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Osteosarcoma (OS) is a primary malignant bone tumour. However, the genetic basis for the pathogenesis of OS remains elusive. In this study, we uncovered the role of the histone methyltransferase NSD2 in regulating tumourigenesis and chemosensitivity in OS. We show that NSD2 knockdown leads to increased apoptosis in OS cells in vitro and in vivo. Additionally, NSD2 knockdown significantly enhances the efficacy of cisplatin against OS cells and accordingly inhibits properties associated with cancer stem cells (CSCs). Furthermore, RNA sequencing (RNAseq) and Gene Ontology (GO) analysis revealed that NSD2 promotes transcription of genes associated with negative regulation of apoptotic signalling pathways and CSC properties. The results of chromatin immunoprecipitation quantitative polymerase chain reaction (ChIP-qPCR) assays indicated that NSD2 knockdown leads to decreased H3K36me2 modification at BCL2 and SOX2 loci, thus inhibiting the transcription of these two genes that are closely correlated with apoptosis, CSC properties and chemosensitivity in OS cells. Pathway analysis demonstrated that the ERK and AKT pathways mediate the regulation of OS progression and chemosensitivity by NSD2. Overall, our study is the first to uncover the function of NSD2 in OS chemosensitivity. NSD2 regulates the expression of the apoptosis regulatory proteins BCL2 and SOX2 through the ERK and AKT pathways. Our results suggest that NSD2 is a new target for combined chemotherapy and is a prognostic factor for OS.

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In a recent paper, Chang and Le proposed an efficient smart card–based authenticated key exchange protocol (which is referred to as CL scheme) for heterogeneous ad hoc wireless sensor networks. However, we found that the CL scheme is subject to sensor capture attack which breaks the session key security of the CL scheme. An improved protocol is proposed to fix this problem.

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How to acquire landslide disaster information quickly and accurately has become the focus and difficulty of disaster prevention and relief by remote sensing. Landslide disasters are generally featured by sudden occurrence, proposing high demand for emergency data acquisition. The low-altitude Unmanned Aerial Vehicle (UAV) remote sensing technology is widely applied to acquire landslide disaster data, due to its convenience, high efficiency, and ability to fly at low altitude under cloud. However, the spectrum information of UAV images is generally deficient and manual interpretation is difficult for meeting the need of quick acquisition of emergency data. Based on this, UAV images of high-occurrence areas of landslide disaster in Wenchuan County and Baoxing County in Sichuan Province, China were selected for research in the paper. Firstly, the acquired UAV images were pre-processed to generate orthoimages. Subsequently, multi-resolution segmentation was carried out to obtain image objects, and the barycenter of each object was calculated to generate a landslide sample database (including positive and negative samples) for deep learning. Next, four landslide feature models of deep learning and transfer learning, namely Histograms of Oriented Gradients (HOG), Bag of Visual Word (BOVW), Convolutional Neural Network (CNN), and Transfer Learning (TL) were compared, and it was found that the TL model possesses the best feature extraction effect, so a landslide extraction method based on the TL model and object-oriented image analysis (TLOEL) was proposed; finally, the TLOEL method was compared with the object-oriented nearest neighbor classification (NNC) method. The research results show that the accuracy of the TLOEL method is higher than the NNC method, which can not only achieve the edge extraction of large landslides, but also detect and extract middle and small landslides accurately that are scatteredly distributed.