Spatial Distribution of Lead in Sacramento, California, USA

Chronic exposure to lead remains a health concern in many urban areas; Sacramento, California is one example, with state surveillance data showing nearly 3% of screened children reported with blood lead levels over 4.5 μg/dL in 2009. To investigate the environmental exposure, 91 soil samples were collected and analyzed by ICP-AES and ICP-MS for 14 elements. An additional 28 samples were collected from areas of focus and analyzed by hand-held X-ray fluorescence spectrometry for Pb and Zn. Analysis of the metals data revealed non-normal distributions and positive skewness, consistent with anthropogenic input. In addition, high correlation coefficients (≥0.75) of metal concentrations in Cd-Pb, Cd-Zn, Pb-Zn, and Sb-Sn pairs suggest similarities in the input mechanisms. Semivariograms generated from Pb and associated metals reveal these metals to exhibit spatial correlation. A prediction map of lead concentrations in soil was generated by ordinary kriging, showing elevated concentrations in soil located in the central, older area of Sacramento where historic traffic density and industrial activity have been historically concentrated. XRF analysis of Pb and Zn from additional samples verifies elevated concentrations in the central areas of Sacramento as predicted

Microwave Spectroscopy

Contains reports on three research projects

A Brain-Machine Interface for Control of Medically-Induced Coma

Medically-induced coma is a drug-induced state of profound brain inactivation and unconsciousness used to treat refractory intracranial hypertension and to manage treatment-resistant epilepsy. The state of coma is achieved by continually monitoring the patient's brain activity with an electroencephalogram (EEG) and manually titrating the anesthetic infusion rate to maintain a specified level of burst suppression, an EEG marker of profound brain inactivation in which bursts of electrical activity alternate with periods of quiescence or suppression. The medical coma is often required for several days. A more rational approach would be to implement a brain-machine interface (BMI) that monitors the EEG and adjusts the anesthetic infusion rate in real time to maintain the specified target level of burst suppression. We used a stochastic control framework to develop a BMI to control medically-induced coma in a rodent model. The BMI controlled an EEG-guided closed-loop infusion of the anesthetic propofol to maintain precisely specified dynamic target levels of burst suppression. We used as the control signal the burst suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state. We characterized the EEG response to propofol using a two-dimensional linear compartment model and estimated the model parameters specific to each animal prior to initiating control. We derived a recursive Bayesian binary filter algorithm to compute the BSP from the EEG and controllers using a linear-quadratic-regulator and a model-predictive control strategy. Both controllers used the estimated BSP as feedback. The BMI accurately controlled burst suppression in individual rodents across dynamic target trajectories, and enabled prompt transitions between target levels while avoiding both undershoot and overshoot. The median performance error for the BMI was 3.6%, the median bias was -1.4% and the overall posterior probability of reliable control was 1 (95% Bayesian credibility interval of [0.87, 1.0]). A BMI can maintain reliable and accurate real-time control of medically-induced coma in a rodent model suggesting this strategy could be applied in patient care.National Institutes of Health (U.S.) (Director's Transformative Award R01 GM104948)National Institutes of Health (U.S.) (Pioneer Award DP1-OD003646)National Institutes of Health (U.S.) (NIH K08-GM094394)Massachusetts General Hospital. Dept. of Anesthesia and Critical Car

Propofol and sevoflurane induce distinct burst suppression patterns in rats

Burst suppression is an EEG pattern characterized by alternating periods of high-amplitude activity (bursts) and relatively low amplitude activity (suppressions). Burst suppression can arise from several different pathological conditions, as well as from general anesthesia. Here we review current algorithms that are used to quantify burst suppression, its various etiologies, and possible underlying mechanisms. We then review clinical applications of anesthetic-induced burst suppression. Finally, we report the results of our new study showing clear electrophysiological differences in burst suppression patterns induced by two common general anesthetics, sevoflurane and propofol. Our data suggest that the circuit mechanisms that generate burst suppression activity may differ among general anesthetics

Microwave Spectroscopy

Contains reports on three research projects

The GNAT library for local and remote gene mention normalization

Summary: Identifying mentions of named entities, such as genes or diseases, and normalizing them to database identifiers have become an important step in many text and data mining pipelines. Despite this need, very few entity normalization systems are publicly available as source code or web services for biomedical text mining. Here we present the Gnat Java library for text retrieval, named entity recognition, and normalization of gene and protein mentions in biomedical text. The library can be used as a component to be integrated with other text-mining systems, as a framework to add user-specific extensions, and as an efficient stand-alone application for the identification of gene and protein names for data analysis. On the BioCreative III test data, the current version of Gnat achieves a Tap-20 score of 0.1987