Matching algorithm of missile tail flame based on back-propagation neural network

NASA Astrophysics Data System (ADS)

Huang, Da; Huang, Shucai; Tang, Yidong; Zhao, Wei; Cao, Wenhuan

2018-02-01

This work presents a spectral matching algorithm of missile plume detection that based on neural network. The radiation value of the characteristic spectrum of the missile tail flame is taken as the input of the network. The network's structure including the number of nodes and layers is determined according to the number of characteristic spectral bands and missile types. We can get the network weight matrixes and threshold vectors through training the network using training samples, and we can determine the performance of the network through testing the network using the test samples. A small amount of data cause the network has the advantages of simple structure and practicality. Network structure composed of weight matrix and threshold vector can complete task of spectrum matching without large database support. Network can achieve real-time requirements with a small quantity of data. Experiment results show that the algorithm has the ability to match the precise spectrum and strong robustness.

Geometrical features assessment of liver's tumor with application of artificial neural network evolved by imperialist competitive algorithm.

PubMed

Keshavarz, M; Mojra, A

2015-05-01

Geometrical features of a cancerous tumor embedded in biological soft tissue, including tumor size and depth, are a necessity in the follow-up procedure and making suitable therapeutic decisions. In this paper, a new socio-politically motivated global search strategy which is called imperialist competitive algorithm (ICA) is implemented to train a feed forward neural network (FFNN) to estimate the tumor's geometrical characteristics (FFNNICA). First, a viscoelastic model of liver tissue is constructed by using a series of in vitro uniaxial and relaxation test data. Then, 163 samples of the tissue including a tumor with different depths and diameters are generated by making use of PYTHON programming to link the ABAQUS and MATLAB together. Next, the samples are divided into 123 samples as training dataset and 40 samples as testing dataset. Training inputs of the network are mechanical parameters extracted from palpation of the tissue through a developing noninvasive technology called artificial tactile sensing (ATS). Last, to evaluate the FFNNICA performance, outputs of the network including tumor's depth and diameter are compared with desired values for both training and testing datasets. Deviations of the outputs from desired values are calculated by a regression analysis. Statistical analysis is also performed by measuring Root Mean Square Error (RMSE) and Efficiency (E). RMSE in diameter and depth estimations are 0.50 mm and 1.49, respectively, for the testing dataset. Results affirm that the proposed optimization algorithm for training neural network can be useful to characterize soft tissue tumors accurately by employing an artificial palpation approach. Copyright © 2015 John Wiley & Sons, Ltd.

Flexible sampling large-scale social networks by self-adjustable random walk

NASA Astrophysics Data System (ADS)

Xu, Xiao-Ke; Zhu, Jonathan J. H.

2016-12-01

Online social networks (OSNs) have become an increasingly attractive gold mine for academic and commercial researchers. However, research on OSNs faces a number of difficult challenges. One bottleneck lies in the massive quantity and often unavailability of OSN population data. Sampling perhaps becomes the only feasible solution to the problems. How to draw samples that can represent the underlying OSNs has remained a formidable task because of a number of conceptual and methodological reasons. Especially, most of the empirically-driven studies on network sampling are confined to simulated data or sub-graph data, which are fundamentally different from real and complete-graph OSNs. In the current study, we propose a flexible sampling method, called Self-Adjustable Random Walk (SARW), and test it against with the population data of a real large-scale OSN. We evaluate the strengths of the sampling method in comparison with four prevailing methods, including uniform, breadth-first search (BFS), random walk (RW), and revised RW (i.e., MHRW) sampling. We try to mix both induced-edge and external-edge information of sampled nodes together in the same sampling process. Our results show that the SARW sampling method has been able to generate unbiased samples of OSNs with maximal precision and minimal cost. The study is helpful for the practice of OSN research by providing a highly needed sampling tools, for the methodological development of large-scale network sampling by comparative evaluations of existing sampling methods, and for the theoretical understanding of human networks by highlighting discrepancies and contradictions between existing knowledge/assumptions of large-scale real OSN data.

Sampling for Global Epidemic Models and the Topology of an International Airport Network

PubMed Central

Bobashev, Georgiy; Morris, Robert J.; Goedecke, D. Michael

2008-01-01

Mathematical models that describe the global spread of infectious diseases such as influenza, severe acute respiratory syndrome (SARS), and tuberculosis (TB) often consider a sample of international airports as a network supporting disease spread. However, there is no consensus on how many cities should be selected or on how to select those cities. Using airport flight data that commercial airlines reported to the Official Airline Guide (OAG) in 2000, we have examined the network characteristics of network samples obtained under different selection rules. In addition, we have examined different size samples based on largest flight volume and largest metropolitan populations. We have shown that although the bias in network characteristics increases with the reduction of the sample size, a relatively small number of areas that includes the largest airports, the largest cities, the most-connected cities, and the most central cities is enough to describe the dynamics of the global spread of influenza. The analysis suggests that a relatively small number of cities (around 200 or 300 out of almost 3000) can capture enough network information to adequately describe the global spread of a disease such as influenza. Weak traffic flows between small airports can contribute to noise and mask other means of spread such as the ground transportation. PMID:18776932

Statistical analysis of stream water-quality data and sampling network design near Oklahoma City, central Oklahoma, 1977-1999

USGS Publications Warehouse

Brigham, Mark E.; Payne, Gregory A.; Andrews, William J.; Abbott, Marvin M.

2002-01-01

The sampling network was evaluated with respect to areal coverage, sampling frequency, and analytical schedules. Areal coverage could be expanded to include one additional watershed that is not part of the current network. A new sampling site on the North Canadian River might be useful because of expanding urbanization west of the city, but sampling at some other sites could be discontinued or reduced based on comparisons of data between the sites. Additional real-time or periodic monitoring for dissolved oxygen may be useful to prevent anoxic conditions in pools behind new low-water dams. The sampling schedules, both monthly and quarterly, are adequate to evaluate trends, but additional sampling during flow extremes may be needed to quantify loads and evaluate water-quality during flow extremes. Emerging water-quality issues may require sampling for volatile organic compounds, sulfide, total phosphorus, chlorophyll-a, Esherichia coli, and enterococci, as well as use of more sensitive laboratory analytical methods for determination of cadmium, mercury, lead, and silver.

Development and progress of Ireland's biobank network: Ethical, legal, and social implications (ELSI), standardized documentation, sample and data release, and international perspective.

PubMed

Mee, Blanaid; Gaffney, Eoin; Glynn, Sharon A; Donatello, Simona; Carroll, Paul; Connolly, Elizabeth; Garrigle, Sarah Mc; Boyle, Terry; Flannery, Delia; Sullivan, Francis J; McCormick, Paul; Griffin, Mairead; Muldoon, Cian; Fay, Joanna; O'Grady, Tony; Kay, Elaine; Eustace, Joe; Burke, Louise; Sheikh, Asim A; Finn, Stephen; Flavin, Richard; Giles, Francis J

2013-02-01

Biobank Ireland Trust (BIT) was established in 2004 to promote and develop an Irish biobank network to benefit patients, researchers, industry, and the economy. The network commenced in 2008 with two hospital biobanks and currently consists of biobanks in the four main cancer hospitals in Ireland. The St. James's Hospital (SJH) Biobank coordinates the network. Procedures, based on ISBER and NCI guidelines, are standardized across the network. Policies and documents-Patient Consent Policy, Patient Information Sheet, Biobank Consent Form, Sample and Data Access Policy (SAP), and Sample Application Form have been agreed upon (after robust discussion) for use in each hospital. An optimum sequence for document preparation and submission for review is outlined. Once consensus is reached among the participating biobanks, the SJH biobank liaises with the Research and Ethics Committees, the Office of the Data Protection Commissioner, The National Cancer Registry (NCR), patient advocate groups, researchers, and other stakeholders. The NCR provides de-identified data from its database for researchers via unique biobank codes. ELSI issues discussed include the introduction of prospective consent across the network and the return of significant research results to patients. Only 4 of 363 patients opted to be re-contacted and re-consented on each occasion that their samples are included in a new project. It was decided, after multidisciplinary discussion, that results will not be returned to patients. The SAP is modeled on those of several international networks. Biobank Ireland is affiliated with international biobanking groups-Marble Arch International Working Group, ISBER, and ESBB. The Irish government continues to deliberate on how to fund and implement biobanking nationally. Meanwhile BIT uses every opportunity to promote awareness of the benefits of biobanking in events and in the media.

Social support networks and depression of women suffering from early-stage breast cancer: a case control study.

PubMed

Gagliardi, Cristina; Vespa, Anna; Papa, Roberta; Mariotti, Carlo; Cascinu, Stefano; Rossini, Simonetta

2009-01-01

The aim of this study was to investigate the areas of depression, anxiety, and social support using the structural model of the social network. By comparing the networks of two samples of breast cancer sufferers and healthy control participants, it was possible to identify differences in their relationships, in the shape of the networks themselves, and in the levels of depression and anxiety. Women with breast cancer described smaller and denser networks, including mainly kins whereas the healthy women included more friends, coworkers, and leisure companions. The levels of anxiety and depression were higher in women with breast cancer. Social network and social support measure correlated differently with depression and anxiety in the two groups.

Multiple contexts and adolescent body mass index: Schools, neighborhoods, and social networks.

PubMed

Evans, Clare R; Onnela, Jukka-Pekka; Williams, David R; Subramanian, S V

2016-08-01

Adolescent health and behaviors are influenced by multiple contexts, including schools, neighborhoods, and social networks, yet these contexts are rarely considered simultaneously. In this study we combine social network community detection analysis and cross-classified multilevel modeling in order to compare the contributions of each of these three contexts to the total variation in adolescent body mass index (BMI). Wave 1 of the National Longitudinal Study of Adolescent to Adult Health is used, and for robustness we conduct the analysis in both the core sample (122 schools; N = 14,144) and a sub-set of the sample (16 schools; N = 3335), known as the saturated sample due to its completeness of neighborhood data. After adjusting for relevant covariates, we find that the school-level and neighborhood-level contributions to the variance are modest compared with the network community-level (σ(2)school = 0.069, σ(2)neighborhood = 0.144, σ(2)network = 0.463). These results are robust to two alternative algorithms for specifying network communities, and to analysis in the saturated sample. While this study does not determine whether network effects are attributable to social influence or selection, it does highlight the salience of adolescent social networks and indicates that they may be a promising context to address in the design of health promotion programs. Copyright © 2016 Elsevier Ltd. All rights reserved.

Variation in density and diversity of species of Phytophthora in two forest stream networks

Treesearch

Jaesoon Hwang; Steven N. Jeffers; Steven W. Oak

2010-01-01

Monitoring occurrence and distribution of Phytophthora species, including Phytophthora ramorum, in forest ecosystems can be achieved in several ways including sampling symptomatic plants, infested soils, and infested streams. Collecting plant and soil samples can be laborious and time consuming due to the distance surveyors...

Application of artificial neural networks for conformity analysis of fuel performed with an optical fiber sensor

NASA Astrophysics Data System (ADS)

Possetti, Gustavo Rafael Collere; Coradin, Francelli Klemba; Côcco, Lílian Cristina; Yamamoto, Carlos Itsuo; de Arruda, Lucia Valéria Ramos; Falate, Rosane; Muller, Marcia; Fabris, José Luís

2008-04-01

The liquid fuel quality control is an important issue that brings benefits for the State, for the consumers and for the environment. The conformity analysis, in special for gasoline, demands a rigorous sampling technique among gas stations and other economic agencies, followed by a series of standard physicochemical tests. Such procedures are commonly expensive and time demanding and, moreover, a specialist is often required to carry out the tasks. Such drawbacks make the development of alternative analysis tools an important research field. The fuel refractive index is an additional parameter to help the fuel conformity analysis, besides the prospective optical fiber sensors, which operate like transducers with singular properties. When this parameter is correlated with the sample density, it becomes possible to determine conformity zones that cannot be analytically defined. This work presents an application of artificial neural networks based on Radial Basis Function to determine these zones. A set of 45 gasoline samples, collected in several gas stations and previously analyzed according to the rules of Agência Nacional do Petróleo, Gás Natural e Biocombustíveis, a Brazilian regulatory agency, constituted the database to build two neural networks. The input variables of first network are the samples refractive indices, measured with an Abbe refractometer, and the density of the samples measured with a digital densimeter. For the second network the input variables included, besides the samples densities, the wavelength response of a long-period grating to the samples refractive indices. The used grating was written in an optical fiber using the point-to-point technique by submitting the fiber to consecutive electrical arcs from a splice machine. The output variables of both Radial Basis Function Networks are represented by the conformity status of each sample, according to report of tests carried out following the American Society for Testing and Materials and/or Brazilian Association of Technical Rules standards. A subset of 35 samples, randomly chosen from the database, was used to design and calibrate (train) both networks. The two networks topologies (numbers of Radial Basis Function neurons of the hidden layer and function radius) were built in order to minimize the root mean square error. The subset composed by the other 10 samples was used to validate the final networks architectures. The obtained results have demonstrated that both networks reach a good predictive capability.

State criminal justice telecommunications (STACOM). Volume 4: Network design software user's guide

NASA Technical Reports Server (NTRS)

Lee, J. J.

1977-01-01

A user's guide to the network design program is presented. The program is written in FORTRAN V and implemented on a UNIVAC 1108 computer under the EXEC-8 operating system which enables the user to construct least-cost network topologies for criminal justice digital telecommunications networks. A complete description of program features, inputs, processing logic, and outputs is presented, and a sample run and a program listing are included.

[Application of simulated annealing method and neural network on optimizing soil sampling schemes based on road distribution].

PubMed

Han, Zong-wei; Huang, Wei; Luo, Yun; Zhang, Chun-di; Qi, Da-cheng

2015-03-01

Taking the soil organic matter in eastern Zhongxiang County, Hubei Province, as a research object, thirteen sample sets from different regions were arranged surrounding the road network, the spatial configuration of which was optimized by the simulated annealing approach. The topographic factors of these thirteen sample sets, including slope, plane curvature, profile curvature, topographic wetness index, stream power index and sediment transport index, were extracted by the terrain analysis. Based on the results of optimization, a multiple linear regression model with topographic factors as independent variables was built. At the same time, a multilayer perception model on the basis of neural network approach was implemented. The comparison between these two models was carried out then. The results revealed that the proposed approach was practicable in optimizing soil sampling scheme. The optimal configuration was capable of gaining soil-landscape knowledge exactly, and the accuracy of optimal configuration was better than that of original samples. This study designed a sampling configuration to study the soil attribute distribution by referring to the spatial layout of road network, historical samples, and digital elevation data, which provided an effective means as well as a theoretical basis for determining the sampling configuration and displaying spatial distribution of soil organic matter with low cost and high efficiency.

Quality in Family Child Care Networks: An Evaluation of All Our Kin Provider Quality

ERIC Educational Resources Information Center

Porter, Toni; Reiman, Kayla; Nelson, Christina; Sager, Jessica; Wagner, Janna

2016-01-01

This article presents findings from a quasi-experimental evaluation of quality with a sample of 28 family child care providers in the All Our Kin Family Child Care Network, a staffed family child care network which offers a range of services including relationship-based intensive consultation, and 20 family child care providers who had no…

A Survey of K-12 Teachers' Utilization of Social Networks as a Professional Resource

ERIC Educational Resources Information Center

Hunter, Leah J.; Hall, Cristin M.

2018-01-01

Teachers are increasingly using social networks, including social media and other Internet applications, to look for educational resources. This study shares results from a survey examining patterns of social network application use among K-12 teachers in the United States. A sample of 154 teachers (18 males, 136 females) in the United States…

Use of randomized sampling for analysis of metabolic networks.

PubMed

Schellenberger, Jan; Palsson, Bernhard Ø

2009-02-27

Genome-scale metabolic network reconstructions in microorganisms have been formulated and studied for about 8 years. The constraint-based approach has shown great promise in analyzing the systemic properties of these network reconstructions. Notably, constraint-based models have been used successfully to predict the phenotypic effects of knock-outs and for metabolic engineering. The inherent uncertainty in both parameters and variables of large-scale models is significant and is well suited to study by Monte Carlo sampling of the solution space. These techniques have been applied extensively to the reaction rate (flux) space of networks, with more recent work focusing on dynamic/kinetic properties. Monte Carlo sampling as an analysis tool has many advantages, including the ability to work with missing data, the ability to apply post-processing techniques, and the ability to quantify uncertainty and to optimize experiments to reduce uncertainty. We present an overview of this emerging area of research in systems biology.

On the Structure of Cortical Microcircuits Inferred from Small Sample Sizes.

PubMed

Vegué, Marina; Perin, Rodrigo; Roxin, Alex

2017-08-30

The structure in cortical microcircuits deviates from what would be expected in a purely random network, which has been seen as evidence of clustering. To address this issue, we sought to reproduce the nonrandom features of cortical circuits by considering several distinct classes of network topology, including clustered networks, networks with distance-dependent connectivity, and those with broad degree distributions. To our surprise, we found that all of these qualitatively distinct topologies could account equally well for all reported nonrandom features despite being easily distinguishable from one another at the network level. This apparent paradox was a consequence of estimating network properties given only small sample sizes. In other words, networks that differ markedly in their global structure can look quite similar locally. This makes inferring network structure from small sample sizes, a necessity given the technical difficulty inherent in simultaneous intracellular recordings, problematic. We found that a network statistic called the sample degree correlation (SDC) overcomes this difficulty. The SDC depends only on parameters that can be estimated reliably given small sample sizes and is an accurate fingerprint of every topological family. We applied the SDC criterion to data from rat visual and somatosensory cortex and discovered that the connectivity was not consistent with any of these main topological classes. However, we were able to fit the experimental data with a more general network class, of which all previous topologies were special cases. The resulting network topology could be interpreted as a combination of physical spatial dependence and nonspatial, hierarchical clustering. SIGNIFICANCE STATEMENT The connectivity of cortical microcircuits exhibits features that are inconsistent with a simple random network. Here, we show that several classes of network models can account for this nonrandom structure despite qualitative differences in their global properties. This apparent paradox is a consequence of the small numbers of simultaneously recorded neurons in experiment: when inferred via small sample sizes, many networks may be indistinguishable despite being globally distinct. We develop a connectivity measure that successfully classifies networks even when estimated locally with a few neurons at a time. We show that data from rat cortex is consistent with a network in which the likelihood of a connection between neurons depends on spatial distance and on nonspatial, asymmetric clustering. Copyright © 2017 the authors 0270-6474/17/378498-13$15.00/0.

Audio Spectrogram Representations for Processing with Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Wyse, L.

2017-05-01

One of the decisions that arise when designing a neural network for any application is how the data should be represented in order to be presented to, and possibly generated by, a neural network. For audio, the choice is less obvious than it seems to be for visual images, and a variety of representations have been used for different applications including the raw digitized sample stream, hand-crafted features, machine discovered features, MFCCs and variants that include deltas, and a variety of spectral representations. This paper reviews some of these representations and issues that arise, focusing particularly on spectrograms for generating audio using neural networks for style transfer.

U.S. Geological Survey Catskill/Delaware Water-Quality Network: Water-Quality Report Water Year 2006

USGS Publications Warehouse

McHale, Michael R.; Siemion, Jason

2010-01-01

The U.S. Geological Survey operates a 60-station streamgaging network in the New York City Catskill/Delaware Water Supply System. Water-quality samples were collected at 13 of the stations in the Catskill/Delaware streamgaging network to provide resource managers with water-quality and water-quantity data from the water-supply system that supplies about 85 percent of the water needed by the more than 9 million residents of New York City. This report summarizes water-quality data collected at those 13 stations plus one additional station operated as a part of the U.S. Environmental Protection Agency's Regional Long-Term Monitoring Network for the 2006 water year (October 1, 2005 to September 30, 2006). An average of 62 water-quality samples were collected at each station during the 2006 water year, including grab samples collected every other week and storm samples collected with automated samplers. On average, 8 storms were sampled at each station during the 2006 water year. The 2006 calendar year was the second warmest on record and the summer of 2006 was the wettest on record for the northeastern United States. A large storm on June 26-28, 2006, caused extensive flooding in the western part of the network where record peak flows were measured at several watersheds.

Environmental Response Laboratory Network Membership and Benefits

EPA Pesticide Factsheets

Member laboratories must meet core requirements including quality systems, policies and procedures, sample and data management, and analytical capabilities. Benefits include training and exercise opportunities, information sharing and technical support.

Graph Curvature for Differentiating Cancer Networks

PubMed Central

Sandhu, Romeil; Georgiou, Tryphon; Reznik, Ed; Zhu, Liangjia; Kolesov, Ivan; Senbabaoglu, Yasin; Tannenbaum, Allen

2015-01-01

Cellular interactions can be modeled as complex dynamical systems represented by weighted graphs. The functionality of such networks, including measures of robustness, reliability, performance, and efficiency, are intrinsically tied to the topology and geometry of the underlying graph. Utilizing recently proposed geometric notions of curvature on weighted graphs, we investigate the features of gene co-expression networks derived from large-scale genomic studies of cancer. We find that the curvature of these networks reliably distinguishes between cancer and normal samples, with cancer networks exhibiting higher curvature than their normal counterparts. We establish a quantitative relationship between our findings and prior investigations of network entropy. Furthermore, we demonstrate how our approach yields additional, non-trivial pair-wise (i.e. gene-gene) interactions which may be disrupted in cancer samples. The mathematical formulation of our approach yields an exact solution to calculating pair-wise changes in curvature which was computationally infeasible using prior methods. As such, our findings lay the foundation for an analytical approach to studying complex biological networks. PMID:26169480

Low-frequency sine wave hard-limiting technique

NASA Technical Reports Server (NTRS)

Anderson, T. O.

1977-01-01

Circuit includes serial-in/parallel-out shift register and weighting network that are used to eliminate effects of noise and other nonrepetitive circuit transients. Register and weighting network average decisions from section of signal where decisions are more dependable or where differences between two consecutive samples are larger.

On-board processing satellite network architectures for broadband ISDN

NASA Technical Reports Server (NTRS)

Inukai, Thomas; Faris, Faris; Shyy, Dong-Jye

1992-01-01

Onboard baseband processing architectures for future satellite broadband integrated services digital networks (B-ISDN's) are addressed. To assess the feasibility of implementing satellite B-ISDN services, critical design issues, such as B-ISDN traffic characteristics, transmission link design, and a trade-off between onboard circuit and fast packet switching, are analyzed. Examples of the two types of switching mechanisms and potential onboard network control functions are presented. A sample network architecture is also included to illustrate a potential onboard processing system.

Protein-protein interaction network of gene expression in the hydrocortisone-treated keloid.

PubMed

Chen, Rui; Zhang, Zhiliang; Xue, Zhujia; Wang, Lin; Fu, Mingang; Lu, Yi; Bai, Ling; Zhang, Ping; Fan, Zhihong

2015-01-01

In order to explore the molecular mechanism of hydrocortisone in keloid tissue, the gene expression profiles of keloid samples treated with hydrocortisone were subjected to bioinformatics analysis. Firstly, the gene expression profiles (GSE7890) of five samples of keloid treated with hydrocortisone and five untreated keloid samples were downloaded from the Gene Expression Omnibus (GEO) database. Secondly, data were preprocessed using packages in R language and differentially expressed genes (DEGs) were screened using a significance analysis of microarrays (SAM) protocol. Thirdly, the DEGs were subjected to gene ontology (GO) function and KEGG pathway enrichment analysis. Finally, the interactions of DEGs in samples of keloid treated with hydrocortisone were explored in a human protein-protein interaction (PPI) network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software. Based on the analysis, 572 DEGs in the hydrocortisone-treated samples were screened; most of these were involved in the signal transduction and cell cycle. Furthermore, three critical genes in the module, including COL1A1, NID1, and PRELP, were screened in the PPI network analysis. These findings enhance understanding of the pathogenesis of the keloid and provide references for keloid therapy. © 2015 The International Society of Dermatology.

Identification of driving network of cellular differentiation from single sample time course gene expression data

NASA Astrophysics Data System (ADS)

Chen, Ye; Wolanyk, Nathaniel; Ilker, Tunc; Gao, Shouguo; Wang, Xujing

Methods developed based on bifurcation theory have demonstrated their potential in driving network identification for complex human diseases, including the work by Chen, et al. Recently bifurcation theory has been successfully applied to model cellular differentiation. However, there one often faces a technical challenge in driving network prediction: time course cellular differentiation study often only contains one sample at each time point, while driving network prediction typically require multiple samples at each time point to infer the variation and interaction structures of candidate genes for the driving network. In this study, we investigate several methods to identify both the critical time point and the driving network through examination of how each time point affects the autocorrelation and phase locking. We apply these methods to a high-throughput sequencing (RNA-Seq) dataset of 42 subsets of thymocytes and mature peripheral T cells at multiple time points during their differentiation (GSE48138 from GEO). We compare the predicted driving genes with known transcription regulators of cellular differentiation. We will discuss the advantages and limitations of our proposed methods, as well as potential further improvements of our methods.

Ground-water-quality assessment of the Central Oklahoma aquifer, Oklahoma; hydrologic, water-quality, and quality-assurance data 1987-90

USGS Publications Warehouse

Ferree, D.M.; Christenson, S.C.; Rea, A.H.; Mesander, B.A.

1992-01-01

This report presents data collected from 202 wells between June 1987 and September 1990 as part of the Central Oklahoma aquifer pilot study of the National Water-Quality Assessment Program. The report describes the sampling networks, the sampling procedures, and the results of the ground-water quality and quality-assurance sample analyses. The data tables consist of information about the wells sampled and the results of the chemical analyses of ground water and quality-assurance sampling. Chemical analyses of ground-water samples in four sampling networks are presented: A geochemical network, a low-density survey bedrock network, a low-density survey alluvium and terrace deposits network, and a targeted urban network. The analyses generally included physical properties, major ions, nutrients, trace substances, radionuclides, and organic constituents. The chemical analyses of the ground-water samples are presented in five tables: (1) Physical properties and concentrations of major ions, nutrients, and trace substances; (2) concentrations of radionuclides and radioactivities; (3) carbon isotope ratios and delta values (d-values) of selected isotopes; (4) concentrations of organic constituents; and (5) organic constituents not reported in ground-water samples. The quality of the ground water sampled varied substantially. The sum of constituents (dissolved solids) concentrations ranged from 71 to 5,610 milligrams per liter, with 38 percent of the wells sampled exceeding the Secondary Maximum Contaminant Level of 500 milligrams per liter established under the Safe Drinking Water Act. Values of pH ranged from 5.7 to 9.2 units with 20 percent of the wells outside the Secondary Maximum Contaminant Level of 6.5 to 8.5 units. Nitrite plus nitrate concentrations ranged from less than 0.1 to 85 milligrams per liter with 8 percent of the wells exceeding the proposed Maximum Contaminant Level of 10 milligrams per liter. Concentrations of trace substances were highly variable, ranging from below the reporting level to concentrations over the Maximum Contaminant Levels for several constituents (arsenic, barium, cadmium, chromium, lead, and selenium). Radionuclide activities also were highly variable. Gross alpha radioactivity ranged from 0.1 to 210 picocuries per liter as 230thorium. Of the wells sampled, 20 percent exceeded the proposed Maximum Contaminant Level of 15 picocuries per liter for gross alpha radioactivity. Organic constituents were detected in 39 percent of the 170 wells sampled for organic constituents; in most cases concentrations were at or near the laboratory minimum reporting levels. Ten of the wells sampled for organic constituents had one or more constituents (chlordane, dieldrin, heptachlor epoxide, trichloroethylene, 1,1-dichloroethylene, 1,1,1-trichloroethane) at concentrations equal to or greater than the Maximum Contaminant Level or acceptable concentrations as suggested in the Environmental Protection Agency's Health Advisory Summaries. Quality-assurance sampling included duplicate samples, repeated samples, blanks, spikes, and blind samples. These samples proved to be essential in evaluating the accuracy of the data, particularly in the case of volatile organic constituents.

Time Spent on Social Network Sites and Psychological Well-Being: A Meta-Analysis.

PubMed

Huang, Chiungjung

2017-06-01

This meta-analysis examines the relationship between time spent on social networking sites and psychological well-being factors, namely self-esteem, life satisfaction, loneliness, and depression. Sixty-one studies consisting of 67 independent samples involving 19,652 participants were identified. The mean correlation between time spent on social networking sites and psychological well-being was low at r = -0.07. The correlations between time spent on social networking sites and positive indicators (self-esteem and life satisfaction) were close to 0, whereas those between time spent on social networking sites and negative indicators (depression and loneliness) were weak. The effects of publication outlet, site on which users spent time, scale of time spent, and participant age and gender were not significant. As most included studies used student samples, future research should be conducted to examine this relationship for adults.

High-Dimensional Function Approximation With Neural Networks for Large Volumes of Data.

PubMed

Andras, Peter

2018-02-01

Approximation of high-dimensional functions is a challenge for neural networks due to the curse of dimensionality. Often the data for which the approximated function is defined resides on a low-dimensional manifold and in principle the approximation of the function over this manifold should improve the approximation performance. It has been show that projecting the data manifold into a lower dimensional space, followed by the neural network approximation of the function over this space, provides a more precise approximation of the function than the approximation of the function with neural networks in the original data space. However, if the data volume is very large, the projection into the low-dimensional space has to be based on a limited sample of the data. Here, we investigate the nature of the approximation error of neural networks trained over the projection space. We show that such neural networks should have better approximation performance than neural networks trained on high-dimensional data even if the projection is based on a relatively sparse sample of the data manifold. We also find that it is preferable to use a uniformly distributed sparse sample of the data for the purpose of the generation of the low-dimensional projection. We illustrate these results considering the practical neural network approximation of a set of functions defined on high-dimensional data including real world data as well.

Unprotected sex of homeless youth: results from a multilevel dyadic analysis of individual, social network, and relationship factors.

PubMed

Kennedy, David P; Tucker, Joan S; Green, Harold D; Golinelli, Daniela; Ewing, Brett

2012-10-01

Homeless youth have elevated risk of HIV through sexual behavior. This project investigates the multiple levels of influence on unprotected sex among homeless youth, including social network, individual, and partner level influences. Findings are based on analyses of an exploratory, semi-structured interview (n = 40) and a structured personal network interview (n = 240) with randomly selected homeless youth in Los Angeles. Previous social network studies of risky sex by homeless youth have collected limited social network data from non-random samples and have not distinguished sex partner influences from other network influences. The present analyses have identified significant associations with unprotected sex at multiple levels, including individual, partner, and, to a lesser extent, the social network. Analyses also distinguished between youth who did or did not want to use condoms when they had unprotected sex. Implications for social network based HIV risk interventions with homeless youth are discussed.

Unprotected Sex of Homeless Youth: Results from a Multilevel Analysis of Individual, Social Network, and Relationship Factors

PubMed Central

Kennedy, David P.; Tucker, Joan S.; Green, Harold D.; Golinelli, Daniela; Ewing, Brett

2012-01-01

Homeless youth have elevated risk of HIV through sexual behavior. This project investigates the multiple levels of influence on unprotected sex among homeless youth, including social network, individual, and partner level influences. Findings are based on analyses of an exploratory, semi-structured interview (n=40) and a structured personal network interview (n=240) with randomly selected homeless youth in Los Angeles. Previous social network studies of risky sex by homeless youth have collected limited social network data from non-random samples and have not distinguished sex partner influences from other network influences. The present analyses have identified significant associations with unprotected sex at multiple levels, including individual, partner, and, to a lesser extent, the social network. Analyses also distinguished between youth who wished they used condoms after having unprotected sex and youth who did not regret having unprotected sex. Implications for social network based HIV risk interventions with homeless youth are discussed. PMID:22610421

Performance Analysis of Optical Mobile Fronthaul for Cloud Radio Access Networks

NASA Astrophysics Data System (ADS)

Zhang, Jiawei; Xiao, Yuming; Li, Hui; Ji, Yuefeng

2017-10-01

Cloud radio access networks (C-RAN) separates baseband units (BBU) of conventional base station to a centralized pool which connects remote radio heads (RRH) through mobile fronthaul. Mobile fronthaul is a new network segment of C-RAN, it is designed to transport digital sampling data between BBU and RRH. Optical transport networks that provide large bandwidth and low latency is a promising fronthaul solution. In this paper, we discuss several optical transport networks which are candidates for mobile fronthaul, analyze their performances including the number of used wavelength, round-trip latency and wavelength utilization.

Saltwater intrusion monitoring in Florida

USGS Publications Warehouse

Prinos, Scott T.

2016-01-01

Florida's communities are largely dependent on freshwater from groundwater aquifers. Existing saltwater in the aquifers, or seawater that intrudes parts of the aquifers that were fresh, can make the water unusable without additional processing. The quality of Florida's saltwater intrusion monitoring networks varies. In Miami-Dade and Broward Counties, for example, there is a well-designed network with recently constructed short open-interval monitoring wells that bracket the saltwater interface in the Biscayne aquifer. Geochemical analyses of water samples from the network help scientists evaluate pathways of saltwater intrusion and movement of the saltwater interface. Geophysical measurements, collected in these counties, aid the mapping of the saltwater interface and the design of monitoring networks. In comparison, deficiencies in the Collier County monitoring network include the positioning of monitoring wells, reliance on wells with long open intervals that when sampled might provide questionable results, and the inability of existing analyses to differentiate between multiple pathways of saltwater intrusion. A state-wide saltwater intrusion monitoring network is being planned; the planned network could improve saltwater intrusion monitoring by adopting the applicable strategies of the networks of Miami-Dade and Broward Counties, and by addressing deficiencies such as those described for the Collier County network.

Parents, Friends, and Romantic Partners: Enmeshment in Deviant Networks and Adolescent Delinquency Involvement

ERIC Educational Resources Information Center

Lonardo, Robert A.; Giordano, Peggy C.; Longmore, Monica A.; Manning, Wendy D.

2009-01-01

Adolescent networks include parents, friends, and romantic partners, but research on the social learning mechanisms related to delinquency has not typically examined the characteristics of all three domains simultaneously. Analyses draw on data from the Toledo Adolescent Relationships Study (n = 957), and our analytic sample contains 51% male and…

Structural bridging network position is associated with HIV status in a younger Black men who have sex with men epidemic.

PubMed

Shah, Nirav S; Iveniuk, James; Muth, Stephen Q; Michaels, Stuart; Jose, Jo-Anne; Laumann, Edward O; Schneider, John A

2014-02-01

Younger Black men who have sex with men (BMSM) ages 16-29 have the highest rates of HIV in the United States. Despite increased attention to social and sexual networks as a framework for biomedical intervention, the role of measured network positions, such as bridging and their relationship to HIV risk has received limited attention. A network sample (N = 620) of BMSM respondents (N = 154) and their MSM and transgendered person network members (N = 466) was generated through respondent driven sampling of BMSM and elicitation of their personal networks. Bridging status of each network member was determined by a constraint measure and was used to assess the relationship between this bridging and unprotected anal intercourse (UAI), sex-drug use (SDU), group sex (GS) and HIV status within the network in South Chicago. Low, moderate and high bridging was observed in 411 (66.8 %), 81 (13.2 %) and 123 (20.0 %) of the network. In addition to age and having sex with men only, moderate and high levels of bridging were associated with HIV status (aOR 3.19; 95 % CI 1.58-6.45 and aOR 3.83; 95 % CI 1.23-11.95, respectively). Risk behaviors observed including UAS, GS, and SDU were not associated with HIV status, however, they clustered together in their associations with one another. Bridging network position but not risk behavior was associated with HIV status in this network sample of younger BMSM. Socio-structural features such as position within the network may be important when implementing effective HIV prevention interventions in younger BMSM populations.

Identifying influencers from sampled social networks

NASA Astrophysics Data System (ADS)

Tsugawa, Sho; Kimura, Kazuma

2018-10-01

Identifying influencers who can spread information to many other individuals from a social network is a fundamental research task in the network science research field. Several measures for identifying influencers have been proposed, and the effectiveness of these influence measures has been evaluated for the case where the complete social network structure is known. However, it is difficult in practice to obtain the complete structure of a social network because of missing data, false data, or node/link sampling from the social network. In this paper, we investigate the effects of node sampling from a social network on the effectiveness of influence measures at identifying influencers. Our experimental results show that the negative effect of biased sampling, such as sample edge count, on the identification of influencers is generally small. For social media networks, we can identify influencers whose influence is comparable with that of those identified from the complete social networks by sampling only 10%-30% of the networks. Moreover, our results also suggest the possible benefit of network sampling in the identification of influencers. Our results show that, for some networks, nodes with higher influence can be discovered from sampled social networks than from complete social networks.

Mean field analysis of algorithms for scale-free networks in molecular biology

PubMed Central

2017-01-01

The sampling of scale-free networks in Molecular Biology is usually achieved by growing networks from a seed using recursive algorithms with elementary moves which include the addition and deletion of nodes and bonds. These algorithms include the Barabási-Albert algorithm. Later algorithms, such as the Duplication-Divergence algorithm, the Solé algorithm and the iSite algorithm, were inspired by biological processes underlying the evolution of protein networks, and the networks they produce differ essentially from networks grown by the Barabási-Albert algorithm. In this paper the mean field analysis of these algorithms is reconsidered, and extended to variant and modified implementations of the algorithms. The degree sequences of scale-free networks decay according to a powerlaw distribution, namely P(k) ∼ k−γ, where γ is a scaling exponent. We derive mean field expressions for γ, and test these by numerical simulations. Generally, good agreement is obtained. We also found that some algorithms do not produce scale-free networks (for example some variant Barabási-Albert and Solé networks). PMID:29272285

Mean field analysis of algorithms for scale-free networks in molecular biology.

PubMed

Konini, S; Janse van Rensburg, E J

2017-01-01

The sampling of scale-free networks in Molecular Biology is usually achieved by growing networks from a seed using recursive algorithms with elementary moves which include the addition and deletion of nodes and bonds. These algorithms include the Barabási-Albert algorithm. Later algorithms, such as the Duplication-Divergence algorithm, the Solé algorithm and the iSite algorithm, were inspired by biological processes underlying the evolution of protein networks, and the networks they produce differ essentially from networks grown by the Barabási-Albert algorithm. In this paper the mean field analysis of these algorithms is reconsidered, and extended to variant and modified implementations of the algorithms. The degree sequences of scale-free networks decay according to a powerlaw distribution, namely P(k) ∼ k-γ, where γ is a scaling exponent. We derive mean field expressions for γ, and test these by numerical simulations. Generally, good agreement is obtained. We also found that some algorithms do not produce scale-free networks (for example some variant Barabási-Albert and Solé networks).

A statistical summary of data from the U.S. Geological Survey's national water quality networks

USGS Publications Warehouse

Smith, R.A.; Alexander, R.B.

1983-01-01

The U.S. Geological Survey Operates two nationwide networks to monitor water quality, the National Hydrologic Bench-Mark Network and the National Stream Quality Accounting Network (NASQAN). The Bench-Mark network is composed of 51 stations in small drainage basins which are as close as possible to their natural state, with no human influence and little likelihood of future development. Stations in the NASQAN program are located to monitor flow from accounting units (subregional drainage basins) which collectively encompass the entire land surface of the nation. Data collected at both networks include streamflow, concentrations of major inorganic constituents, nutrients, and trace metals. The goals of the two water quality sampling programs include the determination of mean constituent concentrations and transport rates as well as the analysis of long-term trends in those variables. This report presents a station-by-station statistical summary of data from the two networks for the period 1974 through 1981. (Author 's abstract)

Network Sampling with Memory: A proposal for more efficient sampling from social networks.

PubMed

Mouw, Ted; Verdery, Ashton M

2012-08-01

Techniques for sampling from networks have grown into an important area of research across several fields. For sociologists, the possibility of sampling from a network is appealing for two reasons: (1) A network sample can yield substantively interesting data about network structures and social interactions, and (2) it is useful in situations where study populations are difficult or impossible to survey with traditional sampling approaches because of the lack of a sampling frame. Despite its appeal, methodological concerns about the precision and accuracy of network-based sampling methods remain. In particular, recent research has shown that sampling from a network using a random walk based approach such as Respondent Driven Sampling (RDS) can result in high design effects (DE)-the ratio of the sampling variance to the sampling variance of simple random sampling (SRS). A high design effect means that more cases must be collected to achieve the same level of precision as SRS. In this paper we propose an alternative strategy, Network Sampling with Memory (NSM), which collects network data from respondents in order to reduce design effects and, correspondingly, the number of interviews needed to achieve a given level of statistical power. NSM combines a "List" mode, where all individuals on the revealed network list are sampled with the same cumulative probability, with a "Search" mode, which gives priority to bridge nodes connecting the current sample to unexplored parts of the network. We test the relative efficiency of NSM compared to RDS and SRS on 162 school and university networks from Add Health and Facebook that range in size from 110 to 16,278 nodes. The results show that the average design effect for NSM on these 162 networks is 1.16, which is very close to the efficiency of a simple random sample (DE=1), and 98.5% lower than the average DE we observed for RDS.

Network Sampling with Memory: A proposal for more efficient sampling from social networks

PubMed Central

Mouw, Ted; Verdery, Ashton M.

2013-01-01

Techniques for sampling from networks have grown into an important area of research across several fields. For sociologists, the possibility of sampling from a network is appealing for two reasons: (1) A network sample can yield substantively interesting data about network structures and social interactions, and (2) it is useful in situations where study populations are difficult or impossible to survey with traditional sampling approaches because of the lack of a sampling frame. Despite its appeal, methodological concerns about the precision and accuracy of network-based sampling methods remain. In particular, recent research has shown that sampling from a network using a random walk based approach such as Respondent Driven Sampling (RDS) can result in high design effects (DE)—the ratio of the sampling variance to the sampling variance of simple random sampling (SRS). A high design effect means that more cases must be collected to achieve the same level of precision as SRS. In this paper we propose an alternative strategy, Network Sampling with Memory (NSM), which collects network data from respondents in order to reduce design effects and, correspondingly, the number of interviews needed to achieve a given level of statistical power. NSM combines a “List” mode, where all individuals on the revealed network list are sampled with the same cumulative probability, with a “Search” mode, which gives priority to bridge nodes connecting the current sample to unexplored parts of the network. We test the relative efficiency of NSM compared to RDS and SRS on 162 school and university networks from Add Health and Facebook that range in size from 110 to 16,278 nodes. The results show that the average design effect for NSM on these 162 networks is 1.16, which is very close to the efficiency of a simple random sample (DE=1), and 98.5% lower than the average DE we observed for RDS. PMID:24159246

The microfluidic bioagent autonomous networked detector (M-BAND): an update. Fully integrated, automated, and networked field identification of airborne pathogens

NASA Astrophysics Data System (ADS)

Sanchez, M.; Probst, L.; Blazevic, E.; Nakao, B.; Northrup, M. A.

2011-11-01

We describe a fully automated and autonomous air-borne biothreat detection system for biosurveillance applications. The system, including the nucleic-acid-based detection assay, was designed, built and shipped by Microfluidic Systems Inc (MFSI), a new subsidiary of PositiveID Corporation (PSID). Our findings demonstrate that the system and assay unequivocally identify pathogenic strains of Bacillus anthracis, Yersinia pestis, Francisella tularensis, Burkholderia mallei, and Burkholderia pseudomallei. In order to assess the assay's ability to detect unknown samples, our team also challenged it against a series of blind samples provided by the Department of Homeland Security (DHS). These samples included natural occurring isolated strains, near-neighbor isolates, and environmental samples. Our results indicate that the multiplex assay was specific and produced no false positives when challenged with in house gDNA collections and DHS provided panels. Here we present another analytical tool for the rapid identification of nine Centers for Disease Control and Prevention category A and B biothreat organisms.

Sampling from complex networks using distributed learning automata

NASA Astrophysics Data System (ADS)

Rezvanian, Alireza; Rahmati, Mohammad; Meybodi, Mohammad Reza

2014-02-01

A complex network provides a framework for modeling many real-world phenomena in the form of a network. In general, a complex network is considered as a graph of real world phenomena such as biological networks, ecological networks, technological networks, information networks and particularly social networks. Recently, major studies are reported for the characterization of social networks due to a growing trend in analysis of online social networks as dynamic complex large-scale graphs. Due to the large scale and limited access of real networks, the network model is characterized using an appropriate part of a network by sampling approaches. In this paper, a new sampling algorithm based on distributed learning automata has been proposed for sampling from complex networks. In the proposed algorithm, a set of distributed learning automata cooperate with each other in order to take appropriate samples from the given network. To investigate the performance of the proposed algorithm, several simulation experiments are conducted on well-known complex networks. Experimental results are compared with several sampling methods in terms of different measures. The experimental results demonstrate the superiority of the proposed algorithm over the others.

Identification of Hot Moments and Hot Spots for Real-Time Adaptive Control of Multi-scale Environmental Sensor Networks

NASA Astrophysics Data System (ADS)

Wietsma, T.; Minsker, B. S.

2012-12-01

Increased sensor throughput combined with decreasing hardware costs has led to a disruptive growth in data volume. This disruption, popularly termed "the data deluge," has placed new demands for cyberinfrastructure and information technology skills among researchers in many academic fields, including the environmental sciences. Adaptive sampling has been well established as an effective means of improving network resource efficiency (energy, bandwidth) without sacrificing sample set quality relative to traditional uniform sampling. However, using adaptive sampling for the explicit purpose of improving resolution over events -- situations displaying intermittent dynamics and unique hydrogeological signatures -- is relatively new. In this paper, we define hot spots and hot moments in terms of sensor signal activity as measured through discrete Fourier analysis. Following this frequency-based approach, we apply the Nyquist-Shannon sampling theorem, a fundamental contribution from signal processing that led to the field of information theory, for analysis of uni- and multivariate environmental signal data. In the scope of multi-scale environmental sensor networks, we present several sampling control algorithms, derived from the Nyquist-Shannon theorem, that operate at local (field sensor), regional (base station for aggregation of field sensor data), and global (Cloud-based, computationally intensive models) scales. Evaluated over soil moisture data, results indicate significantly greater sample density during precipitation events while reducing overall sample volume. Using these algorithms as indicators rather than control mechanisms, we also discuss opportunities for spatio-temporal modeling as a tool for planning/modifying sensor network deployments. Locally adaptive model based on Nyquist-Shannon sampling theorem Pareto frontiers for local, regional, and global models relative to uniform sampling. Objectives are (1) overall sampling efficiency and (2) sampling efficiency during hot moments as identified using heuristic approach.

Triangle area water supply monitoring project, October 1988 through September 2001, North Carolina -- description of the water-quality network, sampling and analysis methods, and quality-assurance practices

USGS Publications Warehouse

Oblinger, Carolyn J.

2004-01-01

The Triangle Area Water Supply Monitoring Project was initiated in October 1988 to provide long-term water-quality data for six area water-supply reservoirs and their tributaries. In addition, the project provides data that can be used to determine the effectiveness of large-scale changes in water-resource management practices, document differences in water quality among water-supply types (large multiuse reservoir, small reservoir, run-of-river), and tributary-loading and in-lake data for water-quality modeling of Falls and Jordan Lakes. By September 2001, the project had progressed in four phases and included as many as 34 sites (in 1991). Most sites were sampled and analyzed by the U.S. Geological Survey. Some sites were already a part of the North Carolina Division of Water Quality statewide ambient water-quality monitoring network and were sampled by the Division of Water Quality. The network has provided data on streamflow, physical properties, and concentrations of nutrients, major ions, metals, trace elements, chlorophyll, total organic carbon, suspended sediment, and selected synthetic organic compounds. Project quality-assurance activities include written procedures for sample collection, record management and archive, collection of field quality-control samples (blank samples and replicate samples), and monitoring the quality of field supplies. In addition to project quality-assurance activities, the quality of laboratory analyses was assessed through laboratory quality-assurance practices and an independent laboratory quality-control assessment provided by the U.S. Geological Survey Branch of Quality Systems through the Blind Inorganic Sample Project and the Organic Blind Sample Project.

Concentrations of hormones, pharmaceuticals and other micropollutants in groundwater affected by septic systems in New England and New York.

PubMed

Phillips, P J; Schubert, C; Argue, D; Fisher, I; Furlong, E T; Foreman, W; Gray, J; Chalmers, A

2015-04-15

Septic-system discharges can be an important source of micropollutants (including pharmaceuticals and endocrine active compounds) to adjacent groundwater and surface water systems. Groundwater samples were collected from well networks tapping glacial till in New England (NE) and sandy surficial aquifer New York (NY) during one sampling round in 2011. The NE network assesses the effect of a single large septic system that receives discharge from an extended health care facility for the elderly. The NY network assesses the effect of many small septic systems used seasonally on a densely populated portion of Fire Island. The data collected from these two networks indicate that hydrogeologic and demographic factors affect micropollutant concentrations in these systems. The highest micropollutant concentrations from the NE network were present in samples collected from below the leach beds and in a well downgradient of the leach beds. Total concentrations for personal care/domestic use compounds, pharmaceutical compounds and plasticizer compounds generally ranged from 1 to over 20 μg/L in the NE network samples. High tris(2-butoxyethyl phosphate) plasticizer concentrations in wells beneath and downgradient of the leach beds (>20 μg/L) may reflect the presence of this compound in cleaning agents at the extended health-care facility. The highest micropollutant concentrations for the NY network were present in the shoreline wells and reflect groundwater that is most affected by septic system discharges. One of the shoreline wells had personal care/domestic use, pharmaceutical, and plasticizer concentrations ranging from 0.4 to 5.7 μg/L. Estradiol equivalency quotient concentrations were also highest in a shoreline well sample (3.1 ng/L). Most micropollutant concentrations increase with increasing specific conductance and total nitrogen concentrations for shoreline well samples. These findings suggest that septic systems serving institutional settings and densely populated areas in coastal settings may be locally important sources of micropollutants to adjacent aquifer and marine systems. Published by Elsevier B.V.

RENEB intercomparisons applying the conventional Dicentric Chromosome Assay (DCA).

PubMed

Oestreicher, Ursula; Samaga, Daniel; Ainsbury, Elizabeth; Antunes, Ana Catarina; Baeyens, Ans; Barrios, Leonardo; Beinke, Christina; Beukes, Philip; Blakely, William F; Cucu, Alexandra; De Amicis, Andrea; Depuydt, Julie; De Sanctis, Stefania; Di Giorgio, Marina; Dobos, Katalin; Dominguez, Inmaculada; Duy, Pham Ngoc; Espinoza, Marco E; Flegal, Farrah N; Figel, Markus; Garcia, Omar; Monteiro Gil, Octávia; Gregoire, Eric; Guerrero-Carbajal, C; Güçlü, İnci; Hadjidekova, Valeria; Hande, Prakash; Kulka, Ulrike; Lemon, Jennifer; Lindholm, Carita; Lista, Florigio; Lumniczky, Katalin; Martinez-Lopez, Wilner; Maznyk, Nataliya; Meschini, Roberta; M'kacher, Radia; Montoro, Alegria; Moquet, Jayne; Moreno, Mercedes; Noditi, Mihaela; Pajic, Jelena; Radl, Analía; Ricoul, Michelle; Romm, Horst; Roy, Laurence; Sabatier, Laure; Sebastià, Natividad; Slabbert, Jacobus; Sommer, Sylwester; Stuck Oliveira, Monica; Subramanian, Uma; Suto, Yumiko; Que, Tran; Testa, Antonella; Terzoudi, Georgia; Vral, Anne; Wilkins, Ruth; Yanti, LusiYanti; Zafiropoulos, Demetre; Wojcik, Andrzej

2017-01-01

Two quality controlled inter-laboratory exercises were organized within the EU project 'Realizing the European Network of Biodosimetry (RENEB)' to further optimize the dicentric chromosome assay (DCA) and to identify needs for training and harmonization activities within the RENEB network. The general study design included blood shipment, sample processing, analysis of chromosome aberrations and radiation dose assessment. After manual scoring of dicentric chromosomes in different cell numbers dose estimations and corresponding 95% confidence intervals were submitted by the participants. The shipment of blood samples to the partners in the European Community (EU) were performed successfully. Outside the EU unacceptable delays occurred. The results of the dose estimation demonstrate a very successful classification of the blood samples in medically relevant groups. In comparison to the 1st exercise the 2nd intercomparison showed an improvement in the accuracy of dose estimations especially for the high dose point. In case of a large-scale radiological incident, the pooling of ressources by networks can enhance the rapid classification of individuals in medically relevant treatment groups based on the DCA. The performance of the RENEB network as a whole has clearly benefited from harmonization processes and specific training activities for the network partners.

Function approximation and documentation of sampling data using artificial neural networks.

PubMed

Zhang, Wenjun; Barrion, Albert

2006-11-01

Biodiversity studies in ecology often begin with the fitting and documentation of sampling data. This study is conducted to make function approximation on sampling data and to document the sampling information using artificial neural network algorithms, based on the invertebrate data sampled in the irrigated rice field. Three types of sampling data, i.e., the curve species richness vs. the sample size, the curve rarefaction, and the curve mean abundance of newly sampled species vs.the sample size, are fitted and documented using BP (Backpropagation) network and RBF (Radial Basis Function) network. As the comparisons, The Arrhenius model, and rarefaction model, and power function are tested for their ability to fit these data. The results show that the BP network and RBF network fit the data better than these models with smaller errors. BP network and RBF network can fit non-linear functions (sampling data) with specified accuracy and don't require mathematical assumptions. In addition to the interpolation, BP network is used to extrapolate the functions and the asymptote of the sampling data can be drawn. BP network cost a longer time to train the network and the results are always less stable compared to the RBF network. RBF network require more neurons to fit functions and generally it may not be used to extrapolate the functions. The mathematical function for sampling data can be exactly fitted using artificial neural network algorithms by adjusting the desired accuracy and maximum iterations. The total numbers of functional species of invertebrates in the tropical irrigated rice field are extrapolated as 140 to 149 using trained BP network, which are similar to the observed richness.

Sampling history and 2009--2010 results for pesticides and inorganic constituents monitored by the Lake Wales Ridge Groundwater Network, central Florida

USGS Publications Warehouse

Choquette, Anne F.; Freiwald, R. Scott; Kraft, Carol L.

2012-01-01

The Lake Wales Ridge Monitoring (LWRM) Network was established to provide a long-term record of water quality of the surficial aquifer in one of the principal citrus-production areas of Florida. This region is underlain by sandy soils that contain minimal organic matter and are highly vulnerable to leaching of chemicals into the subsurface. This report documents the 1989 through May 2010 sampling history of the LWRM Network and summarizes monitoring results for 38 Network wells that were sampled during the period January 2009 through May 2010. During 1989 through May 2010, the Network’s citrus land-use wells were sampled intermittently to 1999, quarterly from April 1999 to October 2009, and thereafter quarterly to semiannually. The water-quality summaries in this report focus on the period January 2009 through May 2010, during which the Network’s citrus land-use wells were sampled six times and the non-citrus land-use wells were sampled two times. Within the citrus land-use wells sampled, a total of 13 pesticide compounds (8 parent pesticides and 5 degradates) were detected of the 37 pesticide compounds analyzed during this period. The most frequently detected compounds included demethyl norflurazon (83 percent of wells), norflurazon (79 percent), aldicarb sulfoxide (41 percent), aldicarb sulfone (38 percent), imidacloprid (38 percent), and diuron (28 percent). Agrichemical concentrations in samples from the citrus land-use wells during the 2009 through May 2010 period exceeded Federal drinking-water standards (maximum contaminant levels, MCLs) in 1.5 to 24 percent of samples for aldicarb and its degradates (sulfone and sulfoxide), and in 68 percent of the samples for nitrate. Florida statutes restrict the distance of aldicarb applications to drinking-water wells; however, these statutes do not apply to monitoring wells. Health-screening benchmark levels that identify unregulated chemicals of potential concern were exceeded for norflurazon and diuron in 29 and 7 percent, respectively, of the 2009–2010 samples. A comparison of agrichemical land-use effects on groundwater quality, determined on the basis of samples from LWRM Network wells in citrus and in non-citrus land-use areas, indicated significantly higher (p<0.05) concentrations of inorganic constituents in samples from citrus land-use areas compared to samples from non-citrus areas. These inorganic constituents include calcium, magnesium, chloride, sulfate, potassium, nitrate, aluminum, manganese, strontium, and total nitrogen, and also specific conductance, an indicator of total dissolved solutes in water. In addition to land use, including irrigation, site differences such as soils and groundwater reduction/oxidation conditions might have contributed to the differences in some of these constituents. Pesticide detections were primarily restricted to the citrus land-use wells, where 22 of 23 wells yielded pesticide detections, with a median of four detected pesticide compounds per well. For the non-citrus land-use wells, typically surrounded by mixed land use including developed and undeveloped land, one of the eight sampled wells yielded pesticide detections consisting of norflurazon and its degradate, and the source(s) of these detections might have been active or recently active citrus orchards in the vicinity of this well. Results from the LWRM Network during the 1989 through May 2010 period have provided early warning of chemicals prone to leaching, guidance for developing or modifying chemical usage practices to minimize impacts to groundwater, and a mechanism for prioritizing State sampling of domestic wells to assure safe drinking-water supplies. Given the typically long time period (years to tens of years or longer) required to remove chemical contamination once it enters the groundwater system, groundwater monitoring is important to protect drinking-water sources as well as the numerous lakes in this region, which are closely connected with the surficial aquifer. Long-term monitoring of the LWRM Network is planned to continue providing early warning of potential for groundwater contamination, and to assess spatial and temporal trends in water quality resulting from changes in pesticide-use patterns and in land use.

The social network index and its relation to later-life depression among the elderly aged ≥80 years in Northern Thailand.

PubMed

Aung, Myo Nyein; Moolphate, Saiyud; Aung, Thin Nyein Nyein; Katonyoo, Chitima; Khamchai, Songyos; Wannakrairot, Pongsak

2016-01-01

Having a diverse social network is considered to be beneficial to a person's well-being. The significance, however, of social network diversity in the geriatric assessment of people aged ≥80 years has not been adequately investigated within the Southeast Asian context. This study explored the social networks belonging to the elderly aged ≥80 years and assessed the relation of social network and geriatric depression. This study was a community-based cross-sectional survey conducted in Chiang Mai Province, Northern Thailand. A representative sample of 435 community residents, aged ≥80 years, were included in a multistage sample. The participants' social network diversity was assessed by applying Cohen's social network index (SNI). The geriatric depression scale and activities of daily living measures were carried out during home visits. Descriptive analyses revealed the distribution of SNI, while the relationship between the SNI and the geriatric depression scale was examined by ordinal logistic regression models controlling possible covariants such as age, sex, and educational attainment. The median age of the sample was 83 years, with females comprising of 54.94% of the sample. The participants' children, their neighbors, and members of Buddhist temples were reported as the most frequent contacts of the study participants. Among the 435 participants, 25% were at risk of social isolation due to having a "limited" social network group (SNI 0-3), whereas 37% had a "medium" social network (SNI 4-5), and 38% had a "diverse" social network (SNI ≥6). The SNI was not different among the two sexes. Activities of daily living scores in the diverse social network group were significantly higher than those in the limited social network group. Multivariate ordinal logistic regression analysis models revealed a significant negative association between social network diversity and geriatric depression. Regular and frequent contact with various social contacts may safeguard common geriatric depression among persons aged ≥80 years. As a result, screening those at risk of social isolation is recommended to be integrated into routine primary health care-based geriatric assessment and intervention programs.

Mixed-method Exploration of Social Network Links to Participation

PubMed Central

Kreider, Consuelo M.; Bendixen, Roxanna M.; Mann, William C.; Young, Mary Ellen; McCarty, Christopher

2015-01-01

The people who regularly interact with an adolescent form that youth's social network, which may impact participation. We investigated the relationship of social networks to participation using personal network analysis and individual interviews. The sample included 36 youth, age 11 – 16 years. Nineteen had diagnoses of learning disability, attention disorder, or high-functioning autism and 17 were typically developing. Network analysis yielded 10 network variables, of which 8 measured network composition and 2 measured network structure, with significant links to at least one measure of participation using the Children's Assessment of Participation and Enjoyment (CAPE). Interviews from youth in the clinical group yielded description of strategies used to negotiate social interactions, as well as processes and reasoning used to remain engaged within social networks. Findings contribute to understanding the ways social networks are linked to youth participation and suggest the potential of social network factors for predicting rehabilitation outcomes. PMID:26594737

Relation of Shallow Water Quality in the Central Oklahoma Aquifer to Geology, Soils, and Land Use

USGS Publications Warehouse

Rea, Alan H.; Christenson, Scott C.; Andrews, William J.

2001-01-01

The purpose of this report is to identify, describe, and explain relations between natural and land-use factors and ground-water quality in the Central Oklahoma aquifer NAWQA study unit. Natural factors compared to water quality included the geologic unit in which the sampled wells were completed and the properties of soils in the areas surrounding the wells. Land-use factors included types of land use and population densities surrounding sampled wells. Ground-water quality was characterized by concentrations of inorganic constituents, and by frequencies of detection of volatile organic compounds and pesticides. Water-quality data were from samples collected from wells 91 meters (300 feet) or less in depth as part of Permian and Quaternary geologic unit survey networks and from an urban survey network. Concentrations of many inorganic constituents were significantly related to geology. In addition, concentrations of many inorganic constituents were greater in water from wells from the Oklahoma City urban sampling network than in water from wells from low-density survey networks designed to evaluate ambient water quality in the Central Oklahoma aquifer study unit. However, sampling bias may have been induced by differences in hydrogeologic factors between sampling networks, limiting the ability to determine land-use effects on concentrations of inorganic constituents. Frequencies of detection of pesticide and volatile organic compounds (VOC's) in ground-water samples were related to land use and population density, with these compounds being more frequently detected in densely-populated areas. Geology and soil properties were not significantly correlated to pesticide or VOC occurrence in ground water. Lesser frequencies of detection of pesticides in water from wells in rural areas may be due to low to moderate use of those compounds on agricultural lands in the study unit, with livestock production being the primary agricultural activity. There are many possible sources of pesticides and VOC's in the urban areas of Central Oklahoma. Because only existing water-supply wells were sampled, it is not clear from the data collected whether pesticides and VOC's: (1) occur in low concentrations throughout upper portions of the aquifer in urban areas, or (2) are present in ground water only in the immediate vicinity of the wells due to back-flow of those chemicals into the wells or to inflow around cement seals and through gravel packs surrounding well casings of surface runoff containing those compounds.

Toward cost-efficient sampling methods

NASA Astrophysics Data System (ADS)

Luo, Peng; Li, Yongli; Wu, Chong; Zhang, Guijie

2015-09-01

The sampling method has been paid much attention in the field of complex network in general and statistical physics in particular. This paper proposes two new sampling methods based on the idea that a small part of vertices with high node degree could possess the most structure information of a complex network. The two proposed sampling methods are efficient in sampling high degree nodes so that they would be useful even if the sampling rate is low, which means cost-efficient. The first new sampling method is developed on the basis of the widely used stratified random sampling (SRS) method and the second one improves the famous snowball sampling (SBS) method. In order to demonstrate the validity and accuracy of two new sampling methods, we compare them with the existing sampling methods in three commonly used simulation networks that are scale-free network, random network, small-world network, and also in two real networks. The experimental results illustrate that the two proposed sampling methods perform much better than the existing sampling methods in terms of achieving the true network structure characteristics reflected by clustering coefficient, Bonacich centrality and average path length, especially when the sampling rate is low.

On-board processing architectures for satellite B-ISDN services

NASA Technical Reports Server (NTRS)

Inukai, Thomas; Shyy, Dong-Jye; Faris, Faris

1991-01-01

Onboard baseband processing architectures for future satellite broadband integrated services digital networks (B-ISDN's) are addressed. To assess the feasibility of implementing satellite B-ISDN services, critical design issues, such as B-ISDN traffic characteristics, transmission link design, and a trade-off between onboard circuit and fast packet switching, are analyzed. Examples of the two types of switching mechanisms and potential onboard network control functions are presented. A sample network architecture is also included to illustrate a potential onboard processing system.

Influences of sampling effort on detected patterns and structuring processes of a Neotropical plant-hummingbird network.

PubMed

Vizentin-Bugoni, Jeferson; Maruyama, Pietro K; Debastiani, Vanderlei J; Duarte, L da S; Dalsgaard, Bo; Sazima, Marlies

2016-01-01

Virtually all empirical ecological interaction networks to some extent suffer from undersampling. However, how limitations imposed by sampling incompleteness affect our understanding of ecological networks is still poorly explored, which may hinder further advances in the field. Here, we use a plant-hummingbird network with unprecedented sampling effort (2716 h of focal observations) from the Atlantic Rainforest in Brazil, to investigate how sampling effort affects the description of network structure (i.e. widely used network metrics) and the relative importance of distinct processes (i.e. species abundances vs. traits) in determining the frequency of pairwise interactions. By dividing the network into time slices representing a gradient of sampling effort, we show that quantitative metrics, such as interaction evenness, specialization (H2 '), weighted nestedness (wNODF) and modularity (Q; QuanBiMo algorithm) were less biased by sampling incompleteness than binary metrics. Furthermore, the significance of some network metrics changed along the sampling effort gradient. Nevertheless, the higher importance of traits in structuring the network was apparent even with small sampling effort. Our results (i) warn against using very poorly sampled networks as this may bias our understanding of networks, both their patterns and structuring processes, (ii) encourage the use of quantitative metrics little influenced by sampling when performing spatio-temporal comparisons and (iii) indicate that in networks strongly constrained by species traits, such as plant-hummingbird networks, even small sampling is sufficient to detect their relative importance for the frequencies of interactions. Finally, we argue that similar effects of sampling are expected for other highly specialized subnetworks. © 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society.

Systematic construction and control of stereo nerve vision network in intelligent manufacturing

NASA Astrophysics Data System (ADS)

Liu, Hua; Wang, Helong; Guo, Chunjie; Ding, Quanxin; Zhou, Liwei

2017-10-01

A system method of constructing stereo vision by using neural network is proposed, and the operation and control mechanism in actual operation are proposed. This method makes effective use of the neural network in learning and memory function, by after training with samples. Moreover, the neural network can learn the nonlinear relationship in the stereoscopic vision system and the internal and external orientation elements. These considerations are Worthy of attention, which includes limited constraints, the scientific of critical group, the operating speed and the operability in technical aspects. The results support our theoretical forecast.

Support network of adolescents with chronic disease: adolescents' perspective.

PubMed

Kyngäs, Helvi

2004-12-01

The purpose of this study was to describe the support network of adolescents with a chronic disease from their own perspective. Data were collected by interviewing adolescents with asthma, epilepsy, juvenile rheumatoid arthritis (JRA) and insulin-dependent diabetes mellitus (IDDM). The sample consisted of 40 adolescents aged between 13 and 17 years. Interview data were examined using content analysis. Six main categories were established to describe the support network of adolescents with a chronic disease: parents, peers, school, health care providers, technology and pets. Peers were divided into two groups: fellow sufferers and peers without a chronic disease. At school, teachers, school nurses and classmates were part of the support network. Health care providers included nurses, physicians and physiotherapists. Technology was also part of the support network and included four techniques that may be used to communicate: computers, mobile telephones, television and videos. The results provided a useful insight into the social network of adolescents with chronic disease and serve to raise awareness of the problems and opinions experienced by adolescents with this condition.

Network analysis of mesoscale optical recordings to assess regional, functional connectivity.

