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While helical gears are critical components of many power transmission systems, their dynamic behavior has not been studied extensively, perhaps due to their complex vibratory motions and lack of experimental data to guide sound modeling efforts. This study proposes a general dynamic model of a helical gear pair supported by realistic shaft-bearing structures. Aiming at the examination of main assumptions regarding the modeling of helical gears, two versions of the model are constructed, a nonlinear time-varying (NTV) version with gear backlash and time variation of gear mesh stiffness included, and a linear time-invariant (LTI) version, both subject to nonproportional damping. An extensive experimental study is performed covering a large portion of the helical gear design space. Simulations of these experiments indicate that the LTI version of the model with both backlash and mesh stiffness variations ignored compares well with the experiments. The model predictions and measurements collectively show that a helical gear pair acts as a linear system with the loaded motion transmission error as its main excitation. They also indicate that the helical gear motions are three-dimensional, requiring an accurate description of the support structures in the model.
Lipid metabolism and glycolysis play crucial roles in the progression and metastasis of cancer, and the use of 3-bromopyruvate (3-BP) as an antiglycolytic agent has shown promise in killing pancreatic cancer cells. However, developing an effective strategy to avoid chemoresistance requires the ability to probe the interaction of cancer drugs with complex tumor-associated microenvironments (TAMs). Unfortunately, no robust and multiplexed molecular imaging technology is currently available to analyze TAMs. In this study, the simultaneous profiling of three protein biomarkers using SERS nanotags and antibody-functionalized nanoparticles in a syngeneic mouse model of pancreatic cancer (PC) is demonstrated. This allows for comprehensive information about biomarkers and TAM alterations before and after treatment. These multimodal imaging techniques include surface-enhanced Raman spectroscopy (SERS), immunohistochemistry (IHC), polarized light microscopy, second harmonic generation (SHG) microscopy, fluorescence lifetime imaging microscopy (FLIM), and untargeted liquid chromatography and mass spectrometry (LC-MS) analysis. The study reveals the efficacy of 3-BP in treating pancreatic cancer and identifies drug treatment-induced lipid species remodeling and associated pathways through bioinformatics analysis.
