Noncollinearity-modulated electronic properties of the monolayer CrI3_3

Introducing noncollinear magnetization into a monolayer CrI$_3$ is proposed to be an effective approach to modulate the local electronic properties of the two-dimensional (2D) magnetic material. Using first-principles calculation, we illustrate that both the conduction and valence bands in the monolayer CrI$_3$ are lowered down by spin spiral states. The distinct electronic structure of the monolayer noncollinear CrI$_3$ can be applied in nanoscale functional devices. As a proof of concept, we show that a magnetic domain wall can form a one-dimensional conducting channel in the 2D semiconductor via proper gating. Other possible applications such as electron-hole separation and identical quantum dots are also discussed

An Adaptive Fault-Tolerant Communication Scheme for Body Sensor Networks

A high degree of reliability for critical data transmission is required in body sensor networks (BSNs). However, BSNs are usually vulnerable to channel impairments due to body fading effect and RF interference, which may potentially cause data transmission to be unreliable. In this paper, an adaptive and flexible fault-tolerant communication scheme for BSNs, namely AFTCS, is proposed. AFTCS adopts a channel bandwidth reservation strategy to provide reliable data transmission when channel impairments occur. In order to fulfill the reliability requirements of critical sensors, fault-tolerant priority and queue are employed to adaptively adjust the channel bandwidth allocation. Simulation results show that AFTCS can alleviate the effect of channel impairments, while yielding lower packet loss rate and latency for critical sensors at runtime.Comment: 10 figures, 19 page

Inhibition of activity of GABA transporter GAT1 by δ-opioid receptor

Analgesia is a well-documented effect of acupuncture. A critical role in pain sensation plays the nervous system, including the GABAergic system and opioid receptor (OR) activation. Here we investigated regulation of GABA transporter GAT1 by δOR in rats and in Xenopus oocytes. Synaptosomes of brain from rats chronically exposed to opiates exhibited reduced GABA uptake, indicating that GABA transport might be regulated by opioid receptors. For further investigation we have expressed GAT1 of mouse brain together with mouse δOR and μOR in Xenopus oocytes. The function of GAT1 was analyzed in terms of Na(+)-dependent [(3)H]GABA uptake as well as GAT1-mediated currents. Coexpression of δOR led to reduced number of fully functional GAT1 transporters, reduced substrate translocation, and GAT1-mediated current. Activation of δOR further reduced the rate of GABA uptake as well as GAT1-mediated current. Coexpression of μOR, as well as μOR activation, affected neither the number of transporters, nor rate of GABA uptake, nor GAT1-mediated current. Inhibition of GAT1-mediated current by activation of δOR was confirmed in whole-cell patch-clamp experiments on rat brain slices of periaqueductal gray. We conclude that inhibition of GAT1 function will strengthen the inhibitory action of the GABAergic system and hence may contribute to acupuncture-induced analgesia

Calcineurin B-Like Proteins CBL4 and CBL10 Mediate Two Independent Salt Tolerance Pathways in Arabidopsis.

In Arabidopsis, the salt overly sensitive (SOS) pathway, consisting of calcineurin B-like protein 4 (CBL4/SOS3), CBL-interacting protein kinase 24 (CIPK24/SOS2) and SOS1, has been well defined as a crucial mechanism to control cellular ion homoeostasis by extruding Na+ to the extracellular space, thus conferring salt tolerance in plants. CBL10 also plays a critical role in salt tolerance possibly by the activation of Na+ compartmentation into the vacuole. However, the functional relationship of the SOS and CBL10-regulated processes remains unclear. Here, we analyzed the genetic interaction between CBL4 and CBL10 and found that the cbl4 cbl10 double mutant was dramatically more sensitive to salt as compared to the cbl4 and cbl10 single mutants, suggesting that CBL4 and CBL10 each directs a different salt-tolerance pathway. Furthermore, the cbl4 cbl10 and cipk24 cbl10 double mutants were more sensitive than the cipk24 single mutant, suggesting that CBL10 directs a process involving CIPK24 and other partners different from the SOS pathway. Although the cbl4 cbl10, cipk24 cbl10, and sos1 cbl10 double mutants showed comparable salt-sensitive phenotype to sos1 at the whole plant level, they all accumulated much lower Na+ as compared to sos1 under high salt conditions, suggesting that CBL10 regulates additional unknown transport processes that play distinct roles from the SOS1 in Na+ homeostasis

Deep graph learning for anomalous citation detection

Anomaly detection is one of the most active research areas in various critical domains, such as healthcare, fintech, and public security. However, little attention has been paid to scholarly data, that is, anomaly detection in a citation network. Citation is considered as one of the most crucial metrics to evaluate the impact of scientific research, which may be gamed in multiple ways. Therefore, anomaly detection in citation networks is of significant importance to identify manipulation and inflation of citations. To address this open issue, we propose a novel deep graph learning model, namely graph learning for anomaly detection (GLAD), to identify anomalies in citation networks. GLAD incorporates text semantic mining to network representation learning by adding both node attributes and link attributes via graph neural networks (GNNs). It exploits not only the relevance of citation contents, but also hidden relationships between papers. Within the GLAD framework, we propose an algorithm called Citation PUrpose (CPU) to discover the purpose of citation based on citation context. The performance of GLAD is validated through a simulated anomalous citation dataset. Experimental results demonstrate the effectiveness of GLAD on the anomalous citation detection task. © 2012 IEEE

Robust normalization and guaranteed cost control for a class of uncertain singular Markovian jump systems via hybrid impulsive control

This paper investigates the problem of robust normalization and guaranteed cost control for a class of uncertain singular Markovian jump systems. The uncertainties exhibit in both system matrices and transition rate matrix of the Markovian chain. A new impulsive and proportional-derivative control strategy is presented, where the derivative gain is to make the closed-loop system of the singular plant to be a normal one, and the impulsive control part is to make the value of the Lyapunov function does not increase at each time instant of the Markovian switching. A linearization approach via congruence transformations is proposed to solve the controller design problem. The cost function is minimized via solving an optimization problem under the designed control scheme. Finally, three examples (two numerical examples and an RC pulse divider circuit example) are provided to illustrate the effectiveness and applicability of the proposed methods

MODEL : motif-based deep feature learning for link prediction

Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms into embedding-based algorithms. However, most existing approaches fail to exploit the fact that real-world networks are different from random networks. In particular, real-world networks are known to contain motifs, natural network building blocks reflecting the underlying network-generating processes. In this article, we propose a novel embedding algorithm that incorporates network motifs to capture higher order structures in the network. To evaluate its effectiveness for link prediction, experiments were conducted on three types of networks: social networks, biological networks, and academic networks. The results demonstrate that our algorithm outperforms both the traditional similarity-based algorithms (by 20%) and the state-of-the-art embedding-based algorithms (by 19%). © 2014 IEEE