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Neighborhood AND spatial modeling

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https://www.readbyqxmd.com/read/28328502/spatial-statistics-for-segmenting-histological-structures-in-h-e-stained-tissue-images
#1
Luong Nguyen, A Burak Tosun, Jeffrey Fine, Adrian Lee, D Lansing Taylor, Chakra Chennubhotla
Segmenting a broad class of histological structures in transmitted light and/or fluorescence-based images is a prerequisite for determining the pathological basis of cancer, elucidating spatial interactions between histological structures in tumor microenvironments (e.g. tumor infiltrating lymphocytes), facilitating precision medicine studies with deep molecular profiling, and providing an exploratory tool for pathologists. Our paper focuses on segmenting histological structures in hematoxylin and eosin (H&E) stained images of breast tissues, e...
March 16, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28317230/cortical-surface-based-threshold-free-cluster-enhancement-and-cortexwise-mediation
#2
Tristram A Lett, Lea Waller, Heike Tost, Ilya M Veer, Arash Nazeri, Susanne Erk, Eva J Brandl, Katrin Charlet, Anne Beck, Sabine Vollstädt-Klein, Anne Jorde, Falk Kiefer, Andreas Heinz, Andreas Meyer-Lindenberg, M Mallar Chakravarty, Henrik Walter
Threshold-free cluster enhancement (TFCE) is a sensitive means to incorporate spatial neighborhood information in neuroimaging studies without using arbitrary thresholds. The majority of methods have applied TFCE to voxelwise data. The need to understand the relationship among multiple variables and imaging modalities has become critical. We propose a new method of applying TFCE to vertexwise statistical images as well as cortexwise (either voxel- or vertexwise) mediation analysis. Here we present TFCE_mediation, a toolbox that can be used for cortexwise multiple regression analysis with TFCE, and additionally cortexwise mediation using TFCE...
March 20, 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/28294963/voxel-based-neighborhood-for-spatial-shape-pattern-classification-of-lidar-point-clouds-with-supervised-learning
#3
Victoria Plaza-Leiva, Jose Antonio Gomez-Ruiz, Anthony Mandow, Alfonso García-Cerezo
Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself...
March 15, 2017: Sensors
https://www.readbyqxmd.com/read/28236183/validation-of-a-google-street-view-based-neighborhood-disorder-observational-scale
#4
Miriam Marco, Enrique Gracia, Manuel Martín-Fernández, Antonio López-Quílez
Recently, there has been a growing interest in developing new tools to measure neighborhood features using the benefits of emerging technologies. This study aimed to assess the psychometric properties of a neighborhood disorder observational scale using Google Street View (GSV). Two groups of raters conducted virtual audits of neighborhood disorder on all census block groups (N = 92) in a district of the city of Valencia (Spain). Four different analyses were conducted to validate the instrument. First, inter-rater reliability was assessed through intraclass correlation coefficients, indicating moderated levels of agreement among raters...
February 24, 2017: Journal of Urban Health: Bulletin of the New York Academy of Medicine
https://www.readbyqxmd.com/read/28225909/tuberculosis-as-a-marker-of-inequities-in-the-context-of-socio-spatial-transformation
#5
Alexandre San Pedro, Gerusa Gibson, Jefferson Pereira Caldas Dos Santos, Luciano Medeiros de Toledo, Paulo Chagastelles Sabroza, Rosely Magalhães de Oliveira
OBJECTIVE: This study aims to analyze the association between the incidence of tuberculosis and different socioeconomic indicators in a territory of intense transformation of the urban space. METHODS: This is an ecological study, whose analysis units were the neighborhoods of the city of Itaboraí, state of Rio de Janeiro, Brazil. The data have been analyzed by generalized linear models. The response variable was incidence of tuberculosis from 2006 to 2011. The independent variables were the socio-demographic indicators...
