DSM-Net: Disentangled Structured Mesh Net for Controllable Generation of Fine Geometry

3D shape generation is a fundamental operation in computer graphics. While significant progress has been made, especially with recent deep generative models, it remains a challenge to synthesize high-quality geometric shapes with rich detail and complex structure, in a controllable manner. To tackle this, we introduce DSM-Net, a deep neural network that learns a disentangled structured mesh representation for 3D shapes, where two key aspects of shapes, geometry and structure, are encoded in a synergistic manner to ensure plausibility of the generated shapes, while also being disentangled as much as possible. This supports a range of novel shape generation applications with intuitive control, such as interpolation of structure (geometry) while keeping geometry (structure) unchanged. To achieve this, we simultaneously learn structure and geometry through variational autoencoders (VAEs) in a hierarchical manner for both, with bijective mappings at each level. In this manner we effectively encode geometry and structure in separate latent spaces, while ensuring their compatibility: the structure is used to guide the geometry and vice versa. At the leaf level, the part geometry is represented using a conditional part VAE, to encode high-quality geometric details, guided by the structure context as the condition. Our method not only supports controllable generation applications, but also produces high-quality synthesized shapes, outperforming state-of-the-art methods

DSG-Net: Learning disentangled structure and geometry for 3D shape generation

3D shape generation is a fundamental operation in computer graphics. While significant progress has been made, especially with recent deep generative models, it remains a challenge to synthesize high-quality shapes with rich geometric details and complex structures, in a controllable manner. To tackle this, we introduce DSG-Net, a deep neural network that learns a disentangled structured & geometric mesh representation for 3D shapes, where two key aspects of shapes, geometry and structure, are encoded in a synergistic manner to ensure plausibility of the generated shapes, while also being disentangled as much as possible. This supports a range of novel shape generation applications with disentangled control, such as interpolation of structure (geometry) while keeping geometry (structure) unchanged. To achieve this, we simultaneously learn structure and geometry through variational autoencoders (VAEs) in a hierarchical manner for both, with bijective mappings at each level. In this manner, we effectively encode geometry and structure in separate latent spaces, while ensuring their compatibility: the structure is used to guide the geometry and vice versa. At the leaf level, the part geometry is represented using a conditional part VAE, to encode high-quality geometric details, guided by the structure context as the condition. Our method not only supports controllable generation applications, but also produces high-quality synthesized shapes, outperforming state-of-the-art methods

Haisor: Human-aware indoor scene optimization via deep reinforcement learning

3D scene synthesis facilitates and benefits many real-world applications. Most scene generators focus on making indoor scenes plausible via learning from training data and leveraging extra constraints such as adjacency and symmetry. Although the generated 3D scenes are mostly plausible with visually realistic layouts, they can be functionally unsuitable for human users to navigate and interact with furniture. Our key observation is that human activity plays a critical role and sufficient free space is essential for human-scene interactions. This is exactly where many existing synthesized scenes fail—the seemingly correct layouts are often not fit for living. To tackle this, we present a human-aware optimization framework Haisor for 3D indoor scene arrangement via reinforcement learning, which aims to find an action sequence to optimize the indoor scene layout automatically. Based on the hierarchical scene graph representation, an optimal action sequence is predicted and performed via Deep Q-Learning with Monte Carlo Tree Search (MCTS), where MCTS is our key feature to search for the optimal solution in long-term sequences and large action space. Multiple human-aware rewards are designed as our core criteria of human-scene interaction, aiming to identify the next smart action by leveraging powerful reinforcement learning. Our framework is optimized end-to-end by giving the indoor scenes with part-level furniture layout including part mobility information. Furthermore, our methodology is extensible and allows utilizing different reward designs to achieve personalized indoor scene synthesis. Extensive experiments demonstrate that our approach optimizes the layout of 3D indoor scenes in a human-aware manner, which is more realistic and plausible than original state-of-the-art generator results, and our approach produces superior smart actions, outperforming alternative baselines

IκBα polymorphism at promoter region (rs2233408) influences the susceptibility of gastric cancer in Chinese

<p>Abstract</p> <p>Background</p> <p>Nuclear factor of kappa B inhibitor alpha (IκBα) protein is implicated in regulating a variety of cellular process from inflammation to tumorigenesis. The objective of this study was to investigate the susceptibility of rs2233408 T/C genotype in the promoter region of <it>IκBα </it>to gastric cancer and the association of this polymorphism with clinicopathologic variables in gastric cancer patients.</p> <p>Methods</p> <p>A population-based case-control study was conducted between 1999 and 2006 in Guangdong Province, China. A total of 564 gastric cancer patients and 566 healthy controls were enrolled in this study. rs2233408 genotypes in <it>IκBα </it>were analyzed by TaqMan SNP genotyping assay.</p> <p>Results</p> <p>Both rs2233408 T homozygote (TT) and T heterozygotes (TC and TT) had significantly reduced gastric cancer risk (TT: OR = 0.250, 95% CI = 0.069-0.909, <it>P </it>= 0.035; TC and TT: OR = 0.721, 95% CI = 0.530-0.981, <it>P </it>= 0.037), compared with rs2233408 C homozygote (CC). rs2233408 T heterozygotes were significantly associated with reduced risk of intestinal-type gastric cancer with ORs of 0.648 (95% CI = 0.459-0.916, <it>P </it>= 0.014), but not with the diffuse or mix type of gastric cancer. The association between rs2233408 T heterozygotes and gastric cancer appeared more apparent in the older patients (age>40) (OR = 0.674, 95% CI = 0.484-0.939, <it>P </it>= 0.02). rs2233408 T heterozygotes was associated with non-cardiac gastric cancer (OR = 0.594, 95% CI = 0.411-0.859, <it>P </it>= 0.006), but not with cardiac gastric cancer. However, rs2233408 polymorphism was not associated with the prognosis of gastric cancer patients.</p> <p>Conclusions</p> <p><it>IκBα </it>rs2233408 T heterozygotes were associated with reduced risk of gastric cancer, especially for the development of certain subtypes of gastric cancer in Chinese population.</p

