Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks

Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study Group) was established to initiate discussions on new IEEE 802.11 features. Coordinated control methods of the access points (APs) in the wireless local area networks (WLANs) are discussed in EHT Study Group. The present study proposes a deep reinforcement learning-based channel allocation scheme using graph convolutional networks (GCNs). As a deep reinforcement learning method, we use a well-known method double deep Q-network. In densely deployed WLANs, the number of the available topologies of APs is extremely high, and thus we extract the features of the topological structures based on GCNs. We apply GCNs to a contention graph where APs within their carrier sensing ranges are connected to extract the features of carrier sensing relationships. Additionally, to improve the learning speed especially in an early stage of learning, we employ a game theory-based method to collect the training data independently of the neural network model. The simulation results indicate that the proposed method can appropriately control the channels when compared to extant methods

青森・岩手県境不法投棄産業廃棄物の処理計画に関する住民意識調査

We carried out the attitude survey of residents of Aomori and Hachinohe to investigate the problems concerning the transaction plan of the industrial waste disposed illegally in Aomori-Iwate prefectural border. As the result of this survey we found that we must completely survey an environment with surrounding area of the treatment facilities and disclose information to habitants in order to advance the transaction scheme of industrial waste. Additionally we must make the best use of the industrial waste,so it becomes easy to obtain the understanding of surrounding habitants

Endotoxemia by Porphyromonas gingivalis Injection Aggravates Non-alcoholic Fatty Liver Disease, Disrupts Glucose/Lipid Metabolism, and Alters Gut Microbiota in Mice

Many risk factors related to the development of non-alcoholic fatty liver disease (NAFLD) have been proposed, including the most well-known of diabetes and obesity as well as periodontitis. As periodontal pathogenic bacteria produce endotoxins, periodontal treatment can result in endotoxemia. The aim of this study was to investigate the effects of intravenous, sonicated Porphyromonas gingivalis (Pg) injection on glucose/lipid metabolism, liver steatosis, and gut microbiota in mice. Endotoxemia was induced in C57BL/6J mice (8 weeks old) by intravenous injection of sonicated Pg; Pg was deactivated but its endotoxin remained. The mice were fed a high-fat diet and administered sonicated Pg (HFPg) or saline (HFco) injections for 12 weeks. Liver steatosis, glucose metabolism, and gene expression in the liver were evaluated. 16S rRNA gene sequencing with metagenome prediction was performed on the gut microbiota. Compared to HFco mice, HFPg mice exhibited impaired glucose tolerance and insulin resistance along with increased liver steatosis. Liver microarray analysis demonstrated that 1278 genes were differentially expressed between HFco and HFPg mice. Gene set enrichment analysis showed that fatty acid metabolism, hypoxia, and TNFα signaling via NFκB gene sets were enriched in HFPg mice. Although sonicated Pg did not directly reach the gut, it changed the gut microbiota and decreased bacterial diversity in HFPg mice. Metagenome prediction in the gut microbiota showed enriched citrate cycle and carbon fixation pathways in prokaryotes. Overall, intravenous injection of sonicated Pg caused impaired glucose tolerance, insulin resistance, and liver steatosis in mice fed high-fat diets. Thus, blood infusion of Pg contributes to NAFLD and alters the gut microbiota

Characteristic Metabolism of Free Amino Acids in Cetacean Plasma: Cluster Analysis and Comparison with Mice

From an evolutionary perspective, the ancestors of cetaceans first lived in terrestrial environments prior to adapting to aquatic environments. Whereas anatomical and morphological adaptations to aquatic environments have been well studied, few studies have focused on physiological changes. We focused on plasma amino acid concentrations (aminograms) since they show distinct patterns under various physiological conditions. Plasma and urine aminograms were obtained from bottlenose dolphins, pacific white-sided dolphins, Risso's dolphins, false-killer whales and C57BL/6J and ICR mice. Hierarchical cluster analyses were employed to uncover a multitude of amino acid relationships among different species, which can help us understand the complex interrelations comprising metabolic adaptations. The cetacean aminograms formed a cluster that was markedly distinguishable from the mouse cluster, indicating that cetaceans and terrestrial mammals have quite different metabolic machinery for amino acids. Levels of carnosine and 3-methylhistidine, both of which are antioxidants, were substantially higher in cetaceans. Urea was markedly elevated in cetaceans, whereas the level of urea cycle-related amino acids was lower. Because diving mammals must cope with high rates of reactive oxygen species generation due to alterations in apnea/reoxygenation and ischemia-reperfusion processes, high concentrations of antioxidative amino acids are advantageous. Moreover, shifting the set point of urea cycle may be an adaption used for body water conservation in the hyperosmotic sea water environment, because urea functions as a major blood osmolyte. Furthermore, since dolphins are kept in many aquariums for observation, the evaluation of these aminograms may provide useful diagnostic indices for the assessment of cetacean health in artificial environments in the future