PubMed

Lim, Diana H; LeDue, Jeffrey M; Murphy, Timothy H

2015-10-01

With modern optical imaging methods, it is possible to map structural and functional connectivity. Optical imaging studies that aim to describe large-scale neural connectivity often need to handle large and complex datasets. In order to interpret these datasets, new methods for analyzing structural and functional connectivity are being developed. Recently, network analysis, based on graph theory, has been used to describe and quantify brain connectivity in both experimental and clinical studies. We outline how to apply regional, functional network analysis to mesoscale optical imaging using voltage-sensitive-dye imaging and channelrhodopsin-2 stimulation in a mouse model. We include links to sample datasets and an analysis script. The analyses we employ can be applied to other types of fluorescence wide-field imaging, including genetically encoded calcium indicators, to assess network properties. We discuss the benefits and limitations of using network analysis for interpreting optical imaging data and define network properties that may be used to compare across preparations or other manipulations such as animal models of disease.

Pressure to Drink but Not to Smoke: Disentangling Selection and Socialization in Adolescent Peer Networks and Peer Groups

ERIC Educational Resources Information Center

Kiuru, Noona; Burk, William J.; Laursen, Brett; Salmela-Aro, Katariina; Nurmi, Jari-Erik

2010-01-01

This paper examined the relative influence of selection and socialization on alcohol and tobacco use in adolescent peer networks and peer groups. The sample included 1419 Finnish secondary education students (690 males and 729 females, mean age 16 years at the outset) from nine schools. Participants identified three school friends and described…

Caregiver Mental Health, Neighborhood, and Social Network Influences on Mental Health Needs among African American Children

ERIC Educational Resources Information Center

Lindsey, Michael A.; Browne, Dorothy C.; Thompson, Richard; Hawley, Kristin M.; Graham, Christopher J.; Weisbart, Cindy; Harrington, Donna; Kotch, Jonathan B.

2008-01-01

In this study, the authors examined the combined effects of caregiver mental health, alcohol use, and social network support/satisfaction on child mental health needs among African American caregiver-child dyads at risk of maltreatment. The sample included 514 eight-year-old African American children and their caregivers who participated in the…

EFDC1D - A ONE DIMENSIONAL HYDRODYNAMIC AND SEDIMENT TRANSPORT MODEL FOR RIVER AND STREAM NETWORKS: MODEL THEORY AND USERS GUIDE

EPA Science Inventory

This technical report describes the new one-dimensional (1D) hydrodynamic and sediment transport model EFDC1D. This model that can be applied to stream networks. The model code and two sample data sets are included on the distribution CD. EFDC1D can simulate bi-directional unstea...

Sampling of temporal networks: Methods and biases

NASA Astrophysics Data System (ADS)

Rocha, Luis E. C.; Masuda, Naoki; Holme, Petter

2017-11-01

Temporal networks have been increasingly used to model a diversity of systems that evolve in time; for example, human contact structures over which dynamic processes such as epidemics take place. A fundamental aspect of real-life networks is that they are sampled within temporal and spatial frames. Furthermore, one might wish to subsample networks to reduce their size for better visualization or to perform computationally intensive simulations. The sampling method may affect the network structure and thus caution is necessary to generalize results based on samples. In this paper, we study four sampling strategies applied to a variety of real-life temporal networks. We quantify the biases generated by each sampling strategy on a number of relevant statistics such as link activity, temporal paths and epidemic spread. We find that some biases are common in a variety of networks and statistics, but one strategy, uniform sampling of nodes, shows improved performance in most scenarios. Given the particularities of temporal network data and the variety of network structures, we recommend that the choice of sampling methods be problem oriented to minimize the potential biases for the specific research questions on hand. Our results help researchers to better design network data collection protocols and to understand the limitations of sampled temporal network data.

Competing endogenous RNA regulatory network in papillary thyroid carcinoma.

PubMed

Chen, Shouhua; Fan, Xiaobin; Gu, He; Zhang, Lili; Zhao, Wenhua

2018-05-11

The present study aimed to screen all types of RNAs involved in the development of papillary thyroid carcinoma (PTC). RNA‑sequencing data of PTC and normal samples were used for screening differentially expressed (DE) microRNAs (DE‑miRNAs), long non‑coding RNAs (DE‑lncRNAs) and genes (DEGs). Subsequently, lncRNA‑miRNA, miRNA‑gene (that is, miRNA‑mRNA) and gene‑gene interaction pairs were extracted and used to construct regulatory networks. Feature genes in the miRNA‑mRNA network were identified by topological analysis and recursive feature elimination analysis. A support vector machine (SVM) classifier was built using 15 feature genes, and its classification effect was validated using two microarray data sets that were downloaded from the Gene Expression Omnibus (GEO) database. In addition, Gene Ontology function and Kyoto Encyclopedia Genes and Genomes pathway enrichment analyses were conducted for genes identified in the ceRNA network. A total of 506 samples, including 447 tumor samples and 59 normal samples, were obtained from The Cancer Genome Atlas (TCGA); 16 DE‑lncRNAs, 917 DEGs and 30 DE‑miRNAs were screened. The miRNA‑mRNA regulatory network comprised 353 nodes and 577 interactions. From these data, 15 feature genes with high predictive precision (>95%) were extracted from the network and were used to form an SVM classifier with an accuracy of 96.05% (486/506) for PTC samples downloaded from TCGA, and accuracies of 96.81 and 98.46% for GEO downloaded data sets. The ceRNA regulatory network comprised 596 lines (or interactions) and 365 nodes. Genes in the ceRNA network were significantly enriched in 'neuron development', 'differentiation', 'neuroactive ligand‑receptor interaction', 'metabolism of xenobiotics by cytochrome P450', 'drug metabolism' and 'cytokine‑cytokine receptor interaction' pathways. Hox transcript antisense RNA, miRNA‑206 and kallikrein‑related peptidase 10 were nodes in the ceRNA regulatory network of the selected feature gene, and they may serve import roles in the development of PTC.

A water-resources data-network evaluation for Monterey County, California; Phase 3, Northern Salinas River drainage basin

USGS Publications Warehouse

Templin, W.E.; Schluter, R.C.

1990-01-01

This report evaluates existing data collection networks and possible additional data collection to monitor quantity and quality of precipitation, surface water, and groundwater in the northern Salinas River drainage basin, California. Of the 34 precipitation stations identified, 20 were active and are concentrated in the northwestern part of the study area. No precipitation quality networks were identified, but possible data collection efforts include monitoring for acid rain and pesticides. Six of ten stream-gaging stations are active. Two surface water quality sites are sampled for suspended sediment, specific conductance, and chloride; one U.S. Geological Survey NASOAN site and one site operated by California Department of Water Resources make up the four active sampling locations; reactivation of 45 inactive surface water quality sites might help to achieve objectives described in the report. Three local networks measure water levels in 318 wells monthly, during peak irrigation, and at the end of the irrigation season. Water quality conditions are monitored in 379 wells; samples are collected in summer to monitor saltwater intrusion near Castroville and are also collected annually throughout the study area for analysis of chloride, specific conductance, and nitrate. An ideal baseline network would be an evenly spaced grid of index wells with a density of one per section. When baseline conditions are established, representative wells within the network could be monitored periodically according to specific data needs. (USGS)

Exploring of the molecular mechanism of rhinitis via bioinformatics methods

PubMed Central

Song, Yufen; Yan, Zhaohui

2018-01-01

The aim of this study was to analyze gene expression profiles for exploring the function and regulatory network of differentially expressed genes (DEGs) in pathogenesis of rhinitis by a bioinformatics method. The gene expression profile of GSE43523 was downloaded from the Gene Expression Omnibus database. The dataset contained 7 seasonal allergic rhinitis samples and 5 non-allergic normal samples. DEGs between rhinitis samples and normal samples were identified via the limma package of R. The webGestal database was used to identify enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the DEGs. The differentially co-expressed pairs of the DEGs were identified via the DCGL package in R, and the differential co-expression network was constructed based on these pairs. A protein-protein interaction (PPI) network of the DEGs was constructed based on the Search Tool for the Retrieval of Interacting Genes database. A total of 263 DEGs were identified in rhinitis samples compared with normal samples, including 125 downregulated ones and 138 upregulated ones. The DEGs were enriched in 7 KEGG pathways. 308 differential co-expression gene pairs were obtained. A differential co-expression network was constructed, containing 212 nodes. In total, 148 PPI pairs of the DEGs were identified, and a PPI network was constructed based on these pairs. Bioinformatics methods could help us identify significant genes and pathways related to the pathogenesis of rhinitis. Steroid biosynthesis pathway and metabolic pathways might play important roles in the development of allergic rhinitis (AR). Genes such as CDC42 effector protein 5, solute carrier family 39 member A11 and PR/SET domain 10 might be also associated with the pathogenesis of AR, which provided references for the molecular mechanisms of AR. PMID:29257233

ERLN Biological Focus Area

EPA Pesticide Factsheets

The Environmental Response Laboratory Network supports the goal to increase national capacity for biological analysis of environmental samples. This includes methods development and verification, technology transfer, and collaboration with USDA, FERN, CDC.

The Influence of Social Network Characteristics on Peer Clustering in Smoking: A Two-Wave Panel Study of 19- and 23-Year-Old Swedes.

PubMed

Miething, Alexander; Rostila, Mikael; Edling, Christofer; Rydgren, Jens

2016-01-01

The present study examines how the composition of social networks and perceived relationship content influence peer clustering in smoking, and how the association changes during the transition from late adolescence to early adulthood. The analysis was based on a Swedish two-wave survey sample comprising ego-centric network data. Respondents were 19 years old in the initial wave, and 23 when the follow-up sample was conducted. 17,227 ego-alter dyads were included in the analyses, which corresponds to an average response rate of 48.7 percent. Random effects logistic regression models were performed to calculate gender-specific average marginal effects of social network characteristics on smoking. The association of egos' and alters' smoking behavior was confirmed and found to be stronger when correlated in the female sample. For females, the associations decreased between age 19 and 23. Interactions between network characteristics and peer clustering in smoking showed that intense social interactions with smokers increase egos' smoking probability. The influence of network structures on peer clustering in smoking decreased during the transition from late adolescence to early adulthood. The study confirmed peer clustering in smoking and revealed that females' smoking behavior in particular is determined by social interactions. Female smokers' propensity to interact with other smokers was found to be associated with the quality of peer relationships, frequent social interactions, and network density. The influence of social networks on peer clustering in smoking decreased during the transition from late adolescence to early adulthood.

The Influence of Social Network Characteristics on Peer Clustering in Smoking: A Two-Wave Panel Study of 19- and 23-Year-Old Swedes

PubMed Central

Rostila, Mikael; Edling, Christofer; Rydgren, Jens

2016-01-01

Objectives The present study examines how the composition of social networks and perceived relationship content influence peer clustering in smoking, and how the association changes during the transition from late adolescence to early adulthood. Methods The analysis was based on a Swedish two-wave survey sample comprising ego-centric network data. Respondents were 19 years old in the initial wave, and 23 when the follow-up sample was conducted. 17,227 ego-alter dyads were included in the analyses, which corresponds to an average response rate of 48.7 percent. Random effects logistic regression models were performed to calculate gender-specific average marginal effects of social network characteristics on smoking. Results The association of egos’ and alters’ smoking behavior was confirmed and found to be stronger when correlated in the female sample. For females, the associations decreased between age 19 and 23. Interactions between network characteristics and peer clustering in smoking showed that intense social interactions with smokers increase egos’ smoking probability. The influence of network structures on peer clustering in smoking decreased during the transition from late adolescence to early adulthood. Conclusions The study confirmed peer clustering in smoking and revealed that females’ smoking behavior in particular is determined by social interactions. Female smokers’ propensity to interact with other smokers was found to be associated with the quality of peer relationships, frequent social interactions, and network density. The influence of social networks on peer clustering in smoking decreased during the transition from late adolescence to early adulthood. PMID:27727314

Social networks and incident stroke among women with suspected myocardial ischemia.

PubMed

Rutledge, Thomas; Linke, Sarah E; Olson, Marian B; Francis, Jennifer; Johnson, B Delia; Bittner, Vera; York, Kaki; McClure, Candace; Kelsey, Sheryl F; Reis, Steven E; Cornell, Carol E; Vaccarino, Viola; Sheps, David S; Shaw, Leslee J; Krantz, David S; Parashar, Susmita; Merz, C Noel Bairey

2008-04-01

To describe the prospective relationship between social networks and nonfatal stroke events in a sample of women with suspected myocardial ischemia. Social networks are an independent predictor of all-cause and cardiovascular mortality, but their relationship with stroke events in at-risk populations is largely unknown. A total of 629 women (mean age = 59.6 +/- 11.6 years) were evaluated at baseline for cardiovascular disease risk factors as part of a protocol including coronary angiography; the subjects were followed over a median 5.9 years to track the incidence of cardiovascular events including stroke. Participants also completed the Social Network Index (SNI), measuring the presence/absence of 12 types of common social relationships. Stroke events occurred among 5.1% of the sample over follow-up. More isolated women were older and less educated, with higher rates of smoking and hypertension, and increased use of cardiovascular medications. Women with smaller social networks were also more likely to show elevations (scores of > or =10) on the Beck Depression Inventory (54% versus 41%, respectively; p = .003). Relative to women with higher SNI scores, Cox regression results indicated that more isolated women experienced strokes at greater than twice the rate of those with more social relationships after adjusting for covariates (hazard ratio = 2.7; 95% Confidence Interval = 1.1-6.7). Smaller social networks are a robust predictor of stroke in at-risk women, and the magnitude of the association rivals that of conventional risk factors.

Prediction of the physical properties of barium titanates using an artificial neural network

NASA Astrophysics Data System (ADS)

Al-Jabar, Ahmed Jaafar Abed; Al-dujaili, Mohammed Assi Ahmed; Al-hydary, Imad Ali Disher

2017-04-01

Barium titanate is one of the most important ceramics amongst those that are widely used in the electronic industry because of their dielectric properties. These properties are related to the physical properties of the material, namely, the density and the porosity. Thus, the prediction of these properties is highly desirable. The aim of the current work is to develop models that can predict the density, porosity, firing shrinkage, and the green density of barium titanate BaTiO3. An artificial neural network was used to fulfill this aim. The modified pechini method was used to prepare barium titanate powders with five different particle size distributions. Eighty samples were prepared using different processing parameters including the pressing rate, pressing pressure, heating rate, sintering temperature, and soaking time. In the artificial neural network (ANN) model, the experimental data set consisted of these 80 samples, 70 samples were used for training the network and 10 samples were employed for testing. A comparison was made between the experimental and the predicted data. Good performance of the ANN model was achieved, in which the results showed that the mean error for the density, porosity, shrinkage, and green density are 0.02, 0.06, 0.04, and 0.002, respectively.

A gene co-expression network model identifies yield-related vicinity networks in Jatropha curcas shoot system.

PubMed

Govender, Nisha; Senan, Siju; Mohamed-Hussein, Zeti-Azura; Wickneswari, Ratnam

2018-06-15

The plant shoot system consists of reproductive organs such as inflorescences, buds and fruits, and the vegetative leaves and stems. In this study, the reproductive part of the Jatropha curcas shoot system, which includes the aerial shoots, shoots bearing the inflorescence and inflorescence were investigated in regard to gene-to-gene interactions underpinning yield-related biological processes. An RNA-seq based sequencing of shoot tissues performed on an Illumina HiSeq. 2500 platform generated 18 transcriptomes. Using the reference genome-based mapping approach, a total of 64 361 genes was identified in all samples and the data was annotated against the non-redundant database by the BLAST2GO Pro. Suite. After removing the outlier genes and samples, a total of 12 734 genes across 17 samples were subjected to gene co-expression network construction using petal, an R library. A gene co-expression network model built with scale-free and small-world properties extracted four vicinity networks (VNs) with putative involvement in yield-related biological processes as follow; heat stress tolerance, floral and shoot meristem differentiation, biosynthesis of chlorophyll molecules and laticifers, cell wall metabolism and epigenetic regulations. Our VNs revealed putative key players that could be adapted in breeding strategies for J. curcas shoot system improvements.

Job Search Correspondence.

ERIC Educational Resources Information Center

Lorenzen, Elizabeth A.; And Others

This paper describes the various types of correspondence used in the job search process and provides guidelines and samples of each type. Types of letters discussed include cover letters (including letters of application and prospecting letters), networking letters, thank-you letters, acceptance letters, withdrawal letters, and rejection of offer…

Water-resources data network evaluation for Monterey County, California; Phase 2, northern and coastal areas of Monterey County

USGS Publications Warehouse

Templin, W.E.; Smith, P.E.; DeBortoli, M.L.; Schluter, R.C.

1995-01-01

This report presents an evaluation of water- resources data-collection networks in the northern and coastal areas of Monterey County, California. This evaluation was done by the U.S. Geological Survey in cooperation with the Monterey County Flood Control and Water Conservation District to evaluate precipitation, surface water, and ground water monitoring networks. This report describes existing monitoring networks in the study areas and areas where possible additional data-collection is needed. During this study, 106 precipitation-quantity gages were identified, of which 84 were active; however, no precipitation-quality gages were identified in the study areas. The precipitaion-quantity gages were concentrated in the Monterey Peninsula and the northern part of the county. If the number of gages in these areas were reduced, coverage would still be adequate to meet most objectives; however, additional gages could improve coverage in the Tularcitos Creek basin and in the coastal areas south of Carmel to the county boundary. If collection of precipitation data were expanded to include monitoring precipitation quality, this expanded monitoring also could include monitoring precipitation for acid rain and pesticides. Eleven continuous streamflow-gaging stations were identified during this study, of which seven were active. To meet the objectives of the streamflow networks outlined in this report, the seven active stations would need to be continued, four stations would need to be reactivated, and an additional six streamflow-gaging stations would need to be added. Eleven stations that routinely were sampled for chemical constituents were identified in the study areas. Surface water in the lower Big Sur River basin was sampled annually for total coli- form and fecal coliform bacteria, and the Big Sur River was sampled monthly at 16 stations for these bacteria. Routine sampling for chemical constituents also was done in the Big Sur River basin. The Monterey County Flood Control and Water Conservation District maintained three networks in the study areas to measure ground-water levels: (1) the summer network, (2) the monthly network, and (3) the annual autumn network. The California American Water Company also did some ground-water-level monitoring in these areas. Well coverage for ground-water monitoring was dense in the seawater-intrusion area north of Moss Landing (possibly because of multiple overlying aquifers), but sparse in other parts of the study areas. During the study, 44 sections were identified as not monitored for ground-water levels. In an ideal ground-water-level network, wells would be evenly spaced, except where local conditions or correlations of wells make monitoring unnecessary. A total of 384 wells that monitor ground-water levels and/or ground-water quality were identified during this study. The Monterey County Flood Control and Water Conservation District sampled ground-water quality monthly during the irrigation season to monitor seawater intrusion. Once each year (during the summer), the wells in this network were monitored for chlorides, specific conductance, and nitrates. Additional samples were collected from each well once every 5 years for complete mineral analysis. The California Department of Health Services, the California American Water Company, the U.S. Army Health Service at Ford Ord, and the Monterey Peninsula Water Management District also monitored ground-water quality in wells in the study areas. Well coverage for the ground-water- quality networks was dense in the seawater- intrusion area north of Moss Landing, but sparse in the rest of the study areas. During this study, 54 sections were identified as not monitored for water quality.

78 FR 37219 - Information Collection Request Submitted to OMB for Review and Approval; Comment Request; RadNet...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-20

... restricted by statute. FOR FURTHER INFORMATION CONTACT: Charles M. Petko, Office of Radiation and Indoor Air... national network of stations collecting sampling media that include air, precipitation, drinking water, and milk. Samples are sent to EPA's National Air and Radiation Environmental Laboratory (NAREL) in...

iSANLA: intelligent sensor and actuator network for life science applications.

PubMed

Schloesser, Mario; Schnitzer, Andreas; Ying, Hong; Silex, Carmen; Schiek, Michael

2008-01-01

In the fields of neurological rehabilitation and neurophysiological research there is a strong need for miniaturized, multi channel, battery driven, wireless networking DAQ systems enabling real-time digital signal processing and feedback experiments. For the scientific investigation on the passive auditory based 3D-orientation of Barn Owls and the scientific research on vegetative locomotor coordination of Parkinson's disease patients during rehabilitation we developed our 'intelligent Sensor and Actuator Network for Life science Application' (iSANLA) system. Implemented on the ultra low power microcontroller MSP430 sample rates up to 96 kHz have been realised for single channel DAQ. The system includes lossless local data storage up to 4 GB. With its outer dimensions of 20mm per rim and less than 15 g of weight including the Lithium-Ion battery our modular designed sensor node is thoroughly capable of up to eight channel recordings with 8 kHz sample rate each and provides sufficient computational power for digital signal processing ready to start our first mobile experiments. For wireless mobility a compact communication protocol based on the IEEE 802.15.4 wireless standard with net data rates up to 141 kbit/s has been implemented. To merge the lossless acquired data of the distributed iNODEs a time synchronization protocol has been developed preserving causality. Hence the necessary time synchronous start of the data acquisition inside a network of multiple sensors with a precision better than the highest sample rate has been realized.

The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies.

PubMed

McCarty, Catherine A; Chisholm, Rex L; Chute, Christopher G; Kullo, Iftikhar J; Jarvik, Gail P; Larson, Eric B; Li, Rongling; Masys, Daniel R; Ritchie, Marylyn D; Roden, Dan M; Struewing, Jeffery P; Wolf, Wendy A

2011-01-26

The eMERGE (electronic MEdical Records and GEnomics) Network is an NHGRI-supported consortium of five institutions to explore the utility of DNA repositories coupled to Electronic Medical Record (EMR) systems for advancing discovery in genome science. eMERGE also includes a special emphasis on the ethical, legal and social issues related to these endeavors. The five sites are supported by an Administrative Coordinating Center. Setting of network goals is initiated by working groups: (1) Genomics, (2) Informatics, and (3) Consent & Community Consultation, which also includes active participation by investigators outside the eMERGE funded sites, and (4) Return of Results Oversight Committee. The Steering Committee, comprised of site PIs and representatives and NHGRI staff, meet three times per year, once per year with the External Scientific Panel. The primary site-specific phenotypes for which samples have undergone genome-wide association study (GWAS) genotyping are cataract and HDL, dementia, electrocardiographic QRS duration, peripheral arterial disease, and type 2 diabetes. A GWAS is also being undertaken for resistant hypertension in ≈ 2,000 additional samples identified across the network sites, to be added to data available for samples already genotyped. Funded by ARRA supplements, secondary phenotypes have been added at all sites to leverage the genotyping data, and hypothyroidism is being analyzed as a cross-network phenotype. Results are being posted in dbGaP. Other key eMERGE activities include evaluation of the issues associated with cross-site deployment of common algorithms to identify cases and controls in EMRs, data privacy of genomic and clinically-derived data, developing approaches for large-scale meta-analysis of GWAS data across five sites, and a community consultation and consent initiative at each site. Plans are underway to expand the network in diversity of populations and incorporation of GWAS findings into clinical care. By combining advanced clinical informatics, genome science, and community consultation, eMERGE represents a first step in the development of data-driven approaches to incorporate genomic information into routine healthcare delivery.

Exotic plant infestation is associated with decreased modularity and increased numbers of connectors in mixed-grass prairie pollination networks

USGS Publications Warehouse

Larson, Diane L.; Rabie, Paul A.; Droege, Sam; Larson, Jennifer L.; Haar, Milton

2016-01-01

The majority of pollinating insects are generalists whose lifetimes overlap flowering periods of many potentially suitable plant species. Such generality is instrumental in allowing exotic plant species to invade pollination networks. The particulars of how existing networks change in response to an invasive plant over the course of its phenology are not well characterized, but may shed light on the probability of long-term effects on plant-pollinator interactions and the stability of network structure. Here we describe changes in network topology and modular structure of infested and non-infested networks during the flowering season of the generalist non-native flowering plant, Cirsium arvense in mixed-grass prairie at Badlands National Park, South Dakota, USA. Objectives were to compare network-level effects of infestation as they propagate over the season in infested and non-infested (with respect to C. arvense) networks. We characterized plant-pollinator networks on 5 non-infested and 7 infested 1-ha plots during 4 sample periods that collectively covered the length of C. arvense flowering period. Two other abundantly-flowering invasive plants were present during this time: Melilotus officinalis had highly variable floral abundance in both C. arvense-infested and non-infested plots andConvolvulus arvensis, which occurred almost exclusively in infested plots and peaked early in the season. Modularity, including roles of individual species, and network topology were assessed for each sample period as well as in pooled infested and non-infested networks. Differences in modularity and network metrics between infested and non-infested networks were limited to the third and fourth sample periods, during flower senescence of C. arvenseand the other invasive species; generality of pollinators rose concurrently, suggesting rewiring of the network and a lag effect of earlier floral abundance. Modularity was lower and number of connectors higher in infested networks, whether they were assessed in individual sample periods or pooled into infested and non-infested networks over the entire blooming period of C.arvense. Connectors typically did not reside within the same modules as C. arvense, suggesting that effects of the other invasive plants may also influence the modularity results, and that effects of infestation extend to co-flowering native plants. We conclude that the presence of abundantly flowering invasive species is associated with greater network stability due to decreased modularity, but whether this is advantageous for the associated native plant-pollinator communities depends on the nature of perturbations they experience.

Increased anterior default-mode network homogeneity in first-episode, drug-naive major depressive disorder: A replication study.

PubMed

Guo, Wenbin; Cui, Xilong; Liu, Feng; Chen, Jindong; Xie, Guangrong; Wu, Renrong; Zhang, Zhikun; Chen, Huafu; Zhao, Jingping

2018-01-01

Abnormal default-mode network (DMN) homogeneity has been involved in the neurophysiology of major depressive disorder (MDD) with inconsistent findings. The inconsistency may be due to clinical and methodological variability, and the reproducibility of the findings is limited. The present study aimed to examine alterations of the DMN homogeneity in two independent samples of patients with first-episode, drug-naive MDD. The samples included 59 patients with MDD and 31 comparison subjects from Sample 1 and 29 patients with MDD and 24 comparison subjects from Sample 2. Network homogeneity (NH) was computed with an overlapping technique, which was employed to define brain regions with abnormal NH common to the MDD samples. Compared with comparison subjects, patients with MDD exhibited increased NH in an overlapped brain region of the left superior medial prefrontal cortex (MPFC). No correlations were found between abnormal NH and HAMD total/subscale scores in the patients of each sample and in the combined patients from both samples. This study is the first to examine alterations of DMN homogeneity in first-episode, drug-naive patients with MDD in two independent samples by using an overlapping technique. Patients with MDD exhibit increased NH in an overlapped region in the anterior DMN. The present study thus highlights the importance of the DMN in the neurophysiology of MDD. Copyright © 2017 Elsevier B.V. All rights reserved.

A Statistical Framework for Microbial Source Attribution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Velsko, S P; Allen, J E; Cunningham, C T

2009-04-28

This report presents a general approach to inferring transmission and source relationships among microbial isolates from their genetic sequences. The outbreak transmission graph (also called the transmission tree or transmission network) is the fundamental structure which determines the statistical distributions relevant to source attribution. The nodes of this graph are infected individuals or aggregated sub-populations of individuals in which transmitted bacteria or viruses undergo clonal expansion, leading to a genetically heterogeneous population. Each edge of the graph represents a transmission event in which one or a small number of bacteria or virions infects another node thus increasing the size ofmore » the transmission network. Recombination and re-assortment events originate in nodes which are common to two distinct networks. In order to calculate the probability that one node was infected by another, given the observed genetic sequences of microbial isolates sampled from them, we require two fundamental probability distributions. The first is the probability of obtaining the observed mutational differences between two isolates given that they are separated by M steps in a transmission network. The second is the probability that two nodes sampled randomly from an outbreak transmission network are separated by M transmission events. We show how these distributions can be obtained from the genetic sequences of isolates obtained by sampling from past outbreaks combined with data from contact tracing studies. Realistic examples are drawn from the SARS outbreak of 2003, the FMDV outbreak in Great Britain in 2001, and HIV transmission cases. The likelihood estimators derived in this report, and the underlying probability distribution functions required to calculate them possess certain compelling general properties in the context of microbial forensics. These include the ability to quantify the significance of a sequence 'match' or 'mismatch' between two isolates; the ability to capture non-intuitive effects of network structure on inferential power, including the 'small world' effect; the insensitivity of inferences to uncertainties in the underlying distributions; and the concept of rescaling, i.e. ability to collapse sub-networks into single nodes and examine transmission inferences on the rescaled network.« less

Optimizing the MAC Protocol in Localization Systems Based on IEEE 802.15.4 Networks

PubMed Central

Claver, Jose M.; Ezpeleta, Santiago

2017-01-01

Radio frequency signals are commonly used in the development of indoor localization systems. The infrastructure of these systems includes some beacons placed at known positions that exchange radio packets with users to be located. When the system is implemented using wireless sensor networks, the wireless transceivers integrated in the network motes are usually based on the IEEE 802.15.4 standard. But, the CSMA-CA, which is the basis for the medium access protocols in this category of communication systems, is not suitable when several users want to exchange bursts of radio packets with the same beacon to acquire the radio signal strength indicator (RSSI) values needed in the location process. Therefore, new protocols are necessary to avoid the packet collisions that appear when multiple users try to communicate with the same beacons. On the other hand, the RSSI sampling process should be carried out very quickly because some systems cannot tolerate a large delay in the location process. This is even more important when the RSSI sampling process includes measures with different signal power levels or frequency channels. The principal objective of this work is to speed up the RSSI sampling process in indoor localization systems. To achieve this objective, the main contribution is the proposal of a new MAC protocol that eliminates the medium access contention periods and decreases the number of packet collisions to accelerate the RSSI collection process. Moreover, the protocol increases the overall network throughput taking advantage of the frequency channel diversity. The presented results show the suitability of this protocol for reducing the RSSI gathering delay and increasing the network throughput in simulated and real environments. PMID:28684666

Optimizing the MAC Protocol in Localization Systems Based on IEEE 802.15.4 Networks.

PubMed

Pérez-Solano, Juan J; Claver, Jose M; Ezpeleta, Santiago

2017-07-06

Radio frequency signals are commonly used in the development of indoor localization systems. The infrastructure of these systems includes some beacons placed at known positions that exchange radio packets with users to be located. When the system is implemented using wireless sensor networks, the wireless transceivers integrated in the network motes are usually based on the IEEE 802.15.4 standard. But, the CSMA-CA, which is the basis for the medium access protocols in this category of communication systems, is not suitable when several users want to exchange bursts of radio packets with the same beacon to acquire the radio signal strength indicator (RSSI) values needed in the location process. Therefore, new protocols are necessary to avoid the packet collisions that appear when multiple users try to communicate with the same beacons. On the other hand, the RSSI sampling process should be carried out very quickly because some systems cannot tolerate a large delay in the location process. This is even more important when the RSSI sampling process includes measures with different signal power levels or frequency channels. The principal objective of this work is to speed up the RSSI sampling process in indoor localization systems. To achieve this objective, the main contribution is the proposal of a new MAC protocol that eliminates the medium access contention periods and decreases the number of packet collisions to accelerate the RSSI collection process. Moreover, the protocol increases the overall network throughput taking advantage of the frequency channel diversity. The presented results show the suitability of this protocol for reducing the RSSI gathering delay and increasing the network throughput in simulated and real environments.

Designing and implementing sample and data collection for an international genetics study: the Type 1 Diabetes Genetics Consortium (T1DGC).

PubMed

Hilner, Joan E; Perdue, Letitia H; Sides, Elizabeth G; Pierce, June J; Wägner, Ana M; Aldrich, Alan; Loth, Amanda; Albret, Lotte; Wagenknecht, Lynne E; Nierras, Concepcion; Akolkar, Beena

2010-01-01

The Type 1 Diabetes Genetics Consortium (T1DGC) is an international project whose primary aims are to: (a) discover genes that modify type 1 diabetes risk; and (b) expand upon the existing genetic resources for type 1 diabetes research. The initial goal was to collect 2500 affected sibling pair (ASP) families worldwide. T1DGC was organized into four regional networks (Asia-Pacific, Europe, North America, and the United Kingdom) and a Coordinating Center. A Steering Committee, with representatives from each network, the Coordinating Center, and the funding organizations, was responsible for T1DGC operations. The Coordinating Center, with regional network representatives, developed study documents and data systems. Each network established laboratories for: DNA extraction and cell line production; human leukocyte antigen genotyping; and autoantibody measurement. Samples were tracked from the point of collection, processed at network laboratories and stored for deposit at National Institute for Diabetes and Digestive and Kidney Diseases (NIDDK) Central Repositories. Phenotypic data were collected and entered into the study database maintained by the Coordinating Center. T1DGC achieved its original ASP recruitment goal. In response to research design changes, the T1DGC infrastructure also recruited trios, cases, and controls. Results of genetic analyses have identified many novel regions that affect susceptibility to type 1 diabetes. T1DGC created a resource of data and samples that is accessible to the research community. Participation in T1DGC was declined by some countries due to study requirements for the processing of samples at network laboratories and/or final deposition of samples in NIDDK Central Repositories. Re-contact of participants was not included in informed consent templates, preventing collection of additional samples for functional studies. T1DGC implemented a distributed, regional network structure to reach ASP recruitment targets. The infrastructure proved robust and flexible enough to accommodate additional recruitment. T1DGC has established significant resources that provide a basis for future discovery in the study of type 1 diabetes genetics.

Identifying a Probabilistic Boolean Threshold Network From Samples.

PubMed

Melkman, Avraham A; Cheng, Xiaoqing; Ching, Wai-Ki; Akutsu, Tatsuya

2018-04-01

This paper studies the problem of exactly identifying the structure of a probabilistic Boolean network (PBN) from a given set of samples, where PBNs are probabilistic extensions of Boolean networks. Cheng et al. studied the problem while focusing on PBNs consisting of pairs of AND/OR functions. This paper considers PBNs consisting of Boolean threshold functions while focusing on those threshold functions that have unit coefficients. The treatment of Boolean threshold functions, and triplets and -tuplets of such functions, necessitates a deepening of the theoretical analyses. It is shown that wide classes of PBNs with such threshold functions can be exactly identified from samples under reasonable constraints, which include: 1) PBNs in which any number of threshold functions can be assigned provided that all have the same number of input variables and 2) PBNs consisting of pairs of threshold functions with different numbers of input variables. It is also shown that the problem of deciding the equivalence of two Boolean threshold functions is solvable in pseudopolynomial time but remains co-NP complete.

Gender Differences of Brain Glucose Metabolic Networks Revealed by FDG-PET: Evidence from a Large Cohort of 400 Young Adults

PubMed Central

Li, Kai; Zhu, Hong; Qi, Rongfeng; Zhang, Zhiqiang; Lu, Guangming

2013-01-01

Background Gender differences of the human brain are an important issue in neuroscience research. In recent years, an increasing amount of evidence has been gathered from noninvasive neuroimaging studies supporting a sexual dimorphism of the human brain. However, there is a lack of imaging studies on gender differences of brain metabolic networks based on a large population sample. Materials and Methods FDG PET data of 400 right-handed, healthy subjects, including 200 females (age: 25∼45 years, mean age±SD: 40.9±3.9 years) and 200 age-matched males were obtained and analyzed in the present study. We first investigated the regional differences of brain glucose metabolism between genders using a voxel-based two-sample t-test analysis. Subsequently, we investigated the gender differences of the metabolic networks. Sixteen metabolic covariance networks using seed-based correlation were analyzed. Seven regions showing significant regional metabolic differences between genders, and nine regions conventionally used in the resting-state network studies were selected as regions-of-interest. Permutation tests were used for comparing within- and between-network connectivity between genders. Results Compared with the males, females showed higher metabolism in the posterior part and lower metabolism in the anterior part of the brain. Moreover, there were widely distributed patterns of the metabolic networks in the human brain. In addition, significant gender differences within and between brain glucose metabolic networks were revealed in the present study. Conclusion This study provides solid data that reveal gender differences in regional brain glucose metabolism and brain glucose metabolic networks. These observations might contribute to the better understanding of the gender differences in human brain functions, and suggest that gender should be included as a covariate when designing experiments and explaining results of brain glucose metabolic networks in the control and experimental individuals or patients. PMID:24358312

Gender differences of brain glucose metabolic networks revealed by FDG-PET: evidence from a large cohort of 400 young adults.

PubMed

Hu, Yuxiao; Xu, Qiang; Li, Kai; Zhu, Hong; Qi, Rongfeng; Zhang, Zhiqiang; Lu, Guangming

2013-01-01

Gender differences of the human brain are an important issue in neuroscience research. In recent years, an increasing amount of evidence has been gathered from noninvasive neuroimaging studies supporting a sexual dimorphism of the human brain. However, there is a lack of imaging studies on gender differences of brain metabolic networks based on a large population sample. FDG PET data of 400 right-handed, healthy subjects, including 200 females (age: 25:45 years, mean age ± SD: 40.9 ± 3.9 years) and 200 age-matched males were obtained and analyzed in the present study. We first investigated the regional differences of brain glucose metabolism between genders using a voxel-based two-sample t-test analysis. Subsequently, we investigated the gender differences of the metabolic networks. Sixteen metabolic covariance networks using seed-based correlation were analyzed. Seven regions showing significant regional metabolic differences between genders, and nine regions conventionally used in the resting-state network studies were selected as regions-of-interest. Permutation tests were used for comparing within- and between-network connectivity between genders. Compared with the males, females showed higher metabolism in the posterior part and lower metabolism in the anterior part of the brain. Moreover, there were widely distributed patterns of the metabolic networks in the human brain. In addition, significant gender differences within and between brain glucose metabolic networks were revealed in the present study. This study provides solid data that reveal gender differences in regional brain glucose metabolism and brain glucose metabolic networks. These observations might contribute to the better understanding of the gender differences in human brain functions, and suggest that gender should be included as a covariate when designing experiments and explaining results of brain glucose metabolic networks in the control and experimental individuals or patients.

An Inside Look at Homeless Youths’ Social Networks: Perceptions of Substance Use Norms1

PubMed Central

Melander, Lisa A.; Tyler, Kimberly A.; Schmitz, Rachel M.

2016-01-01

Substance use among homeless young people is a pervasive problem, and there have been many efforts to understand more about the dynamics of this health compromising behavior. The current study examined perceived substance use norms within homeless youths’ social networks utilizing in-depth interviews. The sample included 19 homeless individuals aged 16 to 21. Four elements of substance use within networks emerged: substance use choices, drug use safety issues, encouragement and/or discouragement, and appropriate situations in which substance use is condoned. These findings provide unique insight into the norms associated with drug and alcohol use within homeless youths’ social networks. PMID:26989340

A convolutional neural network-based screening tool for X-ray serial crystallography

PubMed Central

Ke, Tsung-Wei; Brewster, Aaron S.; Yu, Stella X.; Ushizima, Daniela; Yang, Chao; Sauter, Nicholas K.

2018-01-01

A new tool is introduced for screening macromolecular X-ray crystallography diffraction images produced at an X-ray free-electron laser light source. Based on a data-driven deep learning approach, the proposed tool executes a convolutional neural network to detect Bragg spots. Automatic image processing algorithms described can enable the classification of large data sets, acquired under realistic conditions consisting of noisy data with experimental artifacts. Outcomes are compared for different data regimes, including samples from multiple instruments and differing amounts of training data for neural network optimization. PMID:29714177

A convolutional neural network-based screening tool for X-ray serial crystallography.

PubMed

Ke, Tsung Wei; Brewster, Aaron S; Yu, Stella X; Ushizima, Daniela; Yang, Chao; Sauter, Nicholas K

2018-05-01

A new tool is introduced for screening macromolecular X-ray crystallography diffraction images produced at an X-ray free-electron laser light source. Based on a data-driven deep learning approach, the proposed tool executes a convolutional neural network to detect Bragg spots. Automatic image processing algorithms described can enable the classification of large data sets, acquired under realistic conditions consisting of noisy data with experimental artifacts. Outcomes are compared for different data regimes, including samples from multiple instruments and differing amounts of training data for neural network optimization. open access.

A convolutional neural network-based screening tool for X-ray serial crystallography

DOE PAGES

Ke, Tsung-Wei; Brewster, Aaron S.; Yu, Stella X.; ...

2018-04-24

A new tool is introduced for screening macromolecular X-ray crystallography diffraction images produced at an X-ray free-electron laser light source. Based on a data-driven deep learning approach, the proposed tool executes a convolutional neural network to detect Bragg spots. Automatic image processing algorithms described can enable the classification of large data sets, acquired under realistic conditions consisting of noisy data with experimental artifacts. Outcomes are compared for different data regimes, including samples from multiple instruments and differing amounts of training data for neural network optimization.

A convolutional neural network-based screening tool for X-ray serial crystallography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ke, Tsung-Wei; Brewster, Aaron S.; Yu, Stella X.

A new tool is introduced for screening macromolecular X-ray crystallography diffraction images produced at an X-ray free-electron laser light source. Based on a data-driven deep learning approach, the proposed tool executes a convolutional neural network to detect Bragg spots. Automatic image processing algorithms described can enable the classification of large data sets, acquired under realistic conditions consisting of noisy data with experimental artifacts. Outcomes are compared for different data regimes, including samples from multiple instruments and differing amounts of training data for neural network optimization.

Groundwater-quality data from the National Water-Quality Assessment Project, January through December 2014 and select quality-control data from May 2012 through December 2014

USGS Publications Warehouse

Arnold, Terri L.; Bexfield, Laura M.; Musgrove, MaryLynn; Lindsey, Bruce D.; Stackelberg, Paul E.; Barlow, Jeannie R.; Desimone, Leslie A.; Kulongoski, Justin T.; Kingsbury, James A.; Ayotte, Joseph D.; Fleming, Brandon J.; Belitz, Kenneth

2017-10-05

Groundwater-quality data were collected from 559 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Program from January through December 2014. The data were collected from four types of well networks: principal aquifer study networks, which are used to assess the quality of groundwater used for public water supply; land-use study networks, which are used to assess land-use effects on shallow groundwater quality; major aquifer study networks, which are used to assess the quality of groundwater used for domestic supply; and enhanced trends networks, which are used to evaluate the time scales during which groundwater quality changes. Groundwater samples were analyzed for a large number of water-quality indicators and constituents, including major ions, nutrients, trace elements, volatile organic compounds, pesticides, radionuclides, and some constituents of special interest (arsenic speciation, chromium [VI] and perchlorate). These groundwater-quality data, along with data from quality-control samples, are tabulated in this report and in an associated data release.

Cascaded deep decision networks for classification of endoscopic images

NASA Astrophysics Data System (ADS)

Murthy, Venkatesh N.; Singh, Vivek; Sun, Shanhui; Bhattacharya, Subhabrata; Chen, Terrence; Comaniciu, Dorin

2017-02-01

Both traditional and wireless capsule endoscopes can generate tens of thousands of images for each patient. It is desirable to have the majority of irrelevant images filtered out by automatic algorithms during an offline review process or to have automatic indication for highly suspicious areas during an online guidance. This also applies to the newly invented endomicroscopy, where online indication of tumor classification plays a significant role. Image classification is a standard pattern recognition problem and is well studied in the literature. However, performance on the challenging endoscopic images still has room for improvement. In this paper, we present a novel Cascaded Deep Decision Network (CDDN) to improve image classification performance over standard Deep neural network based methods. During the learning phase, CDDN automatically builds a network which discards samples that are classified with high confidence scores by a previously trained network and concentrates only on the challenging samples which would be handled by the subsequent expert shallow networks. We validate CDDN using two different types of endoscopic imaging, which includes a polyp classification dataset and a tumor classification dataset. From both datasets we show that CDDN can outperform other methods by about 10%. In addition, CDDN can also be applied to other image classification problems.

Circle of Care: Extending Beyond Primary Caregivers to Examine Collaborative Caretaking in Adolescent Development

PubMed Central

Margolis, Kathryn L.; Fosco, Gregory M.; Stormshak, Elizabeth A.

2013-01-01

In the contemporary family, which is increasingly shaped by multicultural influences, parents rarely are the sole caretakers of their children. To improve understanding of family dynamics, researchers must redefine caregiving networks to include multiple caregivers, such as extended family members. This study explored the influences of caregiving networks on youth depression by examining who youths perceived as caretakers, how many caretakers were in their networks, the youths’ connectedness with adults in their network, and harmony of relationships among adults within the network. Data from an ethnically diverse, urban sample of 180 middle school youths revealed participation of multiple caregivers for all groups, but ethnic differences existed in network composition. These differences in network composition are discussed within a socio-cultural context, considering how positive relationships with specific caregivers may buffer future depression. Longitudinal analyses confirmed the importance of positive relationships with caregiving networks for youth of color when predicting future depression. PMID:27453615

Queuing theory models for computer networks

NASA Technical Reports Server (NTRS)

Galant, David C.

1989-01-01

A set of simple queuing theory models which can model the average response of a network of computers to a given traffic load has been implemented using a spreadsheet. The impact of variations in traffic patterns and intensities, channel capacities, and message protocols can be assessed using them because of the lack of fine detail in the network traffic rates, traffic patterns, and the hardware used to implement the networks. A sample use of the models applied to a realistic problem is included in appendix A. Appendix B provides a glossary of terms used in this paper. This Ames Research Center computer communication network is an evolving network of local area networks (LANs) connected via gateways and high-speed backbone communication channels. Intelligent planning of expansion and improvement requires understanding the behavior of the individual LANs as well as the collection of networks as a whole.

Network Sampling and Classification:An Investigation of Network Model Representations

PubMed Central

Airoldi, Edoardo M.; Bai, Xue; Carley, Kathleen M.

2011-01-01

Methods for generating a random sample of networks with desired properties are important tools for the analysis of social, biological, and information networks. Algorithm-based approaches to sampling networks have received a great deal of attention in recent literature. Most of these algorithms are based on simple intuitions that associate the full features of connectivity patterns with specific values of only one or two network metrics. Substantive conclusions are crucially dependent on this association holding true. However, the extent to which this simple intuition holds true is not yet known. In this paper, we examine the association between the connectivity patterns that a network sampling algorithm aims to generate and the connectivity patterns of the generated networks, measured by an existing set of popular network metrics. We find that different network sampling algorithms can yield networks with similar connectivity patterns. We also find that the alternative algorithms for the same connectivity pattern can yield networks with different connectivity patterns. We argue that conclusions based on simulated network studies must focus on the full features of the connectivity patterns of a network instead of on the limited set of network metrics for a specific network type. This fact has important implications for network data analysis: for instance, implications related to the way significance is currently assessed. PMID:21666773

Primary care research conducted in networks: getting down to business.

PubMed

Mold, James W

2012-01-01

This seventh annual practice-based research theme issue of the Journal of the American Board of Family Medicine highlights primary care research conducted in practice-based research networks (PBRNs). The issue includes discussion of (1) theoretical and methodological research, (2) health care research (studies addressing primary care processes), (3) clinical research (studies addressing the impact of primary care on patients), and (4) health systems research (studies of health system issues impacting primary care including the quality improvement process). We had a noticeable increase in submissions from PBRN collaborations, that is, studies that involved multiple networks. As PBRNs cooperate to recruit larger and more diverse patient samples, greater generalizability and applicability of findings lead to improved primary care processes.

Macrostructure from Microstructure: Generating Whole Systems from Ego Networks

PubMed Central

Smith, Jeffrey A.

2014-01-01

This paper presents a new simulation method to make global network inference from sampled data. The proposed simulation method takes sampled ego network data and uses Exponential Random Graph Models (ERGM) to reconstruct the features of the true, unknown network. After describing the method, the paper presents two validity checks of the approach: the first uses the 20 largest Add Health networks while the second uses the Sociology Coauthorship network in the 1990's. For each test, I take random ego network samples from the known networks and use my method to make global network inference. I find that my method successfully reproduces the properties of the networks, such as distance and main component size. The results also suggest that simpler, baseline models provide considerably worse estimates for most network properties. I end the paper by discussing the bounds/limitations of ego network sampling. I also discuss possible extensions to the proposed approach. PMID:25339783

Peer network influence on intimate partner violence perpetration among urban Tanzanian men.

PubMed

Mulawa, Marta I; Kajula, Lusajo J; Maman, Suzanne

2018-04-01

Male perpetration of intimate partner violence (IPV) against women in Tanzania is widespread. Theory and empirical evidence suggest peer networks may play an important role in shaping IPV perpetration, although research on this topic in sub-Saharan Africa is limited. Grounded in social learning theory, social influence theory, and the theory of gender and power, the purpose of this study was to examine whether and how peer networks influence men's perpetration of IPV in Dar es Salaam, Tanzania. We conducted in-depth interviews (n = 40) with a sub-sample of 20 men enrolled in the control condition of an ongoing cluster-randomised controlled trial. We purposively sampled participants who previously reported perpetrating physical IPV. To analyse the data, we generated narrative summaries and conducted thematic and interpretative coding. We saw no evidence that men self-selected into peer networks with certain values or behaviours. Rather, men described several mechanisms through which their peers influenced the perpetration of IPV, including: (1) the internalisation of peer network norms, (2) pressure to conform to peer network norms and (3) the direct involvement of peers in shaping couple power dynamics. Our findings suggest that peer networks influence men's perpetration of IPV and should be targeted in future programmes and interventions.

Upstream and downstream correlates of older people's engagement in social networks: what are their effects on health over time?

PubMed

Stephens, Christine; Noone, Jack; Alpass, Fiona

2014-01-01

This study tested the effects of social network engagement and social support on the health of older people moving into retirement, using a model which includes social context variables. A prospective survey of a New Zealand population sample aged 54-70 at baseline (N = 2,282) was used to assess the effects on mental and physical health across time. A structural equation model assessed pathways from the social context variables through network engagement to social support and then to mental and physical health 2 years later. The proposed model of effects on mental health was supported when gender, economic living standards, and ethnicity were included along with the direct effects of these variables on social support. These findings confirm the importance of taking social context variables into account when considering social support networks. Social engagement appears to be an important aspect of social network functioning which could be investigated further.

Dynamic networks of PTSD symptoms during conflict.

PubMed

Greene, Talya; Gelkopf, Marc; Epskamp, Sacha; Fried, Eiko

2018-02-28

Conceptualizing posttraumatic stress disorder (PTSD) symptoms as a dynamic system of causal elements could provide valuable insights into the way that PTSD develops and is maintained in traumatized individuals. We present the first study to apply a multilevel network model to produce an exploratory empirical conceptualization of dynamic networks of PTSD symptoms, using data collected during a period of conflict. Intensive longitudinal assessment data were collected during the Israel-Gaza War in July-August 2014. The final sample (n = 96) comprised a general population sample of Israeli adult civilians exposed to rocket fire. Participants completed twice-daily reports of PTSD symptoms via smartphone for 30 days. We used a multilevel vector auto-regression model to produce contemporaneous and temporal networks, and a partial correlation network model to obtain a between-subjects network. Multilevel network analysis found strong positive contemporaneous associations between hypervigilance and startle response, avoidance of thoughts and avoidance of reminders, and between flashbacks and emotional reactivity. The temporal network indicated the central role of startle response as a predictor of future PTSD symptomatology, together with restricted affect, blame, negative emotions, and avoidance of thoughts. There were some notable differences between the temporal and contemporaneous networks, including the presence of a number of negative associations, particularly from blame. The between-person network indicated flashbacks and emotional reactivity to be the most central symptoms. This study suggests various symptoms that could potentially be driving the development of PTSD. We discuss clinical implications such as identifying particular symptoms as targets for interventions.

Adaptive model predictive process control using neural networks

DOEpatents

Buescher, K.L.; Baum, C.C.; Jones, R.D.

1997-08-19

A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.

Adaptive model predictive process control using neural networks

DOEpatents

Buescher, Kevin L.; Baum, Christopher C.; Jones, Roger D.

1997-01-01

A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.

E-SMART system for in-situ detection of environmental contaminants. Quarterly technical progress report, July--September 1996

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1996-10-01

General Atomics (GA) leads a team of industrial, academic, and government organizations to develop the Environmental Systems Management, Analysis and Reporting neTwork (E-SMART) for the Defense Advanced Research Project Agency (DARPA), by way of this Technology Reinvestment Project (TRP). E-SMART defines a standard by which networks of smart sensing, sampling, and control devices can interoperate. E-SMART is intended to be an open standard, available to any equipment manufacturer. The user will be provided a standard platform on which a site-specific monitoring plan can be implemented using sensors and actuators from various manufacturers and upgraded as new monitoring devices become commerciallymore » available. This project will further develop and advance the E-SMART standardized network protocol to include new sensors, sampling systems, and graphical user interfaces.« less

Coupled protein-ligand dynamics in truncated hemoglobin N from atomistic simulations and transition networks.

PubMed

Cazade, Pierre-André; Berezovska, Ganna; Meuwly, Markus

2015-05-01

The nature of ligand motion in proteins is difficult to characterize directly using experiment. Specifically, it is unclear to what degree these motions are coupled. All-atom simulations are used to sample ligand motion in truncated Hemoglobin N. A transition network analysis including ligand- and protein-degrees of freedom is used to analyze the microscopic dynamics. Clustering of two different subsets of MD trajectories highlights the importance of a diverse and exhaustive description to define the macrostates for a ligand-migration network. Monte Carlo simulations on the transition matrices from one particular clustering are able to faithfully capture the atomistic simulations. Contrary to clustering by ligand positions only, including a protein degree of freedom yields considerably improved coarse grained dynamics. Analysis with and without imposing detailed balance agree closely which suggests that the underlying atomistic simulations are converged with respect to sampling transitions between neighboring sites. Protein and ligand dynamics are not independent from each other and ligand migration through globular proteins is not passive diffusion. Transition network analysis is a powerful tool to analyze and characterize the microscopic dynamics in complex systems. This article is part of a Special Issue entitled Recent developments of molecular dynamics. Copyright © 2014 Elsevier B.V. All rights reserved.

Social support network characteristics of incarcerated women with co-occurring major depressive and substance use disorders.

PubMed

Nargiso, Jessica E; Kuo, Caroline C; Zlotnick, Caron; Johnson, Jennifer E

2014-01-01

The nature of social support available to incarcerated women is not well-understood, particularly among women at high risk of negative outcomes, including women dually diagnosed with Major Depressive Disorder and a Substance Use Disorder (MDD-SUD). Descriptive statistics and paired-tests were conducted on 60 incarcerated MDD-SUD women receiving in-prison substance use and depression treatments to characterize the women's social networks, including the strength of support, network characteristics, and types of support provided as well as to determine what aspects of social support may be amenable to change during incarceration and post-release. Study results showed that, on average, women perceived they had moderately supportive individuals in their lives, although more than a quarter of the sample could not identify any regular supporters in their network at baseline. During incarceration, women's social networks significantly increased in general supportiveness, and decreased in network size and percentage of substance users in their networks. Participants maintained positive social support gains post-release in most areas while also significantly increasing the size of their support network post-release. Findings suggest that there are aspects of incarcerated MDD-SUD women's social networks that are amenable to change during incarceration and post-release and provide insight into treatment targets for this vulnerable population.

Social support network characteristics of incarcerated women with co-occurring major depressive and substance use disorders

PubMed Central

Nargiso, Jessica E.; Kuo, Caroline C.; Zlotnick, Caron; Johnson, Jennifer E.

2014-01-01

The nature of social support available to incarcerated women is not well understood, particularly among women at high risk of negative outcomes, including women dually-diagnosed with Major Depressive Disorder and a Substance Use Disorder (MDD-SUD). Descriptive statistics and paired-tests were conducted on 60 incarcerated MDD-SUD women receiving in-prison substance use and depression treatments to characterize the women’s social networks, including the strength of support, network characteristics, and types of support provided as well as to determine what aspects of social support may be amenable to change during incarceration and post-release. Study results showed that on average women perceived they had moderately supportive individuals in their lives, although more than a quarter of the sample could not identify any regular supporters in their network at baseline. During incarceration, women’s social networks significantly increased in general supportiveness, and decreased in network size and percentage of substance users in their networks. Participants maintained positive social support gains post-release in most areas while also significantly increasing the size of their support network post-release. Findings suggest that there are aspects of incarcerated MDD-SUD women’s social networks that are amenable to change during incarceration and post-release and provide insight into treatment targets for this vulnerable population. PMID:25052785

An interactive graphics program for manipulation and display of panel method geometry

NASA Technical Reports Server (NTRS)

Hall, J. F.; Neuhart, D. H.; Walkley, K. B.

1983-01-01

Modern aerodynamic panel methods that handle large, complex geometries have made evident the need to interactively manipulate, modify, and view such configurations. With this purpose in mind, the GEOM program was developed. It is a menu driven, interactive program that uses the Tektronix PLOT 10 graphics software to display geometry configurations which are characterized by an abutting set of networks. These networks are composed of quadrilateral panels which are described by the coordinates of their corners. GEOM is divided into fourteen executive controlled functions. These functions are used to build configurations, scale and rotate networks, transpose networks defining M and N lines, graphically display selected networks, join and split networks, create wake networks, produce symmetric images of networks, repanel and rename networks, display configuration cross sections, and output network geometry in two formats. A data base management system is used to facilitate data transfers in this program. A sample session illustrating various capabilities of the code is included as a guide to program operation.

Personal networks of women in residential and outpatient substance abuse treatment

PubMed Central

Kim, HyunSoo; Tracy, Elizabeth; Brown, Suzanne; Jun, MinKyoung; Park, Hyunyong; Min, Meeyoung; McCarty, Chris

2015-01-01

This study compared compositional, social support, and structural characteristics of personal networks among women in residential (RT) and intensive outpatient (IOP) substance abuse treatment. The study sample included 377 women from inner-city substance use disorder treatment facilities. Respondents were asked about 25 personal network members known within the past 6 months, characteristics of each (relationship, substance use, types of support), and relationships between each network member. Differences between RT women and IOP women in personal network characteristics were identified using Chi-square and t-tests. Compared to IOP women, RT women had more substance users in their networks, more network members with whom they had used substances and fewer network members who provided social support. These findings suggest that women in residential treatment have specific network characteristics, not experienced by women in IOP, which may make them more vulnerable to relapse; they may therefore require interventions that target these specific network characteristics in order to reduce their vulnerability to relapse. PMID:27011762

Personal networks of women in residential and outpatient substance abuse treatment.

PubMed

Kim, HyunSoo; Tracy, Elizabeth; Brown, Suzanne; Jun, MinKyoung; Park, Hyunyong; Min, Meeyoung; McCarty, Chris

This study compared compositional, social support, and structural characteristics of personal networks among women in residential (RT) and intensive outpatient (IOP) substance abuse treatment. The study sample included 377 women from inner-city substance use disorder treatment facilities. Respondents were asked about 25 personal network members known within the past 6 months, characteristics of each (relationship, substance use, types of support), and relationships between each network member. Differences between RT women and IOP women in personal network characteristics were identified using Chi-square and t -tests. Compared to IOP women, RT women had more substance users in their networks, more network members with whom they had used substances and fewer network members who provided social support. These findings suggest that women in residential treatment have specific network characteristics, not experienced by women in IOP, which may make them more vulnerable to relapse; they may therefore require interventions that target these specific network characteristics in order to reduce their vulnerability to relapse.

Generalization of Clustering Coefficients to Signed Correlation Networks

PubMed Central

Costantini, Giulio; Perugini, Marco

2014-01-01

The recent interest in network analysis applications in personality psychology and psychopathology has put forward new methodological challenges. Personality and psychopathology networks are typically based on correlation matrices and therefore include both positive and negative edge signs. However, some applications of network analysis disregard negative edges, such as computing clustering coefficients. In this contribution, we illustrate the importance of the distinction between positive and negative edges in networks based on correlation matrices. The clustering coefficient is generalized to signed correlation networks: three new indices are introduced that take edge signs into account, each derived from an existing and widely used formula. The performances of the new indices are illustrated and compared with the performances of the unsigned indices, both on a signed simulated network and on a signed network based on actual personality psychology data. The results show that the new indices are more resistant to sample variations in correlation networks and therefore have higher convergence compared with the unsigned indices both in simulated networks and with real data. PMID:24586367

A Smart Sensor Web for Ocean Observation: Integrated Acoustics, Satellite Networking, and Predictive Modeling

NASA Astrophysics Data System (ADS)

Arabshahi, P.; Chao, Y.; Chien, S.; Gray, A.; Howe, B. M.; Roy, S.

2008-12-01

In many areas of Earth science, including climate change research, there is a need for near real-time integration of data from heterogeneous and spatially distributed sensors, in particular in-situ and space- based sensors. The data integration, as provided by a smart sensor web, enables numerous improvements, namely, 1) adaptive sampling for more efficient use of expensive space-based sensing assets, 2) higher fidelity information gathering from data sources through integration of complementary data sets, and 3) improved sensor calibration. The specific purpose of the smart sensor web development presented here is to provide for adaptive sampling and calibration of space-based data via in-situ data. Our ocean-observing smart sensor web presented herein is composed of both mobile and fixed underwater in-situ ocean sensing assets and Earth Observing System (EOS) satellite sensors providing larger-scale sensing. An acoustic communications network forms a critical link in the web between the in-situ and space-based sensors and facilitates adaptive sampling and calibration. After an overview of primary design challenges, we report on the development of various elements of the smart sensor web. These include (a) a cable-connected mooring system with a profiler under real-time control with inductive battery charging; (b) a glider with integrated acoustic communications and broadband receiving capability; (c) satellite sensor elements; (d) an integrated acoustic navigation and communication network; and (e) a predictive model via the Regional Ocean Modeling System (ROMS). Results from field experiments, including an upcoming one in Monterey Bay (October 2008) using live data from NASA's EO-1 mission in a semi closed-loop system, together with ocean models from ROMS, are described. Plans for future adaptive sampling demonstrations using the smart sensor web are also presented.

Note: Design and development of wireless controlled aerosol sampling network for large scale aerosol dispersion experiments.

PubMed

Gopalakrishnan, V; Subramanian, V; Baskaran, R; Venkatraman, B

2015-07-01

Wireless based custom built aerosol sampling network is designed, developed, and implemented for environmental aerosol sampling. These aerosol sampling systems are used in field measurement campaign, in which sodium aerosol dispersion experiments have been conducted as a part of environmental impact studies related to sodium cooled fast reactor. The sampling network contains 40 aerosol sampling units and each contains custom built sampling head and the wireless control networking designed with Programmable System on Chip (PSoC™) and Xbee Pro RF modules. The base station control is designed using graphical programming language LabView. The sampling network is programmed to operate in a preset time and the running status of the samplers in the network is visualized from the base station. The system is developed in such a way that it can be used for any other environment sampling system deployed in wide area and uneven terrain where manual operation is difficult due to the requirement of simultaneous operation and status logging.

Note: Design and development of wireless controlled aerosol sampling network for large scale aerosol dispersion experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gopalakrishnan, V.; Subramanian, V.; Baskaran, R.

2015-07-15

Wireless based custom built aerosol sampling network is designed, developed, and implemented for environmental aerosol sampling. These aerosol sampling systems are used in field measurement campaign, in which sodium aerosol dispersion experiments have been conducted as a part of environmental impact studies related to sodium cooled fast reactor. The sampling network contains 40 aerosol sampling units and each contains custom built sampling head and the wireless control networking designed with Programmable System on Chip (PSoC™) and Xbee Pro RF modules. The base station control is designed using graphical programming language LabView. The sampling network is programmed to operate in amore » preset time and the running status of the samplers in the network is visualized from the base station. The system is developed in such a way that it can be used for any other environment sampling system deployed in wide area and uneven terrain where manual operation is difficult due to the requirement of simultaneous operation and status logging.« less

Generalized friendship paradox in complex networks: The case of scientific collaboration

NASA Astrophysics Data System (ADS)

Eom, Young-Ho; Jo, Hang-Hyun

2014-04-01

The friendship paradox states that your friends have on average more friends than you have. Does the paradox ``hold'' for other individual characteristics like income or happiness? To address this question, we generalize the friendship paradox for arbitrary node characteristics in complex networks. By analyzing two coauthorship networks of Physical Review journals and Google Scholar profiles, we find that the generalized friendship paradox (GFP) holds at the individual and network levels for various characteristics, including the number of coauthors, the number of citations, and the number of publications. The origin of the GFP is shown to be rooted in positive correlations between degree and characteristics. As a fruitful application of the GFP, we suggest effective and efficient sampling methods for identifying high characteristic nodes in large-scale networks. Our study on the GFP can shed lights on understanding the interplay between network structure and node characteristics in complex networks.

Generalized friendship paradox in complex networks: The case of scientific collaboration

PubMed Central

Eom, Young-Ho; Jo, Hang-Hyun

2014-01-01

The friendship paradox states that your friends have on average more friends than you have. Does the paradox “hold” for other individual characteristics like income or happiness? To address this question, we generalize the friendship paradox for arbitrary node characteristics in complex networks. By analyzing two coauthorship networks of Physical Review journals and Google Scholar profiles, we find that the generalized friendship paradox (GFP) holds at the individual and network levels for various characteristics, including the number of coauthors, the number of citations, and the number of publications. The origin of the GFP is shown to be rooted in positive correlations between degree and characteristics. As a fruitful application of the GFP, we suggest effective and efficient sampling methods for identifying high characteristic nodes in large-scale networks. Our study on the GFP can shed lights on understanding the interplay between network structure and node characteristics in complex networks. PMID:24714092

WGCNA: an R package for weighted correlation network analysis.

PubMed

Langfelder, Peter; Horvath, Steve

2008-12-29

Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.

A neural network approach for enhancing information extraction from multispectral image data

USGS Publications Warehouse

Liu, J.; Shao, G.; Zhu, H.; Liu, S.

2005-01-01

A back-propagation artificial neural network (ANN) was applied to classify multispectral remote sensing imagery data. The classification procedure included four steps: (i) noisy training that adds minor random variations to the sampling data to make the data more representative and to reduce the training sample size; (ii) iterative or multi-tier classification that reclassifies the unclassified pixels by making a subset of training samples from the original training set, which means the neural model can focus on fewer classes; (iii) spectral channel selection based on neural network weights that can distinguish the relative importance of each channel in the classification process to simplify the ANN model; and (iv) voting rules that adjust the accuracy of classification and produce outputs of different confidence levels. The Purdue Forest, located west of Purdue University, West Lafayette, Indiana, was chosen as the test site. The 1992 Landsat thematic mapper imagery was used as the input data. High-quality airborne photographs of the same Lime period were used for the ground truth. A total of 11 land use and land cover classes were defined, including water, broadleaved forest, coniferous forest, young forest, urban and road, and six types of cropland-grassland. The experiment, indicated that the back-propagation neural network application was satisfactory in distinguishing different land cover types at US Geological Survey levels II-III. The single-tier classification reached an overall accuracy of 85%. and the multi-tier classification an overall accuracy of 95%. For the whole test, region, the final output of this study reached an overall accuracy of 87%. ?? 2005 CASI.

