The chiD gene in Escherichja coli is required for the incorporation and utilization of molybdenum when the cells are grown with low concentrations of molybdate. We constructed chiD-lac operon fusions and measured expression of the fusion, Mo cofactor, and nitrate reductase activities under a variety of growth conditions. The chlD-lac fusion was highly expressed when cells were grown with less than 10 nm molybdate. Increasing concentrations of molybdate caused loss of activity, with less than 5% of the activity remaining at 500 nM molybdate; when tungstate replaced molybdate, it bad an identical affect on chiD expression. Expression of chiD-lac was increased in cells grown with nitrate. Strains with chiD-lac plus an additional mutation in a chl or nar gene were constructed to test whether the regulation of chiD-lac required the concerted action of gene products involved with Mo cofactor or nitrate reductase synthesis. Mutations in narL prevented the increase in activity in response' to nitrate; mutations in chM, narC, or narI resulted in partial constitutive expression of the chiD-lac fusion: the fusion was regulated by molybdate, but it no longer required the presence of nitrate for maximal activity. Mutations in chMA, chE, or chiG which affect Mo cofactor metabolism, did not affect the expression of chiD-lac.

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We describe and apply novel methodology for whole-brain analysis of resting state fMRI functional connectivity data, combining conventional multi-channel Pearson correlation with covariance analysis. Unlike correlation, covariance analysis preserves signal amplitude information, which feature of fMRI time series may carry physiological significance. Additionally, we demonstrate that dimensionality reduction of the fMRI data offers several computational advantages including projection onto a space of manageable dimension, enabling linear operations on functional connectivity measures and exclusion of variance unrelated to resting state network structure. We show that group-averaged, dimensionality reduced, covariance and correlation matrices are related, to reasonable approximation, by a single scalar factor. We apply this methodology to the analysis of a large, resting state fMRI data set acquired in a prospective, controlled study of mindfulness training and exercise in older, sedentary participants at risk for developing cognitive decline. Results show marginally significant effects of both mindfulness training and exercise in both covariance and correlation measures of functional connectivity.

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