DataSheet2_Ray tracing model for long-range acoustic vortex wave propagation underwater.ZIP

The use of vortex waves in multiple environments is of increasing interest for numerous applications including underwater acoustic communications, particle manipulations, and sonothrombolysis. Finite element methods are limited in the range for which the propagation of these vortex beams may be simulated. On the other hand, ray tracing programs simulate well over long ranges, though are generally limited in their ability to resolve the features of a propagating vortex. Methods for overcoming these difficulties in simulating the long-range propagation of such waves in inhomogeneous environments have been developed and employed, though their specific implementation has not been thoroughly discussed. This manuscript provides the methods by which existing ray tracing programs may be used to approximate the long-range propagation of acoustic vortex beams in complex environments.</p

DataSheet1_Ray tracing model for long-range acoustic vortex wave propagation underwater.pdf

The use of vortex waves in multiple environments is of increasing interest for numerous applications including underwater acoustic communications, particle manipulations, and sonothrombolysis. Finite element methods are limited in the range for which the propagation of these vortex beams may be simulated. On the other hand, ray tracing programs simulate well over long ranges, though are generally limited in their ability to resolve the features of a propagating vortex. Methods for overcoming these difficulties in simulating the long-range propagation of such waves in inhomogeneous environments have been developed and employed, though their specific implementation has not been thoroughly discussed. This manuscript provides the methods by which existing ray tracing programs may be used to approximate the long-range propagation of acoustic vortex beams in complex environments.</p

Figure S1 from Molecular Pathology of Patient Tumors, Patient-Derived Xenografts, and Cancer Cell Lines

Figure S1. Comparison of gene expression within and between cancer types when the number of pairwise DEGs is 3000.</p

Supplemental Table S2 from Molecular Pathology of Patient Tumors, Patient-Derived Xenografts, and Cancer Cell Lines

PDX list</p

Table S1 from Molecular Pathology of Patient Tumors, Patient-Derived Xenografts, and Cancer Cell Lines

Table S1. List of cell lines mentioned/described in this study</p

Table S3 and S4 from Molecular Pathology of Patient Tumors, Patient-Derived Xenografts, and Cancer Cell Lines

Table S3. Biomarkers for lung origin and colon origins. Table S4. IHC analysis of the outlier models</p

Figure S2 from Molecular Pathology of Patient Tumors, Patient-Derived Xenografts, and Cancer Cell Lines

Figure S2. The relationship between number of unique genes and number of pairwise DEGs in the TCGA dataset.</p

Supplemental Figure Legends from Molecular Pathology of Patient Tumors, Patient-Derived Xenografts, and Cancer Cell Lines

Supplemental Figure Legends</p

Figure S3 from Molecular Pathology of Patient Tumors, Patient-Derived Xenografts, and Cancer Cell Lines

Figure S3. Immunohistochemistry (IHC) staining for confirmation of tumor types.</p

Supplementary Figure 4 from Tumor Purity in Preclinical Mouse Tumor Models

The distribution of tumor purity in human cells of 2115 PDX models within passage 10.</p