GRAINS AND OILSEEDS OUTLOOK FOR 2004

Crop Production/Industries,

A Comparison of Risk Preference Measurements with Implications for Extension Programming

Risk and Uncertainty, Teaching/Communication/Extension/Profession,

Kinetics of Cellulose Dissolution in N-Methyl Morpholine-N-Oxide and Evaporative Processes of Similar Solutions

The lyocell process is an environmentally friendly process for producing regenerated cellulose fibers, but is not entirely understood. The lyocell process uses the hygroscopic solvent N-methyl morpholine N-oxide (NMMO) to dissolve cellulose; the resulting solution is often termed a lyocell solution [1-4]. It is the objective of this study to better understand the process by which cellulose dissolves and the nature of lyocell solutions. By observing the disappearance of cellulose fibers into the solvent, rate data may be obtained from which kinetic parameters may be developed. Additionally an independent method for determining the concentration of cellulose in lyocell solutions was desired so as to better to gauge the effect of concentration on the behavior of the solution. Water affects the behavior of NMMO, making it an important factor in the lyocell process. The water content in lyocell samples may be determined by a number of methods including NMR spectroscopy and Fischer’s method. Unfortunately, these methods each require additional chemicals that add to the cost of the analysis. Therefore a novel method was sought for determining the water content of lyocell samples without the use of additional chemicals. Samples of NMMO, some containing dissolved cellulose, were subjected to thermogravimetric analysis on a Pyris 1 TGA to observe the evaporative process and note any effects of cellulose on that process in an effort to develop a rudimentary approach to determining water content on lyocell samples. Additionally, the dissolution of cellulose into NMMO was observed under a Fourier Transform Infrared Spectrometer and a light microscope. Digital photographs with corresponding time measurements were taken of the dissolving cellulose that resulted in dissolution data for single fibers. This was done at several temperatures to extract rate constants for the dissolution process. The results of this project confirmed that cellulose depresses the melting point of NMMO monohydrate and led to a novel method for determining water content in lyocell samples. Detailed mid-infrared spectra were collected for cellulose, NMMO monohydrate, and lyocell samples which were used to develop a predictive model for determining cellulose content in lyocell solutions. Finally, the temperature and surface area dependence for the process of cellulose dissolution in NMMO monohydrate were demonstrated and a rate constant and Arrhenius parameters for the process were obtained. An examination of the phase behavior of NMMO at the onset of cellulose solubility would aid in understanding the dissolution process as would a DSC analysis of NMMO crystallization versus water content. A more detailed multivariate analysis of mid-infrared spectra from lyocell solutions may be performed in the future to improve the predictive model

EEG complexity as a biomarker for autism spectrum disorder risk

BACKGROUND: Complex neurodevelopmental disorders may be characterized by subtle brain function signatures early in life before behavioral symptoms are apparent. Such endophenotypes may be measurable biomarkers for later cognitive impairments. The nonlinear complexity of electroencephalography (EEG) signals is believed to contain information about the architecture of the neural networks in the brain on many scales. Early detection of abnormalities in EEG signals may be an early biomarker for developmental cognitive disorders. The goal of this paper is to demonstrate that the modified multiscale entropy (mMSE) computed on the basis of resting state EEG data can be used as a biomarker of normal brain development and distinguish typically developing children from a group of infants at high risk for autism spectrum disorder (ASD), defined on the basis of an older sibling with ASD. METHODS: Using mMSE as a feature vector, a multiclass support vector machine algorithm was used to classify typically developing and high-risk groups. Classification was computed separately within each age group from 6 to 24 months. RESULTS: Multiscale entropy appears to go through a different developmental trajectory in infants at high risk for autism (HRA) than it does in typically developing controls. Differences appear to be greatest at ages 9 to 12 months. Using several machine learning algorithms with mMSE as a feature vector, infants were classified with over 80% accuracy into control and HRA groups at age 9 months. Classification accuracy for boys was close to 100% at age 9 months and remains high (70% to 90%) at ages 12 and 18 months. For girls, classification accuracy was highest at age 6 months, but declines thereafter. CONCLUSIONS: This proof-of-principle study suggests that mMSE computed from resting state EEG signals may be a useful biomarker for early detection of risk for ASD and abnormalities in cognitive development in infants. To our knowledge, this is the first demonstration of an information theoretic analysis of EEG data for biomarkers in infants at risk for a complex neurodevelopmental disorder.This research was supported by a grant from Autism Speaks (to HTF), National Institute on Deafness and Other Communication Disorders (NIDCD) grant R21 DC08647 (to HTF), NIDCD grant R01 DC 10290 (to HTF and CAN) and a grant from the Simons Foundation (to CAN and WJB). We thank the following people for their help in data collection: Tara Augenstein, Leah Casner, Laura Kasparian, Nina Leezenbaum, Vanessa Vogel-Farley and Annemarie Zuluaga. We are especially grateful to the families who participated in this study. (Autism Speaks; R21 DC08647 - National Institute on Deafness and Other Communication Disorders (NIDCD); R01 DC 10290 - National Institute on Deafness and Other Communication Disorders (NIDCD); Simons Foundation