Activating Transcription Factor 4 Confers a Multidrug Resistance Phenotype to Gastric Cancer Cells through Transactivation of SIRT1 Expression

BACKGROUND: Multidrug resistance (MDR) in gastric cancer remains a major challenge to clinical treatment. Activating transcription factor 4 (ATF4) is a stress response gene involved in homeostasis and cellular protection. However, the expression and function of ATF4 in gastric cancer MDR remains unknown. In this study, we investigate whether ATF4 play a role in gastric cancer MDR and its potential mechanisms. METHODOLOGY/PRINCIPAL FINDINGS: We demonstrated that ATF4 overexpression confered the MDR phenotype to gastric cancer cells, while knockdown of ATF4 in the MDR variants induced re-sensitization. In this study we also showed that the NAD(+)-dependent histone deacetylase SIRT1 was required for ATF4-induced MDR effect in gastric cancer cells. We demonstrated that ATF4 facilitated MDR in gastric cancer cells through direct binding to the SIRT1 promoter, resulting in SIRT1 up-regulation. Significantly, inhibition of SIRT1 by small interfering RNA (siRNA) or a specific inhibitor (EX-527) reintroduced therapeutic sensitivity. Also, an increased Bcl-2/Bax ratio and MDR1 expression level were found in ATF4-overexpressing cells. CONCLUSIONS/SIGNIFICANCE: We showed that ATF4 had a key role in the regulation of MDR in gastric cancer cells in response to chemotherapy and these findings suggest that targeting ATF4 could relieve therapeutic resistance in gastric cancer

MiR-218 Inhibits Invasion and Metastasis of Gastric Cancer by Targeting the Robo1 Receptor

MicroRNAs play key roles in tumor metastasis. Here, we describe the regulation and function of miR-218 in gastric cancer (GC) metastasis. miR-218 expression is decreased along with the expression of one of its host genes, Slit3 in metastatic GC. However, Robo1, one of several Slit receptors, is negatively regulated by miR-218, thus establishing a negative feedback loop. Decreased miR-218 levels eliminate Robo1 repression, which activates the Slit-Robo1 pathway through the interaction between Robo1 and Slit2, thus triggering tumor metastasis. The restoration of miR-218 suppresses Robo1 expression and inhibits tumor cell invasion and metastasis in vitro and in vivo. Taken together, our results describe a Slit-miR-218-Robo1 regulatory circuit whose disruption may contribute to GC metastasis. Targeting miR-218 may provide a strategy for blocking tumor metastasis

Performance of Toluene Removal in a Nonthermal Plasma Catalysis System over Flake-Like HZSM-5 Zeolite with Tunable Pore Size and Evaluation of Its Byproducts

In this study, a series of HZSM-5 catalysts were prepared by the chemical liquid-phase deposition method, and low concentration toluene degradation was carried out in an atmospheric pressure dielectric barrier discharge (DBD) reactor. The catalysts were characterized by X-ray powder diffraction (XRD), SEM, TEM, and N2 adsorption analysis techniques. In addition, several organic contaminants were used to evaluate the adsorption performance of the prepared catalysts, and the effect of pore size on the removal efficiency of toluene and byproduct formation was also investigated. The unmodified HZSM-5 zeolite (Z0) exhibited good performance in toluene removal and CO2 selectivity due to the diffusion resistance of ozone and the amounts of active species (OH• and O•). Meanwhile, the time of flight mass spectrometry (TOF-MS) result showed that there were more byproducts of the benzene ring in the gas phase under the action of small micropore size catalysts. Moreover, the surface byproducts were detected by gas chromatography–mass spectrometry (GC-MS)

Efficacy of Scutellaria baicalensis for the Treatment of Hand, Foot, and Mouth Disease Associated with Encephalitis in Patients Infected with EV71: A Multicenter, Retrospective Analysis

This study aimed to evaluate the clinical efficacy and safety of using the traditional Chinese herbal medicine Scutellaria baicalensis for the treatment of severe HFMD in 725 patients aged >1 year in a multicenter, retrospective analysis. The patients were divided into the S. baicalensis and ribavirin groups, and the temperatures, presence or absence of skin rashes and oral lesions, nervous system (NS) involvement, and viral loads of the patients, as well as the safety of the treatments, were evaluated. The median duration of fever, median time to NS involvement, and the number of patients with oral ulcers and/or vesicles, as well as skin rashes, were decreased in the S. baicalensis group compared with the ribavirin group. In addition, the EV71 viral loads were decreased in the S. baicalensis group, suggesting that S. baicalensis exerted more potent antiviral effects compared with ribavirin. The present study demonstrated that S. baicalensis was suitable for the treatment of severe HFMD in patients aged >1 year, since it was shown to rapidly relieve fever, attenuate oral lesions and rashes, and improve NS involvement. Furthermore, it was demonstrated to be relatively safe for topical application