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"Wang, Xiao-Yi"
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CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture
Intelligence has been considered as the major challenge in promoting economic potential and production efficiency of precision agriculture. In order to apply advanced deep-learning technology to complete various agricultural tasks in online and offline ways, a large number of crop vision datasets with domain-specific annotation are urgently needed. To encourage further progress in challenging realistic agricultural conditions, we present the CropDeep species classification and detection dataset, consisting of 31,147 images with over 49,000 annotated instances from 31 different classes. In contrast to existing vision datasets, images were collected with different cameras and equipment in greenhouses, captured in a wide variety of situations. It features visually similar species and periodic changes with more representative annotations, which have supported a stronger benchmark for deep-learning-based classification and detection. To further verify the application prospect, we provide extensive baseline experiments using state-of-the-art deep-learning classification and detection models. Results show that current deep-learning-based methods achieve well performance in classification accuracy over 99%. While current deep-learning methods achieve only 92% detection accuracy, illustrating the difficulty of the dataset and improvement room of state-of-the-art deep-learning models when applied to crops production and management. Specifically, we suggest that the YOLOv3 network has good potential application in agricultural detection tasks.
Journal Article
A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model
The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due to the difficult recognition of the practical system and the empirical parameter estimation in the traditional Kalman filter, a neuron-based Kalman filter was proposed in the paper. Firstly, the framework of the improved Kalman filter was designed, in which the neuro units were introduced. Secondly, the functions of the neuro units were excavated with the nonlinear autoregressive model. The neuro units optimized the filtering process to reduce the effect of the unpractical system model and hypothetical parameters. Thirdly, the adaptive filtering algorithm was proposed based on the new Kalman filter. Finally, the filter was verified with the simulation signals and practical measurements. The results proved that the filter was effective in noise elimination within the soft computing solution.
Journal Article
Safe and Effective Antioxidant: The Biological Mechanism and Potential Pathways of Ergothioneine in the Skin
Ergothioneine, a sulfur-containing micromolecular histidine derivative, has attracted increasing attention from scholars since it was confirmed in the human body. In the human body, ergothioneine is transported and accumulated specifically through OCTN-1, especially in the mitochondria and nucleus, suggesting that it can target damaged cells and tissues as an antioxidant. It shows excellent antioxidant, anti-inflammatory effects, and anti-aging properties, and inhibits melanin production. It is a mega antioxidant that may participate in the antioxidant network system and promote the reducing glutathione regeneration cycle. This review summarizes studies on the antioxidant effects of ergothioneine on various free radicals in vitro to date and systematically introduces its biological activities and potential mechanisms, mostly in dermatology. Additionally, the application of ergothioneine in cosmetics is briefly summarized. Lastly, we propose some problems that require solutions to understand the mechanism of action of ergothioneine. We believe that ergothioneine has good prospects in the food and cosmetics industries, and can thus meet some needs of the health and beauty industry.
Journal Article
Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization
Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power system management. To solve the limitation of the existing load forecasting methods in dealing with time-series data, causing the poor stability and non-ideal forecasting accuracy, this paper proposed an attention-based encoder-decoder network with Bayesian optimization to do the accurate short-term power load forecasting. Proposed model is based on an encoder-decoder architecture with a gated recurrent units (GRU) recurrent neural network with high robustness on time-series data modeling. The temporal attention layer focuses on the key features of input data that play a vital role in promoting the prediction accuracy for load forecasting. Finally, the Bayesian optimization method is used to confirm the model’s hyperparameters to achieve optimal predictions. The verification experiments of 24 h load forecasting with real power load data from American Electric Power (AEP) show that the proposed model outperforms other models in terms of prediction accuracy and algorithm stability, providing an effective approach for migrating time-serial power load prediction by deep-learning technology.
Journal Article
Structure Identification of Ganoderma lucidum Spore Polysaccharides and Their Antitumor Activity In Vivo
Ganoderma lucidum spore powder, valued for its nutritional and medicinal properties, contains polysaccharides crucial for its efficacy. However, the complex structural nature of these polysaccharides necessitates further investigation to fully realize their potential. This study aimed to investigate the effects of acid heat treatment on Ganoderma lucidum spore polysaccharides (GLSPs) to enhance their properties and application in antitumor activity. The GLSP was obtained via acid heat treatment, concentration, and centrifugal separation. This process led to a notable reduction in polysaccharide molecular weight, increasing water solubility and bioavailability. Analytical techniques including NMR spectroscopy and methylation analysis revealed a polysaccharide composition comprising four distinct monosaccharides, with molecular weights of 3291 Da (Mw) and 3216 Da (Mn). Six different linkage modes were identified, with a molar ratio of 1:5:2:3:4:3. In vivo experiments demonstrated the GLSP’s significant inhibitory effect on the growth of four tumor models (sarcoma S180, Lewis lung cancer, liver cancer H22, and colon cancer C26) in mice, with no observed toxicity. These findings suggest the GLSP’s potential as an antitumor therapeutic agent for clinical use.
