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Wood-boring pests are one of the most destructive forest pests. However, the early detection of wood-boring pests is extremely difficult because their larvae live in tree trunks and have high invisibility. Borehole listening technology is a new and effective method to detect the larvae of insect pests. It identifies infested trees by analyzing wood-boring vibration signals. However, the collected wood-boring vibration signals are often disturbed by various noises existing in the field environment, which reduces the accuracy of pest detection. Therefore, it is necessary to filter out the noise and enhance the wood-boring vibration signals to facilitate the subsequent identification of pests. The current signal enhancement models are all designed based on deep learning models, which have complex scales, a large number of parameters, high demands for storage resources, large computational complexity, and high time costs. They often run on resource-rich computers or servers, and they are difficult to deploy to resource-limited field environments to realize the real-time monitoring of pests; as well, they have low practicability. Therefore, this study designs and implements two model lightweight optimization algorithms, one is a pre-training pruning algorithm based on masks, and the other is a knowledge distillation algorithm based on the separate transfer of vibration signal knowledge and noise signal knowledge. We apply the two lightweight optimization algorithms to the signal enhancement model T-CENV with good performance outcomes and conduct a series of ablation experiments. The experimental results show that the proposed methods effectively reduce the volume of the T-CENV model, which make them useful for the deployment of signal enhancement models on embedded devices, improve the usability of the model, and help to realize the real-time monitoring of wood-boring pest larvae. [ABSTRACT FROM AUTHOR]

Simple Summary: Wood-boring pests pose a significant threat to the health of trees in forest and urban ecosystems. Their larvae live deep within the xylem of tree trunks, where visible signs of their activity on the trunk surface are minimal. Traditional monitoring methods are time-consuming and labor-intensive, making it difficult to detect infestations early, thereby complicating pest control efforts. Taking the emerald ash borer (EAB), Agrilus planipennis Fairmaire, 1888 (Coleoptera: Buprestidae), and the small carpenter moth (SCM), Streltzoviella insularis Staudinger, 1892 (Lepidoptera: Cossidae), commonly found on ash trees (Fraxinus chinensis Roxb) in northern China, as examples, acoustic sensors can be used to collect the feeding vibration signals of their larvae, providing a new approach to pest identification. This paper proposes a deep learning recognition model, BorerNet, which uses these boring vibration signals as input and incorporates an attention mechanism. Experimental results show that this model achieves high recognition accuracy. The method can provide technical support for the identification and control of wood-boring pests. Wood-boring pests are difficult to monitor due to their concealed lifestyle. To effectively control these wood-boring pests, it is first necessary to efficiently and accurately detect their presence and identify their species, which requires addressing the limitations of traditional monitoring methods. This paper proposes a deep learning-based model called BorerNet, which incorporates an attention mechanism to accurately identify wood-boring pests using the limited vibration signals generated by feeding larvae. Acoustic sensors can be used to collect boring vibration signals from the larvae of the emerald ash borer (EAB), Agrilus planipennis Fairmaire, 1888 (Coleoptera: Buprestidae), and the small carpenter moth (SCM), Streltzoviella insularis Staudinger, 1892 (Lepidoptera: Cossidae). After preprocessing steps such as clipping and segmentation, Mel-frequency cepstral coefficients (MFCCs) are extracted as inputs for the BorerNet model, with noisy signals from real environments used as the test set. BorerNet learns from the input features and outputs identification results. The research findings demonstrate that BorerNet achieves an identification accuracy of 96.67% and exhibits strong robustness and generalization capabilities. Compared to traditional methods, this approach offers significant advantages in terms of automation, recognition efficiency, and cost-effectiveness. It enables the early detection and treatment of pest infestations and allows for the development of targeted control strategies for different pests. This introduces innovative technology into the field of tree health monitoring, enhancing the ability to detect wood-boring pests early and making a substantial contribution to forestry-related research and practical applications. [ABSTRACT FROM AUTHOR]

The abnormality in N6-methyladenosine (m6A) methylation is involved in the course of Alzheimer's disease (AD), while the intervention of 27-Hydroxycholesterol (27-OHC) can affect the m6A methylation modification in the brain cortex. Disordered gut microbiota is a key link in 27-OHC leading to cognitive impairment, and further studies have found that the abundance of Roseburia intestinalis in the gut is significantly reduced under the intervention of 27-OHC. This study aims to investigate the association of 27-OHC, Roseburia intestinalis in the gut, and brain m6A modification in the learning and memory ability injury. In this study, 9-month-old male C57BL/6J mice were treated with antibiotic cocktails for 6 weeks to sweep the intestinal flora, followed by 27-OHC or normal saline subcutaneous injection, and then Roseburia intestinalis or normal saline gavage were applied to the mouse. The 27-OHC level in the brain, the gut barrier function, the m6A modification in the brain, and the memory ability were measured. From the results, we observed that 27-OHC impairs the gut barrier function, causing a disturbance in the expression of m6A methylation-related enzymes and reducing the m6A methylation modification level in the brain cortex, and finally leads to learning and memory impairment. However, Roseburia intestinalis supplementation could reverse the negative effects mentioned above. This study suggests that 27-OHC-induced learning and memory impairment might be linked to brain m6A methylation modification disturbance, while Roseburia intestinalis, as a probiotic with great potential, could reverse the damage caused by 27-OHC. This research could help reveal the mechanism of 27-OHC-induced neural damage and provide important scientific evidence for the future use of Roseburia intestinalis in neuroprotection. [ABSTRACT FROM AUTHOR]

