Beijing University of Agriculture

As a large genus in Rosaceae, Rubus contains approximately 863 species worldwide, which distributed in the temperate regions of the Northern Hemisphere. The number of species in China accounts for approximately one quarter of the total, so China is the diversity distribution center of Rubus in the world. Given this, in this study, the chloroplast genomes and their characteristics of Chinese Rubus species were sequenced, assembled, and analyzed to provide molecular evidence for the Rubus systematic classification. The results indicated that the chloroplast genomes of Rubus exhibited a typical circular quadripartite structure, which consists of a large single-copy region (LSC), a small single-copy region (SSC), and a pair of inverted repeat regions (IR). Its size ranged from 155,472 to 156,886 base pairs (bp). It contained 131 genes, including 88 protein-coding genes (PCGs), 35 tRNA genes, and 8 rRNA genes. The Rubus chloroplast genome showed a high degree of conservation in gene arrangement, in the occurrence frequencies of different types of long repeat sequences, in simple sequence repeats (SSRs), in codon usage patterns, and in selection pressures on PCG s. Differences were mainly manifested in subtle expansion and contraction changes in the inverted repeat (IR) regions, as well as variations in the number of SSRs and long repeat sequences. In some branches, certain genes underwent enhanced or relaxed purifying selection. Phylogenetic analysis revealed that Chinese endemic Rubus species could be divided into eight evolutionary branches, which corresponding to the eight subgenera of Rubus . These data not only clarify the phylogenetic relationships among the Chinese Rubus species, but also provide the basic data for the taxonomic revision under the level of Chinese Rubus subgenera.

Pseudomonas aeruginosa is a ubiquitous opportunistic pathogen of significant clinical and public health concern, necessitating the development of rapid and reliable detection methods. Traditional diagnostic approaches, which rely on culture-dependent techniques and biochemical identification, are often labor-intensive, time-consuming, and technically demanding. This study describes a novel single-tube, two-step, rapid detection platform that integrates recombinase polymerase amplification (RPA) with clustered regularly interspaced short palindromic repeats-associated protein Cas12b technology. Through systematic experimental optimization, the study identified an optimal RPA primer pair (F2-R1) and single-guide ribonucleic acid 553 that targets the lasR gene of P. aeruginosa, with reaction conditions optimized at 42°C and a primer concentration of 10 μM. The RPA-clustered regularly interspaced short palindromic repeats/Cas12b fluorescence detection system (RPA-Cas12b-Fluo) demonstrated a sensitivity threshold of 10 copies of deoxyribonucleic acid (DNA) per reaction and a bacterial detection limit of 50 colony-forming units (CFU) per reaction. When coupled with a lateral flow strip (RPA-Cas12b-LFS), the sensitivity was slightly reduced but remained robust, achieving detection limits of 10² copies and 200 CFU per reaction. Specificity assays confirmed a high discriminatory capacity for P. aeruginosa with no cross-reactivity observed against P. fluorescens, P. putida, or six common foodborne pathogens, thereby validating the specificity profile of the platform. The applicability of the method was further validated by analyzing 20 water samples, which demonstrated 100% concordance with the national standard culture method. These findings have significant implications for improving outbreak surveillance and mitigating the risk of foodborne transmission associated with P. aeruginosa.

Robotic pollination represents a pivotal component of smart agriculture, with foundational architectures for target recognition, path planning, and motion control having been progressively established. However, developing an efficient and robust pollination system that integrates perception, decision-making, and execution within real-world scenarios remains confronted with complex challenges. This study systematically reviews recent advancements in the field and distills the core technical issues of greenhouse robotic pollination into three primary domains: target detection and pose estimation, end-effector design, and pollination strategies combined with motion control. Focusing on the visual perception of flowers, actuator architecture, and operational tactics, this review synthesizes existing academic findings to evaluate the state-of-the-art in flower detection and pose estimation, characterize diverse end-effector designs, and analyze the evolutionary trajectory of motion control techniques. Specifically, the analysis encompasses the impact of detection algorithms on recognition accuracy and robustness, the structural classification and performance attributes of pollination mechanisms, and the optimization of control strategies. Furthermore, the study categorizes global research backgrounds, technical methodologies, and paradigmatic system cases, offering a critical evaluation of experiences in constructing automated pollination systems. Despite these advances, current robotic pollination technologies for peppers (chili) face significant bottlenecks characterized by immature methods for precise flower detection and pose estimation, the need for optimized specialized end-effector designs, and insufficient robustness in decision-making systems under dynamic environmental conditions. To address these issues, future development should prioritize constructing diverse, large-scale flower image and pose datasets while developing detection algorithms adaptable to complex environments to achieve high-precision identification. Additionally, implementing this system requires a hierarchical architecture where perception drives adaptive actuation. Deep learning models must localize flower targets and assess maturity in real-time, feeding coordinates to path planners that generate collision-free trajectories through foliage. These trajectories are executed via multimodal motion control, synchronizing the rigid manipulator with soft end-effectors. By embedding tactile feedback into the machine learning loop, the system creates a unified sensorimotor framework. This enables dynamic force modulation based on physical resistance, ensuring precise, non-destructive pollination tailored to chili plants.

ABSTRACTDue to the high incidence of periodontal diseases in pet cats and dogs, the purpose of this study is to screen bacteria with beneficial oral health effects and assess the effectiveness of its freeze‐dried cell. In this study, we explored the effects of oral probiotics by determining the bacteriostatic ability, percentages of self‐aggregation and co‐aggregation in vitro, and through microbial community analysis, the alleviating effect of freeze‐dried cells on periodontitis and its mechanism of action were evaluated. Lactobacillus zeae N165 significantly inhibited Fusobacterium nucleatum and Porphyromonas gingivalis, and it had high co‐aggregation rates of 96.78% and 88.94% with F. nucleatum and P. gingivalis, respectively. In vivo, freeze‐dried L. zeae N165 cells significantly reduced alveolar bone resorption, TNF‐α, and IL‐6 levels in rats with periodontitis, maintained a healthy oral and intestinal microbial community structure, and regulated the dominant species to alleviate periodontitis. The prediction of functions by the KEGG database analysis of oral flora revealed that freeze‐dried L. zeae N165 cells may alleviate periodontitis via four pathways: reduction of glutamine degradation, modulation of lipopolysaccharide levels, biosynthesis of polyketide glycan units, and biotin metabolism. L. zeae N165 showed promising results in both in vitro and in vivo experiments, suggesting new directions for the oral probiotics industry.