Long non-coding RNA PTPRG-AS1/microRNA-124-3p regulates radiosensitivity of nasopharyngeal carcinoma via the LIM Homeobox 2-dependent Notch pathway through competitive endogenous RNA mechanism.

Nasopharyngeal carcinoma (NPC) is a malignant tumor in the nasopharyngeal cavity. LncRNA PTPRG-AS1 is essential in NPC radiosensitivity. This study sought to explore the mechanism of PTPRG-AS1 in NPC radiosensitivity by regulating the miR-124-3p/LHX2 axis. First, NPC-related microarray was analyzed to screen differentially expressed lncRNAs. PTPRG-AS1 and miR-124-3p expression patterns in NPC tissues and adjacent tissues of NPC patients and NPC cell lines were detected by RT-qPCR. PTPRG-AS1 was knocked down in CNE2 and 5-8 F cells by transfection. The radiosensitivity, proliferation and apoptosis before and after radiotherapy (0/6 Gy) were detected by cloning formation assay, CCK-8 assay, and flow cytometry. Bioinformatics, Pearson correlation analysis, RNA pull-down, and luciferase reporter assays were performed to explore the regulatory relationship of the lncRNA PTPRG-AS1/miR-124-3/LHX2 axis. The corresponding functions were verified in the complementation test. The levels of LHX2 and Notch pathway-related proteins were detected by Western blot. PTPRG-AS1 was upregulated in NPC cell lines and tissues. PTPRG-AS1 knockdown decreased NPC cell proliferation and promoted radiotherapy-induced apoptosis and cell radiosensitivity. PTPRG-AS1 upregulated LHX2 as a ceRNA of miR-124-3p. miR-124-3p inhibition partially reversed PTPRG-AS1 silencing-induced NPC cell radiosensitivity. miR-124-3p targeted LHX2. LHX2 overexpression attenuated the miR-124-3p overexpression-induced NPC cell radiosensitivity. LHX2 attenuated NPC cell radiosensitivity by activating the Notch pathway. Briefly, lncRNA PTPRG-AS1 reduced NPC cell radiosensitivity by regulating the miR-124-3p/LHX2 axis through the ceRNA mechanism.

Spatiotemporal Variability of Soil Nitrogen in Relation to Environmental Factors in a Low Hilly Region of Southeastern China.

Soil total nitrogen (TN) plays a major role in agriculture, geochemical cycles and terrestrial ecosystem functions. Knowledge regarding the TN distribution is crucial for the sustainable use of soil resources. This paper therefore aims to characterize the spatiotemporal distribution of soil TN and improve the current understanding of how various factors influence changes in TN. Natural characteristics and remote sensing (RS) variables were used in conjunction with the random forest (RF) model to map the TN distribution in a low hilly region of southeastern China in 1979, 2004 and 2014. The means and changes of TN in different geographic regions and farmland protection regions were also analyzed. The results showed that: (1) the TN showed an increasing trend in the early periods and exhibited a decreasing trend from 2004 to 2014; (2) the geographic and RS variables played more important roles in predicting TN distribution than did the other variables; and (3) changes in the fertilization and crop planting structure caused by soil testing and formulated fertilization techniques (STFFT-Soil Testing and Formulated Fertilization Techniques) as well as farmland protection policies influenced the spatiotemporal variability of TN. Evidently, more attention should be focused on improving the quality and soil fertility in the surrounding low mountainous areas.

Nitrogen diagnosis based on dynamic characteristics of rice leaf image.

Digital image processing is widely used in the non-destructive diagnosis of plant nutrition. Previous plant nitrogen diagnostic studies have mostly focused on characteristics of the rice canopy or leaves at some specific points in time, with the long sampling intervals unable to provide detailed and specific "dynamic features." According to plant growth mechanisms, the dynamic changing rate in leaf shape and color differ between different nitrogen supplements. Therefore, the objective of this study was to diagnose nitrogen stress levels by analyzing the dynamic characteristics of rice leaves. Scanning technology was implemented to collect rice leaf images every 3 days, with the characteristics of the leaves from different leaf positions extracted utilizing MATLAB. Newly developed shape characteristics such as etiolation area (EA) and etiolation degree (ED), in addition to shape (area, perimeter) and color characteristics (green, normalized red index, etc.), were used to quantify the process of leaf change. These characteristics allowed sensitive indices to be established for further model validation. Our results indicate that the changing rates in dynamic characteristics, in particular the shape characteristics of the first incomplete leaf (FIL) and the characteristics of the 3rd leaf (leaf color and etiolation indices), expressed obvious distinctions among different nitrogen treatments. Consequently, we achieved acceptable diagnostic accuracy (training accuracy 77.3%, validation accuracy 64.4%) by using the FIL at six days after leaf emergence, and the new shape characteristics developed in this article (ED and EA) also showed good performance in nitrogen diagnosis. Based on the aforementioned results, dynamic analysis is valuable not only in further studies but also in practice.

Dynamics of Hierarchical Urban Green Space Patches and Implications for Management Policy.