Journal Article
Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model
Smart agricultural sensing has enabled great advantages in practical applications recently, making it one of the most important and valuable systems. For outdoor plantation farms, the prediction of climate data, such as temperature, wind speed, and humidity, enables the planning and control of agricultural production to improve the yield and quality of crops. However, it is not easy to accurately predict climate trends because the sensing data are complex, nonlinear, and contain multiple components. This study proposes a hybrid deep learning predictor, in which an empirical mode decomposition (EMD) method is used to decompose the climate data into fixed component groups with different frequency characteristics, then a gated recurrent unit (GRU) network is trained for each group as the sub-predictor, and finally the results from the GRU are added to obtain the prediction result. Experiments based on climate data from an agricultural Internet of Things (IoT) system verify the development of the proposed model. The prediction results show that the proposed predictor can obtain more accurate predictions of temperature, wind speed, and humidity data to meet the needs of precision agricultural production.
Journal Article
Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse
Smart agricultural greenhouses provide well-controlled conditions for crop cultivation but require accurate prediction of environmental factors to ensure ideal crop growth and management efficiency. Due to the limitations of existing predictors in dealing with massive, nonlinear, and dynamic temporal data, this study proposes a bidirectional self-attentive encoder–decoder framework (BEDA) to construct the long-time predictor for multiple environmental factors with high nonlinearity and noise in a smart greenhouse. Firstly, the original data are denoised by wavelet threshold filter and pretreatment operations. Secondly, the bidirectional long short-term-memory is selected as the fundamental unit to extract time-serial features. Then, the multi-head self-attention mechanism is incorporated into the encoder–decoder framework to improve the prediction performance. Experimental investigations are conducted in a practical greenhouse to accurately predict indoor environmental factors (temperature, humidity, and CO2) from noisy IoT-based sensors. The best model for all datasets was the proposed BEDA method, with the root mean square error of three factors’ prediction reduced to 2.726, 3.621, and 49.817, and with an R of 0.749 for temperature, 0.848 for humidity, and 0.8711 for CO2 concentration, respectively. The experimental results show that the favorable prediction accuracy, robustness, and generalization of the proposed method make it suitable to more precisely manage greenhouses.
Journal Article
Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System
Based on the collected weather data from the agricultural Internet of Things (IoT) system, changes in the weather can be obtained in advance, which is an effective way to plan and control sustainable agricultural production. However, it is not easy to accurately predict the future trend because the data always contain complex nonlinear relationship with multiple components. To increase the prediction performance of the weather data in the precision agriculture IoT system, this study used a deep learning predictor with sequential two-level decomposition structure, in which the weather data were decomposed into four components serially, then the gated recurrent unit (GRU) networks were trained as the sub-predictors for each component. Finally, the results from GRUs were combined to obtain the medium- and long-term prediction result. The experiments were verified for the proposed model based on weather data from the IoT system in Ningxia, China, for wolfberry planting, in which the prediction results showed that the proposed predictor can obtain the accurate prediction of temperature and humidity and meet the needs of precision agricultural production.
Journal Article
Conventional type 1 dendritic cells in the lymph nodes aggravate neuroinflammation after spinal cord injury by promoting CD8+ T cell expansion
Background
Adaptive immune response is at the core of the mechanism of secondary spinal cord injury (SCI). This study aims to explore the molecular mechanism by which classical dendritic cells (cDC1s) influence CD8
+
T cell expansion in SCI.
Methods
Peripheral blood samples from patients with SCI and spinal cord tissues from SCI mice were collected, and the population of cDC1 subset was analyzed by flow cytometry. In vivo, the fms-like tyrosine kinase 3 (Flt3) inhibitor quizartinib was administered to deplete cDC1s, while intraperitoneal injection of recombinant Flt3L and immunosuppressive drug FTY-720 was used to expand cDC1s and prevent T cell egress from lymph nodes (LNs), respectively. In vitro, the conditioned medium (CM) of isolated LN fibroblastic stromal cells (FSCs) and pre-DCs were co-cultured. Subsequently, FSC CM-induced DCs were stimulated and co-cultured with CD8
+
T cells for proliferation assay.
Results
The cDC1 subset was increased in the peripheral blood of SCI patients and in the injured spinal cord of SCI mice. Depletion of cDC1s decreased the proportion of infiltrating CD8
+
T cells in the injured spinal cord of SCI mice and reduced the inflammatory response. The Basso Mouse Scale score of SCI mice was increased and the proportion of CD8
+
T cells in blood and spinal cord tissue was decreased after FTY-720 injection. Both migratory cDC1s (CD103
+
) and resident cDC1s (CD8α
+
) were present in the LNs surrounding the injured spinal cord of SCI mice. Among them, CD103
+
cells were derived from the migration of cDC1s in spinal cord tissues, and CD8α
+
cDC1s were directionally differentiated from pre-DCs after co-culture with LN-FSCs. Interferon-γ promoted the secretion of Flt3L by LN-FSCs through the activation of JAK/STAT signaling pathway and enhanced the differentiation of pre-DCs into CD8α
+
cells.
Conclusion
Migratory cDC1s and resident cDC1s promote the expansion of CD8
+
T cells in LNs around the injured spinal cord and mediate the adaptive immune response to aggravate neuroinflammation in SCI.
Journal Article