Organic Materials Promote Rhododendron simsii Growth and Rhizosphere Soil Properties in a Lead-Zinc Mining Wasteland.

The mining of metal minerals generates considerable mining wasteland areas, which are characterized by poor soil properties that hinder plant growth. In this study, a field plot experiment was carried out in the mining wasteland of the Lanping lead-zinc mine in Yunnan Province to study the effects of applying three organic materials-biochar (B), organic fertilizer (OF), and sludge (S)-at concentrations of 1% (mass fraction), on promoting the soil of mining wasteland and the growth of two plant varieties (Huolieniao and Yingshanhong). The results showed that the amount of available nutrients in the surface soil of a mining wasteland could be considerably increased by S and OF compared to the control check (CK). In the rhizosphere soils of two Rhododendron simsii varieties, the application of S increased the available phosphorus (P) content by 66.4% to 108.8% and the alkali-hydrolyzed nitrogen (N) content by 61.7% to 295.5%. However, the contents of available cadmium (Cd) and available lead (Pb) were reduced by 17.1% to 32.0% and 14.8% to 19.0%, respectively. Moreover, three organic materials increased the photosynthetic rate and biomass of two R. simsii varieties. Specifically, OF and S were found to significantly increase the biomass of R. simsii. Organic materials have direct impacts on the increased plant height and biomass of R. simsii. Additionally, organic materials indirectly contribute to the growth of R. simsii by reducing the content of available Cd and available Pb in rhizosphere soil while increasing the content of available nutrients according to the structural equation model (SEM). Overall, S can stabilize Cd and Pb, increase soil nutrient contents, and promote the growth of R. simsii effectively, and has great potential in the vegetation reconstruction of mining wasteland.

Research on the detection of leakage points in vertical barrier walls using a combined method of ERT and tracer methods.

In order to improve the detection accuracy of vertical barrier leakage, three contamination leakage working conditions, including point leakage only, point and vertical leakage, and horizontal leakage, were simulated by small-scale soil tank tests. The dynamic evolution of soil resistivity over time was monitored by electrical resistivity tomography (ERT). The accuracy of the ERT detection results was validated through thermal tracer method, chloride tracer method, and soil true resistivity experiments. The results indicate that the resistivity profiles at different times can more accurately reflect information on the location of leakage points, the extent of contamination plumes, and the migration pathways of pollutants under different working conditions. The extent of anomalous areas in resistivity profiles is a crucial factor in representing the geometric shape of pollution leakage. However, the preferential seepage or lateral migration of contaminant in the soil significantly reduces the detection accuracy of ERT for identifying leakage points. The thermal tracer method and the chloride tracer method can produce better complementary interpretations of ERT monitoring results. The measurement points near the leakage point exhibit faster temperature response rates, which can serve as a characteristic for identifying the location of leakage points. Compared with the thermal tracer method, the chloride tracer method can monitor the migration of contaminants over a larger range. Therefore, the proposed combined diagnostic detection method in this paper presents a feasible solution with promising engineering applications in leakage detection for vertical barrier barriers.

RNA G-quadruplex in live cells lighted-up by a thiazole orange analogue for SCA36 identification.

SCA36 is a neurodegenerative disease mainly caused by the abnormal expansion of the GGGCCT repeat sequence in intron 1 of NOP56. The RNA sequences of this gene are expected to form large amounts of G-quadruplexes in the cytoplasm, which may be a potential intervention and detection target for SCA36. Here, we have developed a small-molecular compound named TCB-1, which shows good selectivity to the G-quadruplex structure, and its fluorescence can be enhanced by hundreds of folds. Interestingly, TCB-1 can avoid lysosome capture, evenly disperse in the cytoplasm, and selectively light up the cytoplasmic RNA G-quadruplexes. This property allows TCB-1 to sensitively detect the increased formation of cytoplasmic RNA G-quadruplexes in SCA36 model cells. This work not only provides new ideas for the design of small-molecule compounds targeting RNA G-quadruplexes in living cells, but also intuitively demonstrates the increased formation of RNA G-quadruplexes caused by NOP56 gene mutation, providing a possible tool for the detection of SCA36.

Towards Lightweight Neural Networks for Garbage Object Detection.

In recent years, garbage classification has become a hot topic in China, and legislation on garbage classification has been proposed. Proper garbage classification and improving the recycling rate of garbage can protect the environment and save resources. In order to effectively achieve garbage classification, a lightweight garbage object detection model based on deep learning techniques was designed and developed in this study, which can locate and classify garbage objects in real-time using embedded devices. Focusing on the problems of low accuracy and poor real-time performances in garbage classification, we proposed a lightweight garbage object detection model, YOLOG (YOLO for garbage detection), which is based on accurate local receptive field dilation and can run on embedded devices at high speed and with high performance. YOLOG improves on YOLOv4 in three key ways, including the design of DCSPResNet with accurate local receptive field expansion based on dilated-deformable convolution, network structure simplification, and the use of new activation functions. We collected the domestic garbage image dataset, then trained and tested the model on it. Finally, in order to compare the performance difference between YOLOG and existing state-of-the-art algorithms, we conducted comparison experiments using a uniform data set training model. The experimental results showed that YOLOG achieved AP0.5 of 94.58% and computation of 6.05 Gflops, thus outperformed YOLOv3, YOLOv4, YOLOv4-Tiny, and YOLOv5s in terms of comprehensive performance indicators. The network proposed in this paper can detect domestic garbage accurately and rapidly, provide a foundation for future academic research and engineering applications.