N-Myc Downstream-Regulated Gene 2 (NDRG2) as a Novel Tumor Suppressor in Multiple Human Cancers

N-myc downstream-regulated gene 2 (NDRG2) was identified as a novel tumor suppressor gene in regulating the proliferation, differentiation, apoptosis and metastasis of multiple cancer types. Consistent with this finding, we and other groups observed the decreased NDRG2 expression in multiple human cancer cell lines and tumors, including breast cancer, colorectal cancer, and cervical cancer. We identified NDRG2 as a stress sensor for hypoxia, DNA damage stimuli and endoplasmic reticulum stress (ERS). Our recent data showed that NDRG2 could promote the differentiation of colorectal cancer cells. Interestingly, we found that reduced NDRG2 expression was a powerful and independent predictor of poor prognosis of colorectal cancer patients. Furthermore, NDRG2 can inhibit epithelial-mesenchymal transition (EMT) by positively regulating E-cadherin expression. Moreover, NDRG2-deficient mice show spontaneous development of various tumor types, including T-cell lymphomas, providing in vivo evidence that NDRG2 functions as a tumor suppressor gene. We believe that NDRG2 is a novel tumor suppressor and might be a therapeutic target for cancer treatment

Accurate and Fast Compressed Video Captioning

Existing video captioning approaches typically require to first sample video frames from a decoded video and then conduct a subsequent process (e.g., feature extraction and/or captioning model learning). In this pipeline, manual frame sampling may ignore key information in videos and thus degrade performance. Additionally, redundant information in the sampled frames may result in low efficiency in the inference of video captioning. Addressing this, we study video captioning from a different perspective in compressed domain, which brings multi-fold advantages over the existing pipeline: 1) Compared to raw images from the decoded video, the compressed video, consisting of I-frames, motion vectors and residuals, is highly distinguishable, which allows us to leverage the entire video for learning without manual sampling through a specialized model design; 2) The captioning model is more efficient in inference as smaller and less redundant information is processed. We propose a simple yet effective end-to-end transformer in the compressed domain for video captioning that enables learning from the compressed video for captioning. We show that even with a simple design, our method can achieve state-of-the-art performance on different benchmarks while running almost 2x faster than existing approaches. Code is available at https://github.com/acherstyx/CoCap

SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic Scenes

Self-supervised monocular depth estimation has shown impressive results in static scenes. It relies on the multi-view consistency assumption for training networks, however, that is violated in dynamic object regions and occlusions. Consequently, existing methods show poor accuracy in dynamic scenes, and the estimated depth map is blurred at object boundaries because they are usually occluded in other training views. In this paper, we propose SC-DepthV3 for addressing the challenges. Specifically, we introduce an external pretrained monocular depth estimation model for generating single-image depth prior, namely pseudo-depth, based on which we propose novel losses to boost self-supervised training. As a result, our model can predict sharp and accurate depth maps, even when training from monocular videos of highly-dynamic scenes. We demonstrate the significantly superior performance of our method over previous methods on six challenging datasets, and we provide detailed ablation studies for the proposed terms. Source code and data will be released at https://github.com/JiawangBian/sc_depth_plComment: Under Review; The code will be available at https://github.com/JiawangBian/sc_depth_p

Yeast Model Uncovers Dual Roles of Mitochondria in the Action of Artemisinin

Artemisinins, derived from the wormwood herb Artemisia annua, are the most potent antimalarial drugs currently available. Despite extensive research, the exact mode of action of artemisinins has not been established. Here we use yeast, Saccharamyces cerevisiae, to probe the core working mechanism of this class of antimalarial agents. We demonstrate that artemisinin's inhibitory effect is mediated by disrupting the normal function of mitochondria through depolarizing their membrane potential. Moreover, in a genetic study, we identify the electron transport chain as an important player in artemisinin's action: Deletion of NDE1 or NDI1, which encode mitochondrial NADH dehydrogenases, confers resistance to artemisinin, whereas overexpression of NDE1 or NDI1 dramatically increases sensitivity to artemisinin. Mutations or environmental conditions that affect electron transport also alter host's sensitivity to artemisinin. Sensitivity is partially restored when the Plasmodium falciparum NDI1 ortholog is expressed in yeast ndi1 strain. Finally, we showed that artemisinin's inhibitory effect is mediated by reactive oxygen species. Our results demonstrate that artemisinin's effect is primarily mediated through disruption of membrane potential by its interaction with the electron transport chain, resulting in dysfunctional mitochondria. We propose a dual role of mitochondria played during the action of artemisinin: the electron transport chain stimulates artemisinin's effect, most likely by activating it, and the mitochondria are subsequently damaged by the locally generated free radicals