Alternative splicing of histone demethylase Kdm6bb mediates temperature-induced sex reversal in the Nile tilapia.

Sex determination in many fish species is remarkably plastic and temperature sensitive. Nile tilapia display a genetic sex-determination system (XX/XY). However, high-temperature treatment during critical thermosensitive periods can induce XX females into XXm pseudo-males, and this phenomenon is termed temperature-induced sex reversal (TISR). To investigate the molecular mechanism of TISR in Nile tilapia, we performed Iso-seq analysis and found a dramatic effect of high temperature on gene alternative splicing (AS). Kdm6bb histone demethylase showed a novel AS at intron 5 that generates Kdm6bb_tv1 transcripts without intron 5 and Kdm6bb_tv2 with intron 5. Kdm6bb_tv1 encodes a full-length protein while Kdm6bb_tv2 encodes a truncated protein. Expression analysis revealed that intron 5 splicing of Kdm6bb is male and gonad biased at larval stage, and only gonad biased at adult stage. High-temperature treatment induced intron 5 splicing in the gonads of XX and XY fish, resulting in increased Kdm6bb_tv1 expression. To directly test the role of Kdm6bb_tv1 in Nile tilapia TISR, we knocked out expression of Kdm6bb_tv1. However, Kdm6bb_tv1-/- homozygous mutants showed embryonic lethality. Overexpression of Kdm6bb_tv1, but not Kdm6bb_tv2, induced sex reversal of XX females into pseudo-males. Overexpression of Kdm6bb_tv1, as with high-temperature treatment, modified the promotor region of Gsdf and Dmrt1 by demethylating the trimethylated lysine 27 of histone 3 (H3K27me3), thereby increasing expression. Collectively, these studies demonstrate that AS of Kdm6bb intron 5 increases the expression of Kdm6bb_tv1, which acts as a direct link between high temperature and activation of Gsdf and Dmrt1 expression, leading to male sex determination.

[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.