The impacts of sodium lauroyl sarcosinate in facial cleanser on facial skin microbiome and lipidome.

BACKGROUND: The human skin microbiome and lipidome are essential for skin homeostasis and barrier function, and have become a focus in both dermatological and cosmetic fields. However, the influence of surfactants commonly used in cosmetic products on the skin resident microbiome and lipidome remains poorly characterized. METHODS: We conducted self-control experiments to systematically study the effects of surfactant (sodium lauroyl sarcosinate [SLS]) on facial skin. Wrinkles, pores, porphyrins, and superficial lipids were examined to evaluate the biophysical state of skin. Quantitative real-time PCR was used to detect the numbers of bacteria and fungi. The diversity and structure of prokaryotic and eukaryotic microbiomes were assessed using 16S rDNA and ITS amplicon sequencing, respectively. Moreover, 22 lipids were identified to evaluate lipidome variations. SPSS software was used for statistical analysis. RESULTS: SLS in facial cleanser did not extensively influence skin biophysical parameters, but caused a decrease in porphyrin. After using the SLS-added facial cleanser for 3 weeks, the alpha diversity of the prokaryotic microbial community decreased significantly, while the eukaryotic microbial community showed a continuous downward trend but no statistically significant. A shift in the structure of prokaryotic microbiome was observed as a result of SLS exposure, mainly reflected by the increase in Acinetobacter, Escherichia-Shigella, Streptococcus, and Ralstonia, while the SLS had little effect on the structure of the eukaryotic microbiome. Furthermore, SLS exposure had a great impact on skin lipidome, mainly manifested by the increase of phosphatidylglycerol (PG) and phosphatidylcholine (PC), and the decrease of ceramides. Spearman's correlations analysis showed that Escherichia-Shigella, Pseudomonas, and Acinetobacter are positively correlated with PG and PC; however, the correlation is not statistically significant. CONCLUSION: In this study, we found the SLS in facial cleanser primarily affected lipidome and the prokaryotic microbiome of facial skin. These findings are useful for reminding us to be vigilant about the ingredients in personal care products, even the common ingredients, and designing effective formulations for repairing ecological balance of skin.

The Illusion of the China-US-Europe Strategic Triangle: Reactions from Germany and the UK.

The dawn of the Sino-US peer competition has broken, denoting a new status quo of instability as Beijing and Washington vie for influence. As leader of the rules-based international order (RIO), Washington under the Trump administration nevertheless adopted an "America first" posture, which triggered a transatlantic discord. Simultaneously, Beijing, having signalled discontent with existing settlements, has sought to alter international arrangements to cement its leadership role. Europe has been identified as a significant theatre for the emerging competition, with Germany and the UK recognized as essential target states for Beijing's efforts, yet are simultaneously major stakeholders in the US-led RIO. As such, this article sets out to operationalize a European narrative of hedging. The article poses the questions, how does the transatlantic discord and Beijing's leadership ambitions impact the hedging strategies of Germany and the UK, and why do such coping behaviors differ in various indices? Utilizing key signals from policy practitioners, policy elites' rhetoric and national strategies, it is contended that while the UK is struggling to formulate a coherent foreign policy and now finds itself aligned with US foreign policy narratives, Germany is unlikely to signal its wishes to become strategically independent, if the US and EU foreign policy imperatives accord with Berlin's political and economic interests. These two case studies reveal that both European powers are decidedly wary about Beijing's signals and intentions, and are likely to retain closer alignment with Washington, foregoing strategic independence and illustrating the temporary nature of the transatlantic discord. Both will not, however, wish to relinquish Beijing, so long as attractive economic benefits can be attained, resulting in an inexplicit strategic hedge.

[Hyperspectral Estimation of Apple Tree Canopy LAI Based on SVM and RF Regression].

Leaf area index (LAI) is the dynamic index of crop population size. Hyperspectral technology can be used to estimate apple canopy LAI rapidly and nondestructively. It can be provide a reference for monitoring the tree growing and yield estimation. The Red Fuji apple trees of full bearing fruit are the researching objects. Ninety apple trees canopies spectral reflectance and LAI values were measured by the ASD Fieldspec3 spectrometer and LAI-2200 in thirty orchards in constant two years in Qixia research area of Shandong Province. The optimal vegetation indices were selected by the method of correlation analysis of the original spectral reflectance and vegetation indices. The models of predicting the LAI were built with the multivariate regression analysis method of support vector machine (SVM) and random forest (RF). The new vegetation indices, GNDVI527, ND-VI676, RVI682, FD-NVI656 and GRVI517 and the previous two main vegetation indices, NDVI670 and NDVI705, are in accordance with LAI. In the RF regression model, the calibration set decision coefficient C-R2 of 0.920 and validation set decision coefficient V-R2 of 0.889 are higher than the SVM regression model by 0.045 and 0.033 respectively. The root mean square error of calibration set C-RMSE of 0.249, the root mean square error validation set V-RMSE of 0.236 are lower than that of the SVM regression model by 0.054 and 0.058 respectively. Relative analysis of calibrating error C-RPD and relative analysis of validation set V-RPD reached 3.363 and 2.520, 0.598 and 0.262, respectively, which were higher than the SVM regression model. The measured and predicted the scatterplot trend line slope of the calibration set and validation set C-S and V-S are close to 1. The estimation result of RF regression model is better than that of the SVM. RF regression model can be used to estimate the LAI of red Fuji apple trees in full fruit period.

Trade-off between angular and spatial resolutions in in vivo fiber tractography.

