Biomedical subjects

Barry M Wise

Publications and source records attributed to Barry M Wise.

2 recordsLinked to original sources

PubMed2006

Prediction of nucleating sequences from amyloidogenic propensities of tau-related peptides.

Physical properties, including amyloid morphology, FTIR and CD spectra, enhancement of Congo red absorbance, polymerization rate, critical monomer concentration, free energy of stabilization, hydrophobicity, and the partition coefficient between soluble and amyloid states, were measured for the tau-related peptide Ac-VQIVYK amide (AcPHF6) and its single site mutants Ac-VQIVXK amide (X not equal Cys). Transmission electron microscopy showed that 15 out of the 19 peptides formed amyloid in buffer, with morphologies ranging from straight and twisted filaments to sheets and rolled sheets. Using principal component analysis (PCA), measured properties were treated in a comprehensive manner, and scores along the most significant principal components were used to define individual amino acid amyloidogenic propensities. Quantitative structure-activity modeling (QSAM) showed that residues with greater size and hydrophobicity made the largest contributions to the propensity of peptides to form amyloid. Using individual amino acid propensities, sequences within tau with high amyloid-forming potential were estimated and found to include 226VAVVR230 in the proline-rich region, 275VQIINK280 (PHF6) and 306VQIVYK311 (PHF6) within the microtubule binding region, and 392IVYK395 in the C-tail region of the protein. The results suggest that regions outside the microtubule-binding region may play important roles in tau aggregation kinetics or paired helical filament structure.

Amino Acid Substitution↗

PubMed2003

Error analysis for estimation of trace vapor concentration pathlength in stack plumes.

Near-infrared hyperspectral imaging is finding utility in remote sensing applications such as detection and quantification of chemical vapor effluents in stack plumes. Optimizing the sensing system or quantification algorithms is difficult because reference images are rarely well characterized. The present work uses a radiance model for a down-looking scene and a detailed noise model for dispersive and Fourier transform spectrometers to generate well-characterized synthetic data. These data were used with a classical least-squares-based estimator in an error analysis to obtain estimates of different sources of concentration-pathlength quantification error in the remote sensing problem. Contributions to the overall quantification error were the sum of individual error terms related to estimating the background, atmospheric corrections, plume temperature, and instrument signal-to-noise ratio. It was found that the quantification error depended strongly on errors in the background estimate and second-most on instrument signal-to-noise ratio. Decreases in net analyte signal (e.g., due to low analyte absorbance or increasing the number of analytes in the plume) led to increases in the quantification error as expected. These observations have implications on instrument design and strategies for quantification. The outlined approach could be used to estimate detection limits or perform variable selection for given sensing problems.

Air Movements↗