Genomic techniques commonly used for assessing distributions of microorganisms in the environment often produce small sample sizes. We investigated artificial neural networks for analyzing the distributions of nitrite reductase genes (nirS and nirK) and two sets of dissimilatory sulfite reductase genes (dsrAB 1 and dsrAB 2 ) in small sample sets. Data reduction (to reduce the number of input parameters), cross-validation (to measure the generalization error), weight decay (to adjust model parameters to reduce generalization error), and importance analysis (to determine which variables had the most influence) were useful in developing and interpreting neural network models that could be used to infer relationships between geochemistry and gene distributions. A robust relationship was observed between geochemistry and the frequencies of genes that were not closely related to known dissimilatory sulfite reductase genes (dsrAB 2 ). Uranium and sulfate appeared to be the most related to distribution of two groups of these unusual dsrAB-related genes. For the other three groups, the distributions appeared to be related to pH, nickel, nonpurgeable organic carbon, and total organic carbon. The models relating the geochemical parameters to the distributions of the nirS, nirK, and dsrAB 1 genes did not generalize as well as the models for dsrAB 2 . The data also illustrate the danger (generating a model that has a high generalization error) of not using a validation approach in evaluating the meaningfulness of the fit of linear or nonlinear models to such small sample sizes.One of the goals of microbial ecology is to understand which abiotic factors control the abundance and distribution of microorganisms in the environment. Environmental microbial ecology is beginning to achieve this goal in a wide range of habitats (6,8,30,59) with the advent of molecular techniques that allow a significant part of the indigenous populations to be identified to some phylogenetic or functional level. For example, microbial distributions and diversity have been examined in relation to spatial factors (1), freshwater and ocean environments (51), and soil type (48, 50). Distribution or diversity has also been linked to dominant environmental characteristics or seasonal variations (29,43,57,63,68). To identify the critical factors that influence population distribution in complex environments, sophisticated data analysis techniques are needed to model the relationships between microbial distributions and environmental characteristics (14, 66).Cloning and sequencing of functional genes from environmental samples are powerful methods for investigating the ecology of microorganisms. These techniques have advanced our understanding of the types of microorganisms and degradation capabilities found in various habitats (6,12,15,43,51). However, relating the population data generated by these techniques to environmental characteristics, such as geochemical measurements, can be challenging. One problem is the small sample size that is typical in thes...
Discrimination of user intent at the computer interface solely from eye gaze can provide a powerful tool, benefiting many applications. An exploratory methodology for discriminating zoom-in, zoom-out, and no-zoom intent was developed for such applications as telerobotics, disability aids, weapons systems, and process control interfaces. Using an eye-tracking system, real-time eye-gaze locations on a display are collected. Using off-line procedures, these data are clustered, using minimum spanning tree representations, and then characterized. The cluster characteristics are fed into a multiple linear discriminant analysis, which attempts to discriminate the zoom-in, zoom-out, and no-zoom conditions. The methodologies, algorithms, and experimental data collection procedure are described, followed by example output from the analysis programs. Although developed specifically for the discrimination of zoom conditions, the methodology has broader potential for discrimination of user intent in other interface operations. Eye Gaze in Computer Interface ControlThe use of eye gaze as a computer interface control device is a recent concept with significant potential. Initially conceived for disability applications and military weapons targeting, eye gaze as an input device may readily extend to the control ofadvanced process interfaces, teierobotics, and camera manipulation (Hutchinson, White, Martin, Reichert, & Frey, 1989) or routine word processing (Frey, White, & Hutchinson, 1990). A great appeal of eye-gaze control is that it may serve as an effective replacement for mouse and keyboard input for high-workload tasks, thus freeing the hands to control other operations. For example, one might access items in a helmet-mounted database using eye gaze. Alternatively, eye gaze might control not only the direction of a wheelchair for a disabled individual, but also such subtle tasks as speed or route planning. Eye-gaze tracking methodologies have been used to control two types of operations at the computer interface: spatial cursor position and object selection. Jacob (1990, 1991) presented an algorithm and demonstration of both ofthese in a videogame interface. He defined fixations after delays of 100 msec and ended fixations if data were received outside the current fixation area for at least 50 msec. Extensive averaging of spatial positions ensured that spurious blinks and or other anomalies were not considered. Objects were selected in this interface ifat least a 150-200-msec dwell time occurred at a specific location; selections were easily reversible by fixating another object. The experimental eye-gaze-driven word processor by Frey et al. (1990) used a dwell time ofl ,000 msec for object selection; it predicted probable letter combinations continuously in order to increase user speed and accuracy. These predictions effectively decreased the number of alternative characters needed to display as "lookpoints" to the user. Starker and Bolt (1990) considered varying models of required dwell time at an object for specifyin...
The relationship between groundwater geochemistry and microbial community structure can be complex and difficult to assess. We applied nonlinear and generalized linear data analysis methods to relate microbial biomarkers (phospholipids fatty acids, PLFA) to groundwater geochemical characteristics at the Shiprock uranium mill tailings disposal site that is primarily contaminated by uranium, sulfate, and nitrate. First, predictive models were constructed using feedforward artificial neural networks (NN) to predict PLFA classes from geochemistry. To reduce the danger of overfitting, parsimonious NN architectures were selected based on pruning of hidden nodes and elimination of redundant predictor (geochemical) variables. The resulting NN models greatly outperformed the generalized linear models. Sensitivity analysis indicated that tritium, which was indicative of riverine influences, and uranium were important in predicting the distributions of the PLFA classes. In contrast, nitrate concentration and inorganic carbon were least important, and total ionic strength was of intermediate importance. Second, nonlinear principal components (NPC) were extracted from the PLFA data using a variant of the feedforward NN. The NPC grouped the samples according to similar geochemistry. PLFA indicators of Gram-negative bacteria and eukaryotes were associated with the groups of wells with lower levels of contamination. The more contaminated samples contained microbial communities that were predominated by terminally branched saturates and branched monounsaturates that are indicative of metal reducers, actinomycetes, and Gram-positive bacteria. These results indicate that the microbial community at the site is coupled to the geochemistry and knowledge of the geochemistry allows prediction of the community composition.
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