•MYB111 is a positive regulator in salt stress response.•MYB111 could bind to specific cis-elements in promoter of key enzyme genes of flavonoid synthesis.•MYB111’s role on salt resistance is ...dependent on its regulation on flavonoid synthesis.
Salt stress is an adverse environmental factor severely disturbing plant growth and development. Correspondingly, plants have evolved in a set of machinery to cope with salt toxicity including transcriptional regulation. In this study, by employing knockout and overproducing plants, we found that MYB111 is a positive regulator in salt stress response. MYB111 deficiency significantly decreased salt tolerance in Arabidopsis, whereas MYB111 overproduction dramatically increased during the stages of seed germination and seedling growth. Physiological analyses suggested that this altered sensitivity may be from different reactive oxygen species (ROS) scavenging capacity. ROS accumulation was higher in myb111 mutants than the wild-type (WT) plants, but lower in MYB111-overexpressed plants. Consistent to previous report that MYB111 is a regulator in flavonoid synthesis, flavonoid accumulation was lower in mutants, but higher in MYB111-overexpressed plants compared with WT. Surprisingly, addition of bioflavonoids, chalcone/ dihydrokaempferole/ quercetin were able to rescue the loss of salt tolerance in myb111 mutants, demonstrating that flavonoids are crucial against salt stress. Furthermore, we found that MYB111 can bind to specific cis-elements in promoter of chalcone synthase (CHS), flavanone carboxylase (F3H), flavonol synthase 1 (FLS1), and in turn activates their transcription. Meanwhile, MYB111 expression was highly induced under salt treatment, indicating its role in responses to salt stress. Taken together, our data clearly showed that MYB111’s role on salt resistance is dependent on its regulation on flavonoid synthesis.
This paper adopts an unmanned aerial vehicle (UAV) relay to forward data from a source sensor to a remote base station (BS). To ensure reliable data transmission, our objective is to minimize the ...average outage probability along the three-dimensional (3D) UAV trajectory under the UAV mobility and source/relay power allocation constraints. The UAV mobility causes the time-varying Rician fading channel, thus the outage probability function considers both the distance-based large-scale fading and a time-varying Rician factor. To tackle our formulated non-convex problem, we approximate the outage probability by its first-order asymptotic form, based on which we propose a block coordinate descent (BCD) iteration algorithm by alternatively solving the 3D UAV trajectory and power allocation subproblems. The successive convex optimization (SCO) method is applied to deal with the 3D UAV trajectory subproblem. To reveal the advantage by designing the 3D UAV trajectory, we also consider joint optimization of the two-dimensional (2D) UAV trajectory and power allocation to minimize the average outage probability. Extensive simulations verify the accuracy of the approximate outage probability, and the results show that considering the time-varying Rician fading channel, the 3D UAV trajectory helps to obtain a lower link outage probability than the 2D UAV trajectory.
Recently, with the remarkable advancements of deep learning in the field of image processing, convolutional neural networks (CNNs) have garnered widespread attention from researchers in the domain of ...hyperspectral image (HSI) classification. Moreover, due to the high performance demonstrated by the transformer architecture in classification tasks, there has been a proliferation of neural networks combining CNNs and transformers for HSI classification. However, the majority of the current methods focus on extracting spatial–spectral features from the HSI data of a single size for a pixel, overlooking the rich multi-scale feature information inherent to the data. To address this problem, we designed a novel transformer network with a CNN-enhanced cross-attention (TNCCA) mechanism for HSI classification. It is a dual-branch network that utilizes different scales of HSI input data to extract shallow spatial–spectral features using a multi-scale 3D and 2D hybrid convolutional neural network. After converting the feature maps into tokens, a series of 2D convolutions and dilated convolutions are employed to generate two sets of Q (queries), K (keys), and V (values) at different scales in a cross-attention module. This transformer with CNN-enhanced cross-attention explores multi-scale CNN-enhanced features and fuses them from both branches. Experimental evaluations conducted on three widely used hyperspectral image (HSI) datasets, under the constraint of limited sample size, demonstrate excellent classification performance of the proposed network.
A palladium-catalyzed oxidative C–H bond functionalization/ortho-acylation of acetanilides using easily accessible aldehyde as the acyl source is described. In the presence of a Pd(TFA)2 catalyst and ...tert-butylhydroperoxide at 90 °C in general, an array of ortho-acylacetanilides can be afforded in good yields.
Twitter has become a major social media platform and has attracted considerable interest among researchers in sentiment analysis. Research into Twitter Sentiment Analysis (TSA) is an active subfield ...of text mining. TSA refers to the use of computers to process the subjective nature of Twitter data, including its opinions and sentiments. In this research, a thorough review of the most recent developments in this area, and a wide range of newly proposed algorithms and applications are explored. Each publication is arranged into a category based on its significance to a particular type of TSA method. The purpose of this survey is to provide a concise, nearly comprehensive overview of TSA techniques and related fields. The primary contributions of the survey are the detailed classifications of numerous recent articles and the depiction of the current direction of research in the field of TSA.
Spectral unmixing is among one of the major hyperspectral image analysis tasks that aims to extract basic features (endmembers) at the subpixel level and estimate their corresponding proportions ...(fractional abundances). Recently, the rapid development of deep learning networks has provided us with a new method to solve the problem of spectral unmixing. In this paper, we propose a spatial-information-assisted spectral information learning unmixing network (SISLU-Net) for hyperspectral images. The SISLU-Net consists of two branches. The upper branch focuses on the extraction of spectral information. The input of the upper branch is a number of pixels randomly extracted from the hyperspectral image. The data are fed into the network as a random combination of different pixel blocks each time. The random combination of batches can boost the network to learn global spectral information. Another branch focuses on learning spatial information from the entire hyperspectral image and transmitting it to the upper branch through the shared weight strategy. This allows the network to take into account the spectral information and spatial information of HSI at the same time. In addition, according to the distribution characteristics of endmembers, we employ Wing loss to solve the problem of uneven distributions of endmembers. Experimental results on one synthetic and three real hyperspectral data sets show that SISLU-Net is effective and competitive compared with several state-of-the-art unmixing algorithms in terms of the spectral angle distance (SAD) of the endmembers and the root mean square error (RMSE) of the abundances.
