Efficient allocation of chain-like task on chain-like network computers

In this paper, we propose an algorithm to improve Bokhari's method for allocation of chain-like task on chain-like network computers. The time complexity of our algorithm is reduced from the time complexity O(m'n) of Bokhari's to O(min( m, n)m*). where m is the number of modules and n is the number of processors. In addition, our algorithm relaxes Bokhari's constraints of all processors to be utilized and n < m

An Obstacle-Free and Power Efficient Deployment Algorithm for Wireless Sensor Networks

[[abstract]]This paper proposes a robot-deployment algorithm that overcomes unpredicted obstacles and employs full-coverage deployment with a minimal number of sensor nodes. Without the location information, node placement and spiral movement policies are proposed for the robot to deploy sensors efficiently to achieve power conservation and full coverage, while an obstacle surrounding movement policy is proposed to reduce the impacts of an obstacle upon deployment. Simulation results reveal that the proposed robot-deployment algorithm outperforms most existing robot-deployment mechanisms in power conservation and obstacle resistance and therefore achieves a better deployment performance.[[notice]]補正完

A Distributed Query Protocol in Wireless Sensor Networks

Abstract. In wireless sensor networks, query execution over a specific geographical region is an essential function for collecting sensed data. However, sensor nodes deployed in sensor networks have limited battery power. Hence, the minimum number of connected sensor nodes that covers the queried region in a sensor network must be determined. This paper proposes an efficient distributed protocol to find a subset of connected sensor nodes to cover the queried region. Each node determines whether to be a sensing node to sense the queried region according to its priority. The proposed protocol can efficiently construct a subset of connected sensing nodes and respond the query request to the sink node. In addition, the proposed protocol is extended to solve the k-coverage request. Simulation results show that our protocol is more efficient and has a lower communication overhead than the existing protocol

ADCA: An Asynchronous Duty Cycle Adjustment MAC Protocol for Wireless Sensor Networks

Abstract--In this paper, we propose an asynchronous duty cycle adjustment MAC protocol, called ADCA, for the wireless sensor network (WSN). ADCA is a sleep/wakeup schedule-based protocol to reduce power consumption without lowering network throughput or lengthening transmission delay. It is asynchronous; it allows each node in the WSN to set its own sleep/wakeup schedule independently. The media access is thus staggered and collisions are reduced. According to the statuses of previous transmissions, ADCA adjusts the duty cycle length for shortening transmission delay and increasing throughput. Simulation results show that ADCA outperforms related ones in terms of energy saving, network throughput and transmission delay

Catenarin Prevents Type 1 Diabetes in Nonobese Diabetic Mice via Inhibition of Leukocyte Migration Involving the MEK6/p38 and MEK7/JNK Pathways

Inflammation contributes to leukocyte migration, termed insulitis, and β-cell loss in type 1 diabetes (T1D). Naturally occurring anthraquinones are claimed as anti-inflammatory compounds; however, their actions are not clear. This study aimed to investigate the effect and mechanism of catenarin on the inflammatory disease, T1D. Catenarin and/or its anthraquinone analogs dose-dependently suppressed C-X-C chemokine receptor type 4 (CXCR4)- and C-C chemokine receptor type 5 (CCR5)-implicated chemotaxis in leukocytes. Catenarin, the most potent anthraquinone tested in the study, prevented T1D in nonobese diabetic mice. Mechanistic study showed that catenarin did not act on the expression of CCR5 and CXCR4. On the contrary, catenarin inhibited CCR5- and CXCR4-mediated chemotaxis via the reduction of the phosphorylation of mitogen-activated protein kinases (p38 and JNK) and their upstream kinases (MKK6 and MKK7), and calcium mobilization. Overall, the data demonstrate the preventive effect and molecular mechanism of action of catenarin on T1D, suggesting its novel use as a prophylactic agent in T1D

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p