oalib

Publish in OALib Journal

ISSN: 2333-9721

APC: Only $99

Submit

Any time

2019 ( 148 )

2018 ( 327 )

2017 ( 297 )

2016 ( 469 )

Custom range...

Search Results: 1 - 10 of 299721 matches for " Emily J. Fox "
All listed articles are free for downloading (OA Articles)
Page 1 /299721
Display every page Item
Walking Adaptability after a Stroke and Its Assessment in Clinical Settings
Chitralakshmi K. Balasubramanian,David J. Clark,Emily J. Fox
Stroke Research and Treatment , 2014, DOI: 10.1155/2014/591013
Abstract: Control of walking has been described by a tripartite model consisting of stepping, equilibrium, and adaptability. This review focuses on walking adaptability, which is defined as the ability to modify walking to meet task goals and environmental demands. Walking adaptability is crucial to safe ambulation in the home and community environments and is often severely compromised after a stroke. Yet quantification of walking adaptability after stroke has received relatively little attention in the clinical setting. The objectives of this review were to examine the conceptual challenges for clinical measurement of walking adaptability and summarize the current state of clinical assessment for walking adaptability. We created nine domains of walking adaptability from dimensions of community mobility to address the conceptual challenges in measurement and reviewed performance-based clinical assessments of walking to determine if the assessments measure walking adaptability in these domains. Our literature review suggests the lack of a comprehensive well-tested clinical assessment tool for measuring walking adaptability. Accordingly, recommendations for the development of a comprehensive clinical assessment of walking adaptability after stroke have been presented. Such a clinical assessment will be essential for gauging recovery of walking adaptability with rehabilitation and for motivating novel strategies to enhance recovery of walking adaptability after stroke. 1. Introduction Approximately, 600.000 individuals incur a stroke each year and stroke is the leading cause of long term disability in the United States [1, 2]. Walking function in those who have sustained a stroke may range from complete dependence to independent walking ability. During the first week after a stroke, only a third of persons are able to walk unaided [3] but at 3 weeks or at hospital discharge 50–80% of survivors can walk unaided [4, 5] and by 6 months approximately 85% of stroke survivors are able to walk independently without physical assistance from another person [6]. Interestingly, while up to 85% of individuals with a stroke regain independent walking ability [6–8], only about 7% of persons discharged from inpatient rehabilitation could manage steps and inclines and walk the speeds and distances required to walk competently in the community [8–10]. Walking in everyday life necessitates walking adaptability, which is the ability to modify walking to meet behavioral task goals and demands of the environment [11–13]. The ability to adapt walking is one component of a tripartite
Streaming Variational Inference for Bayesian Nonparametric Mixture Models
Alex Tank,Nicholas J. Foti,Emily B. Fox
Statistics , 2014,
Abstract: In theory, Bayesian nonparametric (BNP) models are well suited to streaming data scenarios due to their ability to adapt model complexity with the observed data. Unfortunately, such benefits have not been fully realized in practice; existing inference algorithms are either not applicable to streaming applications or not extensible to BNP models. For the special case of Dirichlet processes, streaming inference has been considered. However, there is growing interest in more flexible BNP models building on the class of normalized random measures (NRMs). We work within this general framework and present a streaming variational inference algorithm for NRM mixture models. Our algorithm is based on assumed density filtering (ADF), leading straightforwardly to expectation propagation (EP) for large-scale batch inference as well. We demonstrate the efficacy of the algorithm on clustering documents in large, streaming text corpora.
Stochastic Variational Inference for Hidden Markov Models
Nicholas J. Foti,Jason Xu,Dillon Laird,Emily B. Fox
Statistics , 2014,
Abstract: Variational inference algorithms have proven successful for Bayesian analysis in large data settings, with recent advances using stochastic variational inference (SVI). However, such methods have largely been studied in independent or exchangeable data settings. We develop an SVI algorithm to learn the parameters of hidden Markov models (HMMs) in a time-dependent data setting. The challenge in applying stochastic optimization in this setting arises from dependencies in the chain, which must be broken to consider minibatches of observations. We propose an algorithm that harnesses the memory decay of the chain to adaptively bound errors arising from edge effects. We demonstrate the effectiveness of our algorithm on synthetic experiments and a large genomics dataset where a batch algorithm is computationally infeasible.
Bayesian Nonparametric Covariance Regression
Emily Fox,David Dunson
Statistics , 2011,
