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ObjectiveThe widespread prevalence of sclerotinia disease poses a significant challenge to the cultivation and supply of oilseed rape, not only results in substantial yield losses and decreased oil content in infected plant seeds but also severely impacts crop productivity and quality, leading to significant economic losses. To solve the problems of complex operation, environmental pollution, sample destruction and low detection efficiency of traditional chemical detection methods, a Bi-directional Gate Recurrent Unit (Bi-GRU) model based on space-spectrum feature fusion was constructed to achieve hyperspectral images (HSIs) segmentation of oilseed rape sclerotinia infected area.MethodsThe spectral characteristics of sclerotinia disease from a spectral perspective was initially explored. Significantly varying spectral reflectance was notably observed around 550 nm and within the wavelength range of 750-1 000 nm at different locations on rapeseed leaves. As the severity of sclerotinia infection increased, the differences in reflectance at these wavelengths became more pronounced. Subsequently, a rapeseed leaf sclerotinia disease dataset comprising 400 HSIs was curated using an intelligent data annotation tool. This dataset was divided into three subsets: a training set with 280 HSIs, a validation set with 40 HSIs, and a test set with 80 HSIs. Expanding on this, a 7×7 pixel neighborhood was extracted as the spatial feature of the target pixel, incorporating both spatial and spectral features effectively. Leveraging the Bi-GRU model enabled simultaneous feature extraction at any point within the sequence data, eliminating the impact of the order of spatial-spectral data fusion on the model's performance. The model comprises four key components: an input layer, hidden layers, fully connected layers, and an output layer. The Bi-GRU model in this study consisted of two hidden layers, each housing 512 GRU neurons. The forward hidden layer computed sequence information at the current time step, while the backward hidden layer retrieves the sequence in reverse, incorporating reversed-order information. These two hidden layers were linked to a fully connected layer, providing both forward and reversed-order information to all neurons during training. The Bi-GRU model included two fully connected layers, each with 1 000 neurons, and an output layer with two neurons representing the healthy and diseased classes, respectively.Results and DiscussionsTo thoroughly validate the comprehensive performance of the proposed Bi-GRU model and assess the effectiveness of the spatial-spectral information fusion mechanism, relevant comparative analysis experiments were conducted. These experiments primarily focused on five key parameters—ClassAP(1), ClassAP(2), mean average precision (mAP), mean intersection over union (mIoU), and Kappa coefficient—to provide a comprehensive evaluation of the Bi-GRU model's performance. The comprehensive performance analysis revealed that the Bi-GRU model, when compared to mainstream convolutional neural network (CNN) and long short-term memory (LSTM) models, demonstrated superior overall performance in detecting rapeseed sclerotinia disease. Notably, the proposed Bi-GRU model achieved an mAP of 93.7%, showcasing a 7.1% precision improvement over the CNN model. The bidirectional architecture, coupled with spatial-spectral fusion data, effectively enhanced detection accuracy. Furthermore, the study visually presented the segmentation results of sclerotinia disease-infected areas using CNN, Bi-LSTM, and Bi-GRU models. A comparison with the Ground-Truth data revealed that the Bi-GRU model outperformed the CNN and Bi-LSTM models in detecting sclerotinia disease at various infection stages. Additionally, the Dice coefficient was employed to comprehensively assess the actual detection performance of different models at early, middle, and late infection stages. The dice coefficients for the Bi-GRU model at these stages were 83.8%, 89.4% and 89.2%, respectively. While early infection detection accuracy was relatively lower, the spatial-spectral data fusion mechanism significantly enhanced the effectiveness of detecting early sclerotinia infections in oilseed rape.ConclusionsThis study introduces a Bi-GRU model that integrates spatial and spectral information to accurately and efficiently identify the infected areas of oilseed rape sclerotinia disease. This approach not only addresses the challenge of detecting early stages of sclerotinia infection but also establishes a basis for high-throughput non-destructive detection of the disease.

Isocyanate‐based monomers and polymers have found widespread industrial application; however, the realization of green water‐based products via the preparation of stable isocyanate aqueous emulsions remains challenging. Herein, a series of organosilicon and organofluorine synergistically modified amphiphilic branched polyurethanes (HBWPU), synthesized by grafting perfluorohexyl ethanol semi‐terminated isophorone diisocyanate (IPDI) onto a hydrophilic polyurethane prepolymer, is presented. Subsequently, using a physical mixing method, stable diisocyanate emulsions are fabricated by preparing diisocyanate latex particles with IPDI as the core and hyperbranched water‐based polyurethanes (HBWPU) as the shell. Structural investigations showed that the branching degree and fluorine content of the HBWPU significantly affects the stability of the diisocyanate emulsions. Increasing the degree of branching from 0.34 to 0.86 causes the amount of residual isocyanate (NCO) groups to decrease from 20.1% to 16.7%. In contrast, an increase in the fluorine content from 0% to 17.8% increases the amount of residual NCO groups from 11.5% to 24.6%. Thus, HBWPU can be used as an emulsifier to prepare waterborne diisocyanate cross‐linking agents. Such polyurethane‐coated isocyanate aqueous emulsions can increase the hardness and significantly improve the acetone resistance of hydroxyl polyacrylate resins. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of cognitive load on the formation and maintenance of cooperation within a public goods game experiment featuring a punishment option. By integrating the experimental designs of prior studies and manipulating cognitive load through the memorization of numbers with varying digits, we reveal that high cognitive load accelerates the breakdown of cooperation, irrespective of the presence of a punishment system. Furthermore, under high cognitive load, participants are more likely to engage in antisocial punishment, while the punishment of free riders remains unaffected. These findings suggest that increased cognitive load depletes the cognitive resources needed for deliberative decision-making, leading to a higher propensity for antisocial punishment. Our study contributes to the literature by demonstrating the significant influence of cognitive load on cooperative behavior and providing new insights into the causes of antisocial punishment. [ABSTRACT FROM AUTHOR]

