He was a co-founder of Seragon, purchased by Genentech/Roche in 2014. J.M. is a science advisor and owns company stock in Scholar Rock. H.C. is an inventor on several patents related to organoid technology. S.W.L. is a co-founder and scientific advisory board member for ORIC Pharm, Blueprint, and Mirimus. He also serves on the scientific advisory board for Constellation, Petra, and PMV and has recently served as a consultant for Forma, Boehringer Ingelheim, and Aileron. J.G.-A. has received support from Medtronic (honorarium for consultancy with Medtronic), Johnson & Johnson (honorarium for delivering a talk), and Intuitive Surgical (honorarium for participating in a webinar by Intuitive Surgical). P.B.R. has received honorarium from Corning to discuss 3D cell culture techniques, has served as a consultant for AstraZeneca, and is a consultant for EMD Serono for work on radiation sensitizers.
Summary
Phosphoinositide-3-kinase (PI3K)-α inhibitors have shown clinical activity in squamous carcinoma (SCC) of head and neck (H&N) bearing PIK3CA mutations or amplification. Studying models of therapeutic resistance we have observed that SCCs cells that become refractory to PI3Kα inhibition maintain PI3K-independent activation of the mammalian target of rapamycin (mTOR). This persistent mTOR activation is mediated by the tyrosine kinase receptor AXL. AXL is overexpressed in resistant tumors from both laboratory models and patients treated with the PI3Kα inhibitor BYL719. AXL dimerizes with and phosphorylates epidermal growth factor receptor (EGFR), resulting in activation of phospholipase Cγ (PLCγ)- protein kinase C (PKC), which in turn activates mTOR. Combined treatment with PI3Kα and either EGFR, AXL, or PKC inhibitors reverts this resistance.
Abstract-Finding appropriate stable grasps for a hand (either robotic or human) on an arbitrary object has proved to be a challenging and difficult problem. The space of grasping parameters coupled with the degrees-of-freedom and geometry of the object to be grasped creates a high-dimensional, non-smooth manifold. Traditional search methods applied to this manifold are typically not powerful enough to find appropriate stable grasping solutions, let alone optimal grasps. We address this issue in this paper, which attempts to find optimal grasps of objects using a grasping simulator. Our unique approach to the problem involves a combination of numerical methods to recover parts of the grasp quality surface with any robotic hand, and contemporary machine learning methods to interpolate that surface, in order to find the optimal grasp.
We present SplashRNA, a sequential classifier to predict potent microRNA-based short hairpin RNAs (shRNAs). Trained on published and novel datasets, SplashRNA outperforms previous algorithms and reliably predicts the most efficient shRNAs for a given gene. Combined with an optimized miR-E backbone, >90% of high-scoring SplashRNA predictions trigger >85% protein knockdown when expressed from a single genomic integration. SplashRNA can significantly improve the accuracy of loss-of-function genetics studies and facilitates the generation of compact shRNA libraries.
Planning realizable and stable grasps on 3D objects is crucial for many robotics applications, but grasp planners often ignore the relative sizes of the robotic hand and the object being grasped or do not account for physical joint and positioning limitations. We present a grasp planner that can consider the full range of parameters of a real hand and an arbitrary object, including physical and material properties as well as environmental obstacles and forces, and produce an output grasp that can be immediately executed. We do this by decomposing a 3D model into a superquadric 'decomposition tree' which we use to prune the intractably large space of possible grasps into a subspace that is likely to contain many good grasps. This subspace can be sampled and evaluated in GraspIt!, our 3D grasping simulator, to find a set of highly stable grasps, all of which are physically realizable. We show grasp results on various models using a Barrett hand.
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