Publications

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

The introduction of molecularly woven three-dimensional (3D) covalent organic framework (COF) crystals into polymers of varying types invokes different forms of contact between filler and polymer. Whereas the combination of woven COFs with amorphous and brittle polymethyl methacrylate results in surface interactions, the use of the liquid-crystalline polymer polyimide induces the formation of polymer-COF junctions. These junctions are generated by the threading of polymer chains through the pores of the nanocrystals, thus allowing for spatial arrangement of polymer strands. This offers a programmable pathway for unthreading polymer strands under stress and leads to the in situ formation of high-aspect-ratio nanofibrils, which dissipate energy during the fracture. Polymer-COF junctions also strengthen the filler-matrix interfaces and lower the percolation thresholds of the composites, enhancing strength, ductility, and toughness of the composites by adding small amounts (~1 weight %) of woven COF nanocrystals. The ability of the polymer strands to closely interact with the woven framework is highlighted as the main parameter to forming these junctions, thus affecting polymer chain penetration and conformation.

The locations of supernovae in the local stellar and gaseous environment in galaxies contain important clues to their progenitor stars. As part of a program to study the environments of supernovae using Hubble Space Telescope (HST) imaging data, we have examined the environment of the Type II-L SN 1979C in NGC 4321 (M100). We place more rigorous constraints on the mass of the SN progenitor, which may have had a mass M \approx 17--18 M_sun. Moreover, we have recovered and measured the brightness of SN 1979C, m=23.37 in F439W (~B; m_B(max) = 11.6), 17 years after explosion. .

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

Abstract Terminal alkenes are easily derivatized, making them desirable functional group targets for polyketide synthase (PKS) engineering. However, they are rarely encountered in natural PKS systems. One mechanism for terminal alkene formation in PKSs is through the activity of an acyl-CoA dehydrogenase (ACAD). Herein, we use biochemical and structural analysis to understand the mechanism of terminal alkene formation catalyzed by an γ,δ-ACAD from the biosynthesis of the polyketide natural product FK506, TcsD. While TcsD is homologous to canonical α,β-ACADs, it acts regioselectively at the γ,δ-position and only on α,β-unsaturated substrates. Furthermore, this regioselectivity is controlled by a combination of bulky residues in the active site and a lateral shift in the positioning of the FAD cofactor within the enzyme. Substrate modeling suggests that TcsD utilizes a novel set of hydrogen bond donors for substrate activation and positioning, preventing dehydrogenation at the α,β position of substrates. From the structural and biochemical characterization of TcsD, key residues that contribute to regioselectivity and are unique to the protein family were determined and used to identify other putative γ,δ-ACADs that belong to diverse natural product biosynthetic gene clusters. These predictions are supported by the demonstration that a phylogenetically distant homolog of TcsD also regioselectively oxidizes α,β-unsaturated substrates. This work exemplifies a powerful approach to understand unique enzymatic reactions and will facilitate future enzyme discovery, inform enzyme engineering, and aid natural product characterization efforts.

Automating precision subtasks such as debridement (removing dead or diseased tissue fragments) with Robotic Surgical Assistants (RSAs) such as the da Vinci Research Kit (dVRK) is challenging due to inherent nOnlinearities in cable-driven systems. We propose and evaluate a novel two-phase coarse-to-fine calibration method. In Phase I (coarse), we place a red calibration marker on the end effector and let it randomly move through a set of open-loop trajectories to obtain a large sample set of camera pixels and internal robot end-effector configurations. This coarse data is then used to train a Deep Neural Network (DNN) to learn the coarse transformation bias. In Phase II (fine), the bias from Phase I is applied to move the end -effector toward a small set of specific target points on a printed sheet. For each target, a human operator manually adjusts the end -effector position by direct contact (not through teleoperation) and the residual compensation bias is recorded. This fine data is then used to train a Random Forest (RF) to learn the fine transformation bias. Subsequent experiments suggest that without calibration, position errors average 4.55mm. Phase I can reduce average error to 2.14mm and the combination of Phase I and Phase II can reduces average error to 1.08mm. We apply these results to debridement of raisins and pumpkin seeds as fragment phantoms. Using an endoscopic stereo camera with standard edge detection, experiments with 120 trials achieved average success rates of 94.5 %, exceeding prior results with much larger fragments (89.4%) and achieving a speedup of 2.1x, decreasing time per fragment from 15.8 seconds to 7.3 seconds. Source code, data, and videos are available at https://sites.google.com/view/calib-icra/.