Topological electronic states in holey graphyne.

We unveil that the holey graphyne (HGY), a two-dimensional carbon allotrope where benzene rings are connected by two -C≡C- bonds fabricated recently in a bottom-up way, exhibits topological electronic states. Using first-principles calculations and Wannier tight-binding modeling, we discover a higher-order topological invariant associated withC2symmetry of the material, and show that the resultant corner modes appear in nanoflakes matching to the structure of precursor reported previously, which are ready for direct experimental observations. In addition, we find that a band inversion between emergentg-like andh-like orbitals gives rise to a nontrivial topology characterized byZ2invariant protected by an energy gap as large as 0.52 eV, manifesting helical edge states mimicking those in the prominent quantum spin Hall effect, which can be accessed experimentally after hydrogenation in HGY. We hope these findings trigger interests towards exploring the topological electronic states in HGY and related future electronics applications.

Neuroprotective effects of insulin-like growth factor-2 in 6-hydroxydopamine-induced cellular and mouse models of Parkinson's disease.

Skin-derived precursor Schwann cells have been reported to play a protective role in the central nervous system. The neuroprotective effects of skin-derived precursor Schwann cells may be attributable to the release of growth factors that nourish host cells. In this study, we first established a cellular model of Parkinson's disease using 6-hydroxydopamine. When SH-SY5Y cells were pretreated with conditioned medium from skin-derived precursor Schwann cells, their activity was greatly increased. The addition of insulin-like growth factor-2 neutralizing antibody markedly attenuated the neuroprotective effects of skin-derived precursor Schwann cells. We also found that insulin-like growth factor-2 levels in the peripheral blood were greatly increased in patients with Parkinson's disease and in a mouse model of Parkinson's disease. Next, we pretreated cell models of Parkinson's disease with insulin-like growth factor-2 and administered insulin-like growth factor-2 intranasally to a mouse model of Parkinson's disease induced by 6-hydroxydopamine and found that the level of tyrosine hydroxylase, a marker of dopamine neurons, was markedly restored, α-synuclein aggregation decreased, and insulin-like growth factor-2 receptor down-regulation was alleviated. Finally, in vitro experiments showed that insulin-like growth factor-2 activated the phosphatidylinositol 3 kinase (PI3K)/AKT pathway. These findings suggest that the neuroprotective effects of skin-derived precursor Schwann cells on the central nervous system were achieved through insulin-like growth factor-2, and that insulin-like growth factor-2 may play a neuroprotective role through the insulin-like growth factor-2 receptor/PI3K/AKT pathway. Therefore, insulin-like growth factor-2 may be an useful target for Parkinson's disease treatment.

Schwann cells differentiated from skin-derived precursors provide neuroprotection via autophagy inhibition in a cellular model of Parkinson's disease.

Autophagy has been shown to play an important role in Parkinson's disease. We hypothesized that skin-derived precursor cells exhibit neuroprotective effects in Parkinson's disease through affecting autophagy. In this study, 6-hydroxydopamine-damaged SH-SY5Y cells were pretreated with a culture medium containing skin-derived precursors differentiated into Schwann cells (SKP-SCs). The results showed that the SKP-SC culture medium remarkably enhanced the activity of SH-SY5Y cells damaged by 6-hydroxydopamine, reduced excessive autophagy, increased tyrosine hydroxylase expression, reduced α-synuclein expression, reduced the autophagosome number, and activated the PI3K/AKT/mTOR pathway. Autophagy activator rapamycin inhibited the effects of SKP-SCs, and autophagy inhibitor 3-methyladenine had the opposite effect. These findings confirm that SKP-SCs modulate the PI3K/AKT/mTOR pathway to inhibit autophagy, thereby exhibiting a neuroprotective effect in a cellular model of Parkinson's disease. This study was approved by the Animal Ethics Committee of Laboratory Animal Center of Nantong University (approval No. S20181009-205) on October 9, 2018.

[Quantitative Analysis of Mn in Soil Samples Using LIBS].

The trace element of Manganese element in the agricultural farm (Anhui Huaiyuan Nongkang) soil was quantitatively analyzed by Laser-induced breakdown spectroscopy. The line of 403.1 nm was selected as the analysis line of Mn. The matrix element of Fe in soil was chosen as the internal calibration element and the analysis line was 407.2 nm. Ten soil samples were used to construct calibration curves with traditional method and internal standard method, and four soil samples were selected as test samples. The experimental results showed that the fitting correlation coefficient (r) is 0.954 when using the traditional method, the maximum relative error of the measurement samples is 5.72%, and the detection limit of Mn in soil is 93 mg x kg(-1). While using the internal standard method to construct the calibration curve, the fitting correlation coefficient (r) is 0.983, the relative error of measurement samples is reduced to 4.1%, and the detection limit of Mn in soil is 71 mg x kg(-1). The result indicates that LIBS technique can be used to detect trace element Mn in soil. In a certain extent, the internal standard method can improve the accuracy of measurement.

[Quantitative analysis of thiram by surface-enhanced raman spectroscopy combined with feature extraction Algorithms].

Three feature extraction algorithms, such as the principal component analysis (PCA), the discrete cosine transform (DCT) and the non-negative factorization (NMF), were used to extract the main information of the spectral data in order to weaken the influence of the spectral fluctuation on the subsequent quantitative analysis results based on the SERS spectra of the pesticide thiram. Then the extracted components were respectively combined with the linear regression algorithm--the partial least square regression (PLSR) and the non-linear regression algorithm--the support vector machine regression (SVR) to develop the quantitative analysis models. Finally, the effect of the different feature extraction algorithms on the different kinds of the regression algorithms was evaluated by using 5-fold cross-validation method. The experiments demonstrate that the analysis results of SVR are better than PLSR for the non-linear relationship between the intensity of the SERS spectrum and the concentration of the analyte. Further, the feature extraction algorithms can significantly improve the analysis results regardless of the regression algorithms which mainly due to extracting the main information of the source spectral data and eliminating the fluctuation. Additionally, PCA performs best on the linear regression model and NMF is best on the non-linear model, and the predictive error can be reduced nearly three times in the best case. The root mean square error of cross-validation of the best regression model (NMF+SVR) is 0.0455 micormol x L(-1) (10(-6) mol x L(-1)), and it attains the national detection limit of thiram, so the method in this study provides a novel method for the fast detection of thiram. In conclusion, the study provides the experimental references the selecting the feature extraction algorithms on the analysis of the SERS spectrum, and some common findings of feature extraction can also help processing of other kinds of spectroscopy.