Measurement of the attachment and assembly of small amyloid-β oligomers on live cell membranes at physiological concentrations using single-molecule tools

It is thought that the pathological cascade in Alzheimer's disease is initiated by the formation of amyloid-β (Aβ) peptide complexes on cell membranes. However, there is considerable debate about the nature of these complexes and the type of solution-phase Aβ aggregates that may contribute to their formation. Also, it is yet to be shown that Aβ attaches strongly to living cell membranes, and that this can happen at low, physiologically relevant Aβ concentrations. Here, we simultaneously measure the aggregate size and fluorescence lifetime of fluorescently labeled Aβ(1-40) on and above the membrane of cultured PC12 cells at near-physiological concentrations. We find that at 350 nM Aβ concentration, large (>>10 nm average hydrodynamic radius) assemblies of codiffusing, membrane-attached Aβ molecules appear on the cell membrane together with a near-monomeric species. When the extracellular concentration is 150 nM, the membrane contains only the smaller species, but with a similar degree of attachment. At both concentrations, the extracellular solution contains only small (∼2.3 nm average hydrodynamic radius) Aβ oligomers or monomers. We conclude that at near-physiological concentrations only the small oligomeric Aβ species are relevant, they are capable of attaching to the cell membrane, and they assemble in situ to form much larger complexes.

Spectroscopic evaluation of the surface quality of apple

Different spectroscopic techniques based on infrared and Raman were used to evaluate the natural wax and related surface quality of apple fruit. Transmission near-infrared (NIR) spectroscopy was applied to solutions of single wax components and extracted apple wax. Fourier transform infrared (FTIR) spectroscopy was used for transmission measurements of wax films on NaCl crystals, diffuse reflectance spectroscopy (DRIFTS) was used to analyze wax powders, and FT-Raman spectroscopy was explored to examine intact wax layers on whole fruit. The natural wax layers of apple fruit from a maximum of three different cultivars (Jonagold, Jonagored, and Elshof) from three picking dates (early, commercial, and late), three controlled atmosphere storage durations (0, 4, and 8 months), and three shelf life periods (0, 1, and 2 weeks) within each storage duration were examined. Canonical discriminant analysis was carried out on the first derivative NIR and FTIR spectra to describe the information contained in the spectra. Discrimination between cultivars and between storage duration based on wax layer properties was achieved with reasonable accuracy from both of the techniques. Information contained in the spectra of apples from different picking dates and shelf life periods was not significant. Differences between cultivars and storage periods in this analysis mostly related to differences in the number of aliphatic chains (e.g., alkanes and esters) and the presence of alpha-farnesene. No satisfactory results were obtained by means of Raman spectroscopy and DRIFTS.

Rapid determination of tetracycline in milk by FT-MIR and FT-NIR spectroscopy

The feasibility of measuring tetracycline at the ppb levels in milk was investigated by Fourier transform mid-infrared (FT-MIR) and Fourier transform near-infrared (FT-NIR) spectroscopic techniques. Milk samples spiked with different concentrations of tetracycline were scanned using FT-MIR and FT-NIR spectroscopy. Suitable spectral wave number regions were selected for principal least square (PLS) regression models development. Prediction errors were high when the calibration model was developed using the wide range of tetracycline concentrations (4 to 2000 ppb) in milk. Maximum correlation coefficient (R2) value of about 0.89 was obtained for the validation models developed using different concentration ranges. Prediction errors were high for FT-NIR method. Results indicated that FT-MIR spectroscopy could be used for rapid detection of tetracycline hydrochloride residues in milk.

Monitoring a bioprocess for ethanol production using FT-MIR and FT-Raman spectroscopy

The application of Fourier transform mid-infrared (FT-MIR) spectroscopy and Fourier transform Raman (FT-Raman) spectroscopy for process and quality control of fermentative production of ethanol was investigated. FT-MIR and FT-Raman spectroscopy along with multivariate techniques were used to determine simultaneously glucose, ethanol, and optical cell density of Saccharomyces cerevisiae during ethanol fermentation. Spectroscopic measurement of glucose and ethanol were compared and validated with the high-performance liquid chromatography (HPLC) method. Spectral wave number regions were selected for partial least-squares (PLS) regression and principal component regression (PCR) and calibration models for glucose, ethanol, and optical cell density were developed for culture samples. Correlation coefficient (R(2)) value for the prediction for glucose and ethanol was more than 0.9 using various calibration methods. The standard error of prediction for the PLS first-derivative calibration models for glucose, ethanol, and optical cell density were 1.938 g/l, 1.150 g/l, and 0.507, respectively. Prediction errors were high with FT-Raman because the Raman scattering of the cultures was weak. Results indicated that FT-MIR spectroscopy could be used for rapid detection of glucose, ethanol, and optical cell density in S. cerevisiae culture during ethanol fermentation.

Examination of full fat and reduced fat Cheddar cheese during ripening by Fourier transform infrared spectroscopy

The Fourier transform infrared spectra of reduced fat Cheddar cheese and full fat Cheddar cheese during ripening were examined. A suitable sampling procedure was used to obtain the spectra of cheese samples. The spectra were affected by the moisture contents of the samples, but this effect could be eliminated by equilibration of the sample for at least 10 min before data collection. Strong and well-separated bands at 1744, 1450, 1240, 1170, and 1115 cm-1 arising from fat were observed in the frozen samples. The bands observed at 1650 and 1540 cm-1 were attributed to the protein contents of the cheese samples. The absorption intensities of protein and fat bands corresponded to the amounts of protein and fat that were present in the samples. Distinct changes in the bands of fat and protein of reduced fat and full fat cheese samples occurred during ripening. This technique could provide a basis for a rapid characterization and determination of the age of cheese undergoing ripening.

Application of a multi-structure neural network (MSNN) to sorting pistachio nuts

A multi-structure neural network (MSNN) classifier consisting of four discriminators followed by a maximum selector was designed and applied to classification of four grades of pistachio nuts. Each discriminator was a multi-layer feed-forward neural network with two hidden layers and a single-neuron output layer. Fourier descriptors of the nuts' boundaries and their area were used as the recognition features. The individual discriminators were trained using a biased technique and a back-propagation algorithm. The MSNN classifier gave an average classification performance of 95.0%. This was an increase of 14.8% over the performance of a multi-layer neural network (MLNN) with similar complexity for classifying the same set of patterns.