miRNA-128-3p inhibits malignant behavior of glioma cells by downregulating KLHDC8A expression / 南方医科大学学报

OBJECTIVE@#To determine whether miRNA-128-3p regulates malignant biological behavior of glioma cells by targeting KLHDC8A.@*METHODS@#Dual-luciferase reporter assays, qRT-PCR and Western blotting were used to verify the targeting of miRNA-128-3p to KLHDC8A. Edu assay, flow cytometry, Transwell assay and would healing assay were used to determine the effects of changes in miRNA-128-3p and KLHDC8A expression levels on malignant behavior of glioma cells. Rescue experiment was carried out to verify that miRNA-128-3p regulated glioma cell proliferation, apoptosis, invasion and migration by targeting KLHDC8A.@*RESULTS@#The expression level of KLHDC8A was significantly increased in high-grade glioma tissue and was closely related to a poor survival outcome of the patients. Overexpression of KLHDC8A promoted glioma cell proliferation, migration and invasion, and miRNA-128-3p overexpression inhibited proliferative and metastatic capacities of glioma cells. Mechanistically, KLHDC8A expression was directly modulated by miRNA-128-3p, which, by targeting KLHDC8A, inhibited malignant behavior of glioma cells.@*CONCLUSION@#Upregulation of miRNA-128-3p inhibits uncontrolled growth of glioma cells by negatively regulating KLHDC8A expression and its downstream effectors, suggesting that the miRNA-128-3p-KLHDC8A axis may serve as a potential prognostic indicator and a therapeutic target for developing new strategies for glioma treatment.

Person Re-Identification by Dual-Regularized KISS Metric Learning.

Person re-identification aims to match the images of pedestrians across different camera views from different locations. This is a challenging intelligent video surveillance problem that remains an active area of research due to the need for performance improvement. Person re-identification involves two main steps: feature representation and metric learning. Although the keep it simple and straightforward (KISS) metric learning method for discriminative distance metric learning has been shown to be effective for the person re-identification, the estimation of the inverse of a covariance matrix is unstable and indeed may not exist when the training set is small, resulting in poor performance. Here, we present dual-regularized KISS (DR-KISS) metric learning. By regularizing the two covariance matrices, DR-KISS improves on KISS by reducing overestimation of large eigenvalues of the two estimated covariance matrices and, in doing so, guarantees that the covariance matrix is irreversible. Furthermore, we provide theoretical analyses for supporting the motivations. Specifically, we first prove why the regularization is necessary. Then, we prove that the proposed method is robust for generalization. We conduct extensive experiments on three challenging person re-identification datasets, VIPeR, GRID, and CUHK 01, and show that DR-KISS achieves new state-of-the-art performance.

An improved software design of ultrasound bone densitometer / 生物医学工程学杂志

In order to meet the requirements of ultrasound bone density measurement, we proposed a software solution to improve the accuracy and speed of measurement of bone mineral density of the ultrasound bone densitometer. We used a high-speed USB interface chip FT232H, along with a high-speed AD converter chip to calculate speed of sound (SOS), broadband ultrasound attenuation (BUA ) and other bone density parameters in the PC software. This solution improved the accuracy of the measurement data, reduced the measurement time and increased the quality of the displayed image. It is well concluded that the new software can greatly improve the accuracy and transmission speed of bone density measurement data through a high-speed USB interface and a software data processing technology.