A three-stage displacement amplification microgripper system based on compliant mechanisms was designed for microassembly and microoperation tasks. The static mechanical models of the microgripper system were established, and the mechanical characteristics of the output displacements, stresses, and strains of the microgripper system were comprehensively simulated and analyzed. It was demonstrated that the microgripper system demonstrated excellent leveling performance, with the stresses and strains being concentrated at the flexible hinges. Subsequently, the tracking control experiments of the finger displacement and gripping force of the microgripper system were conducted using sinusoidal signals, variable amplitude signals, and frequency signals. It was clearly illustrated that the dynamic performances of the microgripper system are remarkable, and the standard deviation of the tracking error of the finger displacement is no more than 0.2 µm, while that of the gripping force is 0.35 mN under closed-loop conditions. The designed microgripper system achieves accurate control of displacements and gripping forces, effectively fulfilling the requirements of high-precision microassembly tasks. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD), the most common form of dementia, remains long-term and challenging to diagnose. Furthermore, there is currently no medication to completely cure AD patients. Rapamycin has been clinically demonstrated to postpone the aging process in mice and improve learning and memory abilities in animal models of AD. Therefore, rapamycin has the potential to be significant in the discovery and development of drugs for AD patients. Objective: The main objective of this systematic review and meta-analysis was to investigate the effects and mechanisms of rapamycin on animal models of AD by examining behavioral indicators and pathological features. Methods: Six databases were searched and 4,277 articles were retrieved. In conclusion, 13 studies were included according to predefined criteria. Three authors independently judged the selected literature and methodological quality. Use of subgroup analyses to explore potential mechanistic effects of rapamycin interventions: animal models of AD, specific types of transgenic animal models, dosage, and periodicity of administration. Results: The results of Morris Water Maze (MWM) behavioral test showed that escape latency was shortened by 15.60 seconds with rapamycin therapy, indicating that learning ability was enhanced in AD mice; and the number of traversed platforms was increased by 1.53 times, indicating that the improved memory ability significantly corrected the memory deficits. CONCLUSIONS: Rapamycin therapy reduced age-related plaque deposition by decreasing AβPP production and down-regulating β-secretase and γ-secretase activities, furthermore increased amyloid-β clearance by promoting autophagy, as well as reduced tau hyperphosphorylation by up-regulating insulin-degrading enzyme levels. [ABSTRACT FROM AUTHOR]

Heavy metal lead (Pb) accumulation in agricultural soil has become a serious threat to human health through the food chain in recent decades. Wheat, being one of the most important crops, is a crucial source of food for humans. Pb in soil can pose a significant risk to the wheat grain, as it can be transported through the root and stem. This transportation not only increases the Pb concentration in wheat, but also threatens the wheat's overall food security. Therefore, it is imperative to investigate the effects of Pb stress on wheat seedlings. Double-pulse laser-induced breakdown spectroscopy (DP-LIBS) and surface-enhanced Raman spectroscopy (SERS) as promising enhanced spectroscopy techniques were coupled to estimate Pb stress in wheat seedlings. Wheat seedling samples under Pb stress at 6 concentration gradients (0(c.k.), 500, 1000, 2000, 3000, and 5000 mg L−1) were cultivated with the same substrate and management. The SERS and DP-LIBS spectra of the root, stem and leaf were obtained by using spectrometers in an enhanced way. Principal component analysis (PCA) was employed for preliminary analysis of DP-LIBS and SERS data to investigate the clustering of samples, including substrates and the wheat seedling root and leaf, under different Pb concentration stresses. However, while PCA enabled the rough differentiation of the samples, the classification accuracy requires improvement. The pre-processed SERS, DP-LIBS, and fusion spectral data (LIBS-Raman) were analyzed using discriminant models such as least squares support vector machines (LS-SVM), multi-layer perceptron-artificial neural network (MLP-ANN), radial basis function-artificial neural network (RBF-ANN) and probabilistic neural networks (PNN). The accuracy of the model built by LIBS-Raman was significantly better than the model built based on SERS and DP-LIBS data alone. LIBS-Raman increased the accuracy of the discrimination model, which highlights the effectiveness of collaborative spectroscopic methods for heavy metal detection. This study investigated the migration of Pb in the substrate-root-leaf. At 0 concentration (c.k.), Pb was not detected in the substrate and wheat seedling root and leaf. At the same concentration, the level of Pb stress in the root was higher than that in the leaf. This might be attributed to the self-protection mechanism of wheat seedlings, which had a weak ability to transport Pb ions. This work provides theoretical support for collaborative spectral data, furnishes a reference for the development of a combined spectrometer, and offers a valuable method for monitoring heavy metal contamination in plants. [ABSTRACT FROM AUTHOR]