Measurement of isotopic uranium in water for compliance monitoring by liquid scintillation counting with alpha/beta discrimination

A simple and inexpensive method is described for analysis of uranium (U) activity and mass in water by liquid scintillation counting using $\alpha$/$\beta$ discrimination. This method appears to offer a solution to the need for an inexpensive protocol for monitoring U activity and mass simultaneously and an alternative to the potential inaccuracy involved when depending on the mass-to-activity conversion factor or activity screen.^ U is extracted virtually quantitatively into 20 ml extractive scintillator from a 1-$\ell$ aliquot of water acidified to less than pH 2. After phase separation, the sample is counted for a 20-minute screening count with a minimum detection level of 0.27 pCi $\ell\sp{-1}$. $\alpha$-particle emissions from the extracted U are counted with close to 100% efficiency with a Beckman LS6000 LL liquid scintillation counter equipped with pulse-shape discrimination electronics. Samples with activities higher than 10 pCi $\ell\sp-1$ are recounted for 500-1000 minutes for isotopic analysis. Isotopic analysis uses events that are automatically stored in spectral files and transferred to a computer during assay. The data can be transferred to a commercially available spreadsheet and retrieved for examination or data manipulation. Values for three readily observable spectral features can be rapidly identified by data examination and substituted into a simple formula to obtain $\sp{234}$U/$\sp{238}$U ratio for most samples. U mass is calculated by substituting the isotopic ratio value into a simple equation.^ The utility of this method for the proposed compliance monitoring of U in public drinking water supplies was field tested with a survey of drinking water from Texas supplies that had previously been known to contain elevated levels of gross $\alpha$ activity. U concentrations in 32 samples from 27 drinking water supplies ranged from 0.26 to 65.5 pCi $\ell\sp{-1}$, with seven samples exceeding the proposed Maximum Contaminant Level of 20 $\mu$g $\ell\sp{-1}$. Four exceeded the proposed activity screening level of 30 pCi $\ell\sp{-1}$. Isotopic ratios ranged from 0.87 to 41.8, while one sample contained $\sp{234}$U activity of 34.6 pCi $\ell\sp{-1}$ in the complete absence of its parent, $\sp{238}$U. U mass in the samples with elevated activity ranged from 0.0 to 103 $\mu$g $\ell\sp{-1}$. A limited test of screening surface and groundwaters for contamination by U from waste sites and natural processes was also successful. ^

POPULATION CODING IN LAMINAR CORTICAL CIRCUITS

One of the fundamental questions in neuroscience is to understand how encoding of sensory inputs is distributed across neuronal networks in cerebral cortex to influence sensory processing and behavioral performance. The fact that the structure of neuronal networks is organized according to cortical layers raises the possibility that sensory information could be processed differently in distinct layers. The goal of my thesis research is to understand how laminar circuits encode information in their population activity, how the properties of the population code adapt to changes in visual input, and how population coding influences behavioral performance. To this end, we performed a series of novel experiments to investigate how sensory information in the primary visual cortex (V1) emerges across laminar cortical circuits. First, it is commonly known that the amount of information encoded by cortical circuits depends critically on whether or not nearby neurons exhibit correlations. We examined correlated variability in V1 circuits from a laminar-specific perspective and observed that cells in the input layer, which have only local projections, encode incoming stimuli optimally by exhibiting low correlated variability. In contrast, output layers, which send projections to other cortical and subcortical areas, encode information suboptimally by exhibiting large correlations. These results argue that neuronal populations in different cortical layers play different roles in network computations. Secondly, a fundamental feature of cortical neurons is their ability to adapt to changes in incoming stimuli. Understanding how adaptation emerges across cortical layers to influence information processing is vital for understanding efficient sensory coding. We examined the effects of adaptation, on the time-scale of a visual fixation, on network synchronization across laminar circuits. Specific to the superficial layers, we observed an increase in gamma-band (30-80 Hz) synchronization after adaptation that was correlated with an improvement in neuronal orientation discrimination performance. Thus, synchronization enhances sensory coding to optimize network processing across laminar circuits. Finally, we tested the hypothesis that individual neurons and local populations synchronize their activity in real-time to communicate information about incoming stimuli, and that the degree of synchronization influences behavioral performance. These analyses assessed for the first time the relationship between changes in laminar cortical networks involved in stimulus processing and behavioral performance.