Effect of Rosiglitazone on Insulin Sensitivity and Body Composition in Type 2 Diabetic Patients

Objective: To investigate the effects of rosiglitazone (RSG) on insulin sensitivity and regional adiposity (including intrahepatic fat) in patients with type 2 diabetes. Research Methods and Procedures: We examined the effect of RSG (8 mg/day, 2 divided doses) compared with placebo on insulin sensitivity and body composition in 33 type 2 diabetic patients. Measurements of insulin sensitivity (euglycemic hyperinsulinemic clamp), body fat (abdominal magnetic resonance imaging and DXA), and liver fat (magnetic resonance spectroscopy) were taken at baseline and repeated after 16 weeks of treatment. Results: There was a significant improvement in glycemic control (glycosylated hemoglobin -0.7 +/- 0.7%, p less than or equal to 0.05) and an 86% increase in insulin sensitivity in the RSG group (glucose-disposal rate change from baseline: 17.5 +/- 14.5 mumol glucose/min/kg free fat mass, P < 0.05), but no significant change in the placebo group compared with baseline. Total body weight and fat mass increased (p &LE; 0.05) with RSG (2.1 +/- 2.0 kg and 1.4 +/- 1.6 kg, respectively) with 95% of the increase in adiposity occurring in nonabdominal regions. In the abdominal region, RSG increased subcutaneous fat area by 8% (25.0 +/- 28.7 cm(2), p = 0.02), did not alter intra-abdominal fat area, and reduced intrahepatic fat levels by 45% (-6.7 +/- 9.7%, concentration relative to water). Discussion: Our data indicate that RSG greatly improves insulin sensitivity in patients with type 2 diabetes and is associated with an increase in adiposity in subcutaneous but not visceral body regions.

Consensus neuropathological diagnosis of common dementia syndromes: testing and standardising the use of multiple diagnostic criteria

The aim of this study was to assess the variation between neuropathologists in the diagnosis of common dementia syndromes when multiple published protocols are applied. Fourteen out of 18 Australian neuropathologists participated in diagnosing 20 cases (16 cases of dementia, 4 age-matched controls) using consensus diagnostic methods. Diagnostic criteria, clinical synopses and slides from multiple brain regions were sent to participants who were asked for case diagnoses. Diagnostic sensitivity, specificity, predictive value, accuracy and variability were determined using percentage agreement and kappa statistics. Using CERAD criteria, there was a high inter-rater agreement for cases with probable and definite Alzheimer's disease but low agreement for cases with possible Alzheimer's disease. Braak staging and the application of criteria for dementia with Lewy bodies also resulted in high inter-rater agreement. There was poor agreement for the diagnosis of frontotemporal dementia and for identifying small vessel disease. Participants rarely diagnosed more than one disease in any case. To improve efficiency when applying multiple diagnostic criteria, several simplifications were proposed and tested on 5 of the original 210 cases. Inter-rater reliability for the diagnosis of Alzheimer's disease and dementia with Lewy bodies significantly improved. Further development of simple and accurate methods to identify small vessel lesions and diagnose frontotemporal dementia is warranted.

Input-driven Language Learning

Input-driven models provide an explicit and readily testable account of language learning. Although we share Ellis's view that the statistical structure of the linguistic environment is a crucial and, until recently, relatively neglected variable in language learning, we also recognize that the approach makes three assumptions about cognition and language learning that are not universally shared. The three assumptions concern (a) the language learner as an intuitive statistician, (b) the constraints on what constitute relevant surface cues, and (c) the redescription problem faced by any system that seeks to derive abstract grammatical relations from the frequency of co-occurring surface forms and functions. These are significant assumptions that must be established if input-driven models are to gain wider acceptance. We comment on these issues and briefly describe a distributed, instance-based approach that retains the key features of the input-driven account advocated by Ellis but that also addresses shortcomings of the current approaches.

Extensive multiple test centre evaluation of the VecTest malaria antigen panel assay

To determine which species and populations of Anopheles transmit malaria in any given situation, immunological assays for malaria sporozoite antigen can replace traditional microscopical examination of freshly dissected Anopheles. We developed a wicking assay for use with mosquitoes that identifies the presence or absence of specific peptide epitopes of circumsporozoite (CS) protein of Plasmodium falciparum and two strains of Plasmodium vivax (variants 210 and 247). The resulting assay (VecTest(TM) Malaria) is a rapid, one-step procedure using a 'dipstick' test strip capable of detecting and distinguishing between P. falciparum and P. vivax infections in mosquitoes. The objective of the present study was to test the efficacy, sensitivity, stability and field-user acceptability of this wicking dipstick assay. In collaboration with 16 test centres world-wide, we evaluated more than 40 000 units of this assay, comparing it to the standard CS ELISA. The 'VecTest(TM) Malaria' was found to show 92% sensitivity and 98.1% specificity, with 97.8% accuracy overall. In accelerated storage tests, the dipsticks remained stable for >15 weeks in dry conditions up to 45degreesC and in humid conditions up to 37degreesC. Evidently, this quick and easy dipstick test performs at an acceptable level of reliability and offers practical advantages for field workers needing to make rapid surveys of malaria vectors.