Abstract: Although there is a rich literature on methods for allowing the variance in a univariate regression model to vary with predictors, time and other factors, relatively little has been done in the multivariate case. Our focus is on developing a class of nonparametric covariance regression models, which allow an unknown p x p covariance matrix to change flexibly with predictors. The proposed modeling framework induces a prior on a collection of covariance matrices indexed by predictors through priors for predictor-dependent loadings matrices in a factor model. In particular, the predictor-dependent loadings are characterized as a sparse combination of a collection of unknown dictionary functions (e.g, Gaussian process random functions). The induced covariance is then a regularized quadratic function of these dictionary elements. Our proposed framework leads to a highly-flexible, but computationally tractable formulation with simple conjugate posterior updates that can readily handle missing data. Theoretical properties are discussed and the methods are illustrated through simulations studies and an application to the Google Flu Trends data.
Genetic Control of Conventional and Pheromone-Stimulated Biofilm Formation in Candida albicans
Ching-Hsuan Lin,Shail Kabrawala,Emily P. Fox,Clarissa J. Nobile,Alexander D. Johnson,Richard J. Bennett
PLOS Pathogens , 2013, DOI: 10.1371/journal.ppat.1003305
Abstract: Candida albicans can stochastically switch between two phenotypes, white and opaque. Opaque cells are the sexually competent form of C. albicans and therefore undergo efficient polarized growth and mating in the presence of pheromone. In contrast, white cells cannot mate, but are induced – under a specialized set of conditions – to form biofilms in response to pheromone. In this work, we compare the genetic regulation of such “pheromone-stimulated” biofilms with that of “conventional” C. albicans biofilms. In particular, we examined a network of six transcriptional regulators (Bcr1, Brg1, Efg1, Tec1, Ndt80, and Rob1) that mediate conventional biofilm formation for their potential roles in pheromone-stimulated biofilm formation. We show that four of the six transcription factors (Bcr1, Brg1, Rob1, and Tec1) promote formation of both conventional and pheromone-stimulated biofilms, indicating they play general roles in cell cohesion and biofilm development. In addition, we identify the master transcriptional regulator of pheromone-stimulated biofilms as C. albicans Cph1, ortholog of Saccharomyces cerevisiae Ste12. Cph1 regulates mating in C. albicans opaque cells, and here we show that Cph1 is also essential for pheromone-stimulated biofilm formation in white cells. In contrast, Cph1 is dispensable for the formation of conventional biofilms. The regulation of pheromone- stimulated biofilm formation was further investigated by transcriptional profiling and genetic analyses. These studies identified 196 genes that are induced by pheromone signaling during biofilm formation. One of these genes, HGC1, is shown to be required for both conventional and pheromone-stimulated biofilm formation. Taken together, these observations compare and contrast the regulation of conventional and pheromone-stimulated biofilm formation in C. albicans, and demonstrate that Cph1 is required for the latter, but not the former.
Modulation of Force below 1 Hz: Age-Associated Differences and the Effect of Magnified Visual Feedback
Emily J. Fox, Harsimran S. Baweja, Changki Kim, Deanna M. Kennedy, David E. Vaillancourt, Evangelos A. Christou
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0055970
Abstract: Oscillations in force output change in specific frequency bins and have important implications for understanding aging and pathological motor control. Although previous studies have demonstrated that oscillations from 0–1 Hz can be influenced by aging and visuomotor processing, these studies have averaged power within this bandwidth and not examined power in specific frequencies below 1 Hz. The purpose was to determine whether a differential modulation of force below 1 Hz contributes to changes in force control related to manipulation of visual feedback and aging. Ten young adults (25±4 yrs, 5 men) and ten older adults (71±5 yrs, 4 men) were instructed to accurately match a target force at 2% of their maximal isometric force for 35 s with abduction of the index finger. Visual feedback was manipulated by changing the visual angle (0.05°, 0.5°, 1.5°) or removing it after 15 s. Modulation of force below 1 Hz was quantified by examining the absolute and normalized power in seven frequency bins. Removal of visual feedback increased normalized power from 0–0.33 Hz and decreased normalized power from 0.66–1.0 Hz. In contrast, magnification of visual feedback (visual angles of 0.5° and 1.5°) decreased normalized power from 0–0.16 Hz and increased normalized power from 0.66–1.0 Hz. Older adults demonstrated a greater increase in the variability of force with magnification of visual feedback compared with young adults (P = 0.05). Furthermore, older adults exhibited differential force modulation of frequencies below 1 Hz compared with young adults (P<0.05). Specifically, older adults exhibited greater normalized power from 0–0.16 Hz and lesser normalized power from 0.66–0.83 Hz. The changes in force modulation predicted the changes in the variability of force with magnification of visual feedback (R2 = 0.80). Our findings indicate that force oscillations below 1 Hz are associated with force control and are modified by aging and visual feedback.
Sparse graphs using exchangeable random measures
Francois Caron,Emily B. Fox
Computer Science , 2014,
