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【Objective】Guizhou Province is an area for annual breeding of Spodoptera frugiperda. The use of molecular markers were used to identify the population biotypes of S. frugiperda in Guiyang, Guizhou, and its adaptability to Zea mays, Sorghum bicolor and Oryza sativa were studied, with a view to clarifying the biotype of S. frugiperda invading Guiyang and understanding the characteristics of its biotypes.【Method】A total of 32 S. frugiperda samples from three counties or cities in Guiyang, Guizhou were amplified, sequenced and analyzed for comparison by using CO Ⅰ and Tpi molecular marker methods, to determine the biotype of the population invading Guiyang, Guizhou. Additionally, the developmental duration, adult emergence rate and survival time of S. frugiperda larvae of various instars after feeding on three host plants (Zea mays, S. bicolor and O. sativa) were recorded to evaluate the adaptability of S. frugiperda to these three host plants.【Result】The results showed that among the 32 S. frugiperda samples, 93.75% belonged to the rice biotype while 6.25% belonged to the corn biotype based on the mitochondrial CO Ⅰ gene analysis. The sequence characteristics were highly consistent with the corresponding biotypes of S. frugiperda population. Meanwhile, the analysis of nuclear genome Tpi gene revealed that all samples showed the haplotype characteristics of the corn strain. The adaptability study indicated that the invasive S. frugiperda in Guiyang had the shortest larval period (13.22 d), longest adult survival time (16.42 d) and highest emergence rate (95.14 %) on Z. mays, followed by S. bicolor; while it had the longest larval period (16.59 d), shortest adult survival time (8.56 d) and lowest emergence rate (75.15 %) on O. sativa.【Conclusion】The biotypes of S. frugiperda invaded Guiyang, Guizhou Province were 93.75% rice type, 6.25% maize type using CO Ⅰ molecular marker, and 100% maize type using Tpi molecu lar marker. The life span of female S. frugiperda feeding on maize was significantly higher than those feeding on rice and S. bicolor. The results provide a basis for the prevention and control of S. frugiperda on S. bicolor.

ObjectiveSclerodermus wasps are important biocontrol agents of a class of wood borers. Bacterial symbionts influence the ecology and biology of their hosts in a variety of ways, including the formation of life-long beneficial or detrimental parasitic infections. However, only a few studies have explored the species and content of the symbionts in the Sclerodermus species.MethodsHere, a high-throughput sequencing study of the V3-V4 region of the 16S ribosomal RNA gene revealed a high level of microbial variety in four Sclerodermus waps, and their diversities and functions were also predictedResultsThe three most prevalent phyla of microorganisms in the sample were Firmicutes, Bacteroides, and Proteus. The KEEG pathways prediction results indicated that the three pathways with the highest relative abundances in the S. sichuanensis species were translation, membrane transport, and nucleotide metabolism. These pathways differed from those observed in S. guani, S. pupariae, and S. alternatusi, which exhibited carbohydrate metabolism, membrane transport, and amino acid metabolism, respectively. Bacteroides were found to be abundant in several species, whereas Wolbachia was the most abundant among S. sichuanensis, with a significant negative correlation between temperature and carriage rate.ConclusionsThese results offer insights into the microbial communities associated with the bethylid wasps, which is crucial for understanding how to increase the reproductive capacity of wasps, enhance their parasitic effects, and lower cost in biocontrol.

The existing network representation learning algorithms mainly model the relationship between network nodes based on the structural features of the network, or use text features, hierarchical features and other external attributes to realize the network joint representation learning. Capturing global features of the network allows the obtained node vectors to retain more comprehensive feature information during training, thereby enhancing the quality of embeddings. In order to preserve the global structural features of the network in the training results, we employed a multi-channel learning approach to perform high-order feature modeling on the network. We proposed a novel algorithm for multi-channel high-order network representation learning, referred to as the Multi-Channel High-Order Network Representation (MHNR) algorithm. This algorithm initially constructs high-order network features from the original network structure, thereby transforming the single-channel network representation learning process into a multi-channel high-order network representation learning process. Then, for each single-channel network representation learning process, the novel graph assimilation mechanism is introduced in the algorithm, so as to realize the high-order network structure modeling mechanism in the single-channel network representation learning. Finally, the algorithm integrates the multi-channel and single-channel mechanism of high-order network structure joint modeling, realizing the efficient use of network structure features and sufficient modeling. Experimental results show that the node classification performance of the proposed MHNR algorithm reaches a good order on Citeseer, Cora, and DBLP data, and its node classification performance is better than that of the comparison algorithm used in this paper. In addition, when the vector length is optimized, the average classification accuracy of nodes of the proposed algorithm is up to 12.24% higher than that of the DeepWalk algorithm. Therefore, the node classification performance of the proposed algorithm can reach the current optimal order only based on the structural features of the network under the condition of no external feature supplementary modeling.

