MSTN, also known as growth and differentiation factor 8 (GDF8), and GDF11 are members of the transforming growth factor-beta (TGF-beta) subfamily. They have been thought to be derived from one ancestral gene. In the present study, we report the isolation and characterization of an invertebrate GDF8/11 homolog from the amphioxus (Branchiostoma belcheri tsingtauense). The amphioxus GDF8/11 gene consists of five exons flanked by four introns, which have two more exons and introns than that of other species. In intron III, a possible transposable element was identified. This suggested that this intron might be derived from transposon. The amphioxus GDF8/11 cDNA encodes a polypeptide of 419 amino acid residues. Phologenetic analysis shows that the GDF8/11 is at the base of vertebrate MSTNs and GDF11s. This result might prove that the GDF8/11 derived from one ancestral gene and the amphioxus GDF8/11 may be the common ancestral gene, and also the gene duplication event generating MSTN and GDF11 occurred before the divergence of vertebrates and after or at the divergence of amphioxus from vertebrates. Reverse transcriptase polymerase chain reaction results showed that the GDF8/11 gene was expressed in new fertilized cell, early gastrulation, and knife-shaped embryo, which was different from that in mammals. It suggested that the GDF8/11 gene might possess additional functions other than regulating muscle growth in amphioxus.

Sky detection plays an essential role in various computer vision applications. Most existing sky detection approaches, being trained on ideal dataset, may lose efficacy when facing unfavorable conditions like the effects of weather and lighting conditions. In this paper, a novel algorithm for sky detection in hazy images is proposed from the perspective of probing the density of haze. We address the problem by an image segmentation and a region-level classification. To characterize the sky of hazy scenes, we unprecedentedly introduce several haze-relevant features that reflect the perceptual hazy density and the scene depth. Based on these features, the sky is separated by two imbalance SVM classifiers and a similarity measurement. Moreover, a sky dataset (named HazySky) with 500 annotated hazy images is built for model training and performance evaluation. To evaluate the performance of our method, we conducted extensive experiments both on our HazySky dataset and the SkyFinder dataset. The results demonstrate that our method performs better on the detection accuracy than previous methods, not only under hazy scenes, but also under other weather conditions.