Tractography is becoming an increasingly popular method to reconstruct white matter connections in vivo. The diffusion MRI data that tractography is based on requires a high angular resolution to resolve crossing fibers whereas high spatial resolution is required to distinguish kissing from crossing fibers. However, scan time increases with increasing spatial and angular resolutions, which can become infeasible in clinical settings. Here we investigated the trade-off between spatial and angular resolutions to determine which of these factors is most worth investing scan time in. We created a unique diffusion MRI dataset with 1.0 mm isotropic resolution and a high angular resolution (100 directions) using an advanced 3D diffusion-weighted multi-slab EPI acquisition. This dataset was reconstructed to create subsets of lower angular (75, 50, and 25 directions) and lower spatial (1.5, 2.0, and 2.5 mm) resolution. Using all subsets, we investigated the effects of angular and spatial resolutions in three fiber bundles-the corticospinal tract, arcuate fasciculus and corpus callosum-by analyzing the volumetric bundle overlap and anatomical correspondence between tracts. Our results indicate that the subsets of 25 and 50 directions provided inferior tract reconstructions compared with the datasets with 75 and 100 directions. Datasets with spatial resolutions of 1.0, 1.5, and 2.0 mm were comparable, while the lowest resolution (2.5 mm) datasets had discernible inferior quality. In conclusion, we found that angular resolution appeared to be more influential than spatial resolution in improving tractography results. Spatial resolutions higher than 2.0 mm only appear to benefit multi-fiber tractography methods if this is not at the cost of decreased angular resolution.

Reliability of brain volume measurements: a test-retest dataset.

Evaluation of neurodegenerative disease progression may be assisted by quantification of the volume of structures in the human brain using magnetic resonance imaging (MRI). Automated segmentation software has improved the feasibility of this approach, but often the reliability of measurements is uncertain. We have established a unique dataset to assess the repeatability of brain segmentation and analysis methods. We acquired 120 T1-weighted volumes from 3 subjects (40 volumes/subject) in 20 sessions spanning 31 days, using the protocol recommended by the Alzheimer's Disease Neuroimaging Initiative (ADNI). Each subject was scanned twice within each session, with repositioning between the two scans, allowing determination of test-retest reliability both within a single session (intra-session) and from day to day (inter-session). To demonstrate the application of the dataset, all 3D volumes were processed using FreeSurfer v5.1. The coefficient of variation of volumetric measurements was between 1.6% (caudate) and 6.1% (thalamus). Inter-session variability exceeded intra-session variability for lateral ventricle volume (P<0.0001), indicating that ventricle volume in the subjects varied between days.

Relation between brain architecture and mathematical ability in children: a DBM study.

Population-based studies indicate that between 5 and 9 percent of US children exhibit significant deficits in mathematical reasoning, yet little is understood about the brain morphological features related to mathematical performances. In this work, deformation-based morphometry (DBM) analyses have been performed on magnetic resonance images of the brains of 79 third graders to investigate whether there is a correlation between brain morphological features and mathematical proficiency. Group comparison was also performed between Math Difficulties (MD-worst math performers) and Normal Controls (NC), where each subgroup consists of 20 age and gender matched subjects. DBM analysis is based on the analysis of the deformation fields generated by non-rigid registration algorithms, which warp the individual volumes to a common space. To evaluate the effect of registration algorithms on DBM results, five nonrigid registration algorithms have been used: (1) the Adaptive Bases Algorithm (ABA); (2) the Image Registration Toolkit (IRTK); (3) the FSL Nonlinear Image Registration Tool; (4) the Automatic Registration Tool (ART); and (5) the normalization algorithm available in SPM8. The deformation field magnitude (DFM) was used to measure the displacement at each voxel, and the Jacobian determinant (JAC) was used to quantify local volumetric changes. Results show there are no statistically significant volumetric differences between the NC and the MD groups using JAC. However, DBM analysis using DFM found statistically significant anatomical variations between the two groups around the left occipital-temporal cortex, left orbital-frontal cortex, and right insular cortex. Regions of agreement between at least two algorithms based on voxel-wise analysis were used to define Regions of Interest (ROIs) to perform an ROI-based correlation analysis on all 79 volumes. Correlations between average DFM values and standard mathematical scores over these regions were found to be significant. We also found that the choice of registration algorithm has an impact on DBM-based results, so we recommend using more than one algorithm when conducting DBM studies. To the best of our knowledge, this is the first study that uses DBM to investigate brain anatomical features related to mathematical performance in a relatively large population of children.

Effect of nonrigid registration algorithms on deformation-based morphometry: a comparative study with control and Williams syndrome subjects.

Deformation-based morphometry (DBM) is a widely used method for characterizing anatomical differences across groups. DBM is based on the analysis of the deformation fields generated by nonrigid registration algorithms, which warp the individual volumes to a DBM atlas. Although several studies have compared nonrigid registration algorithms for segmentation tasks, few studies have compared the effect of the registration algorithms on group differences that may be uncovered through DBM. In this study, we compared group atlas creation and DBM results obtained with five well-established nonrigid registration algorithms using 13 subjects with Williams syndrome and 13 normal control subjects. The five nonrigid registration algorithms include the following: (1) the adaptive bases algorithm, (2) the image registration toolkit, (3) The FSL nonlinear image registration tool, (4) the automatic registration tool, and (5) the normalization algorithm available in Statistical Parametric Mapping (SPM8). Results indicate that the choice of algorithm has little effect on the creation of group atlases. However, regions of differences between groups detected with DBM vary from algorithm to algorithm both qualitatively and quantitatively. Some regions are detected by several algorithms, but their extent varies. Others are detected only by a subset of the algorithms. Based on these results, we recommend using more than one algorithm when performing DBM studies.