Pancreatic cancer (PC) is an aggressive malignancy with an exceptionally high mortality rate because it lacks effective early diagnosis methods. To improve the ability to diagnose PC, the identification of biomarkers that can differentiate PC patients from both normal controls (NC) and those with chronic pancreatitis (CP) is vital. This study demonstrates the detection of extracellular vesicles (EVs) as an excellent resource of diagnostic biomarkers in serum samples from NC individuals, and CP and PC patients using a surface enhanced Raman spectroscopy (SERS)-based immunoassay technique. The assay uses the Au-CD81-EVs-EphA2-Au complex to capture PC tumor-derived EVs specifically and produces highly localized regions of intense field enhancement (hot spots) concurrently. Applying a machine learning algorithm to the analysis of the expression level of EVs biomarkers in PC, CP, and NC individuals, the sensitivity and specificity were measured as 0.95 and 0.96, respectively.Measuring PC tumor-derived EVs' expression levels in serum of PC patients, CP patients, and NC individuals suggests the great potential of using this biomarker to differentiate pancreatic cancers from chronic pancreatitis.
A brief summary of direct solution approaches for finite element methods (FEM) in computational electromagnetics (CEM) is given along with an alternative direct solution based on domain decomposition (DD). Unlike recent trends in approximate/low-rank solvers, this work focuses on 'numerically exact' solution methods as they are more reliable for complex 'real-life' models. Preliminary studies on general three dimensional geometries with unstructured FEM meshes suggest that the proposed direct DD methodology offers significant memory advantages over highly optimized, high-performance sparse direct solver libraries, while maintaining approximately comparable or slightly slower serial serial execution speed but with significantly better parallel and GPU processing prospects.
An exact arithmetic, memory efficient direct solution method for finite element method (FEM) computations is outlined. Unlike conventional black-box or low-rank direct solvers that are opaque to the underlying physical problem, the proposed method leverages physical insights at every stage of the development through a new symmetric domain decomposition method (DDM) with one set of Lagrange multipliers. Comparisons with state-of-the-art exact direct solvers on electrically large problems suggest up to 10 times less memory and better run-time complexity while maintaining the same accuracy.
A parallel direct solution approach based on domain decomposition method (DDM) and directed acyclic graph (DAG) scheduling is outlined. Computations are represented as a sequence of small tasks that operate on domains of DDM or dense matrix blocks of a reduced matrix. These tasks can be statically scheduled for parallel execution using their DAG dependencies and weights that depend on estimates of computation and communication costs. Performance comparison with MUMPS 5.1.2 on electrically large problems suggest up to 20% better parallel efficiency, 30% less memory and slightly faster in runtime, while maintaining the same accuracy.
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