6,066 research outputs found
Meta-Analysis of Genome-Wide Linkage Studies in Celiac Disease.
OBJECTIVE: A meta-analysis of genome-wide linkage studies allows us to summarize the extensive information available from family-based studies, as the field moves into genome-wide association studies.
METHODS: Here we apply the genome scan meta-analysis (GSMA) method, a rank-based, model-free approach, to combine results across eight independent genome-wide linkages performed on celiac disease (CD), including 554 families with over 1,500 affected individuals. We also investigate the agreement between signals we identified from this meta-analysis of linkage studies and those identified from genome-wide association analysis using a hypergeometric distribution.
RESULTS: Not surprisingly, the most significant result was obtained in the HLA region. Outside the HLA region, suggestive evidence for linkage was obtained at the telomeric region of chromosome 10 (10q26.12-qter; p = 0.00366), and on chromosome 8 (8q22.2-q24.21; p = 0.00491). Testing signals of association and linkage within bins showed no significant evidence for co-localization of results.
CONCLUSION: This meta-analysis allowed us to pool the results from available genome-wide linkage studies and to identify novel regions potentially harboring predisposing genetic variation contributing to CD. This study also shows that linkage and association studies may identify different types of disease-predisposing variants
Online optimal and adaptive integral tracking control for varying discreteâtime systems using reinforcement learning
Conventional closedâform solution to the optimal control problem using optimal control theory is only available under the assumption that there are known system dynamics/models described as differential equations. Without such models, reinforcement learning (RL) as a candidate technique has been successfully applied to iteratively solve the optimal control problem for unknown or varying systems. For the optimal tracking control problem, existing RL techniques in the literature assume either the use of a predetermined feedforward input for the tracking control, restrictive assumptions on the reference model dynamics, or discounted tracking costs. Furthermore, by using discounted tracking costs, zero steadyâstate error cannot be guaranteed by the existing RL methods. This article therefore presents an optimal online RL tracking control framework for discreteâtime (DT) systems, which does not impose any restrictive assumptions of the existing methods and equally guarantees zero steadyâstate tracking error. This is achieved by augmenting the original system dynamics with the integral of the error between the reference inputs and the tracked outputs for use in the online RL framework. It is further shown that the resulting value function for the DT linear quadratic tracker using the augmented formulation with integral control is also quadratic. This enables the development of Bellman equations, which use only the system measurements to solve the corresponding DT algebraic Riccati equation and obtain the optimal tracking control inputs online. Two RL strategies are thereafter proposed based on both the value function approximation and the Qâlearning along with bounds on excitation for the convergence of the parameter estimates. Simulation case studies show the effectiveness of the proposed approach
Neural superposition and oscillations in the eye of the blowfly
Neural superposition in the eye of the blowfly Calliphora erythrocephala was investigated by stimulating single photoreceptors using corneal neutralization through water immersion. Responses in Large Monopolar Cells (LMCs) in the lamina were measured, while stimulating one or more of the six photoreceptors connected to the LMC. Responses to flashes of low light intensity on individual photoreceptors add approximately linearly at the LMC. Higher intensity light flashes produce a maximum LMC response to illumination of single photoreceptors which is about half the maximum response to simultaneous illumination of the six connecting photoreceptors. This observation indicates that a saturation can occur at a stage of synaptic transmission which precedes the change in the post-synaptic membrane potential.
Stimulation of single photoreceptors yields high frequency oscillations (about 200 Hz) in the LMC potential, much larger in amplitude than produced by simultaneous stimulation of the six photoreceptors connected to the LMC. It is discussed that these oscillations also arise from a mechanism that precedes the change in the postsynaptic membrane potential.
Hsp21potentiates antifungal drug tolerance in Candida albicans
Peer reviewedPublisher PD
Colored Resonant Signals at the LHC: Largest Rate and Simplest Topology
We study the colored resonance production at the LHC in a most general
approach. We classify the possible colored resonances based on group theory
decomposition, and construct their effective interactions with light partons.
The production cross section from annihilation of valence quarks or gluons may
be on the order of 400 - 1000 pb at LHC energies for a mass of 1 TeV with
nominal couplings, leading to the largest production rates for new physics at
the TeV scale, and simplest event topology with dijet final states. We apply
the new dijet data from the LHC experiments to put bounds on various possible
colored resonant states. The current bounds range from 0.9 to 2.7 TeV. The
formulation is readily applicable for future searches including other decay
modes.Comment: 29 pages, 9 figures. References updated and additional K-factors
include
Computer-supported feedback message tailoring: Theory-informed adaptation of clinical audit and feedback for learning and behavior change
Background: Evidence shows that clinical audit and feedback can significantly improve compliance with desired practice, but it is unclear when and how it is effective. Audit and feedback is likely to be more effective when feedback messages can influence barriers to behavior change, but barriers to change differ across individual health-care providers, stemming from differences in providers' individual characteristics. Discussion: The purpose of this article is to invite debate and direct research attention towards a novel audit and feedback component that could enable interventions to adapt to barriers to behavior change for individual health-care providers: computer-supported tailoring of feedback messages. We argue that, by leveraging available clinical data, theory-informed knowledge about behavior change, and the knowledge of clinical supervisors or peers who deliver feedback messages, a software application that supports feedback message tailoring could improve feedback message relevance for barriers to behavior change, thereby increasing the effectiveness of audit and feedback interventions. We describe a prototype system that supports the provision of tailored feedback messages by generating a menu of graphical and textual messages with associated descriptions of targeted barriers to behavior change. Supervisors could use the menu to select messages based on their awareness of each feedback recipient's specific barriers to behavior change. We anticipate that such a system, if designed appropriately, could guide supervisors towards giving more effective feedback for health-care providers. Summary: A foundation of evidence and knowledge in related health research domains supports the development of feedback message tailoring systems for clinical audit and feedback. Creating and evaluating computer-supported feedback tailoring tools is a promising approach to improving the effectiveness of clinical audit and feedback
Testing for Network and Spatial Autocorrelation
Testing for dependence has been a well-established component of spatial
statistical analyses for decades. In particular, several popular test
statistics have desirable properties for testing for the presence of spatial
autocorrelation in continuous variables. In this paper we propose two
contributions to the literature on tests for autocorrelation. First, we propose
a new test for autocorrelation in categorical variables. While some methods
currently exist for assessing spatial autocorrelation in categorical variables,
the most popular method is unwieldy, somewhat ad hoc, and fails to provide
grounds for a single omnibus test. Second, we discuss the importance of testing
for autocorrelation in data sampled from the nodes of a network, motivated by
social network applications. We demonstrate that our proposed statistic for
categorical variables can both be used in the spatial and network setting
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