No abstract

We introduce Similarity Group Proposal Network (SGPN), a simple and intuitive deep learning framework for 3D object instance segmentation on point clouds. SGPN uses a single network to predict point grouping proposals and a corresponding semantic class for each proposal, from which we can directly extract instance segmentation results. Important to the effectiveness of SGPN is its novel representation of 3D instance segmentation results in the form of a similarity matrix that indicates the similarity between each pair of points in embedded feature space, thus producing an accurate grouping proposal for each point. Experimental results on various 3D scenes show the effectiveness of our method on 3D instance segmentation, and we also evaluate the capability of SGPN to improve 3D object detection and semantic segmentation results. We also demonstrate its flexibility by seamlessly incorporating 2D CNN features into the framework to boost performance.

The V82F/I84V double mutation is considered as the key residue mutation of the HIV-1 protease drug resistance because it can significantly lower the binding affinity of protease inhibitors in clinical uses. In the current work, the binding of amprenavir to both of the wild-type and the drug-resistant V82F/I84V mutant of the HIV-1 protease was investigated by molecular dynamics (MD) simulations and was compared to those of two inhibitors in development, TMC126 and TMC114. Absolute binding free energies were calculated by molecular mechanics/Poisson-Boltzmann surface area (MM/ PBSA) methodology. The predicted binding affinities give a good explanation of structure-affinity relationship (SAR) of three studied inhibitors. Furthermore, in the 18 ns MD simulations on the free wild-type and the mutated proteases, we observed that the free mutated protease shows similar dynamic characteristics of the flap opening and a little higher structural stability than the free wild-type protease. This suggests that the effect of the mutations may not significantly affect the equilibrium between the semiopen and the closed conformations. Finally, decomposition analysis of binding free energies and the further structural analysis indicate that the dominating effect of the V82F/I84V double mutation is to distort the geometry of the binding site and hence weaken the interactions of inhibitors preshaped to the wild-type binding site.

The low rate of approval of novel anti-cancer agents underscores the need for better preclinical models of therapeutic response as neither xenografts nor early-generation genetically engineered mouse models (GEMMs) reliably predict human clinical outcomes. Whereas recent, sporadic GEMMs emulate many aspects of their human disease counterpart more closely, their ability to predict clinical therapeutic responses has never been tested systematically. We evaluated the utility of two state-of-the-art, mutant Kras-driven GEMMs--one of non-small-cell lung carcinoma and another of pancreatic adenocarcinoma--by assessing responses to existing standard-of-care chemotherapeutics, and subsequently in combination with EGFR and VEGF inhibitors. Standard clinical endpoints were modeled to evaluate efficacy, including overall survival and progression-free survival using noninvasive imaging modalities. Comparisons with corresponding clinical trials indicate that these GEMMs model human responses well, and lay the foundation for the use of validated GEMMs in predicting outcome and interrogating mechanisms of therapeutic response and resistance.

A series of compounds were designed and synthesized as antagonists of cIAP1/2, ML-IAP, and XIAP based on the N-terminus, AVPI, of mature Smac. Compound 1 (GDC-0152) has the best profile of these compounds; it binds to the XIAP BIR3 domain, the BIR domain of ML-IAP, and the BIR3 domains of cIAP1 and cIAP2 with Ki values of 28, 14, 17 and 43 nM, respectively. These compounds promote degradation of cIAP1, induce activation of caspase-3/7, and lead to decreased viability of breast cancer cells without affecting normal mammary epithelial cells. Compound 1 inhibits tumor growth when dosed orally in the MDA-MB-231 breast cancer xenograft model. Compound 1 was advanced to human clinical trials and it exhibited linear pharmacokinetics over the dose range (0.049 to 1.48 mg/kg) tested. Mean plasma clearance in humans was 9 ± 3 mL/min/kg and volume of distribution was 0.6 ± 0.2 L/kg.