Eyes of diff ering colors in Alvinocaris longirostris from deep-sea chemosynthetic ecosystems: genetic and molecular evidence of its formation mechanism

Coloration is an important phenotypic trait for multiple adaptive functions.It is interesting to fi nd white-eye(AW)and orange-eye(AO)phenotypes in the shrimp Alvinocaris longirostris inhabiting the deep-sea cold seep and hydrothermal vent areas of the northwestern Pacifi c.By comparative transcriptome analyses,1491 diff erentially expressed genes(DEGs)were identified between AW and AO.Among them,many DEGs were associated with immunity,antioxidation,and detoxifi cation.Two signifi cant enzyme encoding genes,xanthine dehydrogenase,and tryptophan oxidase involved in pigment biosynthesis pathways were up-regulated in AW and AO,respectively,which might be related to the diff erences of white and orange eye phenotypes.Moreover,single nucleotide polymorphism(SNP)calling detected that genotypes of 28 SNP distributing in 14 unigenes were completely diff erent between AW and AO.Particularly,there were three and two non-synonymous mutations in immune genes crustin Pm5 and antimicrobial peptide,respectively.Results indicate that the diff erence in eye color is probably resulted from immune response to variable micro-environmental stressors encountered in the dispersal process of the shrimps,such as symbiotic microbes,pathogens,and toxic substances,and might be genetically fi xed at last.The suggested pathway preliminarily explained the formation mechanism of diff erent eye phenotypes in Alvinocaridid shrimps,providing a basis for further study on adaptive evolution of eyes in deep-sea chemosynthetic faunas.

Driver Fatigue Detection System Based on Colored and Infrared Eye Features Fusion

Real-time detection of driver fatigue status is of great significance for road traffic safety.In this paper,a proposed novel driver fatigue detection method is able to detect the driver’s fatigue status around the clock.The driver’s face images were captured by a camera with a colored lens and an infrared lens mounted above the dashboard.The landmarks of the driver’s face were labeled and the eye-area was segmented.By calculating the aspect ratios of the eyes,the duration of eye closure,frequency of blinks and PERCLOS of both colored and infrared,fatigue can be detected.Based on the change of light intensity detected by a photosensitive device,the weight matrix of the colored features and the infrared features was adjusted adaptively to reduce the impact of lighting on fatigue detection.Video samples of the driver’s face were recorded in the test vehicle.After training the classification model,the results showed that our method has high accuracy on driver fatigue detection in both daytime and nighttime.