CircMAN1A2 promotes vasculogenic mimicry of nasopharyngeal carcinoma cells through upregulating ERBB2 via sponging miR-940

Nasopharyngeal carcinoma(NPC)is the most prevalent human primary malignancy of the head and neck,and the presence of vasculogenic mimicry(VM)renders anti-angiogenic therapy ineffective and poorly prognostic.However,the underlying mechanisms are unclear.In the present study,we used miR-940 silencing and overexpression for in vitro NPC cell EdU staining,wound healing assay and 3D cell culture assay,and in vivo xenograft mouse model and VM formation to assess miR-940 function.We found that ectopic miR-940 expression reduced NPC cell proliferation,migration and VM,as well as tumorigenesis in vivo.By bioinformatic analysis,circMAN1A2 was identified as a circRNA that binds to miR-940.Mechanistically,we confirmed that circMAN1A2 acts as a sponge for miR-940,impairs the inhibitory effect of miR-940 on target ERBB2,and then activates the PI3K/AKT/mTOR signaling pathway using RNA-FISH,dual luciferase reporter gene and rescue analysis assays.In addition,upregulation of ERBB2 expression is associated with clinical staging and poor prognosis of NPC.Taken together,the present findings suggest that circMAN1A2 promotes VM formation and progression of NPC through miR-940/ERBB2 axis and further activates the PI3K/AKT/mTOR pathway.Therefore,circMAN1A2 may become a biomarker and therapeutic target for anti-angiogenic therapy in patients with nasopharyngeal carcinoma.

Visual exploration of latent space for traditional Chinese music

Generating compact and effective numerical representations of data is a fundamental step for many machine learning tasks.Traditionally,handcrafted features are used but as deep learning starts to show its potential,using deep learning models to extract compact representations becomes a new trend.Among them,adopting vectors from the model’s latent space is the most popular.There are several studies focused on visual analysis of latent space in NLP and computer vision.However,relatively little work has been done for music information retrieval(MIR)especially incorporating visualization.To bridge this gap,we propose a visual analysis system utilizing Autoencoders to facilitate analysis and exploration of traditional Chinese music.Due to the lack of proper traditional Chinese music data,we construct a labeled dataset from a collection of pre-recorded audios and then convert them into spectrograms.Our system takes music features learned from two deep learning models(a fully-connected Autoencoder and a Long Short-Term Memory(LSTM)Autoencoder)as input.Through interactive selection,similarity calculation,clustering and listening,we show that the latent representations of the encoded data allow our system to identify essential music elements,which lay the foundation for further analysis and retrieval of Chinese music in the future.