The cholesterol-oxidized metabolite 27-hydroxycholesterol (27-OHC) is synthesized by CYP27A1, which is a key factor in vitamin D and oxysterol metabolism. Both vitamin D and 27-OHC are considered to play important roles in Alzheimer's disease (AD). The study aims to research the effects of co-supplementation of vitamin D, folic acid, and vitamin B12 on learning and memory ability in vitamin D-deficient mice, and to explore the underlying mechanism. In this study, C57BL/6J mice were fed a vitamin D-deficient diet for 13 weeks to establish a vitamin D-deficient mice model. The vitamin D-deficient mice were then orally gavaged with vitamin D (VD), folic acid (FA), and vitamin B12 (VB12) alone or together for eight weeks. Following the gavage, the learning and memory ability of the mice were evaluated by Morris Water Maze and Novel object recognition test. The CYP27A1-related gene and protein expressions in the liver and brain were determined by qRT-PCR. The serum level of 27-OHC was detected by HPLC-MS. Serum levels of 25(OH)D, homocysteine (Hcy), and S-Adenosylmethionine (SAM) were measured by ELISA. After feeding with the vitamin D-deficient diet, the mice performed longer latency to a platform (p < 0.001), lower average speed (p = 0.026) in the Morris Water Maze, a lower time discrimination index (p = 0.009) in Novel object recognition, and performances were reversed after vitamin D, folic acid and vitamin B12 supplementation alone or together (p < 0.05). The gene expressions of CYP27A1 in the liver and brain were upregulated in the vitamin D-deficiency (VDD) group compared with the control (CON) group (p = 0.015), while it was downregulated in VDD + VD and VDD + VD-FA/VB12 groups compared with the VDD group (p < 0.05), with a similar trend in the protein expression of CYP27A1. The serum levels of 27-OHC were higher in the VDD group, compared with CON, VDD + VD, and VDD + VD-FA/VB12 group (p < 0.05), and a similar trend was found in the brain. The serum 25(OH)D levels were significantly decreased in the vitamin D-deficiency group (p = 0.008), and increased in the vitamin D-supplemented group (p < 0.001). The serum levels of SAM were higher in the B vitamins-supplemented group, compared with CON and VDD groups (p < 0.05). This study suggests that CYP27A1 expression may be involved in the mechanism of learning and memory impairment induced by vitamin D deficiency. Co-supplementation with vitamin D, folic acid, and vitamin B12 significantly reverses this effect by affecting the expression of CYP27A1, which in turn regulates the metabolism of 27-OHC, 25(OH)D, and SAM. [ABSTRACT FROM AUTHOR]

The cholesterol-oxidized metabolite 27-hydroxycholesterol (27-OHC) is synthesized by CYP27A1, which is a key factor in vitamin D and oxysterol metabolism. Both vitamin D and 27-OHC are considered to play important roles in Alzheimer’s disease (AD). The study aims to research the effects of co-supplementation of vitamin D, folic acid, and vitamin B12 on learning and memory ability in vitamin D-deficient mice, and to explore the underlying mechanism. In this study, C57BL/6J mice were fed a vitamin D-deficient diet for 13 weeks to establish a vitamin D-deficient mice model. The vitamin D-deficient mice were then orally gavaged with vitamin D (VD), folic acid (FA), and vitamin B12 (VB12) alone or together for eight weeks. Following the gavage, the learning and memory ability of the mice were evaluated by Morris Water Maze and Novel object recognition test. The CYP27A1-related gene and protein expressions in the liver and brain were determined by qRT-PCR. The serum level of 27-OHC was detected by HPLC-MS. Serum levels of 25(OH)D, homocysteine (Hcy), and S-Adenosylmethionine (SAM) were measured by ELISA. After feeding with the vitamin D-deficient diet, the mice performed longer latency to a platform (p < 0.001), lower average speed (p = 0.026) in the Morris Water Maze, a lower time discrimination index (p = 0.009) in Novel object recognition, and performances were reversed after vitamin D, folic acid and vitamin B12 supplementation alone or together (p < 0.05). The gene expressions of CYP27A1 in the liver and brain were upregulated in the vitamin D-deficiency (VDD) group compared with the control (CON) group (p = 0.015), while it was downregulated in VDD + VD and VDD + VD-FA/VB12 groups compared with the VDD group (p < 0.05), with a similar trend in the protein expression of CYP27A1. The serum levels of 27-OHC were higher in the VDD group, compared with CON, VDD + VD, and VDD + VD-FA/VB12 group (p < 0.05), and a similar trend was found in the brain. The serum 25(OH)D levels were significantly decreased in the vitamin D-deficiency group (p = 0.008), and increased in the vitamin D-supplemented group (p < 0.001). The serum levels of SAM were higher in the B vitamins-supplemented group, compared with CON and VDD groups (p < 0.05). This study suggests that CYP27A1 expression may be involved in the mechanism of learning and memory impairment induced by vitamin D deficiency. Co-supplementation with vitamin D, folic acid, and vitamin B12 significantly reverses this effect by affecting the expression of CYP27A1, which in turn regulates the metabolism of 27-OHC, 25(OH)D, and SAM.