Research overview on the genetic mechanism underlying the biosynthesis of polysaccharide in tuber plants.

Tuber plants are of great significance in the world as human food crops. Polysaccharides, important metabolites in tuber plants, also serve as a source of innovative drugs with significant pharmacological effects. These drugs are particularly known for their immunomodulation and antitumor properties. To fully exploit the potential value of tuber plant polysaccharides and establish a synthetic system for their targeted synthesis, it is crucial to dissect their metabolic processes and genetic regulatory mechanisms. In this article, we provide a comprehensive summary of the basic pathways involved in the synthesis of various types of tuber plant polysaccharides. We also outline the key research progress that has been made in this area in recent years. We classify the main types and functions of tuber plant polysaccharides and analyze the biosynthetic processes and genetic regulation mechanisms of key enzymes involved in the metabolic pathways of starch, cellulose, pectin, and fructan in tuber plants. We have identified hexokinase and glycosyltransferase as the key enzymes involved in the polysaccharide synthesis process. By elucidating the synthesis pathway of polysaccharides in tuber plants and understanding the underlying mechanism of action of key enzymes in the metabolic pathway, we can provide a theoretical framework for enhancing the yield of polysaccharides and other metabolites in plant culture cells. This will ultimately lead to increased production efficiency.

Identification, Biological Function Profiling and Biosynthesis of Secondary Metabolites in Medicinal Orchids.

The secondary metabolites present in medicinal orchids are diverse and possess a vast array of biological activities. They represent valuable raw materials for modern pharmaceuticals and clinical medicine and have tremendous potential for future development. A systematic collation of secondary metabolites' composition and a summary of the biological activities of medicinal orchids represent a crucial step in unlocking the potential of these valuable resources in drug development. Furthermore, such information can provide essential guidance for comprehensively analyzing the pharmacological and therapeutic mechanisms of these valuable herbs in traditional Chinese herbal medicine. This review article presents an overview of the types and main biological functions of the secondary metabolites found in medicinal orchids, as well as the conventional synthesis methods for these compounds. Our aim is to provide a useful reference for future research and the drug development of secondary metabolic products of medicinal orchids.

SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perception.

Due to the wavelength-dependent light attenuation, refraction and scattering, underwater images usually suffer from color distortion and blurred details. However, due to the limited number of paired underwater images with undistorted images as reference, training deep enhancement models for diverse degradation types is quite difficult. To boost the performance of data-driven approaches, it is essential to establish more effective learning mechanisms that mine richer supervised information from limited training sample resources. In this paper, we propose a novel underwater image enhancement network, called SGUIE-Net, in which we introduce semantic information as high-level guidance via region-wise enhancement feature learning. Accordingly, we propose semantic region-wise enhancement module to better learn local enhancement features for semantic regions with multi-scale perception. After using them as complementary features and feeding them to the main branch, which extracts the global enhancement features on the original image scale, the fused features bring semantically consistent and visually superior enhancements. Extensive experiments on the publicly available datasets and our proposed dataset demonstrate the impressive performance of SGUIE-Net. The code and proposed dataset are available at https://trentqq.github.io/SGUIE-Net.html.

Recent Advances in Conventional and Deep Learning-Based Depth Completion: A Survey.

Depth completion aims to recover pixelwise depth from incomplete and noisy depth measurements with or without the guidance of a reference RGB image. This task attracted considerable research interest due to its importance in various computer vision-based applications, such as scene understanding, autonomous driving, 3-D reconstruction, object detection, pose estimation, trajectory prediction, and so on. As the system input, an incomplete depth map is usually generated by projecting the 3-D points collected by ranging sensors, such as LiDAR in outdoor environments, or obtained directly from RGB-D cameras in indoor areas. However, even if a high-end LiDAR is employed, the obtained depth maps are still very sparse and noisy, especially in the regions near the object boundaries, which makes the depth completion task a challenging problem. To address this issue, a few years ago, conventional image processing-based techniques were employed to fill the holes and remove the noise from the relatively dense depth maps obtained by RGB-D cameras, while deep learning-based methods have recently become increasingly popular and inspiring results have been achieved, especially for the challenging situation of LiDAR-image-based depth completion. This article systematically reviews and summarizes the works related to the topic of depth completion in terms of input modalities, data fusion strategies, loss functions, and experimental settings, especially for the key techniques proposed in deep learning-based multiple input methods. On this basis, we conclude by presenting the current status of depth completion and discussing several prospects for its future research directions.

SaCoseg: object cosegmentation by shape conformability.

In this paper, an object cosegmentation method based on shape conformability is proposed. Different from the previous object cosegmentation methods which are based on the region feature similarity of the common objects in image set, our proposed SaCoseg cosegmentation algorithm focuses on the shape consistency of the foreground objects in image set. In the proposed method, given an image set where the implied foreground objects may be varied in appearance but share similar shape structures, the implied common shape pattern in the image set can be automatically mined and regarded as the shape prior of those unsatisfactorily segmented images. The SaCoseg algorithm mainly consists of four steps: 1) the initial Grabcut segmentation; 2) the shape mapping by coherent point drift registration; 3) the common shape pattern discovery by affinity propagation clustering; and 4) the refinement by Grabcut with common shape constraint. To testify our proposed algorithm and establish a benchmark for future work, we built the CoShape data set to evaluate the shape-based cosegmentation. The experiments on CoShape data set and the comparison with some related cosegmentation algorithms demonstrate the good performance of the proposed SaCoseg algorithm.