Micellization of Lactosylammonium Surfactants with Different Counter Ions and Their Interaction with DNA

So far, the studies about the physical chemical properties of sugar-based surfactants have been still unsystematic; most of the studies have been focused on nonionic sugar-based surfactants. In the present work, we studied the micellization of four lactose-based surfactants, with the same headgroup (lactosylammonium) and the same hydrophobic alkyl chain (dodecyl) but different counterions (malonate, adipate, propionate, and hexanoate), at 25.0 and/or 50.0 °C. We found that these four surfactants could decrease the surface tension of water to ca. 30 mN/m. When the number of carboxylate groups in the counterion was the same, the counterion having a shorter alkyl chain could lead to a smaller minimum area per surfactant molecule. Moreover, the surfactants with monocarboxylates as counterions had much lower critical micelle concentrations than those with dicarboxylates as counterions, and the micelles from the former surfactants had a lower counterion binding degree. The lactosylammonium surfactants could bind with DNA, and low content of the surfactant could decrease the CD signal of DNA, while high content of the surfactant could make DNA unfold somewhat

Late Palaeozoic ⁴⁰Ar/³⁹Ar ages of the HP-LT metamorphic rocks from the Kekesu Valley, Chinese southwestern Tianshan: new constraints on exhumation tectonics

<div><p></p><p>Although numerous ages have been obtained for the Chinese southwestern Tianshan high pressure/ultrahigh pressure-low temperature (HP/UHP-LT) metamorphic belt in the past two decades, its exhumation history is still controversial. The poor age constraint was related to the appealing low metamorphic temperatures and excess Ar commonly present under HP/UHP conditions. This study aims to provide new age constraints on the orogen’s exhumation by obtaining ⁴⁰Ar/³⁹Ar mica ages using the conventional step-heating technique, with emphasis on the avoidance of excess Ar contamination. From a cross section along the Kekesu Valley, four samples, three from the HP-LT metamorphic belt (TK050, TK051, and TK081) and one from the southern margin of the low pressure metamorphic belt (TK097), were selected for ⁴⁰Ar/³⁹Ar dating. Phengites from garnet glaucophane schist TK050 and the surrounding rock garnet phengite schist TK051 yield comparable plateau ages of 321.4 ± 1.6 and 318.6 ± 1.6 Ma, respectively, while epidote mica schist TK081 gives a younger plateau age of 293.3 ± 1.5 Ma. Considering the chemical compositions of phengites, mineral assemblages, and microstructures in the thin slices, we suppose that the former represents the time the HP rocks retrograded from the peak stage (eclogite facies) to the (epidote)-blueschist facies, whereas the latter reflects greenschist facies overprinting. Biotite and muscovite from two-mica quartzite TK097 give similar plateau ages of 253.0 ± 1.3 and 247.1 ± 1.2 Ma, interpreted to date movement on the post collisional transcrustal South Nalati ductile shear zone. By combining our new ages with published data, a two-stage exhumation model is suggested for the Chinese southwestern Tianshan HP/UHP-LT metamorphic belt: initial fast exhumation to a depth of about 30–35 km by ~320 Ma was followed by relatively slow (~1 mm year^–1) uplift to ~10 km by ~293 Ma.</p></div

Harmonizing the pixel size in retrospective computed tomography radiomics studies

<div><p>Consistent pixel sizes are of fundamental importance for assessing texture features that relate intensity and spatial information in radiomics studies. To correct for the effects of variable pixel sizes, we combined image resampling with Butterworth filtering in the frequency domain and tested the correction on computed tomography (CT) scans of lung cancer patients reconstructed 5 times with pixel sizes varying from 0.59 to 0.98 mm. One hundred fifty radiomics features were calculated for each preprocessing and field-of-view combination. Intra-patient agreement and inter-patient agreement were compared using the overall concordance correlation coefficient (OCCC). To further evaluate the corrections, hierarchical clustering was used to identify patient scans before and after correction. To assess the general applicability of the corrections, they were applied to 17 CT scans of a radiomics phantom. The reduction in the inter-scanner variability relative to non–small cell lung cancer patient scans was quantified. The variation in pixel sizes caused the intra-patient variability to be large (OCCC <95%) relative to the inter-patient variability in 79% of the features. However, with the resampling and filtering corrections, the intra-patient variability was relatively large in only 10% of the features. With the filtering correction, 8 of 8 patients were correctly clustered, in contrast to only 2 of 8 without the correction. In the phantom study, resampling and filtering the images of a rubber particle cartridge substantially reduced variability in 61% of the radiomics features and substantially increased variability in only 6% of the features. Surprisingly, resampling without filtering tended to increase the variability. In conclusion, applying a correction based on resampling and Butterworth low-pass filtering in the frequency domain effectively reduced variability in CT radiomics features caused by variations in pixel size. This correction may also reduce the variability introduced by other CT scan acquisition parameters.</p></div

Scaled contrast.

<p>Scaled contrast for the CCR phantom’s rubber particle cartridge scanned with 17 different CT scanner configurations. (a) Feature values without image preprocessing. (b) Feature values calculated after all images had been resampled to 1 mm/pixel. (c) Feature values calculated after all images had been resampled to 1 mm/pixel and filtered with Butterworth filter (order 2, frequency cutoff 75). The points are color coded and labeled according to the manufacturer of the scanner: GE indicates GE Healthcare (green); P, Philips Healthcare (purple); S, Siemens Healthineers (pink); T, Toshiba Medical Systems (cyan).</p

Summary of the OCCC values for 150 radiomics features.

<p>Summary of the OCCC values for 150 radiomics features.</p

The credence cartridge radiomics phantom.

<p>The credence cartridge radiomics phantom.</p

Image correction level.

<p>Image correction level.</p

Heat map comparing the scaled variability for the 7 pixel size correction levels for the rubber particle cartridge of the CCR phantom.

<p>The features were calculated for the phantom for 17 scans. The color is rescaled on a row-by-row basis; darker colors indicate more variability. The values in the cells are the scaled variability values. BW indicates Butterworth; px, pixel.</p

Heat map comparing the scaled variability for the 7 pixel size correction levels for the sycamore wood cartridge of the CCR phantom.

<p>The features were calculated for the phantom for 17 scans. The color is rescaled on a row-by-row basis, and darker colors indicate more variability. The values in the cell are the scaled variability values. BW indicates Butterworth; px, pixel.</p

Hierarchical clusters of lung cancer patient CT scans using the Euclidean distance of the features entropy, busyness, and gray level non-uniformity.

<p>The features were extracted from images that had (a) no preprocessing, (b) resampling to 1 mm/pixel, and (c) resampling to 1 mm/pixel and filtering with a Butterworth filter (order 2, frequency cutoff 125). Boxes indicate incorrect (red) and correct (blue) groupings of the 5 FOV scans for each patient.</p