Image-to-image translation (I2IT) is an important visual task that aims to learn a mapping of images from one domain to another while preserving the representation of the content. The phenomenon ...known as mode collapse makes this task challenging. Most existing methods usually learn the relationship between the data and latent distributions to train more robust latent models. However, these methods often ignore the structural information among latent variables, leading to patterns in the data being obscured during the process. In addition, the inflexibility of data modes caused by ignoring the latent mapping of two domains is also one of the factors affecting the performance of existing methods. To make the data schema stable, this paper develops a novel binary noise guidance learning (BnGLGAN) framework for image translation to solve these problems. Specifically, to eliminate uncertainty of domain distribution, a noise prior inference learning (NPIL) module is designed to infer an estimated distribution from a certain domain. In addition, to improve the authenticity of reconstructed images, a distribution-guided noise reconstruction learning (DgNRL) module is introduced to reconstruct the noise from the source domain, which can provide source semantic information to guide the GAN’s generation. Extensive experiments fully prove the efficiency of our proposed framework and its advantages over comparable methods.
Short-term prices prediction is a crucial task for participants in the electricity market, as it enables them to optimize their bidding strategies and mitigate risks. However, the price signal is ...subject to various factors, including supply, demand, weather conditions, and renewable energy sources, resulting in high volatility and nonlinearity. In this study, a novel approach is introduced that combines Artificial Neural Networks (ANN) with a newly developed Snake Optimization Algorithm (SOA) to forecast short-term price signals in the Nord Pool market. The snake optimization algorithm is utilized to optimize both the structure and weights of the neural network, as well as to select relevant input data based on the similarity of price curves and wind production. To evaluate the effectiveness of the proposed technique, experiments have been conducted using data from two regions of the Nord Pool market, namely DK-1 and SE-1, across different seasons and time horizons. The results demonstrate that the proposed technique surpasses two alternative methods based on Particle Swarm Optimization (PSO) and Genetic Algorithms-based Neural Network (PSOGANN) and Gravitational Search Optimization Algorithm-based Neural Network (GSONN), exhibiting superior accuracy and minimal error rates in short-term price prediction. The results show that the average MAPE index of the proposed technique for the DK-1 region is 3.1292%, which is 32.5% lower than the PSOGA method and 47.1% lower than the GSONN method. For the SE-1 region, the average MAPE index of the proposed technique is 2.7621%, which is 40.4% lower than the PSOGA method and 64.7% lower than the GSONN method. Consequently, the proposed technique holds significant potential as a valuable tool for market participants to enhance their decision-making and planning activities.
Secondary metabolites, such as flavonoids, are key participants in biotic and abiotic stress responses in plants. Production of Flavonol Glycosides 3 (PFG3), an R2R3-MYB transcription factor, has ...been reported to regulate flavonoid biosynthesis and accumulation in Arabidopsis, but its role in drought and other stress responses remains unclear. In this study, we created PFG3-deficient mutants (pfg3 and pfg3-d1) to investigate how PFG3 influences plant flavonoid synthesis and drought/osmotic stress responses. PFG3 was localized in the nucleus and had transcriptional activator activity. The transcript levels of key flavonoid synthesis genes were significantly reduced in pfg3 and pfg3-d1. PFG3 was expressed mainly in leaves, and its expression was induced by osmotic stress. pfg3 and pfg3-d1 showed impaired accumulation of flavonoids, especially flavonols, and were more sensitive to osmotic and drought stresses. The pfg3 and pfg3-d1 mutants showed less biomass accumulation and a weaker ability to acclimate during drought treatment compared with Col-0 plants. Taken together, our data show that PFG3 is important for plant drought/osmotic stress tolerance by regulating flavonoid biosynthesis.
•PFG3 regulates the expression of key flavonoid biosynthesis genes and affects flavonoid accumulation in plants.•PFG3 is expressed mainly in the leaves and is induced by osmotic stress.•PFG3 plays important roles in plant drought/osmotic stress tolerance through the regulation of flavonoid biosynthesis.
In the field of object detection, deep learning models have achieved great success in recent years. Despite these advances, detecting small objects remains difficult. Most objects in aerial images ...have features that are a challenge for traditional object detection techniques, including small size, high density, high variability, and varying orientation. Previous approaches have used slicing methods on high-resolution images or feature maps to improve performance. However, existing slicing methods inevitably lead to redundant computation. Therefore, in this article we present a novel adaptive slicing method named ASAHI (Adaptive Slicing Aided Hyper Inference), which can dramatically reduce redundant computation using an adaptive slicing size. Specifically, ASAHI focuses on the number of slices rather than the slicing size, that is, it adaptively adjusts the slicing size to control the number of slices according to the image resolution. Additionally, we replace the standard non-maximum suppression technique with Cluster-DIoU-NMS due to its improved accuracy and inference speed in the post-processing stage. In extensive experiments, ASAHI achieves competitive performance on the VisDrone and xView datasets. The results show that the mAP50 is increased by 0.9% and the computation time is reduced by 20–25% compared with state-of-the-art slicing methods on the TPH-YOLOV5 pretrained model. On the VisDrone2019-DET-val dataset, our mAP50 result is 56.4% higher, demonstrating the superiority of our approach.
