Dalian University of Technology

About: Dalian University of Technology is a education organization based out in Dalian, China. It is known for research contribution in the topics: Catalysis & Finite element method. The organization has 60890 authors who have published 71921 publications receiving 1188356 citations. The organization is also known as: Dàlián Lǐgōng Dàxué.

Unimolecular Photodynamic O2-Economizer To Overcome Hypoxia Resistance in Phototherapeutics

Abstract: Tumor hypoxia has proven to be the major bottleneck of photodynamic therapy (PDT) to clinical transformation. Different from traditional O2 delivery approaches, here we describe an innovative binary photodynamic O2-economizer (PDOE) tactic to reverse hypoxia-driven resistance by designing a superoxide radical (O2•-) generator targeting mitochondria respiration, termed SORgenTAM. This PDOE system is able to block intracellular O2 consumption and down-regulate HIF-1α expression, which successfully rescues cancer cells from becoming hypoxic and relieves the intrinsic hypoxia burden of tumors in vivo, thereby sparing sufficient endogenous O2 for the PDT process. Photosensitization mechanism studies demonstrate that SORgenTAM has an ideal intersystem crossing rate and triplet excited state lifetime for generating O2•- through type-I photochemistry, and the generated O2•- can further trigger a biocascade to reduce the PDT's demand for O2 in an O2-recycble manner. Furthermore, SORgenTAM also serves to activate the AMPK metabolism signaling pathway to inhibit cell repair and promote cell death. Consequently, using this two-step O2-economical strategy, under relatively low light dose irradiation, excellent therapeutic responses toward hypoxic tumors are achieved. This study offers a conceptual while practical paradigm for overcoming the pitfalls of phototherapeutics.

Accurate Monocular 3D Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving

Abstract: In this paper, we propose a monocular 3D object detection framework in the domain of autonomous driving. Unlike previous image-based methods which focus on RGB feature extracted from 2D images, our method solves this problem in the reconstructed 3D space in order to exploit 3D contexts explicitly. To this end, we first leverage a stand-alone module to transform the input data from 2D image plane to 3D point clouds space for a better input representation, then we perform the 3D detection using PointNet backbone net to obtain objects’ 3D locations, dimensions and orientations. To enhance the discriminative capability of point clouds, we propose a multi-modal feature fusion module to embed the complementary RGB cue into the generated point clouds representation. We argue that it is more effective to infer the 3D bounding boxes from the generated 3D scene space (i.e., X,Y, Z space) compared to the image plane (i.e., R,G,B image plane). Evaluation on the challenging KITTI dataset shows that our approach boosts the performance of state-of-the-art monocular approach by a large margin.

Highly Efficient Oxidation of Water by a Molecular Catalyst Immobilized on Carbon Nanotubes

Abstract: A successful team: A molecular device based on multiwalled carbon nanotubes functionalized by a mononuclear ruthenium catalyst has been shown to split water electrochemically (see picture; ITO=indium tin oxide). The readily prepared electrode showed excellent electrocatalytic activity for the oxidation of water, a high current density, and a low overpotential, and constitutes one step forward in the design of artificial photosynthetic systems.

Prediction and Validation of Disease Genes Using HeteSim Scores

Abstract: Deciphering the gene disease association is an important goal in biomedical research. In this paper, we use a novel relevance measure, called HeteSim, to prioritize candidate disease genes. Two methods based on heterogeneous networks constructed using protein-protein interaction, gene-phenotype associations, and phenotype-phenotype similarity, are presented. In HeteSim_MultiPath (HSMP), HeteSim scores of different paths are combined with a constant that dampens the contributions of longer paths. In HeteSim_SVM (HSSVM), HeteSim scores are combined with a machine learning method. The 3-fold experiments show that our non-machine learning method HSMP performs better than the existing non-machine learning methods, our machine learning method HSSVM obtains similar accuracy with the best existing machine learning method CATAPULT. From the analysis of the top 10 predicted genes for different diseases, we found that HSSVM avoid the disadvantage of the existing machine learning based methods, which always predict similar genes for different diseases. The data sets and Matlab code for the two methods are freely available for download at http://lab.malab.cn/data/HeteSim/index.jsp .