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A green and efficient ultrasonic-assisted acidic natural deep eutectic solvents for the extraction of anthocyanins from Aronia Melanocarpa was developed. The extraction conditions were optimized by the artificial neural networks and genetic algorithms, and the stability and antioxidant activity of anthocyanins extracts were investigated. A series of acidic natural deep eutectic solvents were prepared using betaine and organic acids as hydrogen bond acceptor and hydrogen bond donor respectively. Their physical and chemical properties such as density, viscosity, pH value were measured. The structure and formation mechanism of natural deep eutectic solvents were studied by infrared spectroscopy. The optimal extraction conditions were optimized by artificial neural network combined with genetic algorithm. The photostability, thermal stability and antioxidant activity of anthocyanins extract were evaluated. The experimental results showed that the natural deep eutectic solvents formed by the hydrogen bond interaction between betaine and lactic acid had the characteristics of density as low as 1.19, viscosity as low as 24.75 mPa·s and pH as 2.89. The optimal extraction conditions were as follows: betaine and lactic acid were used to prepare natural deep eutectic solvents, molar ratio was 1:3, water content was 32%, ultrasonic power was 124 W, ultrasonic time was 24 min and initial ultrasonic temperature was 32 ℃. Under this condition, the anthocyanins extraction yield reached 23.62 mg/g. Compared with traditional solvents and other methods, the method is green, efficient and simple to operate. The results of stability and antioxidant experiments showed that light can accelerate the degradation of anthocyanins in the extract. When the temperature was higher than 50 ℃, the thermal degradation of anthocyanins accelerated with the first-order kinetic degradation constant k>0.0234. When the mass concentration was 200 μg/mL, the DPPH free radical scavenging rate and total antioxidant capacity of anthocyanins extracts were 62.30% and 6.39 mmol/L FeSO4·7H2O equivalent, and at a mass concentration of 30 μg/mL, the ABTS+ free radical scavenging rate of anthocyanins extracts was 85.14%. The antioxidant activity of anthocyanins extracts was significantly (P

The red imported fire ant (Solenopsis invicta Buren) is a social pest species with a robust reproductive ability that causes extensive damage. Identification of the genes involved in queen fertility is critical in order to better understand the reproductive biology and screening for the potential molecular targets in S. invicta. Here, we used the mRNA deep sequencing (RNA-seq) approach to identify differentially expressed genes (DEGs) in the transcriptomes of three reproductive caste types of S. invicta, including queen (QA) and winged female (FA) and male (MA) ants. The genes that were specific to and highly expressed in the queens were then screened, and the Vg2 and Vg3 genes were chosen as targets to explore their functions in oogenesis and fertility. A minimum of 6.08 giga bases (Gb) of clean reads was obtained from all samples, with a mapping rate > 89.78%. There were 7524, 7133, and 977 DEGs identified in the MA vs. QA, MA vs. FA, and FA vs. QA comparisons, respectively. qRT–PCR was used to validate 10 randomly selected DEGs, including vitellogenin 2 (Vg2) and 3 (Vg3), and their expression patterns were mostly consistent with the RNA-seq data. The S. invicta Vgs included conserved domains and motifs that are commonly found in most insect Vgs. SiVg2 and SiVg3 were highly expressed in queens and winged females and were most highly expressed in the thorax, followed by the fat body, head, and epidermis. Evaluation based on a loss-of-function-based knockdown analysis showed that the downregulation of either or both of these genes resulted in smaller ovaries, less oogenesis, and less egg production. The results of transcriptional sequencing provide a foundation for clarifying the regulators of queen fertility in S. invicta. The functions of SiVg2 and SiVg3 as regulators of oogenesis highlight their importance in queen fecundity and their potential as targets of reproductive disruption in S. invicta control.

Attention mechanism has been extensively used in video captioning tasks, which enables further development of deeper visual understanding. However, most existing video captioning methods apply the attention mechanism on the frame level, which only model the temporal structure and generated words, but ignore the region-level spatial information that provides accurate visual features corresponding to the semantic content. In this paper, we propose a fine-grained spatial-temporal attention model (FSTA), and the spatial information of objects appearing in the video will be our main concern. In the proposed FSTA, we achieve the spatial-hard attention at a fine-grained region level of objects through the mask pooling module and compute the temporal soft attention by using a two-layer LSTM network with attention mechanism to generate sentences. We test the proposed model on two benchmark datasets, namely, MSVD and MSR-VTT. The results indicate that our proposed FSTA model can achieve competitive performance against the state of the arts on both datasets.