External quality assurance project report for the National Atmospheric Deposition Program’s National Trends Network and Mercury Deposition Network, 2015–16

USGS Publications Warehouse

Wetherbee, Gregory A.; Martin, RoseAnn

2018-06-29

The U.S. Geological Survey Precipitation Chemistry Quality Assurance project operated five distinct programs to provide external quality assurance monitoring for the National Atmospheric Deposition Program’s (NADP) National Trends Network and Mercury Deposition Network during 2015–16. The National Trends Network programs include (1) a field audit program to evaluate sample contamination and stability, (2) an interlaboratory comparison program to evaluate analytical laboratory performance, and (3) a colocated sampler program to evaluate bias and variability attributed to automated precipitation samplers. The Mercury Deposition Network programs include the (4) system blank program and (5) an interlaboratory comparison program. The results indicate that NADP data continue to be of sufficient quality for the analysis of spatial distributions and time trends for chemical constituents in wet deposition.The field audit program results indicate increased sample contamination for calcium, magnesium, and potassium relative to 2010 levels, and slight fluctuation in sodium contamination. Nitrate contamination levels dropped slightly during 2014–16, and chloride contamination leveled off between 2007 and 2016. Sulfate contamination is similar to the 2000 level. Hydrogen ion contamination has steadily decreased since 2012. Losses of ammonium and nitrate resulting from potential sample instability were negligible.The NADP Central Analytical Laboratory produced interlaboratory comparison results with low bias and variability compared to other domestic and international laboratories that support atmospheric deposition monitoring. Significant absolute bias above the magnitudes of the detection limits was observed for nitrate and sulfate concentrations, but no analyte determinations exceeded the detection limits for blanks.Colocated sampler program results from dissimilar colocated collectors indicate that the retrofit of the National Trends Network with N-CON Systems Company, Inc. precipitation collectors could cause substantial shifts in NADP annual deposition (concentration multiplied by depth) values. Median weekly relative percent differences for analyte concentrations ranged from -4 to +76 percent for cations, from 5 to 6 percent for ammonium, from +14 to +25 percent for anions, and from -21 to +8 percent for hydrogen ion contamination. By comparison, weekly absolute concentration differences for paired identical N-CON Systems Company, Inc., collectors ranged from 4–22 percent for cations; 2–9 percent for anions; 4–5 percent for ammonium; and 13–14 percent for hydrogen ion contamination. The N-CON Systems Company, Inc. collector caught more precipitation than the Aerochem Metrics Model 301 collector (ACM) at the WA99/99WA sites, but it typically caught slightly less precipitation than the ACM at ND11/11ND, sites which receive more wind and snow than WA99/99WA.Paired, identical OTT Pluvio-2 and ETI Noah IV precipitation gages were operated at the same sites. Median absolute percent differences for daily measured precipitation depths ranged from 0 to 7 percent. Annual absolute differences ranged from 0.08 percent (ETI Noah IV precipitation gages) to 11 percent (OTT Pluvio-2 precipitation gages).The Mercury Deposition Network programs include the system blank program and an interlaboratory comparison program. System blank results indicate that maximum total mercury contamination concentrations in samples were less than the third percentile of all Mercury Deposition Network sample concentrations (1.098 nanograms per liter; ng/L). The Mercury Analytical Laboratory produced chemical concentration results with low bias and variability compared with other domestic and international laboratories that support atmospheric-deposition monitoring. The laboratory’s performance results indicate a +1-ng/L shift in bias between 2015 (-0.4 ng/L) and 2016 (+0.5 ng/L).

Atmospheric Methane Mixing Ratios--The NOAA/CMDL Global Cooperative Air Sampling Network\\, 1983-1993

DOE Data Explorer

Dlugokencky, E. J. [National Oceanic and Atmospheric Administration, Boulder, Colorado (USA); Lang, P. M. [National Oceanic and Atmospheric Administration, Boulder, Colorado (USA); Masarie, K. A. [National Oceanic and Atmospheric Administration, Boulder, Colorado (USA); Steele, L. P. [Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria, Australia

1994-01-01

This data base presents atmospheric methane (CH4) mixing ratios from flask air samples collected over the period 1983-1993 by the National Oceanic and Atmospheric Administration, Climate Monitoring and Diagnostics Laboratory's (NOAA/CMDL's) global cooperative air sampling network. Air samples were collected approximately once per week at 44 fixed sites (37 of which were still active at the end of 1993). Samples were also collected at 5 degree latitude intervals along shipboard cruise tracks in the Pacific Ocean between North America and New Zealand (or Australia) and at 3 degree latitude intervals along cruise tracks in the South China Sea between Singapore and Hong Kong. The shipboard measurements were made approximately every 3 weeks per latitude zone by each of two ships in the Pacific Ocean and approximately once every week per latitude zone in the South China Sea. All samples were analyzed for CH4 at the NOAA/CMDL laboratory in Boulder, Colorado, by gas chromatography with flame ionization detection, and each aliquot was referenced to the NOAA/CMDL methane standard scale. In addition to providing the complete set of atmospheric CH4 measurements from flask air samples collected at the NOAA/CMDL network sites, this data base also includes files which list monthly mean mixing ratios derived from the individual flask air measurements. These monthly summary data are available for 35 of the fixed sites and 21 of the shipboard sampling sites.

Composing Music with Complex Networks

NASA Astrophysics Data System (ADS)

Liu, Xiaofan; Tse, Chi K.; Small, Michael

In this paper we study the network structure in music and attempt to compose music artificially. Networks are constructed with nodes and edges corresponding to musical notes and their co-occurrences. We analyze sample compositions from Bach, Mozart, Chopin, as well as other types of music including Chinese pop music. We observe remarkably similar properties in all networks constructed from the selected compositions. Power-law exponents of degree distributions, mean degrees, clustering coefficients, mean geodesic distances, etc. are reported. With the network constructed, music can be created by using a biased random walk algorithm, which begins with a randomly chosen note and selects the subsequent notes according to a simple set of rules that compares the weights of the edges, weights of the nodes, and/or the degrees of nodes. The newly created music from complex networks will be played in the presentation.

Science and ethics meet: a mathematical view on one kind of violation of publication ethics

NASA Astrophysics Data System (ADS)

Shinyaeva, Taisiya S.; Tarasevich, Yuri Yu

2018-01-01

When a person who did not make a significant intellectual contribution to a published research is included into the co-author list, the person is called gift or guest author depending on the reason why the person has been added to the co-authors. Essential deviation of properties of a particular co-author network from typical values may evidenced that the network is artificial. Using network analysis, we have performed an attempt to characterize a typical co-author network. We performed analysis of the co-author networks using references in the thesis on Physics and Mathematics, Economics defended from 2012 to 2017 and planned to be defended in 2017 and 2018 in Russia. Properties of the co-author networks are expected to be a reference sample in future research.

Assessing respondent-driven sampling.

PubMed

Goel, Sharad; Salganik, Matthew J

2010-04-13

Respondent-driven sampling (RDS) is a network-based technique for estimating traits in hard-to-reach populations, for example, the prevalence of HIV among drug injectors. In recent years RDS has been used in more than 120 studies in more than 20 countries and by leading public health organizations, including the Centers for Disease Control and Prevention in the United States. Despite the widespread use and growing popularity of RDS, there has been little empirical validation of the methodology. Here we investigate the performance of RDS by simulating sampling from 85 known, network populations. Across a variety of traits we find that RDS is substantially less accurate than generally acknowledged and that reported RDS confidence intervals are misleadingly narrow. Moreover, because we model a best-case scenario in which the theoretical RDS sampling assumptions hold exactly, it is unlikely that RDS performs any better in practice than in our simulations. Notably, the poor performance of RDS is driven not by the bias but by the high variance of estimates, a possibility that had been largely overlooked in the RDS literature. Given the consistency of our results across networks and our generous sampling conditions, we conclude that RDS as currently practiced may not be suitable for key aspects of public health surveillance where it is now extensively applied.

Assessing respondent-driven sampling

PubMed Central

Goel, Sharad; Salganik, Matthew J.

2010-01-01

Respondent-driven sampling (RDS) is a network-based technique for estimating traits in hard-to-reach populations, for example, the prevalence of HIV among drug injectors. In recent years RDS has been used in more than 120 studies in more than 20 countries and by leading public health organizations, including the Centers for Disease Control and Prevention in the United States. Despite the widespread use and growing popularity of RDS, there has been little empirical validation of the methodology. Here we investigate the performance of RDS by simulating sampling from 85 known, network populations. Across a variety of traits we find that RDS is substantially less accurate than generally acknowledged and that reported RDS confidence intervals are misleadingly narrow. Moreover, because we model a best-case scenario in which the theoretical RDS sampling assumptions hold exactly, it is unlikely that RDS performs any better in practice than in our simulations. Notably, the poor performance of RDS is driven not by the bias but by the high variance of estimates, a possibility that had been largely overlooked in the RDS literature. Given the consistency of our results across networks and our generous sampling conditions, we conclude that RDS as currently practiced may not be suitable for key aspects of public health surveillance where it is now extensively applied. PMID:20351258

Network inference using informative priors

PubMed Central

Mukherjee, Sach; Speed, Terence P.

2008-01-01

Recent years have seen much interest in the study of systems characterized by multiple interacting components. A class of statistical models called graphical models, in which graphs are used to represent probabilistic relationships between variables, provides a framework for formal inference regarding such systems. In many settings, the object of inference is the network structure itself. This problem of “network inference” is well known to be a challenging one. However, in scientific settings there is very often existing information regarding network connectivity. A natural idea then is to take account of such information during inference. This article addresses the question of incorporating prior information into network inference. We focus on directed models called Bayesian networks, and use Markov chain Monte Carlo to draw samples from posterior distributions over network structures. We introduce prior distributions on graphs capable of capturing information regarding network features including edges, classes of edges, degree distributions, and sparsity. We illustrate our approach in the context of systems biology, applying our methods to network inference in cancer signaling. PMID:18799736

Network inference using informative priors.

PubMed

Mukherjee, Sach; Speed, Terence P

2008-09-23

Recent years have seen much interest in the study of systems characterized by multiple interacting components. A class of statistical models called graphical models, in which graphs are used to represent probabilistic relationships between variables, provides a framework for formal inference regarding such systems. In many settings, the object of inference is the network structure itself. This problem of "network inference" is well known to be a challenging one. However, in scientific settings there is very often existing information regarding network connectivity. A natural idea then is to take account of such information during inference. This article addresses the question of incorporating prior information into network inference. We focus on directed models called Bayesian networks, and use Markov chain Monte Carlo to draw samples from posterior distributions over network structures. We introduce prior distributions on graphs capable of capturing information regarding network features including edges, classes of edges, degree distributions, and sparsity. We illustrate our approach in the context of systems biology, applying our methods to network inference in cancer signaling.

An improved sampling method of complex network

NASA Astrophysics Data System (ADS)

Gao, Qi; Ding, Xintong; Pan, Feng; Li, Weixing

2014-12-01

Sampling subnet is an important topic of complex network research. Sampling methods influence the structure and characteristics of subnet. Random multiple snowball with Cohen (RMSC) process sampling which combines the advantages of random sampling and snowball sampling is proposed in this paper. It has the ability to explore global information and discover the local structure at the same time. The experiments indicate that this novel sampling method could keep the similarity between sampling subnet and original network on degree distribution, connectivity rate and average shortest path. This method is applicable to the situation where the prior knowledge about degree distribution of original network is not sufficient.

Analog Delta-Back-Propagation Neural-Network Circuitry

NASA Technical Reports Server (NTRS)

Eberhart, Silvio

1990-01-01

Changes in synapse weights due to circuit drifts suppressed. Proposed fully parallel analog version of electronic neural-network processor based on delta-back-propagation algorithm. Processor able to "learn" when provided with suitable combinations of inputs and enforced outputs. Includes programmable resistive memory elements (corresponding to synapses), conductances (synapse weights) adjusted during learning. Buffer amplifiers, summing circuits, and sample-and-hold circuits arranged in layers of electronic neurons in accordance with delta-back-propagation algorithm.

Social networks and well-being: a comparison of older people in Mediterranean and non-Mediterranean countries.

PubMed

Litwin, Howard

2010-09-01

This study examined whether the social networks of older persons in Mediterranean and non-Mediterranean countries were appreciably different and whether they functioned in similar ways in relation to well-being outcomes. The sample included family household respondents aged 60 years and older from the first wave of the Survey of Health, Ageing and Retirement in Europe in 5 Mediterranean (n = 3,583) and 7 non-Mediterranean (n = 5,471) countries. Region was regressed separately by gender on variables from 4 network domains: structure and interaction, exchange, engagement and relationship quality, and controlling for background and health characteristics. In addition, 2 well-being outcomes-depressive symptoms and perceived income inadequacy-were regressed on the study variables, including regional social network interaction terms. The results revealed differences across the 2 regional settings in each of the realms of social network, above and beyond the differences that exist in background characteristics and health status. The findings also showed that the social network variables had different effects on the well-being outcomes in the respective settings. The findings underscore that the social network phenomenon is contextually bound. The social networks of older people should be seen within their unique regional milieu and in relation to the values and social norms that prevail in different sets of societies.

The precision of wet atmospheric deposition data from national atmospheric deposition program/national trends network sites determined with collocated samplers

USGS Publications Warehouse

Nilles, M.A.; Gordon, J.D.; Schroder, L.J.

1994-01-01

A collocated, wet-deposition sampler program has been operated since October 1988 by the U.S. Geological Survey to estimate the overall sampling precision of wet atmospheric deposition data collected at selected sites in the National Atmospheric Deposition Program and National Trends Network (NADP/NTN). A duplicate set of wet-deposition sampling instruments was installed adjacent to existing sampling instruments at four different NADP/NTN sites for each year of the study. Wet-deposition samples from collocated sites were collected and analysed using standard NADP/NTN procedures. Laboratory analyses included determinations of pH, specific conductance, and concentrations of major cations and anions. The estimates of precision included all variability in the data-collection system, from the point of sample collection through storage in the NADP/NTN database. Sampling precision was determined from the absolute value of differences in the analytical results for the paired samples in terms of median relative and absolute difference. The median relative difference for Mg2+, Na+, K+ and NH4+ concentration and deposition was quite variable between sites and exceeded 10% at most sites. Relative error for analytes whose concentrations typically approached laboratory method detection limits were greater than for analytes that did not typically approach detection limits. The median relative difference for SO42- and NO3- concentration, specific conductance, and sample volume at all sites was less than 7%. Precision for H+ concentration and deposition ranged from less than 10% at sites with typically high levels of H+ concentration to greater than 30% at sites with low H+ concentration. Median difference for analyte concentration and deposition was typically 1.5-2-times greater for samples collected during the winter than during other seasons at two northern sites. Likewise, the median relative difference in sample volume for winter samples was more than double the annual median relative difference at the two northern sites. Bias accounted for less than 25% of the collocated variability in analyte concentration and deposition from weekly collocated precipitation samples at most sites.A collocated, wet-deposition sampler program has been operated since OCtober 1988 by the U.S Geological Survey to estimate the overall sampling precision of wet atmospheric deposition data collected at selected sites in the National Atmospheric Deposition Program and National Trends Network (NADP/NTN). A duplicate set of wet-deposition sampling instruments was installed adjacent to existing sampling instruments four different NADP/NTN sites for each year of the study. Wet-deposition samples from collocated sites were collected and analysed using standard NADP/NTN procedures. Laboratory analyses included determinations of pH, specific conductance, and concentrations of major cations and anions. The estimates of precision included all variability in the data-collection system, from the point of sample collection through storage in the NADP/NTN database.

Characterizing the Daily Life, Needs, and Priorities of Adults with Autism Spectrum Disorder from Interactive Autism Network Data

ERIC Educational Resources Information Center

Gotham, Katherine; Marvin, Alison R.; Taylor, Julie Lounds; Warren, Zachary; Anderson, Connie M.; Law, Paul A.; Law, Jessica K.; Lipkin, Paul H.

2015-01-01

Using online survey data from a large sample of adults with autism spectrum disorder and legal guardians, we first report outcomes across a variety of contexts for participants with a wide range of functioning, and second, summarize these stakeholders' priorities for future research. The sample included n?=?255 self-reporting adults with autism…

E-SMART system for in-situ detection of environmental contaminants. Quarterly technical progress report, April--June 1997

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1997-08-01

General Atomics (GA) leads a team of industrial, academic, and government organizations in the development of the Environmental Systems Management, Analysis and Reporting neTwork (E-SMART) for the Defense Advanced Research Project Agency (DARPA), by way of this Technology Reinvestment Project (TRP). E-SMART defines a standard by which networks of smart sensing, sampling, and control devices can interoperate. E-SMART is intended to be an open standard, available to any equipment manufacturer. The user will be provided a standard platform on which a site-specific monitoring plan can be implemented using sensors and actuators from various manufacturers and upgraded as new monitoring devicesmore » become commercially available. This project will further develop and advance the E-SMART standardized network protocol to include new sensors, sampling systems, and graphical user interfaces.« less

Challenges to Recruiting Population Representative Samples of Female Sex Workers in China Using Respondent Driven Sampling1

PubMed Central

Merli, M. Giovanna; Moody, James; Smith, Jeffrey; Li, Jing; Weir, Sharon; Chen, Xiangsheng

2014-01-01

We explore the network coverage of a sample of female sex workers (FSWs) in China recruited through Respondent Drive Sampling (RDS) as part of an effort to evaluate the claim of RDS of population representation with empirical data. We take advantage of unique information on the social networks of FSWs obtained from two overlapping studies --RDS and a venue-based sampling approach (PLACE) -- and use an exponential random graph modeling (ERGM) framework from local networks to construct a likely network from which our observed RDS sample is drawn. We then run recruitment chains over this simulated network to assess the assumption that the RDS chain referral process samples participants in proportion to their degree and the extent to which RDS satisfactorily covers certain parts of the network. We find evidence that, contrary to assumptions, RDS oversamples low degree nodes and geographically central areas of the network. Unlike previous evaluations of RDS which have explored the performance of RDS sampling chains on a non-hidden population, or the performance of simulated chains over previously mapped realistic social networks, our study provides a robust, empirically grounded evaluation of the performance of RDS chains on a real-world hidden population. PMID:24834869

WGCNA: an R package for weighted correlation network analysis

PubMed Central

Langfelder, Peter; Horvath, Steve

2008-01-01

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at . PMID:19114008

Maximizing capture of gene co-expression relationships through pre-clustering of input expression samples: an Arabidopsis case study.

PubMed

Feltus, F Alex; Ficklin, Stephen P; Gibson, Scott M; Smith, Melissa C

2013-06-05

In genomics, highly relevant gene interaction (co-expression) networks have been constructed by finding significant pair-wise correlations between genes in expression datasets. These networks are then mined to elucidate biological function at the polygenic level. In some cases networks may be constructed from input samples that measure gene expression under a variety of different conditions, such as for different genotypes, environments, disease states and tissues. When large sets of samples are obtained from public repositories it is often unmanageable to associate samples into condition-specific groups, and combining samples from various conditions has a negative effect on network size. A fixed significance threshold is often applied also limiting the size of the final network. Therefore, we propose pre-clustering of input expression samples to approximate condition-specific grouping of samples and individual network construction of each group as a means for dynamic significance thresholding. The net effect is increase sensitivity thus maximizing the total co-expression relationships in the final co-expression network compendium. A total of 86 Arabidopsis thaliana co-expression networks were constructed after k-means partitioning of 7,105 publicly available ATH1 Affymetrix microarray samples. We term each pre-sorted network a Gene Interaction Layer (GIL). Random Matrix Theory (RMT), an un-supervised thresholding method, was used to threshold each of the 86 networks independently, effectively providing a dynamic (non-global) threshold for the network. The overall gene count across all GILs reached 19,588 genes (94.7% measured gene coverage) and 558,022 unique co-expression relationships. In comparison, network construction without pre-sorting of input samples yielded only 3,297 genes (15.9%) and 129,134 relationships. in the global network. Here we show that pre-clustering of microarray samples helps approximate condition-specific networks and allows for dynamic thresholding using un-supervised methods. Because RMT ensures only highly significant interactions are kept, the GIL compendium consists of 558,022 unique high quality A. thaliana co-expression relationships across almost all of the measurable genes on the ATH1 array. For A. thaliana, these networks represent the largest compendium to date of significant gene co-expression relationships, and are a means to explore complex pathway, polygenic, and pleiotropic relationships for this focal model plant. The networks can be explored at sysbio.genome.clemson.edu. Finally, this method is applicable to any large expression profile collection for any organism and is best suited where a knowledge-independent network construction method is desired.

Maximizing capture of gene co-expression relationships through pre-clustering of input expression samples: an Arabidopsis case study

PubMed Central

2013-01-01

Background In genomics, highly relevant gene interaction (co-expression) networks have been constructed by finding significant pair-wise correlations between genes in expression datasets. These networks are then mined to elucidate biological function at the polygenic level. In some cases networks may be constructed from input samples that measure gene expression under a variety of different conditions, such as for different genotypes, environments, disease states and tissues. When large sets of samples are obtained from public repositories it is often unmanageable to associate samples into condition-specific groups, and combining samples from various conditions has a negative effect on network size. A fixed significance threshold is often applied also limiting the size of the final network. Therefore, we propose pre-clustering of input expression samples to approximate condition-specific grouping of samples and individual network construction of each group as a means for dynamic significance thresholding. The net effect is increase sensitivity thus maximizing the total co-expression relationships in the final co-expression network compendium. Results A total of 86 Arabidopsis thaliana co-expression networks were constructed after k-means partitioning of 7,105 publicly available ATH1 Affymetrix microarray samples. We term each pre-sorted network a Gene Interaction Layer (GIL). Random Matrix Theory (RMT), an un-supervised thresholding method, was used to threshold each of the 86 networks independently, effectively providing a dynamic (non-global) threshold for the network. The overall gene count across all GILs reached 19,588 genes (94.7% measured gene coverage) and 558,022 unique co-expression relationships. In comparison, network construction without pre-sorting of input samples yielded only 3,297 genes (15.9%) and 129,134 relationships. in the global network. Conclusions Here we show that pre-clustering of microarray samples helps approximate condition-specific networks and allows for dynamic thresholding using un-supervised methods. Because RMT ensures only highly significant interactions are kept, the GIL compendium consists of 558,022 unique high quality A. thaliana co-expression relationships across almost all of the measurable genes on the ATH1 array. For A. thaliana, these networks represent the largest compendium to date of significant gene co-expression relationships, and are a means to explore complex pathway, polygenic, and pleiotropic relationships for this focal model plant. The networks can be explored at sysbio.genome.clemson.edu. Finally, this method is applicable to any large expression profile collection for any organism and is best suited where a knowledge-independent network construction method is desired. PMID:23738693

Adaptive Peer Sampling with Newscast

NASA Astrophysics Data System (ADS)

Tölgyesi, Norbert; Jelasity, Márk

The peer sampling service is a middleware service that provides random samples from a large decentralized network to support gossip-based applications such as multicast, data aggregation and overlay topology management. Lightweight gossip-based implementations of the peer sampling service have been shown to provide good quality random sampling while also being extremely robust to many failure scenarios, including node churn and catastrophic failure. We identify two problems with these approaches. The first problem is related to message drop failures: if a node experiences a higher-than-average message drop rate then the probability of sampling this node in the network will decrease. The second problem is that the application layer at different nodes might request random samples at very different rates which can result in very poor random sampling especially at nodes with high request rates. We propose solutions for both problems. We focus on Newscast, a robust implementation of the peer sampling service. Our solution is based on simple extensions of the protocol and an adaptive self-control mechanism for its parameters, namely—without involving failure detectors—nodes passively monitor local protocol events using them as feedback for a local control loop for self-tuning the protocol parameters. The proposed solution is evaluated by simulation experiments.

Phylogenetic structure of European Salmonella Enteritidis outbreak correlates with national and international egg distribution network

PubMed Central

Inns, Thomas; Jombart, Thibaut; Ashton, Philip; Loman, Nicolas; Chatt, Carol; Messelhaeusser, Ute; Rabsch, Wolfgang; Simon, Sandra; Nikisins, Sergejs; Bernard, Helen; le Hello, Simon; Jourdan da-Silva, Nathalie; Kornschober, Christian; Mossong, Joel; Hawkey, Peter; de Pinna, Elizabeth; Grant, Kathie; Cleary, Paul

2016-01-01

Outbreaks of Salmonella Enteritidis have long been associated with contaminated poultry and eggs. In the summer of 2014 a large multi-national outbreak of Salmonella Enteritidis phage type 14b occurred with over 350 cases reported in the United Kingdom, Germany, Austria, France and Luxembourg. Egg supply network investigation and microbiological sampling identified the source to be a Bavarian egg producer. As part of the international investigation into the outbreak, over 400 isolates were sequenced including isolates from cases, implicated UK premises and eggs from the suspected source producer. We were able to show a clear statistical correlation between the topology of the UK egg distribution network and the phylogenetic network of outbreak isolates. This correlation can most plausibly be explained by different parts of the egg distribution network being supplied by eggs solely from independent premises of the Bavarian egg producer (Company X). Microbiological sampling from the source premises, traceback information and information on the interventions carried out at the egg production premises all supported this conclusion. The level of insight into the outbreak epidemiology provided by whole-genome sequencing (WGS) would not have been possible using traditional microbial typing methods. PMID:28348865

Phylogenetic structure of European Salmonella Enteritidis outbreak correlates with national and international egg distribution network.

PubMed

Dallman, Tim; Inns, Thomas; Jombart, Thibaut; Ashton, Philip; Loman, Nicolas; Chatt, Carol; Messelhaeusser, Ute; Rabsch, Wolfgang; Simon, Sandra; Nikisins, Sergejs; Bernard, Helen; le Hello, Simon; Jourdan da-Silva, Nathalie; Kornschober, Christian; Mossong, Joel; Hawkey, Peter; de Pinna, Elizabeth; Grant, Kathie; Cleary, Paul

2016-08-01

Outbreaks of Salmonella Enteritidis have long been associated with contaminated poultry and eggs. In the summer of 2014 a large multi-national outbreak of Salmonella Enteritidis phage type 14b occurred with over 350 cases reported in the United Kingdom, Germany, Austria, France and Luxembourg. Egg supply network investigation and microbiological sampling identified the source to be a Bavarian egg producer. As part of the international investigation into the outbreak, over 400 isolates were sequenced including isolates from cases, implicated UK premises and eggs from the suspected source producer. We were able to show a clear statistical correlation between the topology of the UK egg distribution network and the phylogenetic network of outbreak isolates. This correlation can most plausibly be explained by different parts of the egg distribution network being supplied by eggs solely from independent premises of the Bavarian egg producer (Company X). Microbiological sampling from the source premises, traceback information and information on the interventions carried out at the egg production premises all supported this conclusion. The level of insight into the outbreak epidemiology provided by whole-genome sequencing (WGS) would not have been possible using traditional microbial typing methods.

Effects of social networks on physical health among people with serious mental illness.

PubMed

Lee, Sungkyu; Wong, Yin-Ling Irene; Rothbard, Aileen

2014-12-01

This study examined the effects of social network characteristics on physical health among people with serious mental illness using social transactions that are reciprocal, and the combination of objective and subjective health measures. The sample consisted of a probability sample of 231 adults with serious mental illness who resided in permanent supportive housing in Philadelphia, Pennsylvania. Path analyses were conducted to examine the relationships between social network characteristics and two aspects of medical comorbidity, objective health and subjective health. Bivariate statistics showed that individuals with medical comorbidity were more likely to have contact with their network members and had a higher level of reciprocal positive tangible support when compared to those who did not have medical comorbidity. The results of the path analyses revealed that none of the social network characteristics were associated with better physical health. The lack of a significant relationship between social networks and better physical health is contrary to prior research findings. However, this is the first study to include both types of social transactions simultaneously as predictors of better physical health for individuals with serious mental illness. A longitudinal study would provide more insight into the temporal relationship of social networks and physical health conditions of people with serious mental illness. Furthermore, the transactional nature of social relationships, particularly for those with mental health issues, requires greater exploration.

Bias in groundwater samples caused by wellbore flow

USGS Publications Warehouse

Reilly, Thomas E.; Franke, O. Lehn; Bennett, Gordon D.

1989-01-01

Proper design of physical installations and sampling procedures for groundwater monitoring networks is critical for the detection and analysis of possible contaminants. Monitoring networks associated with known contaminant sources sometimes include an array of monitoring wells with long well screens. The purpose of this paper is: (a) to report the results of a numerical experiment indicating that significant borehole flow can occur within long well screens installed in homogeneous aquifers with very small head differences in the aquifer (less than 0.01 feet between the top and bottom of the screen); (b) to demonstrate that contaminant monitoring wells with long screens may completely fail to fulfill their purpose in many groundwater environments.

Towards a system level understanding of non-model organisms sampled from the environment: a network biology approach.

PubMed

Williams, Tim D; Turan, Nil; Diab, Amer M; Wu, Huifeng; Mackenzie, Carolynn; Bartie, Katie L; Hrydziuszko, Olga; Lyons, Brett P; Stentiford, Grant D; Herbert, John M; Abraham, Joseph K; Katsiadaki, Ioanna; Leaver, Michael J; Taggart, John B; George, Stephen G; Viant, Mark R; Chipman, Kevin J; Falciani, Francesco

2011-08-01

The acquisition and analysis of datasets including multi-level omics and physiology from non-model species, sampled from field populations, is a formidable challenge, which so far has prevented the application of systems biology approaches. If successful, these could contribute enormously to improving our understanding of how populations of living organisms adapt to environmental stressors relating to, for example, pollution and climate. Here we describe the first application of a network inference approach integrating transcriptional, metabolic and phenotypic information representative of wild populations of the European flounder fish, sampled at seven estuarine locations in northern Europe with different degrees and profiles of chemical contaminants. We identified network modules, whose activity was predictive of environmental exposure and represented a link between molecular and morphometric indices. These sub-networks represented both known and candidate novel adverse outcome pathways representative of several aspects of human liver pathophysiology such as liver hyperplasia, fibrosis, and hepatocellular carcinoma. At the molecular level these pathways were linked to TNF alpha, TGF beta, PDGF, AGT and VEGF signalling. More generally, this pioneering study has important implications as it can be applied to model molecular mechanisms of compensatory adaptation to a wide range of scenarios in wild populations.

Towards a System Level Understanding of Non-Model Organisms Sampled from the Environment: A Network Biology Approach

PubMed Central

Williams, Tim D.; Turan, Nil; Diab, Amer M.; Wu, Huifeng; Mackenzie, Carolynn; Bartie, Katie L.; Hrydziuszko, Olga; Lyons, Brett P.; Stentiford, Grant D.; Herbert, John M.; Abraham, Joseph K.; Katsiadaki, Ioanna; Leaver, Michael J.; Taggart, John B.; George, Stephen G.; Viant, Mark R.; Chipman, Kevin J.; Falciani, Francesco

2011-01-01

The acquisition and analysis of datasets including multi-level omics and physiology from non-model species, sampled from field populations, is a formidable challenge, which so far has prevented the application of systems biology approaches. If successful, these could contribute enormously to improving our understanding of how populations of living organisms adapt to environmental stressors relating to, for example, pollution and climate. Here we describe the first application of a network inference approach integrating transcriptional, metabolic and phenotypic information representative of wild populations of the European flounder fish, sampled at seven estuarine locations in northern Europe with different degrees and profiles of chemical contaminants. We identified network modules, whose activity was predictive of environmental exposure and represented a link between molecular and morphometric indices. These sub-networks represented both known and candidate novel adverse outcome pathways representative of several aspects of human liver pathophysiology such as liver hyperplasia, fibrosis, and hepatocellular carcinoma. At the molecular level these pathways were linked to TNF alpha, TGF beta, PDGF, AGT and VEGF signalling. More generally, this pioneering study has important implications as it can be applied to model molecular mechanisms of compensatory adaptation to a wide range of scenarios in wild populations. PMID:21901081

Self-organizing adaptive map: autonomous learning of curves and surfaces from point samples.

PubMed

Piastra, Marco

2013-05-01

Competitive Hebbian Learning (CHL) (Martinetz, 1993) is a simple and elegant method for estimating the topology of a manifold from point samples. The method has been adopted in a number of self-organizing networks described in the literature and has given rise to related studies in the fields of geometry and computational topology. Recent results from these fields have shown that a faithful reconstruction can be obtained using the CHL method only for curves and surfaces. Within these limitations, these findings constitute a basis for defining a CHL-based, growing self-organizing network that produces a faithful reconstruction of an input manifold. The SOAM (Self-Organizing Adaptive Map) algorithm adapts its local structure autonomously in such a way that it can match the features of the manifold being learned. The adaptation process is driven by the defects arising when the network structure is inadequate, which cause a growth in the density of units. Regions of the network undergo a phase transition and change their behavior whenever a simple, local condition of topological regularity is met. The phase transition is eventually completed across the entire structure and the adaptation process terminates. In specific conditions, the structure thus obtained is homeomorphic to the input manifold. During the adaptation process, the network also has the capability to focus on the acquisition of input point samples in critical regions, with a substantial increase in efficiency. The behavior of the network has been assessed experimentally with typical data sets for surface reconstruction, including suboptimal conditions, e.g. with undersampling and noise. Copyright © 2012 Elsevier Ltd. All rights reserved.

Substance Abuse Treatment Stage and Personal Networks of Women in Substance Abuse Treatment

PubMed Central

Tracy, Elizabeth M.; Kim, HyunSoo; Brown, Suzanne; Min, Meeyoung O.; Jun, Min Kyoung; McCarty, Christopher

2012-01-01

This study examines the relationship among 4 treatment stages (i.e., engagement, persuasion, active treatment, relapse prevention) and the composition, social support, and structural characteristics of personal networks. The study sample includes 242 women diagnosed with substance dependence who were interviewed within their first month of intensive outpatient treatment. Using EgoNet software, the women reported on their 25 alter personal networks and the characteristics of each alter. With one exception, few differences were found in the network compositions at different stages of substance abuse treatment. The exception was the network composition of women in the active treatment stage, which included more network members from treatment programs or 12-Step meetings. Although neither the type nor amount of social support differed across treatment stages, reciprocity differed between women in active treatment and those in the engagement stage. Networks of women in active treatment were less connected, as indicated by a higher number of components, whereas networks of women in the persuasion stage had a higher degree of centralization, as indicated by networks dominated by people with the most ties. Overall, we find social network structural variables to relate to the stage of treatment, whereas network composition, type of social support, and sociodemographic variables (with a few exceptions) do not relate to treatment stage. Results suggest that social context, particularly how social contacts are arranged around clients, should be incorporated into treatment programs, regardless of demographic background. PMID:22639705

Line width measurement below 60 nm using an optical interferometer and artificial neural network

NASA Astrophysics Data System (ADS)

See, Chung W.; Smith, Richard J.; Somekh, Michael G.; Yacoot, Andrew

2007-03-01

We have recently described a technique for optical line-width measurements. The system currently is capable of measuring line-width down to 60 nm with a precision of 2 nm, and potentially should be able to measure down to 10nm. The system consists of an ultra-stable interferometer and artificial neural networks (ANNs). The former is used to generate optical profiles which are input to the ANNs. The outputs of the ANNs are the desired sample parameters. Different types of samples have been tested with equally impressive results. In this paper we will discuss the factors that are essential to extend the application of the technique. Two of the factors are signal conditioning and sample classification. Methods, including principal component analysis, that are capable of performing these tasks will be considered.

Social Networks Across Common Cancer Types: The Evidence, Gaps, and Areas of Potential Impact.

PubMed

Rice, L J; Halbert, C H

2017-01-01

Although the association between social context and health has been demonstrated previously, much less is known about network interactions by gender, race/ethnicity, and sociodemographic characteristics. Given the variability in cancer outcomes among groups, research on these relationships may have important implications for addressing cancer health disparities. We examined the literature on social networks and cancer across the cancer continuum among adults. Relevant studies (N=16) were identified using two common databases: PubMed and Google Scholar. Most studies used a prospective cohort study design (n=9), included women only (n=11), and were located in the United States (n=14). Seventy-five percent of the studies reviewed used a validated scale or validated items to measure social networks (n=12). Only one study examined social network differences by race, 57.1% (n=8) focused on breast cancer alone, 14.3% (n=2) explored colorectal cancer or multiple cancers simultaneously, and 7.1% (n=1) only prostate cancer. More than half of the studies included multiple ethnicities in the sample, while one study included only low-income subjects. Despite findings of associations between social networks and cancer survival, risk, and screening, none of the studies utilized social networks as a mechanism for reducing health disparities; however, such an approach has been utilized for infectious disease control. Social networks and the support provided within these networks have important implications for health behaviors and ultimately cancer disparities. This review serves as the first step toward dialog on social networks as a missing component in the social determinants of cancer disparities literature that could move the needle upstream to target adverse cancer outcomes among vulnerable populations. © 2017 Elsevier Inc. All rights reserved.

optGpSampler: an improved tool for uniformly sampling the solution-space of genome-scale metabolic networks.

PubMed

Megchelenbrink, Wout; Huynen, Martijn; Marchiori, Elena

2014-01-01

Constraint-based models of metabolic networks are typically underdetermined, because they contain more reactions than metabolites. Therefore the solutions to this system do not consist of unique flux rates for each reaction, but rather a space of possible flux rates. By uniformly sampling this space, an estimated probability distribution for each reaction's flux in the network can be obtained. However, sampling a high dimensional network is time-consuming. Furthermore, the constraints imposed on the network give rise to an irregularly shaped solution space. Therefore more tailored, efficient sampling methods are needed. We propose an efficient sampling algorithm (called optGpSampler), which implements the Artificial Centering Hit-and-Run algorithm in a different manner than the sampling algorithm implemented in the COBRA Toolbox for metabolic network analysis, here called gpSampler. Results of extensive experiments on different genome-scale metabolic networks show that optGpSampler is up to 40 times faster than gpSampler. Application of existing convergence diagnostics on small network reconstructions indicate that optGpSampler converges roughly ten times faster than gpSampler towards similar sampling distributions. For networks of higher dimension (i.e. containing more than 500 reactions), we observed significantly better convergence of optGpSampler and a large deviation between the samples generated by the two algorithms. optGpSampler for Matlab and Python is available for non-commercial use at: http://cs.ru.nl/~wmegchel/optGpSampler/.

Social network and individual correlates of sexual risk behavior among homeless young men who have sex with men.

PubMed

Tucker, Joan S; Hu, Jianhui; Golinelli, Daniela; Kennedy, David P; Green, Harold D; Wenzel, Suzanne L

2012-10-01

There is growing interest in network-based interventions to reduce HIV sexual risk behavior among both homeless youth and men who have sex with men. The goal of this study was to better understand the social network and individual correlates of sexual risk behavior among homeless young men who have sex with men (YMSM) to inform these HIV prevention efforts. A multistage sampling design was used to recruit a probability sample of 121 homeless YMSM (ages: 16-24 years) from shelters, drop-in centers, and street venues in Los Angeles County. Face-to-face interviews were conducted. Because of the different distributions of the three outcome variables, three distinct regression models were needed: ordinal logistic regression for unprotected sex, zero-truncated Poisson regression for number of sex partners, and logistic regression for any sex trade. Homeless YMSM were less likely to engage in unprotected sex and had fewer sex partners if their networks included platonic ties to peers who regularly attended school, and had fewer sex partners if most of their network members were not heavy drinkers. Most other aspects of network composition were unrelated to sexual risk behavior. Individual predictors of sexual risk behavior included older age, Hispanic ethnicity, lower education, depressive symptoms, less positive condom attitudes, and sleeping outdoors because of nowhere else to stay. HIV prevention programs for homeless YMSM may warrant a multipronged approach that helps these youth strengthen their ties to prosocial peers, develop more positive condom attitudes, and access needed mental health and housing services. Copyright © 2012 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Bibliometric trends of health economic evaluation in Sub-Saharan Africa.

PubMed

Hernandez-Villafuerte, Karla; Li, Ryan; Hofman, Karen J

2016-08-24

Collaboration between Sub-Saharan African researchers is important for the generation and transfer of health technology assessment (HTA) evidence, in order to support priority-setting in health. The objective of this analysis was to evaluate collaboration patterns between countries. We conducted a rapid evidence assessment that included a random sample of health economic evaluations carried out in 20 countries (Angola, Botswana, Congo, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia, Zimbabwe, Ghana, Kenya, Nigeria, Ethiopia, Uganda). We conducted bibliometric network analysis based on all first authors with a Sub-Saharan African academic affiliation and their co-authored publications ("network-articles"). Then we produced a connection map of collaboration patterns among Sub-Saharan African researchers, reflecting the number of network-articles and the country of affiliation of the main co-authors. The sample of 119 economic evaluations mostly related to treatments of communicable diseases, in particular HIV/AIDS (42/119, 35.29 %) and malaria (26/119, 21.85 %). The 39 first authors from Sub-Saharan African institutions together co-authored 729 network-articles. The network analysis showed weak collaboration between health economic researchers in Sub-Saharan Africa, with researchers being more likely to collaborate with Europe and North America than with other African countries. South Africa stood out as producing the highest number of health economic evaluations and collaborations. The development and evaluation of HTA research networks in Sub-Saharan Africa should be supported, with South Africa central to any such efforts. Organizations and institutions from high income countries interested in supporting priority setting in Sub-Saharan Africa should include promoting collaboration as part of their agendas, in order to take advantage of the potential transferability of results and methods of the available health economic analyses in Africa and internationally.

Integrated expression analysis identifies transcription networks in mouse and human gastric neoplasia.

PubMed

Chen, Zheng; Soutto, Mohammed; Rahman, Bushra; Fazili, Muhammad W; Peng, DunFa; Blanca Piazuelo, Maria; Chen, Heidi; Kay Washington, M; Shyr, Yu; El-Rifai, Wael

2017-07-01

Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide. The Tff1 knockout (KO) mouse model develops gastric lesions that include low-grade dysplasia (LGD), high-grade dysplasia (HGD), and adenocarcinomas. In this study, we used Affymetrix microarrays gene expression platforms for analysis of molecular signatures in the mouse stomach [Tff1-KO (LGD) and Tff1 wild-type (normal)] and human gastric cancer tissues and their adjacent normal tissue samples. Combined integrated bioinformatics analysis of mouse and human datasets indicated that 172 genes were consistently deregulated in both human gastric cancer samples and Tff1-KO LGD lesions (P < .05). Using Ingenuity pathway analysis, these genes mapped to important transcription networks that include MYC, STAT3, β-catenin, RELA, NFATC2, HIF1A, and ETS1 in both human and mouse. Further analysis demonstrated activation of FOXM1 and inhibition of TP53 transcription networks in human gastric cancers but not in Tff1-KO LGD lesions. Using real-time RT-PCR, we validated the deregulated expression of several genes (VCAM1, BGN, CLDN2, COL1A1, COL1A2, COL3A1, EpCAM, IFITM1, MMP9, MMP12, MMP14, PDGFRB, PLAU, and TIMP1) that map to altered transcription networks in both mouse and human gastric neoplasia. Our study demonstrates significant similarities in deregulated transcription networks in human gastric cancer and gastric tumorigenesis in the Tff1-KO mouse model. The data also suggest that activation of MYC, STAT3, RELA, and β-catenin transcription networks could be an early molecular step in gastric carcinogenesis. © 2017 Wiley Periodicals, Inc.

Publications of the Jet Propulsion Laboratory 1982

NASA Technical Reports Server (NTRS)

1983-01-01

A bibliography of articles concerning topics on the deep space network, data acquisition, telecommunication, and related aerospace studies is presented. A sample of the diverse subjects include, solar energy remote sensing, computer science, Earth resources, astronomy, and satellite communication.

Friends, Depressive Symptoms, and Life Satisfaction Among Older Korean Americans.

PubMed

Roh, Soonhee; Lee, Yeon-Shim; Lee, Kyoung Hag; Shibusawa, Tazuko; Yoo, Grace J

2015-08-01

This study examined the interactive effects of social network support and depressive symptoms on life satisfaction among older Korean Americans (KAs). Using data from a sample of 200 elders in a large metropolitan area (M age = 72.50, SD = 5.15), hierarchical regression analysis was used to examine the interaction between social network support and depressive symptoms on life satisfaction among older KAs. After controlling for demographic variables, both social network support and depressive symptoms were identified as predictors for life satisfaction. Interaction effects indicated strong associations between higher social network support specifically from friends and lower depressive symptoms with higher levels of life satisfaction. Findings highlight the important role that friends play in terms of social network support for the mental health of older KAs, and the need for geriatric practitioners to monitor and assess the quality of social network support-including friendships-when working with older KAs.

Random walks and diffusion on networks

NASA Astrophysics Data System (ADS)

Masuda, Naoki; Porter, Mason A.; Lambiotte, Renaud

2017-11-01

Random walks are ubiquitous in the sciences, and they are interesting from both theoretical and practical perspectives. They are one of the most fundamental types of stochastic processes; can be used to model numerous phenomena, including diffusion, interactions, and opinions among humans and animals; and can be used to extract information about important entities or dense groups of entities in a network. Random walks have been studied for many decades on both regular lattices and (especially in the last couple of decades) on networks with a variety of structures. In the present article, we survey the theory and applications of random walks on networks, restricting ourselves to simple cases of single and non-adaptive random walkers. We distinguish three main types of random walks: discrete-time random walks, node-centric continuous-time random walks, and edge-centric continuous-time random walks. We first briefly survey random walks on a line, and then we consider random walks on various types of networks. We extensively discuss applications of random walks, including ranking of nodes (e.g., PageRank), community detection, respondent-driven sampling, and opinion models such as voter models.

A Social Network Analysis of the Financial Links Backing Health and Fitness Apps.

PubMed

Grundy, Quinn; Held, Fabian; Bero, Lisa

2017-11-01

To identify the major stakeholders in mobile health app development and to describe their financial relationships using social network analysis. We conducted a structured content analysis of a purposive sample of prominent health and fitness apps available in November 2015 in the United States, Canada, and Australia. We conducted a social network analysis of apps' developers, investors, other funding sources, and content advisors to describe the financial relationships underpinning health app development. Prominent health and fitness apps are largely developed by private companies based in North America, with an average of 4.7 (SD = 5.5) financial relations, including founders, external investors, acquiring companies, and commercial partnerships. Network analysis revealed a core of 41 sampled apps connected to 415 other entities by 466 financial relations. This core largely comprised apps published by major technology, pharmaceutical, and fashion corporations. About one third of apps named advisors, many of whom had commercial affiliations. Public health needs to extend its scrutiny and advocacy beyond the health messages contained within apps to understanding commercial influences on health and, when necessary, challenging them.

Temperamental factors in remitted depression: The role of effortful control and attentional mechanisms.

PubMed

Marchetti, Igor; Shumake, Jason; Grahek, Ivan; Koster, Ernst H W

2018-08-01

Temperamental effortful control and attentional networks are increasingly viewed as important underlying processes in depression and anxiety. However, it is still unknown whether these factors facilitate depressive and anxiety symptoms in the general population and, more specifically, in remitted depressed individuals. We investigated to what extent effortful control and attentional networks (i.e., Attention Network Task) explain concurrent depressive and anxious symptoms in healthy individuals (n = 270) and remitted depressed individuals (n = 90). Both samples were highly representative of the US population. Increased effortful control predicted a substantial decrease in symptoms of both depression and anxiety in the whole sample, whereas decreased efficiency of executive attention predicted a modest increase in depressive symptoms. Remitted depressed individuals did not show less effortful control nor less efficient attentional networks than healthy individuals. Moreover, clinical status did not moderate the relationship between temperamental factors and either depressive or anxiety symptoms. Limitations include the cross-sectional nature of the study. Our study shows that temperamental effortful control represents an important transdiagnostic process for depressive and anxiety symptoms in adults. Copyright © 2018 Elsevier B.V. All rights reserved.

Selective Self-Presentation and Social Comparison Through Photographs on Social Networking Sites.

PubMed

Fox, Jesse; Vendemia, Megan A

2016-10-01

Through social media and camera phones, users enact selective self-presentation as they choose, edit, and post photographs of themselves (such as selfies) to social networking sites for an imagined audience. Photos typically focus on users' physical appearance, which may compound existing sociocultural pressures about body image. We identified users of social networking sites among a nationally representative U.S. sample (N = 1,686) and examined women's and men's photo-related behavior, including posting photos, editing photos, and feelings after engaging in upward and downward social comparison with others' photos on social networking sites. We identified some sex differences: women edited photos more frequently and felt worse after upward social comparison than men. Body image and body comparison tendency mediated these effects.

Water-quality assessment of the Cambrian-Ordovician aquifer system in the northern Midwest, United States

USGS Publications Warehouse

Wilson, John T.

2012-01-01

This report provides a regional assessment of groundwater quality of the Cambrian-Ordovician aquifer system, based primarily on raw water samples collected by the NAWQA Program during 1995 through 2007. The NAWQA Program has published findings in local study-unit reports encompassing parts of the Cambrian-Ordovician aquifer system. Data collected from the aquifer system were used in national synthesis reports on selected topics such as specific water-quality constituent classes, well type, or aquifer material; however, a synthesis of groundwater quality at the principal aquifer scale has not been completed and is therefore the major purpose of this report. Water samples collected by the NAWQA Program were analyzed for various classes of characteristics including physical properties, major ions, trace elements, nutrients and dissolved organic carbon, radionuclides (tritium, radon, and radium), pesticides, and volatile organic compounds. Subsequent sections of this report provide discussions on these classes of characteristics. The assessment objectives of this report are to (1) summarize constituent concentrations and compare them to human-health benchmarks and non-health guidelines; (2) determine the geographic distribution of constituent concentrations and relate them to various factors such as confining conditions, well type, land use, and groundwater age; and (3) evaluate near-decadal-scale changes in nitrate concentrations and pesticide detections. The most recent sample collected from each well by the NAWQA Program was used for most analyses. Near-decadal-scale changes in nitrate concentrations and pesticide detections were evaluated for selected well networks by using the most recent sample from each well and comparing it to the results from a sample collected 7 or 11 years earlier. Because some of the NAWQA well networks provide a limited areal coverage of the aquifer system, data for raw water samples from other USGS sources and state agencies were included to expand the data coverage into areas between the NAWQA well networks and into northeastern Missouri. Many of the maps in this report that show concentrations of selected constituents include data from other sources to expand on the geographic area covered by the NAWQA data.

Reduced Left Lateralization of Language in Congenitally Blind Individuals.

PubMed

Lane, Connor; Kanjlia, Shipra; Richardson, Hilary; Fulton, Anne; Omaki, Akira; Bedny, Marina

2017-01-01

Language processing depends on a left-lateralized network of frontotemporal cortical regions. This network is remarkably consistent across individuals and cultures. However, there is also evidence that developmental factors, such as delayed exposure to language, can modify this network. Recently, it has been found that, in congenitally blind individuals, the typical frontotemporal language network expands to include parts of "visual" cortices. Here, we report that blindness is also associated with reduced left lateralization in frontotemporal language areas. We analyzed fMRI data from two samples of congenitally blind adults (n = 19 and n = 13) and one sample of congenitally blind children (n = 20). Laterality indices were computed for sentence comprehension relative to three different control conditions: solving math equations (Experiment 1), a memory task with nonwords (Experiment 2), and a "does this come next?" task with music (Experiment 3). Across experiments and participant samples, the frontotemporal language network was less left-lateralized in congenitally blind than in sighted individuals. Reduction in left lateralization was not related to Braille reading ability or amount of occipital plasticity. Notably, we observed a positive correlation between the lateralization of frontotemporal cortex and that of language-responsive occipital areas in blind individuals. Blind individuals with right-lateralized language responses in frontotemporal cortices also had right-lateralized occipital responses to language. Together, these results reveal a modified neurobiology of language in blindness. Our findings suggest that, despite its usual consistency across people, the neurobiology of language can be modified by nonlinguistic experiences.

Analysing the 21 cm signal from the epoch of reionization with artificial neural networks

NASA Astrophysics Data System (ADS)

Shimabukuro, Hayato; Semelin, Benoit

2017-07-01

The 21 cm signal from the epoch of reionization should be observed within the next decade. While a simple statistical detection is expected with Square Kilometre Array (SKA) pathfinders, the SKA will hopefully produce a full 3D mapping of the signal. To extract from the observed data constraints on the parameters describing the underlying astrophysical processes, inversion methods must be developed. For example, the Markov Chain Monte Carlo method has been successfully applied. Here, we test another possible inversion method: artificial neural networks (ANNs). We produce a training set that consists of 70 individual samples. Each sample is made of the 21 cm power spectrum at different redshifts produced with the 21cmFast code plus the value of three parameters used in the seminumerical simulations that describe astrophysical processes. Using this set, we train the network to minimize the error between the parameter values it produces as an output and the true values. We explore the impact of the architecture of the network on the quality of the training. Then we test the trained network on the new set of 54 test samples with different values of the parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameters at a given redshift, that including thermal noise and sample variance decreases the quality of the reconstruction and that using the power spectrum at several redshifts as an input to the ANN improves the quality of the reconstruction. We conclude that ANNs are a viable inversion method whose main strength is that they require a sparse exploration of the parameter space and thus should be usable with full numerical simulations.

A Data Scheduling and Management Infrastructure for the TEAM Network

NASA Astrophysics Data System (ADS)

Andelman, S.; Baru, C.; Chandra, S.; Fegraus, E.; Lin, K.; Unwin, R.

2009-04-01

The objective of the Tropical Ecology Assessment and Monitoring Network (www.teamnetwork.org) is "To generate real time data for monitoring long-term trends in tropical biodiversity through a global network of TEAM sites (i.e. field stations in tropical forests), providing an early warning system on the status of biodiversity to effectively guide conservation action". To achieve this, the TEAM Network operates by collecting data via standardized protocols at TEAM Sites. The standardized TEAM protocols include the Climate, Vegetation and Terrestrial Vertebrate Protocols. Some sites also implement additional protocols. There are currently 7 TEAM Sites with plans to grow the network to 15 by June 30, 2009 and 50 TEAM Sites by the end of 2010. Climate Protocol The Climate Protocol entails the collection of climate data via meteorological stations located at the TEAM Sites. This includes information such as precipitation, temperature, wind direction and strength and various solar radiation measurements. Vegetation Protocol The Vegetation Protocol collects standardized information on tropical forest trees and lianas. A TEAM Site will have between 6-9 1ha plots where trees and lianas larger than a pre-specified size are mapped, identified and measured. This results in each TEAM Site repeatedly measuring between 3000-5000 trees annually. Terrestrial Vertebrate Protocol The Terrestrial Vertebrate Protocol collects standardized information on mid-sized tropical forest fauna (i.e. birds and mammals). This information is collected via camera traps (i.e. digital cameras with motion sensors housed in weather proof casings). The images taken by the camera trap are reviewed to identify what species are captured in the image by the camera trap. The image and the interpretation of what is in the image are the data for the Terrestrial Vertebrate Protocol. The amount of data collected through the TEAM protocols provides a significant yet exciting IT challenge. The TEAM Network is currently partnering with the San Diego Super Computer Center to build the data management infrastructure. Data collected from the three core protocols as well as others are currently made available through the TEAM Network portal, which provides the content management framework, the data scheduling and management framework, an administrative framework to implement and manage TEAM sites, collaborative tools and a number of tools and applications utilizing Google Map and Google Earth products. A critical element of the TEAM Network data management infrastructure is to make the data publicly available in as close to real-time as possible (the TEAM Network Data Use Policy: http://www.teamnetwork.org/en/data/policy). This requires two essential tasks to be accomplished, 1) A data collection schedule has to be planned, proposed and approved for a given TEAM site. This is a challenging process since TEAM sites are geographically distributed across the tropics and hence have different seasons where they schedule field sampling for the different TEAM protocols. Capturing this information and ensuring that TEAM sites follow the outlined legal contract is key to the data collection process and 2) A stream-lined and efficient information management system to ensure data collected from the field meet the minimum data standards (i.e. are of the highest scientific quality) and are securely transferred, archived, processed and be rapidly made publicaly available, as a finished consumable product via the TEAM Network portal. The TEAM Network is achieving these goals by implementing an end-to-end framework consisting of the Sampling Scheduler application and the Data Management Framework. Sampling Scheduler The Sampling Scheduler is a project management, calendar based portal application that will allow scientists at a TEAM site to schedule field sampling for each of the TEAM protocols implemented at that site. The sampling scheduler addresses the specific requirements established in the TEAM protocols with the logistical scheduling needs of each TEAM Site. For example, each TEAM protocol defines when data must be collected (e.g. time of day, number of times per year, during which seasons, etc) as well as where data must be collected (from which sampling units, which trees, etc). Each TEAM Site has a limited number of resources and must create plans that will both satisfy the requirements of the protocols as well as be logistically feasible for their TEAM Site. With 15 TEAM Sites (and many more coming soon) the schedules of each TEAM Site must be communicated to the Network Office to ensure data are being collected as scheduled and to address the many problems when working in difficult environments like Tropical Forests. The Sampling Schedule provides built-in proposal and approval functionality to ensure that the TEAM Sites are and the Network office are in sync as well as provides the capability to modify schedules when needed. The Data Management Framework The Data Management framework is a three-tier data ingestion, edit and review application for protocols defined in the TEAM network. The data ingestion framework provides online web forms for field personnel to submit and edit data collected at TEAM Sites. These web forms will be accessible from the TEAM content management site. Once the data is securely uploaded, cured, processed and approved, it will be made publicly available for consumption by the scientific community. The Data Management framework, when combined with the Sampling Scheduler provides a closed loop Data Scheduling and Management infrastructure. All information starting from data collection plan, tools to input, modify and curate data, review and run QA/QC tests, as well as verify data are collected as planed are included. Finally, TEAM Network data are available for download via the Data Query and Download Application. This application utilizes a Google Maps custom interface to search, visualize, and download TEAM Network data. References • TEAM Network, http://www.teamnetwork.org • Center for Applied Biodiversity Science, Conservation International. http://science.conservation.org/portal/server.pt • TEAM Data Query and Download Application, http://www.teamnetwork.org/en/data/query

Sampling Migrants from their Social Networks: The Demography and Social Organization of Chinese Migrants in Dar es Salaam, Tanzania.

PubMed

Merli, M Giovanna; Verdery, Ashton; Mouw, Ted; Li, Jing

2016-07-01

The streams of Chinese migration to Africa are growing in tandem with rising Chinese investments and trade flows in and to the African continent. In spite of the high profile of this phenomenon in the media, there are few rich and broad descriptions of Chinese communities in Africa. Reasons for this include the rarity of official statistics on foreign-born populations in African censuses, the absence of predefined sampling frames required to draw representative samples with conventional survey methods and difficulties to reach certain segments of this population. Here, we use a novel network-based approach, Network Sampling with Memory, which overcomes the challenges of sampling 'hidden' populations in the absence of a sampling frame, to recruit a sample of recent Chinese immigrants in Dar es Salaam, Tanzania and collect information on the demographic characteristics, migration histories and social ties of members of this sample. These data reveal a heterogeneous Chinese community composed of "state-led" migrants who come to Africa to work on projects undertaken by large Chinese state-owned enterprises and "independent" migrants who come on their own accord to engage in various types of business ventures. They offer a rich description of the demographic profile and social organization of this community, highlight key differences between the two categories of migrants and map the structure of the social ties linking them. We highlight needs for future research on inter-group differences in individual motivations for migration, economic activities, migration outcomes, expectations about future residence in Africa, social integration and relations with local communities.

Sampling Migrants from their Social Networks: The Demography and Social Organization of Chinese Migrants in Dar es Salaam, Tanzania

PubMed Central

Merli, M. Giovanna; Verdery, Ashton; Mouw, Ted; Li, Jing

2016-01-01

The streams of Chinese migration to Africa are growing in tandem with rising Chinese investments and trade flows in and to the African continent. In spite of the high profile of this phenomenon in the media, there are few rich and broad descriptions of Chinese communities in Africa. Reasons for this include the rarity of official statistics on foreign-born populations in African censuses, the absence of predefined sampling frames required to draw representative samples with conventional survey methods and difficulties to reach certain segments of this population. Here, we use a novel network-based approach, Network Sampling with Memory, which overcomes the challenges of sampling ‘hidden’ populations in the absence of a sampling frame, to recruit a sample of recent Chinese immigrants in Dar es Salaam, Tanzania and collect information on the demographic characteristics, migration histories and social ties of members of this sample. These data reveal a heterogeneous Chinese community composed of “state-led” migrants who come to Africa to work on projects undertaken by large Chinese state-owned enterprises and “independent” migrants who come on their own accord to engage in various types of business ventures. They offer a rich description of the demographic profile and social organization of this community, highlight key differences between the two categories of migrants and map the structure of the social ties linking them. We highlight needs for future research on inter-group differences in individual motivations for migration, economic activities, migration outcomes, expectations about future residence in Africa, social integration and relations with local communities. PMID:27746912

Bias and precision of selected analytes reported by the National Atmospheric Deposition Program and National Trends Network, 1984

USGS Publications Warehouse

Brooks, M.H.; Schroder, L.J.; Willoughby, T.C.

1987-01-01

The U.S. Geological Survey operated a blind audit sample program during 1974 to test the effects of the sample handling and shipping procedures used by the National Atmospheric Deposition Program and National Trends Network on the quality of wet deposition data produced by the combined networks. Blind audit samples, which were dilutions of standard reference water samples, were submitted by network site operators to the central analytical laboratory disguised as actual wet deposition samples. Results from the analyses of blind audit samples were used to calculate estimates of analyte bias associated with all network wet deposition samples analyzed in 1984 and to estimate analyte precision. Concentration differences between double blind samples that were submitted to the central analytical laboratory and separate analyses of aliquots of those blind audit samples that had not undergone network sample handling and shipping were used to calculate analyte masses that apparently were added to each blind audit sample by routine network handling and shipping procedures. These calculated masses indicated statistically significant biases for magnesium, sodium , potassium, chloride, and sulfate. Median calculated masses were 41.4 micrograms (ug) for calcium, 14.9 ug for magnesium, 23.3 ug for sodium, 0.7 ug for potassium, 16.5 ug for chloride and 55.3 ug for sulfate. Analyte precision was estimated using two different sets of replicate measures performed by the central analytical laboratory. Estimated standard deviations were similar to those previously reported. (Author 's abstract)

Affective network and default mode network in depressive adolescents with disruptive behaviors

PubMed Central

Kim, Sun Mi; Park, Sung Yong; Kim, Young In; Son, Young Don; Chung, Un-Sun; Min, Kyung Joon; Han, Doug Hyun

2016-01-01

Aim Disruptive behaviors are thought to affect the progress of major depressive disorder (MDD) in adolescents. In resting-state functional connectivity (RSFC) studies of MDD, the affective network (limbic network) and the default mode network (DMN) have garnered a great deal of interest. We aimed to investigate RSFC in a sample of treatment-naïve adolescents with MDD and disruptive behaviors. Methods Twenty-two adolescents with MDD and disruptive behaviors (disrup-MDD) and 20 age- and sex-matched healthy control (HC) participants underwent resting-state functional magnetic resonance imaging (fMRI). We used a seed-based correlation approach concerning two brain circuits including the affective network and the DMN, with two seed regions including the bilateral amygdala for the limbic network and the bilateral posterior cingulate cortex (PCC) for the DMN. We also observed a correlation between RSFC and severity of depressive symptoms and disruptive behaviors. Results The disrup-MDD participants showed lower RSFC from the amygdala to the orbitofrontal cortex and parahippocampal gyrus compared to HC participants. Depression scores in disrup-MDD participants were negatively correlated with RSFC from the amygdala to the right orbitofrontal cortex. The disrup-MDD participants had higher PCC RSFC compared to HC participants in a cluster that included the left precentral gyrus, left insula, and left parietal lobe. Disruptive behavior scores in disrup-MDD patients were positively correlated with RSFC from the PCC to the left insular cortex. Conclusion Depressive mood might be correlated with the affective network, and disruptive behavior might be correlated with the DMN in adolescent depression. PMID:26770059

Respondent-driven sampling and the recruitment of people with small injecting networks.

PubMed

Paquette, Dana; Bryant, Joanne; de Wit, John

2012-05-01

Respondent-driven sampling (RDS) is a form of chain-referral sampling, similar to snowball sampling, which was developed to reach hidden populations such as people who inject drugs (PWID). RDS is said to reach members of a hidden population that may not be accessible through other sampling methods. However, less attention has been paid as to whether there are segments of the population that are more likely to be missed by RDS. This study examined the ability of RDS to capture people with small injecting networks. A study of PWID, using RDS, was conducted in 2009 in Sydney, Australia. The size of participants' injecting networks was examined by recruitment chain and wave. Participants' injecting network characteristics were compared to those of participants from a separate pharmacy-based study. A logistic regression analysis was conducted to examine the characteristics independently associated with having small injecting networks, using the combined RDS and pharmacy-based samples. In comparison with the pharmacy-recruited participants, RDS participants were almost 80% less likely to have small injecting networks, after adjusting for other variables. RDS participants were also more likely to have their injecting networks form a larger proportion of those in their social networks, and to have acquaintances as part of their injecting networks. Compared to those with larger injecting networks, individuals with small injecting networks were equally likely to engage in receptive sharing of injecting equipment, but less likely to have had contact with prevention services. These findings suggest that those with small injecting networks are an important group to recruit, and that RDS is less likely to capture these individuals.

Phenotypic constraints promote latent versatility and carbon efficiency in metabolic networks.

PubMed

Bardoscia, Marco; Marsili, Matteo; Samal, Areejit

2015-07-01

System-level properties of metabolic networks may be the direct product of natural selection or arise as a by-product of selection on other properties. Here we study the effect of direct selective pressure for growth or viability in particular environments on two properties of metabolic networks: latent versatility to function in additional environments and carbon usage efficiency. Using a Markov chain Monte Carlo (MCMC) sampling based on flux balance analysis (FBA), we sample from a known biochemical universe random viable metabolic networks that differ in the number of directly constrained environments. We find that the latent versatility of sampled metabolic networks increases with the number of directly constrained environments and with the size of the networks. We then show that the average carbon wastage of sampled metabolic networks across the constrained environments decreases with the number of directly constrained environments and with the size of the networks. Our work expands the growing body of evidence about nonadaptive origins of key functional properties of biological networks.

Validation of artificial neural network models for predicting biochemical markers associated with male infertility.

PubMed

Vickram, A S; Kamini, A Rao; Das, Raja; Pathy, M Ramesh; Parameswari, R; Archana, K; Sridharan, T B

2016-08-01

Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres. AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous epididymal sperm spiration; RBFN: radical basis function network; SRNN: simple recurrent neural network; SVM: support vector machines; TSE: testicular sperm extraction; WHO: World Health Organization.

Social Networks and Well-being: A Comparison of Older People in Mediterranean and Non-Mediterranean Countries

PubMed Central

2010-01-01

Objectives. This study examined whether the social networks of older persons in Mediterranean and non-Mediterranean countries were appreciably different and whether they functioned in similar ways in relation to well-being outcomes. Methods. The sample included family household respondents aged 60 years and older from the first wave of the Survey of Health, Ageing and Retirement in Europe in 5 Mediterranean (n = 3,583) and 7 non-Mediterranean (n = 5,471) countries. Region was regressed separately by gender on variables from 4 network domains: structure and interaction, exchange, engagement and relationship quality, and controlling for background and health characteristics. In addition, 2 well-being outcomes—depressive symptoms and perceived income inadequacy—were regressed on the study variables, including regional social network interaction terms. Results. The results revealed differences across the 2 regional settings in each of the realms of social network, above and beyond the differences that exist in background characteristics and health status. The findings also showed that the social network variables had different effects on the well-being outcomes in the respective settings. Discussion. The findings underscore that the social network phenomenon is contextually bound. The social networks of older people should be seen within their unique regional milieu and in relation to the values and social norms that prevail in different sets of societies. PMID:20008485

Using a two-phase evolutionary framework to select multiple network spreaders based on community structure

NASA Astrophysics Data System (ADS)

Fu, Yu-Hsiang; Huang, Chung-Yuan; Sun, Chuen-Tsai

2016-11-01

Using network community structures to identify multiple influential spreaders is an appropriate method for analyzing the dissemination of information, ideas and infectious diseases. For example, data on spreaders selected from groups of customers who make similar purchases may be used to advertise products and to optimize limited resource allocation. Other examples include community detection approaches aimed at identifying structures and groups in social or complex networks. However, determining the number of communities in a network remains a challenge. In this paper we describe our proposal for a two-phase evolutionary framework (TPEF) for determining community numbers and maximizing community modularity. Lancichinetti-Fortunato-Radicchi benchmark networks were used to test our proposed method and to analyze execution time, community structure quality, convergence, and the network spreading effect. Results indicate that our proposed TPEF generates satisfactory levels of community quality and convergence. They also suggest a need for an index, mechanism or sampling technique to determine whether a community detection approach should be used for selecting multiple network spreaders.

