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Objective: To explore a green and efficient method for extracting chitin. Methods: The crayfish shells were crushed through three mesh sieves to obtain the raw material. Utilizing ultrasonic wave and citric acid and urea to remove calcium and protein from the sample, decolorize it with hydrogen peroxide, filter it, and then dry it to produce chitin products. The particle size of the chitin sample was analyzed by laser scattering particle size distribution analyzer. Infrared absorption characteristics were measured by an infrared spectrometer. Scanning electron microscopy was used to observe the apparent structure of the sample. Results: It was shown that the larger the mesh size, the higher the purity of the chitin product. The yield of chitin from 200 mesh crayfish shell powder increased to 43.93%, and the purity increased to 55.39%. The particle size of chitin samples obtained from shrimp shell powder of three mesh numbers after plasma treatment decreased from 340.05, 184.30, and 137.39 μm without plasma treatment to 195.95, 159.02, and 53.51 μm respectively. Infrared spectrum analysis showed that the crayfish shell powder treated by plasma-assisted ultrasonic-assisted weak acid and weak base method displayed the absorption characteristics of chitin and magenta, indicating that the main component of the treated sample was chitin. The scanning electron microscope results showed that the structure of the chitin sample was looser and the surface was uneven after plasma treatment. Multiple statistical analyses showed that screening and plasma treatment had a significant impact on the whole properties of chitin samples. Conclusion: In this study, plasma and ultrasonic-assisted weak acid and weak base extraction of chitin from crayfish shells can improve the traditional acid-base process, reduce the environmental pollution of acid-base reagents, and effectively improve the extraction rate and purity of chitin.

Agricultural nitrous oxide (N2O) emission accounts for a non-trivial fraction of global greenhouse gas (GHG) budget. To date, estimating N2O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or uncertainties of model parameters, and due to leverage recent advances in machine learning (ML) a new method is needed to unlock the "black box"to overcome its limitations such as low interpretability, out-of-sample failure and massive data demand. In this study, we developed a first-of-its-kind knowledge-guided machine learning model for agroecosystems (KGML-ag) by incorporating biogeophysical and chemical domain knowledge from an advanced PB model, ecosys, and tested it by comparing simulating daily N2O fluxes with real observed data from mesocosm experiments. The gated recurrent unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including (1) using initial values of intermediate variables (IMVs) instead of time series as model input to reduce data demand; (2) building hierarchical structures to explicitly estimate IMVs for further N2O prediction; (3) using multi-task learning to balance the simultaneous training on multiple variables; and (4) pre-training with millions of synthetic data generated from ecosys and fine-tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosm N2O fluxes (overall r2Combining double low line0.81, and RMSECombining double low line3.6g€¯mgNm-2d-1 from cross validation). Importantly, KGML-ag always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML-ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes.

DNA double-strand break (DSB) repair by homologous recombination (HR) is crucial for the maintenance of genome stability and integrity. In this study, we aim to identify novel RNA binding proteins (RBPs) involved in HR repair because little is known about RBP function in HR. For this purpose, we carry out pulldown assays using a synthetic ssDNA/dsDNA structure coated with replication protein A (RPA) to mimic resected DNA, a crucial intermediate in HR-mediated DSB repair. Using this approach, we identify RNA-binding motif protein 14 (RBM14) as a potential binding partner. We further show that RBM14 interacts with an essential HR repair factor, CtIP. RBM14 is crucial for CtIP recruitment to DSB sites and for subsequent RPA coating and RAD51 replacement, facilitating efficient HR repair. Moreover, inhibition of RBM14 expression sensitizes cancer cells to X-ray irradiation. Together, our results demonstrate that RBM14 promotes DNA end resection to ensure HR repair and may serve as a potential target for cancer therapy.

Considering the problems that quantitative analysis and optimization of the geometrical model of scroll compressor created from involute of circle with variable radii are incomplete, the changing trend of the working chamber volume from the beginning of the suction process to the end of the discharge process is given. The volume calculation differences of Green’s theorem method, the graphical method, and the normal isometric method are compared, the reasons for calculation errors are analyzed, and the correctness of the geometrical model is verified. The calculation formula of teeth area utilization rate is deduced, and the sensitivity models of suction volume and teeth area utilization rate are constructed. It is concluded that the influence of the base circle radius variation coefficient and the occurrence angle of the inner profile are particularly significant. A multi-objective optimization analysis model considering nonlinear constraints and taking the suction volume and teeth area utilization rate as the optimization goals is established. The results show that the multi-objective genetic algorithm NSGA-II has a better comprehensive performance than the multi-objective particle swarm algorithm MOPSO and the suction volume of the scroll compressor created from the involute of circle with variable radii for an automobile air conditioner has been increased by 6.5% and the teeth area utilization rate has been reduced by 4.27%. This paper can provide further theoretical reference for the optimal design and quantitative research of the scroll compressor constructed from involute of circle with variable radii.