Developing axons are attracted to the CNS midline by Netrin proteins and other as yet unidentified signals. Netrin signals are transduced in part by Frazzled (Fra)/DCC receptors. Genetic analysis in Drosophila indicates that additional unidentified receptors are needed to mediate the attractive response to Netrin. Analysis of Bolwig's nerve reveals that Netrin mutants have a similar phenotype to Down Syndrome Cell Adhesion Molecule (Dscam) mutants. Netrin and Dscam mutants display dose sensitive interactions, suggesting that Dscam could act as a Netrin receptor. We show using cell overlay assays that Netrin binds to fly and vertebrate Dscam, and that Dscam binds Netrin with the same affinity as DCC. At the CNS midline, we find that Dscam and its paralog Dscam3 act redundantly to promote midline crossing. Simultaneous genetic knockout of the two Dscam genes and the Netrin receptor fra produces a midline crossing defect that is stronger than the removal of Netrin proteins, suggesting that Dscam proteins also function in a pathway parallel to Netrins. Additionally, overexpression of Dscam in axons that do not normally cross the midline is able to induce ectopic midline crossing, consistent with an attractive receptor function. Our results support the model that Dscam proteins function as attractive receptors for Netrin and also act in parallel to Frazzled/DCC. Furthermore, the results suggest that Dscam proteins have the ability to respond to multiple ligands and act as receptors for an unidentified midline attractive cue. These functions in axon guidance have implications for the pathogenesis of Down Syndrome.

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Abstract-The uptake, distribution, and subsequent emission of mercury to the atmosphere were investigated in five plant species ( [Labill]) with different ecological and physiological attributes. Transfer coefficients for mercury in the soilplant system were calculated. Plant-to-atmosphere emissions of mercury were determined using a controlled environment gasexchange system and ranged from 10 to 93 ng/m 2 /h in the light; emissions in the dark were an order of magnitude less. Transfer coefficients for mercury within the soil-plant system increased acropetally (root-to-leaf axis) by orders of magnitude. Estimated mercury emissions from plants in the Carson River Drainage Basin of Nevada over the growing season (0.5 mg/m 2 ) add to the previously reported soil mercury emissions (8.5 mg/m 2 ), resulting in total landscape emissions of 9 mg/m 2 . For L. latifolium, 70% of the mercury taken up by the roots during the growing season was emitted to the atmosphere. For every one molecule of mercury retained in foliage of L. latifolium, 12 molecules of mercury were emitted. Within this arid ecosystem, mercury emissions are a dominant pathway of the mercury cycle. Plants function as conduits for the interfacial transport of mercury from the geosphere to the atmosphere, and this role is undervalued in models of the behavior of mercury in terrestrial ecosystems and in the atmosphere on a global scale.

Analysis of variance (ANOVA) is a commonly used statistical analysis in agricultural experiments. Additivity, variance homogeneity, and normality are often considered prerequisites for ANOVA (Cochran, 1943; Eisenhart, 1947). The interpretation of ANOVA is valid when the random errors are independently distributed according to a normal distribution with zero mean and an unknown but fixed variance (Kempthorne, 1952; Scheffe, 1959; Steel and Torrie, 1980). Failure to meet one or more of these assumptions affects the significance levels and the sensitivity of the F test (Gomez and Gomez, 1984; Kempthorne, 1952; Little and Hills, 1978) Thus, strong deviations from one or more of the assumptions must be checked and corrected before the statistical analysis and interpretation of the results. Discrepancies of many kinds between an assumed model and the data can be detected by studying the error component or residuals (Anscombe and Tukey, 1963; Emerson and Stoto, 1983). The residuals are the deviation from the observed and the predicted values according to the assumed model. If the assumptions about the validity of the model are valid, a residual plot (scatter plot between the residuals and the predicted values) will have a random distribution. If the residual plot has an unexplained systematic pattern, then the ANOVA model is not appropriate. Residual plots can be used to detect the violation of assumptions in ANOVA, such as variance heterogeneity (unequal variance], auto-correlated error (nonindependence), and the presence of outliers. Thus, it is crucial to examine the residuals before interpreting the data. Violation of assumptions in ANOVA Nonadditivity, variance heterogeneity, and nonnormality. The additivity requirement implies that the block and treatment effects should be additive. For example, in a randomized complete-block design, the differ