Accurately quantifying the variation of urban green space is the prerequisite for fully understanding its ecosystem services. However, knowledge about the spatiotemporal dynamics of urban green space is still insufficient due to multiple challenges that remain in mapping green spaces within heterogeneous urban environments. This paper uses the city of Hangzhou to demonstrate an analysis methodology that integrates sub-pixel mapping technology and landscape analysis to fully investigate the spatiotemporal pattern and variation of hierarchical urban green space patches. Firstly, multiple endmember spectral mixture analysis was applied to time series Landsat data to derive green space coverage at the sub-pixel level. Landscape metric analysis was then employed to characterize the variation pattern of urban green space patches. Results indicate that Hangzhou has experienced a significant loss of urban greenness, producing a more fragmented and isolated vegetation landscape. Additionally, a remarkable amelioration of urban greenness occurred in the city core from 2002 to 2013, characterized by the significant increase of small-sized green space patches. The green space network has been formed as a consequence of new urban greening strategies in Hangzhou. These strategies have greatly fragmented the built-up areas and enriched the diversity of the urban landscape. Gradient analysis further revealed a distinct pattern of urban green space landscape variation in the process of urbanization. By integrating both sub-pixel mapping technology and landscape analysis, our approach revealed the subtle variation of urban green space patches which are otherwise easy to overlook. Findings from this study will help us to refine our understanding of the evolution of heterogeneous urban environments.

[Study on soil carbon estimation by on-the-go near-infrared spectra and partial least squares regression with variable selection].

The present paper tried to evaluate the effectiveness and improvement of variable selection before modeling with partial least squares regression (PLSR). Based on the independent test dataset, and compared with the PLSR model derived from all spectral variables, the prediction accuracy by modeling after variable selection has been improved. Thus, the results showed that variable selection was beneficial and necessary for soil carbon modeling by on-the-go NIRS. UVE (uninformative variable elimination) and UVE-SPA (successive projection algorithm) could perform effective variable selection and created promising models, and SPA and GA-PLS (genetic algorithm PLS) failed to make appropriate models. For synergy interval PLS (siPLS), change in interval number and number of interval for modeling could affect the prediction accuracy obviously. Promising models could be made by selecting appropriate interval number and number of interval for modeling, and siPLS could achieve similar prediction accuracy to UVE or UVE-SPA, and the shortcoming was that siPLS required a lot of computing time to find optimal combination of intervals for modeling.

[Diagnosis study of rice leaf under phosphorus insufficiency based on spectral features of scan image and pattern recognition].

Insufficiency of phosphorus could greatly effect rice production, thus it is significant to adopt quick and nondestructive diagnosis of phosphorus content. The present paper focused on first expanded leaves with different phosphorus fertilization levels, comprehensively extracted 26 features' spectral information such as color, texture and shape etc. Single feature index analysis was conducted. Then features were collected to integrate CfsSubsetEval + Scattersearch method for optimizing, evaluation and choosing. Based on the feature selection for different leave positions, leaves in different phosphorus fertilization levels were finally classified into three grades (extremly insufficient, significant insufficient and normal) according to rough set theory. Results showed that the accuracy of recognition was very high while few phosphorus contained in the leaves. Moreover, the third expanded leaf is the best part for phosphorus-nutrient diagnosis.

[Application of mutual information to variable selection in diagnosis of phosphorus nutrition in rice].

The present study obtained data of rice canopy spectrum, and P and chlorophyll content at typical growth stages with different rates of P supply by means of solution experiment. The effects of P treatments on leaf P and chlorophyll content were analyzed statistically using LSD's multiple comparison at a probability of 0.05; By mutual information (MI) variable selection procedure, the optimal spectral variables were identified at 536, 630, 1040, 551 and 656 nm, and their corresponding mutual information values were 1.0575, 1.1039, 1.135 3, 1.1417 and 1.1494 respectively; based on these sensitive bands, the built feed-forward artificial neural network model (ANN) had higher precision for P content estimation than the multiple linear regression model (MLR). Its RMSE of cross-validation and R were 0.038 8 and 0.9882, respectively, for the calibration data set, and the RMSE of prediction and R were 0.0505 and 0.9892, respectively, for the test data set. Therefore, it was suggested that MI was encouraged for quantitative prediction of leaf P content in rice with visible/near infrared hyperspectral information without assumption on the relationship between independent and dependent variables. But more work is needed to explain why these bands are sensitive to leaf P content in rice.

[Study on relationship between on-the-go near-infrared spectroscopy and soil texture].

Near infrared spectroscopy (NIRS) is a rapid, pioximal-sensed method that has proven useful in quantifying soil constituents mainly in laboratory. However, very little is known about how NIRS performs in a field setting by newly developed on-the-go NIRS measurements. The objective of the present study was to evaluate the relationship between on-the-go field NIRS measurements and soil texture in a glacial till soil. It was found that NIRS band combination based on difference, normalized difference and ratio could apparently improve the coefficient of relationship between NIRS and soil texture, and this might be a new and effective analytical procedure for field NIRS measurements.

[Impacts of farmland consolidation on farmland landscape].

Farmland consolidation is the act of regulating, improving, and comprehensively renovating the structure, quality, and layout of field, water, road, forestry, and village in the countryside in a certain area by means of administration, economy, law, and engineering techniques according to the goal and usage defined by land use planning, so as to improve farmland use rate and its output rate, to increase farmland area, and to achieve better productive, living, and ecological environment. Recently, farmland consolidation has been carried out all over the country, especially in its economy-developed regions. But, unscientific planning and unsuitable farmland consolidation engineering have negative effects on field ecological system. In this paper, based on the technology of GIS and RS, the basic theories and methods of landscape ecology and a compositive grading method were applied to analysis the dynamics of farmland landscape fragmentation in Tongxiang county. The results showed that the farmland landscape fragmentation in this county was strongly affected by consolidation. More attention should be paid to the protection of farmland landscape during consolidation, and to avoid or decrease the negative effects resulted from unscientific planning and unsuitable farmland consolidation engineering.