Prediction of soil organic matter content based on characteristic band selection method.

Soil organic matter (SOM) is a key index for evaluating soil fertility and plays a vital role in the terrestrial carbon cycle. Visible and near-infrared (Vis-NIR) spectroscopy is an effective method for determining soil properties and is often used to predict SOM content. However, the key prerequisite for effective prediction of SOM content by Vis-NIR spectroscopy lies in the selection of appropriate preprocessing methods and effective data mining techniques. Therefore, in this study, six commonly used spectral preprocessing methods and effective characteristic band selection methods were selected to process the spectrum to predict SOM content. This study aims to determine a stable spectral preprocessing method and explore the predictive performance of different characteristic band selection methods. The results showed that: (i) The first derivative (FD) is the most stable spectral preprocessing method that can effectively improve the spectral characteristic information and the prediction effect of the model. (ii) The prediction effect of SOM content based on characteristic band selection methods is generally better than the full-spectra data. (iii) The precision of FD preprocessing spectrum combined with successive projections algorithm (SPA) in the partial least square regression prediction model of SOM content is the best. (iv) Although the prediction effect of the model based on the optimal band combination algorithm is slightly lower than that of SPA, it shows stable prediction performance, which provides a feasible method for SOM content prediction. In summary, the characteristic band selection method combined with FD can significantly improve the prediction accuracy of SOM content.

Research on estimation models of the spectral characteristics of soil organic matter based on the soil particle size.

Soil organic matter (SOM) is an important part of soil fertility and the main nutrient source for crop growth. The establishment of an effective SOM content estimation model can provide technical support for the improvement of saline soil and the implementation of precision agriculture. In this paper, a laboratory spectrometer was used to measure the spectral reflectance of saline soils with particle sizes of 1 mm, 0.50 mm, 0.25 mm and 0.15 mm collected from Kenli County. After spectral preprocessing and spectral transformation, the characteristic bands of the SOM spectrum were extracted by the successive projections algorithm (SPA). Finally, stepwise multiple linear regression (SMLR), principal component regression (PCR) and partial least squares regression (PLSR) were used to establish SOM content estimation models based on soil particle size. The results showed the following. (i) Soil particle size had a significant impact on soil spectral reflectance. The smaller the soil particle size was, the greater the soil spectral reflectance. (ii) The sensitive bands for SOM were mainly concentrated in the visible light region (400-760 nm). First derivative (FD) transformation can effectively improve the characteristic spectral information obtained from SOM. (iii) Among the three models established with the characteristic bands, the estimation ability of the PLSR model was better than that of the PCR and SMLR models. (iv) The FD of the original spectral reflectance of the 0.25 mm particles combined with the PLSR model gave the best estimation of the SOM content. When the soil particle size was less than 0.25 mm, the estimation results of the model were not improved. These results provide a basis for effective estimation of the SOM content and improvement of saline-alkali soil in Kenli County in the Yellow River Delta.

High EMP3 expression might independently predict poor overall survival in glioblastoma and its expression is related to DNA methylation.

In this study, we analyzed the prognostic value of epithelial membrane protein 3 (EMP3) in terms of overall survival (OS) in glioblastoma multiforme (GBM) and the association between its expression and DNA methylation.Bioinformatic analysis was performed by using data from the Cancer Genome Atlas (TCGA) database.EMP3 expression was markedly higher in GBM tissues than in normal brain tissues. High EMP3 expression was associated with significantly worse OS in patients with GBM. Univariate and multivariate analysis showed that EMP3 expression was an independent prognostic factor of poor OS no matter converting its expression into categorical variables (Hazard Ratio [HR] = 1.359, 95%CI: 1.118-1.652, P = .002) or setting it as a continuous variable (HR = 1.178, 95%CI: 1.101-1.260, P < .001). Among different subtypes of GBM, proneural subtype had the lowest EMP3 expression. The lowest EMP3 expression was observed in cluster 5 DNA methylation, which all belong to G-CIMP phenotype. Regression analysis confirmed a moderate negative correlation between EMP3 expression and its DNA methylation (Pearson's r = -0.61).Based on these findings, we infer that high EMP3 expression might be an independent indicator of unfavorable OS in GBM. EMP3 expression might be repressed by DNA methylation.

MDC1-AS, an antisense long noncoding RNA, regulates cell proliferation of glioma.

BACKGROUND: Growing number of long noncoding RNAs (lncRNAs) are emerging as new modulators in cancer origination and progression. A lncRNA, mediator of DNA damage checkpoint protein 1antisense RNA (MDC1-AS), with unknown function, is the antisense transcript of tumor suppressor MDC1. METHOD: In this study, we investigated the expression pattern and functional role of lncRNA MDC1-AS in glioma by using real time PCR and gain-/loss-of-function studies. RESULT: The results showed that the expression levels of lncRNA MDC1-AS and MDC1 were significantly downregulated in glioma tissues compared with normal brain tissues, and in glioma cell lines U87MG, U251 and HEB. Overexpression of MDC1-AS resulted in significant inhibition of cell proliferation and cell cycle in U87MG and U251. We also found that MDC1-AS expression was positively correlated with MDC1 expression. In addition, the inhibitory role of MDC1-AS was remarkably diminished when MDC1 was knockdown. CONCLUSION: Together, the results suggest that MDC1-AS is a novel tumor suppressor through up-regulation of its antisense tumor-suppressing gene MDC1 in glioma and leads us to propose that MDC1-AS may serve as a potential biomarker and therapeutic target for glioma.