602 research outputs found
Clinical efficacy of perampanel for partial-onset and primary generalized tonic-clonic seizures
Background and purpose: Perampanel, a selective noncompetitive antagonist at the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor, is highly effective in a wide range of experimental models. Although initially licensed as adjunctive therapy for partial seizures with or without secondary generalization in patients aged 12 years or older, the US Food and Drug Administration has recently approved its use in the treatment of primary generalized tonic–clonic seizures (PGTCS). This paper reviews the pharmacokinetics, efficacy, and tolerability of perampanel as an antiepileptic drug. / Results: After oral ingestion, perampanel is rapidly absorbed (Tmax, 0.5–2.5 hours), has a bioavailability of ~100%, and is highly protein bound (~95%) in plasma. It undergoes extensive (>90%) hepatic metabolism, primarily via cytochrome P450 3A4 (CYP3A4), with a half-life of 48 hours. Carbamazepine and other antiepileptic drugs can enhance its metabolism via induction of CYP3A4. Efficacy of perampanel in focal seizures has been extensively evaluated in Phase II and randomized, placebo-controlled Phase III trials. The efficacy in PGTCS has been reported in one class I study. In the treatment of focal seizures, perampanel showed significant dose-dependent median seizure reductions: 4 mg/d, 23%; 8 mg/d, 26%–31%; 12 mg/d, 18%–35%; and placebo, 10%–21%. The 50% responder rates were 15%–26%, 29%, 33%–38%, and 34%–36% for placebo, 4 mg/d, 8 mg/d, and 12 mg/d perampanel, respectively. Freedom from seizures was recorded in 0%–1.7% of the placebo group, 1.9% of the 2 mg group, 2.6%–4.4% of the 8 mg group, and 2.6%–6.5% of the 12 mg group. For PGTCS, the median seizure reduction was 76.5% for perampanel and 38.4% for placebo. The 50% responder rate was 64.2% for perampanel and 39.5% for placebo. Seizure freedom during maintenance phase was 30.9% for perampanel and 12.3% for placebo. Adverse effects included dose-dependent increases in the frequency of dizziness, somnolence, fatigue, irritability, falls, and probably nausea. / Conclusion: Perampanel is effective in treating both partial-onset seizures and PGTCS
Valproate-Associated Parkinsonism: A Critical Review of the Literature
Valproate was first approved as an antiepileptic drug in 1962 and has since also become established as a mood stabiliser and as prophylaxis for migraine. In 1979, Lautin published the first description of a valproate-associated extrapyramidal syndrome. Many cases of valproate-associated parkinsonism have subsequently been published, but uncertainties remain concerning its prevalence, risk factors and prognosis. The aim of this paper is to provide a critical review of the existing literature on valproate-associated parkinsonism and to discuss possible mechanisms. Literature databases were searched systematically: we identified a total of 116 patients with valproate-associated parkinsonism published in case reports, case series and systematic analyses. Prevalence rates ranged widely, between 1.4 and 75 % of patients taking valproate. There was great heterogeneity with regard to clinical presentation, age of onset, valproate dose, concomitant conditions and imaging findings. In all patients apart from three, valproate plasma concentrations were within or even below the recommended reference range when the parkinsonism occurred. Parkinsonism was reversible in the majority of patients, although recovery was often prolonged and sometimes incomplete. A dopaminergic deficit was confirmed in three of six patients investigated with dopamine transporter imaging. Seven of 14 patients who were treated with dopaminergic medication had a good response. The quality of the evidence was assessed and probability of causation was examined using the Naranjo score, which ranged from 0 to 7 (median: 5.0). Several pathophysiological mechanisms, including altered gene expression and neurotransmitter signalling, enhanced neurodegeneration or unmasking subclinical dopaminergic degeneration, could theoretically lead to valproate-associated parkinsonism. Further studies are warranted to elucidate this entity and its underlying pathophysiology
Composite Correlation Quantization for Efficient Multimodal Retrieval
Efficient similarity retrieval from large-scale multimodal database is
pervasive in modern search engines and social networks. To support queries
across content modalities, the system should enable cross-modal correlation and
computation-efficient indexing. While hashing methods have shown great
potential in achieving this goal, current attempts generally fail to learn
isomorphic hash codes in a seamless scheme, that is, they embed multiple
modalities in a continuous isomorphic space and separately threshold embeddings
into binary codes, which incurs substantial loss of retrieval accuracy. In this
paper, we approach seamless multimodal hashing by proposing a novel Composite
Correlation Quantization (CCQ) model. Specifically, CCQ jointly finds
correlation-maximal mappings that transform different modalities into
isomorphic latent space, and learns composite quantizers that convert the
isomorphic latent features into compact binary codes. An optimization framework
