ApoG2 induces cell cycle arrest of nasopharyngeal carcinoma cells by suppressing the c-Myc signaling pathway

<p>Abstract</p> <p>Background</p> <p>apogossypolone (ApoG2) is a novel derivate of gossypol. We previously have reported that ApoG2 is a promising compound that kills nasopharyngeal carcinoma (NPC) cells by inhibiting the antiapoptotic function of Bcl-2 proteins. However, some researchers demonstrate that the antiproliferative effect of gossypol on breast cancer cells is mediated by induction of cell cycle arrest. So this study was aimed to investigate the effect of ApoG2 on cell cycle proliferation in NPC cells.</p> <p>Results</p> <p>We found that ApoG2 significantly suppressed the expression of c-Myc in NPC cells and induced arrest at the DNA synthesis (S) phase in a large percentage of NPC cells. Immunoblot analysis showed that expression of c-Myc protein was significantly downregulated by ApoG2 and that the expression of c-Myc's downstream molecules cyclin D1 and cyclin E were inhibited whereas p21 was induced. To further identify the cause-effect relationship between the suppression of c-Myc signaling pathway and induction of cell cycle arrest, the expression of c-Myc was interfered by siRNA. The results of cell cycle analysis showed that the downregulation of c-Myc signaling pathway by siRNA interference could cause a significant arrest of NPC cell at S phase of the cell cycle. In CNE-2 xenografts, ApoG2 significantly downregulated the expression of c-Myc and suppressed tumor growth <it>in vivo</it>.</p> <p>Conclusion</p> <p>Our findings indicated that ApoG2 could potently disturb the proliferation of NPC cells by suppressing c-Myc signaling pathway. This data suggested that the inhibitory effect of ApoG2 on NPC cell cycle proliferation might contribute to its use in anticancer therapy.</p

Sketch-based Video Object Segmentation: Benchmark and Analysis

Reference-based video object segmentation is an emerging topic which aims to segment the corresponding target object in each video frame referred by a given reference, such as a language expression or a photo mask. However, language expressions can sometimes be vague in conveying an intended concept and ambiguous when similar objects in one frame are hard to distinguish by language. Meanwhile, photo masks are costly to annotate and less practical to provide in a real application. This paper introduces a new task of sketch-based video object segmentation, an associated benchmark, and a strong baseline. Our benchmark includes three datasets, Sketch-DAVIS16, Sketch-DAVIS17 and Sketch-YouTube-VOS, which exploit human-drawn sketches as an informative yet low-cost reference for video object segmentation. We take advantage of STCN, a popular baseline of semi-supervised VOS task, and evaluate what the most effective design for incorporating a sketch reference is. Experimental results show sketch is more effective yet annotation-efficient than other references, such as photo masks, language and scribble.Comment: BMVC 202

Construction and Validation of a Geometry-based Mathematical Model for Hard X-ray Imager

Quantitative and analytical analysis of modulation process of the collimator is a great challenge, and is also of great value to the design and development of Fourier transform imaging telescopes. The Hard X-ray Imager (HXI), as one of the three payloads onboard the Advanced Space-based Solar Observatory(ASO-S) mission, adopts modulating Fourier-Transformation imaging technique and will be used to explore mechanism of energy release and transmission in solar flare activities. In this paper, a mathematical model is developed to analyze the modulation function under a simplified condition first. Then its behavior under six degrees of freedom is calculated after adding the rotation matrix and translation change to the model. In addition, unparalleled light and extended sources also are considered so that our model can be used to analyze the X-ray beam experiment. Next, applied to the practical HXI conditions, the model has been confirmed not only by Geant4 simulations but also by some verification experiments. Furthermore, how this model will help to improve the image reconstruction process after the launch of ASO-S is also presented

Improving Multi-Task Generalization via Regularizing Spurious Correlation

Multi-Task Learning (MTL) is a powerful learning paradigm to improve generalization performance via knowledge sharing. However, existing studies find that MTL could sometimes hurt generalization, especially when two tasks are less correlated. One possible reason that hurts generalization is spurious correlation, i.e., some knowledge is spurious and not causally related to task labels, but the model could mistakenly utilize them and thus fail when such correlation changes. In MTL setup, there exist several unique challenges of spurious correlation. First, the risk of having non-causal knowledge is higher, as the shared MTL model needs to encode all knowledge from different tasks, and causal knowledge for one task could be potentially spurious to the other. Second, the confounder between task labels brings in a different type of spurious correlation to MTL. We theoretically prove that MTL is more prone to taking non-causal knowledge from other tasks than single-task learning, and thus generalize worse. To solve this problem, we propose Multi-Task Causal Representation Learning framework, aiming to represent multi-task knowledge via disentangled neural modules, and learn which module is causally related to each task via MTL-specific invariant regularization. Experiments show that it could enhance MTL model's performance by 5.5% on average over Multi-MNIST, MovieLens, Taskonomy, CityScape, and NYUv2, via alleviating spurious correlation problem.Comment: Published on NeurIPS 202