Chloroplast heterogeneity and historical admixture within the genus Malus.

PubMed

Volk, Gayle M; Henk, Adam D; Baldo, Angela; Fazio, Gennaro; Chao, C Thomas; Richards, Christopher M

2015-07-01

• The genus Malus represents a unique and complex evolutionary context in which to study domestication. Several Malus species have provided novel alleles and traits to the cultivars. The extent of admixture among wild Malus species has not been well described, due in part to limited sampling of individuals within a taxon.• Four chloroplast regions (1681 bp total) were sequenced and aligned for 412 Malus individuals from 30 species. Phylogenetic relationships were reconstructed using maximum parsimony. The distribution of chloroplast haplotypes among species was examined using statistical parsimony, phylogenetic trees, and a median-joining network.• Chloroplast haplotypes are shared among species within Malus. Three major haplotype-sharing networks were identified. One includes species native to China, Western North America, as well as Malus domestica Borkh, and its four primary progenitor species: M. sieversii (Ledeb.) M. Roem., M. orientalis Uglitzk., M. sylvestris (L.) Mill., and M. prunifolia (Willd.) Borkh; another includes five Chinese Malus species, and a third includes the three Malus species native to Eastern North America.• Chloroplast haplotypes found in M. domestica belong to a single, highly admixed network. Haplotypes shared between the domesticated apple and its progenitors may reflect historical introgression or the retention of ancestral polymorphisms. Multiple individuals should be sampled within Malus species to reveal haplotype heterogeneity, if complex maternal contributions to named species are to be recognized. © 2015 Botanical Society of America, Inc.

ARM Carbon Cycle Gases Flasks at SGP Site

DOE Data Explorer

Biraud, Sebastien

2013-03-26

Data from flasks are sampled at the Atmospheric Radiation Measurement Program ARM, Southern Great Plains Site and analyzed by the National Oceanic and Atmospheric Administration NOAA, Earth System Research Laboratory ESRL. The SGP site is included in the NOAA Cooperative Global Air Sampling Network. The surface samples are collected from a 60 m tower at the ARM SGP Central Facility, usually once per week in the afternoon. The aircraft samples are collected approximately weekly from a chartered aircraft, and the collection flight path is centered over the tower where the surface samples are collected. The samples are collected by the ARM and LBNL Carbon Project.

Local synchronization of chaotic neural networks with sampled-data and saturating actuators.

PubMed

Wu, Zheng-Guang; Shi, Peng; Su, Hongye; Chu, Jian

2014-12-01

This paper investigates the problem of local synchronization of chaotic neural networks with sampled-data and actuator saturation. A new time-dependent Lyapunov functional is proposed for the synchronization error systems. The advantage of the constructed Lyapunov functional lies in the fact that it is positive definite at sampling times but not necessarily between sampling times, and makes full use of the available information about the actual sampling pattern. A local stability condition of the synchronization error systems is derived, based on which a sampled-data controller with respect to the actuator saturation is designed to ensure that the master neural networks and slave neural networks are locally asymptotically synchronous. Two optimization problems are provided to compute the desired sampled-data controller with the aim of enlarging the set of admissible initial conditions or the admissible sampling upper bound ensuring the local synchronization of the considered chaotic neural networks. A numerical example is used to demonstrate the effectiveness of the proposed design technique.

An inventory of terrestrial mammals at national parks in the Northeast Temperate Network and Sagamore Hill National Historic Site

USGS Publications Warehouse

Gilbert, Andrew T.; O'Connell, Allan F.; Annand, Elizabeth M.; Talancy, Neil W.; Sauer, John R.; Nichols, James D.

2008-01-01

An inventory of mammals was conducted during 2004 at nine national park sites in the Northeast Temperate Network (NETN): Acadia National Park (NP), Marsh-Billings-Rockefeller National Historical Park (NHP), Minute Man NHP, Morristown NHP, Roosevelt-Vanderbilt National Historic Site (NHS), Saint-Gaudens NHS, Saugus Iron Works NHS, Saratoga NHP, and Weir Farm NHS. Sagamore Hill NHS, part of the Northeast Coastal and Barrier Network (NCBN), was also surveyed. Each park except Acadia NP was sampled twice, once in the winter/spring and again in the summer/fall. During the winter/spring visit, indirect measure (IM) sampling arrays were employed at 2 to 16 stations and included sampling by remote cameras, cubby boxes (covered trackplates), and hair traps. IM stations were established and re-used during the summer/fall sampling period. Trapping was conducted at 2 to 12 stations at all parks except Acadia NP during the summer/fall period and consisted of arrays of small-mammal traps, squirrel-sized live traps, and some fox-sized live traps. We used estimation-based procedures and probabilistic sampling techniques to design this inventory. A total of 38 species was detected by IM sampling, trapping, and field observations. Species diversity (number of species) varied among parks, ranging from 8 to 24, with Minute Man NHP having the most species detected. Raccoon (Procyon lotor), Virginia Opossum (Didelphis virginiana), Fisher (Martes pennanti), and Domestic Cat (Felis silvestris) were the most common medium-sized mammals detected in this study and White-footed Mouse (Peromyscus leucopus), Northern Short-tailed Shrew (Blarina brevicauda), Deer Mouse (P. maniculatus), and Meadow Vole (Microtus pennsylvanicus) the most common small mammals detected. All species detected are considered fairly common throughout their range including the Fisher, which has been reintroduced in several New England states. We did not detect any state or federal endangered or threatened species.

Surface-water-quality assessment of the Kentucky River Basin, Kentucky; fixed-station network and selected water-quality data, April 1987 through August 1991

USGS Publications Warehouse

Griffin, M.S.; Martin, G.R.; White, K.D.

1994-01-01

This report describes selected data-collection activities and the associated data collected during the Kentucky River Basin pilot study of the U.S. Geological Survey's National Water-Quality Assessment Program. The data are intended to provide a nationally consistent description and improved understanding of current water quality in the basin. The data were collected at seven fixed stations that represent stream cross sections where constituent transport and water-quality trends can be evaluated. The report includes descriptions of (1) the basin; (2) the design of the fixed-station network; (3) the fixed-station sites; (4) the physical and chemical measurements; (5) the methods of sample collection, processing, and analysis; and (6) the quality-assurance and quality-control procedures. Water-quality data collected at the fixed stations during routine periodic sampling and supplemental high-flow sampling from April 1987 to August 1991 are presented.

High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection.

PubMed

Cruz-Roa, Angel; Gilmore, Hannah; Basavanhally, Ajay; Feldman, Michael; Ganesan, Shridar; Shih, Natalie; Tomaszewski, John; Madabhushi, Anant; González, Fabio

2018-01-01

Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. However, CNNs are typically only tenable with relatively small image sizes (200 × 200 pixels). Only recently, Fully convolutional networks (FCN) are able to deal with larger image sizes (500 × 500 pixels) for semantic segmentation. Hence, the direct application of CNNs to WSI is not computationally feasible because for a WSI, a CNN would require billions or trillions of parameters. To alleviate this issue, this paper presents a novel method, High-throughput Adaptive Sampling for whole-slide Histopathology Image analysis (HASHI), which involves: i) a new efficient adaptive sampling method based on probability gradient and quasi-Monte Carlo sampling, and, ii) a powerful representation learning classifier based on CNNs. We applied HASHI to automated detection of invasive breast cancer on WSI. HASHI was trained and validated using three different data cohorts involving near 500 cases and then independently tested on 195 studies from The Cancer Genome Atlas. The results show that (1) the adaptive sampling method is an effective strategy to deal with WSI without compromising prediction accuracy by obtaining comparative results of a dense sampling (∼6 million of samples in 24 hours) with far fewer samples (∼2,000 samples in 1 minute), and (2) on an independent test dataset, HASHI is effective and robust to data from multiple sites, scanners, and platforms, achieving an average Dice coefficient of 76%.

High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection

PubMed Central

Gilmore, Hannah; Basavanhally, Ajay; Feldman, Michael; Ganesan, Shridar; Shih, Natalie; Tomaszewski, John; Madabhushi, Anant; González, Fabio

2018-01-01

Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. However, CNNs are typically only tenable with relatively small image sizes (200 × 200 pixels). Only recently, Fully convolutional networks (FCN) are able to deal with larger image sizes (500 × 500 pixels) for semantic segmentation. Hence, the direct application of CNNs to WSI is not computationally feasible because for a WSI, a CNN would require billions or trillions of parameters. To alleviate this issue, this paper presents a novel method, High-throughput Adaptive Sampling for whole-slide Histopathology Image analysis (HASHI), which involves: i) a new efficient adaptive sampling method based on probability gradient and quasi-Monte Carlo sampling, and, ii) a powerful representation learning classifier based on CNNs. We applied HASHI to automated detection of invasive breast cancer on WSI. HASHI was trained and validated using three different data cohorts involving near 500 cases and then independently tested on 195 studies from The Cancer Genome Atlas. The results show that (1) the adaptive sampling method is an effective strategy to deal with WSI without compromising prediction accuracy by obtaining comparative results of a dense sampling (∼6 million of samples in 24 hours) with far fewer samples (∼2,000 samples in 1 minute), and (2) on an independent test dataset, HASHI is effective and robust to data from multiple sites, scanners, and platforms, achieving an average Dice coefficient of 76%. PMID:29795581

Revealing the potential pathogenesis of glioma by utilizing a glioma associated protein-protein interaction network.

PubMed

Pan, Weiran; Li, Gang; Yang, Xiaoxiao; Miao, Jinming

2015-04-01

This study aims to explore the potential mechanism of glioma through bioinformatic approaches. The gene expression profile (GSE4290) of glioma tumor and non-tumor samples was downloaded from Gene Expression Omnibus database. A total of 180 samples were available, including 23 non-tumor and 157 tumor samples. Then the raw data were preprocessed using robust multiarray analysis, and 8,890 differentially expressed genes (DEGs) were identified by using t-test (false discovery rate < 0.0005). Furthermore, 16 known glioma related genes were abstracted from Genetic Association Database. After mapping 8,890 DEGs and 16 known glioma related genes to Human Protein Reference Database, a glioma associated protein-protein interaction network (GAPN) was constructed. In addition, 51 sub-networks in GAPN were screened out through Molecular Complex Detection (score ≥ 1), and sub-network 1 was found to have the closest interaction (score = 3). What' more, for the top 10 sub-networks, Gene Ontology (GO) enrichment analysis (p value < 0.05) was performed, and DEGs involved in sub-network 1 and 2, such as BRMS1L and CCNA1, were predicted to regulate cell growth, cell cycle, and DNA replication via interacting with known glioma related genes. Finally, the overlaps of DEGs and human essential, housekeeping, tissue-specific genes were calculated (p value = 1.0, 1.0, and 0.00014, respectively) and visualized by Venn Diagram package in R. About 61% of human tissue-specific genes were DEGs as well. This research shed new light on the pathogenesis of glioma based on DEGs and GAPN, and our findings might provide potential targets for clinical glioma treatment.

Traffic-Adaptive, Flow-Specific Medium Access for Wireless Networks

DTIC Science & Technology

2009-09-01

hybrid, contention and non-contention schemes are shown to be special cases. This work also compares the energy efficiency of centralized and distributed...solutions and proposes an energy efficient version of traffic-adaptive CWS-MAC that includes an adaptive sleep cycle coordinated through the use of...preamble sampling. A preamble sampling probability parameter is introduced to manage the trade-off between energy efficiency and throughput and delay

Methods for evaluating temporal groundwater quality data and results of decadal-scale changes in chloride, dissolved solids, and nitrate concentrations in groundwater in the United States, 1988-2010

USGS Publications Warehouse

Lindsey, Bruce D.; Rupert, Michael G.

2012-01-01

Decadal-scale changes in groundwater quality were evaluated by the U.S. Geological Survey National Water-Quality Assessment (NAWQA) Program. Samples of groundwater collected from wells during 1988-2000 - a first sampling event representing the decade ending the 20th century - were compared on a pair-wise basis to samples from the same wells collected during 2001-2010 - a second sampling event representing the decade beginning the 21st century. The data set consists of samples from 1,236 wells in 56 well networks, representing major aquifers and urban and agricultural land-use areas, with analytical results for chloride, dissolved solids, and nitrate. Statistical analysis was done on a network basis rather than by individual wells. Although spanning slightly more or less than a 10-year period, the two-sample comparison between the first and second sampling events is referred to as an analysis of decadal-scale change based on a step-trend analysis. The 22 principal aquifers represented by these 56 networks account for nearly 80 percent of the estimated withdrawals of groundwater used for drinking-water supply in the Nation. Well networks where decadal-scale changes in concentrations were statistically significant were identified using the Wilcoxon-Pratt signed-rank test. For the statistical analysis of chloride, dissolved solids, and nitrate concentrations at the network level, more than half revealed no statistically significant change over the decadal period. However, for networks that had statistically significant changes, increased concentrations outnumbered decreased concentrations by a large margin. Statistically significant increases of chloride concentrations were identified for 43 percent of 56 networks. Dissolved solids concentrations increased significantly in 41 percent of the 54 networks with dissolved solids data, and nitrate concentrations increased significantly in 23 percent of 56 networks. At least one of the three - chloride, dissolved solids, or nitrate - had a statistically significant increase in concentration in 66 percent of the networks. Statistically significant decreases in concentrations were identified in 4 percent of the networks for chloride, 2 percent of the networks for dissolved solids, and 9 percent of the networks for nitrate. A larger percentage of urban land-use networks had statistically significant increases in chloride, dissolved solids, and nitrate concentrations than agricultural land-use networks. In order to assess the magnitude of statistically significant changes, the median of the differences between constituent concentrations from the first full-network sampling event and those from the second full-network sampling event was calculated using the Turnbull method. The largest median decadal increases in chloride concentrations were in networks in the Upper Illinois River Basin (67 mg/L) and in the New England Coastal Basins (34 mg/L), whereas the largest median decadal decrease in chloride concentrations was in the Upper Snake River Basin (1 mg/L). The largest median decadal increases in dissolved solids concentrations were in networks in the Rio Grande Valley (260 mg/L) and the Upper Illinois River Basin (160 mg/L). The largest median decadal decrease in dissolved solids concentrations was in the Apalachicola-Chattahoochee-Flint River Basin (6.0 mg/L). The largest median decadal increases in nitrate as nitrogen (N) concentrations were in networks in the South Platte River Basin (2.0 mg/L as N) and the San Joaquin-Tulare Basins (1.0 mg/L as N). The largest median decadal decrease in nitrate concentrations was in the Santee River Basin and Coastal Drainages (0.63 mg/L). The magnitude of change in networks with statistically significant increases typically was much larger than the magnitude of change in networks with statistically significant decreases. The magnitude of change was greatest for chloride in the urban land-use networks and greatest for dissolved solids and nitrate in the agricultural land-use networks. Analysis of data from all networks combined indicated statistically significant increases for chloride, dissolved solids, and nitrate. Although chloride, dissolved solids, and nitrate concentrations were typically less than the drinking-water standards and guidelines, a statistical test was used to determine whether or not the proportion of samples exceeding the drinking-water standard or guideline changed significantly between the first and second full-network sampling events. The proportion of samples exceeding the U.S. Environmental Protection Agency (USEPA) Secondary Maximum Contaminant Level for dissolved solids (500 milligrams per liter) increased significantly between the first and second full-network sampling events when evaluating all networks combined at the national level. Also, for all networks combined, the proportion of samples exceeding the USEPA Maximum Contaminant Level (MCL) of 10 mg/L as N for nitrate increased significantly. One network in the Delmarva Peninsula had a significant increase in the proportion of samples exceeding the MCL for nitrate. A subset of 261 wells was sampled every other year (biennially) to evaluate decadal-scale changes using a time-series analysis. The analysis of the biennial data set showed that changes were generally similar to the findings from the analysis of decadal-scale change that was based on a step-trend analysis. Because of the small number of wells in a network with biennial data (typically 4-5 wells), the time-series analysis is more useful for understanding water-quality responses to changes in site-specific conditions rather than as an indicator of the change for the entire network.

Rotor assembly and method for automatically processing liquids

DOEpatents

Burtis, Carl A.; Johnson, Wayne F.; Walker, William A.

1992-01-01

A rotor assembly for performing a relatively large number of processing steps upon a sample, such as a whole blood sample, and a diluent, such as water, includes a rotor body for rotation about an axis and including a network of chambers within which various processing steps are performed upon the sample and diluent and passageways through which the sample and diluent are transferred. A transfer mechanism is movable through the rotor body by the influence of a magnetic field generated adjacent the transfer mechanism and movable along the rotor body, and the assembly utilizes centrifugal force, a transfer of momentum and capillary action to perform any of a number of processing steps such as separation, aliquoting, transference, washing, reagent addition and mixing of the sample and diluent within the rotor body. The rotor body is particularly suitable for automatic immunoassay analyses.

Surface-water data and statistics from U.S. Geological Survey data-collection networks in New Jersey on the World Wide Web

USGS Publications Warehouse

Reiser, Robert G.; Watson, Kara M.; Chang, Ming; Nieswand, Steven P.

2002-01-01

The U.S. Geological Survey (USGS), in cooperation with other Federal, State, and local agencies, operates and maintains a variety of surface-water data-collection networks throughout the State of New Jersey. The networks include streamflow-gaging stations, low-flow sites, crest-stage gages, tide gages, tidal creststage gages, and water-quality sampling sites. Both real-time and historical surface-water data for many of the sites in these networks are available at the USGS, New Jersey District, web site (http://nj.usgs.gov/), and water-quality data are available at the USGS National Water Information System (NWIS) web site (http://waterdata.usgs.gov/nwis/). These data are an important source of information for water managers, engineers, environmentalists, and private citizens.

Social Network, Activity Participation, and Cognition: A Complex Relationship.

PubMed

Litwin, Howard; Stoeckel, Kimberly J

2016-01-01

This study examined how two domains of engagement-social network and activity participation-associate with objective and subjective cognitive function in later life. Specific consideration was given as to how these two spheres intersect in regard to recall and memory. The analytic sample included Europeans aged 60 and older drawn from the fourth wave of the Survey of Health Ageing and Retirement in Europe in which a new name-generated social network inventory was implemented. Multivariate analyses revealed that activity participation yielded stronger positive associations with word recall and self-rated memory than social network alone. However, the interactions indicate that this association lessened in strength for both the objective and subjective cognitive outcome measures as social network resources increased. The findings suggest that the social component of activity participation may be partially contributing to the positive role that such engagement has on cognitive well-being in later life. © The Author(s) 2015.

Neural network recognition of chemical class information in mobility spectra obtained at high temperatures

NASA Technical Reports Server (NTRS)

Bell, S.; Nazarov, E.; Wang, Y. F.; Rodriguez, J. E.; Eiceman, G. A.

2000-01-01

A minimal neural network was applied to a large library of high-temperature mobility spectra drawn from 16 chemical classes including 154 substances with 2000 spectra at various concentrations. A genetic algorithm was used to create a representative subset of points from the mobility spectrum as input to a cascade-type back-propagation network. This network demonstrated that significant information specific to chemical class was located in the spectral region near the reactant ions. This network failed to generalize the solution to unfamiliar compounds necessitating the use of complete spectra in network processing. An extended back-propagation network classified unfamiliar chemicals by functional group with a mean for average values of 0.83 without sulfides and 0.79 with sulfides. Further experiments confirmed that chemical class information was resident in the spectral region near the reactant ions. Deconvolution of spectra demonstrated the presence of ions, merged with the reactant ion peaks that originated from introduced samples. The ability of the neural network to generalize the solution to unfamiliar compounds suggests that these ions are distinct and class specific.

Fundamental Principles of Network Formation among Preschool Children1

PubMed Central

Schaefer, David R.; Light, John M.; Fabes, Richard A.; Hanish, Laura D.; Martin, Carol Lynn

2009-01-01

The goal of this research was to investigate the origins of social networks by examining the formation of children’s peer relationships in 11 preschool classes throughout the school year. We investigated whether several fundamental processes of relationship formation were evident at this age, including reciprocity, popularity, and triadic closure effects. We expected these mechanisms to change in importance over time as the network crystallizes, allowing more complex structures to evolve from simpler ones in a process we refer to as structural cascading. We analyzed intensive longitudinal observational data of children’s interactions using the SIENA actor-based model. We found evidence that reciprocity, popularity, and triadic closure all shaped the formation of preschool children’s networks. The influence of reciprocity remained consistent, whereas popularity and triadic closure became increasingly important over the course of the school year. Interactions between age and endogenous network effects were nonsignificant, suggesting that these network formation processes were not moderated by age in this sample of young children. We discuss the implications of our longitudinal network approach and findings for the study of early network developmental processes. PMID:20161606

Sample selection via angular distance in the space of the arguments of an artificial neural network

NASA Astrophysics Data System (ADS)

Fernández Jaramillo, J. M.; Mayerle, R.

2018-05-01

In the construction of an artificial neural network (ANN) a proper data splitting of the available samples plays a major role in the training process. This selection of subsets for training, testing and validation affects the generalization ability of the neural network. Also the number of samples has an impact in the time required for the design of the ANN and the training. This paper introduces an efficient and simple method for reducing the set of samples used for training a neural network. The method reduces the required time to calculate the network coefficients, while keeping the diversity and avoiding overtraining the ANN due the presence of similar samples. The proposed method is based on the calculation of the angle between two vectors, each one representing one input of the neural network. When the angle formed among samples is smaller than a defined threshold only one input is accepted for the training. The accepted inputs are scattered throughout the sample space. Tidal records are used to demonstrate the proposed method. The results of a cross-validation show that with few inputs the quality of the outputs is not accurate and depends on the selection of the first sample, but as the number of inputs increases the accuracy is improved and differences among the scenarios with a different starting sample have and important reduction. A comparison with the K-means clustering algorithm shows that for this application the proposed method with a smaller number of samples is producing a more accurate network.

Covalently Cross-linked Elastomers with Self-Healing and Malleable Abilities Enabled by Boronic Ester Bonds.

PubMed

Chen, Yi; Tang, Zhenghai; Zhang, Xuhui; Liu, Yingjun; Wu, Siwu; Guo, Baochun

2018-06-26

Covalently cross-linked rubbers are renowned for their high elasticity that play an indispensable role in various applications including tires, seals, medical implants. Development of self-healing and malleable rubbers is highly desirable as it allows for damage repair and reprocessibility to extend the lifetime and alleviate environmental pollution. Herein, we propose a facile approach to prepare permanently cross-linked yet self-healing and recyclable diene-rubber by programming dynamic boronic ester linkages into the network. The network is synthesized through one-pot thermally initiated thiol-ene "click" reaction between a novel dithiol-containing boronic ester cross-linker and commonly used styrene-butadiene rubber (SBR) without modifying the macromolecular structure. The resulted samples are covalently cross-linked and possess relatively high mechanical strength which can be readily tailored by varying boronic ester content. Owning to the transesterification of boronic ester bonds, the samples can alter network topologies, endowing the materials with self-healing ability and malleability.

Nested sampling at karst springs: from basic patterns to event triggered sampling and on-line monitoring.

NASA Astrophysics Data System (ADS)

Stadler, Hermann; Skritek, Paul; Zerobin, Wolfgang; Klock, Erich; Farnleitner, Andreas H.

2010-05-01

In the last year, global changes in ecosystems, the growth of population, and modifications of the legal framework within the EU have caused an increased need of qualitative groundwater and spring water monitoring with the target to continue to supply the consumers with high-quality drinking water in the future. Additionally the demand for sustainable protection of drinking water resources effected the initiated implementation of early warning systems and quality assurance networks in water supplies. In the field of hydrogeological investigations, event monitoring and event sampling is worst case scenario monitoring. Therefore, such tools become more and more indispensible to get detailed information about aquifer parameter and vulnerability. In the framework of water supplies, smart sampling designs combined with in-situ measurements of different parameters and on-line access can play an important role in early warning systems and quality surveillance networks. In this study nested sampling tiers are presented, which were designed to cover total system dynamic. Basic monitoring sampling (BMS), high frequency sampling (HFS) and automated event sampling (AES) were combined. BMS was organized with a monthly increment for at least two years, and HFS was performed during times of increased groundwater recharge (e.g. during snowmelt). At least one AES tier was embedded in this system. AES was enabled by cross-linking of hydrological stations, so the system could be run fully automated and could include real-time availability of data. By means of networking via Low Earth Orbiting Satellites (LEO-satellites), data from the precipitation station (PS) in the catchment area are brought together with data from the spring sampling station (SSS) without the need of terrestrial infrastructure for communication and power supply. Furthermore, the whole course of input and output parameters, like precipitation (input system) and discharge (output system), and the status of the sampling system is transmitted via LEO-Satellites to a Central Monitoring Station (CMS), which can be linked with a web-server to have unlimited real-time data access. The automatically generated notice of event to a local service team of the sampling station is transmitted in combination with internet, GSM, GPRS or LEO-Satellites. If a GPRS-network is available for the stations, this system could be realized also via this network. However, one great problem of these terrestrial communication systems is the risk of default when their networks are overloaded, like during flood events or thunderstorms. Therefore, in addition, it is necessary to have the possibility to transmit the measured values via communication satellites when a terrestrial infrastructure is not available. LEO-satellites are especially useful in the alpine regions because they have no deadspots, but only sometimes latency periods. In the workouts we combined in-situ measurements (precipitation, electrical conductivity, discharge, water temperature, spectral absorption coefficient, turbidity) with time increments from 1 to 15 minutes with data from the different sampling tires (environmental isotopes, chemical, mineralogical and bacteriological data).

Viking Mars encounter

NASA Technical Reports Server (NTRS)

1976-01-01

Various phases of planetary operations related to the Viking mission to Mars are described. Topics discussed include: approach phase, Mars orbit insertion, prelanding orbital activities, separation, descent and landing, surface operations, surface sampling and operations starting, orbiter science and radio science, Viking 2, Deep Space Network and data handling.

Are social network correlates of heavy drinking similar among black homeless youth and white homeless youth?

PubMed

Wenzel, Suzanne L; Hsu, Hsun-Ta; Zhou, Annie; Tucker, Joan S

2012-11-01

Understanding factors associated with heavy drinking among homeless youth is important for prevention efforts. Social networks are associated with drinking among homeless youth, and studies have called for attention to racial differences in networks that may affect drinking behavior. This study investigates differences in network characteristics by the racial background of homeless youth, and associations of network characteristics with heavy drinking. (Heavy drinking was defined as having five or more drinks of alcohol in a row within a couple of hours on at least one day within the past 30 days.) A probability sample of 235 Black and White homeless youths ages 13-24 were interviewed in Los Angeles County. We used chi-square or one-way analysis of variance tests to examine network differences by race and logistic regressions to identify network correlates of heavy drinking among Black and White homeless youth. The networks of Black youth included significantly more relatives and students who attend school regularly, whereas the networks of White youth were more likely to include homeless persons, relatives who drink to intoxication, and peers who drink to intoxication. Having peers who drink heavily was significantly associated with heavy drinking only among White youth. For all homeless youth, having more students in the network who regularly attend school was associated with less risk of heavy drinking. This study is the first to our knowledge to investigate racial differences in network characteristics and associations of network characteristics with heavy drinking among homeless youth. White homeless youth may benefit from interventions that reduce their ties with peers who drink. Enhancing ties to school-involved peers may be a promising intervention focus for both Black and White homeless youth.

Are Social Network Correlates of Heavy Drinking Similar Among Black Homeless Youth and White Homeless Youth?

PubMed Central

Wenzel, Suzanne L.; Hsu, Hsun-Ta; Zhou, Annie; Tucker, Joan S.

2012-01-01

Objective: Understanding factors associated with heavy drinking among homeless youth is important for prevention efforts. Social networks are associated with drinking among homeless youth, and studies have called for attention to racial differences in networks that may affect drinking behavior. This study investigates differences in network characteristics by the racial background of homeless youth, and associations of network characteristics with heavy drinking. (Heavy drinking was defined as having five or more drinks of alcohol in a row within a couple of hours on at least one day within the past 30 days.) Method: A probability sample of 235 Black and White homeless youths ages 13–24 were interviewed in Los Angeles County. We used chi-square or one-way analysis of variance tests to examine network differences by race and logistic regressions to identify network correlates of heavy drinking among Black and White homeless youth. Results: The networks of Black youth included significantly more relatives and students who attend school regularly, whereas the networks of White youth were more likely to include homeless persons, relatives who drink to intoxication, and peers who drink to intoxication. Having peers who drink heavily was significantly associated with heavy drinking only among White youth. For all homeless youth, having more students in the network who regularly attend school was associated with less risk of heavy drinking. Conclusions: This study is the first to our knowledge to investigate racial differences in network characteristics and associations of network characteristics with heavy drinking among homeless youth. White homeless youth may benefit from interventions that reduce their ties with peers who drink. Enhancing ties to school-involved peers may be a promising intervention focus for both Black and White homeless youth. PMID:23036205

Transcriptomic context of DRD1 is associated with prefrontal activity and behavior during working memory.

PubMed

Fazio, Leonardo; Pergola, Giulio; Papalino, Marco; Di Carlo, Pasquale; Monda, Anna; Gelao, Barbara; Amoroso, Nicola; Tangaro, Sabina; Rampino, Antonio; Popolizio, Teresa; Bertolino, Alessandro; Blasi, Giuseppe

2018-05-22

Dopamine D 1 receptor (D 1 R) signaling shapes prefrontal cortex (PFC) activity during working memory (WM). Previous reports found higher WM performance associated with alleles linked to greater expression of the gene coding for D 1 Rs ( DRD1 ). However, there is no evidence on the relationship between genetic modulation of DRD1 expression in PFC and patterns of prefrontal activity during WM. Furthermore, previous studies have not considered that D 1 Rs are part of a coregulated molecular environment, which may contribute to D 1 R-related prefrontal WM processing. Thus, we hypothesized a reciprocal link between a coregulated (i.e., coexpressed) molecular network including DRD1 and PFC activity. To explore this relationship, we used three independent postmortem prefrontal mRNA datasets (total n = 404) to characterize a coexpression network including DRD1 Then, we indexed network coexpression using a measure (polygenic coexpression index- DRD1 -PCI) combining the effect of single nucleotide polymorphisms (SNPs) on coexpression. Finally, we associated the DRD1 -PCI with WM performance and related brain activity in independent samples of healthy participants (total n = 371). We identified and replicated a coexpression network including DRD1 , whose coexpression was correlated with DRD1 -PCI. We also found that DRD1 -PCI was associated with lower PFC activity and higher WM performance. Behavioral and imaging results were replicated in independent samples. These findings suggest that genetically predicted expression of DRD1 and of its coexpression partners stratifies healthy individuals in terms of WM performance and related prefrontal activity. They also highlight genes and SNPs potentially relevant to pharmacological trials aimed to test cognitive enhancers modulating DRD1 signaling.

An equation-free probabilistic steady-state approximation: dynamic application to the stochastic simulation of biochemical reaction networks.

PubMed

Salis, Howard; Kaznessis, Yiannis N

2005-12-01

Stochastic chemical kinetics more accurately describes the dynamics of "small" chemical systems, such as biological cells. Many real systems contain dynamical stiffness, which causes the exact stochastic simulation algorithm or other kinetic Monte Carlo methods to spend the majority of their time executing frequently occurring reaction events. Previous methods have successfully applied a type of probabilistic steady-state approximation by deriving an evolution equation, such as the chemical master equation, for the relaxed fast dynamics and using the solution of that equation to determine the slow dynamics. However, because the solution of the chemical master equation is limited to small, carefully selected, or linear reaction networks, an alternate equation-free method would be highly useful. We present a probabilistic steady-state approximation that separates the time scales of an arbitrary reaction network, detects the convergence of a marginal distribution to a quasi-steady-state, directly samples the underlying distribution, and uses those samples to accurately predict the state of the system, including the effects of the slow dynamics, at future times. The numerical method produces an accurate solution of both the fast and slow reaction dynamics while, for stiff systems, reducing the computational time by orders of magnitude. The developed theory makes no approximations on the shape or form of the underlying steady-state distribution and only assumes that it is ergodic. We demonstrate the accuracy and efficiency of the method using multiple interesting examples, including a highly nonlinear protein-protein interaction network. The developed theory may be applied to any type of kinetic Monte Carlo simulation to more efficiently simulate dynamically stiff systems, including existing exact, approximate, or hybrid stochastic simulation techniques.

Pathway and network-based analysis of genome-wide association studies and RT-PCR validation in polycystic ovary syndrome

PubMed Central

Shen, Haoran; Liang, Zhou; Zheng, Saihua; Li, Xuelian

2017-01-01

The purpose of this study was to identify promising candidate genes and pathways in polycystic ovary syndrome (PCOS). Microarray dataset GSE345269 obtained from the Gene Expression Omnibus database includes 7 granulosa cell samples from PCOS patients, and 3 normal granulosa cell samples. Differentially expressed genes (DEGs) were screened between PCOS and normal samples. Pathway enrichment analysis was conducted for DEGs using ClueGO and CluePedia plugin of Cytoscape. A Reactome functional interaction (FI) network of the DEGs was built using ReactomeFIViz, and then network modules were extracted, followed by pathway enrichment analysis for the modules. Expression of DEGs in granulosa cell samples was measured using quantitative RT-PCR. A total of 674 DEGs were retained, which were significantly enriched with inflammation and immune-related pathways. Eight modules were extracted from the Reactome FI network. Pathway enrichment analysis revealed significant pathways of each module: module 0, Regulation of RhoA activity and Signaling by Rho GTPases pathways shared ARHGAP4 and ARHGAP9; module 2, GlycoProtein VI-mediated activation cascade pathway was enriched with RHOG; module 3, Thromboxane A2 receptor signaling, Chemokine signaling pathway, CXCR4-mediated signaling events pathways were enriched with LYN, the hub gene of module 3. Results of RT-PCR confirmed the finding of the bioinformatic analysis that ARHGAP4, ARHGAP9, RHOG and LYN were significantly upregulated in PCOS. RhoA-related pathways, GlycoProtein VI-mediated activation cascade pathway, ARHGAP4, ARHGAP9, RHOG and LYN may be involved in the pathogenesis of PCOS. PMID:28949383

Fusion of shallow and deep features for classification of high-resolution remote sensing images

NASA Astrophysics Data System (ADS)

Gao, Lang; Tian, Tian; Sun, Xiao; Li, Hang

2018-02-01

Effective spectral and spatial pixel description plays a significant role for the classification of high resolution remote sensing images. Current approaches of pixel-based feature extraction are of two main kinds: one includes the widelyused principal component analysis (PCA) and gray level co-occurrence matrix (GLCM) as the representative of the shallow spectral and shape features, and the other refers to the deep learning-based methods which employ deep neural networks and have made great promotion on classification accuracy. However, the former traditional features are insufficient to depict complex distribution of high resolution images, while the deep features demand plenty of samples to train the network otherwise over fitting easily occurs if only limited samples are involved in the training. In view of the above, we propose a GLCM-based convolution neural network (CNN) approach to extract features and implement classification for high resolution remote sensing images. The employment of GLCM is able to represent the original images and eliminate redundant information and undesired noises. Meanwhile, taking shallow features as the input of deep network will contribute to a better guidance and interpretability. In consideration of the amount of samples, some strategies such as L2 regularization and dropout methods are used to prevent over-fitting. The fine-tuning strategy is also used in our study to reduce training time and further enhance the generalization performance of the network. Experiments with popular data sets such as PaviaU data validate that our proposed method leads to a performance improvement compared to individual involved approaches.

Identifying causal networks linking cancer processes and anti-tumor immunity using Bayesian network inference and metagene constructs.

PubMed

Kaiser, Jacob L; Bland, Cassidy L; Klinke, David J

2016-03-01

Cancer arises from a deregulation of both intracellular and intercellular networks that maintain system homeostasis. Identifying the architecture of these networks and how they are changed in cancer is a pre-requisite for designing drugs to restore homeostasis. Since intercellular networks only appear in intact systems, it is difficult to identify how these networks become altered in human cancer using many of the common experimental models. To overcome this, we used the diversity in normal and malignant human tissue samples from the Cancer Genome Atlas (TCGA) database of human breast cancer to identify the topology associated with intercellular networks in vivo. To improve the underlying biological signals, we constructed Bayesian networks using metagene constructs, which represented groups of genes that are concomitantly associated with different immune and cancer states. We also used bootstrap resampling to establish the significance associated with the inferred networks. In short, we found opposing relationships between cell proliferation and epithelial-to-mesenchymal transformation (EMT) with regards to macrophage polarization. These results were consistent across multiple carcinomas in that proliferation was associated with a type 1 cell-mediated anti-tumor immune response and EMT was associated with a pro-tumor anti-inflammatory response. To address the identifiability of these networks from other datasets, we could identify the relationship between EMT and macrophage polarization with fewer samples when the Bayesian network was generated from malignant samples alone. However, the relationship between proliferation and macrophage polarization was identified with fewer samples when the samples were taken from a combination of the normal and malignant samples. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:470-479, 2016. © 2016 American Institute of Chemical Engineers.

NEON, Establishing a Standardized Network for Groundwater Observations

NASA Astrophysics Data System (ADS)

Fitzgerald, M.; Schroeter, N.; Goodman, K. J.; Roehm, C. L.

2013-12-01

The National Ecological Observatory Network (NEON) is establishing a standardized set of data collection systems comprised of in-situ sensors and observational sampling to obtain data fundamental to the analysis of environmental change at a continental scale. NEON will be collecting aquatic, terrestrial, and atmospheric data using Observatory-wide standardized designs and methods via a systems engineering approach. This approach ensures a wealth of high quality data, data algorithms, and models that will be freely accessible to all communities such as academic researchers, policy makers, and the general public. The project is established to provide 30 years of data which will enable prediction and forecasting of drivers and responses of ecological change at scales ranging from localized responses through regional gradients and up to the continental scale. The Observatory is a distributed system of sites spread across the United States, including Alaska, Hawaii, and Puerto Rico, which is subdivided into 20 statistically unique domains, based on a set of 18 ecologically important parameters. Each domain contains at least one core aquatic and terrestrial site which are located in unmanaged lands, and up to 2 additional sites selected to study domain specific questions such as nitrogen deposition gradients and responses of land use change activities on the ecosystem. Here, we present the development of NEON's groundwater observation well network design and the timing strategy for sampling groundwater chemistry. Shallow well networks, up to 100 feet in depth, will be installed at NEON aquatic sites and will allow for observation of localized ecohydrologic site conditions, by providing basic spatio-temporal near-real time data on groundwater parameters (level, temperature, conductivity) collected from in situ high-resolution instrumentation positioned in each well; and biannual sampling of geochemical and nutrient (N and P) concentrations in a subset of wells for each site. These data will be used to calculate several higher level data products such as hydrologic gradients which drive nutrient fluxes and their change over time. When coupled with other NEON data products, these data will allow for examining surface water/groundwater interactions as well as additional terrestrial and aquatic linkages, such as riparian vegetation response to changing ecohydrologic conditions (i.e. groundwater withdraw for irrigation, land use change) and natural sources (i.e. drought and changing precipitation patterns). This work will present the well network arrays designed for the different types of aquatic sites (1st/2nd order streams, larger rivers, and lakes) including variations on the well network designs for sites where physical constraints hinder a consistent design due to topographic (steep topography, wetlands) or physical constraints (such as permafrost). A generalized sampling strategy for each type of environment will also be detailed indicating the time of year, largely governed by hydrologic conditions, when sampling should take place to provide consistent groundwater chemistry data to allow for analyzing geochemical trends spatially across the network and through time.

Network Structure and Biased Variance Estimation in Respondent Driven Sampling

PubMed Central

Verdery, Ashton M.; Mouw, Ted; Bauldry, Shawn; Mucha, Peter J.

2015-01-01

This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of variance estimation for the construction of confidence intervals and hypothesis tests. In this paper, we show that the estimators of RDS sampling variance rely on a critical assumption that the network is First Order Markov (FOM) with respect to the dependent variable of interest. We demonstrate, through intuitive examples, mathematical generalizations, and computational experiments that current RDS variance estimators will always underestimate the population sampling variance of RDS in empirical networks that do not conform to the FOM assumption. Analysis of 215 observed university and school networks from Facebook and Add Health indicates that the FOM assumption is violated in every empirical network we analyze, and that these violations lead to substantially biased RDS estimators of sampling variance. We propose and test two alternative variance estimators that show some promise for reducing biases, but which also illustrate the limits of estimating sampling variance with only partial information on the underlying population social network. PMID:26679927

A tree-like Bayesian structure learning algorithm for small-sample datasets from complex biological model systems.

PubMed

Yin, Weiwei; Garimalla, Swetha; Moreno, Alberto; Galinski, Mary R; Styczynski, Mark P

2015-08-28

There are increasing efforts to bring high-throughput systems biology techniques to bear on complex animal model systems, often with a goal of learning about underlying regulatory network structures (e.g., gene regulatory networks). However, complex animal model systems typically have significant limitations on cohort sizes, number of samples, and the ability to perform follow-up and validation experiments. These constraints are particularly problematic for many current network learning approaches, which require large numbers of samples and may predict many more regulatory relationships than actually exist. Here, we test the idea that by leveraging the accuracy and efficiency of classifiers, we can construct high-quality networks that capture important interactions between variables in datasets with few samples. We start from a previously-developed tree-like Bayesian classifier and generalize its network learning approach to allow for arbitrary depth and complexity of tree-like networks. Using four diverse sample networks, we demonstrate that this approach performs consistently better at low sample sizes than the Sparse Candidate Algorithm, a representative approach for comparison because it is known to generate Bayesian networks with high positive predictive value. We develop and demonstrate a resampling-based approach to enable the identification of a viable root for the learned tree-like network, important for cases where the root of a network is not known a priori. We also develop and demonstrate an integrated resampling-based approach to the reduction of variable space for the learning of the network. Finally, we demonstrate the utility of this approach via the analysis of a transcriptional dataset of a malaria challenge in a non-human primate model system, Macaca mulatta, suggesting the potential to capture indicators of the earliest stages of cellular differentiation during leukopoiesis. We demonstrate that by starting from effective and efficient approaches for creating classifiers, we can identify interesting tree-like network structures with significant ability to capture the relationships in the training data. This approach represents a promising strategy for inferring networks with high positive predictive value under the constraint of small numbers of samples, meeting a need that will only continue to grow as more high-throughput studies are applied to complex model systems.

HepSim: A repository with predictions for high-energy physics experiments

DOE PAGES

Chekanov, S. V.

2015-02-03

A file repository for calculations of cross sections and kinematic distributions using Monte Carlo generators for high-energy collisions is discussed. The repository is used to facilitate effective preservation and archiving of data from theoretical calculations and for comparisons with experimental data. The HepSim data library is publicly accessible and includes a number of Monte Carlo event samples with Standard Model predictions for current and future experiments. The HepSim project includes a software package to automate the process of downloading and viewing online Monte Carlo event samples. Data streaming over a network for end-user analysis is discussed.

Bayesian Analysis for Exponential Random Graph Models Using the Adaptive Exchange Sampler.

PubMed

Jin, Ick Hoon; Yuan, Ying; Liang, Faming

2013-10-01

Exponential random graph models have been widely used in social network analysis. However, these models are extremely difficult to handle from a statistical viewpoint, because of the intractable normalizing constant and model degeneracy. In this paper, we consider a fully Bayesian analysis for exponential random graph models using the adaptive exchange sampler, which solves the intractable normalizing constant and model degeneracy issues encountered in Markov chain Monte Carlo (MCMC) simulations. The adaptive exchange sampler can be viewed as a MCMC extension of the exchange algorithm, and it generates auxiliary networks via an importance sampling procedure from an auxiliary Markov chain running in parallel. The convergence of this algorithm is established under mild conditions. The adaptive exchange sampler is illustrated using a few social networks, including the Florentine business network, molecule synthetic network, and dolphins network. The results indicate that the adaptive exchange algorithm can produce more accurate estimates than approximate exchange algorithms, while maintaining the same computational efficiency.

The effects of types of social networks, perceived social support, and loneliness on the health of older people: accounting for the social context.

PubMed

Stephens, Christine; Alpass, Fiona; Towers, Andy; Stevenson, Brendan

2011-09-01

To use an ecological model of ageing (Berkman, Glass, Brissette, & Seeman, 2000) which includes upstream social context factors and downstream social support factors to examine the effects of social networks on health. Postal survey responses from a representative population sample of New Zealanders aged 55 to 70 years (N = 6,662). Correlations and multiple regression analyses provided support for a model in which social context contributes to social network type, which affects perceived social support and loneliness, and consequent mental and physical health. Ethnicity was related to social networks and health but this was largely accounted for by other contextual variables measuring socioeconomic status. Gender and age were also significant variables in the model. Social network type is a useful way to assess social integration within this model of cascading effects. More detailed information could be gained through the development of our network assessment instruments for older people.

Motivation for and use of social networking sites: Comparisons among college students with and without histories of non-suicidal self-injury.

PubMed

Jarvi, Stephanie M; Swenson, Lance P; Batejan, Kristen L

2017-07-01

This research examines potential differences in social network use and motivation for social network use by non-suicidal self-injury (NSSI) status. 367 (73% women; M age = 20.60) college students were recruited in November-December 2011. A random sample of 2,500 students was accessed through a university registrar to recruit students interested in an online survey assessing NSSI and various health-related behaviors. Social network use and motivations for social networks did not differ by NSSI status. Results suggest that it is not patterns of use or motivation to use social networks that could lead to concern about online behavior (i.e., behavior increasing risk of future NSSI) among those with NSSI history. Rather, future preventive and intervention efforts should address the NSSI-related content that is available online, since this is unregulated, often explicit, and commonly includes "pro-NSSI" content that may be problematic and increase risk among vulnerable individuals.

Use of artificial neural networks on optical track width measurements.

PubMed

Smith, Richard J; See, Chung W; Somekh, Mike G; Yacoot, Andrew

2007-08-01

We have demonstrated recently that, by using an ultrastable optical interferometer together with artificial neural networks (ANNs), track widths down to 60 nm can be measured with a 0.3 NA objective lens. We investigate the effective conditions for training ANNs. Experimental results will be used to show the characteristics of the training samples and the data format of the ANN inputs required to produce suitably trained ANNs. Results obtained with networks measuring double tracks, and classifying different structures, will be presented to illustrate the capability of the technique. We include a discussion on expansion of the application areas of the system, allowing it to be used as a general purpose instrument.

Use of artificial neural networks on optical track width measurements

NASA Astrophysics Data System (ADS)

Smith, Richard J.; See, Chung W.; Somekh, Mike G.; Yacoot, Andrew

2007-08-01

We have demonstrated recently that, by using an ultrastable optical interferometer together with artificial neural networks (ANNs), track widths down to 60 nm can be measured with a 0.3 NA objective lens. We investigate the effective conditions for training ANNs. Experimental results will be used to show the characteristics of the training samples and the data format of the ANN inputs required to produce suitably trained ANNs. Results obtained with networks measuring double tracks, and classifying different structures, will be presented to illustrate the capability of the technique. We include a discussion on expansion of the application areas of the system, allowing it to be used as a general purpose instrument.

A multisample study of longitudinal changes in brain network architecture in 4-13-year-old children.

PubMed

Wierenga, Lara M; van den Heuvel, Martijn P; Oranje, Bob; Giedd, Jay N; Durston, Sarah; Peper, Jiska S; Brown, Timothy T; Crone, Eveline A

2018-01-01

Recent advances in human neuroimaging research have revealed that white-matter connectivity can be described in terms of an integrated network, which is the basis of the human connectome. However, the developmental changes of this connectome in childhood are not well understood. This study made use of two independent longitudinal diffusion-weighted imaging data sets to characterize developmental changes in the connectome by estimating age-related changes in fractional anisotropy (FA) for reconstructed fibers (edges) between 68 cortical regions. The first sample included 237 diffusion-weighted scans of 146 typically developing children (4-13 years old, 74 females) derived from the Pediatric Longitudinal Imaging, Neurocognition, and Genetics (PLING) study. The second sample included 141 scans of 97 individuals (8-13 years old, 62 females) derived from the BrainTime project. In both data sets, we compared edges that had the most substantial age-related change in FA to edges that showed little change in FA. This allowed us to investigate if developmental changes in white matter reorganize network topology. We observed substantial increases in edges connecting peripheral and a set of highly connected hub regions, referred to as the rich club. Together with the observed topological differences between regions connecting to edges showing the smallest and largest changes in FA, this indicates that changes in white matter affect network organization, such that highly connected regions become even more strongly imbedded in the network. These findings suggest that an important process in brain development involves organizing patterns of inter-regional interactions. Hum Brain Mapp 39:157-170, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Gender differences in the structural connectome of the teenage brain revealed by generalized q-sampling MRI.

PubMed

Tyan, Yeu-Sheng; Liao, Jan-Ray; Shen, Chao-Yu; Lin, Yu-Chieh; Weng, Jun-Cheng

2017-01-01

The question of whether there are biological differences between male and female brains is a fraught one, and political positions and prior expectations seem to have a strong influence on the interpretation of scientific data in this field. This question is relevant to issues of gender differences in the prevalence of psychiatric conditions, including autism, attention deficit hyperactivity disorder (ADHD), Tourette's syndrome, schizophrenia, dyslexia, depression, and eating disorders. Understanding how gender influences vulnerability to these conditions is significant. Diffusion magnetic resonance imaging (dMRI) provides a non-invasive method to investigate brain microstructure and the integrity of anatomical connectivity. Generalized q-sampling imaging (GQI) has been proposed to characterize complicated fiber patterns and distinguish fiber orientations, providing an opportunity for more accurate, higher-order descriptions through the water diffusion process. Therefore, we aimed to investigate differences in the brain's structural network between teenage males and females using GQI. This study included 59 (i.e., 33 males and 26 females) age- and education-matched subjects (age range: 13 to 14 years). The structural connectome was obtained by graph theoretical and network-based statistical (NBS) analyses. Our findings show that teenage male brains exhibit better intrahemispheric communication, and teenage female brains exhibit better interhemispheric communication. Our results also suggest that the network organization of teenage male brains is more local, more segregated, and more similar to small-world networks than teenage female brains. We conclude that the use of an MRI study with a GQI-based structural connectomic approach like ours presents novel insights into network-based systems of the brain and provides a new piece of the puzzle regarding gender differences.

HIV health center affiliation networks of black men who have sex with men: disentangling fragmented patterns of HIV prevention service utilization.

PubMed

Schneider, John A; Walsh, Tim; Cornwell, Benjamin; Ostrow, David; Michaels, Stuart; Laumann, Edward O

2012-08-01

In the United States, black men who have sex with men (BMSM) are at highest risk for HIV infection and are at high risk for limited health service utilization. We describe HIV health center (HHC) affiliation network patterns and their potential determinants among urban BMSM. The Men's Assessment of Social and Risk Network instrument was used to elicit HHC utilization, as reported by study respondents recruited through respondent-driven sampling. In 2010, 204 BMSM were systematically recruited from diverse venues in Chicago, IL. A 2-mode data set was constructed that included study participants and 9 diverse HHCs. Associations between individual-level characteristics and HHC utilization were analyzed using Multiple Regression Quadratic Assignment Procedure. Visualization analyses included computation of HHC centrality and faction membership. High utilization of HHCs (45.9%-70.3%) was evident among BMSM, 44.4% who were HIV infected. Multiple Regression Quadratic Assignment Procedure revealed that age, social network size, and HIV status were associated with HHC affiliation patterns (coeff., 0.13-0.27; all P < 0.05). With the exception of one HHC, HHCs offering HIV prevention services to HIV-infected participants occupied peripheral positions within the network of health centers. High-risk HIV-uninfected participants affiliated most with an HHC that offers only treatment services. Subcategories of BMSM in this sample affiliated with HHCs that may not provide appropriate HIV prevention services. Using 2-mode data, public health authorities may be better able to match prevention services to BMSM need; in particular, HIV prevention services for high-risk HIV-uninfected men and HIV "prevention for positives" services for HIV-infected men.

Psychoacoustical evaluation of natural and urban sounds in soundscapes.

PubMed

Yang, Ming; Kang, Jian

2013-07-01

Among various sounds in the environment, natural sounds, such as water sounds and birdsongs, have proven to be highly preferred by humans, but the reasons for these preferences have not been thoroughly researched. This paper explores differences between various natural and urban environmental sounds from the viewpoint of objective measures, especially psychoacoustical parameters. The sound samples used in this study include the recordings of single sound source categories of water, wind, birdsongs, and urban sounds including street music, mechanical sounds, and traffic noise. The samples are analyzed with a number of existing psychoacoustical parameter algorithmic models. Based on hierarchical cluster and principal components analyses of the calculated results, a series of differences has been shown among different sound types in terms of key psychoacoustical parameters. While different sound categories cannot be identified using any single acoustical and psychoacoustical parameter, identification can be made with a group of parameters, as analyzed with artificial neural networks and discriminant functions in this paper. For artificial neural networks, correlations between network predictions and targets using the average and standard deviation data of psychoacoustical parameters as inputs are above 0.95 for the three natural sound categories and above 0.90 for the urban sound category. For sound identification/classification, key parameters are fluctuation strength, loudness, and sharpness.

Gated integrator with signal baseline subtraction

DOEpatents

Wang, X.

1996-12-17

An ultrafast, high precision gated integrator includes an opamp having differential inputs. A signal to be integrated is applied to one of the differential inputs through a first input network, and a signal indicative of the DC offset component of the signal to be integrated is applied to the other of the differential inputs through a second input network. A pair of electronic switches in the first and second input networks define an integrating period when they are closed. The first and second input networks are substantially symmetrically constructed of matched components so that error components introduced by the electronic switches appear symmetrically in both input circuits and, hence, are nullified by the common mode rejection of the integrating opamp. The signal indicative of the DC offset component is provided by a sample and hold circuit actuated as the integrating period begins. The symmetrical configuration of the integrating circuit improves accuracy and speed by balancing out common mode errors, by permitting the use of high speed switching elements and high speed opamps and by permitting the use of a small integrating time constant. The sample and hold circuit substantially eliminates the error caused by the input signal baseline offset during a single integrating window. 5 figs.

Gated integrator with signal baseline subtraction

DOEpatents

Wang, Xucheng

1996-01-01

An ultrafast, high precision gated integrator includes an opamp having differential inputs. A signal to be integrated is applied to one of the differential inputs through a first input network, and a signal indicative of the DC offset component of the signal to be integrated is applied to the other of the differential inputs through a second input network. A pair of electronic switches in the first and second input networks define an integrating period when they are closed. The first and second input networks are substantially symmetrically constructed of matched components so that error components introduced by the electronic switches appear symmetrically in both input circuits and, hence, are nullified by the common mode rejection of the integrating opamp. The signal indicative of the DC offset component is provided by a sample and hold circuit actuated as the integrating period begins. The symmetrical configuration of the integrating circuit improves accuracy and speed by balancing out common mode errors, by permitting the use of high speed switching elements and high speed opamps and by permitting the use of a small integrating time constant. The sample and hold circuit substantially eliminates the error caused by the input signal baseline offset during a single integrating window.

A soil sampling intercomparison exercise for the ALMERA network.

PubMed

Belli, Maria; de Zorzi, Paolo; Sansone, Umberto; Shakhashiro, Abduhlghani; Gondin da Fonseca, Adelaide; Trinkl, Alexander; Benesch, Thomas

2009-11-01

Soil sampling and analysis for radionuclides after an accidental or routine release is a key factor for the dose calculation to members of the public, and for the establishment of possible countermeasures. The IAEA organized for selected laboratories of the ALMERA (Analytical Laboratories for the Measurement of Environmental Radioactivity) network a Soil Sampling Intercomparison Exercise (IAEA/SIE/01) with the objective of comparing soil sampling procedures used by different laboratories. The ALMERA network is a world-wide network of analytical laboratories located in IAEA member states capable of providing reliable and timely analysis of environmental samples in the event of an accidental or intentional release of radioactivity. Ten ALMERA laboratories were selected to participate in the sampling exercise. The soil sampling intercomparison exercise took place in November 2005 in an agricultural area qualified as a "reference site", aimed at assessing the uncertainties associated with soil sampling in agricultural, semi-natural, urban and contaminated environments and suitable for performing sampling intercomparison. In this paper, the laboratories sampling performance were evaluated.

Default and Executive Network Coupling Supports Creative Idea Production

PubMed Central

Beaty, Roger E.; Benedek, Mathias; Barry Kaufman, Scott; Silvia, Paul J.

2015-01-01

The role of attention in creative cognition remains controversial. Neuroimaging studies have reported activation of brain regions linked to both cognitive control and spontaneous imaginative processes, raising questions about how these regions interact to support creative thought. Using functional magnetic resonance imaging (fMRI), we explored this question by examining dynamic interactions between brain regions during a divergent thinking task. Multivariate pattern analysis revealed a distributed network associated with divergent thinking, including several core hubs of the default (posterior cingulate) and executive (dorsolateral prefrontal cortex) networks. The resting-state network affiliation of these regions was confirmed using data from an independent sample of participants. Graph theory analysis assessed global efficiency of the divergent thinking network, and network efficiency was found to increase as a function of individual differences in divergent thinking ability. Moreover, temporal connectivity analysis revealed increased coupling between default and salience network regions (bilateral insula) at the beginning of the task, followed by increased coupling between default and executive network regions at later stages. Such dynamic coupling suggests that divergent thinking involves cooperation between brain networks linked to cognitive control and spontaneous thought, which may reflect focused internal attention and the top-down control of spontaneous cognition during creative idea production. PMID:26084037

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI

PubMed Central

Xu, Tingting; Cullen, Kathryn R.; Mueller, Bryon; Schreiner, Mindy W.; Lim, Kelvin O.; Schulz, S. Charles; Parhi, Keshab K.

2016-01-01

Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03–0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03–0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new knowledge to the current understanding of functional brain networks in BPD. However, due to limitation of small sample sizes, the results of the current study should be viewed as exploratory and need to be validated on large samples in future works. PMID:26977400

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI.

PubMed

Xu, Tingting; Cullen, Kathryn R; Mueller, Bryon; Schreiner, Mindy W; Lim, Kelvin O; Schulz, S Charles; Parhi, Keshab K

2016-01-01

Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03-0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03-0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new knowledge to the current understanding of functional brain networks in BPD. However, due to limitation of small sample sizes, the results of the current study should be viewed as exploratory and need to be validated on large samples in future works.

Polymerization speed and diffractive experiments in polymer network LC test cells

NASA Astrophysics Data System (ADS)

Braun, Larissa; Gong, Zhen; Habibpourmoghadam, Atefeh; Schafforz, Samuel L.; Wolfram, Lukas; Lorenz, Alexander

2018-02-01

Polymer-network liquid crystals (LCs), where the response properties of a LC can be enhanced by the presence of a porous polymer network, are investigated. In the reported experiments, liquid crystals were doped with a small amount (< 10%) of photo-curable acrylate monomers. Samples with surface grafted photoinitiators, dissolvable photoinitiators, and samples with both kinds of photoinitiators were prepared. Both conventional (planar electrodes) and diffractive (interdigitated electrodes) test cells were used. These samples were exposed with a UV light source and changes of their capacitance were investigated with an LCR meter during exposure. Due to the presence of the in-situ generated polymer network, the electro-optic response properties of photo cured samples were enhanced. For example, their continuous phase modulation properties led to more localized responses in samples with interdigitated electrodes, which caused suppression of selected diffraction orders in the diffraction patterns recorded in polymer network LC samples. Moreover, capacitance changes were investigated during photopolymerization of a blue phase LC.

Lognormal kriging for the assessment of reliability in groundwater quality control observation networks

USGS Publications Warehouse

Candela, L.; Olea, R.A.; Custodio, E.

1988-01-01

Groundwater quality observation networks are examples of discontinuous sampling on variables presenting spatial continuity and highly skewed frequency distributions. Anywhere in the aquifer, lognormal kriging provides estimates of the variable being sampled and a standard error of the estimate. The average and the maximum standard error within the network can be used to dynamically improve the network sampling efficiency or find a design able to assure a given reliability level. The approach does not require the formulation of any physical model for the aquifer or any actual sampling of hypothetical configurations. A case study is presented using the network monitoring salty water intrusion into the Llobregat delta confined aquifer, Barcelona, Spain. The variable chloride concentration used to trace the intrusion exhibits sudden changes within short distances which make the standard error fairly invariable to changes in sampling pattern and to substantial fluctuations in the number of wells. ?? 1988.

Relationships between Perron-Frobenius eigenvalue and measurements of loops in networks

NASA Astrophysics Data System (ADS)

Chen, Lei; Kou, Yingxin; Li, Zhanwu; Xu, An; Chang, Yizhe

2018-07-01

The Perron-Frobenius eigenvalue (PFE) is widely used as measurement of the number of loops in networks, but what exactly the relationship between the PFE and the number of loops in networks is has not been researched yet, is it strictly monotonically increasing? And what are the relationships between the PFE and other measurements of loops in networks? Such as the average loop degree of nodes, and the distribution of loop ranks. We make researches on these questions based on samples of ER random network, NW small-world network and BA scale-free network, and the results confirm that, both the number of loops in network and the average loop degree of nodes of all samples do increase with the increase of the PFE in general trend, but neither of them are strictly monotonically increasing, so the PFE is capable to be used as a rough estimative measurement of the number of loops in networks and the average loop degree of nodes. Furthermore, we find that a majority of the loop ranks of all samples obey Weibull distribution, of which the scale parameter A and the shape parameter B have approximate power-law relationships with the PFE of the samples.

Analysis of the streamflow-gaging station network in Ohio for effectiveness in providing regional streamflow information

USGS Publications Warehouse

Straub, D.E.

1998-01-01

The streamflow-gaging station network in Ohio was evaluated for its effectiveness in providing regional streamflow information. The analysis involved application of the principles of generalized least squares regression between streamflow and climatic and basin characteristics. Regression equations were developed for three flow characteristics: (1) the instantaneous peak flow with a 100-year recurrence interval (P100), (2) the mean annual flow (Qa), and (3) the 7-day, 10-year low flow (7Q10). All active and discontinued gaging stations with 5 or more years of unregulated-streamflow data with respect to each flow characteristic were used to develop the regression equations. The gaging-station network was evaluated for the current (1996) condition of the network and estimated conditions of various network strategies if an additional 5 and 20 years of streamflow data were collected. Any active or discontinued gaging station with (1) less than 5 years of unregulated-streamflow record, (2) previously defined basin and climatic characteristics, and (3) the potential for collection of more unregulated-streamflow record were included in the network strategies involving the additional 5 and 20 years of data. The network analysis involved use of the regression equations, in combination with location, period of record, and cost of operation, to determine the contribution of the data for each gaging station to regional streamflow information. The contribution of each gaging station was based on a cost-weighted reduction of the mean square error (average sampling-error variance) associated with each regional estimating equation. All gaging stations included in the network analysis were then ranked according to their contribution to the regional information for each flow characteristic. The predictive ability of the regression equations developed from the gaging station network could be improved for all three flow characteristics with the collection of additional streamflow data. The addition of new gaging stations to the network would result in an even greater improvement of the accuracy of the regional regression equations. Typically, continued data collection at stations with unregulated streamflow for all flow conditions that had less than 11 years of record with drainage areas smaller than 200 square miles contributed the largest cost-weighted reduction to the average sampling-error variance of the regional estimating equations. The results of the network analyses can be used to prioritize the continued operation of active gaging stations or the reactivation of discontinued gaging stations if the objective is to maximize the regional information content in the streamflow-gaging station network.

Assessing new patient access to mental health providers in HMO networks.

PubMed

Barry, Colleen L; Venkatesh, Mohini; Busch, Susan H

2008-12-01

This study examined access to mental health providers in health maintenance organization (HMO) networks. A telephone survey was conducted with a stratified random sample of mental health providers listed as being in a network for at lease one of six HMOs operating in Connecticut (response rate=72%; N=366). Data were collected between December 2006 and March 2007. Measures included the accuracy of network listings, acceptance rates of new patients, and reasons for not accepting new patients. Acceptance of new patients was defined as scheduling an appointment within two weeks from the time of the initial contact. Logistic regression was used to examine acceptance rates of new patients while controlling for type of provider (social worker, nurse, psychologist, or psychiatrist) and practice characteristics. Findings indicate that 17% of sampled HMO network listings were inaccurate. Among the providers with an accurate listing, 73% were accepting new HMO patients and 76% were accepting new self-pay patients. These aggregate acceptance rates of new patients mask differences among providers, with psychiatrists significantly less likely than other providers to accept new patients (55% of psychiatrists were accepting new patients). The most common reason for not accepting new patients was the lack of available appointments. Results indicate that access to mental health providers in HMO networks varied by type of provider. For HMO enrollees seeking treatment for mental health problems from a provider with a master's degree in social work (M.S.W. degree), network access was not a major problem. Scheduling an appointment with a psychiatrist, particularly a psychiatrist treating children only, was more difficult.

Rotor assembly and method for automatically processing liquids

DOEpatents

Burtis, C.A.; Johnson, W.F.; Walker, W.A.

1992-12-22

A rotor assembly is described for performing a relatively large number of processing steps upon a sample, such as a whole blood sample, and a diluent, such as water. It includes a rotor body for rotation about an axis and includes a network of chambers within which various processing steps are performed upon the sample and diluent and passageways through which the sample and diluent are transferred. A transfer mechanism is movable through the rotor body by the influence of a magnetic field generated adjacent the transfer mechanism and movable along the rotor body, and the assembly utilizes centrifugal force, a transfer of momentum and capillary action to perform any of a number of processing steps such as separation, aliquoting, transference, washing, reagent addition and mixing of the sample and diluent within the rotor body. The rotor body is particularly suitable for automatic immunoassay analyses. 34 figs.

Semi-Autonomous Small Unmanned Aircraft Systems for Sampling Tornadic Supercell Thunderstorms

NASA Astrophysics Data System (ADS)

Elston, Jack S.

