Predicting the global potential distribution of Bursaphelenchus xylophilus using an ecological niche model: expansion trend and the main driving factors.

Bursaphelenchus xylophilus (Steiner&Buhrer) Nickle is a global quarantine pest that causes devastating mortality in pine species. The rapid and uncontrollable parasitic spread of this organism results in substantial economic losses to pine forests annually. In this study, we used the MaxEnt model and GIS software ArcGIS10.8 to predict the distribution of B. xylophilus based on collected distribution points and 19 environmental variables (with a correlation coefficient of|R| > 0.8) for the contemporary period (1970-2000), 2041-2060 (2050s), 2061-2080 (2070s), and 2081-2100 (2090s) under four shared socioeconomic pathways (SSPs). We conducted a comprehensive analysis of the key environmental factors affecting the geographical distribution of B. xylophilus and suitable distribution areas. Our results indicate that in current prediction maps B. xylophilus had potential suitable habitats in all continents except Antarctica, with East Asia being the region with the most highly suitable areas and the most serious epidemic area currently. Precipitation of the warmest quarter, temperature seasonality, precipitation of the wettest month, and maximum temperature of the warmest month were identified as key environmental variables that determine the distribution of B. xylophilus. Under future climatic conditions, the potential geographic distribution of B. xylophilus will expand relative to current conditions. In particular, under the SSP5-8.5 scenario in 2081-2100, suitable areas will expand to higher latitudes, and there will be significant changes in suitable areas in Europe, East Asia, and North America. These findings are crucial for future prevention and control management and monitoring.

Integrated transcriptomic and WGCNA analyses reveal candidate genes regulating mainly flavonoid biosynthesis in Litsea coreana var. sinensis.

Litsea coreana Levl. var. sinensis (Allen) Yang et P. H. Huang is a popular ethnic herb and beverage plant known for its high flavonoid content, which has been linked to a variety of pharmacological benefits and crucial health-promoting impacts in humans. The progress in understanding the molecular mechanisms of flavonoid accumulation in this plant has been hindered due to the deficiency of genomic and transcriptomic resources. We utilized a combination of Illumina and Oxford Nanopore Technology (ONT) sequencing to generate a de novo hybrid transcriptome assembly. In total, 126,977 unigenes were characterized, out of which 107,977 were successfully annotated in seven public databases. Within the annotated unigenes, 3,781 were categorized into 58 transcription factor families. Furthermore, we investigated the presence of four valuable flavonoids-quercetin-3-O-ß-D-galactoside, quercetin-3-O-ß-D-glucoside, kaempferol-3-O-ß-D-galactoside, and kaempferol-3-O-ß-D-glucoside in 98 samples, using high-performance liquid chromatography. A weighted gene co-expression network analysis identified two co-expression modules, MEpink and MEturquoise, that showed strong positive correlation with flavonoid content. Within these modules, four transcription factor genes (R2R3-MYB, NAC, WD40, and ARF) and four key enzyme-encoding genes (CHI, F3H, PAL, and C4H) emerged as potential hub genes. Among them, the R2R3-MYB (LcsMYB123) as a homologous gene to AtMYB123/TT2, was speculated to play a significant role in flavonol biosynthesis based on phylogenetic analysis. Our findings provided a theoretical foundation for further research into the molecular mechanisms of flavonoid biosynthesis. Additionally, The hybrid transcriptome sequences will serve as a valuable molecular resource for the transcriptional annotation of L. coreana var. sinensis, which will contribute to the improvement of high-flavonoid materials.

A fusion of VGG-16 and ViT models for improving bone tumor classification in computed tomography.

Background and Objective: Bone tumors present significant challenges in orthopedic medicine due to variations in clinical treatment approaches for different tumor types, which includes benign, malignant, and intermediate cases. Convolutional Neural Networks (CNNs) have emerged as prominent models for tumor classification. However, their limited perception ability hinders the acquisition of global structural information, potentially affecting classification accuracy. To address this limitation, we propose an optimized deep learning algorithm for precise classification of diverse bone tumors. Materials and Methods: Our dataset comprises 786 computed tomography (CT) images of bone tumors, featuring sections from two distinct bone species, namely the tibia and femur. Sourced from The Second Affiliated Hospital of Fujian Medical University, the dataset was meticulously preprocessed with noise reduction techniques. We introduce a novel fusion model, VGG16-ViT, leveraging the advantages of the VGG-16 network and the Vision Transformer (ViT) model. Specifically, we select 27 features from the third layer of VGG-16 and input them into the Vision Transformer encoder for comprehensive training. Furthermore, we evaluate the impact of secondary migration using CT images from Xiangya Hospital for validation. Results: The proposed fusion model demonstrates notable improvements in classification performance. It effectively reduces the training time while achieving an impressive classification accuracy rate of 97.6%, marking a significant enhancement of 8% in sensitivity and specificity optimization. Furthermore, the investigation into secondary migration's effects on experimental outcomes across the three models reveals its potential to enhance system performance. Conclusion: Our novel VGG-16 and Vision Transformer joint network exhibits robust classification performance on bone tumor datasets. The integration of these models enables precise and efficient classification, accommodating the diverse characteristics of different bone tumor types. This advancement holds great significance for the early detection and prognosis of bone tumor patients in the future.