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Sample pretreatment is an essential procedure in pesticide analysis, as the matrix effect can significantly influence the results. In this study, a covalent organic framework (COF) was synthesized using 1,2,4,5-tetrakis-(4-formylphenyl)benzene (TFPB) and benzidine (BD) to mitigate the matrix effect in vegetable and fruit samples. This COF was then used to develop a solid-phase extraction (CSPE) method. In addition, the COF was used to create a magnetic COF (MCOF) for use in magnetic solid-phase extraction (MSPE). The synthesized COF and MCOF were thoroughly characterized using scanning electron microscopy (SEM) for morphological analysis, Fourier-transform infrared spectroscopy (FT-IR) for chemical bond identification, and N2 adsorption–desorption measurements for porosity and surface area assessment. Key pretreatment parameters such as buffers, dilution rate, sorbent dosage, extraction time, elution solvent, and reuse number were optimized. The developed CSPE and MSPE showed excellent purification ability for the matrix of vegetable and fruit samples. The reuse test demonstrated that the synthesized COF and MCOF can be reused up to 15 times. Moreover, the developed CSPE and MSPE showed acceptable recoveries in spiked recovery tests, suggesting that these pretreatment methods were feasible for sample purification in pesticide analysis. [ABSTRACT FROM AUTHOR]

Hydration resistance of CaO aggregates is the key to the successful application of lime-based refractories. Granulation technology on limestone with additives and one step calcination method was used in this study to investigate the effects of Al(OH)3 and La2O3 on the sintering behavior and the hydration resistance of CaO granules. The result showed that the hydration resistance of CaO granules was improved significantly by adding Al(OH)3 and La2O3. The shell of sintered CaO granules was relatively dense after calcination and the specific surface area of CaO granules decreased from 7.5×10-2 m2/g to 1.2×10-2 m2/g. The average pore size of CaO granular was decreased from 3.54 μm to 0.83 μm due to the liquid phase sintering when Al(OH)3 additive was used. La2O3 promoted the densification of CaO compact by the ion substitution and solution with CaO. La3+ approached into CaO lattices, which enlarged the vacancy concentration of Ca2+ and accelerated the diffusion of Ca2+.

Background: Premature ovarian failure (POF) is a type of pathological aging, which seriously interferes with the fertility of affected women. Electroacupuncture (EA) may have a beneficial effect; however, its mechanism of action is unknown. The purpose of this study was to determine the effect of EA on ovarian function in ovarian granulosa cells (OGCs) in a cyclophosphamide (CTX)-induced mouse model of POF. Methods: Mice were divided into three groups: wild type (WT) group, CTX group and CTX + EA group. EA was administered under isoflurane anesthesia at CV4, ST36 and SP6 for 30 min every 2 days, 2–3 times per week for a total of 4 weeks. Effects of EA on ovarian weight and level of estrogen were examined. The mRNA and protein expression levels of cell cycle–associated proteins were detected and mRNA modifications were analyzed. Results: EA significantly increased ovarian weight and reduced the proportion of atretic follicles in mice with CTX-induced POF (p < 0.05). EA increased the level of estrogen in the peripheral blood of mice and inhibited the modification of total mRNA N4-acetylcytidine (ac4C). A significant increase in the expression of P16 and N-acetyltransferase 10 (NAT10) and a significant decrease in the expression of Cyclin D (CCND1) and cyclin-dependent kinase 6 (CDK6) were observed in the OGCs of POF mice (p<0.05). After EA, P16 and NAT10 expression was decreased, and CCND1 and CDK6 expression was increased. Finally, EA reduced the ac4C modification of P16 mRNA-specific sites in the OGCs of POF mice. Conclusion: This study demonstrated that EA promoted the repair of the ovarian microenvironment by inhibiting the ac4C modification of P16 mRNA to decrease its stability and expression intensity, and by altering the activity of the P16/CDK6/CCND1 axis in OGCs. [ABSTRACT FROM AUTHOR]

CaZrO3 samples were prepared by in situ decomposition pore forming technology in this paper. The influence of calcia/zirconia molar ratio and heating treatment on the microstructure and thermal conductivity of the sintered samples were investigated. The phase analysis of CaZrO3 samples was detected by X‐ray diffraction, and the microscopic morphology of CaZrO3 was observed by scanning electron microscope combined with energy dispersive spectrometer for microzone composition analysis. The results showed that a satisfied CaZrO3 sample could be obtained by in situ decomposition pore forming technology with calcium/zirconium molar ratio of 1:1.02. The CaZrO3 grains were developed better when the samples were preheated at 200°C first and then heated at 1600°C for 3 h. The apparent porosity of CaZrO3 samples were increased to 28.5%, and the thermal conductivity reduced to 1.219 W/(m·K) compared with the heavy CaZrO3 samples, which could be used as CaZrO3 refractory aggregates with the lower thermal conductivity. [ABSTRACT FROM AUTHOR]

Predicting the destination of a moving object is a popular research subject in location‐based services. By predicting destinations, suggestions can be offered to people regarding their trips. At present, there are problems such as data sparsity and long‐term dependence based on historical trajectory prediction methods, which affect the accuracy of prediction. To solve data sparsity problem, this paper has devised an improved minimum description length method, which incorporates weighting parameters and optimizes the partitioning of trajectories with undirected complete graph. Long–short‐term memory is a trajectory‐prediction model that solves the problem of long‐term dependence, but the model tends to have vanishing gradient issues when used to process longer sequences. This is because the hidden layers of long–short‐term memory is largely affected by the lengths of sequences. Using embedded technology, the authors convert trajectory sequences into embedded vector sequences, and thus propose a deep‐learning prediction model, EP‐LSTM (Embedded Processing ‐ Long Short Term Memory), which integrates embedded technology and long–short‐term memory. The authors have conducted a great amount of testing with real data sets, comparing EP‐LSTM with currently available predicting methods. The results have shown that EP‐LSTM not only effectively solves data sparsity and long‐term dependence but also achieves a high degree of prediction accuracy. [ABSTRACT FROM AUTHOR]