This work describes the development of a network-centric unmanned aircraft system (UAS) for in situ sampling of supercell thunderstorms. UAS have been identified as a well-suited platform for meteorological observations given their portability, endurance, and ability to mitigate atmospheric disturbances. They represent a unique tool for performing targeted sampling in regions of a supercell thunderstorm previously unreachable through other methods. Doppler radar can provide unique measurements of the wind field in and around supercell thunderstorms. In order to exploit this capability, a planner was developed that can optimize ingress trajectories for severe storm penetration. The resulting trajectories were examined to determine the feasibility of such a mission, and to optimize ingress in terms of flight time and exposure to precipitation. A network-centric architecture was developed to handle the large amount of distributed data produced during a storm sampling mission. Creation of this architecture was performed through a bottom-up design approach which reflects and enhances the interplay between networked communication and autonomous aircraft operation. The advantages of the approach are demonstrated through several field and hardware-in-the-loop experiments containing different hardware, networking protocols, and objectives. Results are provided from field experiments involving the resulting network-centric architecture. An airmass boundary was sampled in the Collaborative Colorado Nebraska Unmanned Aircraft Experiment (CoCoNUE). Utilizing lessons learned from CoCoNUE, a new concept of operations (CONOPS) and UAS were developed to perform in situ sampling of supercell thunderstorms. Deployment during the Verification of the Origins of Rotation in Tornadoes Experiment 2 (VORTEX2) resulted in the first ever sampling of the airmass associated with the rear flank downdraft of a tornadic supercell thunderstorm by a UAS. Hardware-in-the-loop simulation capability was added to the UAS to enable further assessment of the system and CONOPS. The simulation combines a full six degree-of-freedom aircraft dynamic model with wind and precipitation data from simulations of severe convective storms. Interfaces were written to involve as much of the system's field hardware as possible, including the creation of a simulated radar product server. A variety of simulations were conducted to evaluate different aspects of the CONOPS used for the 2010 VORTEX2 field campaign.

Network Influences on the Sexual Risk Behaviors of Gay, Bisexual and Other Men Who Have Sex with Men Using Geosocial Networking Applications.

PubMed

Holloway, Ian W; Pulsipher, Craig A; Gibbs, Jeremy; Barman-Adhikari, Anamika; Rice, Eric

2015-06-01

Geosocial networking applications (GSN apps) have become increasingly popular among gay, bisexual and other men who have sex with men (MSM). Our study sought to understand whether inclusion of individuals met via GSN apps in participants' social networks was associated with increased HIV risk behaviors among a probability sample of GSN app using MSM (N = 295) recruited in Los Angeles, California. Approximately 20 % of participants included a GSN app-met individual as one of their top five closest social network members. Those with a GSN app-met network member had more recent (past 30-day) sexual partners (B = 1.21, p < 0.05), were nearly twice as likely to have engaged in unprotected anal intercourse (UAI) with their last sexual partner (AOR = 2.02, p < 0.05), and were nearly four times as likely to have engaged in UAI with their last GSN app-met sexual partner (AOR = 3.98, p < 0.001). Network-based interventions delivered via GSN apps may be useful in preventing the spread of HIV among MSM.

Features of the Correlation Structure of Price Indices

PubMed Central

Gao, Xiangyun; An, Haizhong; Zhong, Weiqiong

2013-01-01

What are the features of the correlation structure of price indices? To answer this question, 5 types of price indices, including 195 specific price indices from 2003 to 2011, were selected as sample data. To build a weighted network of price indices each price index is represented by a vertex, and a positive correlation between two price indices is represented by an edge. We studied the features of the weighted network structure by applying economic theory to the analysis of complex network parameters. We found that the frequency of the price indices follows a normal distribution by counting the weighted degrees of the nodes, and we identified the price indices which have an important impact on the network's structure. We found out small groups in the weighted network by the methods of k-core and k-plex. We discovered structure holes in the network by calculating the hierarchy of the nodes. Finally, we found that the price indices weighted network has a small-world effect by calculating the shortest path. These results provide a scientific basis for macroeconomic control policies. PMID:23593399

Network Influences on the Sexual Risk Behaviors of Gay, Bisexual and Other Men Who Have Sex With Men Using Geosocial Networking Applications

PubMed Central

Holloway, Ian W.; Pulsipher, Craig A.; Gibbs, Jeremy; Barman-Adhikari, Anamika; Rice, Eric

2016-01-01

Geosocial networking applications (GSN apps) have become increasingly popular among gay, bisexual and other men who have sex with men (MSM). Our study sought to understand whether inclusion of individuals met via GSN apps in participants’ social networks was associated with increased HIV risk behaviors among a probability sample of GSN app using MSM (N=295) recruited in Los Angeles, California. Approximately 20% of participants included a GSN app-met individual as one of their top five closest social network members. Those with a GSN app-met network member had more recent (past 30-day) sexual partners (B=1.21, p<0.05), were nearly twice as likely to have engaged in unprotected anal intercourse (UAI) with their last sexual partner (AOR=2.02, p<0.05), and were nearly four times as likely to have engaged in UAI with their last GSN app-met sexual partner (AOR=3.98, p<0.001). Network-based interventions delivered via GSN apps may be useful in preventing the spread of HIV among MSM. PMID:25572832

Stochastic ecological network occupancy (SENO) models: a new tool for modeling ecological networks across spatial scales

USGS Publications Warehouse

Lafferty, Kevin D.; Dunne, Jennifer A.

2010-01-01

Stochastic ecological network occupancy (SENO) models predict the probability that species will occur in a sample of an ecological network. In this review, we introduce SENO models as a means to fill a gap in the theoretical toolkit of ecologists. As input, SENO models use a topological interaction network and rates of colonization and extinction (including consumer effects) for each species. A SENO model then simulates the ecological network over time, resulting in a series of sub-networks that can be used to identify commonly encountered community modules. The proportion of time a species is present in a patch gives its expected probability of occurrence, whose sum across species gives expected species richness. To illustrate their utility, we provide simple examples of how SENO models can be used to investigate how topological complexity, species interactions, species traits, and spatial scale affect communities in space and time. They can categorize species as biodiversity facilitators, contributors, or inhibitors, making this approach promising for ecosystem-based management of invasive, threatened, or exploited species.

Maximum entropy methods for extracting the learned features of deep neural networks.

PubMed

Finnegan, Alex; Song, Jun S

2017-10-01

New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

Microarray analysis to identify the similarities and differences of pathogenesis between aortic occlusive disease and abdominal aortic aneurysm.

PubMed

Wang, Guofu; Bi, Lechang; Wang, Gaofeng; Huang, Feilai; Lu, Mingjing; Zhu, Kai

2018-06-01

Objectives Expression profile of GSE57691 was analyzed to identify the similarities and differences between aortic occlusive disease and abdominal aortic aneurysm. Methods The expression profile of GSE57691 was downloaded from Gene Expression Omnibus database, including 20 small abdominal aortic aneurysm samples, 29 large abdominal aortic aneurysm samples, 9 aortic occlusive disease samples, and 10 control samples. Using the limma package in R, the differentially expressed genes were screened. Followed by enrichment analysis was performed for the differentially expressed genes using database for annotation, visualization, and integrated discovery online tool. Based on string online tool and Cytoscape software, protein-protein interaction network and module analyses were carried out. Moreover, integrated TF platform database and Cytoscape software were used for constructing transcriptional regulatory networks. Results As a result, 1757, 354, and 396 differentially expressed genes separately were identified in aortic occlusive disease, large abdominal aortic aneurysm, and small abdominal aortic aneurysm samples. UBB was significantly enriched in proteolysis related pathways with a high degree in three groups. SPARCL1 was another gene shared by these groups and regulated by NFIA, which had a high degree in transcriptional regulatory network. ACTB, a significant upregulated gene in abdominal aortic aneurysm samples, could be regulated by CLIC4, which was significantly enriched in cell motions. ACLY and NFIB were separately identified in aortic occlusive disease and small abdominal aortic aneurysm samples, and separately enriched in lipid metabolism and negative regulation of cell proliferation. Conclusions The downregulated UBB, NFIA, and SPARCL1 might play key roles in both aortic occlusive disease and abdominal aortic aneurysm, while the upregulated ACTB might only involve in abdominal aortic aneurysm. ACLY and NFIB were specifically involved in aortic occlusive disease and small abdominal aortic aneurysm separately.

Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images.

PubMed

Li, Wei; Cao, Peng; Zhao, Dazhe; Wang, Junbo

2016-01-01

Computer aided detection (CAD) systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR) in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO). Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs) samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC) database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods.

Cellular network entropy as the energy potential in Waddington's differentiation landscape

PubMed Central

Banerji, Christopher R. S.; Miranda-Saavedra, Diego; Severini, Simone; Widschwendter, Martin; Enver, Tariq; Zhou, Joseph X.; Teschendorff, Andrew E.

2013-01-01

Differentiation is a key cellular process in normal tissue development that is significantly altered in cancer. Although molecular signatures characterising pluripotency and multipotency exist, there is, as yet, no single quantitative mark of a cellular sample's position in the global differentiation hierarchy. Here we adopt a systems view and consider the sample's network entropy, a measure of signaling pathway promiscuity, computable from a sample's genome-wide expression profile. We demonstrate that network entropy provides a quantitative, in-silico, readout of the average undifferentiated state of the profiled cells, recapitulating the known hierarchy of pluripotent, multipotent and differentiated cell types. Network entropy further exhibits dynamic changes in time course differentiation data, and in line with a sample's differentiation stage. In disease, network entropy predicts a higher level of cellular plasticity in cancer stem cell populations compared to ordinary cancer cells. Importantly, network entropy also allows identification of key differentiation pathways. Our results are consistent with the view that pluripotency is a statistical property defined at the cellular population level, correlating with intra-sample heterogeneity, and driven by the degree of signaling promiscuity in cells. In summary, network entropy provides a quantitative measure of a cell's undifferentiated state, defining its elevation in Waddington's landscape. PMID:24154593

Dynamic Functional Connectivity States Reflecting Psychotic-like Experiences.

PubMed

Barber, Anita D; Lindquist, Martin A; DeRosse, Pamela; Karlsgodt, Katherine H

2018-05-01

Psychotic-like experiences (PLEs) are associated with lower social and occupational functioning, and lower executive function. Emerging evidence also suggests that PLEs reflect neural dysfunction resembling that of psychotic disorders. The present study examined dynamic connectivity related to a measure of PLEs derived from the Achenbach Adult Self-Report, in an otherwise-healthy sample of adults from the Human Connectome Project. A total of 76 PLE-endorsing and 153 control participants were included in the final sample. To characterize network dysfunction, dynamic connectivity states were examined across large-scale resting-state networks using dynamic conditional correlation and k-means clustering. Three dynamic states were identified. The PLE-endorsing group spent more time than the control group in state 1, a state reflecting hyperconnectivity within visual regions and hypoconnectivity within the default mode network, and less time in state 2, a state characterized by robust within-network connectivity for all networks and strong default mode network anticorrelations. Within the PLE-endorsing group, worse executive function was associated with more time spent in and more transitions into state 1 and less time spent in and fewer transitions into state 3. PLEs are associated with altered large-scale brain dynamics, which tip the system away from spending more time in states reflecting more "typical" connectivity patterns toward more time in states reflecting visual hyperconnectivity and default mode hypoconnectivity. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Spatial Patterns of Greenhouse Gases Across an Urbanization Gradient in a Suburban River Network

NASA Astrophysics Data System (ADS)

Robison, A.; Balch, E.; Wollheim, W. M.

2017-12-01

River networks are important components of the global carbon cycle, processing significant quantities of terrestrial carbon and are most often sources of greenhouse gases (GHGs) to the atmosphere. While recent investigations have begun to incorporate aquatic systems into continental carbon budgets, our understanding of what drives the variability in space and time of these dynamics is poorly constrained. Meanwhile, urban areas continue to expand rapidly across the globe, with wide ranging effects on aquatic systems. A better understanding of the effect of human activities on aquatic carbon and GHG dynamics at both local and global scales is needed. We address the question: How does urbanization affect GHG dynamics in river networks? To address this question, we conducted a synoptic survey of 45 sites in a suburban river network in New England (Ipswich River, MA), analyzing samples for physical and chemical characteristics, including dissolved GHGs, carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Sampling sites were selected across an urbanization gradient (1.4-90% developed) and included headwater streams, major tributaries, the basin mouth, and additional sites along the main stem. Initial results indicate dissolved N2O concentration in headwater streams is related to catchment development, while CO2 and CH4 are not correlated to land use generally. CO2 and CH4 signals from urban areas are likely modified by fluvial wetlands that are abundant along larger tributaries and the mainstem. Developed watersheds are inherently altered and heterogeneous landscapes. To fully quantify the role of urbanized waters in the larger carbon cycle, GHG dynamics must be considered at the river network scale. The work presented here begins this process, allowing for an examination of the interaction between land use and GHG concentrations. Additional analyses will focus on further constraining GHG patterns across the river network, and modeling gas transport through and flux out of the system. This relationship should also be examined across time and under varying flow conditions.

Evaluation of water-quality characteristics and sampling design for streams in North Dakota, 1970–2008

USGS Publications Warehouse

Galloway, Joel M.; Vecchia, Aldo V.; Vining, Kevin C.; Densmore, Brenda K.; Lundgren, Robert F.

2012-01-01

In response to the need to examine the large amount of historic water-quality data comprehensively across North Dakota and evaluate the efficiency of the State-wide sampling programs, a study was done by the U.S. Geological Survey in cooperation with the North Dakota State Water Commission and the North Dakota Department of Health to describe the water-quality data collected for the various programs and determine an efficient State-wide sampling design for monitoring future water-quality conditions. Although data collected for the North Dakota State Water Commission High-Low Sampling Program, the North Dakota Department of Health Ambient Water-Quality Network, and other projects and programs provide valuable information on the quality of water in streams in North Dakota, the objectives vary among the programs, some of the programs overlap spatially and temporally, and the various sampling designs may not be the most efficient or relevant to the objectives of the individual programs as they have changed through time. One objective of a State-wide sampling program was to evaluate ways to describe the spatial variability of water-quality conditions across the State in the most efficient manner. Weighted least-squares regression analysis was used to relate the average absolute difference between paired downstream and upstream concentrations, expressed as a percent of the average downstream concentration, to the average absolute difference in daily flow between the downstream and upstream pairs, expressed as a percent of the average downstream flow. The analysis showed that a reasonable spatial network would consist of including the most downstream sites in large basins first, followed by the next upstream site(s) that roughly bisect the downstream flows at the first sites, followed by the next upstream site(s) that roughly bisect flows for the second sites. Sampling sites to be included in a potential State-wide network were prioritized into 3 design levels: level 1 (highest priority), level 2 (second priority), and level 3 (third priority). Given the spatial distribution and priority designation (levels 1–3) of sites in the potential spatial network, the next consideration was to determine the appropriate temporal sampling frequency to use for monitoring future water-quality conditions. The time-series model used to detect concentration trends for this report also was used to evaluate sampling designs to monitor future water-quality trends. Sampling designs were evaluated with regard to their sensitivity to detect seasonal trends that occurred during three 4-month seasons—March through June, July through October, and November through February. For the 34 level-1 sites, samples would be collected for major ions, trace metals, nutrients, bacteria, and sediment eight times per year, with samples in January, April (2 samples),May, June, July, August, and October. For the 21 level-2 sites, samples would be collected for major ions, trace metals, and nutrients six times per year (January, April, May, June, August, and October), and for the 26 level-3 sites, samples would be collected for these constituents four times per year (April, June, August, and October).

Screening for genes and subnetworks associated with pancreatic cancer based on the gene expression profile.

PubMed

Long, Jin; Liu, Zhe; Wu, Xingda; Xu, Yuanhong; Ge, Chunlin

2016-05-01

The present study aimed to screen for potential genes and subnetworks associated with pancreatic cancer (PC) using the gene expression profile. The expression profile GSE 16515 was downloaded from the Gene Expression Omnibus database, which included 36 PC tissue samples and 16 normal samples. Limma package in R language was used to screen differentially expressed genes (DEGs), which were grouped as up‑ and downregulated genes. Then, PFSNet was applied to perform subnetwork analysis for all the DEGs. Moreover, Gene Ontology (GO) and REACTOME pathway enrichment analysis of up‑ and downregulated genes was performed, followed by protein‑protein interaction (PPI) network construction using Search Tool for the Retrieval of Interacting Genes Search Tool for the Retrieval of Interacting Genes. In total, 1,989 DEGs including 1,461 up‑ and 528 downregulated genes were screened out. Subnetworks including pancreatic cancer in PC tissue samples and intercellular adhesion in normal samples were identified, respectively. A total of 8 significant REACTOME pathways for upregulated DEGs, such as hemostasis and cell cycle, mitotic were identified. Moreover, 4 significant REACTOME pathways for downregulated DEGs, including regulation of β‑cell development and transmembrane transport of small molecules were screened out. Additionally, DEGs with high connectivity degrees, such as CCNA2 (cyclin A2) and PBK (PDZ binding kinase), of the module in the protein‑protein interaction network were mainly enriched with cell‑division cycle. CCNA2 and PBK of the module and their relative pathway cell‑division cycle, and two subnetworks (pancreatic cancer and intercellular adhesion subnetworks) may be pivotal for further understanding of the molecular mechanism of PC.

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

PubMed

Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System

PubMed Central

Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP). PMID:26829639

A Comparison of Multivariate and Pre-Processing Methods for Quantitative Laser-Induced Breakdown Spectroscopy of Geologic Samples

NASA Technical Reports Server (NTRS)

Anderson, R. B.; Morris, R. V.; Clegg, S. M.; Bell, J. F., III; Humphries, S. D.; Wiens, R. C.

2011-01-01

The ChemCam instrument selected for the Curiosity rover is capable of remote laser-induced breakdown spectroscopy (LIBS).[1] We used a remote LIBS instrument similar to ChemCam to analyze 197 geologic slab samples and 32 pressed-powder geostandards. The slab samples are well-characterized and have been used to validate the calibration of previous instruments on Mars missions, including CRISM [2], OMEGA [3], the MER Pancam [4], Mini-TES [5], and Moessbauer [6] instruments and the Phoenix SSI [7]. The resulting dataset was used to compare multivariate methods for quantitative LIBS and to determine the effect of grain size on calculations. Three multivariate methods - partial least squares (PLS), multilayer perceptron artificial neural networks (MLP ANNs) and cascade correlation (CC) ANNs - were used to generate models and extract the quantitative composition of unknown samples. PLS can be used to predict one element (PLS1) or multiple elements (PLS2) at a time, as can the neural network methods. Although MLP and CC ANNs were successful in some cases, PLS generally produced the most accurate and precise results.

Analyzing hidden populations online: topic, emotion, and social network of HIV-related users in the largest Chinese online community.

PubMed

Liu, Chuchu; Lu, Xin

2018-01-05

Traditional survey methods are limited in the study of hidden populations due to the hard to access properties, including lack of a sampling frame, sensitivity issue, reporting error, small sample size, etc. The rapid increase of online communities, of which members interact with others via the Internet, have generated large amounts of data, offering new opportunities for understanding hidden populations with unprecedented sample sizes and richness of information. In this study, we try to understand the multidimensional characteristics of a hidden population by analyzing the massive data generated in the online community. By elaborately designing crawlers, we retrieved a complete dataset from the "HIV bar," the largest bar related to HIV on the Baidu Tieba platform, for all records from January 2005 to August 2016. Through natural language processing and social network analysis, we explored the psychology, behavior and demand of online HIV population and examined the network community structure. In HIV communities, the average topic similarity among members is positively correlated to network efficiency (r = 0.70, p < 0.001), indicating that the closer the social distance between members of the community, the more similar their topics. The proportion of negative users in each community is around 60%, weakly correlated with community size (r = 0.25, p = 0.002). It is found that users suspecting initial HIV infection or first in contact with high-risk behaviors tend to seek help and advice on the social networking platform, rather than immediately going to a hospital for blood tests. Online communities have generated copious amounts of data offering new opportunities for understanding hidden populations with unprecedented sample sizes and richness of information. It is recommended that support through online services for HIV/AIDS consultation and diagnosis be improved to avoid privacy concerns and social discrimination in China.

Ground-water quality and geochemistry of Las Vegas Valley, Clark County, Nevada, 1981-83; implementation of a monitoring network

USGS Publications Warehouse

Dettinger, M.D.

1987-01-01

As a result of rapid urban growth in Las Vegas Valley, rates of water use and wastewater disposal have grown rapidly during the last 25 years. Concern has developed over the potential water quality effects of this growth. The deep percolation of wastewater and irrigation return flow (much of which originates as imported water from Lake Mead), along with severe overdraft conditions in the principal aquifers of the valley, could combine to pose a long-term threat to groundwater quality. The quantitative investigations of groundwater quality and geochemical conditions in the valley necessary to address these concerns would include the establishment of data collection networks on a valley-wide scale that differ substantially from existing networks. The valley-wide networks would have a uniform areal distribution of sampling sites, would sample from all major depth zones, and would entail repeated sampling from each site. With these criteria in mind, 40 wells were chosen for inclusion in a demonstration monitoring network. Groundwater in the northern half of the valley generally contains 200 to 400 mg/L of dissolved solids, and is dominated by calcium, magnesium , and bicarbonate ions, reflecting a chemical equilibrium between the groundwater and the dominantly carbonate rocks in the aquifers of this area. The intermediate to deep groundwater in the southern half of the valley is of poorer quality (containing 700 to 1,500 mg/L of dissolved solids) and is dominated by calcium, magnesium, sulfate, and bicarbonate ions, reflecting the occurrence of other rock types including evaporite minerals among the still-dominant carbonate rocks in the aquifers of this part of the valley. The poorest quality groundwater in the valley is generally in the lowland parts of the valley in the first few feet beneath the water table, where dissolved solids concentrations range from 2,000 to > 7,000 mg/L , and probably reflects the effects of evaporite dissolution, secondary recharge, and evapotranspiration. The most common water quality constraint on potential groundwater use is the high salinity. No evidence of large-scale contamination of deep groundwater was found in this study. (Author 's abstract)

Security and SCADA protocols

DOE Office of Scientific and Technical Information (OSTI.GOV)

Igure, V. M.; Williams, R. D.

2006-07-01

Supervisory control and data acquisition (SCADA) networks have replaced discrete wiring for many industrial processes, and the efficiency of the network alternative suggests a trend toward more SCADA networks in the future. This paper broadly considers SCADA to include distributed control systems (DCS) and digital control systems. These networks offer many advantages, but they also introduce potential vulnerabilities that can be exploited by adversaries. Inter-connectivity exposes SCADA networks to many of the same threats that face the public internet and many of the established defenses therefore show promise if adapted to the SCADA differences. This paper provides an overview ofmore » security issues in SCADA networks and ongoing efforts to improve the security of these networks. Initially, a few samples from the range of threats to SCADA network security are offered. Next, attention is focused on security assessment of SCADA communication protocols. Three challenges must be addressed to strengthen SCADA networks. Access control mechanisms need to be introduced or strengthened, improvements are needed inside of the network to enhance security and network monitoring, and SCADA security management improvements and policies are needed. This paper discusses each of these challenges. This paper uses the Profibus protocol as an example to illustrate some of the vulnerabilities that arise within SCADA networks. The example Profibus security assessment establishes a network model and an attacker model before proceeding to a list of example attacks. (authors)« less

Social Networks and the Diffusion of Adolescent Problem Behavior: Reliable Estimates of Selection and Influence from Sixth Through Ninth Grades.

PubMed

Osgood, D Wayne; Feinberg, Mark E; Ragan, Daniel T

2015-08-01

Seeking to reduce problematic peer influence is a prominent theme of programs to prevent adolescent problem behavior. To support the refinement of this aspect of prevention programming, we examined peer influence and selection processes for three problem behaviors (delinquency, alcohol use, and smoking). We assessed not only the overall strengths of these peer processes, but also their consistency versus variability across settings. We used dynamic stochastic actor-based models to analyze five waves of friendship network data across sixth through ninth grades for a large sample of U.S. adolescents. Our sample included two successive grade cohorts of youth in 26 school districts participating in the PROSPER study, yielding 51 longitudinal social networks based on respondents' friendship nominations. For all three self-reported antisocial behaviors, we found evidence of both peer influence and selection processes tied to antisocial behavior. There was little reliable variance in these processes across the networks, suggesting that the statistical imprecision of the peer influence and selection estimates in previous studies likely accounts for inconsistencies in results. Adolescent friendship networks play a strong role in shaping problem behavior, but problem behaviors also inform friendship choices. In addition to preferring friends with similar levels of problem behavior, adolescents tend to choose friends who engage in problem behaviors, thus creating broader diffusion.

Social Networks and the Diffusion of Adolescent Problem Behavior: Reliable Estimates of Selection and Influence from 6th through 9th Grade

PubMed Central

Osgood, D. Wayne; Feinberg, Mark E.; Ragan, Daniel T.

2015-01-01

Seeking to reduce problematic peer influence is a prominent theme of programs to prevent adolescent problem behavior. To support the refinement of this aspect of prevention programming, we examined peer influence and selection processes for three problem behaviors (delinquency, alcohol use, and smoking). We assessed not only the overall strengths of these peer processes, but also their consistency versus variability across settings. We used dynamic stochastic actor-based models to analyze five waves of friendship network data across sixth through ninth grades for a large sample of U.S. adolescents. Our sample included two successive grade cohorts of youth in 26 school districts participating in the PROSPER study, yielding 51 longitudinal social networks based on respondents’ friendship nominations. For all three self-reported antisocial behaviors, we found evidence of both peer influence and selection processes tied to antisocial behavior. There was little reliable variance in these processes across the networks, suggesting that the statistical imprecision of the peer influence and selection estimates in previous studies likely accounts for inconsistencies in results. Adolescent friendship networks play a strong role in shaping problem behavior, but problem behaviors also inform friendship choices. In addition to preferring friends with similar levels of problem behavior, adolescents tend to choose friends who engage in problem behaviors, thus creating broader diffusion. PMID:25943034

IndeCut evaluates performance of network motif discovery algorithms.

PubMed

Ansariola, Mitra; Megraw, Molly; Koslicki, David

2018-05-01

Genomic networks represent a complex map of molecular interactions which are descriptive of the biological processes occurring in living cells. Identifying the small over-represented circuitry patterns in these networks helps generate hypotheses about the functional basis of such complex processes. Network motif discovery is a systematic way of achieving this goal. However, a reliable network motif discovery outcome requires generating random background networks which are the result of a uniform and independent graph sampling method. To date, there has been no method to numerically evaluate whether any network motif discovery algorithm performs as intended on realistically sized datasets-thus it was not possible to assess the validity of resulting network motifs. In this work, we present IndeCut, the first method to date that characterizes network motif finding algorithm performance in terms of uniform sampling on realistically sized networks. We demonstrate that it is critical to use IndeCut prior to running any network motif finder for two reasons. First, IndeCut indicates the number of samples needed for a tool to produce an outcome that is both reproducible and accurate. Second, IndeCut allows users to choose the tool that generates samples in the most independent fashion for their network of interest among many available options. The open source software package is available at https://github.com/megrawlab/IndeCut. megrawm@science.oregonstate.edu or david.koslicki@math.oregonstate.edu. Supplementary data are available at Bioinformatics online.

Pathway and network-based analysis of genome-wide association studies and RT-PCR validation in polycystic ovary syndrome.

PubMed

Shen, Haoran; Liang, Zhou; Zheng, Saihua; Li, Xuelian

2017-11-01

The purpose of this study was to identify promising candidate genes and pathways in polycystic ovary syndrome (PCOS). Microarray dataset GSE345269 obtained from the Gene Expression Omnibus database includes 7 granulosa cell samples from PCOS patients, and 3 normal granulosa cell samples. Differentially expressed genes (DEGs) were screened between PCOS and normal samples. Pathway enrichment analysis was conducted for DEGs using ClueGO and CluePedia plugin of Cytoscape. A Reactome functional interaction (FI) network of the DEGs was built using ReactomeFIViz, and then network modules were extracted, followed by pathway enrichment analysis for the modules. Expression of DEGs in granulosa cell samples was measured using quantitative RT-PCR. A total of 674 DEGs were retained, which were significantly enriched with inflammation and immune-related pathways. Eight modules were extracted from the Reactome FI network. Pathway enrichment analysis revealed significant pathways of each module: module 0, Regulation of RhoA activity and Signaling by Rho GTPases pathways shared ARHGAP4 and ARHGAP9; module 2, GlycoProtein VI-mediated activation cascade pathway was enriched with RHOG; module 3, Thromboxane A2 receptor signaling, Chemokine signaling pathway, CXCR4-mediated signaling events pathways were enriched with LYN, the hub gene of module 3. Results of RT-PCR confirmed the finding of the bioinformatic analysis that ARHGAP4, ARHGAP9, RHOG and LYN were significantly upregulated in PCOS. RhoA-related pathways, GlycoProtein VI-mediated activation cascade pathway, ARHGAP4, ARHGAP9, RHOG and LYN may be involved in the pathogenesis of PCOS.

Gestational Age is Dimensionally Associated with Structural Brain Network Abnormalities Across Development.

PubMed

Nassar, Rula; Kaczkurkin, Antonia N; Xia, Cedric Huchuan; Sotiras, Aristeidis; Pehlivanova, Marieta; Moore, Tyler M; Garcia de La Garza, Angel; Roalf, David R; Rosen, Adon F G; Lorch, Scott A; Ruparel, Kosha; Shinohara, Russell T; Davatzikos, Christos; Gur, Ruben C; Gur, Raquel E; Satterthwaite, Theodore D

2018-04-21

Prematurity is associated with diverse developmental abnormalities, yet few studies relate cognitive and neurostructural deficits to a dimensional measure of prematurity. Leveraging a large sample of children, adolescents, and young adults (age 8-22 years) studied as part of the Philadelphia Neurodevelopmental Cohort, we examined how variation in gestational age impacted cognition and brain structure later in development. Participants included 72 preterm youth born before 37 weeks' gestation and 206 youth who were born at term (37 weeks or later). Using a previously-validated factor analysis, cognitive performance was assessed in three domains: (1) executive function and complex reasoning, (2) social cognition, and (3) episodic memory. All participants completed T1-weighted neuroimaging at 3 T to measure brain volume. Structural covariance networks were delineated using non-negative matrix factorization, an advanced multivariate analysis technique. Lower gestational age was associated with both deficits in executive function and reduced volume within 11 of 26 structural covariance networks, which included orbitofrontal, temporal, and parietal cortices as well as subcortical regions including the hippocampus. Notably, the relationship between lower gestational age and executive dysfunction was accounted for in part by structural network deficits. Together, these findings emphasize the durable impact of prematurity on cognition and brain structure, which persists across development.

Dental problems and Familismo: social network discussion of oral health issues among adults of Mexican origin living in the Midwest United States.

PubMed

Maupome, G; McConnell, W R; Perry, B L

2016-12-01

To examine the influence of collectivist orientation (often called familismo when applied to the Latino sub-group in the United States) in oral health discussion networks. Through respondent-driven sampling and face-to-face interviews, we identified respondents' (egos) personal social network members (alters). Egos stated whom they talked with about oral health, and how often they discussed dental problems in the preceding 12 months. An urban community of adult Mexican-American immigrants in the Midwest United States. We interviewed 332 egos (90% born in Mexico); egos named an average of 3.9 alters in their networks, 1,299 in total. We applied egocentric network methods to examine the ego, alter, and network variables that characterize health discussion networks. Kin were most often leveraged when dental problems arose; egos relied on individuals whom they perceive to have better knowledge about dental matters. However, reliance on knowledgeable alters decreased among egos with greater behavioral acculturation. This paper developed a network-based conceptualization of familismo. We describe the structure of oral health networks, including kin, fictive kin, peers, and health professionals, and examine how networks and acculturation help shape oral health among these Mexican-Americans. Copyright© 2016 Dennis Barber Ltd

Long-Term Network Experiments and Interdisciplinary Campaigns Conducted by the USDA-Agricultural Research Service

NASA Astrophysics Data System (ADS)

Goodrich, D. C.; Kustas, W. P.; Cosh, M. H.; Moran, S. M.; Marks, D. G.; Jackson, T. J.; Bosch, D. D.; Rango, A.; Seyfried, M. S.; Scott, R. L.; Prueger, J. H.; Starks, P. J.; Walbridge, M. R.

2014-12-01

The USDA-Agricultural Research Service has led, or been integrally involved in, a myriad of interdisciplinary field campaigns in a wide range of locations both nationally and internationally. Many of the shorter campaigns were anchored over the existing national network of ARS Experimental Watersheds and Rangelands. These long-term outdoor laboratories provided a critical knowledge base for designing the campaigns as well as historical data, hydrologic and meteorological infrastructure coupled with shop, laboratory, and visiting scientist facilities. This strong outdoor laboratory base enabled cost-efficient campaigns informed by historical context, local knowledge, and detailed existing watershed characterization. These long-term experimental facilities have also enabled much longer term lower intensity experiments, observing and building an understanding of both seasonal and inter-annual biosphere-hydrosphere-atmosphere interactions across a wide range of conditions. A sampling of these experiments include MONSOON'90, SGP97, SGP99, Washita'92, Washita'94, SMEX02-05 and JORNEX series of experiments, SALSA, CLASIC and longer-term efforts over the ARS Little Washita, Walnut Gulch, Little River, Reynolds Creek, and OPE3 Experimental Watersheds. This presentation will review some of the highlights and key findings of these campaigns and long-term efforts including the inclusion of many of the experimental watersheds and ranges in the Long-Term Agro-ecosystems Research (LTAR) network. The LTAR network also contains several locations that are also part of other observational networks including the CZO, LTER, and NEON networks. Lessons learned will also be provided for scientists initiating their participation in large-scale, multi-site interdisciplinary science.

A deep convolutional neural network to analyze position averaged convergent beam electron diffraction patterns.

PubMed

Xu, W; LeBeau, J M

2018-05-01

We establish a series of deep convolutional neural networks to automatically analyze position averaged convergent beam electron diffraction patterns. The networks first calibrate the zero-order disk size, center position, and rotation without the need for pretreating the data. With the aligned data, additional networks then measure the sample thickness and tilt. The performance of the network is explored as a function of a variety of variables including thickness, tilt, and dose. A methodology to explore the response of the neural network to various pattern features is also presented. Processing patterns at a rate of  ∼ 0.1 s/pattern, the network is shown to be orders of magnitude faster than a brute force method while maintaining accuracy. The approach is thus suitable for automatically processing big, 4D STEM data. We also discuss the generality of the method to other materials/orientations as well as a hybrid approach that combines the features of the neural network with least squares fitting for even more robust analysis. The source code is available at https://github.com/subangstrom/DeepDiffraction. Copyright © 2018 Elsevier B.V. All rights reserved.

Coexpression network based on natural variation in human gene expression reveals gene interactions and functions

PubMed Central

Nayak, Renuka R.; Kearns, Michael; Spielman, Richard S.; Cheung, Vivian G.

2009-01-01

Genes interact in networks to orchestrate cellular processes. Analysis of these networks provides insights into gene interactions and functions. Here, we took advantage of normal variation in human gene expression to infer gene networks, which we constructed using correlations in expression levels of more than 8.5 million gene pairs in immortalized B cells from three independent samples. The resulting networks allowed us to identify biological processes and gene functions. Among the biological pathways, we found processes such as translation and glycolysis that co-occur in the same subnetworks. We predicted the functions of poorly characterized genes, including CHCHD2 and TMEM111, and provided experimental evidence that TMEM111 is part of the endoplasmic reticulum-associated secretory pathway. We also found that IFIH1, a susceptibility gene of type 1 diabetes, interacts with YES1, which plays a role in glucose transport. Furthermore, genes that predispose to the same diseases are clustered nonrandomly in the coexpression network, suggesting that networks can provide candidate genes that influence disease susceptibility. Therefore, our analysis of gene coexpression networks offers information on the role of human genes in normal and disease processes. PMID:19797678

Symmetry compression method for discovering network motifs.

PubMed

Wang, Jianxin; Huang, Yuannan; Wu, Fang-Xiang; Pan, Yi

2012-01-01

Discovering network motifs could provide a significant insight into systems biology. Interestingly, many biological networks have been found to have a high degree of symmetry (automorphism), which is inherent in biological network topologies. The symmetry due to the large number of basic symmetric subgraphs (BSSs) causes a certain redundant calculation in discovering network motifs. Therefore, we compress all basic symmetric subgraphs before extracting compressed subgraphs and propose an efficient decompression algorithm to decompress all compressed subgraphs without loss of any information. In contrast to previous approaches, the novel Symmetry Compression method for Motif Detection, named as SCMD, eliminates most redundant calculations caused by widespread symmetry of biological networks. We use SCMD to improve three notable exact algorithms and two efficient sampling algorithms. Results of all exact algorithms with SCMD are the same as those of the original algorithms, since SCMD is a lossless method. The sampling results show that the use of SCMD almost does not affect the quality of sampling results. For highly symmetric networks, we find that SCMD used in both exact and sampling algorithms can help get a remarkable speedup. Furthermore, SCMD enables us to find larger motifs in biological networks with notable symmetry than previously possible.

Individual-Based Ant-Plant Networks: Diurnal-Nocturnal Structure and Species-Area Relationship

PubMed Central

Dáttilo, Wesley; Fagundes, Roberth; Gurka, Carlos A. Q.; Silva, Mara S. A.; Vieira, Marisa C. L.; Izzo, Thiago J.; Díaz-Castelazo, Cecília; Del-Claro, Kleber; Rico-Gray, Victor

2014-01-01

Despite the importance and increasing knowledge of ecological networks, sampling effort and intrapopulation variation has been widely overlooked. Using continuous daily sampling of ants visiting three plant species in the Brazilian Neotropical savanna, we evaluated for the first time the topological structure over 24 h and species-area relationships (based on the number of extrafloral nectaries available) in individual-based ant-plant networks. We observed that diurnal and nocturnal ant-plant networks exhibited the same pattern of interactions: a nested and non-modular pattern and an average level of network specialization. Despite the high similarity in the ants’ composition between the two collection periods, ant species found in the central core of highly interacting species totally changed between diurnal and nocturnal sampling for all plant species. In other words, this “night-turnover” suggests that the ecological dynamics of these ant-plant interactions can be temporally partitioned (day and night) at a small spatial scale. Thus, it is possible that in some cases processes shaping mutualistic networks formed by protective ants and plants may be underestimated by diurnal sampling alone. Moreover, we did not observe any effect of the number of extrafloral nectaries on ant richness and their foraging on such plants in any of the studied ant-plant networks. We hypothesize that competitively superior ants could monopolize individual plants and allow the coexistence of only a few other ant species, however, other alternative hypotheses are also discussed. Thus, sampling period and species-area relationship produces basic information that increases our confidence in how individual-based ant-plant networks are structured, and the need to consider nocturnal records in ant-plant network sampling design so as to decrease inappropriate inferences. PMID:24918750

Building Virtual Watersheds: A Global Opportunity to Strengthen Resource Management and Conservation.

PubMed

Benda, Lee; Miller, Daniel; Barquin, Jose; McCleary, Richard; Cai, TiJiu; Ji, Y

2016-03-01

Modern land-use planning and conservation strategies at landscape to country scales worldwide require complete and accurate digital representations of river networks, encompassing all channels including the smallest headwaters. The digital river networks, integrated with widely available digital elevation models, also need to have analytical capabilities to support resource management and conservation, including attributing river segments with key stream and watershed data, characterizing topography to identify landforms, discretizing land uses at scales necessary to identify human-environment interactions, and connecting channels downstream and upstream, and to terrestrial environments. We investigate the completeness and analytical capabilities of national to regional scale digital river networks that are available in five countries: Canada, China, Russia, Spain, and United States using actual resource management and conservation projects involving 12 university, agency, and NGO organizations. In addition, we review one pan-European and one global digital river network. Based on our analysis, we conclude that the majority of the regional, national, and global scale digital river networks in our sample lack in network completeness, analytical capabilities or both. To address this limitation, we outline a general framework to build as complete as possible digital river networks and to integrate them with available digital elevation models to create robust analytical capabilities (e.g., virtual watersheds). We believe this presents a global opportunity for in-country agencies, or international players, to support creation of virtual watersheds to increase environmental problem solving, broaden access to the watershed sciences, and strengthen resource management and conservation in countries worldwide.

Building Virtual Watersheds: A Global Opportunity to Strengthen Resource Management and Conservation

NASA Astrophysics Data System (ADS)

Benda, Lee; Miller, Daniel; Barquin, Jose; McCleary, Richard; Cai, TiJiu; Ji, Y.

2016-03-01

Modern land-use planning and conservation strategies at landscape to country scales worldwide require complete and accurate digital representations of river networks, encompassing all channels including the smallest headwaters. The digital river networks, integrated with widely available digital elevation models, also need to have analytical capabilities to support resource management and conservation, including attributing river segments with key stream and watershed data, characterizing topography to identify landforms, discretizing land uses at scales necessary to identify human-environment interactions, and connecting channels downstream and upstream, and to terrestrial environments. We investigate the completeness and analytical capabilities of national to regional scale digital river networks that are available in five countries: Canada, China, Russia, Spain, and United States using actual resource management and conservation projects involving 12 university, agency, and NGO organizations. In addition, we review one pan-European and one global digital river network. Based on our analysis, we conclude that the majority of the regional, national, and global scale digital river networks in our sample lack in network completeness, analytical capabilities or both. To address this limitation, we outline a general framework to build as complete as possible digital river networks and to integrate them with available digital elevation models to create robust analytical capabilities (e.g., virtual watersheds). We believe this presents a global opportunity for in-country agencies, or international players, to support creation of virtual watersheds to increase environmental problem solving, broaden access to the watershed sciences, and strengthen resource management and conservation in countries worldwide.

Qualitative analysis of social network influences on quitting smoking among individuals with serious mental illness.

PubMed

Aschbrenner, Kelly A; Naslund, John A; Gill, Lydia; Hughes, Terence; O'Malley, Alistair J; Bartels, Stephen J; Brunette, Mary F

2017-07-04

The prevalence of cigarette smoking among adults with serious mental illness (SMI) remains high in the United States despite the availability of effective smoking cessation treatment. Identifying social influences on smoking and smoking cessation may help enhance intervention strategies to help smokers with SMI quit. The objective of this qualitative study was to explore social network influences on efforts to quit smoking among adults with SMI enrolled in a cessation treatment program. Participants were 41 individuals with SMI enrolled in a Medicaid Demonstration Project of smoking cessation at community mental health centers. A convenience sampling strategy was used to recruit participants for social network interviews exploring the influence of family, friends, peers, and significant others on quitting smoking. A team-based analysis of qualitative data involved descriptive coding, grouping coded data into categories, and identifying themes across the data. Social barriers to quitting smoking included pro-smoking social norms, attitudes, and behaviors of social network members, and negative interactions with network members, either specific to smoking or that triggered smoking. Social facilitators to quitting included quitting with network members, having cessation role models, and social support for quitting from network members. Similar to the general population, social factors appear to influence efforts to quit smoking among individuals with SMI enrolled in cessation treatment. Interventions that leverage positive social influences on smoking cessation have the potential to enhance strategies to help individuals with SMI quit smoking.

Comparison of four approaches to a rock facies classification problem

USGS Publications Warehouse

Dubois, M.K.; Bohling, Geoffrey C.; Chakrabarti, S.

2007-01-01

In this study, seven classifiers based on four different approaches were tested in a rock facies classification problem: classical parametric methods using Bayes' rule, and non-parametric methods using fuzzy logic, k-nearest neighbor, and feed forward-back propagating artificial neural network. Determining the most effective classifier for geologic facies prediction in wells without cores in the Panoma gas field, in Southwest Kansas, was the objective. Study data include 3600 samples with known rock facies class (from core) with each sample having either four or five measured properties (wire-line log curves), and two derived geologic properties (geologic constraining variables). The sample set was divided into two subsets, one for training and one for testing the ability of the trained classifier to correctly assign classes. Artificial neural networks clearly outperformed all other classifiers and are effective tools for this particular classification problem. Classical parametric models were inadequate due to the nature of the predictor variables (high dimensional and not linearly correlated), and feature space of the classes (overlapping). The other non-parametric methods tested, k-nearest neighbor and fuzzy logic, would need considerable improvement to match the neural network effectiveness, but further work, possibly combining certain aspects of the three non-parametric methods, may be justified. ?? 2006 Elsevier Ltd. All rights reserved.

An Amorphous Network Model for Capillary Flow and Dispersion in a Partially Saturated Porous Medium

NASA Astrophysics Data System (ADS)

Simmons, C. S.; Rockhold, M. L.

2013-12-01

Network models of capillary flow are commonly used to represent conduction of fluids at pore scales. Typically, a flow system is described by a regular geometric lattice of interconnected tubes. Tubes constitute the pore throats, while connection junctions (nodes) are pore bodies. Such conceptualization of the geometry, however, is questionable for the pore scale, where irregularity clearly prevails, although prior published models using a regular lattice have demonstrated successful descriptions of the flow in the bulk medium. Here a network is allowed to be amorphous, and is not subject to any particular lattice structure. Few network flow models have treated partially saturated or even multiphase conditions. The research trend is toward using capillary tubes with triangular or square cross sections that have corners and always retain some fluid by capillarity when drained. In contrast, this model uses only circular capillaries, whose filled state is controlled by a capillary pressure rule for the junctions. The rule determines which capillary participate in the flow under an imposed matric potential gradient during steady flow conditions. Poiseuille's Law and Laplace equation are used to describe flow and water retention in the capillary units of the model. A modified conjugate gradient solution for steady flow that tracks which capillary in an amorphous network contribute to fluid conduction was devised for partially saturated conditions. The model thus retains the features of classical capillary models for determining hydraulic flow properties under unsaturated conditions based on distribution of non-interacting tubes, but now accounts for flow exchange at junctions. Continuity of the flow balance at every junction is solved simultaneously. The effective water retention relationship and unsaturated permeability are evaluated for an extensive enough network to represent a small bulk sample of porous medium. The model is applied for both a hypothetically randomly generate network and for a directly measured porous medium structure, by means of xray-CT scan. A randomly generated network has the benefit of providing ensemble averages for sample replicates of a medium's properties, whereas network structure measurements are expected to be more predictive. Dispersion of solute in a network flow is calculate by using particle tracking to determine the travel time breakthrough between inflow and outflow boundaries. The travel time distribution can exhibit substantial skewness that reflects both network velocity variability and mixing dilution at junctions. When local diffusion is not included, and transport is strictly advective, then the skew breakthrough is not due to mobile-immobile flow region behavior. The approach of dispersivity to its asymptotic value with sample size is examined, and may be only an indicator of particular stochastic flow variation. It is not proven that a simplified network flow model can accurately predict the hydraulic properties of a sufficiently large-size medium sample, but such a model can at least demonstrate macroscopic flow resulting from the interaction of physical processes at pore scales.

A new computer code for discrete fracture network modelling

NASA Astrophysics Data System (ADS)

Xu, Chaoshui; Dowd, Peter

2010-03-01

The authors describe a comprehensive software package for two- and three-dimensional stochastic rock fracture simulation using marked point processes. Fracture locations can be modelled by a Poisson, a non-homogeneous, a cluster or a Cox point process; fracture geometries and properties are modelled by their respective probability distributions. Virtual sampling tools such as plane, window and scanline sampling are included in the software together with a comprehensive set of statistical tools including histogram analysis, probability plots, rose diagrams and hemispherical projections. The paper describes in detail the theoretical basis of the implementation and provides a case study in rock fracture modelling to demonstrate the application of the software.

An evaluation of potential sampling locations in a reservoir with emphasis on conserved spatial correlation structure.

PubMed

Yenilmez, Firdes; Düzgün, Sebnem; Aksoy, Aysegül

2015-01-01

In this study, kernel density estimation (KDE) was coupled with ordinary two-dimensional kriging (OK) to reduce the number of sampling locations in measurement and kriging of dissolved oxygen (DO) concentrations in Porsuk Dam Reservoir (PDR). Conservation of the spatial correlation structure in the DO distribution was a target. KDE was used as a tool to aid in identification of the sampling locations that would be removed from the sampling network in order to decrease the total number of samples. Accordingly, several networks were generated in which sampling locations were reduced from 65 to 10 in increments of 4 or 5 points at a time based on kernel density maps. DO variograms were constructed, and DO values in PDR were kriged. Performance of the networks in DO estimations were evaluated through various error metrics, standard error maps (SEM), and whether the spatial correlation structure was conserved or not. Results indicated that smaller number of sampling points resulted in loss of information in regard to spatial correlation structure in DO. The minimum representative sampling points for PDR was 35. Efficacy of the sampling location selection method was tested against the networks generated by experts. It was shown that the evaluation approach proposed in this study provided a better sampling network design in which the spatial correlation structure of DO was sustained for kriging.

Estimation of Global Network Statistics from Incomplete Data

PubMed Central

Bliss, Catherine A.; Danforth, Christopher M.; Dodds, Peter Sheridan

2014-01-01

Complex networks underlie an enormous variety of social, biological, physical, and virtual systems. A profound complication for the science of complex networks is that in most cases, observing all nodes and all network interactions is impossible. Previous work addressing the impacts of partial network data is surprisingly limited, focuses primarily on missing nodes, and suggests that network statistics derived from subsampled data are not suitable estimators for the same network statistics describing the overall network topology. We generate scaling methods to predict true network statistics, including the degree distribution, from only partial knowledge of nodes, links, or weights. Our methods are transparent and do not assume a known generating process for the network, thus enabling prediction of network statistics for a wide variety of applications. We validate analytical results on four simulated network classes and empirical data sets of various sizes. We perform subsampling experiments by varying proportions of sampled data and demonstrate that our scaling methods can provide very good estimates of true network statistics while acknowledging limits. Lastly, we apply our techniques to a set of rich and evolving large-scale social networks, Twitter reply networks. Based on 100 million tweets, we use our scaling techniques to propose a statistical characterization of the Twitter Interactome from September 2008 to November 2008. Our treatment allows us to find support for Dunbar's hypothesis in detecting an upper threshold for the number of active social contacts that individuals maintain over the course of one week. PMID:25338183

CHRR: coordinate hit-and-run with rounding for uniform sampling of constraint-based models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haraldsdóttir, Hulda S.; Cousins, Ben; Thiele, Ines

In constraint-based metabolic modelling, physical and biochemical constraints define a polyhedral convex set of feasible flux vectors. Uniform sampling of this set provides an unbiased characterization of the metabolic capabilities of a biochemical network. However, reliable uniform sampling of genome-scale biochemical networks is challenging due to their high dimensionality and inherent anisotropy. Here, we present an implementation of a new sampling algorithm, coordinate hit-and-run with rounding (CHRR). This algorithm is based on the provably efficient hit-and-run random walk and crucially uses a preprocessing step to round the anisotropic flux set. CHRR provably converges to a uniform stationary sampling distribution. Wemore » apply it to metabolic networks of increasing dimensionality. We show that it converges several times faster than a popular artificial centering hit-and-run algorithm, enabling reliable and tractable sampling of genome-scale biochemical networks.« less

CHRR: coordinate hit-and-run with rounding for uniform sampling of constraint-based models

DOE PAGES

Haraldsdóttir, Hulda S.; Cousins, Ben; Thiele, Ines; ...

2017-01-31

In constraint-based metabolic modelling, physical and biochemical constraints define a polyhedral convex set of feasible flux vectors. Uniform sampling of this set provides an unbiased characterization of the metabolic capabilities of a biochemical network. However, reliable uniform sampling of genome-scale biochemical networks is challenging due to their high dimensionality and inherent anisotropy. Here, we present an implementation of a new sampling algorithm, coordinate hit-and-run with rounding (CHRR). This algorithm is based on the provably efficient hit-and-run random walk and crucially uses a preprocessing step to round the anisotropic flux set. CHRR provably converges to a uniform stationary sampling distribution. Wemore » apply it to metabolic networks of increasing dimensionality. We show that it converges several times faster than a popular artificial centering hit-and-run algorithm, enabling reliable and tractable sampling of genome-scale biochemical networks.« less

Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis

NASA Astrophysics Data System (ADS)

Wang, Ting; Plecháč, Petr

2017-12-01

Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.

Synchronization of Hierarchical Time-Varying Neural Networks Based on Asynchronous and Intermittent Sampled-Data Control.

PubMed

Xiong, Wenjun; Patel, Ragini; Cao, Jinde; Zheng, Wei Xing

In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.

Efficient sampling of complex network with modified random walk strategies

NASA Astrophysics Data System (ADS)

Xie, Yunya; Chang, Shuhua; Zhang, Zhipeng; Zhang, Mi; Yang, Lei

2018-02-01

We present two novel random walk strategies, choosing seed node (CSN) random walk and no-retracing (NR) random walk. Different from the classical random walk sampling, the CSN and NR strategies focus on the influences of the seed node choice and path overlap, respectively. Three random walk samplings are applied in the Erdös-Rényi (ER), Barabási-Albert (BA), Watts-Strogatz (WS), and the weighted USAir networks, respectively. Then, the major properties of sampled subnets, such as sampling efficiency, degree distributions, average degree and average clustering coefficient, are studied. The similar conclusions can be reached with these three random walk strategies. Firstly, the networks with small scales and simple structures are conducive to the sampling. Secondly, the average degree and the average clustering coefficient of the sampled subnet tend to the corresponding values of original networks with limited steps. And thirdly, all the degree distributions of the subnets are slightly biased to the high degree side. However, the NR strategy performs better for the average clustering coefficient of the subnet. In the real weighted USAir networks, some obvious characters like the larger clustering coefficient and the fluctuation of degree distribution are reproduced well by these random walk strategies.

The Southeast Asian Influenza Clinical Research Network: development and challenges for a new multilateral research endeavor.

PubMed

Higgs, Elizabeth S; Hayden, Frederick G; Chotpitayasunondh, Tawee; Whitworth, Jimmy; Farrar, Jeremy

2008-04-01

The Southeast Asia Influenza Clinical Research Network (SEA ICRN) (www.seaclinicalresearch.org) is a recently developed multilateral, collaborative partnership that aims to advance scientific knowledge and management of human influenza through integrated clinical investigation. The partnership of hospitals and institutions in Indonesia, Thailand, United Kingdom, United States, and Viet Nam was established in late 2005 after agreement on the general principles and mission of the initiative and after securing initial financial support. The establishment of the SEA ICRN was both a response to the re-emergence of the highly pathogenic avian influenza A(H5N1) virus in Southeast Asia in late 2003 and an acknowledgment that clinical trials on emerging infectious diseases require prepared and coordinated research capacity. The objectives of the Network also include building sustainable research capacity in the region, compliance with international standards, and prompt dissemination of information and sharing of samples. The scope of research includes diagnosis, pathogenesis, treatment and prevention of human influenza due to seasonal or novel viruses. The Network has overcome numerous logistical and scientific challenges but has now successfully initiated several clinical trials. The establishment of a clinical research network is a vital part of preparedness and an important element during an initial response phase to a pandemic.

A natural experiment of social network formation and dynamics.

PubMed

Phan, Tuan Q; Airoldi, Edoardo M

2015-05-26

Social networks affect many aspects of life, including the spread of diseases, the diffusion of information, the workers' productivity, and consumers' behavior. Little is known, however, about how these networks form and change. Estimating causal effects and mechanisms that drive social network formation and dynamics is challenging because of the complexity of engineering social relations in a controlled environment, endogeneity between network structure and individual characteristics, and the lack of time-resolved data about individuals' behavior. We leverage data from a sample of 1.5 million college students on Facebook, who wrote more than 630 million messages and 590 million posts over 4 years, to design a long-term natural experiment of friendship formation and social dynamics in the aftermath of a natural disaster. The analysis shows that affected individuals are more likely to strengthen interactions, while maintaining the same number of friends as unaffected individuals. Our findings suggest that the formation of social relationships may serve as a coping mechanism to deal with high-stress situations and build resilience in communities.

A natural experiment of social network formation and dynamics

PubMed Central

Phan, Tuan Q.; Airoldi, Edoardo M.

2015-01-01

Social networks affect many aspects of life, including the spread of diseases, the diffusion of information, the workers' productivity, and consumers' behavior. Little is known, however, about how these networks form and change. Estimating causal effects and mechanisms that drive social network formation and dynamics is challenging because of the complexity of engineering social relations in a controlled environment, endogeneity between network structure and individual characteristics, and the lack of time-resolved data about individuals' behavior. We leverage data from a sample of 1.5 million college students on Facebook, who wrote more than 630 million messages and 590 million posts over 4 years, to design a long-term natural experiment of friendship formation and social dynamics in the aftermath of a natural disaster. The analysis shows that affected individuals are more likely to strengthen interactions, while maintaining the same number of friends as unaffected individuals. Our findings suggest that the formation of social relationships may serve as a coping mechanism to deal with high-stress situations and build resilience in communities. PMID:25964337

A Community "Hub" Network Intervention for HIV Stigma Reduction: A Case Study.

PubMed

Prinsloo, Catharina D; Greeff, Minrie

2016-01-01

We describe the implementation of a community "hub" network intervention to reduce HIV stigma in the Tlokwe Municipality, North West Province, South Africa. A holistic case study design was used, focusing on community members with no differentiation by HIV status. Participants were recruited through accessibility sampling. Data analyses used open coding and document analysis. Findings showed that the HIV stigma-reduction community hub network intervention successfully activated mobilizers to initiate change; lessened the stigma experience for people living with HIV; and addressed HIV stigma in a whole community using a combination of strategies including individual and interpersonal levels, social networks, and the public. Further research is recommended to replicate and enhance the intervention. In particular, the hub network system should be extended, the intervention period should be longer, there should be a stronger support system for mobilizers, and the multiple strategy approach should be continued on individual and social levels. Copyright © 2016 Association of Nurses in AIDS Care. Published by Elsevier Inc. All rights reserved.

Ecological Networks in Stored Grain: Key Postharvest Nodes for Emerging Pests, Pathogens, and Mycotoxins.

PubMed

Hernandez Nopsa, John F; Daglish, Gregory J; Hagstrum, David W; Leslie, John F; Phillips, Thomas W; Scoglio, Caterina; Thomas-Sharma, Sara; Walter, Gimme H; Garrett, Karen A

2015-10-01

Wheat is at peak quality soon after harvest. Subsequently, diverse biota use wheat as a resource in storage, including insects and mycotoxin-producing fungi. Transportation networks for stored grain are crucial to food security and provide a model system for an analysis of the population structure, evolution, and dispersal of biota in networks. We evaluated the structure of rail networks for grain transport in the United States and Eastern Australia to identify the shortest paths for the anthropogenic dispersal of pests and mycotoxins, as well as the major sources, sinks, and bridges for movement. We found important differences in the risk profile in these two countries and identified priority control points for sampling, detection, and management. An understanding of these key locations and roles within the network is a new type of basic research result in postharvest science and will provide insights for the integrated pest management of high-risk subpopulations, such as pesticide-resistant insect pests.

Ecological Networks in Stored Grain: Key Postharvest Nodes for Emerging Pests, Pathogens, and Mycotoxins

PubMed Central

Hernandez Nopsa, John F.; Daglish, Gregory J.; Hagstrum, David W.; Leslie, John F.; Phillips, Thomas W.; Scoglio, Caterina; Thomas-Sharma, Sara; Walter, Gimme H.; Garrett, Karen A.

2015-01-01

Wheat is at peak quality soon after harvest. Subsequently, diverse biota use wheat as a resource in storage, including insects and mycotoxin-producing fungi. Transportation networks for stored grain are crucial to food security and provide a model system for an analysis of the population structure, evolution, and dispersal of biota in networks. We evaluated the structure of rail networks for grain transport in the United States and Eastern Australia to identify the shortest paths for the anthropogenic dispersal of pests and mycotoxins, as well as the major sources, sinks, and bridges for movement. We found important differences in the risk profile in these two countries and identified priority control points for sampling, detection, and management. An understanding of these key locations and roles within the network is a new type of basic research result in postharvest science and will provide insights for the integrated pest management of high-risk subpopulations, such as pesticide-resistant insect pests. PMID:26955074

Leveraging Educational, Research and Facility Expertise to Improve Global Seismic Monitoring: Preparing a Guide on Sustainable Networks

NASA Astrophysics Data System (ADS)

Nybade, A.; Aster, R.; Beck, S.; Ekstrom, G.; Fischer, K.; Lerner-Lam, A.; Meltzer, A.; Sandvol, E.; Willemann, R. J.

2008-12-01

Building a sustainable earthquake monitoring system requires well-informed cooperation between commercial companies that manufacture components or deliver complete systems and the government or other agencies that will be responsible for operating them. Many nations or regions with significant earthquake hazard lack the financial, technical, and human resources to establish and sustain permanent observatory networks required to return the data needed for hazard mitigation. Government agencies may not be well- informed about the short-term and long-term challenges of managing technologically advanced monitoring systems, much less the details of how they are built and operated. On the relatively compressed time scale of disaster recovery efforts, it can be difficult to find a reliable, disinterested source of information, without which government agencies may be dependent on partial information. If system delivery fails to include sufficient development of indigenous expertise, the performance of local and regional networks may decline quickly, and even data collected during an early high-performance period may be degraded or lost. Drawing on unsurpassed educational capabilities of its members working in close cooperation with its facility staff, IRIS is well prepared to contribute to sustainability through a wide variety of training and service activities that further promote standards for network installation, data exchange protocols, and free and open access to data. Members of the Consortium and staff of its Core Programs together could write a guide on decisions about network design, installation and operation. The intended primary audience would be government officials seeking to understand system requirements, the acquisition and installation process, and the expertise needed operate a system. The guide would cover network design, procurement, set-up, data use and archiving. Chapters could include advice on network data processing, archiving data (including information on the value of standards), installing and servicing stations, building a data processing and management center (including information on evaluating bids), using results from earthquake monitoring, and sustaining an earthquake monitoring system. Appendices might include profiles of well-configured and well- run networks and sample RFPs. Establishing permanent networks could provide a foundation for international research and educational collaborations and critical new data for imaging Earth structure while supporting scientific capacity building and strengthening hazard monitoring around the globe.

Quality of surface water in Missouri, water year 2009

USGS Publications Warehouse

Barr, Miya N.

2010-01-01

The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designs and operates a series of monitoring stations on streams throughout Missouri known as the Ambient Water-Quality Monitoring Network. During the 2009 water year (October 1, 2008, through September 30, 2009), data were collected at 75 stations-69 Ambient Water-Quality Monitoring Network stations, 2 U.S. Geological Survey National Stream Quality Accounting Network stations, 1 spring sampled in cooperation with the U.S. Forest Service, and 3 stations sampled in cooperation with the Elk River Watershed Improvement Association. Dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, fecal coliform bacteria, Escherichia coli bacteria, dissolved nitrate plus nitrite, total phosphorus, dissolved and total recoverable lead and zinc, and select pesticide compound summaries are presented for 72 of these stations. The stations primarily have been classified into groups corresponding to the physiography of the State, primary land use, or unique station types. In addition, a summary of hydrologic conditions in the State including peak discharges, monthly mean discharges, and seven-day low flow is presented.

A quality-assurance assessment for constituents reported by the national atmospheric deposition program and the national trends network

NASA Astrophysics Data System (ADS)

See, Randolph B.; Schroder, LeRoy J.; Willoughby, Timothy C.

A continuing quality-assurance program has been operated by the U.S. Geological Survey to evaluate any bias introduced by routine handling, shipping, and laboratory analyses of wet-deposition samples collected in the National Atmospheric Deposition Program (NADP) and National Trends Network (NTN). Blind-audit samples having a variety of constituent concentrations and values were selected. Only blind-audit samples with constituent concentrations and values less than the 95th-percentile concentration for natural wet-deposition samples were included in the analysis. Of the major ions, there was a significant increase of Ca 2+, Mg 2+, Na 2+, K +, SO 42- and Cl -1 in samples handled according to standard protocols and shipped in NADP/NTN sample-collection buckets. For 1979-1987, graphs of smoothed data showing the estimated contamination in blind-audit samples indicate a decrease in the median concentration and ranges of Ca 2+, Mg 2+ and SO 42- contamination of blind-audit samples shipped in sample-collection buckets. Part of the contamination detected in blind-audit samples can be attributed to contact with the sample-collection bucket and lid; however, additional sources also seem to contaminate the blind-audit sample. Apparent decreases in the magnitude and range of sample contamination may be caused by differences in sample-collection bucket- and lid-washing procedures by the NADP/NTN Central Analytical Laboratory. Although the degree of bias is minimal for most constituents, summaries of the NADP/NTN data base may contain overestimates of Ca 2+, Mg 2+, Na -, K + and SO 42- and Cl - concentrations, and underestimates of H + concentrations.

A quality-assurance assessment for constituents reported by the National Atmospheric Deposition Program and the National Trends Network

USGS Publications Warehouse

See, R.B.; Schroder, L.J.; Willoughby, T.C.

1989-01-01

A continuing quality-assurance program has been operated by the U.S. Geographical Survey to evaluate any bias introduced by routine handling, shipping, and laboratory analyses of wet-deposition samples collected in the National Atmospheric Deposition Program (NADP) and National Trends Network (NTN). Blind-audit samples having a variety of constituent concentrations and values were selected. Only blind-audit samples with constituent concentrations and values less than the 95th-percentile concentration for natural wet-deposition samples were included in the analysis. Of the major ions, there was a significant increase of Ca2+, Mg2+, K+ SO42+ and Cl- in samples handled according to standard protocols and shipped in NADP/NTN sample-collection buckets. For 1979-1987, graphs of smoothed data showing the estimated contaminations in blind-audit samples indicate a decrease in the median concentration and ranges of Ca2+, Mg2+ and SO42- contamination of blind-audit samples shipped in sample-collection buckets. Part of the contamination detected in blind-audit samples can be attributed to contact with the sample-collection bucket and lid; however, additional sources also seem to contaminate the blind-audit sample. Apparent decreases in the magnitude and range of sample contamination may be caused by differences in sample-collection bucket- and lid-washing procedures by the NADP/NTN Central Analytical Laboratory. Although the degree of bias is minimal for most constituents, summaries of the NADP/NTN data base may contain overestimates of Ca2+, Mg2+, Na-, K+, SO42- and Cl- concentrations, and underestimates of H+ concentrations.

Social networks of men who have sex with men: a study of recruitment chains using Respondent Driven Sampling in Salvador, Bahia State, Brazil.

PubMed

Brignol, Sandra Mara Silva; Dourado, Inês; Amorim, Leila Denise; Miranda, José Garcia Vivas; Kerr, Lígia R F S

2015-11-01

Social and sexual contact networks between men who have sex with men (MSM) play an important role in understanding the transmission of HIV and other sexually transmitted infections (STIs). In Salvador (Bahia State, Brazil), one of the cities in the survey Behavior, Attitudes, Practices, and Prevalence of HIV and Syphilis among Men Who Have Sex with Men in 10 Brazilian Cities, data were collected in 2008/2009 from a sample of 383 MSM using Respondent Driven Sampling (RDS). Network analysis was used to study friendship networks and sexual partner networks. The study also focused on the association between the number of links (degree) and the number of sexual partners, in addition to socio-demographic characteristics. The networks' structure potentially facilitates HIV transmission. However, the same networks can also be used to spread messages on STI/HIV prevention, since the proximity and similarity of MSM in these networks can encourage behavior change and positive attitudes towards prevention.

Feasibility of conducting wetfall chemistry investigations around the Bowen Power Plant

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, N.C.J.; Patrinos, A.A.N.

