Effect of Waste Discharges into a Silt-laden Estuary: A Case Study of Cook Inlet, Alaska

Cook Inlet is not well known. Although its thirty-foot tidal range is widely appreciated, its other characteristics, such as turbulence, horizontal velocities of flow, suspended sediment loads, natural biological productivity, the effects of fresh water inflows, temperature, and wind stresses, are seldom acknowledged. The fact that the Inlet has not been used for recreation nor for significant commercial activity explains why the average person is not more aware of these characteristics. Because of the gray cast created by the suspended sediments in the summer and the ice floes in the winter, the Inlet does not have the aura of a beautiful bay or fjord. The shoreline is inhospitable for parks and development, the currents too strong for recreational activities, and, because of the high silt concentration, there is little fishing. Yet, Cook Inlet, for all its negative attributes, can in no way be considered an unlimited dumping ground for the wastes of man. It may be better suited for this purpose than many bays in North America, but it does have a finite capacity for receiving wastes without unduly disturbing natural conditions. This report was written for the interested layman by engineers and scientists who tried to present some highly technical information in such a manner that it could be understood by environmentalists, concerned citizens, students, decision makers, and lawmakers alike. In attempting to address such a diverse audience, we risked failing to be completely understood by any one group. However, all too often research results are written solely for other researchers, a practice which leads to the advancement of knowledge but not necessarily to its immediate use by practicing engineers nor to its inclusion in social, economic, and political decision-making processes. We hope this report will shorten the usual time lag between the acquisition of new information and its use. Several additional reports will be available for a limited distribution. These will be directed to technicians who wish to know the mathematical derivations, assumptions, and other scientific details used in the study. Technical papers by the individual authors, published in national and international scientific and engineering journals, are also anticipated.The work upon which this report is based was supported in part by funds (Proj. B-015-ALAS) provided by the United States Department of the Interior, Office of Water Resources Research, as authorized under the Water Resources Act of 1964, as amended

Improvement of oral reading skills for accelerated children: grade six

Thesis (Ed.M.)--Boston Universit

The Rocketdyne Multifunction Tester. Part 1: Test Method

The Rocketdyne Multifunction Tester is a general purpose test apparatus which utilizes axial and radial magnetic bearings as shaft excitation devices. The tester is modular in design so that different seal and bearing packages can be tested on the same test stand. The tester will be used for rotordynamic coefficient extraction, as well as life and fluid/material compatibility evaluations. Use of a magnetic bearing as a shaft excitation device opens up many possibilities for shaft excitation and rotordynamic coefficient extraction. In addition to describing the basic apparatus, some of the excitation and extraction methods are described. Some of the excitation methods to be discussed include random, aperiodic, harmonic, impulse and chirp

Multiparameter Ultrasonic Evaluation of Ceramic Matrix Composites

Quantitative multiparameter ultrasonic methods are under development for the evaluation of composite materials. Dual frequency, throughtransmission, tone burst signals are used to provide calibrated attenuation data and the frequency dependence thereof. Pulse-echo, spike pulse signals are used to obtain depth information for internal reflectors. Monostatic and bistatic backscatter and bistatic forwardscatter techniques are also used to increase sensitivity to small flaws. Example results are presented for silicon carbide (SiC) fiber reinforced lithium alumino silicate (LAS) glass-ceramic matrix composites. Results include those for manufactured and naturally occurring flaws

Human Capital, Fertility, and Economic Growth

Our model of growth departs from both the Malthusian and neoclassical approaches by including investments in human capital. We assume, crucially, that rates of return on human capital investments rise, rather than, decline, as the stock of human capital increases, until the stock becomes large. This arises because the education sector uses human capital note intensively than either the capital producing sector of the goods producing sector. This produces multiple steady scares: an undeveloped steady stare with little human capital, low rates of return on human capital investments and high fertility, and a developed steady stats with higher rates of return a large, and, perhaps, growing stock of human capital and low fertility. Multiple steady states mean that history and luck are critical determinants of a country's growth experience.

