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Although antibodies targeting specific tumor-expressed antigens are the standard of care for some cancers, the identification of cancer-specific targets amenable to antibody binding has remained a bottleneck in development of new therapeutics. To overcome this challenge, we developed a high-throughput platform that allows for the unbiased, simultaneous discovery of antibodies and targets based on phenotypic binding profiles. Applying this platform to ovarian cancer, we identified a wide diversity of cancer targets including receptor tyrosine kinases, adhesion and migration proteins, proteases and proteins regulating angiogenesis in a single round of screening using genomics, flow cytometry, and mass spectrometry. In particular, we identified BCAM as a promising candidate for targeted therapy in high-grade serous ovarian cancers. More generally, this approach provides a rapid and flexible framework to identify cancer targets and antibodies. [ABSTRACT FROM AUTHOR]

3D neural networks are widely used in real-world applications (e.g., AR/VR headsets, self-driving cars). They are required to be fast and accurate; however, limited hardware resources on edge devices make these requirements rather challenging. Previous work processes 3D data using either voxel-based or point-based neural networks, but both types of 3D models are not hardware-efficient due to the large memory footprint and random memory access. In this paper, we study 3D deep learning from the efficiency perspective. We first systematically analyze the bottlenecks of previous 3D methods. We then combine the best from point-based and voxel-based models together and propose a novel hardware-efficient 3D primitive, Point-Voxel Convolution (PVConv). We further enhance this primitive with the sparse convolution to make it more effective in processing large (outdoor) scenes. Based on our designed 3D primitive, we introduce 3D Neural Architecture Search (3D-NAS) to explore the best 3D network architecture given a resource constraint. We evaluate our proposed method on six representative benchmark datasets, achieving state-of-the-art performance with 1.8-23.7× measured speedup. Furthermore, our method has been deployed to the autonomous racing vehicle of MIT Driverless, achieving larger detection range, higher accuracy and lower latency. [ABSTRACT FROM AUTHOR]

The thermodynamics of model host–guest-binding reactions is examined in depth using isothermal titration calorimetry. In conflict with classical thermodynamics, the results indicate that the equilibrium-binding quotient, K, is not a constant for all pairings. This outcome is predicted by an equation for binding equilibria that includes an explicit term for the change in solvation free energy that accompanies the formation of a binary complex. Application of this framework to the experimentally observed concentration dependence of K allows one to obtain the energetic contribution of the solvent, a linked equilibrium denoted here as ΔGH2O. The estimated values of ΔGH2O are large and unfavourable for the binding of selected guest molecules to two hosts, cucurbit[7]uril and p-sulfonatocalix[4]arene. Intriguingly, the estimated values of ΔGH2O are near zero for the binding of two hydrophobic guest molecules to β-cyclodextrin, leading to a thought-provoking discussion on the driving force behind the hydrophobic effect. [ABSTRACT FROM AUTHOR]

Ethylene plays pleiotropic roles in plant growth, plant development, and stress responses. Although the effects of ethylene on plants are well documented, little is known about molecular-level events that result in transcriptional repression during the ethylene response. In this study, we found that two histone deacetylases, SRT1 and SRT2, interact with ENAP1, which associates with EIN2 in the nucleus. Genetic and transcriptome analyses revealed that SRT1 and SRT2 are required for negative regulation of certain ethylene-responsive genes. The acetylation of HISTONE3 at K9 (H3K9Ac) is specifically regulated by SRT1 and SRT2 in ethylene-repressed genes. In addition, the srt1 srt2 double mutation in Arabidopsis thaliana suppresses both the ENAP1ox and the EIN3ox constitutive ethylene response phenotypes, and the ethylene-induced transcriptional repression observed in EIN3ox plants is derepressed in the EIN3ox/srt1 srt2 mutant. SRT2 and ENAP1 both bind to promoter regions of genes negatively regulated by ethylene, reducing H3K9Ac levels and resulting in transcriptional repression. This work establishes a mechanism by which histone deacetylases SRT1 and SRT2 interact with ENAP1 to mediate transcriptional repression by regulating the levels of H3K9 acetylation in the ethylene signaling. [ABSTRACT FROM AUTHOR]