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Natural rubber is an important national strategic and industrial raw material. The leaf anthracnose of rubber trees caused by the Colletotrichum species is one of the important factors restricting the yields of natural rubber. In this study, we isolated and identified strain Bacillus velezensis SF334, which exhibited significant antagonistic activity against both C. australisinense and C. siamense, the dominant species of Colletotrichum causing rubber tree leaf anthracnose in the Hainan province of China, from a pool of 223 bacterial strains. The cell suspensions of SF334 had a significant prevention effect for the leaf anthracnose of rubber trees, with an efficacy of 79.67% against C. siamense and 71.8% against C. australisinense. We demonstrated that SF334 can lead to the lysis of C. australisinense and C. siamense mycelia by causing mycelial expansion, resulting in mycelial rupture and subsequent death. B. velezensis SF334 also harbors some plant probiotic traits, such as secreting siderophore, protease, cellulase, pectinase, and the auxin of indole-3-acetic acid (IAA), and it has broad-spectrum antifungal activity against some important plant pathogenic fungi. The genome combined with comparative genomic analyses indicated that SF334 possesses most genes of the central metabolic and gene clusters of secondary metabolites in B. velezensis strains. To our knowledge, this is the first time a Bacillus velezensis strain has been reported as a promising biocontrol agent against the leaf anthracnose of rubber trees caused by C. siamense and C. australisinense. The results suggest that B. velezensis could be a potential candidate agent for the leaf anthracnose of rubber trees. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Clustering electric load curves is an important part of the load data mining process. In this paper, we propose a clustering algorithm by combining singular value decomposition and KICIC clustering algorithm (SVD-KICIC) for analyzing the characteristics of daily load curves to mitigate some of the traditional clustering algorithm problems, such as only considering intra-class distance and low computational efficiency when dealing with massive load data. Our method identifies effective daily load curve characteristics using the singular value decomposition technique to improve dimensionality reduction, which improves low computational efficiency by reducing the number of dimensions inherent in big data. Additionally, the method performs SVD on the load data to obtain singular values for determination of weight of the KICIC algorithm, which leverages intra-class and inter-class distances of the load data and further improves the computational efficiency of the algorithm. Finally, we perform a series of simulations of actual load curves from a certain city to validate that the algorithm proposed in this paper has a short operation time, high clustering quality, and solid robustness that improves the clustering performance of the load curves. [ABSTRACT FROM AUTHOR]

Through analyzing the transient components and transient characteristics in transient zero-sequence current (TZSC), a novel fault feeder detection method based on the transient correlation of non-power frequency components (NPFCs) for the resonant grounded system is proposed. Firstly, using variational mode decomposition combined with fast Fourier transformation (VMD–FFT) to decompose the TZSC, by removing the power frequency components and noise signals, the transient NPFCs can be obtained. Secondly, to reflect the overall changing trend between faulty and healthy currents, the moving average filter is introduced to smooth the NPFCs; in this way, the fault transient features can be accurately revealed. Finally, the faulty feeder can be detected by comparing the threshold with the maximum difference value of comprehensive correlation coefficient of NPFCs. The detection results show that the proposed fault detection method can accurately select the faulty feeder; it is unaffected by fault resistances, fault phase angles, etc. Moreover, the detection method can resist noise interference. [ABSTRACT FROM AUTHOR]