1979-10-01

The feasibility of expanding the Meteorological Effects of Thermal Energy Releases - Oak Ridge National Laboratory (METER-ORNL) research at Bower Power Plant, a coal-fired power plant in northwest Georgia, to include wetfall chemistry is evaluated using results of similar studies around other power plants, several atmospheric washout models, analysis of spatial variability in precipitation, and field logistical considerations. An optimal wetfall chemistry network design is proposed, incorporating the inner portion of the existing rain-gauge network and augmented by additional sites to ensure adequate coverage of probable target areas. The predicted sulfate production rate differs by about four orders of magnitudemore » among the models reviewed with a pH of 3. No model can claim superiority over any other model without substantive data verification. The spatial uniformity in rain amount is evaluated using four storms that occurred at the METER-ORNL network. Values of spatial variability ranged from 8 to 31% and decreased as the mean rainfall increased. The field study of wetfall chemistry will require a minimum of 5 persons to operate the approximately 50 collectors covering an area of 740 km/sup 2/. Preliminary wetfall-only samples collected on an event basis showed lower pH and higher electrical conductivity of precipitation collected about 5 km downwind of the power plant relative to samples collected upwind. Wetfall samples collected on a weekly basis using automatic samplers, however, showed variable results, with no consistent pattern. This suggests the need for event sampling to minimize variable rain volume and multiple-source effects often associated with weekly samples.« less

Water Security Toolkit User Manual: Version 1.3 | Science ...

EPA Pesticide Factsheets

User manual: Data Product/Software The Water Security Toolkit (WST) is a suite of tools that help provide the information necessary to make good decisions resulting in the minimization of further human exposure to contaminants, and the maximization of the effectiveness of intervention strategies. WST assists in the evaluation of multiple response actions in order to select the most beneficial consequence management strategy. It includes hydraulic and water quality modeling software and optimization methodologies to identify: (1) sensor locations to detect contamination, (2) locations in the network in which the contamination was introduced, (3) hydrants to remove contaminated water from the distribution system, (4) locations in the network to inject decontamination agents to inactivate, remove or destroy contaminants, (5) locations in the network to take grab sample to confirm contamination or cleanup and (6) valves to close in order to isolate contaminated areas of the network.

Artificial Neural Networks and Instructional Technology.

ERIC Educational Resources Information Center

Carlson, Patricia A.

1991-01-01

Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…

Use of a probabilistic neural network to reduce costs of selecting construction rock

USGS Publications Warehouse

Singer, Donald A.; Bliss, James D.

2003-01-01

Rocks used as construction aggregate in temperate climates deteriorate to differing degrees because of repeated freezing and thawing. The magnitude of the deterioration depends on the rock's properties. Aggregate, including crushed carbonate rock, is required to have minimum geotechnical qualities before it can be used in asphalt and concrete. In order to reduce chances of premature and expensive repairs, extensive freeze-thaw tests are conducted on potential construction rocks. These tests typically involve 300 freeze-thaw cycles and can take four to five months to complete. Less time consuming tests that (1) predict durability as well as the extended freeze-thaw test or that (2) reduce the number of rocks subject to the extended test, could save considerable amounts of money. Here we use a probabilistic neural network to try and predict durability as determined by the freeze-thaw test using four rock properties measured on 843 limestone samples from the Kansas Department of Transportation. Modified freeze-thaw tests and less time consuming specific gravity (dry), specific gravity (saturated), and modified absorption tests were conducted on each sample. Durability factors of 95 or more as determined from the extensive freeze-thaw tests are viewed as acceptable—rocks with values below 95 are rejected. If only the modified freeze-thaw test is used to predict which rocks are acceptable, about 45% are misclassified. When 421 randomly selected samples and all four standardized and scaled variables were used to train aprobabilistic neural network, the rate of misclassification of 422 independent validation samples dropped to 28%. The network was trained so that each class (group) and each variable had its own coefficient (sigma). In an attempt to reduce errors further, an additional class was added to the training data to predict durability values greater than 84 and less than 98, resulting in only 11% of the samples misclassified. About 43% of the test data was classed by the neural net into the middle group—these rocks should be subject to full freeze-thaw tests. Thus, use of the probabilistic neural network would meanthat the extended test would only need be applied to 43% of the samples, and 11% of the rocks classed as acceptable would fail early.

Network correlates of sexual health advice seeking and substance use among members of the Los Angeles House and Ball communities

PubMed Central

Holloway, Ian W.; Schrager, Sheree M.; Wong, Carolyn F.; Dunlap, Shannon L.; Kipke, Michele D.

2014-01-01

House and Ball communities (HBCs), represent a prime context for human immunodeficiency virus prevention with African American young men who have sex with men and transgender persons. This study sought to understand the composition and function of social support and sexual networks of HBC members in Los Angeles, California (N = 263). Participants were recruited using venue-based sampling and asked to report on sexual health advice seeking, alcohol use and illicit substance use. Participants were more likely to seek sexual health advice from social support network members compared with sexual network members [odds ratio (OR): 2.50, P < 0.001]. HBC members were more likely to get drunk (OR: 1.57; P < 0.05) and use illicit substances (OR: 1.87; P < 0.10) with House members and sexual network members compared with non-House members and social support network members. Health promotion programs tailored for the HBC should encourage open communication regarding sexual health; these interventions must include information about the role of substance use in sexual risk taking. PMID:24452228

The Relationship of Self-Awareness to Leadership Effectiveness for Experienced Leaders

ERIC Educational Resources Information Center

Sullivan, Patricia A.

2017-01-01

The purpose of this research was to investigate the relationship between leaders' self-awareness and their effectiveness. The population included leaders with at least five years of experience in a leadership role. Participants were recruited by snowball sampling methods; the researcher used a diverse network of professionals to recruit other…

Social Support and Low-Income, Urban Mothers: Longitudinal Associations with Adolescent Delinquency

ERIC Educational Resources Information Center

Ghazarian, Sharon R.; Roche, Kathleen M.

2010-01-01

The current study examined the role of engaged parenting in explaining longitudinal associations between maternal perceptions of social network support and whether youth engage in delinquent behaviors during the transition into adolescence. The sample included 432 low-income, African American and Latino youth (49% female) and their mothers…

An analog silicon retina with multichip configuration.

PubMed

Kameda, Seiji; Yagi, Tetsuya

2006-01-01

The neuromorphic silicon retina is a novel analog very large scale integrated circuit that emulates the structure and the function of the retinal neuronal circuit. We fabricated a neuromorphic silicon retina, in which sample/hold circuits were embedded to generate fluctuation-suppressed outputs in the previous study [1]. The applications of this silicon retina, however, are limited because of a low spatial resolution and computational variability. In this paper, we have fabricated a multichip silicon retina in which the functional network circuits are divided into two chips: the photoreceptor network chip (P chip) and the horizontal cell network chip (H chip). The output images of the P chip are transferred to the H chip with analog voltages through the line-parallel transfer bus. The sample/hold circuits embedded in the P and H chips compensate for the pattern noise generated on the circuits, including the analog communication pathway. Using the multichip silicon retina together with an off-chip differential amplifier, spatial filtering of the image with an odd- and an even-symmetric orientation selective receptive fields was carried out in real time. The analog data transfer method in the present multichip silicon retina is useful to design analog neuromorphic multichip systems that mimic the hierarchical structure of neuronal networks in the visual system.

Depression and social networks in community dwelling elders: a descriptive study.

PubMed

Wilby, Frances

2011-04-01

Social isolation and inadequate social support have been identified as correlates of depression in older adults, although the relationship between depression and social isolation is not entirely understood (Dorfman et al., 1995). This study was conducted to describe the social networks of depressed older adults living in the community and to compare the social networks of depressed and nondepressed individuals, thus adding to the body of knowledge regarding social networks, older adults, and depression. The sample consisted of 91 respondents aged 65 and older who were randomly selected using the voter registry. About 27% (25) respondents reported significant levels of depressive symptomology as measured by the Center for Epidemiological Studies-Depression Scale (CES-D). All respondents completed semistructured interviews that included questions about social contacts with family and others during the prior week. All participants reported social contact with family and friends during this period. In this sample, depressed elders were not socially isolated. They were more likely to report contacts with friends than those who were not depressed, and equally likely to report involvement in volunteer activities. Their likelihood of seeking social support was also comparable. Results emphasize the importance of peer relationships and suggest that, in some groups of older adults, social isolation may not be a hallmark of depressive symptoms.

Impact of weak social ties and networks on poor sleep quality: A case study of Iranian employees.

PubMed

Masoudnia, Ebrahim

2015-12-01

The poor sleep quality is one of the major risk factors of somatic, psychiatric and social disorders and conditions as well as the major predictors of quality of employees' performance. The previous studies in Iran had neglected the impacts of social factors including social networks and ties on adults sleep quality. Thus, the aim of the current research was to determine the relationship between social networks and adult employees' sleep quality. This study was conducted with a correlational and descriptive design. Data were collected from 360 participants (183 males and 177 females) who were employed in Yazd public organizations in June and July of 2014. These samples were selected based on random sampling method. In addition, the measuring tools were the Pittsburgh Sleep Quality Index (PSQI) and Social Relations Inventory (SRI). Based on the results, the prevalence rate of sleep disorder among Iranian adult employees was 63.1% (total PSQI>5). And, after controlling for socio-demographic variables, there was significant difference between individuals with strong and poor social network and ties in terms of overall sleep quality (p<.01), subjective sleep quality (p<.01), habitual sleep efficiency (p<.05), and daytime dysfunction (p<.01). The results also revealed that the employees with strong social network and ties had better overall sleep quality, had the most habitual sleep efficiency, and less daytime dysfunction than employees with poor social network and ties. It can be implied that the weak social network and ties serve as a risk factor for sleep disorders or poor sleep quality for adult employees. Therefore, the social and behavioral interventions seem essential to improve the adult's quality sleep. Copyright © 2015 Elsevier B.V. All rights reserved.

Optimal design of monitoring networks for multiple groundwater quality parameters using a Kalman filter: application to the Irapuato-Valle aquifer.

PubMed

Júnez-Ferreira, H E; Herrera, G S; González-Hita, L; Cardona, A; Mora-Rodríguez, J

2016-01-01

A new method for the optimal design of groundwater quality monitoring networks is introduced in this paper. Various indicator parameters were considered simultaneously and tested for the Irapuato-Valle aquifer in Mexico. The steps followed in the design were (1) establishment of the monitoring network objectives, (2) definition of a groundwater quality conceptual model for the study area, (3) selection of the parameters to be sampled, and (4) selection of a monitoring network by choosing the well positions that minimize the estimate error variance of the selected indicator parameters. Equal weight for each parameter was given to most of the aquifer positions and a higher weight to priority zones. The objective for the monitoring network in the specific application was to obtain a general reconnaissance of the water quality, including water types, water origin, and first indications of contamination. Water quality indicator parameters were chosen in accordance with this objective, and for the selection of the optimal monitoring sites, it was sought to obtain a low-uncertainty estimate of these parameters for the entire aquifer and with more certainty in priority zones. The optimal monitoring network was selected using a combination of geostatistical methods, a Kalman filter and a heuristic optimization method. Results show that when monitoring the 69 locations with higher priority order (the optimal monitoring network), the joint average standard error in the study area for all the groundwater quality parameters was approximately 90 % of the obtained with the 140 available sampling locations (the set of pilot wells). This demonstrates that an optimal design can help to reduce monitoring costs, by avoiding redundancy in data acquisition.

Independence through social networks: bridging potential among older women and men.

PubMed

Cornwell, Benjamin

2011-11-01

Most studies of older adults' social networks focus on their access to dense networks that yield access to social support. This paper documents gender differences in the extent to which older adults maintain a related, but distinct, form of social capital-bridging potential, which involves serving as a tie between two unconnected parties and thus boosts independence and control of everyday social life. I use egocentric social network data from a national sample of 3,005 older adults--collected in 2005-2006 by the National Social Life, Health, and Aging Project--to compare older men's and women's network bridging potential using multivariate regression analysis. Older women are more likely than older men to have bridging potential in their networks-between both kin and non-kin contacts. These gender differences increase with age. Older women are also more likely to have network members who are not connected to or monopolized by their spouse or partner. Some, but not all, of these gender differences are due to the fact that older women have larger social networks and maintain more ties to people outside of the household. These findings raise important questions about the relational advantages older women have over older men, including greater autonomy, and contradict stereotypes about women having more closely knit, kin-centered networks than men.

Pore network extraction from pore space images of various porous media systems

NASA Astrophysics Data System (ADS)

Yi, Zhixing; Lin, Mian; Jiang, Wenbin; Zhang, Zhaobin; Li, Haishan; Gao, Jian

2017-04-01

Pore network extraction, which is defined as the transformation from irregular pore space to a simplified network in the form of pores connected by throats, is significant to microstructure analysis and network modeling. A physically realistic pore network is not only a representation of the pore space in the sense of topology and morphology, but also a good tool for predicting transport properties accurately. We present a method to extract pore network by employing the centrally located medial axis to guide the construction of maximal-balls-like skeleton where the pores and throats are defined and parameterized. To validate our method, various rock samples including sand pack, sandstones, and carbonates were used to extract pore networks. The pore structures were compared quantitatively with the structures extracted by medial axis method or maximal ball method. The predicted absolute permeability and formation factor were verified against the theoretical solutions obtained by lattice Boltzmann method and finite volume method, respectively. The two-phase flow was simulated through the networks extracted from homogeneous sandstones, and the generated relative permeability curves were compared with the data obtained from experimental method and other numerical models. The results show that the accuracy of our network is higher than that of other networks for predicting transport properties, so the presented method is more reliable for extracting physically realistic pore network.

Social Network Clustering and the Spread of HIV/AIDS Among Persons Who Inject Drugs in 2 Cities in the Philippines.

PubMed

Verdery, Ashton M; Siripong, Nalyn; Pence, Brian W

2017-09-01

The Philippines has seen rapid increases in HIV prevalence among people who inject drugs. We study 2 neighboring cities where a linked HIV epidemic differed in timing of onset and levels of prevalence. In Cebu, prevalence rose rapidly from below 1% to 54% between 2009 and 2011 and remained high through 2013. In nearby Mandaue, HIV remained below 4% through 2011 then rose rapidly to 38% by 2013. We hypothesize that infection prevalence differences in these cities may owe to aspects of social network structure, specifically levels of network clustering. Building on previous research, we hypothesize that higher levels of network clustering are associated with greater epidemic potential. Data were collected with respondent-driven sampling among men who inject drugs in Cebu and Mandaue in 2013. We first examine sample composition using estimators for population means. We then apply new estimators of network clustering in respondent-driven sampling data to examine associations with HIV prevalence. Samples in both cities were comparable in composition by age, education, and injection locations. Dyadic needle-sharing levels were also similar between the 2 cities, but network clustering in the needle-sharing network differed dramatically. We found higher clustering in Cebu than Mandaue, consistent with expectations that higher clustering is associated with faster epidemic spread. This article is the first to apply estimators of network clustering to empirical respondent-driven samples, and it offers suggestive evidence that researchers should pay greater attention to network structure's role in HIV transmission dynamics.

JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data.

PubMed

Ji, Jiadong; He, Di; Feng, Yang; He, Yong; Xue, Fuzhong; Xie, Lei

2017-10-01

A complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application. We propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data. R scripts available at https://github.com/jijiadong/JDINAC. lxie@iscb.org. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Fish assemblages

USGS Publications Warehouse

McGarvey, Daniel J.; Falke, Jeffrey A.; Li, Hiram W.; Li, Judith; Hauer, F. Richard; Lamberti, G.A.

2017-01-01

Methods to sample fishes in stream ecosystems and to analyze the raw data, focusing primarily on assemblage-level (all fish species combined) analyses, are presented in this chapter. We begin with guidance on sample site selection, permitting for fish collection, and information-gathering steps to be completed prior to conducting fieldwork. Basic sampling methods (visual surveying, electrofishing, and seining) are presented with specific instructions for estimating population sizes via visual, capture-recapture, and depletion surveys, in addition to new guidance on environmental DNA (eDNA) methods. Steps to process fish specimens in the field including the use of anesthesia and preservation of whole specimens or tissue samples (for genetic or stable isotope analysis) are also presented. Data analysis methods include characterization of size-structure within populations, estimation of species richness and diversity, and application of fish functional traits. We conclude with three advanced topics in assemblage-level analysis: multidimensional scaling (MDS), ecological networks, and loop analysis.

Machine Learning Toolkit for Extreme Scale

DOE Office of Scientific and Technical Information (OSTI.GOV)

2014-03-31

Support Vector Machines (SVM) is a popular machine learning technique, which has been applied to a wide range of domains such as science, finance, and social networks for supervised learning. MaTEx undertakes the challenge of designing a scalable parallel SVM training algorithm for large scale systems, which includes commodity multi-core machines, tightly connected supercomputers and cloud computing systems. Several techniques are proposed for improved speed and memory space usage including adaptive and aggressive elimination of samples for faster convergence , and sparse format representation of data samples. Several heuristics for earliest possible to lazy elimination of non-contributing samples are consideredmore » in MaTEx. In many cases, where an early sample elimination might result in a false positive, low overhead mechanisms for reconstruction of key data structures are proposed. The proposed algorithm and heuristics are implemented and evaluated on various publicly available datasets« less

Role of Social Media in Diabetes Management in the Middle East Region: Systematic Review

PubMed Central

2018-01-01

Background Diabetes is a major health care burden in the Middle East region. Social networking tools can contribute to the management of diabetes with improved educational and care outcomes using these popular tools in the region. Objective The objective of this review was to evaluate the impact of social networking interventions on the improvement of diabetes management and health outcomes in patients with diabetes in the Middle East. Methods Peer-reviewed articles from PubMed (1990-2017) and Google Scholar (1990-2017) were identified using various combinations of predefined terms and search criteria. The main inclusion criterion consisted of the use of social networking apps on mobile phones as the primary intervention. Outcomes were grouped according to study design, type of diabetes, category of technological intervention, location, and sample size. Results This review included 5 articles evaluating the use of social media tools in the management of diabetes in the Middle East. In most studies, the acceptance rate for the use of social networking to optimize the management of diabetes was relatively high. Diabetes-specific management tools such as the Saudi Arabia Networking for Aiding Diabetes and Diabetes Intelligent Management System for Iraq systems helped collect patient information and lower hemoglobin A1c (HbA1c) levels, respectively. Conclusions The reviewed studies demonstrated the potential of social networking tools being adopted in regions in the Middle East to improve the management of diabetes. Future studies consisting of larger sample sizes spanning multiple regions would provide further insight into the use of social media for improving patient outcomes. PMID:29439941

Evaluation of corrosion and scaling tendency indices in a drinking water distribution system: a case study of Bandar Abbas city, Iran.

PubMed

Alipour, Vali; Dindarloo, Kavoos; Mahvi, Amir Hossein; Rezaei, Leila

2015-03-01

Corrosion and scaling is a major problem in water distribution systems, thus evaluation of water corrosivity properties is a routine test in water networks. To evaluate water stability in the Bandar Abbas water distribution system, the network was divided into 15 clusters and 45 samples were taken. Langelier, Ryznar, Puckorius, Larson-Skold (LS) and Aggressive indices were determined and compared to the marble test. The mean parameters included were pH (7.8 ± 0.1), electrical conductivity (1,083.9 ± 108.7 μS/cm), total dissolved solids (595.7 ± 54.7 mg/L), Cl (203.5 ± 18.7 mg/L), SO₄(174.7 ± 16.0 mg/L), alkalinity (134.5 ± 9.7 mg/L), total hardness (156.5 ± 9.3 mg/L), HCO₃(137.4 ± 13.0 mg/L) and calcium hardness (71.8 ± 4.3 mg/L). According to the Ryznar, Puckorius and Aggressive Indices, all samples were stable; based on the Langelier Index, 73% of samples were slightly corrosive and the rest were scale forming; according to the LS index, all samples were corrosive. Marble test results showed tested water of all 15 clusters tended to scale formation. Water in Bandar Abbas is slightly scale forming. The most appropriate indices for the network conditions are the Aggressive, Puckorius and Ryznar indices that were consistent with the marble test.

A new class of enhanced kinetic sampling methods for building Markov state models

NASA Astrophysics Data System (ADS)

Bhoutekar, Arti; Ghosh, Susmita; Bhattacharya, Swati; Chatterjee, Abhijit

2017-10-01

Markov state models (MSMs) and other related kinetic network models are frequently used to study the long-timescale dynamical behavior of biomolecular and materials systems. MSMs are often constructed bottom-up using brute-force molecular dynamics (MD) simulations when the model contains a large number of states and kinetic pathways that are not known a priori. However, the resulting network generally encompasses only parts of the configurational space, and regardless of any additional MD performed, several states and pathways will still remain missing. This implies that the duration for which the MSM can faithfully capture the true dynamics, which we term as the validity time for the MSM, is always finite and unfortunately much shorter than the MD time invested to construct the model. A general framework that relates the kinetic uncertainty in the model to the validity time, missing states and pathways, network topology, and statistical sampling is presented. Performing additional calculations for frequently-sampled states/pathways may not alter the MSM validity time. A new class of enhanced kinetic sampling techniques is introduced that aims at targeting rare states/pathways that contribute most to the uncertainty so that the validity time is boosted in an effective manner. Examples including straightforward 1D energy landscapes, lattice models, and biomolecular systems are provided to illustrate the application of the method. Developments presented here will be of interest to the kinetic Monte Carlo community as well.

A Dynamic Model of Adolescent Friendship Networks, Parental Influences, and Smoking.

PubMed

Lakon, Cynthia M; Wang, Cheng; Butts, Carter T; Jose, Rupa; Timberlake, David S; Hipp, John R

2015-09-01

Peer and parental influences are critical socializing forces shaping adolescent development, including the co-evolving processes of friendship tie choice and adolescent smoking. This study examines aspects of adolescent friendship networks and dimensions of parental influences shaping friendship tie choice and smoking, including parental support, parental monitoring, and the parental home smoking environment using a Stochastic Actor-Based model. With data from three waves of the National Longitudinal Study of Adolescent Health of youth in grades 7 through 12, including the In-School Survey, the first wave of the In-Home survey occurring 6 months later, and the second wave of the In-Home survey, occurring one year later, this study utilizes two samples based on the social network data collected in the longitudinal saturated sample of sixteen schools. One consists of twelve small schools (n = 1,284, 50.93 % female), and the other of one large school (n = 976, 48.46 % female). The findings indicated that reciprocity, choosing a friend of a friend as a friend, and smoking similarity increased friendship tie choice behavior, as did parental support. Parental monitoring interacted with choosing friends who smoke in affecting friendship tie choice, as at higher levels of parental monitoring, youth chose fewer friends that smoked. A parental home smoking context conducive to smoking decreased the number of friends adolescents chose. Peer influence and a parental home smoking environment conducive to smoking increased smoking, while parental monitoring decreased it in the large school. Overall, peer and parental factors affected the coevolution of friendship tie choice and smoking, directly and multiplicatively.

A Dynamic Model of Adolescent Friendship Networks, Parental Influences, and Smoking

PubMed Central

Wang, Cheng; Butts, Carter T.; Jose, Rupa; Timberlake, David S.; Hipp, John R.

2015-01-01

Peer and parental influences are critical socializing forces shaping adolescent development, including the co-evolving processes of friendship tie choice and adolescent smoking. This study examines aspects of adolescent friendship networks and dimensions of parental influences shaping friendship tie choice and smoking, including parental support, parental monitoring, and the parental home smoking environment using a Stochastic Actor-Based model. With data from three waves of the National Longitudinal Study of Adolescent Health of youth in grades 7 through 12, including the In-School Survey, the first wave of the In-Home survey occurring 6 months later, and the second wave of the In-Home survey, occurring one year later, this study utilizes two samples based on the social network data collected in the longitudinal saturated sample of sixteen schools. One consists of twelve small schools (n = 1,284, 50.93 % female), and the other of one large school (n = 976, 48.46 % female). The findings indicated that reciprocity, choosing a friend of a friend as a friend, and smoking similarity increased friendship tie choice behavior, as did parental support. Parental monitoring interacted with choosing friends who smoke in affecting friendship tie choice, as at higher levels of parental monitoring, youth chose fewer friends that smoked. A parental home smoking context conducive to smoking decreased the number of friends adolescents chose. Peer influence and a parental home smoking environment conducive to smoking increased smoking, while parental monitoring decreased it in the large school. Overall, peer and parental factors affected the coevolution of friendship tie choice and smoking, directly and multiplicatively. PMID:25239115

Investigation of candidate genes for osteoarthritis based on gene expression profiles.

PubMed

Dong, Shuanghai; Xia, Tian; Wang, Lei; Zhao, Qinghua; Tian, Jiwei

2016-12-01

To explore the mechanism of osteoarthritis (OA) and provide valid biological information for further investigation. Gene expression profile of GSE46750 was downloaded from Gene Expression Omnibus database. The Linear Models for Microarray Data (limma) package (Bioconductor project, http://www.bioconductor.org/packages/release/bioc/html/limma.html) was used to identify differentially expressed genes (DEGs) in inflamed OA samples. Gene Ontology function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were performed based on Database for Annotation, Visualization and Integrated Discovery data, and protein-protein interaction (PPI) network was constructed based on the Search Tool for the Retrieval of Interacting Genes/Proteins database. Regulatory network was screened based on Encyclopedia of DNA Elements. Molecular Complex Detection was used for sub-network screening. Two sub-networks with highest node degree were integrated with transcriptional regulatory network and KEGG functional enrichment analysis was processed for 2 modules. In total, 401 up- and 196 down-regulated DEGs were obtained. Up-regulated DEGs were involved in inflammatory response, while down-regulated DEGs were involved in cell cycle. PPI network with 2392 protein interactions was constructed. Moreover, 10 genes including Interleukin 6 (IL6) and Aurora B kinase (AURKB) were found to be outstanding in PPI network. There are 214 up- and 8 down-regulated transcription factor (TF)-target pairs in the TF regulatory network. Module 1 had TFs including SPI1, PRDM1, and FOS, while module 2 contained FOSL1. The nodes in module 1 were enriched in chemokine signaling pathway, while the nodes in module 2 were mainly enriched in cell cycle. The screened DEGs including IL6, AGT, and AURKB might be potential biomarkers for gene therapy for OA by being regulated by TFs such as FOS and SPI1, and participating in the cell cycle and cytokine-cytokine receptor interaction pathway. Copyright © 2016 Turkish Association of Orthopaedics and Traumatology. Production and hosting by Elsevier B.V. All rights reserved.

Reconstruction of the regulatory network for Bacillus subtilis and reconciliation with gene expression data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Faria, Jose P.; Overbeek, Ross; Taylor, Ronald C.

Here, we introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of B. subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, wemore » reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches and small regulatory RNAs. Overall, regulatory information is included in the model for approximately 2500 of the ~4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same “ON” and “OFF” gene expression profiles across multiple samples of experimental data. We show how atomic regulons for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how atomic regulons can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental conditions, gaining insights into novel biology.« less

Reconstruction of the regulatory network for Bacillus subtilis and reconciliation with gene expression data

DOE PAGES

Faria, Jose P.; Overbeek, Ross; Taylor, Ronald C.; ...

2016-03-18

Here, we introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of B. subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, wemore » reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches and small regulatory RNAs. Overall, regulatory information is included in the model for approximately 2500 of the ~4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same “ON” and “OFF” gene expression profiles across multiple samples of experimental data. We show how atomic regulons for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how atomic regulons can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental conditions, gaining insights into novel biology.« less

FGF2 and FAM201A affect the development of osteonecrosis of the femoral head after femoral neck fracture.

PubMed

Huang, Gangyong; Zhao, Guanglei; Xia, Jun; Wei, Yibing; Chen, Feiyan; Chen, Jie; Shi, Jingsheng

2018-04-30

Osteonecrosis of the femoral head (ONFH) is a common orthopedic disease associated with high disability, and femoral neck fracture (FNF) is one of the most common reasons for traumatic ONFH. This study was designed to reveal the mechanisms underlying ONFH. Using fastx_toolkit and prinseq-lite tools, quality control was conducted for the sequencing data. The differentially expressed genes (DEGs, including both mRNAs and lncRNAs) between ONFH and FNF samples were identified using the edgeR package in R, and were then subjected to enrichment analysis using the BioCloud platform. Subsequently, protein-protein interaction (PPI) networks were constructed using Cytoscape software. After the target genes of DE-lncRNAs were predicted based on Spearman's rank correlation coefficient, lncRNA-gene coexpression network was visualized using the Cytoscape software. Furthermore, functional enrichment analysis was carried out for the target genes using the clusterprofiler package in R. Additionally, the key genes were detected by quantitative real-time polymerase chain reaction (qRT-PCR). A total of 2965 DEGs were identified from the ONFH samples, including 602 DE-lncRNAs (such as downregulated FAM201A). In the PPI networks, eight upregulated genes (including FGF2, IGF1, SOX9, and COL2A1) and 11 downregulated genes were among the top 20 genes according to all of the scores, such as degree centrality, closeness centrality, and betweenness centrality scores. Functional enrichment analysis showed that IGF1, SOX9, and COL2A1 were significantly enriched during skeletal system development. Moreover, qRT-PCR experiments detected the upregulation of FGF2 and downregulation of FAM201A in ONFH samples. FGF2 and FAM201A were correlated with the development of ONFH. Besides, IGF1, SOX9, and COL2A1 might also affect the pathogenesis of ONFH. Copyright © 2018. Published by Elsevier B.V.

The national DBS brain tissue network pilot study: need for more tissue and more standardization.

PubMed

Vedam-Mai, V; Krock, N; Ullman, M; Foote, K D; Shain, W; Smith, K; Yachnis, A T; Steindler, D; Reynolds, B; Merritt, S; Pagan, F; Marjama-Lyons, J; Hogarth, P; Resnick, A S; Zeilman, P; Okun, M S

2011-08-01

Over 70,000 DBS devices have been implanted worldwide; however, there remains a paucity of well-characterized post-mortem DBS brains available to researchers. We propose that the overall understanding of DBS can be improved through the establishment of a Deep Brain Stimulation-Brain Tissue Network (DBS-BTN), which will further our understanding of DBS and brain function. The objectives of the tissue bank are twofold: (a) to provide a complete (clinical, imaging and pathological) database for DBS brain tissue samples, and (b) to make available DBS tissue samples to researchers, which will help our understanding of disease and underlying brain circuitry. Standard operating procedures for processing DBS brains were developed as part of the pilot project. Complete data files were created for individual patients and included demographic information, clinical information, imaging data, pathology, and DBS lead locations/settings. 19 DBS brains were collected from 11 geographically dispersed centers from across the U.S. The average age at the time of death was 69.3 years (51-92, with a standard deviation or SD of 10.13). The male:female ratio was almost 3:1. Average post-mortem interval from death to brain collection was 10.6 h (SD of 7.17). The DBS targets included: subthalamic nucleus, globus pallidus interna, and ventralis intermedius nucleus of the thalamus. In 16.7% of cases the clinical diagnosis failed to match the pathological diagnosis. We provide neuropathological findings from the cohort, and perilead responses to DBS. One of the most important observations made in this pilot study was the missing data, which was approximately 25% of all available data fields. Preliminary results demonstrated the feasibility and utility of creating a National DBS-BTN resource for the scientific community. We plan to improve our techniques to remedy omitted clinical/research data, and expand the Network to include a larger donor pool. We will enhance sample preparation to facilitate advanced molecular studies and progenitor cell retrieval.

The social support and social network characteristics of smokers in methadone maintenance treatment.

PubMed

de Dios, Marcel Alejandro; Stanton, Cassandra A; Caviness, Celeste M; Niaura, Raymond; Stein, Michael

2013-01-01

Previous studies have shown social support and social network variables to be important factors in smoking cessation treatment. Tobacco use is highly prevalent among individuals in methadone maintenance treatment (MMT). However, smoking cessation treatment outcomes in this vulnerable subpopulation have been poor and social support and social network variables may contribute. The current study examined the social support and social network characteristics of 151 MMT smokers involved in a randomized clinical trial of smoking cessation treatments. Participants were 50% women and 78% Caucasian. A high proportion (57%) of MMT smokers had spouses or partners who smoke and over two-thirds of households (68.5%) included at least one smoker. Our sample was characterized by relatively small social networks, but high levels of general social support and quitting support. The number of cigarettes per day was found to be positively associated with the number of smokers in the social network (r = .239, p < .05) and quitting self-efficacy was negatively associated with partner smoking (r = -.217, p < .001). Findings are discussed in the context of developing smoking cessation interventions that address the influential role of social support and social networks of smokers in MMT.

Feasibility of Recruiting a Diverse Sample of Men Who Have Sex with Men: Observation from Nanjing, China

PubMed Central

Tang, Weiming; Yang, Haitao; Mahapatra, Tanmay; Huan, Xiping; Yan, Hongjing; Li, Jianjun; Fu, Gengfeng; Zhao, Jinkou; Detels, Roger

2013-01-01

Background Respondent-driven-sampling (RDS) has well been recognized as a method for sampling from most hard-to-reach populations like commercial sex workers, drug users and men who have sex with men. However the feasibility of this sampling strategy in terms of recruiting a diverse spectrum of these hidden populations has not been understood well yet in developing countries. Methods In a cross sectional study in Nanjing city of Jiangsu province of China, 430 MSM were recruited including 9 seeds in 14 weeks of study period using RDS. Information regarding socio-demographic characteristics and sexual risk behavior were collected and testing was done for HIV and syphilis. Duration, completion, participant characteristics and the equilibrium of key factors were used for assessing feasibility of RDS. Homophily of key variables, socio-demographic distribution and social network size were used as the indicators of diversity. Results In the study sample, adjusted HIV and syphilis prevalence were 6.6% and 14.6% respectively. Majority (96.3%) of the participants were recruited by members of their own social network. Although there was a tendency for recruitment within the same self-identified group (homosexuals recruited 60.0% homosexuals), considerable cross-group recruitment (bisexuals recruited 52.3% homosexuals) was also seen. Homophily of the self-identified sexual orientations was 0.111 for homosexuals. Upon completion of the recruitment process, participant characteristics and the equilibrium of key factors indicated that RDS was feasible for sampling MSM in Nanjing. Participants recruited by RDS were found to be diverse after assessing the homophily of key variables in successive waves of recruitment, the proportion of characteristics after reaching equilibrium and the social network size. The observed design effects were nearly the same or even better than the theoretical design effect of 2. Conclusion RDS was found to be an efficient and feasible sampling method for recruiting a diverse sample of MSM in a reasonable time. PMID:24244280

Rapid Sampling of Hydrogen Bond Networks for Computational Protein Design.

PubMed

Maguire, Jack B; Boyken, Scott E; Baker, David; Kuhlman, Brian

2018-05-08

Hydrogen bond networks play a critical role in determining the stability and specificity of biomolecular complexes, and the ability to design such networks is important for engineering novel structures, interactions, and enzymes. One key feature of hydrogen bond networks that makes them difficult to rationally engineer is that they are highly cooperative and are not energetically favorable until the hydrogen bonding potential has been satisfied for all buried polar groups in the network. Existing computational methods for protein design are ill-equipped for creating these highly cooperative networks because they rely on energy functions and sampling strategies that are focused on pairwise interactions. To enable the design of complex hydrogen bond networks, we have developed a new sampling protocol in the molecular modeling program Rosetta that explicitly searches for sets of amino acid mutations that can form self-contained hydrogen bond networks. For a given set of designable residues, the protocol often identifies many alternative sets of mutations/networks, and we show that it can readily be applied to large sets of residues at protein-protein interfaces or in the interior of proteins. The protocol builds on a recently developed method in Rosetta for designing hydrogen bond networks that has been experimentally validated for small symmetric systems but was not extensible to many larger protein structures and complexes. The sampling protocol we describe here not only recapitulates previously validated designs with performance improvements but also yields viable hydrogen bond networks for cases where the previous method fails, such as the design of large, asymmetric interfaces relevant to engineering protein-based therapeutics.

How representative is pesticide monitoring in Swiss streams?

NASA Astrophysics Data System (ADS)

Munz, Nicole; Wittmer, Irene; Strahm, Ivo; Leu, Christian; Stamm, Christian

2013-04-01

The surveillance of surface water quality in Switzerland is the task of the 26 cantons. This includes the assessment of the level of pesticide pollution. Each of the cantons may follow different procedures, which makes a comparison difficult and cumbersome. Nevertheless, in this study presents the main results of the first nation-wide compilation and interpretation of cantonal and federal monitoring data as well as results from specific research projects on agricultural and urban pesticides are presented. Overall, more than 345'000 concentration data of 281 biocidal compounds have been analyzed. This set of substances includes 203 compounds that have been registered either only as agricultural plant protection (N = 149) product or only as urban biocide (N = 18), but also some (N = 36) which were registered for both uses. This data set contains 70 out of the 100 most sold agricultural plant protection products in 2010. A comparable assessment for the representativeness of the biocide data is hardly possible due to a lack of systematic use data. The data stem from 565 measuring sites. However, these sites are not representative for all size classes of the Swiss stream network. While about 75% of the total length of the stream network is made up by small streams (Strahler order 1 and 2), only 28% of the measuring sites are located on such streams. In combination with the sampling strategies that have been used - about 50% grab samples and 50% composite samples - it can be concluded that the 2% of measured values > 100 ng L-1 most probably severely underestimates the true level of pesticide pollution in the Swiss stream network. In the future, more emphasis has to be put on small streams, where higher concentrations are expected and thus also actual ecological effects.

NEON: High Frequency Monitoring Network for Watershed-Scale Processes and Aquatic Ecology

NASA Astrophysics Data System (ADS)

Vance, J. M.; Fitzgerald, M.; Parker, S. M.; Roehm, C. L.; Goodman, K. J.; Bohall, C.; Utz, R.

2014-12-01

Networked high frequency hydrologic and water quality measurements needed to investigate physical and biogeochemical processes at the watershed scale and create robust models are limited and lacking standardization. Determining the drivers and mechanisms of ecological changes in aquatic systems in response to natural and anthropogenic pressures is challenging due to the large amounts of terrestrial, aquatic, atmospheric, biological, chemical, and physical data it requires at varied spatiotemporal scales. The National Ecological Observatory Network (NEON) is a continental-scale infrastructure project designed to provide data to address the impacts of climate change, land-use, and invasive species on ecosystem structure and function. Using a combination of standardized continuous in situ measurements and observational sampling, the NEON Aquatic array will produce over 200 data products across its spatially-distributed field sites for 30 years to facilitate spatiotemporal analysis of the drivers of ecosystem change. Three NEON sites in Alabama were chosen to address linkages between watershed-scale processes and ecosystem changes along an eco-hydrological gradient within the Tombigbee River Basin. The NEON Aquatic design, once deployed, will include continuous measurements of surface water physical, chemical, and biological parameters, groundwater level, temperature and conductivity and local meteorology. Observational sampling will include bathymetry, water chemistry and isotopes, and a suite of organismal sampling from microbes to macroinvertebrates to vertebrates. NEON deployed a buoy to measure the temperature profile of the Black Warrior River from July - November, 2013 to determine the spatiotemporal variability across the water column from a daily to seasonal scale. In July 2014 a series of water quality profiles were performed to assess the contribution of physical and biogeochemical drivers over a diurnal cycle. Additional river transects were performed across our site reach to capture the spatial variability of surface water parameters. Our preliminary data show differing response times to precipitation events and diurnal processes informing our infrastructure designs and sampling protocols aimed at providing data to address the eco-hydrological gradient.

Repeatability of road pavement condition assessment based on three-dimensional analysis of linear accelerations of vehicles

NASA Astrophysics Data System (ADS)

Staniek, Marcin

2018-05-01

The article provides a discussion concerning a tool used for road pavement condition assessment based on signals of linear accelerations recorded with high sampling frequency for typical vehicles traversing the road network under real-life road traffic conditions. Specific relationships have been established for the sake of road pavement condition assessment, including identification of road sections of poor technical condition. The data thus acquired have been verified with regard to repeatability of estimated road pavement assessment indices. The data make it possible to describe the road network status against an area in which users of the system being developed move. What proves to be crucial in the assessment process is the scope of the data set based on multiple transfers within the road network.

A developmental neuroimaging investigation of the change paradigm

PubMed Central

Thomas, Laura A.; Hall, Julie M.; Skup, Martha; Jenkins, Sarah E.; Pine, Daniel S.; Leibenluft, Ellen

2010-01-01

This neuroimaging study examines the development of cognitive flexibility using the Change task in a sample of youths and adults. The Change task requires subjects to inhibit a prepotent response and substitute an alternate response, and the task incorporates an algorithm that adjusts task difficulty in response to subject performance. Data from both groups combined show a network of prefrontal and parietal areas that are active during the task. For adults vs. youths, a distributed network was more active for successful change trials versus go, baseline, or unsuccessful change trials. This network included areas involved in rule representation, retrieval (lateral PFC), and switching (medial PFC and parietal regions). These results are consistent with data from previous task-switching experiments and inform developmental understandings of cognitive flexibility. PMID:21159096

School, Friends, and Substance Use: Gender Differences on the Influence of Attitudes Toward School and Close Friend Networks on Cannabis Involvement.

PubMed

Zaharakis, Nikola; Mason, Michael J; Mennis, Jeremy; Light, John; Rusby, Julie C; Westling, Erika; Crewe, Stephanie; Flay, Brian R; Way, Thomas

2018-02-01

The school environment is extremely salient in young adolescents' lives. Adolescents who have unfavorable attitudes toward school and teachers are at elevated risk for dropping out of school and engaging in behavioral health risks. Peer network health-a summation of the positive and negative behaviors in which one's close friend group engages-may be one way by which attitudes toward school exert influence on youth substance use. Utilizing a sample of 248 primarily African-American young urban adolescents, we tested a moderated mediation model to determine if the indirect effect of attitude to school on cannabis involvement through peer network health was conditioned on gender. Attitude toward school measured at baseline was the predictor (X), peer network health measured at 6 months was the mediator (M), cannabis involvement (including use, offers to use, and refusals to use) measured at 24 months was the outcome (Y), and gender was the moderator (W). Results indicated that negative attitudes toward school were indirectly associated with increased cannabis involvement through peer network health. This relationship was not moderated by gender. Adolescents in our sample with negative attitudes toward school were more likely to receive more offers to use cannabis and to use cannabis more frequently through the perceived health behaviors of their close friends. Implications from these results point to opportunities to leverage the dynamic associations among school experiences, friends, and cannabis involvement, such as offers and use.

Latin Hypercube Sampling (LHS) at variable resolutions for enhanced watershed scale Soil Sampling and Digital Soil Mapping.

NASA Astrophysics Data System (ADS)

Hamalainen, Sampsa; Geng, Xiaoyuan; He, Juanxia

2017-04-01

Latin Hypercube Sampling (LHS) at variable resolutions for enhanced watershed scale Soil Sampling and Digital Soil Mapping. Sampsa Hamalainen, Xiaoyuan Geng, and Juanxia, He. AAFC - Agriculture and Agr-Food Canada, Ottawa, Canada. The Latin Hypercube Sampling (LHS) approach to assist with Digital Soil Mapping has been developed for some time now, however the purpose of this work was to complement LHS with use of multiple spatial resolutions of covariate datasets and variability in the range of sampling points produced. This allowed for specific sets of LHS points to be produced to fulfil the needs of various partners from multiple projects working in the Ontario and Prince Edward Island provinces of Canada. Secondary soil and environmental attributes are critical inputs that are required in the development of sampling points by LHS. These include a required Digital Elevation Model (DEM) and subsequent covariate datasets produced as a result of a Digital Terrain Analysis performed on the DEM. These additional covariates often include but are not limited to Topographic Wetness Index (TWI), Length-Slope (LS) Factor, and Slope which are continuous data. The range of specific points created in LHS included 50 - 200 depending on the size of the watershed and more importantly the number of soil types found within. The spatial resolution of covariates included within the work ranged from 5 - 30 m. The iterations within the LHS sampling were run at an optimal level so the LHS model provided a good spatial representation of the environmental attributes within the watershed. Also, additional covariates were included in the Latin Hypercube Sampling approach which is categorical in nature such as external Surficial Geology data. Some initial results of the work include using a 1000 iteration variable within the LHS model. 1000 iterations was consistently a reasonable value used to produce sampling points that provided a good spatial representation of the environmental attributes. When working within the same spatial resolution for covariates, however only modifying the desired number of sampling points produced, the change of point location portrayed a strong geospatial relationship when using continuous data. Access to agricultural fields and adjacent land uses is often "pinned" as the greatest deterrent to performing soil sampling for both soil survey and soil attribute validation work. The lack of access can be a result of poor road access and/or difficult geographical conditions to navigate for field work individuals. This seems a simple yet continuous issue to overcome for the scientific community and in particular, soils professionals. The ability to assist with the ease of access to sampling points will be in the future a contribution to the Latin Hypercube Sampling (LHS) approach. By removing all locations in the initial instance from the DEM, the LHS model can be restricted to locations only with access from the adjacent road or trail. To further the approach, a road network geospatial dataset can be included within spatial Geographic Information Systems (GIS) applications to access already produced points using a shortest-distance network method.

Exact sampling of graphs with prescribed degree correlations

NASA Astrophysics Data System (ADS)

Bassler, Kevin E.; Del Genio, Charo I.; Erdős, Péter L.; Miklós, István; Toroczkai, Zoltán

2015-08-01

Many real-world networks exhibit correlations between the node degrees. For instance, in social networks nodes tend to connect to nodes of similar degree and conversely, in biological and technological networks, high-degree nodes tend to be linked with low-degree nodes. Degree correlations also affect the dynamics of processes supported by a network structure, such as the spread of opinions or epidemics. The proper modelling of these systems, i.e., without uncontrolled biases, requires the sampling of networks with a specified set of constraints. We present a solution to the sampling problem when the constraints imposed are the degree correlations. In particular, we develop an exact method to construct and sample graphs with a specified joint-degree matrix, which is a matrix providing the number of edges between all the sets of nodes of a given degree, for all degrees, thus completely specifying all pairwise degree correlations, and additionally, the degree sequence itself. Our algorithm always produces independent samples without backtracking. The complexity of the graph construction algorithm is {O}({NM}) where N is the number of nodes and M is the number of edges.

Generating Seismograms with Deep Neural Networks

NASA Astrophysics Data System (ADS)

Krischer, L.; Fichtner, A.

2017-12-01

The recent surge of successful uses of deep neural networks in computer vision, speech recognition, and natural language processing, mainly enabled by the availability of fast GPUs and extremely large data sets, is starting to see many applications across all natural sciences. In seismology these are largely confined to classification and discrimination tasks. In this contribution we explore the use of deep neural networks for another class of problems: so called generative models.Generative modelling is a branch of statistics concerned with generating new observed data samples, usually by drawing from some underlying probability distribution. Samples with specific attributes can be generated by conditioning on input variables. In this work we condition on seismic source (mechanism and location) and receiver (location) parameters to generate multi-component seismograms.The deep neural networks are trained on synthetic data calculated with Instaseis (http://instaseis.net, van Driel et al. (2015)) and waveforms from the global ShakeMovie project (http://global.shakemovie.princeton.edu, Tromp et al. (2010)). The underlying radially symmetric or smoothly three dimensional Earth structures result in comparatively small waveform differences from similar events or at close receivers and the networks learn to interpolate between training data samples.Of particular importance is the chosen misfit functional. Generative adversarial networks (Goodfellow et al. (2014)) implement a system in which two networks compete: the generator network creates samples and the discriminator network distinguishes these from the true training examples. Both are trained in an adversarial fashion until the discriminator can no longer distinguish between generated and real samples. We show how this can be applied to seismograms and in particular how it compares to networks trained with more conventional misfit metrics. Last but not least we attempt to shed some light on the black-box nature of neural networks by estimating the quality and uncertainties of the generated seismograms.

Face recognition: a convolutional neural-network approach.

PubMed

Lawrence, S; Giles, C L; Tsoi, A C; Back, A D

1997-01-01

We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.

Adaptive sampling in behavioral surveys.

PubMed

Thompson, S K

1997-01-01

Studies of populations such as drug users encounter difficulties because the members of the populations are rare, hidden, or hard to reach. Conventionally designed large-scale surveys detect relatively few members of the populations so that estimates of population characteristics have high uncertainty. Ethnographic studies, on the other hand, reach suitable numbers of individuals only through the use of link-tracing, chain referral, or snowball sampling procedures that often leave the investigators unable to make inferences from their sample to the hidden population as a whole. In adaptive sampling, the procedure for selecting people or other units to be in the sample depends on variables of interest observed during the survey, so the design adapts to the population as encountered. For example, when self-reported drug use is found among members of the sample, sampling effort may be increased in nearby areas. Types of adaptive sampling designs include ordinary sequential sampling, adaptive allocation in stratified sampling, adaptive cluster sampling, and optimal model-based designs. Graph sampling refers to situations with nodes (for example, people) connected by edges (such as social links or geographic proximity). An initial sample of nodes or edges is selected and edges are subsequently followed to bring other nodes into the sample. Graph sampling designs include network sampling, snowball sampling, link-tracing, chain referral, and adaptive cluster sampling. A graph sampling design is adaptive if the decision to include linked nodes depends on variables of interest observed on nodes already in the sample. Adjustment methods for nonsampling errors such as imperfect detection of drug users in the sample apply to adaptive as well as conventional designs.

Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample.

PubMed

Freedson, Patty S; Lyden, Kate; Kozey-Keadle, Sarah; Staudenmayer, John

2011-12-01

Previous work from our laboratory provided a "proof of concept" for use of artificial neural networks (nnets) to estimate metabolic equivalents (METs) and identify activity type from accelerometer data (Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P, J Appl Physiol 107: 1330-1307, 2009). The purpose of this study was to develop new nnets based on a larger, more diverse, training data set and apply these nnet prediction models to an independent sample to evaluate the robustness and flexibility of this machine-learning modeling technique. The nnet training data set (University of Massachusetts) included 277 participants who each completed 11 activities. The independent validation sample (n = 65) (University of Tennessee) completed one of three activity routines. Criterion measures were 1) measured METs assessed using open-circuit indirect calorimetry; and 2) observed activity to identify activity type. The nnet input variables included five accelerometer count distribution features and the lag-1 autocorrelation. The bias and root mean square errors for the nnet MET trained on University of Massachusetts and applied to University of Tennessee were +0.32 and 1.90 METs, respectively. Seventy-seven percent of the activities were correctly classified as sedentary/light, moderate, or vigorous intensity. For activity type, household and locomotion activities were correctly classified by the nnet activity type 98.1 and 89.5% of the time, respectively, and sport was correctly classified 23.7% of the time. Use of this machine-learning technique operates reasonably well when applied to an independent sample. We propose the creation of an open-access activity dictionary, including accelerometer data from a broad array of activities, leading to further improvements in prediction accuracy for METs, activity intensity, and activity type.

E-nose based rapid prediction of early mouldy grain using probabilistic neural networks

PubMed Central

Ying, Xiaoguo; Liu, Wei; Hui, Guohua; Fu, Jun

2015-01-01

In this paper, early mouldy grain rapid prediction method using probabilistic neural network (PNN) and electronic nose (e-nose) was studied. E-nose responses to rice, red bean, and oat samples with different qualities were measured and recorded. E-nose data was analyzed using principal component analysis (PCA), back propagation (BP) network, and PNN, respectively. Results indicated that PCA and BP network could not clearly discriminate grain samples with different mouldy status and showed poor predicting accuracy. PNN showed satisfying discriminating abilities to grain samples with an accuracy of 93.75%. E-nose combined with PNN is effective for early mouldy grain prediction. PMID:25714125

Size matters: concurrency and the epidemic potential of HIV in small networks.

PubMed

Carnegie, Nicole Bohme; Morris, Martina

2012-01-01

Generalized heterosexual epidemics are responsible for the largest share of the global burden of HIV. These occur in populations that do not have high rates of partner acquisition, and research suggests that a pattern of fewer, but concurrent, partnerships may be the mechanism that provides the connectivity necessary for sustained transmission. We examine how network size affects the impact of concurrency on network connectivity. We use a stochastic network model to generate a sample of networks, varying the size of the network and the level of concurrency, and compare the largest components for each scenario to the asymptotic expected values. While the threshold for the growth of a giant component does not change, the transition is more gradual in the smaller networks. As a result, low levels of concurrency generate more connectivity in small networks. Generalized HIV epidemics are by definition those that spread to a larger fraction of the population, but the mechanism may rely in part on the dynamics of transmission in a set of linked small networks. Examples include rural populations in sub-Saharan Africa and segregated minority populations in the US, where the effective size of the sexual network may well be in the hundreds, rather than thousands. Connectivity emerges at lower levels of concurrency in smaller networks, but these networks can still be disconnected with small changes in behavior. Concurrency remains a strategic target for HIV combination prevention programs in this context.

Adolescent social networks: general and smoking-specific characteristics associated with smoking.

PubMed

Roberts, Megan E; Nargiso, Jessica E; Gaitonde, Linda Brazil; Stanton, Cassandra A; Colby, Suzanne M

2015-03-01

Converging lines of research suggest that adolescents' smoking behaviors are strongly influenced by the characteristics of their social network and the social processes their network facilitates. The primary goal of this study was to conduct a detailed comparison of the social networks of adolescent smokers and nonsmokers to determine what aspects relate the most to smoking status. A secondary goal was to conduct within-group analyses to examine relationships between key measures of behavior-specific social support and (a) smoking susceptibility among nonsmokers, and (b) readiness to quit smoking among smokers. A matched sample of 190 adolescent smokers and nonsmokers (Mage = 16.8 years; 51% female) completed a questionnaire in which they nominated and reported on up to 10 important people in their lives. This measure allowed us to examine adolescents' overall networks (both peers and family) and to investigate numerous aspects, including general network characteristics (e.g., size of network, average contact with network members), social support (e.g., importance of people in the network), and the pervasiveness of smoking in the network (e.g., percentage of smoking peers). The pervasiveness of smoking in adolescents' social network was the strongest distinguisher of smokers versus nonsmokers. In addition, behavior-specific social support was strongly associated with susceptibility to initiate smoking among nonsmokers and readiness to quit among smokers. This research offers insight into potential targets for prevention and early intervention by demonstrating how social networks can both promote and attenuate risk for smoking.

Stochastic inference with spiking neurons in the high-conductance state

NASA Astrophysics Data System (ADS)

Petrovici, Mihai A.; Bill, Johannes; Bytschok, Ilja; Schemmel, Johannes; Meier, Karlheinz

2016-10-01

The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference but stand in apparent contrast to the deterministic response of neurons measured in vitro. Based on a propagation of the membrane autocorrelation across spike bursts, we provide an analytical derivation of the neural activation function that holds for a large parameter space, including the high-conductance state. On this basis, we show how an ensemble of leaky integrate-and-fire neurons with conductance-based synapses embedded in a spiking environment can attain the correct firing statistics for sampling from a well-defined target distribution. For recurrent networks, we examine convergence toward stationarity in computer simulations and demonstrate sample-based Bayesian inference in a mixed graphical model. This points to a new computational role of high-conductance states and establishes a rigorous link between deterministic neuron models and functional stochastic dynamics on the network level.

Identification and Discrimination of Brands of Fuels by Gas Chromatography and Neural Networks Algorithm in Forensic Research

PubMed Central

Ugena, L.; Moncayo, S.; Manzoor, S.; Rosales, D.

2016-01-01

The detection of adulteration of fuels and its use in criminal scenes like arson has a high interest in forensic investigations. In this work, a method based on gas chromatography (GC) and neural networks (NN) has been developed and applied to the identification and discrimination of brands of fuels such as gasoline and diesel without the necessity to determine the composition of the samples. The study included five main brands of fuels from Spain, collected from fifteen different local petrol stations. The methodology allowed the identification of the gasoline and diesel brands with a high accuracy close to 100%, without any false positives or false negatives. A success rate of three blind samples was obtained as 73.3%, 80%, and 100%, respectively. The results obtained demonstrate the potential of this methodology to help in resolving criminal situations. PMID:27375919

Parameter diagnostics of phases and phase transition learning by neural networks

NASA Astrophysics Data System (ADS)

Suchsland, Philippe; Wessel, Stefan

2018-05-01

We present an analysis of neural network-based machine learning schemes for phases and phase transitions in theoretical condensed matter research, focusing on neural networks with a single hidden layer. Such shallow neural networks were previously found to be efficient in classifying phases and locating phase transitions of various basic model systems. In order to rationalize the emergence of the classification process and for identifying any underlying physical quantities, it is feasible to examine the weight matrices and the convolutional filter kernels that result from the learning process of such shallow networks. Furthermore, we demonstrate how the learning-by-confusing scheme can be used, in combination with a simple threshold-value classification method, to diagnose the learning parameters of neural networks. In particular, we study the classification process of both fully-connected and convolutional neural networks for the two-dimensional Ising model with extended domain wall configurations included in the low-temperature regime. Moreover, we consider the two-dimensional XY model and contrast the performance of the learning-by-confusing scheme and convolutional neural networks trained on bare spin configurations to the case of preprocessed samples with respect to vortex configurations. We discuss these findings in relation to similar recent investigations and possible further applications.

Potential upstream regulators of cannabinoid receptor 1 signaling in prostate cancer: a Bayesian network analysis of data from a tissue microarray.

PubMed

Häggström, Jenny; Cipriano, Mariateresa; Forshell, Linus Plym; Persson, Emma; Hammarsten, Peter; Stella, Nephi; Fowler, Christopher J

2014-08-01

The endocannabinoid system regulates cancer cell proliferation, and in prostate cancer a high cannabinoid CB1 receptor expression is associated with a poor prognosis. Down-stream mediators of CB1 receptor signaling in prostate cancer are known, but information on potential upstream regulators is lacking. Data from a well-characterized tumor tissue microarray were used for a Bayesian network analysis using the max-min hill-climbing method. In non-malignant tissue samples, a directionality of pEGFR (the phosphorylated form of the epidermal growth factor receptor) → CB1 receptors were found regardless as to whether the endocannabinoid metabolizing enzyme fatty acid amide hydrolase (FAAH) was included as a parameter. A similar result was found in the tumor tissue, but only when FAAH was included in the analysis. A second regulatory pathway, from the growth factor receptor ErbB2 → FAAH was also identified in the tumor samples. Transfection of AT1 prostate cancer cells with CB1 receptors induced a sensitivity to the growth-inhibiting effects of the CB receptor agonist CP55,940. The sensitivity was not dependent upon the level of receptor expression. Thus a high CB1 receptor expression alone does not drive the cells towards a survival phenotype in the presence of a CB receptor agonist. The data identify two potential regulators of the endocannabinoid system in prostate cancer and allow the construction of a model of a dysregulated endocannabinoid signaling network in this tumor. Further studies should be designed to test the veracity of the predictions of the network analysis in prostate cancer and other solid tumors. © 2014 The Authors. The Prostate published by Wiley Periodicals, Inc.

Screening the molecular targets of ovarian cancer based on bioinformatics analysis.

PubMed

Du, Lei; Qian, Xiaolei; Dai, Chenyang; Wang, Lihua; Huang, Ding; Wang, Shuying; Shen, Xiaowei

2015-01-01

Ovarian cancer (OC) is the most lethal gynecologic malignancy. This study aims to explore the molecular mechanisms of OC and identify potential molecular targets for OC treatment. Microarray gene expression data (GSE14407) including 12 normal ovarian surface epithelia samples and 12 OC epithelia samples were downloaded from Gene Expression Omnibus database. Differentially expressed genes (DEGs) between 2 kinds of ovarian tissue were identified by using limma package in R language (|log2 fold change| gt;1 and false discovery rate [FDR] lt;0.05). Protein-protein interactions (PPIs) and known OC-related genes were screened from COXPRESdb and GenBank database, respectively. Furthermore, PPI network of top 10 upregulated DEGs and top 10 downregulated DEGs was constructed and visualized through Cytoscape software. Finally, for the genes involved in PPI network, functional enrichment analysis was performed by using DAVID (FDR lt;0.05). In total, 1136 DEGs were identified, including 544 downregulated and 592 upregulated DEGs. Then, PPI network was constructed, and DEGs CDKN2A, MUC1, OGN, ZIC1, SOX17, and TFAP2A interacted with known OC-related genes CDK4, EGFR/JUN, SRC, CLI1, CTNNB1, and TP53, respectively. Moreover, functions about oxygen transport and embryonic development were enriched by the genes involved in the network of downregulated DEGs. We propose that 4 DEGs (OGN, ZIC1, SOX17, and TFAP2A) and 2 functions (oxygen transport and embryonic development) might play a role in the development of OC. These 4 DEGs and known OC-related genes might serve as therapeutic targets for OC. Further studies are required to validate these predictions.

Aberrant gene expression in mucosa adjacent to tumor reveals a molecular crosstalk in colon cancer

PubMed Central

2014-01-01

Background A colorectal tumor is not an isolated entity growing in a restricted location of the body. The patient’s gut environment constitutes the framework where the tumor evolves and this relationship promotes and includes a complex and tight correlation of the tumor with inflammation, blood vessels formation, nutrition, and gut microbiome composition. The tumor influence in the environment could both promote an anti-tumor or a pro-tumor response. Methods A set of 98 paired adjacent mucosa and tumor tissues from colorectal cancer (CRC) patients and 50 colon mucosa from healthy donors (246 samples in total) were included in this work. RNA extracted from each sample was hybridized in Affymetrix chips Human Genome U219. Functional relationships between genes were inferred by means of systems biology using both transcriptional regulation networks (ARACNe algorithm) and protein-protein interaction networks (BIANA software). Results Here we report a transcriptomic analysis revealing a number of genes activated in adjacent mucosa from CRC patients, not activated in mucosa from healthy donors. A functional analysis of these genes suggested that this active reaction of the adjacent mucosa was related to the presence of the tumor. Transcriptional and protein-interaction networks were used to further elucidate this response of normal gut in front of the tumor, revealing a crosstalk between proteins secreted by the tumor and receptors activated in the adjacent colon tissue; and vice versa. Remarkably, Slit family of proteins activated ROBO receptors in tumor whereas tumor-secreted proteins transduced a cellular signal finally activating AP-1 in adjacent tissue. Conclusions The systems-level approach provides new insights into the micro-ecology of colorectal tumorogenesis. Disrupting this intricate molecular network of cell-cell communication and pro-inflammatory microenvironment could be a therapeutic target in CRC patients. PMID:24597571

CASTNet Air Toxics Monitoring Program (CATMP): VOC and carbonyl data for July, 1993 through March, 1994

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harlos, D.P.; Edgerton, E.S.

1994-12-31

The US EPA has, under the auspices of the CASTNet program (Clean Air Status and Trends Network), initiated the CASTNet Air Toxics Monitoring Program (CATMP). Volatile Organic Compounds (VOC) and carbonyls and metals are sampled for 24-hour periods on a 12-day schedule using TO-14 samplers (SUMMA canisters) and dinitrophenylhydrazine-coated (dmph) sorbent cartridges and high volume particle samplers. Sampling was begun at most sites in July of 1993. The sites are operated by state and local air pollution control programs and all analysis is performed by Environmental Science and Engineering (ESE) in Gainesville, Florida. The network currently supports 15 VOC sites,more » of which 7 also sample carbonyls. Three sites sample metals only in Pinellas County, Florida. The limits of detection of 0.05 ppb for VOCs allow routine tracking of a wide range of pollutants including several greenhouse gases, transportation pollutants and photochemically-derived compounds. The sites range from major urban areas (Chicago, St. Louis) to a rural village (Waterbury, Vermont). Results of the first three quarters of VOC and carbonyl data collection are summarized in this presentation.« less

Robust gene network analysis reveals alteration of the STAT5a network as a hallmark of prostate cancer.

PubMed

Reddy, Anupama; Huang, C Chris; Liu, Huiqing; Delisi, Charles; Nevalainen, Marja T; Szalma, Sandor; Bhanot, Gyan

2010-01-01

We develop a general method to identify gene networks from pair-wise correlations between genes in a microarray data set and apply it to a public prostate cancer gene expression data from 69 primary prostate tumors. We define the degree of a node as the number of genes significantly associated with the node and identify hub genes as those with the highest degree. The correlation network was pruned using transcription factor binding information in VisANT (http://visant.bu.edu/) as a biological filter. The reliability of hub genes was determined using a strict permutation test. Separate networks for normal prostate samples, and prostate cancer samples from African Americans (AA) and European Americans (EA) were generated and compared. We found that the same hubs control disease progression in AA and EA networks. Combining AA and EA samples, we generated networks for low low (<7) and high (≥7) Gleason grade tumors. A comparison of their major hubs with those of the network for normal samples identified two types of changes associated with disease: (i) Some hub genes increased their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with gain of regulatory control in cancer (e.g. possible turning on of oncogenes). (ii) Some hubs reduced their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with loss of regulatory control in cancer (e.g. possible loss of tumor suppressor genes). A striking result was that for both AA and EA tumor samples, STAT5a, CEBPB and EGR1 are major hubs that gain neighbors compared to the normal prostate network. Conversely, HIF-lα is a major hub that loses connections in the prostate cancer network compared to the normal prostate network. We also find that the degree of these hubs changes progressively from normal to low grade to high grade disease, suggesting that these hubs are master regulators of prostate cancer and marks disease progression. STAT5a was identified as a central hub, with ~120 neighbors in the prostate cancer network and only 81 neighbors in the normal prostate network. Of the 120 neighbors of STAT5a, 57 are known cancer related genes, known to be involved in functional pathways associated with tumorigenesis. Our method is general and can easily be extended to identify and study networks associated with any two phenotypes.

Cyber Exercise Playbook

DTIC Science & Technology

2014-11-01

unclassified tools and techniques that can be shared with PNs, to include social engineering, spear phishing , fake web sites, physical access attempts, and...and instead rely on commercial services such as Yahoo or Google . Some nations have quite advanced cyber security practices, but may take vastly...unauthorized access to data/systems Inject external network scanning, email phishing , malicious website access, social engineering Sample

Bioassessment metrics and deposited sediments in tributaries of the Chattooga river watershed

Treesearch

Erica Chiao; J. Bruce Wallace

2003-01-01

Excessive sedimentation places waters of the Chattooga River network at risk of biological impairment. Monitoring efforts could be improved by including metrics that are responsive to changes in levels of fine sediments. We sampled three habitats (riffle, depositional, bedrock outcrop) for benthic macroinvertebrates at four sites in three low-order, tributary reaches...

Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks

NASA Astrophysics Data System (ADS)

Jiang, Fei-Bo; Dai, Qian-Wei; Dong, Li

2016-06-01

Conventional artificial neural networks used to solve electrical resistivity imaging (ERI) inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network (PBNN) nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter α k , which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.

The value and limitations of global air-sampling networks for improving our understanding trace gas behavior

NASA Astrophysics Data System (ADS)

Montzka, S. A.

2016-12-01

Measurements from global surface-based air sampling networks provide a fundamental understanding of how and why concentrations of long-lived trace gases are changing over time. Results from these networks are used to quantify trace-gas concentrations and their time-dependent changes on global and smaller scales, and thus provide a means to quantify emission rates, loss frequencies, and mixing processes. Substantial advances in measurement and sampling technologies and the ability of these programs to create and maintain reliable gas standards mean that spatial concentration gradients and time-dependent changes are often very reliably measured. The presence of multiple independent networks allows an assessment of this reliability. Furthermore, recent global `snap-shot' surveys (e.g., HIPPO and ATom) and ongoing atmospheric profiling programs help us assess the ability of surface-based data to describe concentration distributions throughout most of the atmosphere ( 80% of its mass). In this overview talk, I'll explore the usefulness and limitations of existing long-term, ongoing sampling network programs and their advantages and disadvantages for characterizing concentrations on global and regional scales, and how recent advances (and short-term sampling programs) help us assess the accuracy of the surface networks to provide estimates of source and sink magnitudes, and inter-annual variability in both.

