Mikania micranthais one of the top 100 worst invasive species that can cause serious damage to natural ecosystems and substantial economic losses. Here, we present its 1.79 Gb chromosome-scale reference genome. Half of the genome is composed of long terminal repeat retrotransposons, 80% of which have been derived from a significant expansion in the past one million years. We identify a whole genome duplication event and recent segmental duplications, which may be responsible for its rapid environmental adaptation. Additionally, we show thatM. micranthaachieves higher photosynthetic capacity by CO₂absorption at night to supplement the carbon fixation during the day, as well as enhanced stem photosynthesis efficiency. Furthermore, the metabolites ofM. micranthacan increase the availability of nitrogen by enriching the microbes that participate in nitrogen cycling pathways. These findings collectively provide insights into the rapid growth and invasive adaptation.

The use of yellow sticky traps is a green pest control method that utilizes the pests’ attraction to the color yellow. The use of yellow sticky traps not only controls pest populations but also enables monitoring, offering a more economical and environmentally friendly alternative to pesticides. However, the small size and dense distribution of pests on yellow sticky traps lead to lower detection accuracy when using lightweight models. On the other hand, large models suffer from longer training times and deployment difficulties, posing challenges for pest detection in the field using edge computing platforms. To address these issues, this paper proposes a lightweight detection method, YOLO-YSTs, based on an improved YOLOv10n model. The method aims to balance pest detection accuracy and model size and has been validated on edge computing platforms. This model incorporates SPD-Conv convolutional modules, the iRMB inverted residual block attention mechanism, and the Inner-SIoU loss function to improve the YOLOv10n network architecture, ultimately addressing the issues of missed and false detections for small and overlapping targets while balancing model speed and accuracy. Experimental results show that the YOLO-YSTs model achieved precision, recall, mAP₅₀, and mAP_50–95 values of 83.2%, 83.2%, 86.8%, and 41.3%, respectively, on the yellow sticky trap dataset. The detection speed reached 139 FPS, with GFLOPs at only 8.8. Compared with the YOLOv10n model, the mAP₅₀ improved by 1.7%. Compared with other mainstream object detection models, YOLO-YSTs also achieved the best overall performance. Through improvements to the YOLOv10n model, the accuracy of pest detection on yellow sticky traps was effectively enhanced, and the model demonstrated good detection performance when deployed on edge mobile platforms. In conclusion, the proposed YOLO-YSTs model offers more balanced performance in the detection of pest images on yellow sticky traps. It performs well when deployed on edge mobile platforms, making it of significant importance for field pest monitoring and integrated pest management.

Background: Biogas production with anaerobic digestion (AD) is one of the most promising solutions for both renewable energy production and resolving the environmental problem caused by the worldwide increase in organic waste. However, the complex structure of the microbiome in AD is poorly understood. Findings: In this study, we constructed a microbial gene catalog of AD (22,840,185 genes) based on 1,817 Gb metagenomic data derived from digestate samples of 56 full-scale biogas plants fed with diverse feedstocks. Among the gene catalog, 73.63% and 2.32% of genes were taxonomically annotated to Bacteria and Archaea, respectively, and 57.07% of genes were functionally annotated with KEGG orthologous groups. Our results confirmed the existence of core microbiome in AD and showed that the type of feedstock (cattle, chicken, and pig manure) has a great influence on carbohydrate hydrolysis and methanogenesis. In addition, 2,426 metagenome-assembled genomes were recovered from all digestate samples, and all genomes were estimated to be ≥80% complete with ≤10% contamination. Conclusions: This study deepens our understanding of the microbial composition and function in the AD process and also provides a huge number of reference genome and gene resources for analysis of anaerobic microbiota.

Amomum tsao-ko is an economically important spice plant in the ginger family (Zingiberaceae). The dried ripe fruit has been widely used as spice and medicine in Southeast Asia due to its distinct flavor metabolites. However, there is little genomic information available to understand the biosynthesis of its characteristic flavor compounds. Here, we present a high-quality chromosome-level genome of A. tsao-ko with a total length of 2.08 Gb assembled into 24 chromosomes. Potential relationships between genetic variation and chemical constituents were analyzed by a genome-wide association study of 119 representative A. tsao-ko specimens in China. Metabolome and transcriptome correlation analysis of different plant organs and fruit developmental stages revealed the proposed biosynthesis of the characteristic bicyclononane aldehydes and aromatic metabolites in A. tsao-ko fruit. Transcription factors of 20 families may be involved in the regulatory network of terpenoids. This study provides genomic and chemical insights into the biosynthesis of characteristic aroma and flavor constituents, which can be used to improve the quality of A. tsao-ko as food and medicine.

The spotted-wing Drosophila (Drosophila suzukii Matsumura) is native to eastern Asia, but has become a global threat to fruit production. In recent years, CRISPR/Cas9 targeting was established in this species allowing for functional genomic and genetic control studies. Here, we report the generation and characterization of Cas9-expressing strains of D. suzukii. Five independent transgenic lines were generated using a piggyBac construct containing the EGFP fluorescent marker gene and the Cas9 gene under the control of the D. melanogaster heat shock protein 70 promoter and 3’UTR. Heat-shock (HS) treated embryos were analyzed by reverse transcriptase PCR, revealing strong heat inducibility of the transgenic Cas9 expression. By injecting gRNA targeting EGFP into one selected line, 50.0% of G₀ flies showed mosaic loss-of-fluorescence phenotype, and 45.5% of G₀ flies produced G₁ mutants without HS. Such somatic and germline mutagenesis rates were increased to 95.4% and 85.7%, respectively, by applying a HS. Parental flies receiving HS resulted in high inheritance of the mutation (92%) in their progeny. Additionally, targeting the endogenous gene yellow led to the lack of pigmentation and male lethality. We discuss the potential use of these efficient and temperature-dependent Cas9-expressing strains for the genetic studies in D. suzukii.

Amid growing challenges to global food security, high-throughput crop phenotyping has become an essential tool, playing a critical role in genetic improvement, biomass estimation, and disease prevention. Unlike controlled laboratory environments, field-based phenotypic data collection is highly vulnerable to unpredictable factors, significantly complicating the data acquisition process. As a result, the choice of appropriate data collection equipment and processing methods has become a central focus of research. Currently, three key technologies for extracting crop phenotypic parameters are Light Detection and Ranging (LiDAR), Multi-View Stereo (MVS), and depth camera systems. LiDAR is valued for its rapid data acquisition and high-quality point cloud output, despite its substantial cost. MVS offers the potential to combine low-cost deployment with high-resolution point cloud generation, though challenges remain in the complexity and efficiency of point cloud processing. Depth cameras strike a favorable balance between processing speed, accuracy, and cost-effectiveness, yet their performance can be influenced by ambient conditions such as lighting. Data processing techniques primarily involve point cloud denoising, registration, segmentation, and reconstruction. This review summarizes advances over the past five years in 3D reconstruction technologies—focusing on both hardware and point cloud processing methods—with the aim of supporting efficient and accurate 3D phenotype acquisition in high-throughput crop research.