Smoking and Genetic Risk Variation Across Populations of E uropean, A sian, and A frican A merican Ancestry—A Meta‐Analysis of Chromosome 15q25

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/91126/1/gepi21627.pd

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

Decision Support for the Optimization of Provider Staffing for Hospital Emergency Departments with a Queue-Based Approach

Deployment or distribution of valuable medical resources has emerged as an increasing challenge to hospital administrators and health policy makers. The hospital emergency department (HED) census and workload can be highly variable. Improvement of emergency services is an important stage in the development of the healthcare system and research on the optimal deployment of medical resources appears to be an important issue for HED long-term management. HED performance, in terms of patient flow and available resources, can be studied using the queue-based approach. The kernel point of this research is to approach the optimal cost on logistics using queuing theory. To model the proposed approach for a qualitative profile, a generic HED system is mapped into the M/M/R/N queue-based model, which assumes an R-server queuing system with Poisson arrivals, exponentially distributed service times and a system capacity of N. A comprehensive quantitative mathematical analysis on the cost pattern was done, while relevant simulations were also conducted to validate the proposed optimization model. The design illustration is presented in this paper to demonstrate the application scenario in a HED platform. Hence, the proposed approach provides a feasibly cost-oriented decision support framework to adapt a HED management requirement

Modeling and Prolonging Techniques on Operational Lifetime of Wireless Sensor Networks