Measurement Error in Monetary Aggregates: A Markov Switching Factor Approach

This paper compares the different dynamics of the simple sum monetary aggregates and the Divisia monetary aggregate indexes over time, over the business cycle, and across high and low inflation and interest rate phases. Although traditional comparisons of the series sometimes suggest that simple sum and Divisia monetary aggregates share similar dynamics, there are important differences during certain periods, such as around turning points. These differences cannot be evaluated by their average behavior. We use a factor model with regime switching. The model separates out the common movements underlying the monetary aggregate indexes, summarized in the dynamic factor, from individual variations in each individual series, captured by the idiosyncratic terms. The idiosyncratic terms and the measurement errors reveal where the monetary indexes differ. We find several new results. In general, the idiosyncratic terms for both the simple sum aggregates and the Divisia indexes display a business cycle pattern, especially since 1980. They generally rise around the end of high interest rate phases – a couple of quarters before the beginning of recessions – and fall during recessions to subsequently converge to their average in the beginning of expansions. We find that the major differences between the simple sum aggregates and Divisia indexes occur around the beginnings and ends of economic recessions, and during some high interest rate phases. We note the inferences’ policy relevance, which is particularly dramatic at the broadest (M3) level of aggregation. Indeed, as Belongia (1996) has observed in this regard, “measurement matters.”Measurement Error, Divisia Index, Aggregation, State Space, Markov Switching, Monetary Policy

Measurement Error in Monetary Aggregates: A Markov Switching Factor Approach

This paper compares the different dynamics of simple sum monetary aggregates and the Divisia indexes over time, over the business cycle, and across high and low inflation and interest rate phases. Although the traditional comparison of the series may suggest that they share similar dynamics, there are important differences during certain times and around turning points that can not be evaluated by their average behavior. We use a factor model with regime switching that offers several ways in which these differences can be analyzed. The model separates out the common movements underlying the monetary aggregate indexes, summarized in the dynamic factor, from individual variations in each one series, captured by the idiosyncratic terms. The idiosyncratic terms and the measurement errors represent exactly where the monetary indexes differ. We find several new results. In general, the idiosyncratic terms for both the simple sum aggregates and the Divisia indexes display a business cycle pattern, especially since 1980. They generally rise around the end of high interest rate phases – a couple of quarters before the beginning of recessions – and fall during recessions to subsequently converge to their average in the beginning of expansions. We also find that the major differences between the simple sum aggregates and Divisia indexes occur around the beginning and end of economic recessions, and during some high interest rate phases.Measurement Error, Divisia Index, Aggregation, State Space, Markov Switching, Monetary Policy

Electronic Health Records: Delivering the Right Information to the Right Health Care Providers at the Right Time

In 1993 I wrote "Communication and information management consume as much as 40 percent of all inpatient costs, yet errors still occur at an unacceptable rate. The Institute of medicine has suggested that electronic medical records (EMRs) will help lower health care costs, maintain quality of care, and provide physicians with better information" (Tierney et al. 1993, 379). Nearly 20 years later I'm here to tell you how far we've come toward implementing EHRs nationwide, and what we've learned from our experience at the Regenstrief Institute in Indiana University. Most of us consider health care to be a service business, because we think in terms of a patient who goes to the doctor to get some thing: advice, medication, devices, surgery, or physical therapy. I'm going to argue that what patients really get, and health care practitioners really provide, is information. Ninety-eight percent of what we who practice medicine do is not the end result, the end service, but the overall process of getting there.electronic medical records, EMRs, EHRs

Measurement Error in Monetary Aggregates: A Markov Switching Factor Approach

This paper compares the different dynamics of the simple sum monetary aggregates and the Divisia monetary aggregate indexes over time, over the business cycle, and across high and low inflation and interest rate phases. Although traditional comparisons of the series sometimes suggest that simple sum and Divisia monetary aggregates share similar dynamics, there are important differences during certain periods, such as around turning points. These differences cannot be evaluated by their average behavior. We use a factor model with regime switching. The model separates out the common movements underlying the monetary aggregate indexes, summarized in the dynamic factor, from individual variations in each individual series, captured by the idiosyncratic terms. The idiosyncratic terms and the measurement errors reveal where the monetary indexes differ. We find several new results. In general, the idiosyncratic terms for both the simple sum aggregates and the Divisia indexes display a business cycle pattern, especially since 1980. They generally rise around the end of high interest rate phases – a couple of quarters before the beginning of recessions – and fall during recessions to subsequently converge to their average in the beginning of expansions. We find that the major differences between the simple sum aggregates and Divisia indexes occur around the beginnings and ends of economic recessions, and during some high interest rate phases. We note the inferences’ policy relevance, which is particularly dramatic at the broadest (M3) level of aggregation. Indeed, as Belongia (1996) has observed in this regard, “measurement matters.”Measurement Error, Divisia Index, Aggregation, State Space, Markov Switching, Monetary Policy

Measurement Error in Monetary Aggregates: A Markov Switching Factor Approach

This paper compares the different dynamics of the simple sum monetary aggregates and the Divisia monetary aggregate indexes over time, over the business cycle, and across high and low inflation and interest rate phases. Although traditional comparisons of the series sometimes suggest that simple sum and Divisia monetary aggregates share similar dynamics, there are important differences during certain periods, such as around turning points. These differences cannot be evaluated by their average behavior. We use a factor model with regime switching. The model separates out the common movements underlying the monetary aggregate indexes, summarized in the dynamic factor, from individual variations in each individual series, captured by the idiosyncratic terms. The idiosyncratic terms and the measurement errors reveal where the monetary indexes differ. We find several new results. In general, the idiosyncratic terms for both the simple sum aggregates and the Divisia indexes display a business cycle pattern, especially since 1980. They generally rise around the end of high interest rate phases – a couple of quarters before the beginning of recessions – and fall during recessions to subsequently converge to their average in the beginning of expansions. We find that the major differences between the simple sum aggregates and Divisia indexes occur around the beginnings and ends of economic recessions, and during some high interest rate phases. We note the policy relevance of the inferences. Indeed, as Belongia (1996) has observed in this regard, "measurement matters."Measurement error; monetary aggregation; Divisia index; aggregation; state space; Markov switching; monetary policy; index number theory; factor models