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

Thompson Sampling-Based Channel Selection through Density Estimation aided by Stochastic Geometry

We propose a sophisticated channel selection scheme based on multi-armed bandits and stochastic geometry analysis. In the proposed scheme, a typical user attempts to estimate the density of active interferers for every channel via the repeated observations of signal-to-interference power ratio (SIR), which demonstrates the randomness induced by randomized interference sources and fading effects. The purpose of this study involves enabling a typical user to identify the channel with the lowest density of active interferers while considering the communication quality during exploration. To resolve the trade-off between obtaining more observations on uncertain channels and using a channel that appears better, we employ a bandit algorithm called Thompson sampling (TS), which is known for its empirical effectiveness. We consider two ideas to enhance TS. First, noticing that the SIR distribution derived through stochastic geometry is useful for updating the posterior distribution of the density, we propose incorporating the SIR distribution into TS to estimate the density of active interferers. Second, TS requires sampling from the posterior distribution of the density for each channel, while it is significantly more complicated for the posterior distribution of the density to generate samples than well-known distribution. The results indicate that this type of sampling process is achieved via the Markov chain Monte Carlo method (MCMC). The simulation results indicate that the proposed method enables a typical user to determine the channel with the lowest density more efficiently than the TS without density estimation aided by stochastic geometry, and ε-greedy strategies

Role of Ca2+-Dependent and Ca2+-Sensitive Mechanisms in Sphingosine 1-Phosphate-Induced Constriction of Isolated Porcine Retinal Arterioles in Vitro

掲載予定博士(医学）旭川医科大

Role of Ca2+-Dependent and Ca2+-Sensitive Mechanisms in Sphingosine 1-Phosphate-Induced Constriction of Isolated Porcine Retinal Arterioles in Vitro

掲載予定博士(医学）旭川医科大

インタラクティブ ナ ガクシュウ ヲ シエン スル ジョウホウ キキ ヲ レイ ニ シタ キョウイン ムケ キョウザイ ノ カイハツ ト ソノ コウカ ノ ケンヨウ

研究報

Cobalt protoporphyrin represses osteoclastogenesis through blocking multiple signaling pathways

Cobalt protoporphyrin (CoPP) is a metallo-protoporphyrin that works as a powerful inducer of heme oxigenase-1 (HO-1) in various tissues and cells. Our recent studies have demonstrated that induction of HO-1 by several reagents inhibited differentiation and activation of osteoclasts (OCLs), which are multinucleated bone resorbing cells. However, the effects of CoPP on osteoclastogenesis remain to be elucidated. In this study, we report that CoPP inhibits receptor activator of nuclear factor κB ligand (RANKL)-induced OCL formation in a dose dependent manner. Importantly, CoPP had little cytotoxicity, but rather enhanced cell proliferation of OCLs. CoPP suppressed the protein levels of nuclear factor of activated T cells cytoplasmic-1 (NFATc1) as well as those of OCLs markers such as Src and cathepsin K, which are transcriptionally regulated by NFATc1 in mature OCLs. Western blot analyses also showed that CoPP abolished RANKL-stimulated phosphorylation of several major signaling pathways such as IκB, Akt, ERK, JNK and p38 MAPKs in OCL precursor cells. Thus, our results show that CoPP represses osteoclastogenesis through blocking multiple signaling pathways

DOCK2 is involved in the host genetics and biology of severe COVID-19

「コロナ制圧タスクフォース」COVID-19疾患感受性遺伝子DOCK2の重症化機序を解明 --アジア最大のバイオレポジトリーでCOVID-19の治療標的を発見--. 京都大学プレスリリース. 2022-08-10.Identifying the host genetic factors underlying severe COVID-19 is an emerging challenge. Here we conducted a genome-wide association study (GWAS) involving 2, 393 cases of COVID-19 in a cohort of Japanese individuals collected during the initial waves of the pandemic, with 3, 289 unaffected controls. We identified a variant on chromosome 5 at 5q35 (rs60200309-A), close to the dedicator of cytokinesis 2 gene (DOCK2), which was associated with severe COVID-19 in patients less than 65 years of age. This risk allele was prevalent in East Asian individuals but rare in Europeans, highlighting the value of genome-wide association studies in non-European populations. RNA-sequencing analysis of 473 bulk peripheral blood samples identified decreased expression of DOCK2 associated with the risk allele in these younger patients. DOCK2 expression was suppressed in patients with severe cases of COVID-19. Single-cell RNA-sequencing analysis (n = 61 individuals) identified cell-type-specific downregulation of DOCK2 and a COVID-19-specific decreasing effect of the risk allele on DOCK2 expression in non-classical monocytes. Immunohistochemistry of lung specimens from patients with severe COVID-19 pneumonia showed suppressed DOCK2 expression. Moreover, inhibition of DOCK2 function with CPYPP increased the severity of pneumonia in a Syrian hamster model of SARS-CoV-2 infection, characterized by weight loss, lung oedema, enhanced viral loads, impaired macrophage recruitment and dysregulated type I interferon responses. We conclude that DOCK2 has an important role in the host immune response to SARS-CoV-2 infection and the development of severe COVID-19, and could be further explored as a potential biomarker and/or therapeutic target