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Traditional agricultural cultivation is labor-intensive and vulnerable to natural climate conditions, such as heavy rainfall and drought. Concerns over food safety have also brought attention to the growth of weeds and the misuse of agricultural chemicals, which can have a serious negative impact on crop growth and safety. We investigated the feasibility of the YoloX model in the field of agricultural weed identification to address the problem of weed handling in growing crops. In order to overcome the effects of climate and environment, we chose a purchased weed model for our study. We used a binocular vision camera for image acquisition and created a database containing 6,000 samples and enhanced the original database of 1,000 samples with data. In order to address the complex background of the weed images, the changing lighting environment of the binocular camera-acquired images, and the noise interference, we performed histogram equalization, image denoising, and background processing on the dataset. These processing measures aim to improve the overall learning efficiency of the model in order to improve the accuracy of deep learning on weeds. Target detection platforms based on the TensorFlow and PyTorch frameworks were established, respectively, and the mainstream target detection models Faster R-CNN and YoloX series target detection models were trained with the same dataset for comparative analysis using the longitudinal comparison method. The results show that the training under the PyTorch framework yields better models than the training under the TensorFlow framework. YoloX-x has higher recognition accuracy, faster recognition speed, and more stable compared to the Faster R-CNN model, with an average recognition rate of 97.07% and an average recognition time of 0.062 s. Moreover, the optimization of YoloX by incorporating an attention-focusing mechanism resulted in an improved accuracy rate with a decrease in recognition time, with an average recognition rate of 97.70% and an average recognition time of 0.029 s. [ABSTRACT FROM AUTHOR]

Owing to significantly prolonged survival, targeted therapy has become standardized recommendation for advanced non-small cell lung cancer patients with mutated driver genes. However, the genetic status of lung cancer patients is dynamic. By dynamically monitoring the evolution of genes status, differential genes and concomitant genes related to progressive disease could be confirmed early, so as to achieve a more accurate and comprehensive insight of the whole process management of targeted therapy for lung cancer patients. Under the guidance of accurate genetic testing results, it is helpful to provide patients with more effective, long-term, and stable individualized targeted therapy. [ABSTRACT FROM AUTHOR]

The rational design and exploitation of highly active and stable catalysts for the electrochemical oxidation of biomass-derived 5-hydroxymethylfurfural (HMF) to valuable chemical 2,5-furandicarboxylic acid (FDCA), is of great significance. Herein, a ternary metal–organic framework nanoarray is in situ hydrothermally deposited on Ni foam via assembling of multiple metal ions (Co2+, Ni2+ and Fe2+) and 2-amino-terephthalic acid. The integrated MOF composite can directly act as a catalytic electrode, which exhibits excellent electrocatalytic HMF oxidation activity with a high current density of 100 mA cm−2 at a potential of only 1.35 V vs. RHE. More importantly, the constant potential electrolysis at 1.4 V vs. RHE in combination with chromatographic analysis reveals a high faradaic efficiency close to 100% towards the production of FDCA with a yield of 99.76%. The high electrocatalytic performance for HMF oxidation is attributed to the abundant accessible active sites of two-dimensional morphology and the optimized electronic structure of the intrinsic catalytic centers in MOFs. The present study sheds light on the rational design and synthesis of new MOF-based catalysts for biomass conversion and stimulate the extensive explorations of MOFs with tunable active sites for potential electrocatalysis applications. [ABSTRACT FROM AUTHOR]

Soil C, N, and P elements are important components of the forest ecosystem. Studying the influence of exogenous carbon input change on the stoichiometry of the forest soil can reveal the element recycling process and the balanced feedback mechanism of the forest ecosystem. In this study, using the research object of a spruce forest in Tianshan Mountain, the short-term effect of exogenous carbon input on soil C, N, and P in the soil was analyzed through Detritus Input and Removal Treatment (DIRT), and then the interrelationship between soil stoichiometry and other soil physicochemical factors under different treatments was discussed. The results showed that:① the soil C, N, and P contents in most soil layers were the highest double litter (DL) treatment, soil ω (C) by soil depth from shallow to deep was 168.92, 119.88, 103.33, and 64.23 g·kg -1 ; soil ω (N) was 10.60, 9.32, 8.78, and 8.07 g·kg -1 ; soil ω (P) was 0.50, 0.45, 0.37, and 0.36 g·kg -1 ; in the no input (NI) treatment, soil ω (C) by soil depth from shallow to deep was 104.56, 89.24, 48.08, and 43.96 g·kg -1 ; soil ω (N) was 6.83, 2.60, 2.63, and 2.22 g·kg -1 ; soil ω (P) was 0.40, 0.34, 0.32, and 0.22 g·kg -1 ; and a decreased trend was shown with the deepening of the soil layer. Except in the NI treatment, C:N was 0-10 cm and significantly higher than that in other soils ( P <0.05), NL soil C:P at 30-50 cm was significantly higher than that in other soils, and NI soil N:P was 0-10 cm and significantly higher than that in other soils ( P <0.05). ② Microbial carbon, nitrogen, and phosphorus were significantly higher from 0-10 cm than that in other soil layers ( P <0.05). ③ Redundancy analysis results showed that soluble organic carbon and microbial nitrogen at different carbon input levels were important factors affecting the stoichiometric characteristics of soil C, N, and P.