Fast Hierarchical Deep Unfolding Network for Image Compressed Sensing

By integrating certain optimization solvers with deep neural network, deep unfolding network (DUN) has attracted much attention in recent years for image compressed sensing (CS). However, there still exist several issues in existing DUNs: 1) For each iteration, a simple stacked convolutional network is usually adopted, which apparently limits the expressiveness of these models. 2) Once the training is completed, most hyperparameters of existing DUNs are fixed for any input content, which significantly weakens their adaptability. In this paper, by unfolding the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA), a novel fast hierarchical DUN, dubbed FHDUN, is proposed for image compressed sensing, in which a well-designed hierarchical unfolding architecture is developed to cooperatively explore richer contextual prior information in multi-scale spaces. To further enhance the adaptability, series of hyperparametric generation networks are developed in our framework to dynamically produce the corresponding optimal hyperparameters according to the input content. Furthermore, due to the accelerated policy in FISTA, the newly embedded acceleration module makes the proposed FHDUN save more than 50% of the iterative loops against recent DUNs. Extensive CS experiments manifest that the proposed FHDUN outperforms existing state-of-the-art CS methods, while maintaining fewer iterations.Comment: Accepted by ACM MM 202

Potential biomarkers of Parkinson’s disease revealed by plasma metabolic profiling

The plasma of Parkinson's disease (PD) patients may contain various altered metabolites associated with the risk or progression of the disease. Characterization of the abnormal metabolic pattern in PD plasma is therefore critical for the search for potential PD biomarkers. We collected blood plasma samples from PD patients and used an LC-MS based metabolomics approach to identify 17 metabolites with significantly altered levels. Metabolic network analysis was performed to place the metabolites linked to different pathways. The metabolic pathways involved were associated with tyrosine biosynthesis, glycerol phospholipid metabolism, carnitine metabolism and bile acid biosynthesis, within which carnitine and bile acid metabolites as potential biomarkers are first time reported. These abnormal metabolic changes in the plasma of patients with PD were mainly related to lipid metabolism and mitochondrial function

A Scale-Adaptive Matching Algorithm for Underwater Acoustic and Optical Images

Underwater acoustic and optical data fusion has been developed in recent decades. Matching of underwater acoustic and optical images is a fundamental and critical problem in underwater exploration because it usually acts as the key step in many applications, such as target detection, ocean observation, and joint positioning. In this study, a method of matching the same underwater object in acoustic and optical images was designed, consisting of two steps. First, an enhancement step is used to enhance the images and ensure the accuracy of the matching results based on iterative processing and estimate similarity. The acoustic and optical images are first pre-processed with the aim of eliminating the influence of contrast degradation, contour blur, and image noise. A method for image enhancement was designed based on iterative processing. In addition, a new similarity estimation method for acoustic and optical images is also proposed to provide the enhancement effect. Second, a matching step is used to accurately find the corresponding object in the acoustic images that appears in the underwater optical images. In the matching process, a correlation filter is applied to determine the correlation for matching between images. Due to the differences of angle and imaging principle between underwater optical and acoustic images, there may be major differences of size between two images of the same object. In order to eliminate the effect of these differences, we introduce the Gaussian scale-space, which is fused with multi-scale detection to determine the matching results. Therefore, the algorithm is insensitive to scale differences. Extensive experiments demonstrate the effectiveness and accuracy of our proposed method in matching acoustic and optical images

A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks

Deployment of surface-level gateways holds potential as an effective method to alleviate high-propagation delays and high-error probability in an underwater wireless sensor network (UWSN). This promise comes from reducing distances to underwater nodes and using radio waves to forward information to a control station. In an UWSN, a dynamic energy efficient surface-level gateway deployment is required to cope with the mobility of underwater nodes while considering the remote and three-dimensional nature of marine space. In general, deployment problems are usually modeled as an optimization problem to satisfy multiple constraints given a set of parameters. One previously published static deployment optimization framework makes assumptions about network workload, routing, medium access control performance, and node mobility. However, in real underwater environments, all these parameters are dynamic. Therefore, the accuracy of performance estimates calculated through static UWSN deployment optimization framework tends to be limited by nature. This paper presents the Prediction-Assisted Dynamic Surface Gateway Placement (PADP) algorithm to maximize the coverage and minimize the average end-to-end delay of a mobile underwater sensor network over a specified period. PADP implements the Interacting Multiple Model (IMM) tracking scheme to predict the positions of sensor nodes. The deployment is determined based on both current and predicted positions of sensor nodes, which enables better coverage and shorter end-to-end delay. PADP uses a branch-and-cut approach to solve the optimization problem efficiently, and employs a disjoint-set data structure to ensure connectivity. Simulation results illustrate that PADP significantly outperforms a static gateway deployment scheme