A Structure-Adaptive Hybrid RBF-BP Classifier with an Optimized Learning Strategy

PubMed Central

Wen, Hui; Xie, Weixin; Pei, Jihong

2016-01-01

This paper presents a structure-adaptive hybrid RBF-BP (SAHRBF-BP) classifier with an optimized learning strategy. SAHRBF-BP is composed of a structure-adaptive RBF network and a BP network of cascade, where the number of RBF hidden nodes is adjusted adaptively according to the distribution of sample space, the adaptive RBF network is used for nonlinear kernel mapping and the BP network is used for nonlinear classification. The optimized learning strategy is as follows: firstly, a potential function is introduced into training sample space to adaptively determine the number of initial RBF hidden nodes and node parameters, and a form of heterogeneous samples repulsive force is designed to further optimize each generated RBF hidden node parameters, the optimized structure-adaptive RBF network is used for adaptively nonlinear mapping the sample space; then, according to the number of adaptively generated RBF hidden nodes, the number of subsequent BP input nodes can be determined, and the overall SAHRBF-BP classifier is built up; finally, different training sample sets are used to train the BP network parameters in SAHRBF-BP. Compared with other algorithms applied to different data sets, experiments show the superiority of SAHRBF-BP. Especially on most low dimensional and large number of data sets, the classification performance of SAHRBF-BP outperforms other training SLFNs algorithms. PMID:27792737

Data from selected U.S. Geological Survey National Stream Water Quality Monitoring Networks

USGS Publications Warehouse

Alexander, Richard B.; Slack, James R.; Ludtke, Amy S.; Fitzgerald, Kathleen K.; Schertz, Terry L.

1998-01-01

A nationally consistent and well-documented collection of water quality and quantity data compiled during the past 30 years for streams and rivers in the United States is now available on CD-ROM and accessible over the World Wide Web. The data include measurements from two U.S. Geological Survey (USGS) national networks for 122 physical, chemical, and biological properties of water collected at 680 monitoring stations from 1962 to 1995, quality assurance information that describes the sample collection agencies, laboratories, analytical methods, and estimates of laboratory measurement error (bias and variance), and information on selected cultural and natural characteristics of the station watersheds. The data are easily accessed via user-supplied software including Web browser, spreadsheet, and word processor, or may be queried and printed according to user-specified criteria using the supplied retrieval software on CD-ROM. The water quality data serve a variety of scientific uses including research and educational applications related to trend detection, flux estimation, investigations of the effects of the natural environment and cultural sources on water quality, and the development of statistical methods for designing efficient monitoring networks and interpreting water resources data.

Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

PubMed

Wan, Ying; Cao, Jinde; Wen, Guanghui

In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.

A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process Control.

PubMed

Wang, Tong; Gao, Huijun; Qiu, Jianbin

2016-02-01

This paper investigates the multirate networked industrial process control problem in double-layer architecture. First, the output tracking problem for sampled-data nonlinear plant at device layer with sampling period T(d) is investigated using adaptive neural network (NN) control, and it is shown that the outputs of subsystems at device layer can track the decomposed setpoints. Then, the outputs and inputs of the device layer subsystems are sampled with sampling period T(u) at operation layer to form the index prediction, which is used to predict the overall performance index at lower frequency. Radial basis function NN is utilized as the prediction function due to its approximation ability. Then, considering the dynamics of the overall closed-loop system, nonlinear model predictive control method is proposed to guarantee the system stability and compensate the network-induced delays and packet dropouts. Finally, a continuous stirred tank reactor system is given in the simulation part to demonstrate the effectiveness of the proposed method.

The Poultry-Associated Microbiome: Network Analysis and Farm-to-Fork Characterizations

PubMed Central

Oakley, Brian B.; Morales, Cesar A.; Line, J.; Berrang, Mark E.; Meinersmann, Richard J.; Tillman, Glenn E.; Wise, Mark G.; Siragusa, Gregory R.; Hiett, Kelli L.; Seal, Bruce S.

2013-01-01

Microbial communities associated with agricultural animals are important for animal health, food safety, and public health. Here we combine high-throughput sequencing (HTS), quantitative-PCR assays, and network analysis to profile the poultry-associated microbiome and important pathogens at various stages of commercial poultry production from the farm to the consumer. Analysis of longitudinal data following two flocks from the farm through processing showed a core microbiome containing multiple sequence types most closely related to genera known to be pathogenic for animals and/or humans, including Campylobacter, Clostridium, and Shigella. After the final stage of commercial poultry processing, taxonomic richness was ca. 2–4 times lower than the richness of fecal samples from the same flocks and Campylobacter abundance was significantly reduced. Interestingly, however, carcasses sampled at 48 hr after processing harboured the greatest proportion of unique taxa (those not encountered in other samples), significantly more than expected by chance. Among these were anaerobes such as Prevotella, Veillonella, Leptrotrichia, and multiple Campylobacter sequence types. Retail products were dominated by Pseudomonas, but also contained 27 other genera, most of which were potentially metabolically active and encountered in on-farm samples. Network analysis was focused on the foodborne pathogen Campylobacter and revealed a majority of sequence types with no significant interactions with other taxa, perhaps explaining the limited efficacy of previous attempts at competitive exclusion of Campylobacter. These data represent the first use of HTS to characterize the poultry microbiome across a series of farm-to-fork samples and demonstrate the utility of HTS in monitoring the food supply chain and identifying sources of potential zoonoses and interactions among taxa in complex communities. PMID:23468931

Dispersive dielectric and conductive effects in 2D resistor-capacitor networks.

PubMed

Hamou, R F; Macdonald, J R; Tuncer, E

2009-01-14

How to predict and better understand the effective properties of disordered material mixtures has been a long-standing problem in different research fields, especially in condensed matter physics. In order to address this subject and achieve a better understanding of the frequency-dependent properties of these systems, a large 2D L × L square structure of resistors and capacitors was used to calculate the immittance response of a network formed by random filling of binary conductor/insulator phases with 1000 Ω resistors and 10 nF capacitors. The effects of percolating clusters on the immittance response were studied statistically through the generation of 10 000 different random network samples at the percolation threshold. The scattering of the imaginary part of the immittance near the dc limit shows a clear separation between the responses of percolating and non-percolating samples, with the gap between their distributions dependent on both network size and applied frequency. These results could be used to monitor connectivity in composite materials. The effects of the content and structure of the percolating path on the nature of the observed dispersion were investigated, with special attention paid to the geometrical fractal concept of the backbone and its influence on the behavior of relaxation-time distributions. For three different resistor-capacitor proportions, the appropriateness of many fitting models was investigated for modeling and analyzing individual resistor-capacitor network dispersed frequency responses using complex-nonlinear-least-squares fitting. Several remarkable new features were identified, including a useful duality relationship and the need for composite fitting models rather than either a simple power law or a single Davidson-Cole one. Good fits of data for fully percolating random networks required two dispersive fitting models in parallel or series, with a cutoff at short times of the distribution of relaxation times of one of them. In addition, such fits surprisingly led to cutoff parameters, including a primitive relaxation or crossover time, with estimated values comparable to those found for real dispersive materials.

Effects of spatial scale of sampling on food web structure

PubMed Central

Wood, Spencer A; Russell, Roly; Hanson, Dieta; Williams, Richard J; Dunne, Jennifer A

2015-01-01

This study asks whether the spatial scale of sampling alters structural properties of food webs and whether any differences are attributable to changes in species richness and connectance with scale. Understanding how different aspects of sampling effort affect ecological network structure is important for both fundamental ecological knowledge and the application of network analysis in conservation and management. Using a highly resolved food web for the marine intertidal ecosystem of the Sanak Archipelago in the Eastern Aleutian Islands, Alaska, we assess how commonly studied properties of network structure differ for 281 versions of the food web sampled at five levels of spatial scale representing six orders of magnitude in area spread across the archipelago. Species (S) and link (L) richness both increased by approximately one order of magnitude across the five spatial scales. Links per species (L/S) more than doubled, while connectance (C) decreased by approximately two-thirds. Fourteen commonly studied properties of network structure varied systematically with spatial scale of sampling, some increasing and others decreasing. While ecological network properties varied systematically with sampling extent, analyses using the niche model and a power-law scaling relationship indicate that for many properties, this apparent sensitivity is attributable to the increasing S and decreasing C of webs with increasing spatial scale. As long as effects of S and C are accounted for, areal sampling bias does not have a special impact on our understanding of many aspects of network structure. However, attention does need be paid to some properties such as the fraction of species in loops, which increases more than expected with greater spatial scales of sampling. PMID:26380704

Regular cannabis and alcohol use is associated with resting-state time course power spectra in incarcerated adolescents.

PubMed

Thijssen, Sandra; Rashid, Barnaly; Gopal, Shruti; Nyalakanti, Prashanth; Calhoun, Vince D; Kiehl, Kent A

2017-09-01

Cannabis and alcohol are believed to have widespread effects on the brain. Although adolescents are at increased risk for substance use, the adolescent brain may also be particularly vulnerable to the effects of drug exposure due to its rapid maturation. Here, we examined the association between cannabis and alcohol use duration and resting-state functional connectivity in a large sample of male juvenile delinquents. The present sample was drawn from the Southwest Advanced Neuroimaging Cohort, Youth sample, and from a youth detention facility in Wisconsin. All participants were scanned at the maximum-security facilities using The Mind Research Network's 1.5T Avanto SQ Mobile MRI scanner. Information on cannabis and alcohol regular use duration was collected using self-report. Resting-state networks were computed using group independent component analysis in 201 participants. Associations with cannabis and alcohol use were assessed using Mancova analyses controlling for age, IQ, smoking and psychopathy scores in the complete case sample of 180 male juvenile delinquents. No associations between alcohol or cannabis use and network spatial maps were found. Longer cannabis use was associated with decreased low frequency power of the default mode network, the executive control networks (ECNs), and several sensory networks, and with decreased functional network connectivity. Duration of alcohol use was associated with decreased low frequency power of the right frontoparietal network, salience network, dorsal attention network, and several sensory networks. Our findings suggest that adolescent cannabis and alcohol use are associated with widespread differences in resting-state time course power spectra, which may persist even after abstinence. Copyright © 2017 Elsevier B.V. All rights reserved.

Application of artificial neural networks to assess pesticide contamination in shallow groundwater

USGS Publications Warehouse

Sahoo, G.B.; Ray, C.; Mehnert, E.; Keefer, D.A.

2006-01-01

In this study, a feed-forward back-propagation neural network (BPNN) was developed and applied to predict pesticide concentrations in groundwater monitoring wells. Pesticide concentration data are challenging to analyze because they tend to be highly censored. Input data to the neural network included the categorical indices of depth to aquifer material, pesticide leaching class, aquifer sensitivity to pesticide contamination, time (month) of sample collection, well depth, depth to water from land surface, and additional travel distance in the saturated zone (i.e., distance from land surface to midpoint of well screen). The output of the neural network was the total pesticide concentration detected in the well. The model prediction results produced good agreements with observed data in terms of correlation coefficient (R = 0.87) and pesticide detection efficiency (E = 89%), as well as good match between the observed and predicted "class" groups. The relative importance of input parameters to pesticide occurrence in groundwater was examined in terms of R, E, mean error (ME), root mean square error (RMSE), and pesticide occurrence "class" groups by eliminating some key input parameters to the model. Well depth and time of sample collection were the most sensitive input parameters for predicting the pesticide contamination potential of a well. This infers that wells tapping shallow aquifers are more vulnerable to pesticide contamination than those wells tapping deeper aquifers. Pesticide occurrences during post-application months (June through October) were found to be 2.5 to 3 times higher than pesticide occurrences during other months (November through April). The BPNN was used to rank the input parameters with highest potential to contaminate groundwater, including two original and five ancillary parameters. The two original parameters are depth to aquifer material and pesticide leaching class. When these two parameters were the only input parameters for the BPNN, they were not able to predict contamination potential. However, when they were used with other parameters, the predictive performance efficiency of the BPNN in terms of R, E, ME, RMSE, and pesticide occurrence "class" groups increased. Ancillary data include data collected during the study such as well depth and time of sample collection. The BPNN indicated that the ancillary data had more predictive power than the original data. The BPNN results will help researchers identify parameters to improve maps of aquifer sensitivity to pesticide contamination. ?? 2006 Elsevier B.V. All rights reserved.

Quantitative Analysis of Signaling Networks across Differentially Embedded Tumors Highlights Interpatient Heterogeneity in Human Glioblastoma

PubMed Central

2015-01-01

Glioblastoma multiforme (GBM) is the most aggressive malignant primary brain tumor, with a dismal mean survival even with the current standard of care. Although in vitro cell systems can provide mechanistic insight into the regulatory networks governing GBM cell proliferation and migration, clinical samples provide a more physiologically relevant view of oncogenic signaling networks. However, clinical samples are not widely available and may be embedded for histopathologic analysis. With the goal of accurately identifying activated signaling networks in GBM tumor samples, we investigated the impact of embedding in optimal cutting temperature (OCT) compound followed by flash freezing in LN2 vs immediate flash freezing (iFF) in LN2 on protein expression and phosphorylation-mediated signaling networks. Quantitative proteomic and phosphoproteomic analysis of 8 pairs of tumor specimens revealed minimal impact of the different sample processing strategies and highlighted the large interpatient heterogeneity present in these tumors. Correlation analyses of the differentially processed tumor sections identified activated signaling networks present in selected tumors and revealed the differential expression of transcription, translation, and degradation associated proteins. This study demonstrates the capability of quantitative mass spectrometry for identification of in vivo oncogenic signaling networks from human tumor specimens that were either OCT-embedded or immediately flash-frozen. PMID:24927040

Sampled-data synchronisation of coupled harmonic oscillators with communication and input delays subject to controller failure

NASA Astrophysics Data System (ADS)

Zhao, Liyun; Zhou, Jin; Wu, Quanjun

2016-01-01

This paper considers the sampled-data synchronisation problems of coupled harmonic oscillators with communication and input delays subject to controller failure. A synchronisation protocol is proposed for such oscillator systems over directed network topology, and then some general algebraic criteria on exponential convergence for the proposed protocol are established. The main features of the present investigation include: (1) both the communication and input delays are simultaneously addressed, and the directed network topology is firstly considered and (2) the effects of time delays on synchronisation performance are theoretically and numerically investigated. It is shown that in the absence of communication delays, coupled harmonic oscillators can achieve synchronisation oscillatory motion. Whereas if communication delays are nonzero at infinite multiple sampled-data instants, its synchronisation (or consensus) state is zero. This conclusion can be used as an effective control strategy to stabilise coupled harmonic oscillators in practical applications. Furthermore, it is interesting to find that increasing either communication or input delays will enhance the synchronisation performance of coupled harmonic oscillators. Subsequently, numerical examples illustrate and visualise theoretical results.

Simulated remodeling of loaded collagen networks via strain-dependent enzymatic degradation and constant-rate fiber growth

PubMed Central

Hadi, M.F.; Sander, E.A.; Ruberti, J.W.; Barocas, V. H.

2011-01-01

Recent work has demonstrated that enzymatic degradation of collagen fibers exhibits strain-dependent kinetics. Conceptualizing how the strain dependence affects remodeling of collagenous tissues is vital to our understanding of collagen management in native and bioengineered tissues. As a first step towards this goal, the current study puts forward a multiscale model for enzymatic degradation and remodeling of collagen networks for two sample geometries we routinely use in experiments as model tissues. The multiscale model, driven by microstructural data from an enzymatic decay experiment, includes an exponential strain-dependent kinetic relation for degradation and constant growth. For a dogbone sample under uniaxial load, the model predicted that the distribution of fiber diameters would spread over the course of degradation because of variation in individual fiber load. In a cross-shaped sample, the central region, which experiences smaller, more isotropic loads, showed more decay and less spread in fiber diameter compared to the arms. There was also a slight shift in average orientation in different regions of the cruciform. PMID:22180691

Microrheology: Structural evolution under static and dynamic conditions by simultaneous analysis of confocal microscopy and diffusing wave spectroscopy

NASA Astrophysics Data System (ADS)

Nicolas, Yves; Paques, Marcel; Knaebel, Alexandra; Steyer, Alain; Munch, Jean-Pierre; Blijdenstein, Theo B. J.; van Aken, George A.

2003-08-01

An oscillatory shear configuration was developed to improve understanding of structural evolution during deformation. It combines an inverted confocal scanning laser microscope (CSLM) and a special sample holder that can apply to the sample specific deformation: oscillatory shear or steady strain. In this configuration, a zero-velocity plane is created in the sample by moving two plates in opposite directions, thereby providing stable observation conditions of the structural behavior under deformation. The configuration also includes diffusion wave spectroscopy (DWS) to monitor the network properties via particle mobility under static and dynamic conditions. CSLM and DWS can be performed simultaneously and three-dimensional images can be obtained under static conditions. This configuration is mainly used to study mechanistic phenomena like particle interaction, aggregation, gelation and network disintegration, interactions at interfaces under static and dynamic conditions in semisolid food materials (desserts, dressings, sauces, dairy products) and in nonfood materials (mineral emulsions, etc.). Preliminary data obtained with this new oscillatory shear configuration are described that demonstrate their capabilities and the potential contribution to other areas of application also.

Co-Inheritance Analysis within the Domains of Life Substantially Improves Network Inference by Phylogenetic Profiling

PubMed Central

Shin, Junha; Lee, Insuk

2015-01-01

Phylogenetic profiling, a network inference method based on gene inheritance profiles, has been widely used to construct functional gene networks in microbes. However, its utility for network inference in higher eukaryotes has been limited. An improved algorithm with an in-depth understanding of pathway evolution may overcome this limitation. In this study, we investigated the effects of taxonomic structures on co-inheritance analysis using 2,144 reference species in four query species: Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, and Homo sapiens. We observed three clusters of reference species based on a principal component analysis of the phylogenetic profiles, which correspond to the three domains of life—Archaea, Bacteria, and Eukaryota—suggesting that pathways inherit primarily within specific domains or lower-ranked taxonomic groups during speciation. Hence, the co-inheritance pattern within a taxonomic group may be eroded by confounding inheritance patterns from irrelevant taxonomic groups. We demonstrated that co-inheritance analysis within domains substantially improved network inference not only in microbe species but also in the higher eukaryotes, including humans. Although we observed two sub-domain clusters of reference species within Eukaryota, co-inheritance analysis within these sub-domain taxonomic groups only marginally improved network inference. Therefore, we conclude that co-inheritance analysis within domains is the optimal approach to network inference with the given reference species. The construction of a series of human gene networks with increasing sample sizes of the reference species for each domain revealed that the size of the high-accuracy networks increased as additional reference species genomes were included, suggesting that within-domain co-inheritance analysis will continue to expand human gene networks as genomes of additional species are sequenced. Taken together, we propose that co-inheritance analysis within the domains of life will greatly potentiate the use of the expected onslaught of sequenced genomes in the study of molecular pathways in higher eukaryotes. PMID:26394049

Selective Narrowing of Social Networks Across Adulthood is Associated With Improved Emotional Experience in Daily Life.

PubMed

English, Tammy; Carstensen, Laura L

2014-03-01

Past research has documented age differences in the size and composition of social networks that suggest that networks grow smaller with age and include an increasingly greater proportion of well-known social partners. According to socioemotional selectivity theory, such changes in social network composition serve an antecedent emotion regulatory function that supports an age-related increase in the priority that people place on emotional well-being. The present study employed a longitudinal design with a sample that spanned the full adult age range to examine whether there is evidence of within-individual (developmental) change in social networks and whether the characteristics of relationships predict emotional experiences in daily life. Using growth curve analyses, social networks were found to increase in size in young adulthood and then decline steadily throughout later life. As postulated by socioemotional selectivity theory, reductions were observed primarily in the number of peripheral partners; the number of close partners was relatively stable over time. In addition, cross-sectional analyses revealed that older adults reported that social network members elicited less negative emotion and more positive emotion. The emotional tone of social networks, particularly when negative emotions were associated with network members, also predicted experienced emotion of participants. Overall, findings were robust after taking into account demographic variables and physical health. The implications of these findings are discussed in the context of socioemotional selectivity theory and related theoretical models.

Social Network Influences on Service Use among Urban, African American Youth with Mental Health Problems

PubMed Central

Lindsey, Michael A.; Barksdale, Crystal L.; Lambert, Sharon F.; Ialongo, Nicholas S.

2010-01-01

Objective To examine the associations between the size and quality of African American adolescents' social networks and their mental health service use, and to examine whether these social networks characteristics moderate the association between need for services due to emotional or behavioral difficulties and use of services. Method Participants were a community sample of African American adolescents (N=465; 46.2% female; mean age 14.78) initially recruited in 1st grade for participation in an evaluation of two preventive intervention trials. Social network influences and adolescents' mental health service use in schools and the community were accessed. Results A significant positive association between adolescents' perception that their social network was helpful and their use of school mental health services was identified. The significant associations between need for services for anxiety, depression, or behavior problems, and school and outpatient service use were moderated by size of the social network. Specifically, among youth in need of services for anxiety or depression, school-based service use was higher for those with larger social networks. Conclusions Implications for enhancing access to formal mental health services include further examination of key social network influences that potentially serve as facilitators or barriers to formal help-seeking. The findings also suggest that it might be important to integrate social network members into interventions to address the mental health needs of adolescents. PMID:20864006

Mapping soil landscape as spatial continua: The Neural Network Approach

NASA Astrophysics Data System (ADS)

Zhu, A.-Xing

2000-03-01

A neural network approach was developed to populate a soil similarity model that was designed to represent soil landscape as spatial continua for hydroecological modeling at watersheds of mesoscale size. The approach employs multilayer feed forward neural networks. The input to the network was data on a set of soil formative environmental factors; the output from the network was a set of similarity values to a set of prescribed soil classes. The network was trained using a conjugate gradient algorithm in combination with a simulated annealing technique to learn the relationships between a set of prescribed soils and their environmental factors. Once trained, the network was used to compute for every location in an area the similarity values of the soil to the set of prescribed soil classes. The similarity values were then used to produce detailed soil spatial information. The approach also included a Geographic Information System procedure for selecting representative training and testing samples and a process of determining the network internal structure. The approach was applied to soil mapping in a watershed, the Lubrecht Experimental Forest, in western Montana. The case study showed that the soil spatial information derived using the neural network approach reveals much greater spatial detail and has a higher quality than that derived from the conventional soil map. Implications of this detailed soil spatial information for hydroecological modeling at the watershed scale are also discussed.

Random sampling of elementary flux modes in large-scale metabolic networks.

PubMed

Machado, Daniel; Soons, Zita; Patil, Kiran Raosaheb; Ferreira, Eugénio C; Rocha, Isabel

2012-09-15

The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks. Source code for a cross-platform implementation in Python is freely available at http://code.google.com/p/emsampler. dmachado@deb.uminho.pt Supplementary data are available at Bioinformatics online.

Big-data-based edge biomarkers: study on dynamical drug sensitivity and resistance in individuals.

PubMed

Zeng, Tao; Zhang, Wanwei; Yu, Xiangtian; Liu, Xiaoping; Li, Meiyi; Chen, Luonan

2016-07-01

Big-data-based edge biomarker is a new concept to characterize disease features based on biomedical big data in a dynamical and network manner, which also provides alternative strategies to indicate disease status in single samples. This article gives a comprehensive review on big-data-based edge biomarkers for complex diseases in an individual patient, which are defined as biomarkers based on network information and high-dimensional data. Specifically, we firstly introduce the sources and structures of biomedical big data accessible in public for edge biomarker and disease study. We show that biomedical big data are typically 'small-sample size in high-dimension space', i.e. small samples but with high dimensions on features (e.g. omics data) for each individual, in contrast to traditional big data in many other fields characterized as 'large-sample size in low-dimension space', i.e. big samples but with low dimensions on features. Then, we demonstrate the concept, model and algorithm for edge biomarkers and further big-data-based edge biomarkers. Dissimilar to conventional biomarkers, edge biomarkers, e.g. module biomarkers in module network rewiring-analysis, are able to predict the disease state by learning differential associations between molecules rather than differential expressions of molecules during disease progression or treatment in individual patients. In particular, in contrast to using the information of the common molecules or edges (i.e.molecule-pairs) across a population in traditional biomarkers including network and edge biomarkers, big-data-based edge biomarkers are specific for each individual and thus can accurately evaluate the disease state by considering the individual heterogeneity. Therefore, the measurement of big data in a high-dimensional space is required not only in the learning process but also in the diagnosing or predicting process of the tested individual. Finally, we provide a case study on analyzing the temporal expression data from a malaria vaccine trial by big-data-based edge biomarkers from module network rewiring-analysis. The illustrative results show that the identified module biomarkers can accurately distinguish vaccines with or without protection and outperformed previous reported gene signatures in terms of effectiveness and efficiency. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Intranetwork and internetwork connectivity in patients with Alzheimer disease and the association with cerebrospinal fluid biomarker levels.

PubMed

Weiler, Marina; de Campos, Brunno Machado; Teixeira, Camila Vieira de Ligo; Casseb, Raphael Fernandes; Carletti-Cassani, Ana Flávia Mac Knight; Vicentini, Jéssica Elias; Magalhães, Thamires Naela Cardoso; Talib, Leda Leme; Forlenza, Orestes Vicente; Balthazar, Marcio Luiz Figueredo

2017-11-01

In the last decade, many studies have reported abnormal connectivity within the default mode network (DMN) in patients with Alzheimer disease. Few studies, however, have investigated other networks and their association with pathophysiological proteins obtained from cerebrospinal fluid (CSF). We performed 3 T imaging in patients with mild Alzheimer disease, patients with amnestic mild cognitive impairment (aMCI) and healthy controls, and we collected CSF samples from the patients with aMCI and mild Alzheimer disease. We analyzed 57 regions from 8 networks. Additionally, we performed correlation tests to investigate possible associations between the networks' functional connectivity and the protein levels obtained from the CSF of patients with aMCI and Alzheimer disease. Our sample included 41 patients with Alzheimer disease, 35 with aMCI and 48 controls. We found that the main connectivity abnormalities in those with Alzheimer disease occurred between the DMN and task-positive networks: these patients presented not only a decreased anticorrelation between some regions, but also an inversion of the correlation signal (positive correlation instead of anticorrelation). Those with aMCI did not present statistically different connectivity from patients with Alzheimer disease or controls. Abnormal levels of CSF proteins were associated with functional disconnectivity between several regions in both the aMCI and mild Alzheimer disease groups, extending well beyond the DMN or temporal areas. The presented data are cross-sectional in nature, and our findings are dependent on the choice of seed regions used. We found that the main functional connectivity abnormalities occur between the DMN and task-positive networks and that the pathological levels of CSF biomarkers correlate with functional connectivity disruption in patients with Alzheimer disease.

Role of Social Media in Diabetes Management in the Middle East Region: Systematic Review.

PubMed

Alanzi, Turki

2018-02-13

Diabetes is a major health care burden in the Middle East region. Social networking tools can contribute to the management of diabetes with improved educational and care outcomes using these popular tools in the region. The objective of this review was to evaluate the impact of social networking interventions on the improvement of diabetes management and health outcomes in patients with diabetes in the Middle East. Peer-reviewed articles from PubMed (1990-2017) and Google Scholar (1990-2017) were identified using various combinations of predefined terms and search criteria. The main inclusion criterion consisted of the use of social networking apps on mobile phones as the primary intervention. Outcomes were grouped according to study design, type of diabetes, category of technological intervention, location, and sample size. This review included 5 articles evaluating the use of social media tools in the management of diabetes in the Middle East. In most studies, the acceptance rate for the use of social networking to optimize the management of diabetes was relatively high. Diabetes-specific management tools such as the Saudi Arabia Networking for Aiding Diabetes and Diabetes Intelligent Management System for Iraq systems helped collect patient information and lower hemoglobin A 1c (HbA 1c ) levels, respectively. The reviewed studies demonstrated the potential of social networking tools being adopted in regions in the Middle East to improve the management of diabetes. Future studies consisting of larger sample sizes spanning multiple regions would provide further insight into the use of social media for improving patient outcomes. ©Turki Alanzi. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 13.02.2018.

Requiring collaboration: Hippocampal-prefrontal networks needed in spatial working memory and ageing. A multivariate analysis approach.

PubMed

Zancada-Menendez, C; Alvarez-Suarez, P; Sampedro-Piquero, P; Cuesta, M; Begega, A

2017-04-01

Ageing is characterized by a decline in the processes of retention and storage of spatial information. We have examined the behavioural performance of adult rats (3months old) and aged rats (18months old) in a spatial complex task (delayed match to sample). The spatial task was performed in the Morris water maze and consisted of three sessions per day over a period of three consecutive days. Each session consisted of two trials (one sample and retention) and inter-session intervals of 5min. Behavioural results showed that the spatial task was difficult for middle aged group. This worse execution could be associated with impairments of processing speed and spatial information retention. We examined the changes in the neuronal metabolic activity of different brain regions through cytochrome C oxidase histochemistry. Then, we performed MANOVA and Discriminant Function Analyses to determine the functional profile of the brain networks that are involved in the spatial learning of the adult and middle-aged groups. This multivariate analysis showed two principal functional networks that necessarily participate in this spatial learning. The first network was composed of the supramammillary nucleus, medial mammillary nucleus, CA3, and CA1. The second one included the anterior cingulate, prelimbic, and infralimbic areas of the prefrontal cortex, dentate gyrus, and amygdala complex (basolateral l and central subregions). There was a reduction in the hippocampal-supramammilar network in both learning groups, whilst there was an overactivation in the executive network, especially in the aged group. This response could be due to a higher requirement of the executive control in a complex spatial memory task in older animals. Copyright © 2017 Elsevier Inc. All rights reserved.

Field-based evaluation of two herbaceous plant community composition sampling methods for long-term monitoring in Northern Great Plains National Parks

USGS Publications Warehouse

Symstad, Amy J.; Wienk, Cody L.; Thorstenson, Andy

2006-01-01

The Northern Great Plains Inventory & Monitoring (I&M) Network (Network) of the National Park Service (NPS) consists of 13 NPS units in North Dakota, South Dakota, Nebraska, and eastern Wyoming. The Network is in the planning phase of a long-term program to monitor the health of park ecosystems. Plant community composition is one of the 'Vital Signs,' or indicators, that will be monitored as part of this program for three main reasons. First, plant community composition is information-rich; a single sampling protocol can provide information on the diversity of native and non-native species, the abundance of individual dominant species, and the abundance of groups of plants. Second, plant community composition is of specific management concern. The abundance and diversity of exotic plants, both absolute and relative to native species, is one of the greatest management concerns in almost all Network parks (Symstad 2004). Finally, plant community composition reflects the effects of a variety of current or anticipated stressors on ecosystem health in the Network parks including invasive exotic plants, large ungulate grazing, lack of fire in a fire-adapted system, chemical exotic plant control, nitrogen deposition, increased atmospheric carbon dioxide concentrations, and climate change. Before the Network begins its Vital Signs monitoring, a detailed plan describing specific protocols used for each of the Vital Signs must go through rigorous development and review. The pilot study on which we report here is one of the components of this protocol development. The goal of the work we report on here was to determine a specific method to use for monitoring plant community composition of the herb layer (< 2 m tall).

[Evaluation on running status of Chinese Polio Laboratories Network in 2008].

PubMed

Zhu, Shuang-li; Yan, Dong-mei; Zhu, Hui

2010-04-01

In order to evaluate the running status and provide the laboratory data for maintaining polio-free status in China, the virology surveillance database of Chinese Polio Laboratories Network (not include Hong Kong, Macao, and Taiwan)in 2008 were analyzed. The case investigation data of Acute Flaccid Paralysis(AFP)cases reported by 31 provinces (municipal, autonomous regions) through EPI surveillance information management system and the database of National Polio Laboratory (NPL) were analyzed, and the indicators of running status of Chinese Polio Laboratories Network were evaluated. 10,116 stool samples were collected from 5116 AFP cases by Chinese Polio Laboratories Network in 2008, and viral isolation and identification of all stool samples were done according to 4th World Health Organization (WHO) Polio Laboratory Manual. The rate of viral isolation and identification performed within 28d was 94.9%. 189 polioviruses (PV) and 597 of non-polio enteroviruses (NPEV) were isolated from AFP cases, the isolatien rates were 3.72% and 11.74% respectively. 251 polio positive isolates were sent to NPL from 31 provincial polio laboratories. There were 318 single serotype PVs were performed VPI sequencing. And no wild polioviruses and Vaccine-derived Polioviruses (VDPVs) were found in 2008. NPL passed the proficiency test and got full accreditation for on-site review by WHO experts in 2008. All 31 provincial Polio laboratories passed the proficiency test with the same panel as NPL, and 13 provincial Polio laboratories joined and passed the on-site review by WHO experts. The running status of Chinese Polio Laboratories Network was good, polio-free status was maintained in China in 2008. The Chinese polio laboratories network running is normaly, the laboratory surveillance system was sensitive and laboratory data were provided for maintaining the polio-free status in China.

Evaluating the stability of DSM-5 PTSD symptom network structure in a national sample of U.S. military veterans.

PubMed

von Stockert, Sophia H H; Fried, Eiko I; Armour, Cherie; Pietrzak, Robert H

2018-03-15

Previous studies have used network models to investigate how PTSD symptoms associate with each other. However, analyses examining the degree to which these networks are stable over time, which are critical to identifying symptoms that may contribute to the chronicity of this disorder, are scarce. In the current study, we evaluated the temporal stability of DSM-5 PTSD symptom networks over a three-year period in a nationally representative sample of trauma-exposed U.S. military veterans. Data were analyzed from 611 trauma-exposed U.S. military veterans who participated in the National Health and Resilience in Veterans Study (NHRVS). We estimated regularized partial correlation networks of DSM-5 PTSD symptoms at baseline (Time 1) and at three-year follow-up (Time 2), and examined their temporal stability. Evaluation of the network structure of PTSD symptoms at Time 1 and Time 2 using a formal network comparison indicated that the Time 1 network did not differ significantly from the Time 2 network with regard to network structure (p = 0.12) or global strength (sum of all absolute associations, i.e. connectivity; p = 0.25). Centrality estimates of both networks (r = 0.86) and adjacency matrices (r = 0.69) were highly correlated. In both networks, avoidance, intrusive, and negative cognition and mood symptoms were among the more central nodes. This study is limited by the use of a self-report instrument to assess PTSD symptoms and recruitment of a relatively homogeneous sample of predominantly older, Caucasian veterans. Results of this study demonstrate the three-year stability of DSM-5 PTSD symptom network structure in a nationally representative sample of trauma-exposed U.S. military veterans. They further suggest that trauma-related avoidance, intrusive, and dysphoric symptoms may contribute to the chronicity of PTSD symptoms in this population. Published by Elsevier B.V.

The effects of individual- and network-level factors on discussion of cancer experiences: Survivors of childhood cancer in Korea.

PubMed

Kim, Min Ah; Yi, Jaehee; Prince, Kort C; Nagelhout, Elizabeth; Wu, Yelena P

2018-01-01

This study aimed to identify young adult Korean cancer survivors' individual- (psychological distress, stigma, sociodemographic variables, and cancer-related variables) and network-level factors (relationship type, social support type) that influence discussion of their cancer experiences. Sixty-eight survivors of childhood cancer who were recruited using snowball sampling nominated 245 individuals from their networks, including family and intimate partners (40%) and friends and acquaintances (60%), as people with whom they most frequently interacted. Results of multilevel modeling analysis indicated that higher levels of internalized shame were a prominent individual-level factor associated with a lack of discussion of cancer experiences. Relationship type and support type at the network-level were also significant correlates of discussion of cancer experiences. Programs for reducing the survivors' shame, improving illness identity, and providing professional training for building social relationships that are intimate and in which they could exchange reciprocal support may help Korean childhood cancer survivors to openly share their cancer experiences with others in their social network and to be successful in the journey of cancer survivorship.

Prediction of contaminant fate and transport in potable water systems using H2OFate

NASA Astrophysics Data System (ADS)

Devarakonda, Venkat; Manickavasagam, Sivakumar; VanBlaricum, Vicki; Ginsberg, Mark

2009-05-01

BlazeTech has recently developed a software called H2OFate to predict the fate and transport of chemical and biological contaminants in water distribution systems. This software includes models for the reactions of these contaminants with residual disinfectant in bulk water and at the pipe wall, and their adhesion/reactions with the pipe walls. This software can be interfaced with sensors through SCADA systems to monitor water distribution networks for contamination events and activate countermeasures, as needed. This paper presents results from parametric calculations carried out using H2OFate for a simulated contaminant release into a sample water distribution network.

Toughening of PMR composites by semi-interpenetrating networks

NASA Technical Reports Server (NTRS)

Tiwari, S. N.; Srinivansan, K.

1991-01-01

Polymerization of monomer reactants (PMR-15) type polyimide and RP46 prepregs were drum wound using IM-7 fibers. Prepregging and processing conditions were optimized to yield good quality laminates with fiber volume fractions of 60 percent (+/- 2 percent). Samples were fabricated and tested to determine comprehensive engineering properties of both systems. These included 0 deg flexure, short beam shear, transverse flexure and tension, 0 deg tension and compression, intralaminar shear, short block compression, mode 1 and 2 fracture toughness, and compression after impact properties. Semi-2-IPN (interpenetrating polymer networks) toughened PMR-15 and RP46 laminates were also fabricated and tested for the same properties.

Analytical performance assessment of orbital configurations

NASA Astrophysics Data System (ADS)

Hitzl, D. L.; Krakowski, D. C.

1981-08-01

The system analysis of an orbital communication network of N satellites has been conducted. Specifically, the problem of connecting, in an optimal way, a set of ground-based laser transmitters to a second set of ground receivers on another part of the earth via a number of relay satellites fitted with retroreflectors has been addressed. A computer program has been developed which can treat either the so-called 'single-bounce' or 'double-bounce' cases. Sample results included in this paper consider a double-bounce orbital network composed of 12 relay satellites in 6 hour elliptical orbits together with 16 transceiver (delivery) satellites in 4.8 hour elliptical orbits.

Team knowledge representation: a network perspective.

PubMed

Espinosa, J Alberto; Clark, Mark A

2014-03-01

We propose a network perspective of team knowledge that offers both conceptual and methodological advantages, expanding explanatory value through representation and measurement of component structure and content. Team knowledge has typically been conceptualized and measured with relatively simple aggregates, without fully accounting for differing knowledge configurations among team members. Teams with similar aggregate values of team knowledge may have very different team dynamics depending on how knowledge isolates, cliques, and densities are distributed across the team; which members are the most knowledgeable; who shares knowledge with whom; and how knowledge clusters are distributed. We illustrate our proposed network approach through a sample of 57 teams, including how to compute, analyze, and visually represent team knowledge. Team knowledge network structures (isolation, centrality) are associated with outcomes of, respectively, task coordination, strategy coordination, and the proportion of team knowledge cliques, all after controlling for shared team knowledge. Network analysis helps to represent, measure, and understand the relationship of team knowledge to outcomes of interest to team researchers, members, and managers. Our approach complements existing team knowledge measures. Researchers and managers can apply network concepts and measures to help understand where team knowledge is held within a team and how this relational structure may influence team coordination, cohesion, and performance.

The cingulo-opercular network provides word-recognition benefit.

PubMed

Vaden, Kenneth I; Kuchinsky, Stefanie E; Cute, Stephanie L; Ahlstrom, Jayne B; Dubno, Judy R; Eckert, Mark A

2013-11-27

Recognizing speech in difficult listening conditions requires considerable focus of attention that is often demonstrated by elevated activity in putative attention systems, including the cingulo-opercular network. We tested the prediction that elevated cingulo-opercular activity provides word-recognition benefit on a subsequent trial. Eighteen healthy, normal-hearing adults (10 females; aged 20-38 years) performed word recognition (120 trials) in multi-talker babble at +3 and +10 dB signal-to-noise ratios during a sparse sampling functional magnetic resonance imaging (fMRI) experiment. Blood oxygen level-dependent (BOLD) contrast was elevated in the anterior cingulate cortex, anterior insula, and frontal operculum in response to poorer speech intelligibility and response errors. These brain regions exhibited significantly greater correlated activity during word recognition compared with rest, supporting the premise that word-recognition demands increased the coherence of cingulo-opercular network activity. Consistent with an adaptive control network explanation, general linear mixed model analyses demonstrated that increased magnitude and extent of cingulo-opercular network activity was significantly associated with correct word recognition on subsequent trials. These results indicate that elevated cingulo-opercular network activity is not simply a reflection of poor performance or error but also supports word recognition in difficult listening conditions.

Dynamic security contingency screening and ranking using neural networks.

PubMed

Mansour, Y; Vaahedi, E; El-Sharkawi, M A

1997-01-01

This paper summarizes BC Hydro's experience in applying neural networks to dynamic security contingency screening and ranking. The idea is to use the information on the prevailing operating condition and directly provide contingency screening and ranking using a trained neural network. To train the two neural networks for the large scale systems of BC Hydro and Hydro Quebec, in total 1691 detailed transient stability simulation were conducted, 1158 for BC Hydro system and 533 for the Hydro Quebec system. The simulation program was equipped with the energy margin calculation module (second kick) to measure the energy margin in each run. The first set of results showed poor performance for the neural networks in assessing the dynamic security. However a number of corrective measures improved the results significantly. These corrective measures included: 1) the effectiveness of output; 2) the number of outputs; 3) the type of features (static versus dynamic); 4) the number of features; 5) system partitioning; and 6) the ratio of training samples to features. The final results obtained using the large scale systems of BC Hydro and Hydro Quebec demonstrates a good potential for neural network in dynamic security assessment contingency screening and ranking.

An empirical Bayes approach to network recovery using external knowledge.

PubMed

Kpogbezan, Gino B; van der Vaart, Aad W; van Wieringen, Wessel N; Leday, Gwenaël G R; van de Wiel, Mark A

2017-09-01

Reconstruction of a high-dimensional network may benefit substantially from the inclusion of prior knowledge on the network topology. In the case of gene interaction networks such knowledge may come for instance from pathway repositories like KEGG, or be inferred from data of a pilot study. The Bayesian framework provides a natural means of including such prior knowledge. Based on a Bayesian Simultaneous Equation Model, we develop an appealing Empirical Bayes (EB) procedure that automatically assesses the agreement of the used prior knowledge with the data at hand. We use variational Bayes method for posterior densities approximation and compare its accuracy with that of Gibbs sampling strategy. Our method is computationally fast, and can outperform known competitors. In a simulation study, we show that accurate prior data can greatly improve the reconstruction of the network, but need not harm the reconstruction if wrong. We demonstrate the benefits of the method in an analysis of gene expression data from GEO. In particular, the edges of the recovered network have superior reproducibility (compared to that of competitors) over resampled versions of the data. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Characterization of articular cartilage by combining microscopic analysis with a fibril-reinforced finite-element model.

PubMed

Julkunen, Petro; Kiviranta, Panu; Wilson, Wouter; Jurvelin, Jukka S; Korhonen, Rami K

2007-01-01

Load-bearing characteristics of articular cartilage are impaired during tissue degeneration. Quantitative microscopy enables in vitro investigation of cartilage structure but determination of tissue functional properties necessitates experimental mechanical testing. The fibril-reinforced poroviscoelastic (FRPVE) model has been used successfully for estimation of cartilage mechanical properties. The model includes realistic collagen network architecture, as shown by microscopic imaging techniques. The aim of the present study was to investigate the relationships between the cartilage proteoglycan (PG) and collagen content as assessed by quantitative microscopic findings, and model-based mechanical parameters of the tissue. Site-specific variation of the collagen network moduli, PG matrix modulus and permeability was analyzed. Cylindrical cartilage samples (n=22) were harvested from various sites of the bovine knee and shoulder joints. Collagen orientation, as quantitated by polarized light microscopy, was incorporated into the finite-element model. Stepwise stress-relaxation experiments in unconfined compression were conducted for the samples, and sample-specific models were fitted to the experimental data in order to determine values of the model parameters. For comparison, Fourier transform infrared imaging and digital densitometry were used for the determination of collagen and PG content in the same samples, respectively. The initial and strain-dependent fibril network moduli as well as the initial permeability correlated significantly with the tissue collagen content. The equilibrium Young's modulus of the nonfibrillar matrix and the strain dependency of permeability were significantly associated with the tissue PG content. The present study demonstrates that modern quantitative microscopic methods in combination with the FRPVE model are feasible methods to characterize the structure-function relationships of articular cartilage.

Validation of Networks Derived from Snowball Sampling of Municipal Science Education Actors

ERIC Educational Resources Information Center

von der Fehr, Ane; Sølberg, Jan; Bruun, Jesper

2018-01-01

Social network analysis (SNA) has been used in many educational studies in the past decade, but what these studies have in common is that the populations in question in most cases are defined and known to the researchers studying the networks. Snowball sampling is an SNA methodology most often used to study hidden populations, for example, groups…

Recovery of Phytophthora species from drainage points and tributaries within two forest stream networks: a preliminary report

Treesearch

J. Hwang; S.W. Oak; S.N. Jeffers

2011-01-01

To evaluate the number of stream sample sites needed to effectively survey a given stream network for species of Phytophthora, two stream networks, Davidson River and Cathey's Creek, in western North Carolina (USA) were studied. One-litre water samples were collected from the terminal drainage points and most of the tributaries in each stream...

Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks.

PubMed

Schillings, Claudia; Sunnåker, Mikael; Stelling, Jörg; Schwab, Christoph

2015-08-01

Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is "non-intrusive" and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design.

Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks

PubMed Central

Schillings, Claudia; Sunnåker, Mikael; Stelling, Jörg; Schwab, Christoph

2015-01-01

Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is “non-intrusive” and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design. PMID:26317784

Do We Really Need to Catch Them All? A New User-Guided Social Media Crawling Method

NASA Astrophysics Data System (ADS)

Erlandsson, Fredrik; Bródka, Piotr; Boldt, Martin; Johnson, Henric

2017-12-01

With the growing use of popular social media services like Facebook and Twitter it is challenging to collect all content from the networks without access to the core infrastructure or paying for it. Thus, if all content cannot be collected one must consider which data are of most importance. In this work we present a novel User-guided Social Media Crawling method (USMC) that is able to collect data from social media, utilizing the wisdom of the crowd to decide the order in which user generated content should be collected to cover as many user interactions as possible. USMC is validated by crawling 160 public Facebook pages, containing content from 368 million users including 1.3 billion interactions, and it is compared with two other crawling methods. The results show that it is possible to cover approximately 75% of the interactions on a Facebook page by sampling just 20% of its posts, and at the same time reduce the crawling time by 53%. In addition, the social network constructed from the 20% sample contains more than 75% of the users and edges compared to the social network created from all posts, and it has similar degree distribution.

TMA Navigator: network inference, patient stratification and survival analysis with tissue microarray data

PubMed Central

Lubbock, Alexander L. R.; Katz, Elad; Harrison, David J.; Overton, Ian M.

2013-01-01

Tissue microarrays (TMAs) allow multiplexed analysis of tissue samples and are frequently used to estimate biomarker protein expression in tumour biopsies. TMA Navigator (www.tmanavigator.org) is an open access web application for analysis of TMA data and related information, accommodating categorical, semi-continuous and continuous expression scores. Non-biological variation, or batch effects, can hinder data analysis and may be mitigated using the ComBat algorithm, which is incorporated with enhancements for automated application to TMA data. Unsupervised grouping of samples (patients) is provided according to Gaussian mixture modelling of marker scores, with cardinality selected by Bayesian information criterion regularization. Kaplan–Meier survival analysis is available, including comparison of groups identified by mixture modelling using the Mantel-Cox log-rank test. TMA Navigator also supports network inference approaches useful for TMA datasets, which often constitute comparatively few markers. Tissue and cell-type specific networks derived from TMA expression data offer insights into the molecular logic underlying pathophenotypes, towards more effective and personalized medicine. Output is interactive, and results may be exported for use with external programs. Private anonymous access is available, and user accounts may be generated for easier data management. PMID:23761446

Performance evaluation of an importance sampling technique in a Jackson network

NASA Astrophysics Data System (ADS)

brahim Mahdipour, E.; Masoud Rahmani, Amir; Setayeshi, Saeed

2014-03-01

Importance sampling is a technique that is commonly used to speed up Monte Carlo simulation of rare events. However, little is known regarding the design of efficient importance sampling algorithms in the context of queueing networks. The standard approach, which simulates the system using an a priori fixed change of measure suggested by large deviation analysis, has been shown to fail in even the simplest network settings. Estimating probabilities associated with rare events has been a topic of great importance in queueing theory, and in applied probability at large. In this article, we analyse the performance of an importance sampling estimator for a rare event probability in a Jackson network. This article carries out strict deadlines to a two-node Jackson network with feedback whose arrival and service rates are modulated by an exogenous finite state Markov process. We have estimated the probability of network blocking for various sets of parameters, and also the probability of missing the deadline of customers for different loads and deadlines. We have finally shown that the probability of total population overflow may be affected by various deadline values, service rates and arrival rates.

Detection of eardrum abnormalities using ensemble deep learning approaches

NASA Astrophysics Data System (ADS)

Senaras, Caglar; Moberly, Aaron C.; Teknos, Theodoros; Essig, Garth; Elmaraghy, Charles; Taj-Schaal, Nazhat; Yua, Lianbo; Gurcan, Metin N.

2018-02-01

In this study, we proposed an approach to report the condition of the eardrum as "normal" or "abnormal" by ensembling two different deep learning architectures. In the first network (Network 1), we applied transfer learning to the Inception V3 network by using 409 labeled samples. As a second network (Network 2), we designed a convolutional neural network to take advantage of auto-encoders by using additional 673 unlabeled eardrum samples. The individual classification accuracies of the Network 1 and Network 2 were calculated as 84.4%(+/- 12.1%) and 82.6% (+/- 11.3%), respectively. Only 32% of the errors of the two networks were the same, making it possible to combine two approaches to achieve better classification accuracy. The proposed ensemble method allows us to achieve robust classification because it has high accuracy (84.4%) with the lowest standard deviation (+/- 10.3%).

Factors determining the social participation of older adults: A comparison between Japan and Korea using EASS 2012.

PubMed

Katagiri, Keiko; Kim, Ju-Hyun

2018-01-01

Japan and Korea are the world's most aged and most rapidly aging nations. They both have low fertility rates, thereby intensifying the importance of social structures to aid a large, dependent population of older adults. Common strategies involve improving their social participation, which enhances their physical and mental health, so they are supporting society rather than being supported. Since the social participation rates in both countries are not as high as those of Western countries, it is critical to shed light on the factors related to social participation of the elderly. A secondary analyses were performed using Japanese and Korean data from the 2012 East Asia Social Survey (EASS), which includes nationally representative samples through random sampling. The analyses only include data from those 65 and older (Japan: N = 683, Korea: N = 362). Social participation is classified into four types: 1) no affiliation; 2) inactive participation; 3) active recreational; and 4) active social. The Japanese respondents had a higher participation rate than Koreans, but more Japanese were inactive. Though the rates of active participations were similar in both countries. Multinomial logistic regressions were conducted to examine the related factors among the four types of social participation. Basic attributes (e.g., living alone) and other factors (e.g., network size) were included as independent variables. The results show that larger non-family networks were linked with increased social participation in both societies. Men were more vulnerable to engaging in no social activities and at a higher risk of social isolation in both countries. One difference between the two nations is that among the Japanese, people with higher social orientations engage in more active social type participation. This study reveals that non-kin social networks are important for social participation in Japan and Korea.

The relationship between social desirability bias and self-reports of health, substance use, and social network factors among urban substance users in Baltimore, Maryland.

PubMed

Latkin, Carl A; Edwards, Catie; Davey-Rothwell, Melissa A; Tobin, Karin E

2017-10-01

Social desirability response bias may lead to inaccurate self-reports and erroneous study conclusions. The present study examined the relationship between social desirability response bias and self-reports of mental health, substance use, and social network factors among a community sample of inner-city substance users. The study was conducted in a sample of 591 opiate and cocaine users in Baltimore, Maryland from 2009 to 2013. Modified items from the Marlowe-Crowne Social Desirability Scale were included in the survey, which was conducted face-to-face and using Audio Computer Self Administering Interview (ACASI) methods. There were highly statistically significant differences in levels of social desirability response bias by levels of depressive symptoms, drug use stigma, physical health status, recent opiate and cocaine use, Alcohol Use Disorders Identification Test (AUDIT) scores, and size of social networks. There were no associations between health service utilization measures and social desirability bias. In multiple logistic regression models, even after including the Center for Epidemiologic Studies Depression Scale (CES-D) as a measure of depressive symptomology, social desirability bias was associated with recent drug use and drug user stigma. Social desirability bias was not associated with enrollment in prior research studies. These findings suggest that social desirability bias is associated with key health measures and that the associations are not primarily due to depressive symptoms. Methods are needed to reduce social desirability bias. Such methods may include the wording and prefacing of questions, clearly defining the role of "study participant," and assessing and addressing motivations for socially desirable responses. Copyright © 2017 Elsevier Ltd. All rights reserved.

The real-time fMRI neurofeedback based stratification of Default Network Regulation Neuroimaging data repository.

PubMed

McDonald, Amalia R; Muraskin, Jordan; Dam, Nicholas T Van; Froehlich, Caroline; Puccio, Benjamin; Pellman, John; Bauer, Clemens C C; Akeyson, Alexis; Breland, Melissa M; Calhoun, Vince D; Carter, Steven; Chang, Tiffany P; Gessner, Chelsea; Gianonne, Alyssa; Giavasis, Steven; Glass, Jamie; Homann, Steven; King, Margaret; Kramer, Melissa; Landis, Drew; Lieval, Alexis; Lisinski, Jonathan; Mackay-Brandt, Anna; Miller, Brittny; Panek, Laura; Reed, Hayley; Santiago, Christine; Schoell, Eszter; Sinnig, Richard; Sital, Melissa; Taverna, Elise; Tobe, Russell; Trautman, Kristin; Varghese, Betty; Walden, Lauren; Wang, Runtang; Waters, Abigail B; Wood, Dylan C; Castellanos, F Xavier; Leventhal, Bennett; Colcombe, Stanley J; LaConte, Stephen; Milham, Michael P; Craddock, R Cameron

2017-02-01

This data descriptor describes a repository of openly shared data from an experiment to assess inter-individual differences in default mode network (DMN) activity. This repository includes cross-sectional functional magnetic resonance imaging (fMRI) data from the Multi Source Interference Task, to assess DMN deactivation, the Moral Dilemma Task, to assess DMN activation, a resting state fMRI scan, and a DMN neurofeedback paradigm, to assess DMN modulation, along with accompanying behavioral and cognitive measures. We report technical validation from n=125 participants of the final targeted sample of 180 participants. Each session includes acquisition of one whole-brain anatomical scan and whole-brain echo-planar imaging (EPI) scans, acquired during the aforementioned tasks and resting state. The data includes several self-report measures related to perseverative thinking, emotion regulation, and imaginative processes, along with a behavioral measure of rapid visual information processing. Technical validation of the data confirms that the tasks deactivate and activate the DMN as expected. Group level analysis of the neurofeedback data indicates that the participants are able to modulate their DMN with considerable inter-subject variability. Preliminary analysis of behavioral responses and specifically self-reported sleep indicate that as many as 73 participants may need to be excluded from an analysis depending on the hypothesis being tested. The present data are linked to the enhanced Nathan Kline Institute, Rockland Sample and builds on the comprehensive neuroimaging and deep phenotyping available therein. As limited information is presently available about individual differences in the capacity to directly modulate the default mode network, these data provide a unique opportunity to examine DMN modulation ability in relation to numerous phenotypic characteristics. Copyright © 2016 Elsevier Inc. All rights reserved.

Social Network Status and Depression among Adolescents: An Examination of Social Network Influences and Depressive Symptoms in a Chinese Sample

PubMed Central

Okamoto, Janet; Johnson, C. Anderson; Leventhal, Adam; Milam, Joel; Pentz, Mary Ann; Schwartz, David; Valente, Thomas W.

2012-01-01

Despite the well established influence of peer experiences on adolescent attitudes, thoughts, and behaviors, surprisingly little research has examined the importance of peer context and the increased prevalence of depressive symptoms accompanying the transition into adolescence. Examination of social networks may provide some insight into the role of peers in the vulnerability of some adolescents to depression. To address this issue, we leveraged an existing sample of 5,563 Chinese 10th graders to incorporate social network data into a multilevel regression model of depressive symptoms. We found that, in this sample, being nominated as a friend was more important than being nominated as most liked. Social network centrality was significantly associated with depression; those adolescents who were less connected were more likely to suffer from depression. The risk of depression for those who were marginal members of classroom social networks was substantial. These findings suggest that a social network perspective could help to increase the effectiveness of programs aimed at preventing adolescent depression. PMID:23226988

Plasma and Serum Metabolite Association Networks: Comparability within and between Studies Using NMR and MS Profiling.

PubMed

Suarez-Diez, Maria; Adam, Jonathan; Adamski, Jerzy; Chasapi, Styliani A; Luchinat, Claudio; Peters, Annette; Prehn, Cornelia; Santucci, Claudio; Spyridonidis, Alexandros; Spyroulias, Georgios A; Tenori, Leonardo; Wang-Sattler, Rui; Saccenti, Edoardo

2017-07-07

Blood is one of the most used biofluids in metabolomics studies, and the serum and plasma fractions are routinely used as a proxy for blood itself. Here we investigated the association networks of an array of 29 metabolites identified and quantified via NMR in the plasma and serum samples of two cohorts of ∼1000 healthy blood donors each. A second study of 377 individuals was used to extract plasma and serum samples from the same individual on which a set of 122 metabolites were detected and quantified using FIA-MS/MS. Four different inference algorithms (ARANCE, CLR, CORR, and PCLRC) were used to obtain consensus networks. The plasma and serum networks obtained from different studies showed different topological properties with the serum network being more connected than the plasma network. On a global level, metabolite association networks from plasma and serum fractions obtained from the same blood sample of healthy people show similar topologies, and at a local level, some differences arise like in the case of amino acids.

Plasma and Serum Metabolite Association Networks: Comparability within and between Studies Using NMR and MS Profiling

PubMed Central

2017-01-01

Blood is one of the most used biofluids in metabolomics studies, and the serum and plasma fractions are routinely used as a proxy for blood itself. Here we investigated the association networks of an array of 29 metabolites identified and quantified via NMR in the plasma and serum samples of two cohorts of ∼1000 healthy blood donors each. A second study of 377 individuals was used to extract plasma and serum samples from the same individual on which a set of 122 metabolites were detected and quantified using FIA–MS/MS. Four different inference algorithms (ARANCE, CLR, CORR, and PCLRC) were used to obtain consensus networks. The plasma and serum networks obtained from different studies showed different topological properties with the serum network being more connected than the plasma network. On a global level, metabolite association networks from plasma and serum fractions obtained from the same blood sample of healthy people show similar topologies, and at a local level, some differences arise like in the case of amino acids. PMID:28517934

An analysis of respondent driven sampling with Injection Drug Users (IDU) in Albania and the Russian Federation.

PubMed

Stormer, Ame; Tun, Waimar; Guli, Lisa; Harxhi, Arjan; Bodanovskaia, Zinaida; Yakovleva, Anna; Rusakova, Maia; Levina, Olga; Bani, Roland; Rjepaj, Klodian; Bino, Silva

2006-11-01

Injection drug users in Tirana, Albania and St. Petersburg, Russia were recruited into a study assessing HIV-related behaviors and HIV serostatus using Respondent Driven Sampling (RDS), a peer-driven recruitment sampling strategy that results in a probability sample. (Salganik M, Heckathorn DD. Sampling and estimation in hidden populations using respondent-driven sampling. Sociol Method. 2004;34:193-239). This paper presents a comparison of RDS implementation, findings on network and recruitment characteristics, and lessons learned. Initiated with 13 to 15 seeds, approximately 200 IDUs were recruited within 8 weeks. Information resulting from RDS indicates that social network patterns from the two studies differ greatly. Female IDUs in Tirana had smaller network sizes than male IDUs, unlike in St. Petersburg where female IDUs had larger network sizes than male IDUs. Recruitment patterns in each country also differed by demographic categories. Recruitment analyses indicate that IDUs form socially distinct groups by sex in Tirana, whereas there was a greater degree of gender mixing patterns in St. Petersburg. RDS proved to be an effective means of surveying these hard-to-reach populations.

CHRR: coordinate hit-and-run with rounding for uniform sampling of constraint-based models.

PubMed

Haraldsdóttir, Hulda S; Cousins, Ben; Thiele, Ines; Fleming, Ronan M T; Vempala, Santosh

2017-06-01

In constraint-based metabolic modelling, physical and biochemical constraints define a polyhedral convex set of feasible flux vectors. Uniform sampling of this set provides an unbiased characterization of the metabolic capabilities of a biochemical network. However, reliable uniform sampling of genome-scale biochemical networks is challenging due to their high dimensionality and inherent anisotropy. Here, we present an implementation of a new sampling algorithm, coordinate hit-and-run with rounding (CHRR). This algorithm is based on the provably efficient hit-and-run random walk and crucially uses a preprocessing step to round the anisotropic flux set. CHRR provably converges to a uniform stationary sampling distribution. We apply it to metabolic networks of increasing dimensionality. We show that it converges several times faster than a popular artificial centering hit-and-run algorithm, enabling reliable and tractable sampling of genome-scale biochemical networks. https://github.com/opencobra/cobratoolbox . ronan.mt.fleming@gmail.com or vempala@cc.gatech.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

Artificial Neural Network for Total Laboratory Automation to Improve the Management of Sample Dilution.

PubMed

Ialongo, Cristiano; Pieri, Massimo; Bernardini, Sergio

2017-02-01

Diluting a sample to obtain a measure within the analytical range is a common task in clinical laboratories. However, for urgent samples, it can cause delays in test reporting, which can put patients' safety at risk. The aim of this work is to show a simple artificial neural network that can be used to make it unnecessary to predilute a sample using the information available through the laboratory information system. Particularly, the Multilayer Perceptron neural network built on a data set of 16,106 cardiac troponin I test records produced a correct inference rate of 100% for samples not requiring predilution and 86.2% for those requiring predilution. With respect to the inference reliability, the most relevant inputs were the presence of a cardiac event or surgery and the result of the previous assay. Therefore, such an artificial neural network can be easily implemented into a total automation framework to sensibly reduce the turnaround time of critical orders delayed by the operation required to retrieve, dilute, and retest the sample.

Urban Land Cover Mapping Accuracy Assessment - A Cost-benefit Analysis Approach

NASA Astrophysics Data System (ADS)

Xiao, T.

2012-12-01

One of the most important components in urban land cover mapping is mapping accuracy assessment. Many statistical models have been developed to help design simple schemes based on both accuracy and confidence levels. It is intuitive that an increased number of samples increases the accuracy as well as the cost of an assessment. Understanding cost and sampling size is crucial in implementing efficient and effective of field data collection. Few studies have included a cost calculation component as part of the assessment. In this study, a cost-benefit sampling analysis model was created by combining sample size design and sampling cost calculation. The sampling cost included transportation cost, field data collection cost, and laboratory data analysis cost. Simple Random Sampling (SRS) and Modified Systematic Sampling (MSS) methods were used to design sample locations and to extract land cover data in ArcGIS. High resolution land cover data layers of Denver, CO and Sacramento, CA, street networks, and parcel GIS data layers were used in this study to test and verify the model. The relationship between the cost and accuracy was used to determine the effectiveness of each sample method. The results of this study can be applied to other environmental studies that require spatial sampling.

Comparing men who have sex with men and transgender women who use Grindr, other similar social and sexual networking apps, or no social and sexual networking apps: Implications for recruitment and health promotion

PubMed Central

Sun, Christina J.; Sutfin, Erin; Bachmann, Laura H.; Stowers, Jason; Rhodes, Scott D.

2018-01-01

Objective Researchers and public health professionals have increased their attention to GPS-based social and sexual networking applications (apps) tailored to gay, bisexual, other men who have sex with men (MSM) and transgender women. These populations continue to be disproportionately affected by HIV in the United States, therefore these apps, in particular Grindr, have become an important sampling venue for the recruitment of HIV-related research participants. As such, it is essential to identify differences among app users to avoid potential sampling bias. This paper seeks to identify differences in MSM and transgender women who use Grindr and those who use other similar apps. Methods A community-based participatory research (CBPR) approach was used to recruit participants online who then completed a 25-item anonymous survey. Five domains were assessed: sociodemographics, HIV testing, sexual risk, substance abuse, and use of GPS-based social and sexual networking apps. Results 457 participants completed surveys. There were significant differences in the sociodemographic characteristics by app use, including age, race/ethnicity, sexual orientation, and outness. After adjusting for the sociodemographic characteristics associated with app use, there were significant differences in HIV risk and substance use between the groups. Conclusion This paper is the first to report on findings that compare MSM and transgender women who report using Grindr to MSM and transgender women who report using other similar apps. GPS-based social and sexual networking apps may offer a valuable recruitment tool for future HIV research seeking to recruit populations at increased risk for HIV or those living with HIV for therapeutic trials. Because of the differences identified across users of different apps, these findings suggest that if researchers recruited participants from just one app, they could end up with a sample quite different than if they had recruited MSM and transgender women from other apps. PMID:29593933

Probabilistic mapping of descriptive health status responses onto health state utilities using Bayesian networks: an empirical analysis converting SF-12 into EQ-5D utility index in a national US sample.

PubMed

Le, Quang A; Doctor, Jason N

2011-05-01

As quality-adjusted life years have become the standard metric in health economic evaluations, mapping health-profile or disease-specific measures onto preference-based measures to obtain quality-adjusted life years has become a solution when health utilities are not directly available. However, current mapping methods are limited due to their predictive validity, reliability, and/or other methodological issues. We employ probability theory together with a graphical model, called a Bayesian network, to convert health-profile measures into preference-based measures and to compare the results to those estimated with current mapping methods. A sample of 19,678 adults who completed both the 12-item Short Form Health Survey (SF-12v2) and EuroQoL 5D (EQ-5D) questionnaires from the 2003 Medical Expenditure Panel Survey was split into training and validation sets. Bayesian networks were constructed to explore the probabilistic relationships between each EQ-5D domain and 12 items of the SF-12v2. The EQ-5D utility scores were estimated on the basis of the predicted probability of each response level of the 5 EQ-5D domains obtained from the Bayesian inference process using the following methods: Monte Carlo simulation, expected utility, and most-likely probability. Results were then compared with current mapping methods including multinomial logistic regression, ordinary least squares, and censored least absolute deviations. The Bayesian networks consistently outperformed other mapping models in the overall sample (mean absolute error=0.077, mean square error=0.013, and R overall=0.802), in different age groups, number of chronic conditions, and ranges of the EQ-5D index. Bayesian networks provide a new robust and natural approach to map health status responses into health utility measures for health economic evaluations.

Comparing men who have sex with men and transgender women who use Grindr, other similar social and sexual networking apps, or no social and sexual networking apps: Implications for recruitment and health promotion.