February 16, 2017: Revista de Saúde Pública
https://www.readbyqxmd.com/read/28208207/-spatial-distribution-of-type-2-diabetes-mellitus-in-berlin-application-of-a-geographically-weighted-regression-analysis-to-identify-location-specific-risk-groups
#6
Boris Kauhl, Jonas Pieper, Jürgen Schweikart, Andrea Keste, Marita Moskwyn
Understanding which population groups in which locations are at higher risk for type 2 diabetes mellitus (T2DM) allows efficient and cost-effective interventions targeting these risk-populations in great need in specific locations. The goal of this study was to analyze the spatial distribution of T2DM and to identify the location-specific, population-based risk factors using global and local spatial regression models. To display the spatial heterogeneity of T2DM, bivariate kernel density estimation was applied...
February 16, 2017: Das Gesundheitswesen
https://www.readbyqxmd.com/read/28160971/inter-relationships-between-objective-and-subjective-measures-of-the-residential-environment-among-urban-african-american-women
#7
Shawnita Sealy-Jefferson, Lynne Messer, Jaime Slaughter-Acey, Dawn P Misra
PURPOSE: The inter-relationships between objective (census based) and subjective (resident reported) measures of the residential environment is understudied in African American (AA) populations. METHODS: Using data from the Life Influences on Fetal Environments Study (2009-2011; n = 1387) of AA women, we quantified the area-level variation in subjective reports of residential healthy food availability, walkability, safety, and disorder that can be accounted for with an objective neighborhood disadvantage index (NDI)...
March 2017: Annals of Epidemiology
https://www.readbyqxmd.com/read/28155876/failure-and-recovery-in-dynamical-networks
#8
L Böttcher, M Luković, J Nagler, S Havlin, H J Herrmann
Failure, damage spread and recovery crucially underlie many spatially embedded networked systems ranging from transportation structures to the human body. Here we study the interplay between spontaneous damage, induced failure and recovery in both embedded and non-embedded networks. In our model the network's components follow three realistic processes that capture these features: (i) spontaneous failure of a component independent of the neighborhood (internal failure), (ii) failure induced by failed neighboring nodes (external failure) and (iii) spontaneous recovery of a component...
February 3, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28145983/statistical-methods-to-study-variation-in-associations-between-food-store-availability-and-body-mass-in-the-multi-ethnic-study-of-atherosclerosis
#9
Jonggyu Baek, Jana A Hirsch, Kari Moore, Loni Philip Tabb, Tonatiuh Barrientos-Gutierrez, Lynda D Lisabeth, Ana V Diez-Roux, Brisa N Sánchez
Research linking characteristics of the neighborhood environment to health has relied on traditional regression methods where pre-specified distances from participant's locations or areas are used to operationalize neighborhood-level measures. Since the relevant spatial scale of neighborhood environment measures may differ across places or individuals, using pre-specified distances could result in biased association estimates or efficiency losses. We use novel hierarchical distributed lag models and data from the Multi-ethnic Study of Atherosclerosis (MESA) to: 1) examine whether and how the association between the availability of favorable food stores and body mass index (BMI) depends on continuous distance from participant locations (instead of traditional buffers), thus allowing us to indirectly infer the spatial scale at which this association operates; 2) examine if the spatial scale and magnitude of the association differs across six MESA sites and 3) across individuals...
January 31, 2017: Epidemiology
https://www.readbyqxmd.com/read/28137675/relative-risk-for-hiv-in-india-an-estimate-using-conditional-auto-regressive-models-with-bayesian-approach
#10
Chandrasekaran Kandhasamy, Kaushik Ghosh
Indian states are currently classified into HIV-risk categories based on the observed prevalence counts, percentage of infected attendees in antenatal clinics, and percentage of infected high-risk individuals. This method, however, does not account for the spatial dependence among the states nor does it provide any measure of statistical uncertainty. We provide an alternative model-based approach to address these issues. Our method uses Poisson log-normal models having various conditional autoregressive structures with neighborhood-based and distance-based weight matrices and incorporates all available covariate information...
