Kalluri R, Xue J, Eatock RA. Ion channels set spike timing regularity of mammalian vestibular afferent neurons. J Neurophysiol 104: 2034-2051, 2010. First published July 21, 2010 doi:10.1152/jn.00396.2010. In the mammalian vestibular nerve, some afferents have highly irregular interspike intervals and others have highly regular intervals. To investigate whether spike timing is determined by the afferents' ion channels, we studied spiking activity in their cell bodies, isolated from the vestibular ganglia of young rats. Whole cell recordings were made with the perforated-patch method. As previously reported, depolarizing current steps revealed distinct firing patterns. Transient neurons fired one or two onset spikes, independent of current level. Sustained neurons were more heterogeneous, firing either trains of spikes or a spike followed by large voltage oscillations. We show that the firing pattern categories are robust, occurring at different temperatures and ages, both in mice and in rats. A difference in average resting potential did not cause the difference in firing patterns, but contributed to differences in afterhyperpolarizations. A low-voltage-activated potassium current (I LV ) was previously implicated in the transient firing pattern. We show that I LV grew from the first to second postnatal week and by the second week comprised Kv1 and Kv7 (KCNQ) components. Blocking I LV converted step-evoked firing patterns from transient to sustained. Separated from their normal synaptic inputs, the neurons did not spike spontaneously. To test whether the firing-pattern categories might correspond to afferent populations of different regularity, we injected simulated excitatory postsynaptic currents at pseudorandom intervals. Sustained neurons responded to a given pattern of input with more regular firing than did transient neurons. Pharmacological block of I LV made firing more regular. Thus ion channel differences that produce transient and sustained firing patterns in response to depolarizing current steps can also produce irregular and regular spike timing.

Hair bundles are critical to mechanotransduction by vestibular hair cells, but quantitative data are lacking on vestibular bundles in mice or other mammals. Here we quantify bundle heights and their variation with macular locus and hair cell type in adult mouse utricular macula. We also determined that macular organization differs from previous reports. The utricle has approximately 3,600 hair cells, half on each side of the line of polarity reversal (LPR). A band of low hair cell density corresponds to a band of calretinin-positive calyces, i.e., the striola. The relation between the LPR and the striola differs from previous reports in two ways. First, the LPR lies lateral to the striola instead of bisecting it. Second, the LPR follows the striolar trajectory anteriorly, but posteriorly it veers from the edge of the striola to reach the posterior margin of the macula. Consequently, more utricular bundles are oriented mediolaterally than previously supposed. Three hair cell classes are distinguished in calretinin-stained material: type II hair cells, type ID hair cells contacting calretinin-negative (dimorphic) afferents, and type IC hair cells contacting calretinin-positive (calyceal) afferents. They differ significantly on most bundle measures. Type II bundles have short stereocilia. Type IC bundles have kinocilia and stereocilia of similar heights, i.e., KS ratios (ratio of kinocilium to stereocilia heights) approximately 1, unlike other receptor classes. In contrast to these class-specific differences, bundles show little regional variation except that KS ratios are lowest in the striola. These low KS ratios suggest that bundle stiffness is greater in the striola than in the extrastriola.

Because of their mouthing behaviors, children have a higher potential for exposure to available chemicals through the nondietary ingestion route; thus, frequency of hand-to-mouth activity is an important variable for exposure assessments. Such data are limited and difficult to collect. Few published studies report such information, and the studies that have been conducted used different data collection approaches (e.g., videography versus real-time observation), data analysis and reporting methods, ages of children, locations, and even definitions of "mouthing." For this article, hand-to-mouth frequency data were gathered from 9 available studies representing 429 subjects and more than 2,000 hours of behavior observation. A meta-analysis was conducted to study differences in hand-to-mouth frequency based on study, age group, gender, and location (indoor vs. outdoor), to fit variability and uncertainty distributions that can be used in probabilistic exposure assessments, and to identify any data gaps. Results of this analysis indicate that age and location are important for hand-to-mouth frequency, but study and gender are not. As age increases, both indoor and outdoor hand-to-mouth frequencies decrease. Hand-to-mouth behavior is significantly greater indoors than outdoors. For both indoor and outdoor hand-to-mouth frequencies, interpersonal, and intra-personal variability are approximately 60% and approximately 30%, respectively. The variance difference among different studies is much bigger than its mean, indicating that different studies with different methodologies have similar central values. Weibull distributions best fit the observed data for the different variables considered and are presented in this article by study, age group, and location. Average indoor hand-to-mouth behavior ranged from 6.7 to 28.0 contacts/hour, with the lowest value corresponding to the 6 to <11 year olds and the highest value corresponding to the 3 to <6 month olds. Average outdoor hand-to-mouth frequency ranged from 2.9 to 14.5 contacts/hour, with the lowest value corresponding to the 6 to <11 year olds and the highest value corresponding to the 6 to <12 month olds. The analysis highlights the need for additional hand-to-mouth data for the <3 months, 3 to <6 months, and 3 to <6 year age groups using standardized collection and analysis because of lack of data or high uncertainty in available data. This is the first publication to report Weibull distributions as the best fitting distribution for hand-to-mouth frequency; using the best fitting exposure factor distribution will help improve estimates of exposure. The analyses also represent a first comprehensive effort to fit hand-to-mouth frequency variability and uncertainty distributions by indoor/outdoor location and by age groups, using the new standard set of age groups recommended by the U.S. Environmental Protection Agency for assessing childhood exposures. Thus, the data presented in this article can be used to update the U.S. EPA's Child-Specific Exposure Factors Handbook ...

BackgroundDietary exposure from food to toxic inorganic arsenic (iAs) in the general U.S. population has not been well studied.ObjectivesThe goal of this research was to quantify dietary As exposure and analyze the major contributors to total As (tAs) and iAs. Another objective was to compare model predictions with observed data.MethodsProbabilistic exposure modeling for dietary As was conducted with the Stochastic Human Exposure and Dose Simulation–Dietary (SHEDS-Dietary) model, based on data from the National Health and Nutrition Examination Survey. The dose modeling was conducted by combining the SHEDS-Dietary model with the MENTOR-3P (Modeling ENvironment for TOtal Risk with Physiologically Based Pharmacokinetic Modeling for Populations) system. Model evaluation was conducted via comparing exposure and dose-modeling predictions against duplicate diet data and biomarker measurements, respectively, for the same individuals.ResultsThe mean modeled tAs exposure from food is 0.38 μg/kg/day, which is approximately 14 times higher than the mean As exposures from the drinking water. The mean iAs exposure from food is 0.05 μg/kg/day (1.96 μg/day), which is approximately two times higher than the mean iAs exposures from the drinking water. The modeled exposure and dose estimates matched well with the duplicate diet data and measured As biomarkers. The major food contributors to iAs exposure were the following: vegetables (24%); fruit juices and fruits (18%); rice (17%); beer and wine (12%); and flour, corn, and wheat (11%). Approximately 10% of tAs exposure from foods is the toxic iAs form.ConclusionsThe general U.S. population may be exposed to tAs and iAs more from eating some foods than from drinking water. In addition, this model evaluation effort provides more confidence in the exposure assessment tools used.