MYB transcription factors in alfalfa (Medicago sativa): genome-wide identification and expression analysis under abiotic stresses

Background Alfalfa is the most widely cultivated forage legume and one of the most economically valuable crops in the world. Its survival and production are often hampered by environmental changes. However, there are few studies on stress-resistance genes in alfalfa because of its incomplete genomic information and rare expression profile data. The MYB proteins are characterized by a highly conserved DNA-binding domain, which is large, functionally diverse, and represented in all eukaryotes. The role of MYB proteins in plant development is essential; they function in diverse biological processes, including stress and defense responses, and seed and floral development. Studies on the MYB gene family have been reported in several species, but they have not been comprehensively analyzed in alfalfa. Methods To identify more comprehensive MYB transcription factor family genes, the sequences of 168 Arabidopsis thaliana, 430 Glycine max, 185 Medicago truncatula, and 130 Oryza sativa MYB proteins were downloaded from the Plant Transcription Factor Database. These sequences were used as queries in a BLAST search against the M. sativa proteome sequences provided by the Noble Research Institute. Results In the present study, a total of 265 MsMYB proteins were obtained, including 50 R1-MYB, 186 R2R3-MYB, 26 R1R2R3-MYB, and three atypical-MYB proteins. These predicted MsMYB proteins were divided into 12 subgroups by phylogenetic analysis, and gene ontology (GO) analysis indicated that most of the MsMYB genes are involved in various biological processes. The expression profiles and quantitative real-time PCR analysis indicated that some MsMYB genes might play a crucial role in the response to abiotic stresses. Additionally, a total of 170 and 914 predicted protein–protein and protein-DNA interactions were obtained, respectively. The interactions between MsMYB043 and MSAD320162, MsMYB253 and MSAD320162, and MsMYB253 and MSAD308489 were confirmed by a yeast two-hybrid system. This work provides information on the MYB family in alfalfa that was previously lacking and might promote the cultivation of stress-resistant alfalfa

Physiological analysis of the effect of altitudinal gradients on Leymus secalinus on the Qinghai-Tibetan Plateau.

On the Qinghai-Tibetan Plateau, the high-altitudinal gradients can negatively affect plant distribution and limit plant growth and reproduction. Leymus secalinus (Georgi) Tzvel. is an important forage crop on the Qinghai-Tibetan Plateau and has an excellent ability to fix sand and improve soil. To evaluate the effect of altitude on the physiological characteristics of L. secalinus on the Qinghai-Tibetan Plateau, we measured the lipid peroxidation; chlorophyll a (Chl a), chlorophyll b (Chl b), total carotenoid (Car), soluble protein, proline and soluble sugar contents; and the activities of superoxide dismutase (SOD), catalase (CAT) and peroxidase (POD) in leaves from eight different altitudes in Minhe County and Huangzhong County. The leaves were collected at the initial bloom stage, and the average vertical distance between two adjacent collection sites was approximately 100 meters. The reduction in Chl a and Chl b contents and the increase in Car contents can allow plants to weaken their light absorption and avoid photodamage to the chloroplast. The decrease in malondialdehyde (MDA) content associated with lower lipid peroxidation, and the changes of CAT, SOD and POD activities reflect a higher reactive oxygen species (ROS) scavenging capacity in high-altitude plants. The increase in proline and soluble sugar contents with elevation suggests that proline and soluble sugar may play a key role in the osmotic adjustment of plants in alpine regions. The results suggested that altitudinal gradients negatively affect L. secalinus on the Qinghai-Tibetan Plateau and that the adaptation mechanism and survival strategies of L. secalinus were attributed to the combined effects of multiple protective strategies