The role of HIF1alpha and HIF2alpha in muscle development and satellite cell function

Hypoxia inducible factors (HIFs) are central mediators of cellular responses to fluctuations of oxygen, an environmental regulator of stem cell activity. Muscle satellite cells are myogenic stem cells whose quiescence, activation, self-renewal and differentiation are influenced by microenvironment oxygen levels. However, the in vivo roles of HIFs in quiescent satellite cells and activated satellite cells (myoblasts) are poorly understood. Expression analyses indicate that HIF1α and HIF2α are preferentially expressed in pre- and post-differentiation myoblasts, respectively. Interestingly, double knockout of HIF1α and HIF2α (HIF1α/2α dKO) in embryonic myoblasts results in apparently normal muscle development and growth. However, HIF1α/2α dKO in postnatal satellite cells impairs injury-induced muscle repair, accompanied by a reduced number of myoblasts during regeneration. Analysis of satellite cell dynamics on myofibers confirms that HIF1α/2α dKO myoblasts exhibit reduced self-renewal but more pronounced myogenic differentiation under hypoxia conditions. Mechanistically, HIF1α/2α dKO blocks hypoxia-induced activation of Notch signaling, a key determinant of satellite cell self-renewal. Constitutive activation of Notch signaling can rescue HIF1α/2α dKO induced inhibition of satellite cell self-renewal. Together, HIF1α and HIF2α are dispensable for muscle stem cell function under normoxia, but are required for maintaining satellite cell self-renewal under hypoxic environment

A Critical Look at the Current Usage of Foundation Model for Dense Recognition Task

In recent years large model trained on huge amount of cross-modality data, which is usually be termed as foundation model, achieves conspicuous accomplishment in many fields, such as image recognition and generation. Though achieving great success in their original application case, it is still unclear whether those foundation models can be applied to other different downstream tasks. In this paper, we conduct a short survey on the current methods for discriminative dense recognition tasks, which are built on the pretrained foundation model. And we also provide some preliminary experimental analysis of an existing open-vocabulary segmentation method based on Stable Diffusion, which indicates the current way of deploying diffusion model for segmentation is not optimal. This aims to provide insights for future research on adopting foundation model for downstream task.Comment: This is a short report on the current usage of foundation model (mainly pretrained diffusion model) for downstream dense recognition task (e.g., open vocabulary segmentation). We hope this short report could give an insight to the future researc