed the upregulation of Jarid2 (Jumonji and AT-rich domain containing 2) expression after 36 °C high-temperature treatment for 12 days during TSP. It was shown that JARID2 forms a complex with polycomb repressive complex 2 (PRC2) that catalyzed H3K27me3, which was strongly associated with transcriptional repression. In this study, Jarid2b was cloned and characterized in Nile tilapia, which was highly conserved among the analyzed fish species. The expression of Jarid2b was upregulated in the gonad of 21 dpf XX genetic females after 12-day high-temperature treatment and reached a similar level to that of males. Similar responses to high-temperature treatment also appeared in the brain, heart, liver, muscle, eye, and skin tissues. Interestingly, Jarid2b expression was only in response to high-temperature treatment, and not to 17α-methyltestosterone (MT) or letrozole treatments; although, these treatments can also induce the sex reversal of genetic Nile tilapia females. Further studies revealed that Jarid2b responded rapidly at the 8th hour after high-temperature treatment. Considering that JARID2 can recruit PRC2 and establish H3K27me3, we speculated that it might be an upstream gene participating in the regulation of Nile tilapia GSD + TE through regulating the H3K27 methylation level at the locus of many sex differentiation-related genes.

[Prediction of Encapsulation Temperatures of Copolymer Films in Photovoltaic Cells Using Hyperspectral Imaging Techniques and Chemometrics].

A novel method of combination of the chemometrics and the hyperspectral imaging techniques was presented to detect the temperatures of Ethylene-Vinyl Acetate copolymer (EVA) films in photovoltaic cells during the thermal encapsulation process. Four varieties of the EVA films which had been heated at the temperatures of 128, 132, 142 and 148 °C during the photovoltaic cells production process were used for investigation in this paper. These copolymer encapsulation films were firstly scanned by the hyperspectral imaging equipment (Spectral Imaging Ltd. Oulu, Finland). The scanning band range of hyperspectral equipemnt was set between 904.58 and 1700.01 nm. The hyperspectral dataset of copolymer films was randomly divided into two parts for the training and test purpose. Each type of the training set and test set contained 90 and 10 instances, respectively. The obtained hyperspectral images of EVA films were dealt with by using the ENVI (Exelis Visual Information Solutions, USA) software. The size of region of interest (ROI) of each obtained hyperspectral image of EVA film was set as 150 x 150 pixels. The average of reflectance hyper spectra of all the pixels in the ROI was used as the characteristic curve to represent the instance. There kinds of chemometrics methods including partial least squares regression (PLSR), multi-class support vector machine (SVM) and large margin nearest neighbor (LMNN) were used to correlate the characteristic hyper spectra with the encapsulation temperatures of of copolymer films. The plot of weighted regression coefficients illustrated that both bands of short- and long-wave near infrared hyperspectral data contributed to enhancing the prediction accuracy of the forecast model. Because the attained reflectance hyperspectral data of EVA materials displayed the strong nonlinearity, the prediction performance of linear modeling method of PLSR declined and the prediction precision only reached to 95%. The kernel-based forecast models were introduced to eliminate the impact of nonlinear hyperspectral data to some extent through mapping the original nonlinear hyperspectral data to the high dimensional linear feature space, so the relationship between the nonlinear hyperspectral data and the encapsulation temperatures of EVA films was fully disclosed finally. Compared with the prediction results of three proposed models, the prediction performance of LMNN was superior to the other two, whose final recognition accuracy achieved 100%. The results indicated that the methods of combination of LMNN model with the hyperspectral imaging techniques was the best one for accurately and rapidly determining the encapsulation temperatures of EVA films of photovoltaic cells. In addition, this paper had created the ideal conditions for automatically monitoring and effectively controlling the encapsulation temperatures of EVA films in the photovoltaic cells production process.