RESUMEN
Stanniocalcin 2 (STC2) is a secreted glycoprotein involved in multiple biological processes. To systemically study the biological role of STC2 in chickens, phylogenetic tree analysis and conservation analysis were conducted. Association analysis between variations in the STC2 gene and the economic traits of Gushi-Anka F2 was conducted. The tissue expression patterns of STC2 expression in different chicken tissues and liver at different stages were detected. The biological role of STC2 in chicken liver was investigated through overexpression and interfering methods in the LMH cell line. Correlation analyses between STC2 expression and lipid components were conducted. (1) The phylogenetic tree displayed that chicken STC2 is most closely related with Japanese quail and most distantly related with Xenopus tropicalis. STC2 has the same identical conserved motifs as other species. (2) rs9949205 (T > C) found in STC2 intron was highly significantly correlated with chicken body weight at 0, 2, 4, 6, 8, 10 and 12 weeks (p < 0.01). Extremely significant correlations of rs9949205 with semi-evisceration weight (SEW), evisceration weight (EW), breast muscle weight (BMW), leg muscle weight (LMW), liver weight and abdominal fat weight (AFW) were revealed (p < 0.01). Significant associations between rs9949205 and abdominal fat percentage, liver weight rate, breast muscle weight rate and leg muscle weight rate were also found (p < 0.05). Individuals with TT or TC genotypes had significantly lower abdominal fat percentage and liver weight rate compared to those with the CC genotype, while their body weight and other carcass traits were higher. (3) STC2 showed a high expression level in chicken liver tissue, which significantly increased with the progression of age (p < 0.05). STC2 was observed to inhibit the content of lipid droplets, triglycerides (TG) and cholesterol (TC), as well the expression level of genes related to lipid metabolism in LMH cells. (4) Correlation analysis showed that the STC2 gene was significantly correlated with 176 lipids in the breast muscle (p < 0.05) and mainly enriched in omega-3 and omega-6 unsaturated fatty acids. In conclusion, the STC2 gene in chicken might potentially play a crucial role in chicken growth and development, as well as liver lipid metabolism and muscle lipid deposition. This study provides a scientific foundation for further investigation into the regulatory mechanism of the STC2 gene on lipid metabolism and deposition in chicken liver.
RESUMEN
Breast lesion segmentation from ultrasound images is essential in computer-aided breast cancer diagnosis. To alleviate the problems of blurry lesion boundaries and irregular morphologies, common practices combine CNN and attention to integrate global and local information. However, previous methods use two independent modules to extract global and local features separately, such feature-wise inflexible integration ignores the semantic gap between them, resulting in representation redundancy/insufficiency and undesirable restrictions in clinic practices. Moreover, medical images are highly similar to each other due to the imaging methods and human tissues, but the captured global information by transformer-based methods in the medical domain is limited within images, the semantic relations and common knowledge across images are largely ignored. To alleviate the above problems, in the neighbor view, this paper develops a pixel neighbor representation learning method (NeighborNet) to flexibly integrate global and local context within and across images for lesion morphology and boundary modeling. Concretely, we design two neighbor layers to investigate two properties (i.e., number and distribution) of neighbors. The neighbor number for each pixel is not fixed but determined by itself. The neighbor distribution is extended from one image to all images in the datasets. With the two properties, for each pixel at each feature level, the proposed NeighborNet can evolve into the transformer or degenerate into the CNN for adaptive context representation learning to cope with the irregular lesion morphologies and blurry boundaries. The state-of-the-art performances on three ultrasound datasets prove the effectiveness of the proposed NeighborNet.
Asunto(s)
Neoplasias de la Mama , Mama , Interpretación de Imagen Asistida por Computador , Ultrasonografía Mamaria , Humanos , Neoplasias de la Mama/diagnóstico por imagen , Femenino , Interpretación de Imagen Asistida por Computador/métodos , Mama/diagnóstico por imagen , Mama/patología , Ultrasonografía Mamaria/métodos , Algoritmos , Aprendizaje Profundo , Redes Neurales de la ComputaciónRESUMEN
Intramuscular fat (IMF) plays an important role in the tenderness, water-holding capacity, and flavor of chicken meat, which directly affect meat quality. In recent years, regulatory mechanisms underlying IMF deposition and the development of effective molecular markers have been hot topics in poultry genetic breeding. Therefore, this review focuses on the current understanding of regulatory mechanisms underlying IMF deposition in chickens, which were identified by multiple genomic approaches, including genome-wide association studies, whole transcriptome sequencing, proteome sequencing, single-cell RNA sequencing (scRNA-seq), high-throughput chromosome conformation capture (HiC), DNA methylation sequencing, and m6A methylation sequencing. This review comprehensively and systematically describes genetic and epigenetic factors associated with IMF deposition, which provides a fundamental resource for biomarkers of IMF deposition and provides promising applications for genetic improvement of meat quality in chicken.