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Search Results: 1 - 10 of 6140 matches for " Karen Joyce "
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The Human Functional Brain Network Demonstrates Structural and Dynamical Resilience to Targeted Attack
Karen E. Joyce ,Satoru Hayasaka,Paul J. Laurienti
PLOS Computational Biology , 2013, DOI: 10.1371/journal.pcbi.1002885
Abstract: In recent years, the field of network science has enabled researchers to represent the highly complex interactions in the brain in an approachable yet quantitative manner. One exciting finding since the advent of brain network research was that the brain network can withstand extensive damage, even to highly connected regions. However, these highly connected nodes may not be the most critical regions of the brain network, and it is unclear how the network dynamics are impacted by removal of these key nodes. This work seeks to further investigate the resilience of the human functional brain network. Network attack experiments were conducted on voxel-wise functional brain networks and region-of-interest (ROI) networks of 5 healthy volunteers. Networks were attacked at key nodes using several criteria for assessing node importance, and the impact on network structure and dynamics was evaluated. The findings presented here echo previous findings that the functional human brain network is highly resilient to targeted attacks, both in terms of network structure and dynamics.
Live Coral Cover Index Testing and Application with Hyperspectral Airborne Image Data
Karen E. Joyce,Stuart R. Phinn,Chris M. Roelfsema
Remote Sensing , 2013, DOI: 10.3390/rs5116116
Abstract: Coral reefs are complex, heterogeneous environments where it is common for the features of interest to be smaller than the spatial dimensions of imaging sensors. While the coverage of live coral at any point in time is a critical environmental management issue, image pixels may represent mixed proportions of coverage. In order to address this, we describe the development, application, and testing of a spectral index for mapping live coral cover using CASI-2 airborne hyperspectral high spatial resolution imagery of Heron Reef, Australia. Field surveys were conducted in areas of varying depth to quantify live coral cover. Image statistics were extracted from co-registered imagery in the form of reflectance, derivatives, and band ratios. Each of the spectral transforms was assessed for their correlation with live coral cover, determining that the second derivative around 564 nm was the most sensitive to live coral cover variations(r 2 = 0.63). Extensive field survey was used to transform relative to absolute coral cover, which was then applied to produce a live coral cover map of Heron Reef. We present the live coral cover index as a simple and viable means to estimate the amount of live coral over potentially thousands of km 2 and in clear-water reefs.
Assessing the consistency of community structure in complex networks
Matthew Steen,Satoru Hayasaka,Karen Joyce,Paul Laurienti
Physics , 2011, DOI: 10.1103/PhysRevE.84.016111
Abstract: In recent years, community structure has emerged as a key component of complex network analysis. As more data has been collected, researchers have begun investigating changing community structure across multiple networks. Several methods exist to analyze changing communities, but most of these are limited to evolution of a single network over time. In addition, most of the existing methods are more concerned with change at the community level than at the level of the individual node. In this paper, we introduce scaled inclusivity, which is a method to quantify the change in community structure across networks. Scaled inclusivity evaluates the consistency of the classiffication of every node in a network independently. In addition, the method can be applied cross-sectionally as well as longitudinally. In this paper, we calculate the scaled inclusivity for a set of simulated networks of United States cities and a set of real networks consisting of teams that play in the top division of American college football. We found that scaled inclusivity yields reasonable results for the consistency of individual nodes in both sets of networks. We propose that scaled inclusivity may provide a useful way to quantify the change in a network's community structure.
Neuroplasticity and Positive Psychology in Clinical Practice: A Review for Combined Benefits  [PDF]
Joyce Shaffer
Psychology (PSYCH) , 2012, DOI: 10.4236/psych.2012.312A164
Abstract:

Research on using positive psychological perspectives to drive brain plasticity in a positive direction is increasingly encouraging and empowering for clinicians and clients. Increased lifespan with neuroplastic gains was found by Diamond in lab rats when they were held and spoken to. Improvements in brain chemistry, architecture and performance associated with lifestyle choices are now being documented in humans with increasing frequency of reports. Positive psychology can strengthen this trend toward increases in wellbeing by using this evolving research for motivation to increase healthy lifestyle choices, for reinforcement of successive approximation toward these goals and for the many gains associated with greater happiness.

