同: homologous restriction factor

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the 8th letter of the Roman alphabet (同)h

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hydrogenの化学記号
鉛筆の硬度 / 《俗》heroin

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出典(authority):フリー百科事典『ウィキペディア（Wikipedia）』「2015/09/08 19:20:04」(JST)

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English Journal

Effect of voluntary repetitive long-lasting muscle contraction activity on the BOLD signal as assessed by optimal hemodynamic response function.

Storti SF1, Formaggio E, Moretto D, Bertoldo A, Pizzini FB, Beltramello A, Fiaschi A, Toffolo GM, Manganotti P.Author information 1Section of Neurology, Department of Neurological and Movement Sciences, University of Verona, P.le L.A. Scuro 10, 37134, Verona, Italy, silviafrancesca.storti@univr.it.AbstractOBJECTIVE: Among other neuroimaging techniques, functional magnetic resonance imaging (fMRI) can be useful for studying the development of motor fatigue. The aim of this study was to identify differences in cortical neuronal activation in nine subjects on three motor tasks: right-hand movement with minimum, maximum, and post-fatigue maximum finger flexion.
Magma (New York, N.Y.).MAGMA.2014 Apr;27(2):171-84. doi: 10.1007/s10334-013-0401-8. Epub 2013 Sep 3.
OBJECTIVE: Among other neuroimaging techniques, functional magnetic resonance imaging (fMRI) can be useful for studying the development of motor fatigue. The aim of this study was to identify differences in cortical neuronal activation in nine subjects on three motor tasks: right-hand movement with
PMID 23999996

Lag-based effective connectivity applied to fMRI: a simulation study highlighting dependence on experimental parameters and formulation.

Rodrigues J1, Andrade A2.Author information 1Institute of Biophysics and Biomedical Engineering, Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisboa, Portugal. Electronic address: jprodrigues@fc.ul.pt.2Institute of Biophysics and Biomedical Engineering, Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisboa, Portugal. Electronic address: aandrade@fc.ul.pt.AbstractA vast repertoire of methods is currently available to study effective brain connectivity based on neuroimaging data, among which lag-based measures can be distinguished. Although several studies have previously assessed the performance of such measures, their validity in different conditions remains unclear. In the current study, several lag-based effective connectivity measures are tested and benchmarked using simulated fMRI data, conceived to reflect a broad range of different situations with practical interest. The main goal is two-fold: 1) to provide a thorough overview of lag-based effective connectivity measures, and 2) to assess their performance in specific experimental conditions, thereby providing guidance for future effective connectivity studies involving fMRI. We focus on well-known lag-based measures, cover existing improvements and alternative formulations in some cases: Granger causality (GC), Geweke's Granger causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), phase slope index (PSI), and transfer entropy (TE). Benchmarking consists in identifying causal relations in local field potential (LFP) networks that have their output convolved with a canonical hemodynamic response function (HRF) with varying node number, topology, coupling strength, neuronal delay, repetition time (TR), signal-to-noise ratio (SNR) and HRF variability. In a first set of simulations, we cover all possible combinations of discretized values of the previous variables, for networks with 2 and 3 nodes, and find that the measure with best performance (time-domain Granger Causality) is able to detect neuronal delays of a few hundreds of milliseconds with TRs between 0.25 and 2s and neuronal delays below 100ms for TRs that are also below 100ms, with more than 80% accuracy in realistic conditions. For networks with more than 3 nodes, we find that the number of nodes and the density of causal links degrade sensitivity, especially if the number of observations does not compensate for the increase in nodes, and that clustered networks can be more easily identified. In conclusion, this study argues in favor of the applicability of lag-based measures in the context of fMRI, provided that a stringent set of experimental specifications is met and that the chosen measure is applied with full knowledge of its limitations and specific constraints.
NeuroImage.Neuroimage.2014 Apr 1;89:358-77. doi: 10.1016/j.neuroimage.2013.10.029. Epub 2013 Oct 25.
A vast repertoire of methods is currently available to study effective brain connectivity based on neuroimaging data, among which lag-based measures can be distinguished. Although several studies have previously assessed the performance of such measures, their validity in different conditions remain
PMID 24513528

A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses.

Zhang L1, Guindani M2, Versace F3, Vannucci M4.Author information 1Department of Statistics, Rice University, Houston, USA. Electronic address: lz17@rice.edu.2Department of Biostatistics, The University of Texas MD, Anderson Cancer Center, Houston, USA. Electronic address: mguindani@mdanderson.org.3Department of Behavioral Science, The University of Texas MD, Anderson Cancer Center, Houston, USA. Electronic address: fversace@mdanderson.org.4Department of Statistics, Rice University, Houston, USA. Electronic address: marina@rice.edu.AbstractIn this paper we present a novel wavelet-based Bayesian nonparametric regression model for the analysis of functional magnetic resonance imaging (fMRI) data. Our goal is to provide a joint analytical framework that allows to detect regions of the brain which exhibit neuronal activity in response to a stimulus and, simultaneously, infer the association, or clustering, of spatially remote voxels that exhibit fMRI time series with similar characteristics. We start by modeling the data with a hemodynamic response function (HRF) with a voxel-dependent shape parameter. We detect regions of the brain activated in response to a given stimulus by using mixture priors with a spike at zero on the coefficients of the regression model. We account for the complex spatial correlation structure of the brain by using a Markov random field (MRF) prior on the parameters guiding the selection of the activated voxels, therefore capturing correlation among nearby voxels. In order to infer association of the voxel time courses, we assume correlated errors, in particular long memory, and exploit the whitening properties of discrete wavelet transforms. Furthermore, we achieve clustering of the voxels by imposing a Dirichlet process (DP) prior on the parameters of the long memory process. For inference, we use Markov Chain Monte Carlo (MCMC) sampling techniques that combine Metropolis-Hastings schemes employed in Bayesian variable selection with sampling algorithms for nonparametric DP models. We explore the performance of the proposed model on simulated data, with both block- and event-related design, and on real fMRI data.
NeuroImage.Neuroimage.2014 Mar 18. pii: S1053-8119(14)00171-2. doi: 10.1016/j.neuroimage.2014.03.024. [Epub ahead of print]
In this paper we present a novel wavelet-based Bayesian nonparametric regression model for the analysis of functional magnetic resonance imaging (fMRI) data. Our goal is to provide a joint analytical framework that allows to detect regions of the brain which exhibit neuronal activity in response to
PMID 24650600

Japanese Journal

ヒスタミン遊離因子(HRF)によるアレルギー炎症増悪メカニズム (特集アレルギー疾患における特異抗体の意義) -- (免疫学的側面)

柏倉淳一,川上裕子,川上敏明
アレルギー・免疫 20(1), 30-37, 2013-01
NAID 40019533229

Histamine releasing factor(HRF)のアレルギー疾患における役割 (特集マスト細胞の活性化をめぐって)

川上敏明
臨床免疫・アレルギー科 57(6), 601-609, 2012-06
NAID 40019365514

A New TCP Congestion Control Supporting RTT-Fairness

OGURA Kazumine,NEMOTO Yohei,SU Zhou,KATTO Jiro
IEICE transactions on information and systems 95(2), 523-531, 2012-02-01
… This paper focuses on RTT-fairness of multiple TCP flows over the Internet, and proposes a new TCP congestion control named "HRF (Hybrid RTT-Fair)-TCP". … From these analyses, we propose HRF-TCP which switches two modes according to observed RTT values and achieves RTT fairness. … Finally, HRF-TCP outperforms conventional methods in RTT-fairness, efficiency and friendliness with TCP-Reno. …
NAID 10030610980

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