February 2017: Spatial and Spatio-temporal Epidemiology
https://www.readbyqxmd.com/read/28125928/conditional-overdispersed-models-application-to-count-area-data
#11
Edilberto Cepeda-Cuervo, Michel Córdoba, Vicente Núñez-Antón
This paper proposes alternative models for the analysis of count data featuring a given spatial structure, which corresponds to geographical areas. We assume that the overdispersion data structure partially results from the existing and well justified spatial correlation between geographical adjacent regions, so an extension of existing overdispersion models that include spatial neighborhood structures within a Bayesian framework is proposed. These models allow practitioners to quantify the association explained by the considered neighborhood structures and the one modelled by additional factors...
January 1, 2017: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/28114945/international-comparison-of-observation-specific-spatial-buffers-maximizing-the-ability-to-estimate-physical-activity
#12
Lawrence D Frank, Eric H Fox, Jared M Ulmer, James E Chapman, Suzanne E Kershaw, James F Sallis, Terry L Conway, Ester Cerin, Kelli L Cain, Marc A Adams, Graham R Smith, Erica Hinckson, Suzanne Mavoa, Lars B Christiansen, Adriano Akira F Hino, Adalberto A S Lopes, Jasper Schipperijn
BACKGROUND: Advancements in geographic information systems over the past two decades have increased the specificity by which an individual's neighborhood environment may be spatially defined for physical activity and health research. This study investigated how different types of street network buffering methods compared in measuring a set of commonly used built environment measures (BEMs) and tested their performance on associations with physical activity outcomes. METHODS: An internationally-developed set of objective BEMs using three different spatial buffering techniques were used to evaluate the relative differences in resulting explanatory power on self-reported physical activity outcomes...
January 23, 2017: International Journal of Health Geographics
https://www.readbyqxmd.com/read/28114342/spatial-analysis-of-dengue-seroprevalence-and-modeling-of-transmission-risk-factors-in-a-dengue-hyperendemic-city-of-venezuela
#13
Maria F Vincenti-Gonzalez, María-Eugenia Grillet, Zoraida I Velasco-Salas, Erley F Lizarazo, Manuel A Amarista, Gloria M Sierra, Guillermo Comach, Adriana Tami
BACKGROUND: Dengue virus (DENV) transmission is spatially heterogeneous. Hence, to stratify dengue prevalence in space may be an efficacious strategy to target surveillance and control efforts in a cost-effective manner particularly in Venezuela where dengue is hyperendemic and public health resources are scarce. Here, we determine hot spots of dengue seroprevalence and the risk factors associated with these clusters using local spatial statistics and a regression modeling approach. METHODOLOGY/PRINCIPAL FINDINGS: From August 2010 to January 2011, a community-based cross-sectional study of 2012 individuals in 840 households was performed in high incidence neighborhoods of a dengue hyperendemic city in Venezuela...
January 2017: PLoS Neglected Tropical Diseases
https://www.readbyqxmd.com/read/28113657/accurate-lungs-segmentation-on-ct-chest-images-by-adaptive-appearance-guided-shape-modeling
#14
Ahmed Soliman, Fahmi Khalifa, Ahmed Elnakib, Mohamed Abou El-Ghar, Neal Dunlap, Brian Wang, Georgy Gimel'farb, Robert Keynton, Ayman El-Baz
To accurately segment pathological and healthy lungs for reliable computer-aided disease diagnostics, a stack of chest CT scans is modeled as a sample of a spatially inhomogeneous joint 3D Markov-Gibbs random field (MGRF) of voxel-wise lung and chest CT image signals (intensities). The proposed learnable MGRF integrates two visual appearance sub models with an adaptive lung shape submodel. The first-order appearance submodel accounts for both the original CT image and its Gaussian scale space (GSS) filtered version to specify local and global signal properties, respectively...
September 12, 2016: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28099997/on-joint-estimation-of-gaussian-graphical-models-for-spatial-and-temporal-data
#15
Zhixiang Lin, Tao Wang, Can Yang, Hongyu Zhao
In this article, we first propose a Bayesian neighborhood selection method to estimate Gaussian Graphical Models (GGMs). We show the graph selection consistency of this method in the sense that the posterior probability of the true model converges to one. When there are multiple groups of data available, instead of estimating the networks independently for each group, joint estimation of the networks may utilize the shared information among groups and lead to improved estimation for each individual network...