Abstract: Statistical network modeling has focused on representing the graph as a discrete structure, namely the adjacency matrix, and considering the exchangeability of this array. In such cases, the Aldous-Hoover representation theorem (Aldous, 1981;Hoover, 1979} applies and informs us that the graph is necessarily either dense or empty. In this paper, we instead consider representing the graph as a measure on $\mathbb{R}_+^2$. For the associated definition of exchangeability in this continuous space, we rely on the Kallenberg representation theorem (Kallenberg, 2005). We show that for certain choices of such exchangeable random measures underlying our graph construction, our network process is sparse with power-law degree distribution. In particular, we build on the framework of completely random measures (CRMs) and use the theory associated with such processes to derive important network properties, such as an urn representation for our analysis and network simulation. Our theoretical results are explored empirically and compared to common network models. We then present a Hamiltonian Monte Carlo algorithm for efficient exploration of the posterior distribution and demonstrate that we are able to recover graphs ranging from dense to sparse---and perform associated tests---based on our flexible CRM-based formulation. We explore network properties in a range of real datasets, including Facebook social circles, a political blogosphere, protein networks, citation networks, and world wide web networks, including networks with hundreds of thousands of nodes and millions of edges.
Autoregressive Models for Variance Matrices: Stationary Inverse Wishart Processes
Emily B. Fox,Mike West
Statistics , 2011,
Abstract: We introduce and explore a new class of stationary time series models for variance matrices based on a constructive definition exploiting inverse Wishart distribution theory. The main class of models explored is a novel class of stationary, first-order autoregressive (AR) processes on the cone of positive semi-definite matrices. Aspects of the theory and structure of these new models for multivariate "volatility" processes are described in detail and exemplified. We then develop approaches to model fitting via Bayesian simulation-based computations, creating a custom filtering method that relies on an efficient innovations sampler. An example is then provided in analysis of a multivariate electroencephalogram (EEG) time series in neurological studies. We conclude by discussing potential further developments of higher-order AR models and a number of connections with prior approaches.
The Diversity of Massive Star Outbursts I: Observations of SN 2009ip, UGC 2773 OT2009-1, and Their Progenitors
Ryan J. Foley,Edo Berger,Ori Fox,Emily M. Levesque,Peter J. Challis,Inese I. Ivans,James E. Rhoads,Alicia M. Soderberg
Physics , 2010, DOI: 10.1088/0004-637X/732/1/32
Abstract: Despite both being outbursts of luminous blue variables (LBVs), SN 2009ip and UGC 2773 OT2009-1 have very different progenitors, spectra, circumstellar environments, and possibly physical mechanisms that generated the outbursts. From pre-eruption HST images, we determine that SN 2009ip and UGC 2773 OT2009-1 have initial masses of >60 and >25 M_sun, respectively. Optical spectroscopy shows that at peak SN 2009ip had a 10,000 K photosphere and its spectrum was dominated by narrow H Balmer emission, similar to classical LBV giant outbursts, also known as "supernova impostors." The spectra of UGC 2773 OT2009-1, which also have narrow H alpha emission, are dominated by a forest of absorption lines, similar to an F-type supergiant. Blueshifted absorption lines corresponding to ejecta at a velocity of 2000 - 7000 km/s are present in later spectra of SN 2009ip -- an unprecedented observation for LBV outbursts, indicating that the event was the result of a supersonic explosion, rather than a subsonic outburst. The velocity of the absorption lines increases between two epochs, suggesting that there were two explosions in rapid succession. A rapid fading and rebrightening event concurrent with the onset of the high-velocity absorption lines is consistent with the double-explosion model. A near-infrared excess is present in the spectra and photometry of UGC 2773 OT2009-1 that is consistent with ~2100 K dust emission. We compare the properties of these two events and place them in the context of other known massive star outbursts such as eta Car, NGC 300 OT2008-1, and SN 2008S. This qualitative analysis suggests that massive star outbursts have many physical differences which can manifest as the different observables seen in these two interesting objects.
The Epistemic Dimension of Competence in the Social Sciences
Liliana Maggioni,Emily Fox,Patricia A. Alexander
Journal of Social Science Education , 2010,
Abstract: To investigate competence in the social sciences, we propose to define competence as a particular configuration of the learner’s cognition, strategic repertoire, motivation, and orientation toward knowing. Specifically, we focus on epistemic beliefs and on the changes that a view of knowing as a complex, effortful, generative, evidence-seeking, and reflective enterprise entails. In this context, we discuss how familiarity with the processes used to justify knowledge claims within specific disciplinary communities can provide useful tools to develop the kind of adaptive and consistent thinking that characterize competence in different domains and how this focus may aid the identification of characteristics common across domains. We use our empirical exploration of adolescents’ development of competence in the domain of history to illustrate the implications of this theoretical framework, to highlight the relations between domain-specific epistemic beliefs and kind of understanding that students built as a result of reading multiple texts, and to suggest what pedagogical practices may have influenced students’ orientations toward knowing in these three history classes.
Page 1 /299721
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.