Whole-genome sequencing of leopard coral grouper(Plectropomus leopardus) and exploration of regulation mechanism of skin color and adaptive evolution

Leopard coral groupers belong to the Plectropomus genus of the Epinephelidae family and are important fish for coral reef ecosystems and the marine aquaculture industry. To promote future research of this species, a high-quality chromosome-level genome was assembled using PacBio sequencing and Hi-C technology. A 787.06 Mb genome was assembled, with 99.7%(784.57 Mb) of bases anchored to 24 chromosomes. The leopard coral grouper genome size was smaller than that of other groupers, which may be related to its ancient status among grouper species. A total of 22 317 proteincoding genes were predicted. This high-quality genome of the leopard coral grouper is the first genomic resource for Plectropomus and should provide a pivotal genetic foundation for further research. Phylogenetic analysis of the leopard coral grouper and 12 other fish species showed that this fish is closely related to the brown-marbled grouper.Expanded genes in the leopard coral grouper genome were mainly associated with immune response and movement ability, which may be related to the adaptive evolution of this species to its habitat. In addition, we also identified differentially expressed genes(DEGs) associated with carotenoid metabolism between red and brown-colored leopard coral groupers. These genes may play roles in skin color decision by regulating carotenoid content in these groupers.

Dietary aflatoxin B1 induces abnormal deposition of melanin in the corium layer of the chicken shank possibly via promoting the expression of melanin synthesis-related genes

San-Huang chicken is a high-quality breed in China with yellow feather, claw and break. However, the abnormal phenomenon of the yellow shank turning into green shank of San-Huang chicken has been a concern, as it seriously reduces the carcass quality and economic benefit of yellow-feathered broilers. In this study, the cause of this abnormal green skin in shank was systematically investigated. Physiological anatomy revealed that the abnormal skin in shank was primarily due to the deposition of melanin under the dermis. After analyzing multiple potential causes such as heredity(pedigree and genetic markers), environment(water quality monitoring) and feed composition(mycotoxin detection), excessive aflatoxin B1(AFB1) in feed was screened, accompanied with a higher L-dihydroxy-phenylalanine(L-DOPA)(P<0.05) and melanin content(P<0.01). So it was speculated that excessive AFB1 might be the main cause of abnormal green skin in shank. Subsequently, the further results showed that a high concentration of AFB1(>170 μg kg–1)indeed induced the abnormal green skin in shank compared to the normal AFB1 content(<10 μg kg–1), and the mRNA levels of TYR, TYRP1, MITE, MC1R and EDN3 genes related to melanin deposition would significantly up-regulate(P<0.01) and the content and activity of tyrosinase(TyR) significantly increased(P<0.05). At the same time, the content of L-DOPA and melanin deposition also increased significantly(P<0.01), which also confirmed the effect of excessive AFB1 on melanin deposition in skin of shank. Results of additional experiments revealed that the AFB1's negative effect on melanin deposition in skin of shank could last for a longer time. Taken together, the results of this study explained the occurrence and possible mechanisms of the abnormal AFB1-related green skin in shank of chickens. Excessive AFB1 in diets increased the L-DOPA content and melanin abnormal deposition in the chicken shank possibly via promoting TyR content and activity, and the expression of melanin synthesis-related genes. Furthermore, our findings once again raised the alarm of the danger of AFB1 in the broiler production.

Face Detection under Complex Background and Illumination

For face detection under complex background and illumination, a detection method that combines the skin color segmentation and cost-sensitive Adaboost algorithm is proposed in this paper. First, by using the characteristic of human skin color clustering in the color space, the skin color area in YC b C r color space is extracted and a large number of irrelevant backgrounds are excluded; then for remedying the deficiencies of Adaboost algorithm, the cost-sensitive function is introduced into the Adaboost algorithm; finally the skin color segmentation and cost-sensitive Adaboost algorithm are combined for the face detection. Experimental results show that the proposed detection method has a higher detection rate and detection speed, which can more adapt to the actual field environment.

Research of Natural Gesture Recognition and Interactive Technology Compatible with YCb Crand HSV Color Space

In view of the current gesture recognition algorithm based on skin color segmentation is not flexible and has weak resistance to the environment, this paper puts forward a new method of skin color modeling to improve the adaptability of gesture segmentation when it face to different states. The modeling built by double color space instead of only one is compatible both in YCbCr and HSV color space to training the Gaussian model which can update the threshold value for binarization. Finally, this paper designed a natural gesture recognition and interactive systems based on the double color space model. It has shown that the system has a good interactive experience in different environments.

Real-time Face Detection using Skin Color Model

This paper presents a new face detection approach to real-time applications, which is based on the skin color model and the morphological filtering. First the non-skin color pixels of the input image are removed based on the skin color model in the YC rC b chrominance space, from which we extract candidate human face regions. Then a mathematical morphological filter is used to remove noisy regions and fill the holes in the candidate skin color regions. We adopt the similarity between the human face features and the candidate face regions to locate the face regions in the original image. We have implemented the algorithm in our smart media system. The experiment results show that this system is effective in real-time applications.

An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition

The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer interfaces.Moreover,sign language recognition is mainly developed for enabling communication between deaf and dumb people.In conventional works,various image processing techniques like segmentation,optimization,and classification are deployed for hand gesture recognition.Still,it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption,increased false positives,error rate,and misclassification outputs.Hence,this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques.During image segmentation,skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion.Then,the Heuristic Manta-ray Foraging Optimization(HMFO)technique is employed for optimally selecting the features by computing the best fitness value.Moreover,the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate.Finally,an Adaptive Extreme Learning Machine(AELM)based classification technique is employed for predicting the recognition output.During results validation,various evaluation measures have been used to compare the proposed model’s performance with other classification approaches.