January 18, 2017: Biometrics
https://www.readbyqxmd.com/read/28054599/estimating-risks-of-inapparent-avian-exposure-for-human-infection-avian-influenza-virus-a-h7n9-in-zhejiang-province-china
#16
Erjia Ge, Renjie Zhang, Dengkui Li, Xiaolin Wei, Xiaomeng Wang, Poh-Chin Lai
Inapparent avian exposure was suspected for the sporadic infection of avian influenza A(H7N9) occurring in China. This type of exposure is usually unnoticed and difficult to model and measure. Infected poultry with avian influenza H7N9 virus typically remains asymptomatic, which may facilitate infection through inapparent poultry/bird exposure, especially in a country with widespread practice of backyard poultry. The present study proposed a novel approach that integrated ecological and case-control methods to quantify the risk of inapparent avian exposure on human H7N9 infection...
January 5, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28045950/a-rough-set-bounded-spatially-constrained-asymmetric-gaussian-mixture-model-for-image-segmentation
#17
Zexuan Ji, Yubo Huang, Quansen Sun, Guo Cao, Yuhui Zheng
Accurate image segmentation is an important issue in image processing, where Gaussian mixture models play an important part and have been proven effective. However, most Gaussian mixture model (GMM) based methods suffer from one or more limitations, such as limited noise robustness, over-smoothness for segmentations, and lack of flexibility to fit data. In order to address these issues, in this paper, we propose a rough set bounded asymmetric Gaussian mixture model with spatial constraint for image segmentation...
2017: PloS One
https://www.readbyqxmd.com/read/28041980/a-family-of-locally-constrained-cca-models-for-detecting-activation-patterns-in-fmri
#18
Xiaowei Zhuang, Zhengshi Yang, Tim Curran, Richard Byrd, Rajesh Nandy, Dietmar Cordes
Canonical correlation analysis (CCA) has been used in Functional Magnetic Resonance Imaging (fMRI) for improved detection of activation by incorporating time series from multiple voxels in a local neighborhood. To improve the specificity of local CCA methods, spatial constraints were previously proposed. In this study, constraints are generalized by introducing a family model of spatial constraints for CCA to further increase both sensitivity and specificity in fMRI activation detection. The proposed locally-constrained CCA (cCCA) model is formulated in terms of a multivariate constrained optimization problem and solved efficiently with numerical optimization techniques...
December 29, 2016: NeuroImage
https://www.readbyqxmd.com/read/28030651/retinal-lateral-inhibition-provides-the-biological-basis-of-long-range-spatial-induction
#19
Jihyun Yeonan-Kim, Marcelo Bertalmío
Retinal lateral inhibition is one of the conventional efficient coding mechanisms in the visual system that is produced by interneurons that pool signals over a neighborhood of presynaptic feedforward cells and send inhibitory signals back to them. Thus, the receptive-field (RF) of a retinal ganglion cell has a center-surround receptive-field (RF) profile that is classically represented as a difference-of-Gaussian (DOG) adequate for efficient spatial contrast coding. The DOG RF profile has been attributed to produce the psychophysical phenomena of brightness induction, in which the perceived brightness of an object is affected by that of its vicinity, either shifting away from it (brightness contrast) or becoming more similar to it (brightness assimilation) depending on the size of the surfaces surrounding the object...
2016: PloS One
https://www.readbyqxmd.com/read/27939095/a-multiscale-bayesian-data-integration-approach-for-mapping-air-dose-rates-around-the-fukushima-daiichi-nuclear-power-plant
#20
Haruko M Wainwright, Akiyuki Seki, Jinsong Chen, Kimiaki Saito
This paper presents a multiscale data integration method to estimate the spatial distribution of air dose rates in the regional scale around the Fukushima Daiichi Nuclear Power Plant. We integrate various types of datasets, such as ground-based walk and car surveys, and airborne surveys, all of which have different scales, resolutions, spatial coverage, and accuracy. This method is based on geostatistics to represent spatial heterogeneous structures, and also on Bayesian hierarchical models to integrate multiscale, multi-type datasets in a consistent manner...
February 2017: Journal of Environmental Radioactivity
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