A New Measure of Centrality for Brain Networks
Karen E. Joyce,Paul J. Laurienti,Jonathan H. Burdette,Satoru Hayasaka
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0012200
Abstract: Recent developments in network theory have allowed for the study of the structure and function of the human brain in terms of a network of interconnected components. Among the many nodes that form a network, some play a crucial role and are said to be central within the network structure. Central nodes may be identified via centrality metrics, with degree, betweenness, and eigenvector centrality being three of the most popular measures. Degree identifies the most connected nodes, whereas betweenness centrality identifies those located on the most traveled paths. Eigenvector centrality considers nodes connected to other high degree nodes as highly central. In the work presented here, we propose a new centrality metric called leverage centrality that considers the extent of connectivity of a node relative to the connectivity of its neighbors. The leverage centrality of a node in a network is determined by the extent to which its immediate neighbors rely on that node for information. Although similar in concept, there are essential differences between eigenvector and leverage centrality that are discussed in this manuscript. Degree, betweenness, eigenvector, and leverage centrality were compared using functional brain networks generated from healthy volunteers. Functional cartography was also used to identify neighborhood hubs (nodes with high degree within a network neighborhood). Provincial hubs provide structure within the local community, and connector hubs mediate connections between multiple communities. Leverage proved to yield information that was not captured by degree, betweenness, or eigenvector centrality and was more accurate at identifying neighborhood hubs. We propose that this metric may be able to identify critical nodes that are highly influential within the network.
Key components of early intervention programs for preterm infants and their parents: a systematic review and meta-analysis
Benzies Karen M,Magill-Evans Joyce E,Hayden K,Ballantyne Marilyn
BMC Pregnancy and Childbirth , 2013, DOI: 10.1186/1471-2393-13-s1-s10
Abstract: Background Preterm infants are at greater risk for neurodevelopmental disabilities than full term infants. Interventions supporting parents to improve the quality of the infant’s environment should improve developmental outcomes for preterm infants. Many interventions that involve parents do not measure parental change, nor is it clear which intervention components are associated with improved parental outcomes. The aim of this review was to categorize the key components of early intervention programs and determine the direct effects of components on parents, as well as their preterm infants. Methods MEDLINE, EMBASE, CINAHL, ERIC, and Cochrane Database of Systematic Reviews were searched between 1990 and December 2011. Eligible randomized controlled trials (RCTs) included an early intervention for preterm infants, involved parents, and had a community component. Of 2465 titles and abstracts identified, 254 full text articles were screened, and 18 met inclusion criteria. Eleven of these studies reported maternal outcomes of stress, anxiety, depressive symptoms, self-efficacy, and sensitivity/responsiveness in interactions with the infant. Meta-analyses using a random effects model were conducted with these 11 studies. Results Interventions employed multiple components categorized as (a) psychosocial support, (b) parent education, and/or (c) therapeutic developmental interventions targeting the infant. All interventions used some form of parenting education. The reporting quality of most trials was adequate, and the risk of bias was low based on the Cochrane Collaboration tool. Meta-analyses demonstrated limited effects of interventions on maternal stress (Z = 0.40, p = 0.69) and sensitivity/responsiveness (Z = 1.84, p = 0.07). There were positive pooled effects of interventions on maternal anxiety (Z = 2.54, p = 0.01), depressive symptoms (Z = 4.04, p <.0001), and self-efficacy (Z = 2.05, p = 0.04). Conclusions Positive and clinically meaningful effects of early interventions were seen in some psychosocial aspects of mothers of preterm infants. This review was limited by the heterogeneity of outcome measures and inadequate reporting of statistics. Implications of key findings Interventions for preterm infants and their mothers should consider including psychosocial support for mothers. If the intervention involves mothers, outcomes for both mothers and preterm infants should be measured to better understand the mechanisms for change.
Identification and Characterization of Poorly Differentiated Invasive Carcinomas in a Mouse Model of Pancreatic Neuroendocrine Tumorigenesis
Karen E. Hunter, Marsha L. Quick, Anguraj Sadanandam, Douglas Hanahan, Johanna A. Joyce
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0064472
Abstract: Pancreatic neuroendocrine tumors (PanNETs) are a relatively rare but clinically challenging tumor type. In particular, high grade, poorly-differentiated PanNETs have the worst patient prognosis, and the underlying mechanisms of disease are poorly understood. In this study we have identified and characterized a previously undescribed class of poorly differentiated PanNETs in the RIP1-Tag2 mouse model. We found that while the majority of tumors in the RIP1-Tag2 model are well-differentiated insulinomas, a subset of tumors had lost multiple markers of beta-cell differentiation and were highly invasive, leading us to term them poorly differentiated invasive carcinomas (PDICs). In addition, we found that these tumors exhibited a high mitotic index, resembling poorly differentiated (PD)-PanNETs in human patients. Interestingly, we identified expression of Id1, an inhibitor of DNA binding gene, and a regulator of differentiation, specifically in PDIC tumor cells by histological analysis. The identification of PDICs in this mouse model provides a unique opportunity to study the pathology and molecular characteristics of PD-PanNETs.
