Assessment of Global Antibiotic Exposure Risk for Crops: Incorporating Soil Adsorption via Machine Learning.

The overuse and misuse of antibiotics could significantly increase their accumulation in soils. Consequently, antibiotics possibly enter food chain through crop uptake, posing a threat to global food security. Assessing the exposure risks of antibiotics for crops is crucial for addressing this global issue. In this study, we assessed global antibiotic exposure risk for crops, incorporating a machine learning adsorption model based on 4893 data sets from nine antibiotics. The optimized machine learning adsorption model, using the eXtreme Gradient Boosting algorithm and the class-specific modeling strategy, demonstrated relatively good performance. Notably, we introduced unsaturated soil conditions and considered spatiotemporal variations in soil moisture and temperature for the first time in such a risk assessment. Global distributions of antibiotic exposure risk for crops were predicted for March, June, September, and December. The results indicate that soil moisture significantly influences the exposure risk assessment. Relatively high exposure risk for crops was observed during months with colder local temperatures: generally June for the Southern Hemisphere and December for the Northern Hemisphere. The resulting map highlights high-risk agricultural regions, including southern Canada, western Russia, and southern Australia.

Genome-Wide Identification and Expression Analysis of Pseudouridine Synthase Family in Arabidopsis and Maize.

Pseudouridine (Ψ), the isomer of uridine (U), is the most abundant type of RNA modification, which is crucial for gene regulation in various cellular processes. Pseudouridine synthases (PUSs) are the key enzymes for the U-to-Ψ conversion. However, little is known about the genome-wide features and biological function of plant PUSs. In this study, we identified 20 AtPUSs and 22 ZmPUSs from Arabidopsis and maize (Zea mays), respectively. Our phylogenetic analysis indicated that both AtPUSs and ZmPUSs could be clustered into six known subfamilies: RluA, RsuA, TruA, TruB, PUS10, and TruD. RluA subfamily is the largest subfamily in both Arabidopsis and maize. It's noteworthy that except the canonical XXHRLD-type RluAs, another three conserved RluA variants, including XXNRLD-, XXHQID-, and XXHRLG-type were also identified in those key nodes of vascular plants. Subcellular localization analysis of representative AtPUSs and ZmPUSs in each subfamily revealed that PUS proteins were localized in different organelles including nucleus, cytoplasm and chloroplasts. Transcriptional expression analysis indicated that AtPUSs and ZmPUSs were differentially expressed in various tissues and diversely responsive to abiotic stresses, especially suggesting their potential roles in response to heat and salt stresses. All these results would facilitate the functional identification of these pseudouridylation in the future.

A UAV Maneuver Decision-Making Algorithm for Autonomous Airdrop Based on Deep Reinforcement Learning.

How to operate an unmanned aerial vehicle (UAV) safely and efficiently in an interactive environment is challenging. A large amount of research has been devoted to improve the intelligence of a UAV while performing a mission, where finding an optimal maneuver decision-making policy of the UAV has become one of the key issues when we attempt to enable the UAV autonomy. In this paper, we propose a maneuver decision-making algorithm based on deep reinforcement learning, which generates efficient maneuvers for a UAV agent to execute the airdrop mission autonomously in an interactive environment. Particularly, the training set of the learning algorithm by the Prioritized Experience Replay is constructed, that can accelerate the convergence speed of decision network training in the algorithm. It is shown that a desirable and effective maneuver decision-making policy can be found by extensive experimental results.

Information Entropy-Based Intention Prediction of Aerial Targets under Uncertain and Incomplete Information.

To improve the effectiveness of air combat decision-making systems, target intention has been extensively studied. In general, aerial target intention is composed of attack, surveillance, penetration, feint, defense, reconnaissance, cover and electronic interference and it is related to the state of a target in air combat. Predicting the target intention is helpful to know the target actions in advance. Thus, intention prediction has contributed to lay a solid foundation for air combat decision-making. In this work, an intention prediction method is developed, which combines the advantages of the long short-term memory (LSTM) networks and decision tree. The future state information of a target is predicted based on LSTM networks from real-time series data, and the decision tree technology is utilized to extract rules from uncertain and incomplete priori knowledge. Then, the target intention is obtained from the predicted data by applying the built decision tree. With a simulation example, the results show that the proposed method is effective and feasible for state prediction and intention recognition of aerial targets under uncertain and incomplete information. Furthermore, the proposed method can make contributions in providing direction and aids for subsequent attack decision-making.

Constitutive Equation and Hot Compression Deformation Behavior of Homogenized Al-7.5Zn-1.5Mg-0.2Cu-0.2Zr Alloy.

The deformation behavior of homogenized Al-7.5Zn-1.5Mg-0.2Cu-0.2Zr alloy has been studied by a set of isothermal hot compression tests, which were carried out over the temperature ranging from 350 °C to 450 °C and the strain rate ranging from 0.001 s-1 to 10 s-1 on Gleeble-3500 thermal simulation machine. The associated microstructure was studied using electron back scattered diffraction (EBSD) and transmission electron microscopy (TEM). The results showed that the flow stress is sensitive to strain rate and deformation temperature. The shape of true stress-strain curves obtained at a low strain rate (≤0.1 s-1) conditions shows the characteristic of dynamic recrystallization (DRX). Two Arrhenius-typed constitutive equation without and with strain compensation were established based on the true stress-strain curves. Constitutive equation with strain compensation has more precise predictability. The main softening mechanism of the studied alloy is dynamic recovery (DRV) accompanied with DRX, particularly at deformation conditions, with low Zener-Holloman parameters.

Killing dental pathogens using antibacterial graphene oxide.

Dental caries and periodontal diseases have a close relationship with microbes such as Streptococcus mutans, Porphyromonas gingivalis and Fusobacterium nucleatum. Graphene oxide (GO), as the derivative of graphene, plays an important role in many areas including biology and medicine. In particular, it has been known as a promising antimicrobial nanomaterial. In this study, we focused on the antimicrobial property of GO against dental pathogens. With the utilization of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) reduced test, colony forming units (CFU) counting, growth curve observation, live/dead fluorescent staining, and confocal laser scanning microscopy (CLSM), we found GO nanosheets were highly effective in inhibiting the growth of dental pathogens. Transmission electron microscopy (TEM) images revealed that the cell wall and membrane of bacteria lost their integrity and the intracellular contents leaked out after they were treated by GO. Therefore, GO nanosheets would be an effective antibacterial material against dental pathogens and the potential applications in dental care and therapy are promising.