Bunch-by-bunch position measurement and analysis at PLS-II.

A bunch-by-bunch measurement system has been developed at Pohang Light Source II. The system consists of a four-channel button pick-up, 20 GHz sampling oscilloscope and an 800 MHz low-pass digital filter. Upon measuring a bunch-by-bunch spatio-temporal beam motion matrix over many turns, singular-value decomposition analysis is used to reveal the dominant coupled-bunch modes. The system can diagnose injection oscillations due to kicker errors and the effect of resistive-wall impedance that gives rise to instability during operation.

Multiple-input nonlinear modelling of cerebral haemodynamics using spontaneous arterial blood pressure, end-tidal CO2 and heart rate measurements.

In order to examine the effect of changes in heart rate (HR) upon cerebral perfusion and autoregulation, we include the HR signal recorded from 18 control subjects as a third input in a two-input model of cerebral haemodynamics that has been used previously to quantify the dynamic effects of changes in arterial blood pressure and end-tidal CO2upon cerebral blood flow velocity (CBFV) measured at the middle cerebral arteries via transcranial Doppler ultrasound. It is shown that the inclusion of HR as a third input reduces the output prediction error in a statistically significant manner, which implies that there is a functional connection between HR changes and CBFV. The inclusion of nonlinearities in the model causes further statistically significant reduction of the output prediction error. To achieve this task, we employ the concept of principal dynamic modes (PDMs) that yields dynamic nonlinear models of multi-input systems using relatively short data records. The obtained PDMs suggest model-driven quantitative hypotheses for the role of sympathetic and parasympathetic activity (corresponding to distinct PDMs) in the underlying physiological mechanisms by virtue of their relative contributions to the model output. These relative PDM contributions are subject-specific and, therefore, may be used to assess personalized characteristics for diagnostic purposes.

Compartmental and Data-Based Modeling of Cerebral Hemodynamics: Linear Analysis.

Compartmental and data-based modeling of cerebral hemodynamics are alternative approaches that utilize distinct model forms and have been employed in the quantitative study of cerebral hemodynamics. This paper examines the relation between a compartmental equivalent-circuit and a data-based input-output model of dynamic cerebral autoregulation (DCA) and CO2-vasomotor reactivity (DVR). The compartmental model is constructed as an equivalent-circuit utilizing putative first principles and previously proposed hypothesis-based models. The linear input-output dynamics of this compartmental model are compared with data-based estimates of the DCA-DVR process. This comparative study indicates that there are some qualitative similarities between the two-input compartmental model and experimental results.

Model-based physiomarkers of cerebral hemodynamics in patients with mild cognitive impairment.

In our previous studies, we have introduced model-based "functional biomarkers" or "physiomarkers" of cerebral hemodynamics that hold promise for improved diagnosis of early-stage Alzheimer's disease (AD). The advocated methodology utilizes subject-specific data-based dynamic nonlinear models of cerebral hemodynamics to compute indices (serving as possible diagnostic physiomarkers) that quantify the state of cerebral blood flow autoregulation to pressure-changes (CFAP) and cerebral CO2 vasomotor reactivity (CVMR) in each subject. The model is estimated from beat-to-beat measurements of mean arterial blood pressure, mean cerebral blood flow velocity and end-tidal CO2, which can be made reliably and non-invasively under resting conditions. In a previous study, it was found that a CVMR index quantifying the impairment in CO2 vasomotor reactivity correlates with clinical indications of early AD, offering the prospect of a potentially useful diagnostic tool. In this paper, we explore the use of the same model-based indices for patients with amnestic Mild Cognitive Impairment (MCI), a preclinical stage of AD, relative to a control subjects and clinical cognitive assessments. It was found that the model-based CVMR values were lower for MCI patients relative to the control subjects.

On parsing the neural code in the prefrontal cortex of primates using principal dynamic modes.

Nonlinear modeling of multi-input multi-output (MIMO) neuronal systems using Principal Dynamic Modes (PDMs) provides a novel method for analyzing the functional connectivity between neuronal groups. This paper presents the PDM-based modeling methodology and initial results from actual multi-unit recordings in the prefrontal cortex of non-human primates. We used the PDMs to analyze the dynamic transformations of spike train activity from Layer 2 (input) to Layer 5 (output) of the prefrontal cortex in primates performing a Delayed-Match-to-Sample task. The PDM-based models reduce the complexity of representing large-scale neural MIMO systems that involve large numbers of neurons, and also offer the prospect of improved biological/physiological interpretation of the obtained models. PDM analysis of neuronal connectivity in this system revealed "input-output channels of communication" corresponding to specific bands of neural rhythms that quantify the relative importance of these frequency-specific PDMs across a variety of different tasks. We found that behavioral performance during the Delayed-Match-to-Sample task (correct vs. incorrect outcome) was associated with differential activation of frequency-specific PDMs in the prefrontal cortex.

