Huiru Li
Research Subjects
Huiru Li has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Binjie Ge
Eastern China Conservation Centre for Wild Endangered Plant Resources, Shanghai Chenshan Botanical Garden, Shanghai, China
1 shared publication - Huijuan Li
School of Veterinary Medicine, Henan University of Animal Husbandry and Economy, 6 Longzihu North Road, Zhengzhou 450046, China
1 shared publication - Jifeng Liu
School of Pharmaceutical Sciences
1 shared publication - Jing Yan
Eastern China Conservation Centre for Wild Endangered Plant Resources, Shanghai Chenshan Botanical Garden, Shanghai 201602, China
1 shared publication - Li-Fei Zhu
School of Veterinary Medicine, Henan University of Animal Husbandry and Economy, Zhengzhou 450046, China
1 shared publication - Liang-Hai Yang
Key Laboratory of East China Plant Conservation and Utilization, National Forestry and Grassland Administration, Shanghai Chenshan Botanical Garden, Shanghai 201602, China
1 shared publication
Latest Publications
- Open Access
Real-Time Classification of Invasive Plant Seeds Based on Improved YOLOv5 with Attention Mechanism
Seeds of exotic plants transported with imported goods pose a risk of alien species invasion in cross-border transportation and logistics. It is critical to develop stringent inspection and quarantine protocols with active management to control the import and export accompanied by exotic seeds. As a result, a method for promptly identifying exotic plant seeds is urgently needed. In this study, we built a database containing 3000 images of seeds of 12 invasive plants and proposed an improved YOLOv5 target detection algorithm that incorporates a channel attention mechanism. Given that certain seeds in the same family and genus are very similar in appearance and are thus difficult to differentiate, we improved the size model of the initial anchor box to converge better; moreover, we introduce three attention modules, SENet, CBAM, and ECA-Net, to enhance the extraction of globally important features while suppressing the weakening of irrelevant features, thereby effectively solving the problem of automated inspection of similar species. Experiments on an invasive alien plant seed data set reveal that the improved network model fused with ECA-Net requires only a small increase in parameters when compared to the original YOLOv5 network model and achieved greater classification and detection accuracy without affecting detection speed.
Open AccessReal-Time Classification of Invasive Plant Seeds Based on Improved YOLOv5 with Attention Mechanism
Seeds of exotic plants transported with imported goods pose a risk of alien species invasion in cross-border transportation and logistics. It is critical to develop stringent inspection and quarantine protocols with active management to control the import and export accompanied by exotic seeds. As a result, a method for promptly identifying exotic plant seeds is urgently needed. In this study, we built a database containing 3000 images of seeds of 12 invasive plants and proposed an improved YOLOv5 target detection algorithm that incorporates a channel attention mechanism. Given that certain seeds in the same family and genus are very similar in appearance and are thus difficult to differentiate, we improved the size model of the initial anchor box to converge better; moreover, we introduce three attention modules, SENet, CBAM, and ECA-Net, to enhance the extraction of globally important features while suppressing the weakening of irrelevant features, thereby effectively solving the problem of automated inspection of similar species. Experiments on an invasive alien plant seed data set reveal that the improved network model fused with ECA-Net requires only a small increase in parameters when compared to the original YOLOv5 network model and achieved greater classification and detection accuracy without affecting detection speed.
- Open Access
Sesquiterpenes and Monoterpenes from the Leaves and Stems of Illicium simonsii and Their Antibacterial Activity
Two undescribed ether derivatives of sesquiterpenes, 1-ethoxycaryolane-1, 9β-diol (1) and 2-ethoxyclovane-2β, 9α-diol (3), and one new monoterpene glycoside, p-menthane-1α,2α,8-triol-4-O-β-D-glucoside (5), were obtained, together with eight known compounds from the stems and leaves of I. simonsii. Their structures were elucidated by spectroscopic methods. Compounds 1–11 were evaluated for their potency against Staphylococcus aureus and clinical methicillin-resistant S. aureus (MRSA). Among them, compound 3 was weakly active against S. aureus (MIC = 128 μg/mL), and compounds 6 and 7 exhibited good antibacterial activity against S. aureus and MRSA (MICs = 2–8 µg/mL). A primary mechanism study revealed that compounds 6 and 7 could kill bacteria by destroying bacterial cell membranes. Moreover, compounds 6 and 7 were not susceptible to drug resistance development.
Open AccessSesquiterpenes and Monoterpenes from the Leaves and Stems of Illicium simonsii and Their Antibacterial Activity
Two undescribed ether derivatives of sesquiterpenes, 1-ethoxycaryolane-1, 9β-diol (1) and 2-ethoxyclovane-2β, 9α-diol (3), and one new monoterpene glycoside, p-menthane-1α,2α,8-triol-4-O-β-D-glucoside (5), were obtained, together with eight known compounds from the stems and leaves of I. simonsii. Their structures were elucidated by spectroscopic methods. Compounds 1–11 were evaluated for their potency against Staphylococcus aureus and clinical methicillin-resistant S. aureus (MRSA). Among them, compound 3 was weakly active against S. aureus (MIC = 128 μg/mL), and compounds 6 and 7 exhibited good antibacterial activity against S. aureus and MRSA (MICs = 2–8 µg/mL). A primary mechanism study revealed that compounds 6 and 7 could kill bacteria by destroying bacterial cell membranes. Moreover, compounds 6 and 7 were not susceptible to drug resistance development.