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Abstract Friction is one of the cruxes of hydrodynamic modeling; flood conditions are highly sensitive to the Friction Factors (FFs) used to calculate momentum losses. However, empirical FFs are challenging to derive, causing flood models to rely on surrogate observations (such as land cover) and introducing uncertainty. This research presents a laboratory‐trained Deep Neural Network (DNN), developed using flume experiments, to estimateManning's nbased on Point Cloud (PC) data. The DNN was deployed on real‐world lidar PCs to directly estimateManning's nunder regulatory and extreme storm events, showing improved modeling capabilities in both 1D and 2D hydrodynamic models. For 1D models, the lidar estimates decreased differences with values assigned by experts through engineering judgment. For 1D/2D coupled models, the lidar values produced better agreement with flood extents obtained from airborne imagery, while better matching flood insurance claim data for Hurricane Harvey. In both 1D and 1D/2D coupled models, lidar resulted in better agreement with validation gauges. For these reasons, the lidar values ofManning's nwere found to improve both regulatory models and forecasts for extreme storm events, while simultaneously providing a pathway to standardize the estimation of FFs. Changing from land cover to lidar estimates significantly affected fluvial and pluvial flood models, while surge flooding was generally unaffected. Downstream flow conditions were found to change the impacts of FFs to fluvial models. This manuscript introduces a reliable, repeatable, and readily accessible avenue for high‐resolution friction estimation based on 3D PCs, improving flood prediction, and removing uncertainty from hydrodynamic modeling. 

Abstract While MYC is a significant oncogenic transcription factor driver of cancer, directly targeting MYC has remained challenging due to its intrinsic disorder and poorly defined structure, deeming it “undruggable.” Whether transient pockets formed within intrinsically disordered and unstructured regions of proteins can be selectively targeted with small molecules remains an outstanding challenge. Here, we developed a bespoke stereochemically-paired spirocyclic oxindole aziridine covalent library and screened this library for degradation of MYC. Through this screen, we identified a hit covalent ligand KL2-236, bearing a unique sulfinyl aziridine warhead, that engaged MYCin vitroas pure MYC/MAX protein complex andin situin cancer cells to destabilize MYC, inhibit MYC transcriptional activity and degrade MYC in a proteasome-dependent manner through targeting intrinsically disordered C203 and D205 residues. Notably, this reactivity was most pronounced for specific stereoisomers of KL2-236 with a diastereomer KL4-019 that was largely inactive. Mutagenesis of both C203 and D205 completely attenuated KL2-236-mediated MYC degradation. We have also optimized our initial KL2-236 hit compound to generate a more durable MYC degrader KL4-219A in cancer cells. Our results reveal a novel ligandable site within MYC and indicate that certain intrinsically disordered regions within high-value protein targets, such as MYC, can be interrogated by isomerically unique chiral small molecules, leading to destabilization and degradation. 

The purpose of this paper is to describe the development of a set of scales intended to measure self-efficacy and mindset relating to advanced manufacturing. The scales were developed as part of a larger National Science Foundation funded project intended to create a set of online course and modules about advanced manufacturing. These courses and modules are intended to be completed by a variety of learners, including community-college students, 4-year university students, industry professionals, and informal learners who are looking to advance their skills. The scales will ultimately be used as measures to gauge the impact of the instructional activities being created as part of the NSF project.