Model-based quantification of cerebral hemodynamics as a physiomarker for Alzheimer's disease?

Previous studies have found that Alzheimer's disease (AD) impairs cerebral vascular function, even at early stages of the disease. This offers the prospect of a useful diagnostic method for AD, if cerebral vascular dysfunction can be quantified reliably within practical clinical constraints. We present a recently developed methodology that utilizes a data-based dynamic nonlinear closed-loop model of cerebral hemodynamics to compute "physiomarkers" quantifying the state of cerebral flow autoregulation to pressure-changes (CA) and cerebral CO2 vasomotor reactivity (CVMR) in each subject. This model is estimated from beat-to-beat measurements of mean arterial blood pressure, mean cerebral blood flow velocity and end-tidal CO2, which can be made reliably and non-invasively under resting conditions. This model may also take an open-loop form and comparisons are made with the closed-loop counterpart. The proposed model-based physiomarkers take the form of two indices that quantify the gain of the CA and CVMR processes in each subject. It was found in an initial set of clinical data that the CVMR index delineates AD patients from control subjects and, therefore, may prove useful in the improved diagnosis of early-stage AD.

Closed-loop dynamic modeling of cerebral hemodynamics.

The dynamics of cerebral hemodynamics have been studied extensively because of their fundamental physiological and clinical importance. In particular, the dynamic processes of cerebral flow autoregulation (CFA) and CO2 vasomotor reactivity have attracted broad attention because of their involvement in a host of pathologies and clinical conditions (e.g., hypertension, syncope, stroke, traumatic brain injury, vascular dementia, Alzheimer's disease, mild cognitive impairment etc.). This raises the prospect of useful diagnostic methods being developed on the basis of quantitative models of cerebral hemodynamics, if cerebral vascular dysfunction can be quantified reliably from data collected within practical clinical constraints. This paper presents a modeling method that utilizes beat-to-beat measurements of mean arterial blood pressure, cerebral blood flow velocity and end-tidal CO2 (collected non-invasively under resting conditions) to quantify the dynamics of CFA and cerebral vasomotor reactivity (CVMR). The unique and novel aspect of this dynamic model is that it is nonlinear and operates in a closed-loop configuration.

Analysis of the association between air pollution and allergic diseases exposure from nearby sources of ambient air pollution within elementary school zones in four Korean cities.

The objectives of this study were to survey elementary school students regarding the environmental conditions of their elementary schools and to assess the relationship between air pollution and allergic disease using the International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire. Therefore, this study was designed as a cross-sectional study. In this study, seven elementary schools were selected and they were classified into three categories. The selection included one school with no traffic-related or other pollutants, three with traffic-related pollutants, and three with traffic-related and other pollutants from industrial and filling station sources. The ISAAC questionnaire survey was given to all of the students except to those in the 1st grade who were presumed to be less likely to be exposed to the school environment than the remainder of the students attending those seven schools. The assessment of allergic disease was conducted on a total of 4,545 students. Three school zones with critical exposure were selected within each school and they were evaluated based on the levels of black carbon (BC), PM10, SO2, NO2, and O3. There was a significant increase in the risks based on the odds ratios of treatment experiences (within 1 year) for allergy-related diseases such as asthma and allergic rhinitis (a) in the school group with traffic-related pollutants and the school group with complex pollutants were 2.12 (1.41-3.19) and 1.59 (1.06-2.37), respectively, in comparison to the school groups with no exposure to pollutants. This was determined based on the odds ratio of symptoms and treatment experiences for allergy-related diseases by group based on the home town zone as a reference. Also, in the case of atopic dermatitis, the odds ratio of treatment experiences (within 1 year) was 1.42 (1.02-1.97), which indicated elevated risks compared to the students in the S1 school. A regression analysis was used to assess the relationship between the substances and the symptomatic experiences within the last year. There were significant increases in the odds ratio of the symptoms associated with allergic rhinitis and the BC and SO2 in the complex pollution areas. The results of the assessment of the relationship between atopic dermatitis-associated symptoms and O3 showed that the odds ratio increased with statistical significance.

