Automatic segmentation of the caudate nucleus from human brain MR images

We describe a knowledge-driven algorithm to automatically delineate the caudate nucleus (CN) region of the human brain from a magnetic resonance (MR) image. Since the lateral ventricles (LVs) are good landmarks for positioning the CN, the algorithm first extracts the LVs, and automatically localizes the CN from this information guided by anatomic knowledge of the structure. The face validity of the algorithm was tested with 55 high-resolution T1-weighted magnetic resonance imaging (MRI) datasets, and segmentation results were overlaid onto the original image data for visual inspection. We further evaluated the algorithm by comparing automated segmentation results to a "gold standard" established by human experts for these 55 MR datasets. Quantitative comparison showed a high intraclass correlation between the algorithm and expert as well as high spatial overlap between the regions-of-interest (ROIs) generated from the two methods. The mean spatial overlap +/- standard deviation (defined by the intersection of the 2 ROIs divided by the union of the 2 ROIs) was equal to 0.873 +/- 0.0234. The algorithm has been incorporated into a public domain software program written in Java and, thus, has the potential to be of broad benefit to neuroimaging investigators interested in basal ganglia anatomy and function.     

A constant transition array switching method for improving SFDR and resolution in a segmented digital to analogue converter

Conventional methods for correcting linearity errors in a digital to analogue converter (DAC) rely on characterising the DAC first. This paper describes a technique that can increase linearity without characterising the DAC. It is in principle, able to remove completely deterministic errors in a segmented DAC and therefore increase SFDR. As no calibration is needed, the improvement can track dynamically changing errors due for example to thermal effects. Results with an 8-bit DAC show an increase in SFDR of at least 25 dB.     

Genetic algorithms for finite mixture model based voxel classification in neuroimaging

Finite mixture models (FMMs) are an indispensable tool for unsupervised classification in brain imaging. Fitting an FMM to the data leads to a complex optimization problem. This optimization problem is difficult to solve by standard local optimization methods, such as the expectation-maximization (EM) algorithm, if a principled initialization is not available. In this paper, we propose a new global optimization algorithm for the FMM parameter estimation problem, which is based on real coded genetic algorithms. Our specific contributions are two-fold: 1) we propose to use blended crossover in order to reduce the premature convergence problem to its minimum and 2) we introduce a completely new permutation operator specifically meant for the FMM parameter estimation. In addition to improving the optimization results, the permutation operator allows for imposing biologically meaningful constraints to the FMM parameter values. We also introduce a hybrid of the genetic algorithm and the EM algorithm for efficient solution of multidimensional FMM fitting problems. We compare our algorithm to the self-annealing EM-algorithm and a standard real coded genetic algorithm with the voxel classification tasks within the brain imaging. The algorithms are tested on synthetic data as well as real three-dimensional image data from human magnetic resonance imaging, positron emission tomography, and mouse brain MRI. The tissue classification results by our method are shown to be consistently more reliable and accurate than with the competing parameter estimation methods.     

Scenario-testing of agricultural best management practices in Lake Erie watersheds

Current research has shown that reductions in nonpoint nutrient loading are needed to reduce the incidence of harmful algal blooms and hypoxia in the western and central basins of Lake Erie. We used the Soil and Water Assessment Tool (SWAT) to test various sediment and nutrient load reduction strategies, including agricultural best management practice (BMP) implementation and source reduction in various combinations for six watersheds. These watersheds, in order of decreasing phosphorus loads, include the Maumee, Sandusky, Cuyahoga, Raisin, Grand, and Huron, and together comprise 53% of the binational Lake Erie Basin area. Hypothetical pristine nutrient yields, after eliminating all anthropogenic influences, were estimated to be an order of magnitude lower than current yields, underscoring the need for stronger management actions. However, cover crops, filter strips, and no-till BMPs, when implemented at levels considered feasible, were minimally effective, reducing sediment and nutrient yields by only 0–11% relative to current values. Sediment yield reduction was greater than nutrient yield reduction, and the greatest reduction was found when all three BMPs were implemented simultaneously. When BMPs were targeted at specific locations rather than at random, greater reduction in nutrient yields was achieved with BMPs placed in high source locations, whereas reduction in sediment yields was greatest when BMPs were located near the river outlet. Modest nutrient source reduction also was minimally effective in reducing yields. Our model results indicate that an “all-of-above” strategy is needed to substantially reduce nutrient yields and that BMPs should be much more widely implemented.     

羊毛/改性涤纶混纺织物干、湿热整理性能研究

研究了干热定形及蒸呢温度对羊毛,改性涤纶混纺织物(WE)定形稳定性、热收缩率及弯曲刚度的影响,并将其与具有相同织造结构的羊毛,常规涤纶织物(WP)及纯羊毛织物(W)的相应性能进行比较。实验结果表明,WE在低于WP热定形温度约l0℃的温度时便可达到最佳热定形稳定性;随着热定形温度的提高,WE的热收缩率及弯曲刚度表现出比wP更明显的提高。随着蒸呢温度的提高,3种织物的热定形稳定性相应提高;在相同的蒸呢温度下,WE的定形稳定性略好于WP;蒸呢温度的提高对织物的弯曲刚度及热收缩率影响较小。     

A five-phase framework for designing a successful multisource feedback system.

Given the increasing popularity and application of multisource feedback (MSF) methodology for both developmental and decision-making purposes in organizations, there is a need in the literature for more attention to the factors involved in creating a successful feedback process. This article describes a 5 phase framework for designing such a MSF system based on the classic organization development consulting skills model and many years of practitioner experience with large-scale feedback-based applications in Fortune 100 organizations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Confusion over Patient Privacy In US Heath Service

A plethora of federal rules intended to protect the security and privacy of health information has the caused the US health care industry to ask the Government to revisit the provisions of the new regulations. The federal Government has just agreed to reopen the process.     

Finite-element programs in integrated software for structural mechanics and CAD

The paper discusses integrated FEM (finite-element method) software for structural analysis and CAD applications. The main purpose of this paper is to emphasize the need to use interactive graphics in structural analysis and CAD. An attempt is made to review general-purpose pre- and post-processing programs developed for FEM applications, and integrated FEM programs for structural mechanics applications. Some large integrated systems for sizing and analysing aircraft structures are presented.     

Intuitive Expertise and Perceptual Templates

We provide the first demonstration of an artificial neural network encoding the perceptual templates that form an important component of the high level strategic understanding developed by experts. Experts have a highly refined sense of knowing where to look, what information is important and what information to ignore. The conclusions these experts reach are of a higher quality and typically made in a shorter amount of time than those of non-experts. Understanding the manifestation of such abilities in terms of both the psychology of expert performance and the underlying neural mechanisms constitutes one of the most challenging problems in the cognitive sciences. Using perceptual templates we show how the amount of contextual information can change significantly even within a given task, the relationship between local and non-local contexts and finally why there is very little correlation between measures of intelligence and level of expertise in many of the most complex tasks performed by humans.     

Robust color texture features under varying illumination conditions

Under varying illumination, both the statistical and structural contents of color texture are modified, leading to changes in the observed texture surface. We model the effect of illumination as a perturbation on an ideal color texture and show that the spectra of the ambient light have a significant impact on the observed texture patterns in the individual color channels. Motivated by studies in human color constancy, we propose a correlation-based transformation that minimizes the effect of illumination variation in color texture analysis. Experimental results are included, which validate the performance of the proposed minvariance model in the analysis of color texture.