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There is discussed in the article the issues of organization and carrying out tasks solving methodic and lessons arrangement in physics of semiconductors

There is discussed in the article the issues of organization and carrying out tasks solving methodic and lessons arrangement in physics of semiconductors

There is discussed in the article the issues of organization and carrying out tasks solving methodic and lessons arrangement in physics of semiconductors

There is discussed in the article the issues of organization and carrying out tasks solving methodic and lessons arrangement in physics of semiconductors

Purpose: This study was aimed to investigate the impact of noise on full-width at half maximum (FWHM) of slice sensitivity profile (SSP) of computed tomography (CT) images which was automatically calculated using Matlab-based software on the AAPM CT performance phantom. Methods: The slice thickness part of AAPM CT performance phantom was scanned by the 128 slice GE Healthcare CT scanner with four different tube currents of 50 mA, 100 mA, 150 mA and 200 mA. The noise calculation was carried out on the homogeneous region of the phantom which contains water. The noise was calculated as the standard deviation (SD) within the region of interest (ROI). The automated measurement of FWHM of SSP was carried out on the middle stair object of the image. Results: The noises were 7.67, 5.95, 4.98, and 4.00 HU for tube currents of 50, 100, 150, and 200 mA, respectively. The FWHMs of SSPs were 5.5 ± 0.1, 5.3 ± 0.3, 5.4 ± 0,0, and 5.2 ± 0.1 mm for noises of 7.67, 5.95, 4.98, and 4.00 HU, respectively. The result of automated FWHM of image with noise of 7.67 HU was 5.5 mm and the difference from the nominal slice thickness was 10%. The FWHM of image with noise of 4.00 HU was 5.2 mm and the difference from set slice thickness was 4%. The automated FWHM results tended to be similar to the nominal slice thickness for lower noise. Conclusions: The automated FWHM results tends to be similar to the nominal slice thickness for lower noise. Although the noise of 7.67 HU still produces an accurate FWHM of SSP, the results suggested to use low noise for measuring FWHM and SSP.

Purpose: This study was aimed to investigate the impact of noise on full-width at half maximum (FWHM) of slice sensitivity profile (SSP) of computed tomography (CT) images which was automatically calculated using Matlab-based software on the AAPM CT performance phantom. Methods: The slice thickness part of AAPM CT performance phantom was scanned by the 128 slice GE Healthcare CT scanner with four different tube currents of 50 mA, 100 mA, 150 mA and 200 mA. The noise calculation was carried out on the homogeneous region of the phantom which contains water. The noise was calculated as the standard deviation (SD) within the region of interest (ROI). The automated measurement of FWHM of SSP was carried out on the middle stair object of the image. Results: The noises were 7.67, 5.95, 4.98, and 4.00 HU for tube currents of 50, 100, 150, and 200 mA, respectively. The FWHMs of SSPs were 5.5 ± 0.1, 5.3 ± 0.3, 5.4 ± 0,0, and 5.2 ± 0.1 mm for noises of 7.67, 5.95, 4.98, and 4.00 HU, respectively. The result of automated FWHM of image with noise of 7.67 HU was 5.5 mm and the difference from the nominal slice thickness was 10%. The FWHM of image with noise of 4.00 HU was 5.2 mm and the difference from set slice thickness was 4%. The automated FWHM results tended to be similar to the nominal slice thickness for lower noise. Conclusions: The automated FWHM results tends to be similar to the nominal slice thickness for lower noise. Although the noise of 7.67 HU still produces an accurate FWHM of SSP, the results suggested to use low noise for measuring FWHM and SSP.

Purpose: This study was aimed to investigate the impact of noise on full-width at half maximum (FWHM) of slice sensitivity profile (SSP) of computed tomography (CT) images which was automatically calculated using Matlab-based software on the AAPM CT performance phantom. Methods: The slice thickness part of AAPM CT performance phantom was scanned by the 128 slice GE Healthcare CT scanner with four different tube currents of 50 mA, 100 mA, 150 mA and 200 mA. The noise calculation was carried out on the homogeneous region of the phantom which contains water. The noise was calculated as the standard deviation (SD) within the region of interest (ROI). The automated measurement of FWHM of SSP was carried out on the middle stair object of the image. Results: The noises were 7.67, 5.95, 4.98, and 4.00 HU for tube currents of 50, 100, 150, and 200 mA, respectively. The FWHMs of SSPs were 5.5 ± 0.1, 5.3 ± 0.3, 5.4 ± 0,0, and 5.2 ± 0.1 mm for noises of 7.67, 5.95, 4.98, and 4.00 HU, respectively. The result of automated FWHM of image with noise of 7.67 HU was 5.5 mm and the difference from the nominal slice thickness was 10%. The FWHM of image with noise of 4.00 HU was 5.2 mm and the difference from set slice thickness was 4%. The automated FWHM results tended to be similar to the nominal slice thickness for lower noise. Conclusions: The automated FWHM results tends to be similar to the nominal slice thickness for lower noise. Although the noise of 7.67 HU still produces an accurate FWHM of SSP, the results suggested to use low noise for measuring FWHM and SSP.