is devised to preserve both intra-modal similarity and inter-modal correlation
through minimizing both reconstruction and quantization errors, which can be
trained from both paired and partially paired data in linear time. A
comprehensive set of experiments clearly show the superior effectiveness and
efficiency of CCQ against the state of the art hashing methods for both
unimodal and cross-modal retrieval
Creation and characterization of vortex clusters in atomic Bose-Einstein condensates
We show that a moving obstacle, in the form of an elongated paddle, can
create vortices that are dispersed, or induce clusters of like-signed vortices
in 2D Bose-Einstein condensates. We propose new statistical measures of
clustering based on Ripley's K-function which are suitable to the small size
and small number of vortices in atomic condensates, which lack the huge number
of length scales excited in larger classical and quantum turbulent fluid
systems. The evolution and decay of clustering is analyzed using these
measures. Experimentally it should prove possible to create such an obstacle by
a laser beam and a moving optical mask. The theoretical techniques we present
are accessible to experimentalists and extend the current methods available to
induce 2D quantum turbulence in Bose-Einstein condensates.Comment: 9 pages, 9 figure
Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks
Undirected graphical models are widely used in statistics, physics and
machine vision. However Bayesian parameter estimation for undirected models is
extremely challenging, since evaluation of the posterior typically involves the
calculation of an intractable normalising constant. This problem has received
much attention, but very little of this has focussed on the important practical
case where the data consists of noisy or incomplete observations of the
underlying hidden structure. This paper specifically addresses this problem,
comparing two alternative methodologies. In the first of these approaches
particle Markov chain Monte Carlo (Andrieu et al., 2010) is used to efficiently
explore the parameter space, combined with the exchange algorithm (Murray et
al., 2006) for avoiding the calculation of the intractable normalising constant
(a proof showing that this combination targets the correct distribution in
found in a supplementary appendix online). This approach is compared with
approximate Bayesian computation (Pritchard et al., 1999). Applications to
estimating the parameters of Ising models and exponential random graphs from
noisy data are presented. Each algorithm used in the paper targets an
approximation to the true posterior due to the use of MCMC to simulate from the
latent graphical model, in lieu of being able to do this exactly in general.
The supplementary appendix also describes the nature of the resulting
approximation.Comment: 26 pages, 2 figures, accepted in Journal of Computational and
Graphical Statistics (http://www.amstat.org/publications/jcgs.cfm
Lumen Border Detection of Intravascular Ultrasound via Denoising of Directional Wavelet Representations
In this paper, intravascular ultrasound (IVUS) grayscale images, acquired with a single-element mechanically rotating transducer, are processed with wavelet denoising and region-based segmentation to extract various layers of lumen contours and plaques. First, IVUS volumetric data is expanded on complex exponential wavelet-like basis functions, also known as Brushlets, which are well localized in time and frequency domains. Brushlets denoising have demonstrated in the past a great aptitude for denoising ultrasound data and removal of blood speckles. A region-based segmentation framework is then applied for detection of lumen border layers, which remains one of the most challenging problems in IVUS image analysis for images acquired with a single element, mechanically rotating 45 MHz transducer. We evaluated hard thresholding for Brushlet denoising, and compared segmentation results to manually traced lumen borders. We observed good agreement and suggest that the proposed algorithm has a great potential to be used as a reliable pre-processing step for accurate lumen border detection
Comparison of artificial neural network analysis with other multimarker methods for detecting genetic association
<p>Abstract</p> <p>Background</p> <p>Debate remains as to the optimal method for utilising genotype data obtained from multiple markers in case-control association studies. I and colleagues have previously described a method of association analysis using artificial neural networks (ANNs), whose performance compared favourably to single-marker methods. Here, the perfomance of ANN analysis is compared with other multi-marker methods, comprising different haplotype-based analyses and locus-based analyses.</p> <p>Results</p> <p>Of several methods studied and applied to simulated SNP datasets, heterogeneity testing of estimated haplotype frequencies using asymptotic <it>p </it>values rather than permutation testing had the lowest power of the methods studied and ANN analysis had the highest power. The difference in power to detect association between these two methods was statistically significant (<it>p </it>= 0.001) but other comparisons between methods were not significant. The raw <it>t </it>statistic obtained from ANN analysis correlated highly with the empirical statistical significance obtained from permutation testing of the ANN results and with the <it>p </it>value obtained from the heterogeneity test.