Evaluation method for ecology-agriculture-urban spaces based on deep learning.

With the increasing global population and escalating ecological and farmland degradation, challenges to the environment and livelihoods have become prominent. Coordinating urban development, food security, and ecological conservation is crucial for fostering sustainable development. This study focuses on assessing the "Ecology-Agriculture-Urban" (E-A-U) space in Yulin City, China, as a representative case. Following the framework proposed by Chinese named "environmental capacity and national space development suitability evaluation" (hereinafter referred to as "Double Evaluation"), we developed a Self-Attention Residual Neural Network (SARes-NET) model to assess the E-U-A space. Spatially, the northwest region is dominated by agriculture, while the southeast is characterized by urban and ecological areas, aligning with regional development patterns. Comparative validations with five other models, including Logistic Regression (LR), Naive Bayes (NB), Gradient Boosting Decision Trees (GBDT), Random Forest (RF) and Artificial Neural Network (ANN), reveal that the SARes-NET model exhibits superior simulation performance, highlighting it's ability to capture intricate non-linear relationships and reduce human errors in data processing. This study establishes deep learning-guided E-A-U spatial evaluation as an innovative approach for national spatial planning, holding broader implications for national-level territorial assessments.

Investigation of hairpin DNA and chelerythrine interaction by a single bio-nanopore sensing interface.

Chelerythrine (CHE) is one of the potential drugs for cancer treatments. The interaction between hairpin DNA and CHE has been investigated by spectral and mass spectrometry methods. In this paper, the stability of hairpin DNA with different loop bases and its interaction with CHE were explored with a single α-hemolysin (α-HL) nanopore sensing interface. The results showed that the characteristic current pulses not only relate to the loop composition changes of the hairpin DNA, but also provide interaction information between CHE and the hairpin DNA molecules. The dwell time of current pulses for hairpin DNA was significantly increased (hundreds of ms) due to the addition of CHE, and two characteristic current distributions were recognized for the hairpin with T3 and C3 loops. The two characteristic current groups could be ascribed to the hairpin DNA and the ones with CHE. This study indicates that it is possible to study the interaction between single CHE and single hairpin DNA molecules by the single-nanopore sensing interface as an alternative method to conventional spectrometric methods for therapeutic mechanism and drug screening purposes.

Assessment of water quality and identification of polluted risky regions based on field observations & GIS in the Honghe River watershed, China.

Water quality assessment at the watershed scale requires not only an investigation of water pollution and the recognition of main pollution factors, but also the identification of polluted risky regions resulted in polluted surrounding river sections. To realize this objective, we collected water samplings from 67 sampling sites in the Honghe River watershed of China with Grid GIS method to analyze six parameters including dissolved oxygen (DO), ammonia nitrogen (NH3-N), nitrate nitrogen (NO3-N), nitrite nitrogen (NO2-N), total nitrogen (TN) and total phosphorus (TP). Single factor pollution index and comprehensive pollution index were adopted to explore main water pollutants and evaluate water quality pollution level. Based on two evaluate methods, Geo-statistical analysis and Geographical Information System (GIS) were used to visualize the spatial pollution characteristics and identifying potential polluted risky regions. The results indicated that the general water quality in the watershed has been exposed to various pollutants, in which TP, NO2-N and TN were the main pollutants and seriously exceeded the standard of Category III. The zones of TP, TN, DO, NO2-N and NH3-N pollution covered 99.07%, 62.22%, 59.72%, 37.34% and 13.82% of the watershed respectively, and they were from medium to serious polluted. 83.27% of the watershed in total was polluted by comprehensive pollutants. These conclusions may provide useful and effective information for watershed water pollution control and management.