Design of optimal stimulation patterns for neuronal ensembles based on Volterra-type hierarchical modeling.

This paper presents a general methodology for the optimal design of stimulation patterns applied to neuronal ensembles in order to elicit a desired effect. The methodology follows a variant of the hierarchical Volterra modeling approach that utilizes input-output data to construct predictive models that describe the effects of interactions among multiple input events in an ascending order of interaction complexity. The illustrative example presented in this paper concerns the multi-unit activity of CA1 neurons in the hippocampus of a rodent performing a learned delayed-nonmatch-to-sample (DNMS) task. The multi-unit activity of the hippocampal CA1 neurons is recorded via chronically implanted multi-electrode arrays during this task. The obtained model quantifies the likelihood of having correct performance of the specific task for a given multi-unit (spatiotemporal) activity pattern of a CA1 neuronal ensemble during the 'sample presentation' phase of the DNMS task. The model can be used to determine computationally (off-line) the 'optimal' multi-unit stimulation pattern that maximizes the likelihood of inducing the correct performance of the DNMS task. Our working hypothesis is that application of this optimal stimulation pattern will enhance performance of the DNMS task due to enhancement of memory formation and storage during the 'sample presentation' phase of the task.

Diagnostic biomarkers for Alzheimer's disease using dynamic nonlinear models based on Principal Dynamic Modes.

Sensitive and robust diagnostic biomarkers for Alzheimer's disease (AD) were sought using dynamic nonlinear models of the causal interrelationships among time-series (beat-to-beat) data of arterial blood pressure, end-tidal CO(2) and cerebral blood flow velocity collected in human subjects (4 AD patients and 4 control subjects). These models were based on Principal Dynamic Modes (PDM) and yielded a reliable biomarker for AD diagnosis in the form of the "Effective CO(2) Reactivity Index" (ECRI). The results from this initial set of subjects corroborated the efficacy of the ECRI biomarker for accurate AD diagnosis.

Dynamic nonlinear modeling of interactions between neuronal ensembles using principal dynamic modes.

We present a novel methodology for modeling the interactions between neuronal ensembles that utilizes the concept of Principal Dynamic Modes (PDM) and their associated nonlinear functions (ANF). This new approach seeks to reduce the complexity of the multi-input/multi-output (MIMO) model of the interactions between neuronal ensembles--an issue of critical practical importance in scaling up the MIMO models to incorporate hundreds (or even thousands) of input-output neurons. Global PDMs were extracted from the data using estimated first-order and second-order kernels and singular value decomposition (SVD). These global PDMs represent an efficient "coordinate system" for the representation of the MIMO model. The ANFs of the PDMs are estimated from the histograms of the combinations of PDM output values that lead to output spikes. For initial testing and validation of this approach, we applied it to a set of data collected at the pre-frontal cortex of a non-human primate during a behavioral task (Delayed Match-to-Sample). Recorded spike trains from Layer-2 neurons were viewed as the "inputs" and from Layer-5 neurons as the outputs. Model prediction performance was evaluated by means of computed Receiver Operating Characteristic (ROC) curves. The results indicate that this methodology may greatly reduce the complexity of the MIMO model without significant degradation of performance.

Restorative encoding memory integrative neural device: "REMIND".

Construction and application of a neural prosthesis device that enhances existing and replaces lost memory capacity in humans is the focus of research described here in rodents. A unique approach for the analysis and application of neural population firing has been developed to decipher the pattern in which information is successfully encoded by the hippocampus where mnemonic accuracy is critical. A nonlinear dynamic multi-input multi-output (MIMO) model is utilized to extract memory relevant firing patterns in CA3 and CA1 and to predict online what the consequences of the encoded firing patterns reflect for subsequent information retrieval for successful performance of delayed-nonmatch-to-sample (DNMS) memory task in rodents. The MIMO model has been tested successfully in a number of different contexts, each of which produced improved performance by a) utilizing online predicted codes to regulate task difficulty, b) employing electrical stimulation of CA1 output areas in the same pattern as successful cell firing, c) employing electrical stimulation to recover cell firing compromised by pharmacological agents and d) transferring and improving performance in naïve animals using the same stimulation patterns that are effective in fully trained animals. The results in rodents formed the basis for extension of the MIMO model to nonhuman primates in the same type of memory task that is now being tested in the last step prior to its application in humans.