</p> <p>Conclusion</p> <p>Although ANN analysis was more powerful than the standard haplotype-based test it is unlikely to be taken up widely. The permutation testing necessary to obtain a valid <it>p </it>value makes it slow to perform and it is not underpinned by a theoretical model relating marker genotypes to disease phenotype. Nevertheless, the superior performance of this method does imply that the widely-used haplotype-based methods for detecting association with multiple markers are not optimal and efforts could be made to improve upon them. The fact that the <it>t </it>statistic obtained from ANN analysis is highly correlated with the statistical significance does suggest a possibility to use ANN analysis in situations where large numbers of markers have been genotyped, since the <it>t</it> value could be used as a proxy for the <it>p </it>value in preliminary analyses.</p
An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration
While statisticians are well-accustomed to performing exploratory analysis in
the modeling stage of an analysis, the notion of conducting preliminary
general-purpose exploratory analysis in the Monte Carlo stage (or more
generally, the model-fitting stage) of an analysis is an area which we feel
deserves much further attention. Towards this aim, this paper proposes a
general-purpose algorithm for automatic density exploration. The proposed
exploration algorithm combines and expands upon components from various
adaptive Markov chain Monte Carlo methods, with the Wang-Landau algorithm at
its heart. Additionally, the algorithm is run on interacting parallel chains --
a feature which both decreases computational cost as well as stabilizes the
algorithm, improving its ability to explore the density. Performance is studied
in several applications. Through a Bayesian variable selection example, the
authors demonstrate the convergence gains obtained with interacting chains. The
ability of the algorithm's adaptive proposal to induce mode-jumping is
illustrated through a trimodal density and a Bayesian mixture modeling
application. Lastly, through a 2D Ising model, the authors demonstrate the
ability of the algorithm to overcome the high correlations encountered in
spatial models.Comment: 33 pages, 20 figures (the supplementary materials are included as
appendices
Bayesian Centroid Estimation for Motif Discovery
Biological sequences may contain patterns that are signal important
biomolecular functions; a classical example is regulation of gene expression by
transcription factors that bind to specific patterns in genomic promoter
regions. In motif discovery we are given a set of sequences that share a common
motif and aim to identify not only the motif composition, but also the binding
sites in each sequence of the set. We present a Bayesian model that is an
extended version of the model adopted by the Gibbs motif sampler, and propose a
new centroid estimator that arises from a refined and meaningful loss function
for binding site inference. We discuss the main advantages of centroid
estimation for motif discovery, including computational convenience, and how
its principled derivation offers further insights about the posterior
distribution of binding site configurations. We also illustrate, using
simulated and real datasets, that the centroid estimator can differ from the
maximum a posteriori estimator.Comment: 24 pages, 9 figure
A survey on independence-based Markov networks learning
This work reports the most relevant technical aspects in the problem of
learning the \emph{Markov network structure} from data. Such problem has become
increasingly important in machine learning, and many other application fields
of machine learning. Markov networks, together with Bayesian networks, are
probabilistic graphical models, a widely used formalism for handling
probability distributions in intelligent systems. Learning graphical models
from data have been extensively applied for the case of Bayesian networks, but
for Markov networks learning it is not tractable in practice. However, this
situation is changing with time, given the exponential growth of computers
capacity, the plethora of available digital data, and the researching on new
learning technologies. This work stresses on a technology called
independence-based learning, which allows the learning of the independence
structure of those networks from data in an efficient and sound manner,
whenever the dataset is sufficiently large, and data is a representative
sampling of the target distribution. In the analysis of such technology, this
work surveys the current state-of-the-art algorithms for learning Markov
networks structure, discussing its current limitations, and proposing a series
of open problems where future works may produce some advances in the area in
terms of quality and efficiency. The paper concludes by opening a discussion
about how to develop a general formalism for improving the quality of the
structures learned, when data is scarce.Comment: 35 pages, 1 figur
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