Simulation of the load-unload response ratio and critical sensitivity in the lattice solid model

The Load-Unload Response Ratio (LURR) method is an intermediate-term earthquake prediction approach that has shown considerable promise. It involves calculating the ratio of a specified energy release measure during loading and unloading where loading and unloading periods are determined from the earth tide induced perturbations in the Coulomb Failure Stress on optimally oriented faults. In the lead-up to large earthquakes, high LURR values are frequently observed a few months or years prior to the event. These signals may have a similar origin to the observed accelerating seismic moment release (AMR) prior to many large earthquakes or may be due to critical sensitivity of the crust when a large earthquake is imminent. As a first step towards studying the underlying physical mechanism for the LURR observations, numerical studies are conducted using the particle based lattice solid model (LSM) to determine whether LURR observations can be reproduced. The model is initialized as a heterogeneous 2-D block made up of random-sized particles bonded by elastic-brittle links. The system is subjected to uniaxial compression from rigid driving plates on the upper and lower edges of the model. Experiments are conducted using both strain and stress control to load the plates. A sinusoidal stress perturbation is added to the gradual compressional loading to simulate loading and unloading cycles and LURR is calculated. The results reproduce signals similar to those observed in earthquake prediction practice with a high LURR value followed by a sudden drop prior to macroscopic failure of the sample. The results suggest that LURR provides a good predictor for catastrophic failure in elastic-brittle systems and motivate further research to study the underlying physical mechanisms and statistical properties of high LURR values. The results provide encouragement for earthquake prediction research and the use of advanced simulation models to probe the physics of earthquakes.

Parallel simulation system for earthquake generation: Fault analysis modules and parallel coupling analysis

Solid earth simulations have recently been developed to address issues such as natural disasters, global environmental destruction and the conservation of natural resources. The simulation of solid earth phenomena involves the analysis of complex structures including strata, faults, and heterogeneous material properties. Simulation of the generation and cycle of earthquakes is particularly important, but such simulations require the analysis of complex fault dynamics. GeoFEM is a parallel finite-element analysis system intended for solid earth field phenomena problems. This paper describes recent development in the GeoFEM project for the simulation of earthquake generation and cycles.

Exchange rate sensitivity of Australian international equity funds

This paper analyses the exchange rate exposure displayed by a sample of Australian international equity trusts (IET). Exchange rate exposure is also examined in the context of differing economic climates with particular emphasis on the Asian crisis in mid-1997. It is found that there is evidence of exchange rate exposure particularly in the context of a multiple exchange rate model. Exposure varies substantially between three alternative time periods with different exposure apparent subsequent to the Asian crisis than prior to this event.

Accuracy of Mothers' Retrospective Reports of Smoking During Pregnancy: Comparison with Twin Sister Informant Ratings

Retrospective assessment of maternal smoking or substance use during pregnancy is sometimes unavoidable. The unusually close relationship of twin sister pairs permits comparison of self-report data versus co-twin informant data on substance use during pregnancy. Information about smoking during pregnancy has been gathered from a series of mothers from an Australian volunteer twin panel (576 women reporting on 995 pregnancies), supplemented in many cases by independent ratings of their smoking by twin sister informants (821 pregnancies). Estimates of the proportion of women who had never smoked regularly (56-58%), who had smoked but did not smoke during a particular pregnancy (16-21%), or who smoked throughout the pregnancy (16-18%), were in good agreement whether based on self-report or twin sister informant data. However, informants underreported cases who smoked during the first trimester but then quit (1-3% versus 7-9% by self-report). Women who smoked throughout pregnancy (by informant report) rarely denied a history of regular smoking (

Computational differentiation of N-terminal signal peptides and transmembrane helices

Signal peptides and transmembrane helices both contain a stretch of hydrophobic amino acids. This common feature makes it difficult for signal peptide and transmembrane helix predictors to correctly assign identity to stretches of hydrophobic residues near the N-terminal methionine of a protein sequence. The inability to reliably distinguish between N-terminal transmembrane helix and signal peptide is an error with serious consequences for the prediction of protein secretory status or transmembrane topology. In this study, we report a new method for differentiating protein N-terminal signal peptides and transmembrane helices. Based on the sequence features extracted from hydrophobic regions (amino acid frequency, hydrophobicity, and the start position), we set up discriminant functions and examined them on non-redundant datasets with jackknife tests. This method can incorporate other signal peptide prediction methods and achieve higher prediction accuracy. For Gram-negative bacterial proteins, 95.7% of N-terminal signal peptides and transmembrane helices can be correctly predicted (coefficient 0.90). Given a sensitivity of 90%, transmembrane helices can be identified from signal peptides with a precision of 99% (coefficient 0.92). For eukaryotic proteins, 94.2% of N-terminal signal peptides and transmembrane helices can be correctly predicted with coefficient 0.83. Given a sensitivity of 90%, transmembrane helices can be identified from signal peptides with a precision of 87% (coefficient 0.85). The method can be used to complement current transmembrane protein prediction and signal peptide prediction methods to improve their prediction accuracies. (C) 2003 Elsevier Inc. All rights reserved.

