Vitamin B2 enhances development of puberty ovaries via regulation of essential elements and plasma endocrine hormones

Purpose: To investigate the effect of vitamin B2 (VB2) on ovarian development during puberty.Methods: Four groups of domestic hens (Jinghong-1 strain, 12 hens/group) were housed under standard conditions and fed basal diet with or without graded doses of VB2 (10 – 40 mg/kg). At 10 weeks old, 9 hens were sacrificed from each group. Plasma levels of AST, ALT, steroid hormones and growth hormones were determined. In addition, some essential mineral elements in the ovarian tissue of the hens were assayed.Results: Treatment with VB2 significantly improved ovary and liver organ indices (p < 0.01), but had no deleterious effect on the liver. The different doses of VB2 exerted regulatory effects on homeostasis of essential elements in the ovary (p < 0.01). Moreover, VB2 treatment elevated plasma levels of progesterone (PR) and estrogen (ES), suggesting that it might regulate steroid hormone levels.Conclusion: These results indicate that VB2 enhances the development of the ovaries during puberty.Keywords: Domestic hen, Ovarian development, Vitamin B2, Steroid hormones, Mineral element

Calibration-based Dual Prototypical Contrastive Learning Approach for Domain Generalization Semantic Segmentation

Prototypical contrastive learning (PCL) has been widely used to learn class-wise domain-invariant features recently. These methods are based on the assumption that the prototypes, which are represented as the central value of the same class in a certain domain, are domain-invariant. Since the prototypes of different domains have discrepancies as well, the class-wise domain-invariant features learned from the source domain by PCL need to be aligned with the prototypes of other domains simultaneously. However, the prototypes of the same class in different domains may be different while the prototypes of different classes may be similar, which may affect the learning of class-wise domain-invariant features. Based on these observations, a calibration-based dual prototypical contrastive learning (CDPCL) approach is proposed to reduce the domain discrepancy between the learned class-wise features and the prototypes of different domains for domain generalization semantic segmentation. It contains an uncertainty-guided PCL (UPCL) and a hard-weighted PCL (HPCL). Since the domain discrepancies of the prototypes of different classes may be different, we propose an uncertainty probability matrix to represent the domain discrepancies of the prototypes of all the classes. The UPCL estimates the uncertainty probability matrix to calibrate the weights of the prototypes during the PCL. Moreover, considering that the prototypes of different classes may be similar in some circumstances, which means these prototypes are hard-aligned, the HPCL is proposed to generate a hard-weighted matrix to calibrate the weights of the hard-aligned prototypes during the PCL. Extensive experiments demonstrate that our approach achieves superior performance over current approaches on domain generalization semantic segmentation tasks.Comment: Accepted by ACM MM'2