A New Species of the Genus Rhacophorus (Anura： Rhacophoridae) from Dabie Mountains in East China

A new species of rhacophorid of the genus Rhacophorus is described from the Dabie Mountains of west Anhui, east China. The new species, Rhacophorus zhoukaiyae sp. nov. is distinguished from its congeners by a combination of the following characters： 1） the ventral surface and front-and-rear of the femur is paler yellowish and decorated with irregular grayish blotching, and without obvious spots on the dorsum of the hand and foot webbing; 2） the outer metatarsal tubercle is small; 3） outer fingers are half-webbed and outer toes two third webbed; 4） the skin on the dorsum is smooth and without compressed warts; 5） the throat, chest and belly are pure paler yellowish; 6） the dorsal part of the fingers and toes are grayish-white; 7） the iris is golden-yellow. In addition, the phylogenetic tree showed that all the individuals of R. zhoukaiyae sp. nov. clustered into one distinct clade which suggested the validity of this species. This results could also be used to the support of species delimitation. Currently, this species is known only from mid-elevation montane evergreen forest in the Dabie Mountains of west Anhui, China.

Multiple functional linear model for association analysis of RNA-seq with imaging

Emerging integrative analysis of genomic and anatomical imaging data which has not been well developed, provides invaluable information for the holistic discovery of the genomic structure of disease and has the potential to open a new avenue for discovering novel disease susceptibility genes which cannot be identified if they are analyzed separately. A key issue to the success of imaging and genomic data analysis is how to reduce their dimensions. Most previous methods for imaging information extraction and RNA-seq data reduction do not explore imaging spatial information and often ignore gene expression variation at the genomic positional level. To overcome these limitations, we extend functional principle component analysis from one dimension to two dimensions （2DFPCA） for representing imaging data and develop a multiple functional linear model （MFLM） in which functional principal scores of images are taken as multiple quantitative traits and RNA-seq profile across a gene is taken as a function predictor for assessing the association of gene expression with images. The developed method has been applied to image and RNA- seq data of ovarian cancer and kidney renal clear cell carcinoma （KIRC） studies. We identified 24 and 84 genes whose expressions were associated with imaging variations in ovarian cancer and KIRC studies, respectively. Our results showed that many significantly associated genes with images were not differentially expressed, but revealed their morphological and metabolic functions. The results also demonstrated that the peaks of the estimated regression coefficient function in the MFLM often allowed the discovery of splicing sites and multiple isoforms of gene expressions.