Purpose: This study was aimed to investigate the impact of noise on full-width at half maximum (FWHM) of slice sensitivity profile (SSP) of computed tomography (CT) images which was automatically calculated using Matlab-based software on the AAPM CT performance phantom. Methods: The slice thickness part of AAPM CT performance phantom was scanned by the 128 slice GE Healthcare CT scanner with four different tube currents of 50 mA, 100 mA, 150 mA and 200 mA. The noise calculation was carried out on the homogeneous region of the phantom which contains water. The noise was calculated as the standard deviation (SD) within the region of interest (ROI). The automated measurement of FWHM of SSP was carried out on the middle stair object of the image. Results: The noises were 7.67, 5.95, 4.98, and 4.00 HU for tube currents of 50, 100, 150, and 200 mA, respectively. The FWHMs of SSPs were 5.5 ± 0.1, 5.3 ± 0.3, 5.4 ± 0,0, and 5.2 ± 0.1 mm for noises of 7.67, 5.95, 4.98, and 4.00 HU, respectively. The result of automated FWHM of image with noise of 7.67 HU was 5.5 mm and the difference from the nominal slice thickness was 10%. The FWHM of image with noise of 4.00 HU was 5.2 mm and the difference from set slice thickness was 4%. The automated FWHM results tended to be similar to the nominal slice thickness for lower noise. Conclusions: The automated FWHM results tends to be similar to the nominal slice thickness for lower noise. Although the noise of 7.67 HU still produces an accurate FWHM of SSP, the results suggested to use low noise for measuring FWHM and SSP.

Gold nanoparticles (AuNPs) are increasingly studied for cancer treatment purposes, as they can potentially improve both control and efficiency of the treatment. Intensive research is conducted in vitro on rodent and human cell lines to objectify the gain of combining AuNPs with cancer treatment and to understand their mechanisms of action. However, using nanoparticles in such studies requires thorough knowledge of their cellular uptake. In this study, we optimized single particle ICPMS (sp-ICPMS) analysis to qualify and quantify intracellular AuNP content after exposure of in vitro human breast cancer cell lines. To this aim, cells were treated with an alkaline digestion method with 5% TMAH, allowing the detection of gold with a yield of 97% on average. Results showed that under our experimental conditions, the AuNP size distribution appeared to be unchanged after internalization and that the uptake of particles depended on the cell line and on the exposure duration. Finally, the comparison of the particle numbers per cell with the estimates based on the gold masses showed excellent agreement, confirming the validity of the sp-ICPMS particle measurements in such complex samples.

¿Podría considerarse excesivo tomar al pie de la letra el exabrupto del eminente Bertrand Russell, que escribió un día: "por su influencia intelectual, Aristóteles ha sido uno de los más grandes azotes de la especie humana"? Parece, a primera vista, que Russell habría tenido en cuenta la extraordinaria confusión cometida por Aristóteles acerca de la palabra griega Logos, entendiéndola al mismo tiempo como discurso y razón; el error consistiría, por lo tanto, en suponer que las formas del lenguaje traducen exactamente las formas del pensamiento.

¿Podría considerarse excesivo tomar al pie de la letra el exabrupto del eminente Bertrand Russell, que escribió un día: "por su influencia intelectual, Aristóteles ha sido uno de los más grandes azotes de la especie humana"? Parece, a primera vista, que Russell habría tenido en cuenta la extraordinaria confusión cometida por Aristóteles acerca de la palabra griega Logos, entendiéndola al mismo tiempo como discurso y razón; el error consistiría, por lo tanto, en suponer que las formas del lenguaje traducen exactamente las formas del pensamiento.

¿Podría considerarse excesivo tomar al pie de la letra el exabrupto del eminente Bertrand Russell, que escribió un día: "por su influencia intelectual, Aristóteles ha sido uno de los más grandes azotes de la especie humana"? Parece, a primera vista, que Russell habría tenido en cuenta la extraordinaria confusión cometida por Aristóteles acerca de la palabra griega Logos, entendiéndola al mismo tiempo como discurso y razón; el error consistiría, por lo tanto, en suponer que las formas del lenguaje traducen exactamente las formas del pensamiento.