Boosting accuracy of automated classification of fluorescence microscope images for location proteomics

BACKGROUND: Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination with methods for fluorescent tagging, is the most suitable current method for proteome-wide determination of subcellular location. Previous work has shown that neural network classifiers can distinguish all major protein subcellular location patterns in both 2D and 3D fluorescence microscope images. Building on these results, we evaluate here new classifiers and features to improve the recognition of protein subcellular location patterns in both 2D and 3D fluorescence microscope images. RESULTS: We report here a thorough comparison of the performance on this problem of eight different state-of-the-art classification methods, including neural networks, support vector machines with linear, polynomial, radial basis, and exponential radial basis kernel functions, and ensemble methods such as AdaBoost, Bagging, and Mixtures-of-Experts. Ten-fold cross validation was used to evaluate each classifier with various parameters on different Subcellular Location Feature sets representing both 2D and 3D fluorescence microscope images, including new feature sets incorporating features derived from Gabor and Daubechies wavelet transforms. After optimal parameters were chosen for each of the eight classifiers, optimal majority-voting ensemble classifiers were formed for each feature set. Comparison of results for each image for all eight classifiers permits estimation of the lower bound classification error rate for each subcellular pattern, which we interpret to reflect the fraction of cells whose patterns are distorted by mitosis, cell death or acquisition errors. Overall, we obtained statistically significant improvements in classification accuracy over the best previously published results, with the overall error rate being reduced by one-third to one-half and with the average accuracy for single 2D images being higher than 90% for the first time. In particular, the classification accuracy for the easily confused endomembrane compartments (endoplasmic reticulum, Golgi, endosomes, lysosomes) was improved by 5–15%. We achieved further improvements when classification was conducted on image sets rather than on individual cell images. CONCLUSIONS: The availability of accurate, fast, automated classification systems for protein location patterns in conjunction with high throughput fluorescence microscope imaging techniques enables a new subfield of proteomics, location proteomics. The accuracy and sensitivity of this approach represents an important alternative to low-resolution assignments by curation or sequence-based prediction

Objective Clustering of Proteins Based on Subcellular Location Patterns

The goal of proteomics is the complete characterization of all proteins. Efforts to characterize subcellular location have been limited to assigning proteins to general categories of organelles. We have previously designed numerical features to describe location patterns in microscope images and developed automated classifiers that distinguish major subcellular patterns with high accuracy (including patterns not distinguishable by visual examination). The results suggest the feasibility of automatically determining which proteins share a single location pattern in a given cell type. We describe an automated method that selects the best feature set to describe images for a given collection of proteins and constructs an effective partitioning of the proteins by location. An example for a limited protein set is presented. As additional data become available, this approach can produce for the first time an objective systematics for protein location and provide an important starting point for discovering sequence motifs that determine localization

A graphical model approach to automated classification of protein subcellular location patterns in multi-cell images

BACKGROUND: Knowledge of the subcellular location of a protein is critical to understanding how that protein works in a cell. This location is frequently determined by the interpretation of fluorescence microscope images. In recent years, automated systems have been developed for consistent and objective interpretation of such images so that the protein pattern in a single cell can be assigned to a known location category. While these systems perform with nearly perfect accuracy for single cell images of all major subcellular structures, their ability to distinguish subpatterns of an organelle (such as two Golgi proteins) is not perfect. Our goal in the work described here was to improve the ability of an automated system to decide which of two similar patterns is present in a field of cells by considering more than one cell at a time. Since cells displaying the same location pattern are often clustered together, considering multiple cells may be expected to improve discrimination between similar patterns. RESULTS: We describe how to take advantage of information on experimental conditions to construct a graphical representation for multiple cells in a field. Assuming that a field is composed of a small number of classes, the classification accuracy can be improved by allowing the computed probability of each pattern for each cell to be influenced by the probabilities of its neighboring cells in the model. We describe a novel way to allow this influence to occur, in which we adjust the prior probabilities of each class to reflect the patterns that are present. When this graphical model approach is used on synthetic multi-cell images in which the true class of each cell is known, we observe that the ability to distinguish similar classes is improved without suffering any degradation in ability to distinguish dissimilar classes. The computational complexity of the method is sufficiently low that improved assignments of classes can be obtained for fields of twelve cells in under 0.04 second on a 1600 megahertz processor. CONCLUSION: We demonstrate that graphical models can be used to improve the accuracy of classification of subcellular patterns in multi-cell fluorescence microscope images. We also describe a novel algorithm for inferring classes from a graphical model. The performance and speed suggest that the method will be particularly valuable for analysis of images from high-throughput microscopy. We also anticipate that it will be useful for analyzing the mixtures of cell types typically present in images of tissues. Lastly, we anticipate that the method can be generalized to other problems