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Additional file 1: Figure S1. AHSA1 induces proteasome inhibitor resistance in vitro and in vivo. (A) Effects of Bortezomib on cell apoptosis in H929 cells with or without overexpression of AHSA1. (B) The analysis of bortezomib-induced apoptosis. (C) Effects of Bufalin (60nM) on the cell viability in flow MRD-positive peripheral cells from first diagnosed and relapsed MM patients. (D) Photographic images of ARP1 AHSA1 WT/OE xenografts taken from NOD-SCID mice treated with vehicle, BTZ, or ADR. (E) Mean tumor weight in the six experimental groups at day 32 after implantation of MM cells. (F) Western blot analysis of AHSA1 expression of the tumors in the experimental groups. (G) Images of representative HE staining of heart, liver, spleen, lung and kidney from control and 5TMM mouse model with or without Bufalin or KU-177 treatment.

Additional file 2. Materials and Methods.

Additional file 1 Figure S1. Elevation of CHEK1 expression is significantly associated with poor outcome in both event free survival (A) and overall survival (B) in GEP database of GMMG-HD4 cohort. Figure S2. Illustration of the annotated genomic region of CHEK1, the putative different RNA splicing forms, and the validation strategy for circular exon 1 to 7 (circCHEK1).

Generative Adversarial Networks (GANs) have the capability of synthesizing images, which have been successfully applied to medical image synthesis tasks. However, most of existing methods merely consider the global contextual information and ignore the fine foreground structures, e.g., vessel, skeleton, which may contain diagnostic indicators for medical image analysis. Inspired by human painting procedure, which is composed of stroking and color rendering steps, we propose a Sketching-rendering Unconditional Generative Adversarial Network (SkrGAN) to introduce a sketch prior constraint to guide the medical image generation. In our SkrGAN, a sketch guidance module is utilized to generate a high quality structural sketch from random noise, then a color render mapping is used to embed the sketch-based representations and resemble the background appearances. Experimental results show that the proposed SkrGAN achieves the state-of-the-art results in synthesizing images for various image modalities, including retinal color fundus, X-Ray, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). In addition, we also show that the performances of medical image segmentation method have been improved by using our synthesized images as data augmentation., Comment: Accepted to MICCAI 2019

Nowadays, deep learning has been widely used. In natural language learning, the analysis of complex semantics has been achieved because of its high degree of flexibility. The deceptive opinions detection is an important application area in deep learning model, and related mechanisms have been given attention and researched. On-line opinions are quite short, varied types and content. In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions, and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis. The detection mechanism based on deep learning has better self-adaptability and can effectively identify all kinds of deceptive opinions. In this paper, we optimize the convolution neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolution neural network more suitable for various text classification and deceptive opinions detection. The TensorFlow-based experiments demonstrate that the detection mechanism proposed in this paper achieve more accurate deceptive opinion detection results.