The electrocatalyst of oxygen reduction reactions is one of the basic components of a fuel cell. In addition to costly Pt/C benchmark catalysts, cost-effective carbon-based catalysts have received the most attention. Enormous efforts have been dedicated to trade off the catalyst performance against the economic benefit. Optimizing composition and/or structure is a universal strategy for improving performance, but it is typically limited by tedious synthesis steps. Herein, we have found that directly introducing CNT into MOF-derived carbonaceous nanopolyhedra, i.e., introduced carbon nanotubes (CNTs) penetrated porous nitrogen-doped carbon polyhedra (NCP) dotted with cobalt nanoparticles (denoted as CNTs-Co@NCP), can optimize the catalytic activity, stability, and methanol tolerance. The hierarchical architecture combines the 0D/1D/3D Co/CNT/NCP interfaces and 1D/3D CNT/NCP junctions with the frameworks with a greatly exposed active surface, strengthened mass transport kinetics, stereoscopic electrical conductivity networks and structural robustness. The sterical self-consistency of MOF-self-assembly triggered by introduced CNTs demonstrates tactful ORR electrocatalytic activity regulation. Eventually, the CNTs-Co@NCP showed a half-wave potential (E1/2) of 0.86 V and a diffusion-limited current density (JL) of 5.94 mA/cm2 in alkaline electrolyte. The CNTs-Co@NCP was integrated into the cathode of a direct methanol fuel cell (DMFC) with an anion-exchange membrane, and an open-circuit voltage (OCV) of 0.93 V and a high power density of 46.6 mW cm−2 were achieved. This work successfully developed a catalyst with competitive ORR performance through plain parameter fine-tuning without complex material design.

Abstract There is a steadily growing range of applications that can benefit from facial reconstruction techniques, leading to an increasing demand for reconstruction of high-quality 3D face models. While it is an important expressive part of the human face, the nose has received less attention than other expressive regions in the face reconstruction literature. When applying existing reconstruction methods to facial images, the reconstructed nose models are often inconsistent with the desired shape and expression. In this paper, we propose a coarse-to-fine 3D nose reconstruction and correction pipeline to build a nose model from a single image, where 3D and 2D nose curve correspondences are adaptively updated and refined. We first correct the reconstruction result coarsely using constraints of 3D-2D sparse landmark correspondences, and then heuristically update a dense 3D-2D curve correspondence based on the coarsely corrected result. A final refinement step is performed to correct the shape based on the updated 3D-2D dense curve constraints. Experimental results show the advantages of our method for 3D nose reconstruction over existing methods.

The relationship between body size and fitness in parasitoid wasps has several effects on parasitic ability, reproductive behavior in female wasps, and progeny fitness. Female wasps with various body sizes were obtained by mass-rearing a gregarious ectoparasitoid, Sclerodermus pupariae, which is one of the excellent parasites to control the larvae and pupae of Buprestidae and Cerambycidae. We investigated the effects of body size of adult females introduced on Thyestilla gebleri (Coleoptera: Cerambycidae) larvae on their paralysis time, pre-oviposition period, oviposition period and fecundity, and the related fitness of their offspring. Results showed that small female wasps needed more time to paralyze a host and had a higher mortality rate than large female wasps. More offspring were produced by large female wasps than by small female wasps, and the percentage and body size of female offspring was not affected by maternal body size. The duration of the egg stage was not affected by foundress size, nor was that of the pupal stage, but the duration of the larval stage and generation time of small female wasps was longer than that of large females. Our findings suggest that the parasitic fitness and offspring performance are affected by maternal size, and there is need to choose reasonable body size of female wasps, to optimally utilize mass rearing and to control target pests with the lowest mortality cost.

Background We sought to determine the comparative diagnostic performance of standard b-value (800-1000 s/mm² ) versus low b-value (400-500 s/mm²) diffusion-weighted magnetic resonance imaging (DW-MRI) in the detection of renal cell carcinoma (RCC). Method After a systematic review of the available literature, studies were included that reported b-values, used a histopathological reference standard, and allowed construction of 2 x 2 contingency tables for detection of RCC lesions using DW-MRI. In addition, a summary receiver operating characteristic (SROC) analysis was performed. Results Four articles that complied with all inclusion and exclusion criteria were selected for data extraction and analysis (n = 248 lesions in 266 patients). All four studies were high quality. Standard b-value DW-MRI displayed a pooled sensitivity of 0.59 (95% confidence interval (CI): 0.51-0.67) and a pooled specificity of 0.50 (95% CI: 0.30-0.70), while low b-value DW- MRI displayed a pooled sensitivity of 0.58 (95% CI: 0.48-0.63) and a pooled specificity of 0.23 (95% CI: 0.09-0.44). The SROC curve of standard b-value DW-MRI displayed an AUC of 0.61 and a Q*index of 0.59, while the SROC curve of low b-value DW-MRI displayed an AUC of 0.68 and a Q*index of 0.64. Conclusion Standard b-value DW-MRI showed a superior specificity but an approximately equivalent sensitivity to low b-value DW-MRI in detecting RCC lesions in the kidney. However, low b- value DW-MRI displayed an overall superior diagnostic accuracy over standard b-value DW-MRI. [ABSTRACT FROM AUTHOR]