High-quality super-resolution mapping using spatial deep learning.

Super-resolution mapping (SRM) is a critical technology in remote sensing. Recently, several deep learning models have been developed for SRM. Most of these models, however, only use a single stream to process remote sensing images and mainly focus on capturing spectral features. This can undermine the quality of the resulting maps. To address this issue, we propose a soft information-constrained network (SCNet) for SRM that leverages spatial transition features represented by soft information as a spatial prior. Our network incorporates a separate branch to process prior spatial features for feature enhancement. SCNet can extract multi-level feature representations simultaneously from both remote sensing images and prior soft information and hierarchically incorporate features from soft information into image features. Experimental results on three datasets demonstrate that SCNet generates more complete spatial details in complex areas, providing an effective means for producing high-quality and high-resolution mapping products from remote sensing images.

Removal of Antibiotic Resistance Genes from Animal Wastewater by Ecological Treatment Technology Based on Plant Absorption.

With the aim of controlling the pollution of antibiotic resistance genes (ARGs) in livestock and poultry wastewater, this paper highlights an ecological treatment technology based on plant absorption and comprehensively discusses the removal effect, driving factors, removal mechanism, and distribution characteristics of ARGs in plant tissues. The review shows that ecological treatment technology based on plant absorption has gradually become an important method of wastewater treatment of livestock and poultry breeding and has a good ARG removal effect. In plant treatment ecosystems, microbial community structure is the main driver of ARGs, while mobile genetic elements, other pollutants, and environmental factors also affect the growth and decline of ARGs. The role of plant uptake and adsorption of matrix particles, which provide attachment sites for microorganisms and contaminants, cannot be ignored. The distribution characteristics of ARGs in different plant tissues were clarified and their transfer mechanism was determined. In conclusion, the main driving factors affecting ARGs in the ecological treatment technology of plant absorption should be grasped, and the removal mechanism of ARGs by root adsorption, rhizosphere microorganisms, and root exudates should be deeply explored, which will be the focus of future research.

Alkylamine-Confined Thickness-Tunable Synthesis of Co(OH)2-CoO Nanosheets toward Oxygen Evolution Catalysis.

Thickness regulation of transition metal hydroxides/oxides nanosheets with superior catalytic properties represents a promising strategy to enhance catalytic performance, but it remains an enormous challenge to achieve precise control, especially when it comes to the ultrathin limit (several atomic layers). In this work, a facile strategy of alkylamine-confined growth is proposed for the synthesis of thickness-tunable metal hydroxide/oxide nanosheets. Specifically, ultrathin cobalt hydroxide and cobaltous oxide hybrid (Co(OH)2-CoO) nanosheets (Co-O NSs) with a thickness in the range of 2-6 nm (5-13 atomic layers) are synthesized by using alkylamines with different carbon chain lengths as the ligand to modulate vertical coordination ability. Co-O NSs with a thickness of 2 nm (Co-O NSs-2 nm) exhibit excellent oxygen evolution reaction (OER) performance with an overpotential of 278 mV at 10 mA/cm2. The maximized number of active sites including oxygen vacancies, optimal adsorption strength, and the highest electrical conductivity are considered as the potential factors contributing to the excellent OER performance of Co-O NSs-2 nm. This work holds great significance for the precise thickness-tunable synthesis of transition metal layered hydroxide nanosheets with modulated and improved catalytic performance.

Seasonal Pollution Characteristics of Antibiotics on Pig Farms of Different Scales.

Scientific interest in pollution from veterinary antibiotics (VAs) on intensive animal farms has been increasing in recent years. However, limited information is available on the seasonal pollution characteristics and the associated ecological risks of VAs, especially about the different scale farms. Therefore, this study investigated the seasonal pollution status and ecological risks of 42 typical VAs (5 classes) on three different scale pig farms (breeding scales of about 30,000, 1200, and 300 heads, respectively) in Tianjin, China. The results showed that large-scale pig farms usually had the highest antibiotic pollution levels, followed by small-scale pig farms and medium-scale pig farms. Among different seasons, antibiotic contamination was more severe in winter and spring than that in the other seasons. Tetracyclines (TCs) usually had higher proportions (over 51.46%) and the residual concentration detected in manure, and wastewater samples ranged from not detected (ND)-1132.64 mg/kg and ND-1692.50 µg/L, respectively, which all occurred for oxytetracycline (OTC) during winter. For the antibiotic ecological risks in the effluent, we found high-risk level of 12 selected VAs accounted for 58% in spring, and 7 kinds of VAs were selected in the amended soil, but nearly all the antibiotics had no obvious ecological risks except OTC (spring and summer). All these data provided an insight into the seasonal variability and the associated ecological risks of antibiotics on intensive pig farms, which can provide scientific guidance on decreasing antibiotic contamination to enhance environmental security in similar areas.

Machine learning to predict dynamic changes of pathogenic Vibrio spp. abundance on microplastics in marine environment.

Microplastics are widely found in the marine environment. Recent studies have shown that pathogenic microorganisms can hitchhike on microplastics, which might act as a vector for the spread of pathogens. Vibrio spp. are known to be pathogenic to humans and can cause serious foodborne diseases. In this study, using datasets from an estuary and a mariculture zone in China, five machine learning models were established to predict the relative abundance of Vibrio spp. on microplastics. The results showed that deep neural network (DNN) model and RandomForest algorithm achieved the best predictive performance. Different data sources, data sampling, and processing methods had a little impact on the prediction performance of DNN and RandomForest models. SHapley Additive exPlanations (SHAP) indicated that salinity and temperature are the primary factors affecting the relative abundance of Vibrio spp. The prediction performances of the five machine learning models were further improved by feature selection, providing information to support future experimental research. The results of this study could help establish a long-term and dynamic monitoring system for the relative abundance of Vibrio spp. on microplastics in response to environmental factors as well as provide useful information for assessing the potential health impacts of microplastics on marine ecology and humans.