Gas impregnation of titanium alloys in heat treatment in a static atmosphere of argon

Translated from Fiziko-Khimicheskaya Mekhanika Materialov, Vol. 23, No. 4, pp. 38–43, July–August, 1987.     

Discrete Fourier transform-based analysis of HeLa cell microtubules after ultrasonic exposure

Cytoskeletal structures can be affected by external factors including ultrasound. Our task was to develop a structure analysis method to evaluate these changes quantitatively. We exposed HeLa cells to continuous ultrasound (1 MHz, 1 and 2 W/cm2, 10 min at 37 degrees C). The microtubules were detected by the monoclonal antibody TU-01/SwAM/FITC, observed in a fluorescence microscope and photographed digitally. The images were processed by "FFT magic" software. The structure analysis is based on frequency domain filtering using discrete Fourier transform. The basic idea is to design filters to extract information describing best the structural changes. The properties of the filter can be enhanced by direction filtering, i.e., extraction of a symmetric angular segment in the frequency domain centered on a zero frequency. The final image is a normalized sum of inverse FFT's of such segmented spectra. We needed a method yielding a single number assigned to the structure, e.g., the ratio of the area of microtubules to the total cell area. Assuming that the image background intensity is constant, we can use thresholding to detect areas occupied by the cells. The information about the area of the microtubules is contained in a wide range of higher intensities. Therefore, we use a gamma correction. The area occupied by microtubules is then considered an area with intensities above the selected threshold. There were tested three different filters to extract information about microtubules. The mathematical method chosen seems sensitive enough for quantitative assessment of changes of the microtubular network.     

Quantification of diacylglycerol species from cellular extracts by electrospray ionization mass spectrometry using a linear regression algorithm

Diacylglycerols (DAGs) play significant roles in both intermediate metabolism and signal transduction. These lipid species are second messengers involved in modulating a plethora of cellular processes. Evaluation of DAG species concentrations has been hampered by the lack of a reliable method for molecular species analysis within a complex mixture of cellular lipids. We describe a new method for quantitative analysis of DAG species from complex biological extracts based on positive mode electrospray ionization mass spectrometry without prior derivatization. Quantification is achieved using internal standards and calibration curves constructed by spiking cell extracts with different concentrations of DAG species containing various acyl chain lengths and degrees of unsaturation. The new mass spectral data processing algorithm incorporates a multiple linear regression model including a factor accountable for possible interactions between experimental preparations and the slope of the curve for the standards, allowing the examinations of the effects of sample origin conditions (such as cell types, phenotypes, etc.) and instrument variability on this slope. Internal standards provide a basis for quantification of 28 DAG molecular species detected in RAW 264.7 cells after stimulation of a G-protein coupled receptor with platelet activating factor. This method displays excellent reproducibility over the established range of concentrations with variations of < or =10% and is highly sensitive with a detection limit of 0.1-0.4 pmol/microL depending upon acyl chain composition. We have shown differential effects on various DAGs in response to a ligand which illustrates the importance of examining lipids at the molecular species level rather than as a single homogeneous entity.