在延長「無線感測網路」運作壽命的相關議題上，功率耗損的研究，是一項重要的課題與有趣的挑戰。為了增加感測網路之運作壽命，前人們之研究重點大都專注在提出各式各樣的「wake-up 策略及duty cycling 調節機制」，期許節約無線通訊成本。針對相同目標，本論文提出一種新穎的節能機制，利用排隊理論中的Min(N, T) policy M/G/1 排隊模型，來減緩各個感測節點之平均功率耗損。這種作法可以提供兩個系統參數(N, T)，來決定啟動感測節點內之無線發射器的時機。經由數理分析及模擬結果，此節能機制可以展現出功率耗損的改善效果。同時配合「無線感測網路」之任務需求，本論文也提出一種設計演算法，來協助網路管理者決定出最佳系統參數。 在一般「無線感測網路」中，位置越接近「sink node」內圈的感測節點們，因為必須擔負起幫忙較外圍之節點們，轉運(發射)封包的工作，大量地額外增加其功率耗損，導致位置越是居於內圈的感測節點們，其有限的電池能量更是快速耗盡。這種現象在「sink node」的附近，會形成所謂的「能量洞 (EHP)」問題。基本上最內圈的感測節點們，「能量洞 (EHP)」問題更是嚴重。將本論文所提出的Min(N, T) policy M/G/1 排隊模型，更進一步地應用到最內圈的感測節點們，來有效地降低其平均功率耗損，減緩「能量洞 (EHP)」問題，進而延壽「無線感測網路」。經由數理分析，吾人先推導出各圈感測節點的平均封包負擔量，再對最內圈的感測節點們，進行網路模擬驗證。利用排隊理論，其模擬結果驗證了本論文所提出的節能機制，在延長「無線感測網路」運作壽命的貢獻上，是一種有效可行且合乎成本效益的作法。Power consumption is an essentially important issue and an interesting challenge to prolong the operational lifetime of wireless sensor network (WSN). To prolong the lifetime of sensor nodes, most research works have focused on how to tune the duty cycling schemes among nodes to save the communication cost using multifarious wake-up strategies. To this aim, we propose a novel design strategy for mitigating average power consumption of sensor node using the Min(N, T) policy M/G/1 queuing theory. There is a heavy overhead for packet collisions and channel contention resulting from restarting the process of medium contention. The basic point of our approach is that Min(N, T) dyadic policy would mitigate the total average times of medium contention by having both a counter (N) and a timer (T) for the control of triggering on a radio server to transmit queued packets, and then power consumption of communication can be alleviated. We show how the improvement level on power consumption can be achieved through analytical and simulated results. To meet the mission requirements in sensor networks, the proposed add-on power saving technique can also provide a design framework for the sensor network manager to optimize relevant system parameters including power consumption and latency delay. Sensors closer to a sink node have a larger forwarding traffic burden and consume more energy than nodes further away from the sink. The whole operational lifetime of WSN is deteriorated because of such an uneven node power consumption patterns, leading to what is known as an energy hole problem (EHP) around the sink node. Basically the innermost shell nodes have a larger forwarding burden and consume more energy than nodes further away from the sink node. In this dissertation, the proposed queue-based power-saving technique can be expanded and applied to alleviate the EHP as well. With little management cost, the proposed queue-based power-saving technique can be applied to prolong the lifetime of sensor network economically and effectively. For the proposed queue-based model, mathematical framework on performance measures have been formulated. And also we will analyze the average traffic load per node for concentric sensor network. Focusing on the nodes located in the innermost shell of WSN, numerical and NS-2 network simulation results validate that the proposed approach indeed provides a feasibly cost-effective approach for lifetime elongation of sensor networks.摘要 .................................................................................................................................. i Abstract ............................................................................................................................. ii 目次 ................................................................................................................................ iv 表目次 ............................................................................................................................. vi 圖目次 ............................................................................................................................ vii 1. Introduction .................................................................................................................. 1 2. Literature Review ......................................................................................................... 8 2.1 Energy-Efficient Design ..................................................................................... 8 2.2 Overview on Energy Hole Problem with Uniform Node Distribution ............. 13 3. Mathematical Preliminaries. ....................................................................................... 16 3.1 Proposed Model for Sensor Nodes ................................................................... 16 3.2 Reduction form of the proposed system model ................................................ 20 3.2.1 Reduction to N-policy M/G/1 system as T approaches infinite: ........... 21 3.2.2 Reduction to T-policy M/G/1 system as N approaches infinite: ............ 21 3.3 System Performance Measures ......................................................................... 23 3.3.1 Expected length of busy period ............................................................. 24 3.3.2 Expected length of idle period ............................................................... 24 3.3.3 Expected length of the busy cycle ......................................................... 24 3.3.4 Probability that the radio server is busy ................................................ 25 3.3.5 Throughput of the system in steady state .............................................. 25 4. Power Consumption and Data Simulation ................................................................. 28 4.1 Power Consumption Function .......................................................................... 28 4.2 Numerical Simulation and Performance Improvement .................................... 29 4.3 Sensitivity Analysis on the Server Utilization .................................................. 34 5. Performance Improvement Study and Design Strategy.............................................. 36 5.1 Performance Improvement of Power Consumption ......................................... 36 5.2 Design Strategy and Numerical Illustration ..................................................... 36 5.3 Collision Probability Issue ............................................................................... 43 5.4 Sensitivity Analysis on System Power Factors................................................. 47 5.4.1 Sensitivity Analysis #1: ......................................................................... 47 5.4.2 Sensitivity Analysis #2: ......................................................................... 55 6. Lifetime Elongation Technique in WSN .................................................................... 62 6.1. Mathematical analysis of average traffic load per node .................................. 62 6.2. Lifetime elongation by proposed queue-based approach ................................ 64 6.3. Network simulation experiment ...................................................................... 68 7. Conclusion .................................................................................................................. 72 References ...................................................................................................................... 74 Appendix: Rationale for adopting prune-and-search strategy ........................................ 8

Toward Green Sensor Field by Optimizing Power Efficiency Using D-Policy M/G/1 Queuing Systems

Power efficiency is a crucially important issue in the IEEE 802.15.4/ZigBee sensor networks (ZSNs) for majority of sensor nodes equipped with non-rechargeable batteries. To increase the lifetime of sensor networks, each node must optimize power consumption as possible. Among open literatures, much research works have focused on how to optimally increase the probability of sleeping states using multifarious wake-up strategies. Making things different, in this article, we propose a novel optimization framework for alleviating power consumption of sensor node with the D-policy M/G/1 queuing approach. Toward green sensor field, the proposed power-saving technique can be applied to prolong the lifetime of ZSN economically and effectively. For the proposed data aggregation model, mathematical framework on performance measures has been formulated. Data simulation using MATLAB tool has been conducted for exploring the feasibility of the proposed approach. And also we analyze the average traffic load per node for tree-based ZSN. Focusing on ZigBee routers deployed at the innermost shell of ZSN, network simulation results validate that the proposed approach indeed provides a feasibly cost-effective approach for prolonging lifetime of ZSNs