Myosin II Motors and F-Actin Dynamics Drive the Coordinated Movement of the Centrosome and Soma during CNS Glial-Guided Neuronal Migration

SummaryLamination of cortical regions of the vertebrate brain depends on glial-guided neuronal migration. The conserved polarity protein Par6α localizes to the centrosome and coordinates forward movement of the centrosome and soma in migrating neurons. The cytoskeletal components that produce this unique form of cell polarity and their relationship to polarity signaling cascades are unknown. We show that F-actin and Myosin II motors are enriched in the neuronal leading process and that Myosin II activity is necessary for leading process actin dynamics. Inhibition of Myosin II decreased the speed of centrosome and somal movement, whereas Myosin II activation increased coordinated movement. Ectopic expression or silencing of Par6α inhibited Myosin II motors by decreasing Myosin light-chain phosphorylation. These findings suggest leading-process Myosin II may function to “pull” the centrosome and soma forward during glial-guided migration by a mechanism involving the conserved polarity protein Par6α

Development of a unified probabilistic framework for segmentation and recognition of semi-rigid objects in complex backgrounds via deformable shape models

This dissertation presents the development, implementation, and application of a unified probabilistic shape and appearance model (PSAM) algorithm for boundary-based segmentation and recognition of semirigid objects on complex backgrounds. The algorithm requires that the boundary be represented by a set of landmark points (LPs). These LPs are iteratively adjusted to fit the boundary of a new object based on a priori information gathered from a training set. PSAM is derived from compound Bayesian decision theory, and the formulation is general enough that it can be used as a starting point to derive a variety of other probabilistic boundary-finding techniques. The motivation for developing PSAM arose from a need to segment and recognize semirigid anatomic structures within medical images that have faint and/or missing edge information. The starting point for this research was the active shape model (ASM). ASM is described, along with some practical improvements that were made to the published algorithm. These practical improvements are demonstrated on synthetic and real dataASM was tested on a a set of 2D medical images of kidneys within X-ray CT images of labora- tory mice. Although ASM performance was improved because of the practical improvements, some remaining fundamental problems led to poor segmentation accuracy in many cases. These fundamental problems inspired the development of the PSAM algorithm. PSAM contains three specific model components: (1) a global shape model (GSM), (2) a local shape model (LSM), and (3) a gray-level model (GLM)The GLM formulation is based on gradient gray-level profiles normal to the object boundary through each LP. All three of the PSAM components are optimized simultaneously when boundary searches are performed within new images. PSAM is formulated so that the influence of each of these components on the final boundary position can be controlled by the system operator. This allows the same PSAM algorithm to be used in applications with predictable global shape and relatively poor object edge strength, as well as in other applications where global shape is unpredictable but object edges are prominent. The new PSAM algorithm formulation provides confidence metrics for each of the three model components that give the operator feedback on the segmentation result. These confidence metrics indicate how well each PSAM component (GSM, LSM, and GLM) of the final boundary fits within the distribution of each component as derived from the training data. These confidence metrics can be monitored to alert an operator to any boundary results where one or more model components were found to be out of bounds relative to the training data. Furthermore, it was demonstrated that for some applications, the GLM confidence metric can be used as a predictor of segmentation accuracy. The performance of the PSAM algorithm is summarized on both synthetic and real-world dataThe results of three cases of real medical image data segmentations are presented. These cases include X-ray tomographic images of anatomic structures within laboratory mice. Specifically, the skull, the heart and lungs, and the kidneys are segmented using PSAM and ASM; and the results of the two algorithms are directly compared. In all cases the PSAM algorithm performed well and in fact, outperformed ASM by a substantial margin. It is shown that PSAM has a much larger degree of success than ASM on the most difficult segmentation cases. The PSAM performance is summarized, and a variety of future research topics are suggested that could lead to improved performance and broader applicability

An Integrated Spatial Signature Analysis and Automatic Defect Classification System

This paper presents a vision of how a promising new technology called spatial signature analysis (SSA) [1, 2] has potential to improve automatic defect classification (ADC) system accuracy and throughput. Optical-based automatic defect classification (ADC) technologies for semiconductor wafer manufacturing have been under heavy development for the past five years [3-10] and are just recently being seriously introduced into major semiconductor fabrication facilities [11-12]. There are many challenges in building a practical and reliable ADC system that is effective in identifying manufacturing problems in a real wafer manufacturing environment. Two closely coupled characteristics of an ADC system that are still very challenging for the system designer are (1) high defect classification accuracy and (2) high wafer throughput. Accuracy can be a problem because there can be many different classes of defects that a fabrication engineer may wish to automatically identify. To compound the accuracy problem, defects that should be grouped into the same category may have very different visual characteristics. The second challenge, throughput, is a issue because automatically classifying a defect off-line requires the defect to be repositioned under a high resolution microscope (e.g. optical or SEM), re-imaged, re-detected, analyzed to determine defect characteristics, and, finally, classified. This is already a time-consuming process compared to the speed at which the in-line wafer inspection is accomplished. As wafer critical dimensions shrink towards 0.18m, optical microscopes will be replaced by slower, higher resolution SEMs for small defect review [13]. This will add more time to the defect imaging step of the process

Subpixel Measurement Of Image Features Based On Paraboloid Surface Fit

A digital image processing inspection system is under development at Oak Ridge National Laboratory that will locate image features on printed material and measure distances between them to accuracies of 0.001 in. An algorithm has been developed for this system that can locate unique image features to subpixel accuracies. It is based on a least-squares fit of a paraboloid function to the surface generated by correlating a reference image feature against a test image search area. Normalizing the correlation surface makes the algorithm robust in the presence of illumination variations and local flaws. Subpixel accuracies of better than 1/16 of a pixel have been achieved using a variety of different reference image features. 1. INTRODUCTION An algorithm has been developed at Oak Ridge National Laboratory (ORNL) that can be used to locate a variety of features or objects in a digitized image to subpixel accuracies. The algorithm uses a set of normalized correlation data 1 generated by co..

Rapid Yield Learning through Optical Defect and Electrical Test Analysis

As semiconductor device density and wafer area continue to increase, the volume of in-line and off-line data required to diagnose yield-limiting conditions is growing exponentially. To manage this data in the future, analysis tools will be required that can automatically reduce this data to useful information, e.g., by assisting the engineer in rapid root-cause diagnosis of defect generating mechanisms. In this paper, we describe a technology known as Spatial Signature Analysis (SSA) and its application to both opticallydetected defect data as well as electrical test (e-test) bin data. The results of a validation study are summarized that demonstrate the effectiveness of the SSA approach on optical defect wafermaps through field-testing at three semiconductor manufacturing sites on ASIC, DRAM and SRAM products. This method has been extended to analyze and interpret electrical test data and to provide a pathway for correlation of this data with in-line optical measurements. The image pr..