Anti-PD-1 therapy is used as a front-line treatment for many cancers, but mechanistic insight into this therapy resistance is still lacking. Here we generate a humanized (Hu)-mouse melanoma model by injecting fetal liver-derived CD34+ cells and implanting autologous thymus in immune-deficient NOD-scid IL2Rγnull (NSG) mice. Reconstituted Hu-mice are challenged with HLA-matched melanomas and treated with anti-PD-1, which results in restricted tumor growth but not complete regression. Tumor RNA-seq, multiplexed imaging and immunohistology staining show high expression of chemokines, as well as recruitment of FOXP3+ Treg and mast cells, in selective tumor regions. Reduced HLA-class I expression and CD8+/Granz B+ T cells homeostasis are observed in tumor regions where FOXP3+ Treg and mast cells co-localize, with such features associated with resistance to anti-PD-1 treatment. Combining anti-PD-1 with sunitinib or imatinib results in the depletion of mast cells and complete regression of tumors. Our results thus implicate mast cell depletion for improving the efficacy of anti-PD-1 therapy.

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Proton beam radiation treatment was first proposed by Robert Wilson in 1946. The advantage of proton beam radiation is that the lethal dose of radiation is delivered by a sharp increase toward the end of the beam range. This sharp increase, known as the Bragg peak, allows for the possibility of reducing the exposure of healthy tissue to radiation when comparing to x-ray radiation treatment. As the proton beam interacts with the molecules in the body, gamma rays are emitted. The origin of the gamma rays gives the location of the proton beam in the body, therefore, gamma ray imaging allows physicians to better take advantage of the benefits of proton beam radiation. These gamma rays are detected using a Compton Camera (CC) while the SOE algorithm is used to reconstruct images of these gamma rays as they are emitted from the patient. This imaging occurs while the radiation dose is delivered, which would allow the physician to make adjustments in real time in the treatment room, provided the image reconstruction is computed fast enough. This project focuses on speeding up the image reconstruction software with the use of parallel computing techniques involving MPI. Additionally, we demonstrate the use of the VTune performance analyzer to identify bottlenecks in a parallel code. KEYWORDS: Proton Beam Therapy; Image Reconstruction; SOE Algorithm; Parallel Computing; High Performance Computing; Medical Imaging; Prompt Gamma Imaging; Radiotherapy

Competitive divers are able to attain much higher scores when they perform a splash-less entry. To achieve this goal, they use the "rip" entry maneuver where they roll their body forward immediately after impact. This dynamic shape change after impact separates them from previously studied entry bodies. An experimental study of a geometrically simplified hinged diver model is presented. The results for five different hinged models are reported. Geometric and hinge stiffness changes are used to identify the most important aspects of the maneuver. The trajectory of these models after impact and the estimated size of the entrained air cavity are reported. The models that deform the fastest also have the largest final estimated air cavity. A non-dimensional time scaled by the time to complete deformation is found to collapse the estimated size of the air cavity for all models that deform before pinch off. Fixed models are introduced to compare the hinged models to non-deforming entry bodies. The hinged models are found to have between 42% - 154% larger estimated air cavities during the final measurement than their fixed counterpart. At the moment of deep seal, two distinct air cavities are formed: one that connects to the atmosphere above the pool that has smooth walls and a lower cavity largely composed of small bubbles. This composition is analogous to observations of competitive divers.Entry bodies that deform are found to significantly change the entry body dynamics and formation of the air cavity.

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