Isolation and determination of anthocyanins in seed coats of black soybean (Glycine max (L.) Merr.).

Anthocyanin pigments in seed coats of black soybean (Glycine max (L.) Merr.) were extracted with 1% HCl-CH(3)OH, and the crude anthocyanin extract was purified by Shepadex LH-20 and Lichroprep RP-18 open-column chromatography. Three major anthocyanins were isolated, and their chemical structures were identified by spectroscopic methods (UV-visible, FABMS, (1)H and (13)C NMR, and by TLC). The complete structures of these anthocyanins were elucidated as delphinidin-3-glucoside, cyanidin-3-glucoside, and petunidin-3-glucoside. Among them, petunidin-3-glucoside was identified as a new anthocyanin in black soybeans. On the basis of RP-HPLC with a UV-vis detector, the contents of delphinidin-3-glucoside, cyanidin-3-glucoside, petunidin-3-glucoside, and total anthocyanins in seed coats of 10 black soybeans were found in the ranges of 0-3.71, 0.94-15.98, 0-1.41, and 1.58-20.18 mg/g, respectively. The results obtained in this study imply that the seed coats of black soybean can be used as a good source for cyanidin-3-glucoside and delphinidin-3-glucoside.

PCDD and PCDF exposures in workers and controls living near an industrial waste incinerator.

This study measured the levels of 17 congeners of PCDDs/PCDFs in serum to compare the levels between potentially exposed workers at an industrial waste incinerator and any residents with no known exposures. The 1,2,3,6,7,8- and 1,2,3,7,8,9-HxCDD were detected in serum of workers but in controls. Likewise, 1,2,3,7,8-PeCDF, 1,2,3,6,7,8- and 1,2,3,7,8,9-HxCDF were detected only in serum of workers. The international toxic equivalent (TEQ) levels of PCDDs/PCDFs in sera of workers are much higher than in controls. Among PCDDs, the proportion of total concentration and TEQ level is dominated predominantly by 1,2,3,6,7,8- and 1,2,3,7,8,9-HxCDD. We need extensive studies to estimate human exposure and are continuing this investigation.

Pesticide poisoning and its related factors among Korean farmers.

This study has been carried out to assess the health damages due to pesticide use and its related risk factors among Korean farmers. Data regarding pesticide handling and poisoning symptoms were collected by means of a questionnaire filled in by 1,032 farmers from two provinces. The results showed that during summer farming 21.9% of the subjects experienced suspected pesticide poisoning. 18.8% mild poisoning, and 2% more serious poisoning. Univariate and logistic regression analyses between "no poisoning", including the "suspected poisoning" and "poisoning" groups, were performed to select significant variables related to pesticide poisoning. Four variables were significantly associated: sex, days of consecutive pesticide use, hours of pesticide use per day, having received safety education (weakly associated), and compliance with safety guidelines for application. Safety education was weakly associated with poisoning, while age, education, wearing protective gear, and compliance with safety guidelines for personal hygiene after pesticide use were not significant risk factors to determine pesticide poisoning.

Diagnosis of acute hepatitis E infection utilizing enzyme immunoassay.

Acute hepatitis E infection was diagnosed in a Pakistani immigrant admitted to the University of Illinois Hospital. Utilizing enzyme immunoassay (EIA) tests, specific IgG and IgM class antibodies to three different epitopes of hepatitis E virus (HEV) were detected 12 weeks after the onset of illness and in the early convalescent stage. Sixteen months after the onset of hepatitis, IgM anti-HEV was no longer detectable. Low levels of IgG class anti-HEV antibodies continued to be detected. We demonstrate the utility of the EIA HEV assay to diagnose prospectively acute HEV infection.

Subjective indicators and the comparative evaluation of the quality of community life.

Use of subjective indicators in the many efforts of comparative evaluation of community life has been confined primarily to the estimation of the proportions of people who are satisfied or dissatisfied with their life experiences. A sample of 1,488 persons in seven Illinois cities were interviewed by telephone and asked to rate their satisfaction with five community services. Findings showed that the measurement of central tendency characteristics of subjective indicators alone can result in an incomplete and misleading picture of the quality of community life.