The ubiquity of small-world networks
Qawi K. Telesford,Karen E. Joyce,Satoru Hayasaka,Jonathan H. Burdette,Paul J. Laurienti
Physics , 2011,
Abstract: Small-world networks by Watts and Strogatz are a class of networks that are highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. These characteristics result in networks with unique properties of regional specialization with efficient information transfer. Social networks are intuitive examples of this organization with cliques or clusters of friends being interconnected, but each person is really only 5-6 people away from anyone else. While this qualitative definition has prevailed in network science theory, in application, the standard quantitative application is to compare path length (a surrogate measure of distributed processing) and clustering (a surrogate measure of regional specialization) to an equivalent random network. It is demonstrated here that comparing network clustering to that of a random network can result in aberrant findings and networks once thought to exhibit small-world properties may not. We propose a new small-world metric, {\omega} (omega), which compares network clustering to an equivalent lattice network and path length to a random network, as Watts and Strogatz originally described. Example networks are presented that would be interpreted as small-world when clustering is compared to a random network but are not small-world according to {\omega}. These findings have significant implications in network science as small-world networks have unique topological properties, and it is critical to accurately distinguish them from networks without simultaneous high clustering and low path length.
The Occurrence of Non-Pulsating Stars in the gamma Dor and delta Sct Pulsation Instability Regions: Results from Kepler Quarter 14-17 Data
Joyce A. Guzik,Paul A. Bradley,Jason Jackiewicz,Joanna Molenda-Zakowicz,Katrien Uytterhoeven,Karen Kinemuchi
Physics , 2015,
Abstract: In our 2013 Astronomical Review article, we discussed the statistics of variability for 633 faint spectral type A-F stars observed by the Kepler spacecraft during Quarters 6-13. We found six stars that showed no variability with amplitude 20 ppm or greater in the range 0.2 to 24.4 cycles/day, but whose positions in the log g--Teff diagram place them in the delta Sct or gamma Dor pulsation instability regions established from pre-Kepler ground-based observations. Here we present results for 2137 additional stars observed during Quarters 14-17, and find 34 stars that lie within the instability regions. In Paper I, we included a +229 K offset to the Kepler Input Catalog Teff to take into account an average systematic difference between the KIC values and the Teff derived from SDSS color photometry for main-sequence F stars (Pinsonneault et al. 2012). Here we compare the KIC Teff value and the Teff derived from spectroscopy taken by the LAMOST instrument (Molenda-Zakowicz et al. 2013, 2014) for 54 stars common to both samples. We find no trend to support applying the offset, but instead find that a small average temperature decrease relative to the KIC Teff may be more appropriate for the stars in our spectral-type range. If the offset is omitted, only 17 of our 34 `constant' stars fall within the instability regions. For the two `constant' stars also observed by LAMOST, the LAMOST Teff values are cooler than the KIC Teff by several hundred K, and would move these stars out of the instability regions. It is possible that a more accurate determination of their Teff and log g would move some of the other `constant' stars out of the instability regions. However, if average (random) errors in Teff are taken into account, 15 to 52 stars may still persist within the instability regions. Explanations for these `constant' stars, both theoretical and observational, remain to be investigated.
Results of a Search for gamma Dor and delta Sct Stars with the Kepler Spacecraft
Paul A. Bradley,Joyce A. Guzik,Lillian F. Miles,Katrien Uytterhoeven,Jason Jackiewicz,Karen Kinemuchi
Physics , 2015, DOI: 10.1088/0004-6256/149/2/68
Abstract: The light curves of 2768 stars with effective temperatures and surface gravities placing them near the gamma Doradus/delta Scuti instability region were observed as part of the Kepler Guest Observer program from Cycles 1 through 5. The light curves were analyzed in a uniform manner to search for gamma Doradus, delta Scuti, and hybrid star pulsations. The gamma Doradus, delta Scuti, and hybrid star pulsations extend asteroseismology to stars slightly more massive (1.4 to 2.5 solar masses) than our Sun. We find 207 gamma Doradus, 84 delta Scuti, and 32 hybrid candidate stars. Many of these stars are cooler than the red edge of the gamma Doradus instability strip as determined from ground-based observations made before Kepler. A few of our gamma Doradus candidate stars lie on the hot side of the ground-based gamma Doradus instability strip. The hybrid candidate stars cover the entire region between 6200 K and the blue edge of the ground-based delta Scuti instability strip. None of our candidate stars are hotter than the hot edge of the ground-based delta Scuti instability strip. Our discoveries, coupled with the work of others, shows that Kepler has discovered over 2000 gamma Doradus, delta Scuti, and hybrid star candidates in the 116 square degree Kepler field of view. We found relatively few variable stars fainter than magnitude 15, which may be because they are far enough away to lie between spiral arms in our Galaxy, where there would be fewer stars.
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