Recent advances of metabolomics in plant biotechnology

Biotechnology, including genetic modification, is a very important approach to regulate the production of particular metabolites in plants to improve their adaptation to environmental stress, to improve food quality, and to increase crop yield. Unfortunately, these approaches do not necessarily lead to the expected results due to the highly complex mechanisms underlying metabolic regulation in plants. In this context, metabolomics plays a key role in plant molecular biotechnology, where plant cells are modified by the expression of engineered genes, because we can obtain information on the metabolic status of cells via a snapshot of their metabolome. Although metabolome analysis could be used to evaluate the effect of foreign genes and understand the metabolic state of cells, there is no single analytical method for metabolomics because of the wide range of chemicals synthesized in plants. Here, we describe the basic analytical advancements in plant metabolomics and bioinformatics and the application of metabolomics to the biological study of plants

Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs With Graph Convolutional Networks

For densely deployed wireless local area networks (WLANs), this paper proposes a deep reinforcement learning-based channel allocation scheme that enables the efficient use of experience. The central idea is that an objective function is modeled relative to communication quality as a parametric function of a pair of observed topologies and channels. This is because communication quality in WLANs is significantly influenced by the carrier sensing relationship between access points. The features of the proposed scheme can be summarized by two points. First, we adopt graph convolutional layers in the model to extract the features of the channel vectors with topology information, which is the adjacency matrix of the graph dependent on the carrier sensing relationships. Second, we filter experiences to reduce the duplication of data for learning, which can often adversely influence the generalization performance. Because fixed experiences tend to be repeatedly observed in WLAN channel allocation problems, the duplication of experiences must be avoided. The simulation results demonstrate that the proposed method enables the allocation of channels in densely deployed WLANs such that the system throughput increases. Moreover, improved channel allocation, compared to other existing methods, is achieved in terms of the system throughput. Furthermore, compared to the immediate reward maximization method, the proposed method successfully achieves greater reward channel allocation or realizes the optimal channel allocation while reducing the number of changes

A γ-Glutamyl Transpeptidase-Independent Pathway of Glutathione Catabolism to Glutamate via 5-Oxoproline in Arabidopsis1[W][OA]

The degradation pathway of glutathione (GSH) in plants is not well understood. In mammals, GSH is predominantly metabolized through the γ-glutamyl cycle, where GSH is degraded by the sequential reaction of γ-glutamyl transpeptidase (GGT), γ-glutamyl cyclotransferase, and 5-oxoprolinase to yield glutamate (Glu) and dipeptides that are subject to peptidase action. In this study, we examined if GSH is degraded through the same pathway in Arabidopsis (Arabidopsis thaliana) as occurs in mammals. In Arabidopsis, the oxoprolinase knockout mutants (oxp1-1 and oxp1-2) accumulate more 5-oxoproline (5OP) and less Glu than wild-type plants, suggesting substantial metabolite flux though 5OP and that 5OP is a major contributor to Glu steady-state levels. In the ggt1-1/ggt4-1/oxp1-1 triple mutant with no GGT activity in any organs except young siliques, the 5OP concentration in leaves was not different from that in oxp1-1, suggesting that GGTs are not major contributors to 5OP production in Arabidopsis. 5OP formation strongly tracked the level of GSH in Arabidopsis plants, suggesting that GSH is the precursor of 5OP in a GGT-independent reaction. Kinetics analysis suggests that γ-glutamyl cyclotransferase is the major source of GSH degradation and 5OP formation in Arabidopsis. This discovery led us to propose a new pathway for GSH turnover in plants where GSH is converted to 5OP and then to Glu by the combined action of γ-glutamyl cyclotransferase and 5-oxoprolinase in the cytoplasm

A γ-Glutamyl Transpeptidase-Independent Pathway of Glutathione Catabolism to Glutamate via 5-Oxoproline in Arabidopsis

The degradation pathway of glutathione (GSH) in plants is not well understood. In mammals, GSH is predominantly metabolized through the γ-glutamyl cycle, where GSH is degraded by the sequential reaction of γ-glutamyl transpeptidase (GGT), γ-glutamyl cyclotransferase, and 5-oxoprolinase to yield glutamate (Glu) and dipeptides that are subject to peptidase action. In this study, we examined if GSH is degraded through the same pathway in Arabidopsis (Arabidopsis thaliana) as occurs in mammals. In Arabidopsis, the oxoprolinase knockout mutants (oxp1-1 and oxp1-2) accumulate more 5-oxoproline (5OP) and less Glu than wild-type plants, suggesting substantial metabolite flux though 5OP and that 5OP is a major contributor to Glu steady-state levels. In the ggt1-1/ggt4-1/oxp1-1 triple mutant with no GGT activity in any organs except young siliques, the 5OP concentration in leaves was not different from that in oxp1-1, suggesting that GGTs are not major contributors to 5OP production in Arabidopsis. 5OP formation strongly tracked the level of GSH in Arabidopsis plants, suggesting that GSH is the precursor of 5OP in a GGT-independent reaction. Kinetics analysis suggests that γ-glutamyl cyclotransferase is the major source of GSH degradation and 5OP formation in Arabidopsis. This discovery led us to propose a new pathway for GSH turnover in plants where GSH is converted to 5OP and then to Glu by the combined action of γ-glutamyl cyclotransferase and 5-oxoprolinase in the cytoplasm.This article is from Plant Physiology 148, no. 3 (November 2008): 1603–1613, doi:10.1104/pp.108.125716.</p