Defect of Neurofibromin I (NF1) in Muscle Progenitors Induces Premature Quiescence and Metabolic Myopathy

The ultimate purpose of this project was to elucidate the function and underlying mechanism of neurofibromin I during postnatal skeletal muscle development. Neurofibromin I is a tumor suppressor protein that works as a GTPase activator (GAP) through negative regulate the canonical Ras-MAPK-ERK signaling. Mutation of the Nf1 gene leads to a reduction of neurofibromin I, which causes an autochrosomal dominated genetic disorder named neurofibromatosis type I (NF1). Clinical features have been reported in NF1 patients, including pathological changes of the musculoskeletal system, particularly reduction of muscle mass and muscle strength. In this project, it is the first time that a muscle-specific Nf1 knock out mouse model was used for functional analysis of Nf1 during postnatal muscle development. Nf1Myf5 mice showed a gradual loss of muscle weight, which recapitulates the patient phenotype. Nf1 deletion in muscle progenitors leads to muscle stem cell pool depletion. Nf1/ERK/NO/Delta-Notch signaling is involved in the regulation of this phenotype. The results suggest that loss of Nf1 in muscle progenitors results in the hyper activation of Ras/ERK signaling, and ERK signaling via Nitric Oxide leads to a stronger stimulation of the Delta-Notch pathway. During early postnatal muscle development, activation of the Notch pathway drives progenitors out of cell cycle and go gradually to quiescent. For Nf1Myf5 muscle progenitors stronger Delta-Notch pathway contribute to the earlier transition of this process and satellite cell pool depletion. The quiescent signature of Nf1 deleted progenitors was identified from transcription of quiescent genes, reduction of glycolysis activity and also epigenetic change. Nf1 knockout mice showed a metabolism defect of glucose glycolysis and sever energy deficiency. As compensatory, energy sensor AMPK stimulated fatty acid oxidative phorsphorlyation which might through Pparg/Pgc1a signaling, accompanied by suppressed protein synthesis rate and increased the degradation process. In addition, a fiber type transition from fast to intermediate slow fibers was also detected. As Nf1 functions specifically before myoblasts differentiation. Thus the metabolic myopathy caused by Nf1 deletion should be generated from muscle progenitors. One possible explanation might be Nf1 deletion caused the epigenetic modification status change, in a certain way the change can be remembered by the nucleus of satellite cells even after they differentiated into mature muscle fibers. The reduction of glycolysis genes expression in both progenitors and mature muscle fibers is a good indicator. However, a further intensive study still needs to be performed to find the potential target mechanism

Wafer Map Defect Patterns Semi-Supervised Classification Using Latent Vector Representation

As the globalization of semiconductor design and manufacturing processes continues, the demand for defect detection during integrated circuit fabrication stages is becoming increasingly critical, playing a significant role in enhancing the yield of semiconductor products. Traditional wafer map defect pattern detection methods involve manual inspection using electron microscopes to collect sample images, which are then assessed by experts for defects. This approach is labor-intensive and inefficient. Consequently, there is a pressing need to develop a model capable of automatically detecting defects as an alternative to manual operations. In this paper, we propose a method that initially employs a pre-trained VAE model to obtain the fault distribution information of the wafer map. This information serves as guidance, combined with the original image set for semi-supervised model training. During the semi-supervised training, we utilize a teacher-student network for iterative learning. The model presented in this paper is validated on the benchmark dataset WM-811K wafer dataset. The experimental results demonstrate superior classification accuracy and detection performance compared to state-of-the-art models, fulfilling the requirements for industrial applications. Compared to the original architecture, we have achieved significant performance improvement.Comment: 6 pages, 2 figures, CIS confernec