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Abstract Adhesive bonding of composite materials has become increasingly crucial for advanced engineering applications, offering unique advantages for lightweight and high-performance designs. This study presents a novel framework, physics-informed failure mode proportion prediction (PIFMP) model, for predicting failure mode proportions in composite adhesive joints, addressing critical gaps in understanding mixed-mode failure behaviors. In contrast to conventional approaches that focus solely on force or stress prediction, this research integrates important parameters from multistage manufacturing processes (MMPs) and simulation data into a physics-informed machine learning (PIML) framework, enabling proactive failure prediction and design optimization. The proposed framework unifies data-driven machine learning models with features derived from finite element analysis (FEA), incorporating cohesive zone modeling (CZM) to capture the physical dynamics of adhesive behavior under lap shearing. By embedding FEA-based physics features into the machine learning process and leveraging a time-series transformer model to analyze the temporal progression of interfacial damage and separation, the framework ensures predictive accuracy and physics-informed consistency, enabling precise analysis of failure mechanisms. The empirical study validates the effectiveness and the reliability of the framework, demonstrating enhanced predictive performance through cross-validation. The work establishes a foundational approach for failure analysis and provides a robust basis for future advancements. 

Metalloproteins tune the electronic properties of metal active sites through a combination of primary and secondary coordination sphere effects (PCS and SCS) to efficiently perform an array of redox chemistry, including electron transfer (ET) and catalysis. A major influence of these effects is the anisotropic spatial distribution of redox-active molecular orbitals (RAMOs), which in turn dictates redox chemistry of the metalloproteins. While much progress has been made in understanding the SCS effects on RAMOs in individual native metalloproteins, it has been difficult to experimentally examine the influence of the same SCS effects on RAMOs with different spatial distributions. Taking advantage of our recent studies of SCS effect on blue copper azurin from Pseudomonas aeruginosa (Blue CuAz) and its M121H/H46E variant that closely mimic the red copper protein (Red CuAz), in which their RAMOs are dominated by either Cu-S or Cu-S interactions, respectively, we herein compare and contrast how the same SCS modifications impact the electronic and geometric structures of blue and red Cu center in the same protein scaffold. Specifically, we expand our understanding of unpaired electron distribution at the Cu-binding site of Red CuAz and its SCS N47S, F114P, and F114N mutations using 1H and 14N electron-nuclear double resonance (ENDOR) spectroscopy, and then further combine these data sets with recent studies and DFT calculations to provide insight into how these mutations differentially (or similarly) impact electronic structure in Red vs. Blue CuAz. We find that electrostatics produce similar effects in both Red and Blue CuAz, where the introduction of dipole moments in the vicinity of Cu and S produce changes in spin density distribution and of the same sign and comparable magnitude. However, disruption of H-bonding with S through the F114P mutation leads to opposing effects in Red vs. Blue CuAz, which we propose arise from differences in the conformation of Cys112 sidechain adapted in the absence the stabilizing SC112•••H-N backbone interaction. 

Type 1 copper (T1Cu) centers are crucial in biological electron transfer (ET) processes, exhibiting a wide range of reduction potentials (E°′T1Cu) to match their redox partners and optimize ET rates. While tuning E°′T1Cu in mononuclear T1Cu proteins like azurin has been successful, it is more difficult for multicopper oxidases. Specifically, while replacing axial methionine to leucine in azurin increased its E°′T1Cu by ~100 mV, the corresponding M298L mutation in small laccase from Streptomyces coelicolor (SLAC) unexpectedly decreased its E°′T1Cu by 12 mV. X-ray crystallography revealed two axial water molecules in M298L-SLAC, leading to the decrease of E°′T1Cu due to decreased hydrophobicity. Structural alignment and molecular dynamics simulation indicated a key difference in T1Cu axial loop position, leading to the different outcome upon methionine to leucine mutation. Based on structural analyses, we introduced additional F195L and I200F mutations, leading to partial removal of axial waters, a 122-mV increase in E°′T1Cu, and a 7-fold increase in kcat/KM from M298L-SLAC. These findings highlight the complexity of tuning E°′T1Cu in multicopper oxidases and provide valuable insights into how structure-based protein engineering can contribute to the broader understanding of T1Cu center, E°′T1Cu and reactivity tuning for applications in solar energy transfer, fuel cells, and bioremediation. 

Neutrophils release neutrophil extracellular traps (NETs) to neutralize infections, a process that also contributes to immunothrombosis. While beneficial in localized infections, excessive NET formation can lead to widespread coagulopathy and organ failure. While the roles of NET-associated proteins such as histones in immunothrombosis are well characterized, NET-derived DNAs are much less known. To address this issue, we report herein the direct interaction between thrombin and DNA scaffolds and further, the identification of short tandem repeats of single-stranded (ATTCC)_nin NETs that selectively bind thrombin, a crucial enzyme involved in both blood clot formation and immune response. We have also developed a strategy of selective targeting ss(ATTCC)_nusing antisense locked nucleic acids (LNAs), effectively disrupting NET–thrombin interactions. This finding reveals an unexplored role of single strand DNA (ssDNA) within NETs and provides a broad avenue for developing targeted therapeutic interventions for immunothrombosis-related disorders. 

Genetically encoded fluorescent protein and fluorogenic RNA sensors are indispensable tools for imaging biomolecules in cells. To expand the toolboxes and improve the generalizability and stability of this type of sensor, we report herein a genetically encoded fluorogenic DNA aptamer (GEFDA) sensor by linking a fluorogenic DNA aptamer for dimethylindole red with an ATP aptamer. The design enhances red fluorescence by 4-fold at 650 nm in the presence of ATP. Additionally, upon dimerization, it improves the signal-to-noise ratio by 2–3 folds. We further integrated the design into a plasmid to create a GEFDA sensor for sensing ATP in live bacterial and mammalian cells. This work expanded genetically encoded sensors by employing fluorogenic DNA aptamers, which offer enhanced stability over fluorogenic proteins and RNAs, providing a novel tool for real-time monitoring of an even broader range of small molecular metabolites in biological systems.