Retinal specializations in the blue marlin: eyes designed for sensitivity to low light levels

The large eyes and well-developed visual system of billfishes suggest that vision is an important sense for the detection and interception of prey and lures. Investigations of visual abilities in these large pelagic fishes are difficult, however anatomical studies of billfish eyes and retinas allow prediction of a number of visual capabilities. From the density of ganglion cells in the blue marlin (Makaira nigricans) retina, visual acuities of less than 10 cycles per degree were derived, a surprisingly low visual resolution given the absolute size of the marlin eye. Cone photoreceptors, on the other hand, were present in high densities, resulting in a presumed summation of cones to ganglion cells at a ratio of 40:1, even in the area of best vision. The optical sensitivity of the marlin eye was high owing to the large dimensions of the cone photoreceptors. These results indicate that the marlin eye is specifically adapted to cope with the low light levels encountered during diving. Since the marlin is likely to use its vision at depth, it is suggested that this line of research could help estimate the limits of vertical distribution based on light level.

In situ characterization of stickiness of sugar-rich foods using a linear actuator driven stickiness testing device

A stickiness testing device based on the probe tack test has been designed and tested. It was used to perform in situ characterization of drying hemispherical drops with an initial radius 3.5 mm. Tests were carried out in two drying temperatures, 63 and 95 degreesC. Moisture and temperature histories of the drying drops of fructose, honey, sucrose, maltodextrin and sucrose-maltodextrin mixtures were determined. The rates of moisture evaporation of the fructose solution was the fastest while those of the maltodextrin solution was the lowest. A profile reversal was observed when the temperature profiles of these materials were compared. Different modes of failure were observed during the stickiness tests. Pure fructose and honey solutions remained completely sticky and failed cohesively until the end of drying. Pure sucrose solution remained sticky and failed cohesively until complete crystallization occurred. The surface of the maltodextrin drops formed a skin shortly after the start of drying. It exhibited adhesive failure and reached a state of non-adhesion. Addition of maltodextrin significantly altered the stickiness of sucrose solution. (C) 2002 Elsevier Science Ltd. All rights reserved.

Methodologies for calibration and predictive analysis of a watershed model

The use of a fitted parameter watershed model to address water quantity and quality management issues requires that it be calibrated under a wide range of hydrologic conditions. However, rarely does model calibration result in a unique parameter set. Parameter nonuniqueness can lead to predictive nonuniqueness. The extent of model predictive uncertainty should be investigated if management decisions are to be based on model projections. Using models built for four neighboring watersheds in the Neuse River Basin of North Carolina, the application of the automated parameter optimization software PEST in conjunction with the Hydrologic Simulation Program Fortran (HSPF) is demonstrated. Parameter nonuniqueness is illustrated, and a method is presented for calculating many different sets of parameters, all of which acceptably calibrate a watershed model. A regularization methodology is discussed in which models for similar watersheds can be calibrated simultaneously. Using this method, parameter differences between watershed models can be minimized while maintaining fit between model outputs and field observations. In recognition of the fact that parameter nonuniqueness and predictive uncertainty are inherent to the modeling process, PEST's nonlinear predictive analysis functionality is then used to explore the extent of model predictive uncertainty.

Sensitivity of Helicoverpa armigera nucleopolyhedrovirus polyhedra to sodium dodecyl sulfate

Sodium dodecyl sulfate (SDS) is commonly used to extract polyhedra from infected cells and diseased dead larval tissues. It was found, however, that 80% of Helicoverpa armigera nucleopolyhedrovirus (HaSNPV) polyhedra produced via cell culture were damaged after 30 min of 0.5% SDS treatment whereas only 20% of in vivo produced polyhedra were damaged by the same treatment. Transmission and scanning electron microscopy revealed that the damaged polyhedra had lost their polyhedron envelopes and virions were dislodged from the polyhedrin matrix, leaving empty spaces that were previously occupied by the occluded virions. Up to 20% in vitro produced polyhedra were resistant to SDS and remained intact, even after a 24 h exposure to SDS. This sensitivity to SDS was observed across a range of cell culture media, including serum supplemented media. Electron microscopy also revealed that the inferior polyhedron envelope of in vitro produced polyhedra is likely due to poor interaction between the polyhedron envelope, polyhedron envelope protein (PEP), and polyhedrin matrix. The PEP gene was cloned and sequenced and mutations in this gene were ruled out as an explanation. In vitro produced polyhedra that were passed through insect larva once were resistant to SDS, indicating that a critical component is lacking in insect cell culture medium used for producing HaSNPV or the cells growing in culture are inefficient in some ways in relation to production of polyhedra. (C) 2002 Elsevier Science (USA). All rights reserved.