Three-dimensional data merging is vital for full-field three-dimensional (3D) shape measurement. All 3D range data patches, acquired from either different sensors or the same sensor in different viewing angles, have to be merged into a single piece to facilitate future data analysis. A novel method for 3D data merging using Holoimage is proposed. Similar to the 3D shape measurement system using a phase-shifting method, Holoimage is a phase-shiftingbased computer synthesized fringe image. The 3D information is retrieved from Holoimage using a phase-shifting method. If two patches of 3D data with overlapping areas are rendered by OpenGL, the overlapping areas are resolved by the graphics pipeline, that is, only the front geometry can be visualized. Therefore, the merging is performed if the front geometry information can be obtained. Holoimage is to obtain the front geometry by projecting the fringe
Macrophage activation and persistent inflammation contribute to the pathological process of spinal cord injury (SCI). It was reported that M2 macrophages were induced at 37 days after SCI but M2 markers were reduced or eliminated after 1 week. By contrast, M1 macrophage response is rapidly induced and then maintained at injured spinal cord. However, factors that modulate macrophage phenotype and function are poorly understood. We developed a model to distinguish bonemarrow derived macrophages (BMDMs) from residential microglia and explored how BMDMs change their phenotype and functions in response to the lesionrelated factors in injured spinal cord. Infiltrating BMDMs expressing higher Mac2 and lower CX3CR1 migrate to the epicenter of injury, while microglia expressing lower Mac2 but higher CX3CR1 distribute to the edges of lesion. Myelin debris at the lesion site switches BMDMs
Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total,> 30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and
We simulate blood flow in patient-specific cerebral arteries. The complicated geometry in the human brain makes the problem challenging. We use a fully unstructured three dimensional mesh to cover the artery, and Galerkin/least-squares finite element method to discretize the incompressible Navier-Stokes equations, that are employed to model the blood flow, and the resulting large sparse nonlinear system of equations is solved by a Newton-Krylov-Schwarz algorithm. From the computed flow fields, we are able to understand certain behavior of the blood flow of this particular patient before and after a stenosis is surgically removed. We also report the robustness and parallel performance of the domain decomposition based algorithm.
This book presents an overview of recent developments in biostatistics and bioinformatics. Written by active researchers in these emerging areas, it is intended to give graduate students and new researchers an idea of where the frontiers of biostatistics and bioinformatics are as well as a forum to learn common techniques in use, so that they can advance the fields via developing new techniques and new results. Extensive references are provided so that researchers can follow the threads to learn more comprehensively what the literature is and to conduct their own research. In particulars, the book covers three important and rapidly advancing topics in biostatistics: analysis of survival and longitudinal data, statistical methods for epidemiology, and bioinformatics.