PubMed

Sun, Christina J; Sutfin, Erin; Bachmann, Laura H; Stowers, Jason; Rhodes, Scott D

2018-01-01

Researchers and public health professionals have increased their attention to GPS-based social and sexual networking applications (apps) tailored to gay, bisexual, other men who have sex with men (MSM) and transgender women. These populations continue to be disproportionately affected by HIV in the United States, therefore these apps, in particular Grindr, have become an important sampling venue for the recruitment of HIV-related research participants. As such, it is essential to identify differences among app users to avoid potential sampling bias. This paper seeks to identify differences in MSM and transgender women who use Grindr and those who use other similar apps. A community-based participatory research (CBPR) approach was used to recruit participants online who then completed a 25-item anonymous survey. Five domains were assessed: sociodemographics, HIV testing, sexual risk, substance abuse, and use of GPS-based social and sexual networking apps. 457 participants completed surveys. There were significant differences in the sociodemographic characteristics by app use, including age, race/ethnicity, sexual orientation, and outness. After adjusting for the sociodemographic characteristics associated with app use, there were significant differences in HIV risk and substance use between the groups. This paper is the first to report on findings that compare MSM and transgender women who report using Grindr to MSM and transgender women who report using other similar apps. GPS-based social and sexual networking apps may offer a valuable recruitment tool for future HIV research seeking to recruit populations at increased risk for HIV or those living with HIV for therapeutic trials. Because of the differences identified across users of different apps, these findings suggest that if researchers recruited participants from just one app, they could end up with a sample quite different than if they had recruited MSM and transgender women from other apps.

Highly preserved consensus gene modules in human papilloma virus 16 positive cervical cancer and head and neck cancers.

PubMed

Zhang, Xianglan; Cha, In-Ho; Kim, Ki-Yeol

2017-12-26

In this study, we investigated the consensus gene modules in head and neck cancer (HNC) and cervical cancer (CC). We used a publicly available gene expression dataset, GSE6791, which included 42 HNC, 14 normal head and neck, 20 CC and 8 normal cervical tissue samples. To exclude bias because of different human papilloma virus (HPV) types, we analyzed HPV16-positive samples only. We identified 3824 genes common to HNC and CC samples. Among these, 977 genes showed high connectivity and were used to construct consensus modules. We demonstrated eight consensus gene modules for HNC and CC using the dissimilarity measure and average linkage hierarchical clustering methods. These consensus modules included genes with significant biological functions, including ATP binding and extracellular exosome. Eigengen network analysis revealed the consensus modules were highly preserved with high connectivity. These findings demonstrate that HPV16-positive head and neck and cervical cancers share highly preserved consensus gene modules with common potentially therapeutic targets.

Highly preserved consensus gene modules in human papilloma virus 16 positive cervical cancer and head and neck cancers

PubMed Central

Zhang, Xianglan; Cha, In-Ho; Kim, Ki-Yeol

2017-01-01

In this study, we investigated the consensus gene modules in head and neck cancer (HNC) and cervical cancer (CC). We used a publicly available gene expression dataset, GSE6791, which included 42 HNC, 14 normal head and neck, 20 CC and 8 normal cervical tissue samples. To exclude bias because of different human papilloma virus (HPV) types, we analyzed HPV16-positive samples only. We identified 3824 genes common to HNC and CC samples. Among these, 977 genes showed high connectivity and were used to construct consensus modules. We demonstrated eight consensus gene modules for HNC and CC using the dissimilarity measure and average linkage hierarchical clustering methods. These consensus modules included genes with significant biological functions, including ATP binding and extracellular exosome. Eigengen network analysis revealed the consensus modules were highly preserved with high connectivity. These findings demonstrate that HPV16-positive head and neck and cervical cancers share highly preserved consensus gene modules with common potentially therapeutic targets. PMID:29371966

Pressure to drink but not to smoke: disentangling selection and socialization in adolescent peer networks and peer groups.

PubMed

Kiuru, Noona; Burk, William J; Laursen, Brett; Salmela-Aro, Katariina; Nurmi, Jari-Erik

2010-12-01

This paper examined the relative influence of selection and socialization on alcohol and tobacco use in adolescent peer networks and peer groups. The sample included 1419 Finnish secondary education students (690 males and 729 females, mean age 16 years at the outset) from nine schools. Participants identified three school friends and described their alcohol and tobacco use on two occasions one year apart. Actor-based models simultaneously examined changes in peer network ties and changes in individual behaviors for all participants within each school. Multi-level analyses examined changes in individual behaviors for adolescents entering new peer groups and adolescents in stable peer groups, both of which were embedded within the school-based peer networks. Similar results emerged from both analytic methods: Selection and socialization contributed to similarity of alcohol use, but only selection was a factor in tobacco use. Copyright © 2010 The Association for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

A wirelessly programmable actuation and sensing system for structural health monitoring

NASA Astrophysics Data System (ADS)

Long, James; Büyüköztürk, Oral

2016-04-01

Wireless sensor networks promise to deliver low cost, low power and massively distributed systems for structural health monitoring. A key component of these systems, particularly when sampling rates are high, is the capability to process data within the network. Although progress has been made towards this vision, it remains a difficult task to develop and program 'smart' wireless sensing applications. In this paper we present a system which allows data acquisition and computational tasks to be specified in Python, a high level programming language, and executed within the sensor network. Key features of this system include the ability to execute custom application code without firmware updates, to run multiple users' requests concurrently and to conserve power through adjustable sleep settings. Specific examples of sensor node tasks are given to demonstrate the features of this system in the context of structural health monitoring. The system comprises of individual firmware for nodes in the wireless sensor network, and a gateway server and web application through which users can remotely submit their requests.

The influence of body weight on social network ties among adolescents.

PubMed

Ali, Mir M; Amialchuk, Aliaksandr; Rizzo, John A

2012-01-01

Evidence of negative stereotypes, prejudice and discrimination towards obese individuals has been widely documented. However, the effect of a larger body size on social network ties or friendship formations is less well understood. In this paper, we explore the extent to which higher body weight results in social marginalization of adolescents. Using data from a nationally representative sample of adolescents, we estimate endogeneity-corrected models including school-level fixed effects that account for bi-directionality and unobserved confounders to ascertain the effect of body weight on social network ties. We find that obese adolescents have fewer friends and are less socially integrated than their non-obese counterparts. We also find that such penalties in friendship networks are present among whites but not African-Americans or Hispanics, with the largest effect among white females. These results are robust to common environmental influences at the school-level and to controls for preferences, risk attitudes, low self-esteem and objective measures of physical attractiveness. Published by Elsevier B.V.

Social Support Networks and HIV/STI Risk Behaviors Among Latino Immigrants in a New Receiving Environment.

PubMed

Althoff, Meghan D; Theall, Katherine; Schmidt, Norine; Hembling, John; Gebrekristos, Hirut T; Thompson, Michelle M; Muth, Stephen Q; Friedman, Samuel R; Kissinger, Patricia

2017-12-01

The objectives of this study were to: (1) describe the quantity and quality of social support networks of Latino immigrants living in a new receiving environment, and (2) determine the role such networks play in their HIV/STI risk behaviors, including substance use. Double incentivized convenience sampling was used to collect egocentric social support network data on 144 Latino immigrants. Latent class analysis was used for data reduction and to identify items best suited to measure quality and quantity of social support. Moderate and high quantity and quality of social support were protective of HIV/STI sexual risk behavior compared to low quantity and quality of support, after adjustment for gender, years in New Orleans and residing with family. Neither measure of social support was associated with binge drinking. The findings suggest that increased quantity and quality of social support decrease HIV/STI sexual risk behaviors but do not influence binge drinking. Interventions that improve the quantity and quality of social support are needed for Latino immigrants.

Folding energy landscape and network dynamics of small globular proteins

PubMed Central

Hori, Naoto; Chikenji, George; Berry, R. Stephen; Takada, Shoji

2009-01-01

The folding energy landscape of proteins has been suggested to be funnel-like with some degree of ruggedness on the slope. How complex the landscape, however, is still rather unclear. Many experiments for globular proteins suggested relative simplicity, whereas molecular simulations of shorter peptides implied more complexity. Here, by using complete conformational sampling of 2 globular proteins, protein G and src SH3 domain and 2 related random peptides, we investigated their energy landscapes, topological properties of folding networks, and folding dynamics. The projected energy surfaces of globular proteins were funneled in the vicinity of the native but also have other quite deep, accessible minima, whereas the randomized peptides have many local basins, including some leading to seriously misfolded forms. Dynamics in the denatured part of the network exhibited basin-hopping itinerancy among many conformations, whereas the protein reached relatively well-defined final stages that led to their native states. We also found that the folding network has the hierarchic nature characterized by the scale-free and the small-world properties. PMID:19114654

Folding energy landscape and network dynamics of small globular proteins.

PubMed

Hori, Naoto; Chikenji, George; Berry, R Stephen; Takada, Shoji

2009-01-06

The folding energy landscape of proteins has been suggested to be funnel-like with some degree of ruggedness on the slope. How complex the landscape, however, is still rather unclear. Many experiments for globular proteins suggested relative simplicity, whereas molecular simulations of shorter peptides implied more complexity. Here, by using complete conformational sampling of 2 globular proteins, protein G and src SH3 domain and 2 related random peptides, we investigated their energy landscapes, topological properties of folding networks, and folding dynamics. The projected energy surfaces of globular proteins were funneled in the vicinity of the native but also have other quite deep, accessible minima, whereas the randomized peptides have many local basins, including some leading to seriously misfolded forms. Dynamics in the denatured part of the network exhibited basin-hopping itinerancy among many conformations, whereas the protein reached relatively well-defined final stages that led to their native states. We also found that the folding network has the hierarchic nature characterized by the scale-free and the small-world properties.

Analyzing the association between functional connectivity of the brain and intellectual performance

PubMed Central

Pamplona, Gustavo S. P.; Santos Neto, Gérson S.; Rosset, Sara R. E.; Rogers, Baxter P.; Salmon, Carlos E. G.

2015-01-01

Measurements of functional connectivity support the hypothesis that the brain is composed of distinct networks with anatomically separated nodes but common functionality. A few studies have suggested that intellectual performance may be associated with greater functional connectivity in the fronto-parietal network and enhanced global efficiency. In this fMRI study, we performed an exploratory analysis of the relationship between the brain's functional connectivity and intelligence scores derived from the Portuguese language version of the Wechsler Adult Intelligence Scale (WAIS-III) in a sample of 29 people, born and raised in Brazil. We examined functional connectivity between 82 regions, including graph theoretic properties of the overall network. Some previous findings were extended to the Portuguese-speaking population, specifically the presence of small-world organization of the brain and relationships of intelligence with connectivity of frontal, pre-central, parietal, occipital, fusiform and supramarginal gyrus, and caudate nucleus. Verbal comprehension was associated with global network efficiency, a new finding. PMID:25713528

A Rapid Identification Method for Calamine Using Near-Infrared Spectroscopy Based on Multi-Reference Correlation Coefficient Method and Back Propagation Artificial Neural Network.

PubMed

Sun, Yangbo; Chen, Long; Huang, Bisheng; Chen, Keli

2017-07-01

As a mineral, the traditional Chinese medicine calamine has a similar shape to many other minerals. Investigations of commercially available calamine samples have shown that there are many fake and inferior calamine goods sold on the market. The conventional identification method for calamine is complicated, therefore as a result of the large scale of calamine samples, a rapid identification method is needed. To establish a qualitative model using near-infrared (NIR) spectroscopy for rapid identification of various calamine samples, large quantities of calamine samples including crude products, counterfeits and processed products were collected and correctly identified using the physicochemical and powder X-ray diffraction method. The NIR spectroscopy method was used to analyze these samples by combining the multi-reference correlation coefficient (MRCC) method and the error back propagation artificial neural network algorithm (BP-ANN), so as to realize the qualitative identification of calamine samples. The accuracy rate of the model based on NIR and MRCC methods was 85%; in addition, the model, which took comprehensive multiple factors into consideration, can be used to identify crude calamine products, its counterfeits and processed products. Furthermore, by in-putting the correlation coefficients of multiple references as the spectral feature data of samples into BP-ANN, a BP-ANN model of qualitative identification was established, of which the accuracy rate was increased to 95%. The MRCC method can be used as a NIR-based method in the process of BP-ANN modeling.

Porosity, permeability and 3D fracture network characterisation of dolomite reservoir rock samples

PubMed Central

Voorn, Maarten; Exner, Ulrike; Barnhoorn, Auke; Baud, Patrick; Reuschlé, Thierry

2015-01-01

With fractured rocks making up an important part of hydrocarbon reservoirs worldwide, detailed analysis of fractures and fracture networks is essential. However, common analyses on drill core and plug samples taken from such reservoirs (including hand specimen analysis, thin section analysis and laboratory porosity and permeability determination) however suffer from various problems, such as having a limited resolution, providing only 2D and no internal structure information, being destructive on the samples and/or not being representative for full fracture networks. In this paper, we therefore explore the use of an additional method – non-destructive 3D X-ray micro-Computed Tomography (μCT) – to obtain more information on such fractured samples. Seven plug-sized samples were selected from narrowly fractured rocks of the Hauptdolomit formation, taken from wellbores in the Vienna basin, Austria. These samples span a range of different fault rocks in a fault zone interpretation, from damage zone to fault core. We process the 3D μCT data in this study by a Hessian-based fracture filtering routine and can successfully extract porosity, fracture aperture, fracture density and fracture orientations – in bulk as well as locally. Additionally, thin sections made from selected plug samples provide 2D information with a much higher detail than the μCT data. Finally, gas- and water permeability measurements under confining pressure provide an important link (at least in order of magnitude) towards more realistic reservoir conditions. This study shows that 3D μCT can be applied efficiently on plug-sized samples of naturally fractured rocks, and that although there are limitations, several important parameters can be extracted. μCT can therefore be a useful addition to studies on such reservoir rocks, and provide valuable input for modelling and simulations. Also permeability experiments under confining pressure provide important additional insights. Combining these and other methods can therefore be a powerful approach in microstructural analysis of reservoir rocks, especially when applying the concepts that we present (on a small set of samples) in a larger study, in an automated and standardised manner. PMID:26549935

Porosity, permeability and 3D fracture network characterisation of dolomite reservoir rock samples.

PubMed

Voorn, Maarten; Exner, Ulrike; Barnhoorn, Auke; Baud, Patrick; Reuschlé, Thierry

2015-03-01

With fractured rocks making up an important part of hydrocarbon reservoirs worldwide, detailed analysis of fractures and fracture networks is essential. However, common analyses on drill core and plug samples taken from such reservoirs (including hand specimen analysis, thin section analysis and laboratory porosity and permeability determination) however suffer from various problems, such as having a limited resolution, providing only 2D and no internal structure information, being destructive on the samples and/or not being representative for full fracture networks. In this paper, we therefore explore the use of an additional method - non-destructive 3D X-ray micro-Computed Tomography (μCT) - to obtain more information on such fractured samples. Seven plug-sized samples were selected from narrowly fractured rocks of the Hauptdolomit formation, taken from wellbores in the Vienna basin, Austria. These samples span a range of different fault rocks in a fault zone interpretation, from damage zone to fault core. We process the 3D μCT data in this study by a Hessian-based fracture filtering routine and can successfully extract porosity, fracture aperture, fracture density and fracture orientations - in bulk as well as locally. Additionally, thin sections made from selected plug samples provide 2D information with a much higher detail than the μCT data. Finally, gas- and water permeability measurements under confining pressure provide an important link (at least in order of magnitude) towards more realistic reservoir conditions. This study shows that 3D μCT can be applied efficiently on plug-sized samples of naturally fractured rocks, and that although there are limitations, several important parameters can be extracted. μCT can therefore be a useful addition to studies on such reservoir rocks, and provide valuable input for modelling and simulations. Also permeability experiments under confining pressure provide important additional insights. Combining these and other methods can therefore be a powerful approach in microstructural analysis of reservoir rocks, especially when applying the concepts that we present (on a small set of samples) in a larger study, in an automated and standardised manner.

Integrated approach for quantification of fractured tight reservoir rocks: Porosity, permeability analyses and 3D fracture network characterisation on fractured dolomite samples

NASA Astrophysics Data System (ADS)

Voorn, Maarten; Barnhoorn, Auke; Exner, Ulrike; Baud, Patrick; Reuschlé, Thierry

2015-04-01

Fractured reservoir rocks make up an important part of the hydrocarbon reservoirs worldwide. A detailed analysis of fractures and fracture networks in reservoir rock samples is thus essential to determine the potential of these fractured reservoirs. However, common analyses on drill core and plug samples taken from such reservoirs (including hand specimen analysis, thin section analysis and laboratory porosity and permeability determination) suffer from various problems, such as having a limited resolution, providing only 2D and no internal structure information, being destructive on the samples and/or not being representative for full fracture networks. In this study, we therefore explore the use of an additional method - non-destructive 3D X-ray micro-Computed Tomography (μCT) - to obtain more information on such fractured samples. Seven plug-sized samples were selected from narrowly fractured rocks of the Hauptdolomit formation, taken from wellbores in the Vienna Basin, Austria. These samples span a range of different fault rocks in a fault zone interpretation, from damage zone to fault core. 3D μCT data is used to extract porosity, fracture aperture, fracture density and fracture orientations - in bulk as well as locally. The 3D analyses are complemented with thin sections made to provide some 2D information with a much higher detail than the μCT data. Finally, gas- and water permeability measurements under confining pressure provide an important link (at least in order of magnitude) of the µCT results towards more realistic reservoir conditions. Our results show that 3D μCT can be applied efficiently on plug-sized samples of naturally fractured rocks, and that several important parameters can be extracted. μCT can therefore be a useful addition to studies on such reservoir rocks, and provide valuable input for modelling and simulations. Also permeability experiments under confining pressure provide important additional insights. Combining these and other methods can therefore be a powerful approach in microstructural analysis of reservoir rocks, especially when applying the concepts that we present (on a small set of samples) in a larger study, in an automated and standardised manner.

CONSISTENCY UNDER SAMPLING OF EXPONENTIAL RANDOM GRAPH MODELS.

PubMed

Shalizi, Cosma Rohilla; Rinaldo, Alessandro

2013-04-01

The growing availability of network data and of scientific interest in distributed systems has led to the rapid development of statistical models of network structure. Typically, however, these are models for the entire network, while the data consists only of a sampled sub-network. Parameters for the whole network, which is what is of interest, are estimated by applying the model to the sub-network. This assumes that the model is consistent under sampling , or, in terms of the theory of stochastic processes, that it defines a projective family. Focusing on the popular class of exponential random graph models (ERGMs), we show that this apparently trivial condition is in fact violated by many popular and scientifically appealing models, and that satisfying it drastically limits ERGM's expressive power. These results are actually special cases of more general results about exponential families of dependent random variables, which we also prove. Using such results, we offer easily checked conditions for the consistency of maximum likelihood estimation in ERGMs, and discuss some possible constructive responses.

CONSISTENCY UNDER SAMPLING OF EXPONENTIAL RANDOM GRAPH MODELS

PubMed Central

Shalizi, Cosma Rohilla; Rinaldo, Alessandro

2015-01-01

The growing availability of network data and of scientific interest in distributed systems has led to the rapid development of statistical models of network structure. Typically, however, these are models for the entire network, while the data consists only of a sampled sub-network. Parameters for the whole network, which is what is of interest, are estimated by applying the model to the sub-network. This assumes that the model is consistent under sampling, or, in terms of the theory of stochastic processes, that it defines a projective family. Focusing on the popular class of exponential random graph models (ERGMs), we show that this apparently trivial condition is in fact violated by many popular and scientifically appealing models, and that satisfying it drastically limits ERGM’s expressive power. These results are actually special cases of more general results about exponential families of dependent random variables, which we also prove. Using such results, we offer easily checked conditions for the consistency of maximum likelihood estimation in ERGMs, and discuss some possible constructive responses. PMID:26166910

A proposal of optimal sampling design using a modularity strategy

NASA Astrophysics Data System (ADS)

Simone, A.; Giustolisi, O.; Laucelli, D. B.

2016-08-01

In real water distribution networks (WDNs) are present thousands nodes and optimal placement of pressure and flow observations is a relevant issue for different management tasks. The planning of pressure observations in terms of spatial distribution and number is named sampling design and it was faced considering model calibration. Nowadays, the design of system monitoring is a relevant issue for water utilities e.g., in order to manage background leakages, to detect anomalies and bursts, to guarantee service quality, etc. In recent years, the optimal location of flow observations related to design of optimal district metering areas (DMAs) and leakage management purposes has been faced considering optimal network segmentation and the modularity index using a multiobjective strategy. Optimal network segmentation is the basis to identify network modules by means of optimal conceptual cuts, which are the candidate locations of closed gates or flow meters creating the DMAs. Starting from the WDN-oriented modularity index, as a metric for WDN segmentation, this paper proposes a new way to perform the sampling design, i.e., the optimal location of pressure meters, using newly developed sampling-oriented modularity index. The strategy optimizes the pressure monitoring system mainly based on network topology and weights assigned to pipes according to the specific technical tasks. A multiobjective optimization minimizes the cost of pressure meters while maximizing the sampling-oriented modularity index. The methodology is presented and discussed using the Apulian and Exnet networks.

Estimating the Size of a Large Network and its Communities from a Random Sample

PubMed Central

Chen, Lin; Karbasi, Amin; Crawford, Forrest W.

2017-01-01

Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties from smaller sub-samples. One of the most important global properties is the number of vertices/nodes in the network. Estimating the number of vertices in a large network is a major challenge in computer science, epidemiology, demography, and intelligence analysis. In this paper we consider a population random graph G = (V, E) from the stochastic block model (SBM) with K communities/blocks. A sample is obtained by randomly choosing a subset W ⊆ V and letting G(W) be the induced subgraph in G of the vertices in W. In addition to G(W), we observe the total degree of each sampled vertex and its block membership. Given this partial information, we propose an efficient PopULation Size Estimation algorithm, called PULSE, that accurately estimates the size of the whole population as well as the size of each community. To support our theoretical analysis, we perform an exhaustive set of experiments to study the effects of sample size, K, and SBM model parameters on the accuracy of the estimates. The experimental results also demonstrate that PULSE significantly outperforms a widely-used method called the network scale-up estimator in a wide variety of scenarios. PMID:28867924

Estimating the Size of a Large Network and its Communities from a Random Sample.

PubMed

Chen, Lin; Karbasi, Amin; Crawford, Forrest W

2016-01-01

Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties from smaller sub-samples. One of the most important global properties is the number of vertices/nodes in the network. Estimating the number of vertices in a large network is a major challenge in computer science, epidemiology, demography, and intelligence analysis. In this paper we consider a population random graph G = ( V, E ) from the stochastic block model (SBM) with K communities/blocks. A sample is obtained by randomly choosing a subset W ⊆ V and letting G ( W ) be the induced subgraph in G of the vertices in W . In addition to G ( W ), we observe the total degree of each sampled vertex and its block membership. Given this partial information, we propose an efficient PopULation Size Estimation algorithm, called PULSE, that accurately estimates the size of the whole population as well as the size of each community. To support our theoretical analysis, we perform an exhaustive set of experiments to study the effects of sample size, K , and SBM model parameters on the accuracy of the estimates. The experimental results also demonstrate that PULSE significantly outperforms a widely-used method called the network scale-up estimator in a wide variety of scenarios.

What Goes into High Educational and Occupational Achievement? Education, Brains, Hard Work, Networks, and Other Factors

ERIC Educational Resources Information Center

Wai, Jonathan; Rindermann, Heiner

2017-01-01

There are many factors that go into high educational and occupational achievement, including hard work, motivation, and luck. But how important is talent? Specifically, how likely were global innovators and leaders intellectually talented or gifted when younger? This paper reviews retrospective data on multiple US samples (Total N = 11,745),…

Effects of equipment performance on data quality from the National Atmospheric Deposition Program/National Trends Network and the Mercury Deposition Network

USGS Publications Warehouse

Wetherbee, Gregory A.; Rhodes, Mark F.

2013-01-01

The U.S. Geological Survey Branch of Quality Systems operates the Precipitation Chemistry Quality Assurance project (PCQA) to provide independent, external quality-assurance for the National Atmospheric Deposition Program (NADP). NADP is composed of five monitoring networks that measure the chemical composition of precipitation and ambient air. PCQA and the NADP Program Office completed five short-term studies to investigate the effects of equipment performance with respect to the National Trends Network (NTN) and Mercury Deposition Network (MDN) data quality: sample evaporation from NTN collectors; sample volume and mercury loss from MDN collectors; mercury adsorption to MDN collector glassware, grid-type precipitation sensors for precipitation collectors, and the effects of an NTN collector wind shield on sample catch efficiency. Sample-volume evaporation from an NTN Aerochem Metrics (ACM) collector ranged between 1.1–33 percent with a median of 4.7 percent. The results suggest that weekly NTN sample evaporation is small relative to sample volume. MDN sample evaporation occurs predominantly in western and southern regions of the United States (U.S.) and more frequently with modified ACM collectors than with N-CON Systems Inc. collectors due to differences in airflow through the collectors. Variations in mercury concentrations, measured to be as high as 47.5 percent per week with a median of 5 percent, are associated with MDN sample-volume loss. Small amounts of mercury are also lost from MDN samples by adsorption to collector glassware irrespective of collector type. MDN 11-grid sensors were found to open collectors sooner, keep them open longer, and cause fewer lid cycles than NTN 7-grid sensors. Wind shielding an NTN ACM collector resulted in collection of larger quantities of precipitation while also preserving sample integrity.

Determination of beryllium concentrations in UK ambient air

NASA Astrophysics Data System (ADS)

Goddard, Sharon L.; Brown, Richard J. C.; Ghatora, Baljit K.

2016-12-01

Air quality monitoring of ambient air is essential to minimise the exposure of the general population to toxic substances such as heavy metals, and thus the health risks associated with them. In the UK, ambient air is already monitored under the UK Heavy Metals Monitoring Network for a number of heavy metals, including nickel (Ni), arsenic (As), cadmium (Cd) and lead (Pb) to ensure compliance with legislative limits. However, the UK Expert Panel on Air Quality Standards (EPAQS) has highlighted a need to limit concentrations of beryllium (Be) in air, which is not currently monitored, because of its toxicity. The aim of this work was to analyse airborne particulate matter (PM) sampled onto filter papers from the UK Heavy Metals Monitoring Network for quantitative, trace level beryllium determination and compare the results to the guideline concentration specified by EPAQS. Samples were prepared by microwave acid digestion in a matrix of 2% sulphuric acid and 14% nitric acid, verified by the use of Certified Reference Materials (CRMs). The digested samples were then analysed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The filters from the UK Heavy Metals Monitoring Network were tested using this procedure and the average beryllium concentration across the network for the duration of the study period was 7.87 pg m-3. The highest site average concentration was 32.0 pg m-3 at Scunthorpe Low Santon, which is significantly lower than levels that are thought to cause harm. However the highest levels were observed at sites monitoring industrial point sources, indicating that beryllium is being used and emitted, albeit at very low levels, from these point sources. Comparison with other metals concentrations and data from the UK National Atmospheric Emissions Inventory suggests that current emissions of beryllium may be significantly overestimated.

Associations between sensory loss and social networks, participation, support, and loneliness: Analysis of the Canadian Longitudinal Study on Aging.

PubMed

Mick, Paul; Parfyonov, Maksim; Wittich, Walter; Phillips, Natalie; Kathleen Pichora-Fuller, M

2018-01-01

To determine if hearing loss, vision loss, and dual sensory loss were associated with social network diversity, social participation, availability of social support, and loneliness, respectively, in a population-based sample of older Canadians and to determine whether age or sex modified the associations. Cross-sectional population-based study. Canada. The sample included 21 241 participants in the Canadian Longitudinal Study on Aging tracking cohort. The sample was nationally representative of English- and French-speaking, non-institutionalized 45- to 89-year-old Canadians who did not live on First Nations reserves and who had normal cognition. Participants with missing data for any of the variables in the multivariable regression models were excluded from analysis. Hearing and vision loss were determined by self-report. Dual sensory loss was defined as reporting both hearing and vision loss. Univariate analyses were performed to assess cross-sectional associations between hearing, vision, and dual sensory loss, and social, demographic, and medical variables. Multivariable regression models were used to analyze cross-sectional associations between each type of sensory loss and social network diversity, social participation, availability of social support, and loneliness. Vision loss (in men) and dual sensory loss (in 65- to 85-year-olds) were independently associated with reduced social network diversity. Vision loss and dual sensory loss (in 65- to 85-year-olds) were each independently associated with reduced social participation. All forms of sensory loss were associated with both low availability of social support and loneliness. Sensory impairment is associated with reduced social function in older Canadians. Interventions and research that address the social needs of older individuals with sensory loss are needed. Copyright© the College of Family Physicians of Canada.

Respondent driven sampling is an effective method for engaging methamphetamine users in HIV prevention research in South Africa

PubMed Central

Kimani, Stephen M.; Watt, Melissa H.; Merli, M. Giovanna; Skinner, Donald; Myers, Bronwyn; Pieterse, Desiree; MacFarlane, Jessica C.; Meade, Christina S.

2014-01-01

Background South Africa, in the midst of the world’s largest HIV epidemic, has a growing methamphetamine problem. Respondent driven sampling (RDS) is a useful tool for recruiting hard-to-reach populations in HIV prevention research, but its use with methamphetamine smokers in South Africa has not been described. This study examined the effectiveness of RDS as a method for engaging methamphetamine users in a Cape Town township into HIV behavioral research. Methods Standard RDS procedures were used to recruit active methamphetamine smokers from a racially diverse peri-urban township in Cape Town. Effectiveness of RDS was determined by examining social network characteristics (network size, homophily, and equilibrium) of recruited participants. Results Beginning with 8 seeds, 345 methamphetamine users were enrolled over 6 months, with a coupon return rate of 67%. The sample included 197 men and 148 women who were racially diverse (73% Coloured, 27% Black African) and had a mean age of 28.8 years (SD=7.2). Social networks were adequate (mean network size >5) and mainly comprised of close social ties. Equilibrium on race was reached after 11 waves of recruitment, and after ≤3 waves for all other variables of interest. There was little to moderate preference for either in- or out-group recruiting in all subgroups. Conclusions Results suggest that RDS is an effective method for engaging methamphetamine users into HIV prevention research in South Africa. Additionally, RDS may be a useful strategy for seeking high-risk methamphetamine users for HIV testing and linkage to HIV care in this and other low resource settings. PMID:25128957

Social networks and social support for healthy eating among Latina breast cancer survivors: implications for social and behavioral interventions.

PubMed

Crookes, Danielle M; Shelton, Rachel C; Tehranifar, Parisa; Aycinena, Corina; Gaffney, Ann Ogden; Koch, Pam; Contento, Isobel R; Greenlee, Heather

2016-04-01

Little is known about Latina breast cancer survivors' social networks or their perceived social support to achieve and maintain a healthy diet. This paper describes the social networks and perceived support for healthy eating in a sample of breast cancer survivors of predominantly Dominican descent living in New York City. Spanish-speaking Latina breast cancer survivors enrolled in a randomized controlled trial of a culturally tailored dietary intervention. Social networks were assessed using Cohen's Social Network Index and a modified General Social Survey Social Networks Module that included assessments of shared health promoting behaviors. Perceived social support from family and friends for healthy, food-related behaviors was assessed. Participants' networks consisted predominantly of family and friends. Family members were more likely than other individuals to be identified as close network members. Participants were more likely to share food-related activities than exercise activities with close network members. Perceived social support for healthy eating was high, although perceived support from spouses and children was higher than support from friends. Despite high levels of perceived support, family was also identified as a barrier to eating healthy foods by nearly half of women. Although friends are part of Latina breast cancer survivors' social networks, spouses and children may provide greater support for healthy eating than friends. Involving family members in dietary interventions for Latina breast cancer survivors may tap into positive sources of support for women, which could facilitate uptake and maintenance of healthy eating behaviors.

Artificial neural network implementation of a near-ideal error prediction controller

NASA Technical Reports Server (NTRS)

Mcvey, Eugene S.; Taylor, Lynore Denise

1992-01-01

A theory has been developed at the University of Virginia which explains the effects of including an ideal predictor in the forward loop of a linear error-sampled system. It has been shown that the presence of this ideal predictor tends to stabilize the class of systems considered. A prediction controller is merely a system which anticipates a signal or part of a signal before it actually occurs. It is understood that an exact prediction controller is physically unrealizable. However, in systems where the input tends to be repetitive or limited, (i.e., not random) near ideal prediction is possible. In order for the controller to act as a stability compensator, the predictor must be designed in a way that allows it to learn the expected error response of the system. In this way, an unstable system will become stable by including the predicted error in the system transfer function. Previous and current prediction controller include pattern recognition developments and fast-time simulation which are applicable to the analysis of linear sampled data type systems. The use of pattern recognition techniques, along with a template matching scheme, has been proposed as one realizable type of near-ideal prediction. Since many, if not most, systems are repeatedly subjected to similar inputs, it was proposed that an adaptive mechanism be used to 'learn' the correct predicted error response. Once the system has learned the response of all the expected inputs, it is necessary only to recognize the type of input with a template matching mechanism and then to use the correct predicted error to drive the system. Suggested here is an alternate approach to the realization of a near-ideal error prediction controller, one designed using Neural Networks. Neural Networks are good at recognizing patterns such as system responses, and the back-propagation architecture makes use of a template matching scheme. In using this type of error prediction, it is assumed that the system error responses be known for a particular input and modeled plant. These responses are used in the error prediction controller. An analysis was done on the general dynamic behavior that results from including a digital error predictor in a control loop and these were compared to those including the near-ideal Neural Network error predictor. This analysis was done for a second and third order system.

The IRIS Data Management Center: An international "network of networks", providing open, automated access to geographically distributed sensors of geophysical and environmental data.

NASA Astrophysics Data System (ADS)

Benson, R. B.; Ahern, T. K.; Trabant, C.

2006-12-01

The IRIS Data Management System has long supported international collaboration for seismology by both deploying a global network of seismometers and creating and maintaining an open and accessible archive in Seattle, WA, known as the Data Management Center (DMC). With sensors distributed on a global scale spanning more than 30 years of digital data, the DMC provides a rich repository of observations across broad time and space domains. Primary seismological data types include strong motion and broadband seismometers, conventional and superconducting gravimeters, tilt and creep meters, GPS measurements, along with other similar sensors that record accurate and calibrated ground motion. What may not be as well understood is the volume of environmental data that accompanies typical seismological data these days. This poster will review the types of time-series data that are currently being collected, how they are collected, and made freely available for download at the IRIS DMC. Environmental sensor data that is often co-located with geophysical data sensors include temperature, barometric pressure, wind direction and speed, humidity, insolation, rain gauge, and sometimes hydrological data like water current, level, temperature and depth. As the primary archival institution of the International Federation of Digital Seismograph Networks (FDSN), the IRIS DMC collects approximately 13,600 channels of real-time data from 69 different networks, from close to 1600 individual stations, currently averaging 10Tb per year in total. A major contribution to the IRIS archive currently is the EarthScope project data, a ten-year science undertaking that is collecting data from a high-resolution, multi-variate sensor network. Data types include magnetotelluric, high-sample rate seismics from a borehole drilled into the San Andreas fault (SAFOD) and various types of strain data from the Plate Boundary Observatory (PBO). In addition to the DMC, data centers located in other countries are networked seamlessly, and are providing access for researchers to these data from national networks around the world utilizing the IRIS developed Data Handling Interface (DHI) system. This poster will highlight some of the DHI enabled clients that allow geophysical information to be directly transferred to the clients. This ability allows one to construct a virtual network of data centers providing the illusion of a single virtual observatory. Furthermore, some of the features that will be shown include direct connections to MATLAB and the ability to access globally distributed sensor data in real time. We encourage discussion and participation from network operators who would like to leverage existing technology, as well as enabling collaboration.

Nonparametric Bayesian inference of the microcanonical stochastic block model

NASA Astrophysics Data System (ADS)

Peixoto, Tiago P.

2017-01-01

A principled approach to characterize the hidden modular structure of networks is to formulate generative models and then infer their parameters from data. When the desired structure is composed of modules or "communities," a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints, i.e., the generated networks are not allowed to violate the patterns imposed by the model. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: (1) deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, which not only remove limitations that seriously degrade the inference on large networks but also reveal structures at multiple scales; (2) a very efficient inference algorithm that scales well not only for networks with a large number of nodes and edges but also with an unlimited number of modules. We show also how this approach can be used to sample modular hierarchies from the posterior distribution, as well as to perform model selection. We discuss and analyze the differences between sampling from the posterior and simply finding the single parameter estimate that maximizes it. Furthermore, we expose a direct equivalence between our microcanonical approach and alternative derivations based on the canonical SBM.

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6sc05720a Click here for additional data file.

PubMed Central

Smith, J. S.

2017-01-01

Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and transferable potential for organic molecules. We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) or ANI for short. ANI is a new method designed with the intent of developing transferable neural network potentials that utilize a highly-modified version of the Behler and Parrinello symmetry functions to build single-atom atomic environment vectors (AEV) as a molecular representation. AEVs provide the ability to train neural networks to data that spans both configurational and conformational space, a feat not previously accomplished on this scale. We utilized ANI to build a potential called ANI-1, which was trained on a subset of the GDB databases with up to 8 heavy atoms in order to predict total energies for organic molecules containing four atom types: H, C, N, and O. To obtain an accelerated but physically relevant sampling of molecular potential surfaces, we also proposed a Normal Mode Sampling (NMS) method for generating molecular conformations. Through a series of case studies, we show that ANI-1 is chemically accurate compared to reference DFT calculations on much larger molecular systems (up to 54 atoms) than those included in the training data set. PMID:28507695

Brain connectivity aberrations in anabolic-androgenic steroid users.

PubMed

Westlye, Lars T; Kaufmann, Tobias; Alnæs, Dag; Hullstein, Ingunn R; Bjørnebekk, Astrid

2017-01-01

Sustained anabolic-androgenic steroid (AAS) use has adverse behavioral consequences, including aggression, violence and impulsivity. Candidate mechanisms include disruptions of brain networks with high concentrations of androgen receptors and critically involved in emotional and cognitive regulation. Here, we tested the effects of AAS on resting-state functional brain connectivity in the largest sample of AAS-users to date. We collected resting-state functional magnetic resonance imaging (fMRI) data from 151 males engaged in heavy resistance strength training. 50 users tested positive for AAS based on the testosterone to epitestosterone (T/E) ratio and doping substances in urine. 16 previous users and 59 controls tested negative. We estimated brain network nodes and their time-series using ICA and dual regression and defined connectivity matrices as the between-node partial correlations. In line with the emotional and behavioral consequences of AAS, current users exhibited reduced functional connectivity between key nodes involved in emotional and cognitive regulation, in particular reduced connectivity between the amygdala and default-mode network (DMN) and between the dorsal attention network (DAN) and a frontal node encompassing the superior and inferior frontal gyri (SFG/IFG) and the anterior cingulate cortex (ACC), with further reductions as a function of dependency, lifetime exposure, and cycle state (on/off).

The national stream quality accounting network: A flux-basedapproach to monitoring the water quality of large rivers

USGS Publications Warehouse

Hooper, R.P.; Aulenbach, Brent T.; Kelly, V.J.

2001-01-01

Estimating the annual mass flux at a network of fixed stations is one approach to characterizing water quality of large rivers. The interpretive context provided by annual flux includes identifying source and sink areas for constituents and estimating the loadings to receiving waters, such as reservoirs or the ocean. Since 1995, the US Geological Survey's National Stream Quality Accounting Network (NASQAN) has employed this approach at a network of 39 stations in four of the largest river basins of the USA: The Mississippi, the Columbia, the Colorado and the Rio Grande. In this paper, the design of NASQAN is described and its effectiveness at characterizing the water quality of these rivers is evaluated using data from the first 3 years of operation. A broad range of constituents was measured by NASQAN, including trace organic and inorganic chemicals, major ions, sediment and nutrients. Where possible, a regression model relating concentration to discharge and season was used to interpolate between chemical observations for flux estimation. For water-quality network design, the most important finding from NASQAN was the importance of having a specific objective (that is, estimating annual mass flux) and, from that, an explicitly stated data analysis strategy, namely the use of regression models to interpolate between observations. The use of such models aided in the design of sampling strategy and provided a context for data review. The regression models essentially form null hypotheses for concentration variation that can be evaluated by the observed data. The feedback between network operation and data collection established by the hypothesis tests places the water-quality network on a firm scientific footing.

Integration and segregation of large-scale brain networks during short-term task automatization

PubMed Central

Mohr, Holger; Wolfensteller, Uta; Betzel, Richard F.; Mišić, Bratislav; Sporns, Olaf; Richiardi, Jonas; Ruge, Hannes

2016-01-01

The human brain is organized into large-scale functional networks that can flexibly reconfigure their connectivity patterns, supporting both rapid adaptive control and long-term learning processes. However, it has remained unclear how short-term network dynamics support the rapid transformation of instructions into fluent behaviour. Comparing fMRI data of a learning sample (N=70) with a control sample (N=67), we find that increasingly efficient task processing during short-term practice is associated with a reorganization of large-scale network interactions. Practice-related efficiency gains are facilitated by enhanced coupling between the cingulo-opercular network and the dorsal attention network. Simultaneously, short-term task automatization is accompanied by decreasing activation of the fronto-parietal network, indicating a release of high-level cognitive control, and a segregation of the default mode network from task-related networks. These findings suggest that short-term task automatization is enabled by the brain's ability to rapidly reconfigure its large-scale network organization involving complementary integration and segregation processes. PMID:27808095

[Influence of the social network on consumption in drug addicts exhibiting psychiatric comorbidity].

PubMed

Acier, D; Nadeau, L; Landry, M

2011-09-01

This research used a qualitative methodology and was conducted on a sample of 22 participants with concomitant substance-related and mental health disorders. Today, dual diagnosis patients represent the standard rather than the exception. Our objectives were to consider the elements and processes of the social network to explain variations in consumption of alcohol and drugs. The social network refers to all bonds established by patients, mainly family, couple, friends and therapist relationships. The 22 patients have used a specialized addiction treatment in Montreal (Canada). A focused qualitative interview was conducted with each participant using an audionumeric recording. The analysis follows the method of the mixed approach of Miles and Huberman, which combines the objectives of the grounded theory and the ethnography. All the interviews were transcribed then coded and analyzed with QSR N' Vivo 2.0. The method uses an iterative process making a constant return between verbatim and codes. The qualitative analyses present patients' perceptions on the increases and reductions in alcohol and drug consumption. Family network refers to participants where the family is named as supporting a decrease in drug consumption: couple network refers to intimate relations supporting a decrease in consumption. Mutual help network refers to alcoholics anonymous (AA) or other self-help groups. Several verbatim have been included. We propose strategies for the substance abuse treatment centers based on: (1) the paradox influence of the social network and the importance of clinical evaluation of patients of social networks; (2) emotions management, especially negative feelings, which include training of feeling, recognizing and naming, ability to the express and communicate to others; (3) importance of groups of mutual aid providing periods of sharing, validating individual experiences and pushing away loneliness; (4) function of social support of the clinical professionals as substitute of an overdrawn network. Copyright © 2011. Published by Elsevier Masson SAS.

Rocky Mountain snowpack physical and chemical data for selected sites, 2009

USGS Publications Warehouse

Ingersoll, George P.; Mast, M. Alisa; Swank, James M.; Campbell, Chelsea D.

2010-01-01

The Rocky Mountain Snowpack program established a network of snowpack-sampling sites in the Rocky Mountain region from New Mexico to Montana to monitor the chemical content of snow and to understand the effects of regional atmospheric deposition. The U.S. Geological Survey, in cooperation with the National Park Service; the U.S. Department of Agriculture Forest Service; the Colorado Department of Public Health and Environment; Teton County, Wyoming; and others, collected and analyzed snowpack samples annually for 48 or more sites in the Rocky Mountain region during 1993-2009. Sixty-three snowpack-sampling sites were sampled once each in 2009 and data are presented in this report. Data include acid-neutralization capacity, specific conductance, pH, hydrogen ion concentrations, dissolved concentrations of major constituents (calcium, magnesium, sodium, potassium, ammonium, chloride, sulfate, and nitrate), dissolved organic carbon concentrations, snow-water equivalent, snow depth, total mercury concentrations, and ionic charge balance. Quality-assurance data for field and laboratory blanks and field replicates for 2009 also are included.

Using Virtual Social Networks for Case Finding in Clinical Studies: An Experiment from Adolescence, Brain, Cognition, and Diabetes Study.

PubMed

Pourabbasi, Ata; Farzami, Jalal; Shirvani, Mahbubeh-Sadat Ebrahimnegad; Shams, Amir Hossein; Larijani, Bagher

2017-01-01

One of the main usages of social networks in clinical studies is facilitating the process of sampling and case finding for scientists. The main focus of this study is on comparing two different methods of sampling through phone calls and using social network, for study purposes. One of the researchers started calling 214 families of children with diabetes during 90 days. After this period, phone calls stopped, and the team started communicating with families through telegram, a virtual social network for 30 days. The number of children who participated in the study was evaluated. Although the telegram method was 60 days shorter than the phone call method, researchers found that the number of participants from telegram (17.6%) did not have any significant differences compared with the ones being phone called (12.9%). Using social networks can be suggested as a beneficial method for local researchers who look for easier sampling methods, winning their samples' trust, following up with the procedure, and an easy-access database.

Language, gesture, and handedness: Evidence for independent lateralized networks.

PubMed

Häberling, Isabelle S; Corballis, Paul M; Corballis, Michael C

2016-09-01

Language, gesture, and handedness are in most people represented in the left cerebral hemisphere. To explore the relations among these attributes, we collected fMRI images in a large sample of left- and right-handers while they performed language tasks and watched action sequences. Regions of interest included the frontal and parietal areas previously identified as comprising an action-observation network, and the frontal and temporal areas comprising the primary areas for language production and comprehension. All of the language areas and most of the action-observation areas showed an overall left-hemispheric bias, despite the participation of equal numbers of left- and right-handers. A factor analysis of the laterality indices derived from the different areas during the tasks indicated three independent networks, one associated with language, one associated with handedness, and one representing action observation independent of handedness. Areas 44 and 45, which together make up Broca's area, were part of the language and action-observation networks, but were not included in the part of the action observation network that was related to handedness, which in turn was strongly linked to areas in the parietal lobe. These results suggest an evolutionary scenario in which the primate mirror neuron system (MNS) became increasingly lateralized, and later fissioned onto subsystems with one mediating language and the other mediating the execution and observation of manual actions. The second network is further subdivided into one dependent on hand preference and one that is not, providing new insight into the tripartite system of language, handedness, and praxis. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons

PubMed Central

Buesing, Lars; Bill, Johannes; Nessler, Bernhard; Maass, Wolfgang

2011-01-01

The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons. PMID:22096452

Experimental Evidence of Volcanic Earthquakes Induced by Different Fluid Types

NASA Astrophysics Data System (ADS)

Clarke, J. A.; Adam, L.; Sarout, J.; van Wijk, K.; Dautriat, J. D.; Kennedy, B.

2017-12-01

Low Frequency volcanic seismicity has long been associated with resonance in fluid-filled cracks or conduits driven by pressure perturbations at depth. In volcano monitoring, fluid movement, fracturing and the conduit geometry are interpreted based on field observations, laboratory experiments, and numerical models. Fluids in a volcanic environment include gasses, brine and magmas with different viscosities. Magma viscosity is a key influence on eruptive behaviour. For example, increasing magma viscosity is known to favour explosive eruptions. How different fluids affect volcano seismicity is not well understood. Here, we explore the effects of fluid type on volcano seismic signals. Frequency content in the signal, frequency of the events, source mechanism and quality factor are studied. We simulate volcano tectonic (fracturing) and volcano seismic (fluid movement) signatures in a controlled laboratory environment using a range of rock samples, fluid types and pressure conditions. The viscosity of the fluids spans six orders of magnitude, representing realistic volcanic fluids. Microseismicity is generated by venting pressurised fluids through pre-generated fracture networks in cylindrical rock core samples and detected by an array of 18 ultrasonic transducers. We fracture samples of two lithologies: 1) low porosity impermeable granite samples and 2) a permeable volcanic ash tuff sample. Permeability and porosity in the granites are due to a fracture network, while in the tuff a high porosity matrix ( 40 %) and a fracture network interact. The fluids used are nitrogen gas, water, and mixtures of water and glycerol. We generate and detect a myriad of seismic event types, some of which resemble well-known families of volcano-tectonic, low-frequency, hybrid and tremor-type seismicity. Samples with fluids of lower density and viscosity generate a higher number of seismic events. We will present an integrated analysis of the event types, frequency content, source locations and mechanisms. In addition, we explore the importance of seismic wave attenuation by studying the relationship between wave path and event frequency content.

Statistical approaches used to assess and redesign surface water-quality-monitoring networks.

PubMed

Khalil, B; Ouarda, T B M J

2009-11-01

An up-to-date review of the statistical approaches utilized for the assessment and redesign of surface water quality monitoring (WQM) networks is presented. The main technical aspects of network design are covered in four sections, addressing monitoring objectives, water quality variables, sampling frequency and spatial distribution of sampling locations. This paper discusses various monitoring objectives and related procedures used for the assessment and redesign of long-term surface WQM networks. The appropriateness of each approach for the design, contraction or expansion of monitoring networks is also discussed. For each statistical approach, its advantages and disadvantages are examined from a network design perspective. Possible methods to overcome disadvantages and deficiencies in the statistical approaches that are currently in use are recommended.

Network Structure and the Risk for HIV Transmission Among Rural Drug Users

PubMed Central

Young, A. M.; Jonas, A. B.; Mullins, U. L.; Halgin, D. S.

2012-01-01

Research suggests that structural properties of drug users’ social networks can have substantial effects on HIV risk. The purpose of this study was to investigate if the structural properties of Appalachian drug users’ risk networks could lend insight into the potential for HIV transmission in this population. Data from 503 drug users recruited through respondent-driven sampling were used to construct a sociometric risk network. Network ties represented relationships in which partners had engaged in unprotected sex and/or shared injection equipment. Compared to 1,000 randomly generated networks, the observed network was found to have a larger main component and exhibit more cohesiveness and centralization than would be expected at random. Thus, the risk network structure in this sample has many structural characteristics shown to be facilitative of HIV transmission. This underscores the importance of primary prevention in this population and prompts further investigation into the epidemiology of HIV in the region. PMID:23184464

Bipartite Network Analysis of the Archaeal Virosphere: Evolutionary Connections between Viruses and Capsidless Mobile Elements

PubMed Central

Prangishvili, David

2016-01-01

ABSTRACT Archaea and particularly hyperthermophilic crenarchaea are hosts to many unusual viruses with diverse virion shapes and distinct gene compositions. As is typical of viruses in general, there are no universal genes in the archaeal virosphere. Therefore, to obtain a comprehensive picture of the evolutionary relationships between viruses, network analysis methods are more productive than traditional phylogenetic approaches. Here we present a comprehensive comparative analysis of genomes and proteomes from all currently known taxonomically classified and unclassified, cultivated and uncultivated archaeal viruses. We constructed a bipartite network of archaeal viruses that includes two classes of nodes, the genomes and gene families that connect them. Dissection of this network using formal community detection methods reveals strong modularity, with 10 distinct modules and 3 putative supermodules. However, compared to similar previously analyzed networks of eukaryotic and bacterial viruses, the archaeal virus network is sparsely connected. With the exception of the tailed viruses related to bacteriophages of the order Caudovirales and the families Turriviridae and Sphaerolipoviridae that are linked to a distinct supermodule of eukaryotic and bacterial viruses, there are few connector genes shared by different archaeal virus modules. In contrast, most of these modules include, in addition to viruses, capsidless mobile elements, emphasizing tight evolutionary connections between the two types of entities in archaea. The relative contributions of distinct evolutionary origins, in particular from nonviral elements, and insufficient sampling to the sparsity of the archaeal virus network remain to be determined by further exploration of the archaeal virosphere. IMPORTANCE Viruses infecting archaea are among the most mysterious denizens of the virosphere. Many of these viruses display no genetic or even morphological relationship to viruses of bacteria and eukaryotes, raising questions regarding their origins and position in the global virosphere. Analysis of 5,740 protein sequences from 116 genomes allowed dissection of the archaeal virus network and showed that most groups of archaeal viruses are evolutionarily connected to capsidless mobile genetic elements, including various plasmids and transposons. This finding could reflect actual independent origins of the distinct groups of archaeal viruses from different nonviral elements, providing important insights into the emergence and evolution of the archaeal virome. PMID:27681128

In Search of Neural Endophenotypes of Postpartum Psychopathology and Disrupted Maternal Caregiving

PubMed Central

Moses-Kolko, E. L.; Horner, M. S.; Phillips, M. L.; Hipwell, A. E.; Swain, J. E.

2015-01-01

This is a selective review that provides the context for the study of perinatal affective disorder mechanisms and outlines directions for future research. We integrate existing literature along neural networks of interest for affective disorders and maternal caregiving: (i) the salience/fear network; (ii) the executive network; (iii) the reward/social attachment network; and (iv) the default mode network. Extant salience/fear network research reveals disparate responses and corticolimbic coupling to various stimuli based upon a predominantly depressive versus anxious (post-traumatic stress disorder) clinical phenotype. Executive network and default mode connectivity abnormalities have been described in postpartum depression (PPD), although studies are very limited in these domains. Reward/social attachment studies confirm a robust ventral striatal response to infant stimuli, including cry and happy infant faces, which is diminished in depressed, insecurely attached and substance-using mothers. The adverse parenting experiences received and the attachment insecurity of current mothers are factors that are associated with a diminution in infant stimulus-related neural activity similar to that in PPD, and raise the need for additional studies that integrate mood and attachment concepts in larger study samples. Several studies examining functional connectivity in resting state and emotional activation functional magnetic resonance imaging paradigms have revealed attenuated corticolimbic connectivity, which remains an important outcome that requires dissection with increasing precision to better define neural treatment targets. Methodological progress is expected in the coming years in terms of refining clinical phenotypes of interest and experimental paradigms, as well as enlarging samples to facilitate the examination of multiple constructs. Functional imaging promises to determine neural mechanisms underlying maternal psychopathology and impaired caregiving, such that earlier and more precise detection of abnormalities will be possible. Ultimately, the discovery of such mechanisms will promote the refinement of treatment approaches toward maternal affective disturbance, parenting behaviours and the augmentation of parenting resiliency. PMID:25059408

Mobile Networked Sensors for Environmental Observatories

NASA Astrophysics Data System (ADS)

Kaiser, W. J.

2005-12-01

The development of the first embedded networked sensing (ENS) systems has been rapidly followed by their successful deployment for investigations in environments ranging from forest ecosystems, to rivers and lakes, and to subsurface soil observations. As ENS systems have been deployed, many technology challenges have been successfully addressed. For example, the requirements for local and remote data access and long operating life have been encountered and solved with a novel hierarchical network architecture and unique, low power platforms. This presentation will describe this progress and also the development and applications of a new ENS system addressing the most current challenges: A robotic ENS platform providing precise, reliable, and sustained observation capability with diverse sensing capabilities that may adapt to environmental dynamics. In the development of methods for autonomous observation by networked sensors, many applications have emerged requiring spatially and temporally intensive data sampling. Examples include the mapping of forest understory solar radiation, autonomous acquisition of imaging for plant phenology, and mapping of contaminant concentration in aquatic systems. Common to these applications is the need to actively and continuously configure the location and orientation of sensors for high fidelity mapping of the spatial distribution of phenomena. To address this primary environmental observation need, a new sensing platform, Networked Infomechanical Systems (NIMS) has been developed. NIMS relies on deployed aerial infrastructure (for example, cable suspension systems) in the natural environment to permit robotic devices to precisely and reliably move or remain stationary as required at elevations that may lie directly in or above the forest canopy or within a river or stream. NIMS systems are suspended to allow devices to translate a sensor node horizontally, and also to raise and lower devices. Examples of sensors that are now carried by NIMS include sensors for visible wavelength imaging, thermal infrared temperature mapping, microclimate, solar radiation, and for water quality and physical characterization of aquatic systems. NIMS devices include compact embedded computing, wireless network connectivity to surrounding static sensors, and remote Internet access. Exploiting this onboard computing allows NIMS devices to follow precise scanning protocols and self-calibration procedures. This presentation will describe permanent facility NIMS systems deployed at the James San Jacinto Mountains Reserve. Rapidly deployable NIMS permitting short term, highly mobile experiments will also be discussed. This includes the Thermal Mapper system that simultaneously samples plant physical structure (using laser position sensing and imaging) along with plant surface temperature (using high spatial resolution thermal infrared sensing). This compact system has been applied to the investigation of thermal characteristics of alpine plants in varying soil surfaces at the White Mountains Research Station. Other NIMS applications and results to be described include novel spatial mapping of nitrate concentration and other variables in flowing streams. Finally, this presentation will also address the many future applications of observatories linking investigators with remote mobile and static sensor networks. This research is supported by the NSF0331481 ITR program. Research has been performed in collaboration with R. Ambrose, K. Bible, D. Estrin, E. Graham, M. Hamilton, M. Hanson, T. Harmon, G. Pottie, P. Rundel, M. Srivastava, and G. Sukhatme

Jello shot consumption among older adolescents: a pilot study of a newly identified public health problem.

PubMed

Binakonsky, Jane; Giga, Noreen; Ross, Craig; Siegel, Michael

2011-01-01

We investigated the extent of jello shot consumption among underage youths. We conducted a pilot study among a nonrandom national sample of 108 drinkers, aged 16-20 years, recruited from the Knowledge Networks Internet panel in 2010 by using consecutive sampling. The prevalence of past 30-day jello shot consumption among the 108 drinkers, aged 16-20 years, in our sample was 21.4%, and among those who consumed jello shots, the percentage of alcohol consumption attributable to jello shots averaged 14.5%. We concluded that jello shot use is prevalent among youths, representing a substantial proportion of their alcohol intake. Surveillance of youth alcohol use should include jello shot consumption.

The Sensitivity of Genetic Connectivity Measures to Unsampled and Under-Sampled Sites

PubMed Central

Koen, Erin L.; Bowman, Jeff; Garroway, Colin J.; Wilson, Paul J.

2013-01-01

Landscape genetic analyses assess the influence of landscape structure on genetic differentiation. It is rarely possible to collect genetic samples from all individuals on the landscape and thus it is important to assess the sensitivity of landscape genetic analyses to the effects of unsampled and under-sampled sites. Network-based measures of genetic distance, such as conditional genetic distance (cGD), might be particularly sensitive to sampling intensity because pairwise estimates are relative to the entire network. We addressed this question by subsampling microsatellite data from two empirical datasets. We found that pairwise estimates of cGD were sensitive to both unsampled and under-sampled sites, and FST, Dest, and deucl were more sensitive to under-sampled than unsampled sites. We found that the rank order of cGD was also sensitive to unsampled and under-sampled sites, but not enough to affect the outcome of Mantel tests for isolation by distance. We simulated isolation by resistance and found that although cGD estimates were sensitive to unsampled sites, by increasing the number of sites sampled the accuracy of conclusions drawn from landscape genetic analyses increased, a feature that is not possible with pairwise estimates of genetic differentiation such as FST, Dest, and deucl. We suggest that users of cGD assess the sensitivity of this measure by subsampling within their own network and use caution when making extrapolations beyond their sampled network. PMID:23409155

Tracking Effects of Problematic Social Networking on Adolescent Psychopathology: The Mediating Role of Sleep Disruptions.

PubMed

Vernon, Lynette; Modecki, Kathryn L; Barber, Bonnie L

2017-01-01

Concerns are growing about adolescents' problematic social networking and possible links to depressed mood and externalizing behavior. Yet there remains little understanding of underlying processes that may account for these associations, including the mediating role of sleep disruption. This study tests this putative mediating process and examines change in problematic social networking investment and disrupted sleep, in relation to change in depressed mood and externalizing behavior. A sample of 874 students (41% male; 57.2% Caucasian; baseline M age = 14.4 years) from 27 high schools were surveyed. Participants' problematic social networking, sleep disruption, and psychopathology (depressed mood, externalizing behaviors) were measured annually over 3 years. Longitudinal mediation was tested using latent trajectories of problematic social networking use, sleep disruption, and psychopathology. Both problematic social networking and sleep disruption underwent positive linear growth over time. Adolescents who increasingly invested in social networking reported increased depressed mood, with around 53% of this association explained by the indirect effect of increased sleep disruptions. Further, adolescents who increasingly invested in social networking also reported increased externalizing behavior; some of this relation was explained (13%) via increased sleep disruptions. However an alternative model in which increased externalizing was associated with increased social networking, mediated by sleep disruptions, indicated a reciprocal relation of similar magnitude. It is important for parents, teachers, and psychologists to minimize the negative effects of social networking on adolescents' psychopathology. Interventions should potentially target promoting healthy sleep habits through reductions in social networking investment and rescheduling usage away from bedtime.

A human functional protein interaction network and its application to cancer data analysis

PubMed Central

2010-01-01

Background One challenge facing biologists is to tease out useful information from massive data sets for further analysis. A pathway-based analysis may shed light by projecting candidate genes onto protein functional relationship networks. We are building such a pathway-based analysis system. Results We have constructed a protein functional interaction network by extending curated pathways with non-curated sources of information, including protein-protein interactions, gene coexpression, protein domain interaction, Gene Ontology (GO) annotations and text-mined protein interactions, which cover close to 50% of the human proteome. By applying this network to two glioblastoma multiforme (GBM) data sets and projecting cancer candidate genes onto the network, we found that the majority of GBM candidate genes form a cluster and are closer than expected by chance, and the majority of GBM samples have sequence-altered genes in two network modules, one mainly comprising genes whose products are localized in the cytoplasm and plasma membrane, and another comprising gene products in the nucleus. Both modules are highly enriched in known oncogenes, tumor suppressors and genes involved in signal transduction. Similar network patterns were also found in breast, colorectal and pancreatic cancers. Conclusions We have built a highly reliable functional interaction network upon expert-curated pathways and applied this network to the analysis of two genome-wide GBM and several other cancer data sets. The network patterns revealed from our results suggest common mechanisms in the cancer biology. Our system should provide a foundation for a network or pathway-based analysis platform for cancer and other diseases. PMID:20482850

Study on pattern recognition of Raman spectrum based on fuzzy neural network

NASA Astrophysics Data System (ADS)

Zheng, Xiangxiang; Lv, Xiaoyi; Mo, Jiaqing

2017-10-01

Hydatid disease is a serious parasitic disease in many regions worldwide, especially in Xinjiang, China. Raman spectrum of the serum of patients with echinococcosis was selected as the research object in this paper. The Raman spectrum of blood samples from healthy people and patients with echinococcosis are measured, of which the spectrum characteristics are analyzed. The fuzzy neural network not only has the ability of fuzzy logic to deal with uncertain information, but also has the ability to store knowledge of neural network, so it is combined with the Raman spectrum on the disease diagnosis problem based on Raman spectrum. Firstly, principal component analysis (PCA) is used to extract the principal components of the Raman spectrum, reducing the network input and accelerating the prediction speed and accuracy of Network based on remaining the original data. Then, the information of the extracted principal component is used as the input of the neural network, the hidden layer of the network is the generation of rules and the inference process, and the output layer of the network is fuzzy classification output. Finally, a part of samples are randomly selected for the use of training network, then the trained network is used for predicting the rest of the samples, and the predicted results are compared with general BP neural network to illustrate the feasibility and advantages of fuzzy neural network. Success in this endeavor would be helpful for the research work of spectroscopic diagnosis of disease and it can be applied in practice in many other spectral analysis technique fields.

[Donors' personal profile in Tuscany's network of milk banks].

PubMed

Strambi, M; Anselmi, A; Coppi, S

2012-10-01

An investigation on human milk donors among the milk banks of Tuscany's network was carried out. Milk banks select, collect, check, process, store and deliver human milk, whose donors should have certain physical and psychological well-being features. The aim of the study was to describe a personal and social profile of milk donors. The study included a sample of 100 milk donors and a sample of 100 non-milk donor mothers; a questionnaire that collected data about mothers' general information, clinical history, pregnancy and delivery, weight variations, state of health, lifestyle, breastfeeding and knowledge about milk banks was administered to all of them. Then information about food history of mothers has also been collected. First the samples of donors were analysed for all variables considered. Subsequently the samples of donors were compared with the samples of non-donors: statistical analysis was carried out with χ2 test and documented significant differences between donors and non-donors for the majority of variables considered in the questionnaire and for food history. Milk donors have a good state of health, and the integration in milk donation initiative headed towards a healthier lifestyle. It is necessary to promote an advertising campaign to integrate social and sanitary politics, fitting to local socio-economical contest. Furthermore, the improvement of milk banks of public hospitals is necessary, as hospitals are places of major stream both of potential donors and newborns.

Tracing the Potential Flow of Consumer Data: A Network Analysis of Prominent Health and Fitness Apps

PubMed Central

Held, Fabian P; Bero, Lisa A

2017-01-01

Background A great deal of consumer data, collected actively through consumer reporting or passively through sensors, is shared among apps. Developers increasingly allow their programs to communicate with other apps, sensors, and Web-based services, which are promoted as features to potential users. However, health apps also routinely pose risks related to information leaks, information manipulation, and loss of information. There has been less investigation into the kinds of user data that developers are likely to collect, and who might have access to it. Objective We sought to describe how consumer data generated from mobile health apps might be distributed and reused. We also aimed to outline risks to individual privacy and security presented by this potential for aggregating and combining user data across apps. Methods We purposively sampled prominent health and fitness apps available in the United States, Canada, and Australia Google Play and iTunes app stores in November 2015. Two independent coders extracted data from app promotional materials on app and developer characteristics, and the developer-reported collection and sharing of user data. We conducted a descriptive analysis of app, developer, and user data collection characteristics. Using structural equivalence analysis, we conducted a network analysis of sampled apps’ self-reported sharing of user-generated data. Results We included 297 unique apps published by 231 individual developers, which requested 58 different permissions (mean 7.95, SD 6.57). We grouped apps into 222 app families on the basis of shared ownership. Analysis of self-reported data sharing revealed a network of 359 app family nodes, with one connected central component of 210 app families (58.5%). Most (143/222, 64.4%) of the sampled app families did not report sharing any data and were therefore isolated from each other and from the core network. Fifteen app families assumed more central network positions as gatekeepers on the shortest paths that data would have to travel between other app families. Conclusions This cross-sectional analysis highlights the possibilities for user data collection and potential paths that data is able to travel among a sample of prominent health and fitness apps. While individual apps may not collect personally identifiable information, app families and the partners with which they share data may be able to aggregate consumer data, thus achieving a much more comprehensive picture of the individual consumer. The organizations behind the centrally connected app families represent diverse industries, including apparel manufacturers and social media platforms that are not traditionally involved in health or fitness. This analysis highlights the potential for anticipated and voluntary but also possibly unanticipated and involuntary sharing of user data, validating privacy and security concerns in mobile health. PMID:28659254

Current Research at the Endeavour Ridge 2000 Integrated Studies Site

NASA Astrophysics Data System (ADS)

Butterfield, D. A.; Kelley, D. S.; Ridge 2000 Community, R.

2004-12-01

Integrated geophysical, geological, chemical, and biological studies are being conducted on the Endeavour segment with primary support from NSF, the W.M. Keck Foundation, and NSERC (Canada). The research includes a seismic network, physical and chemical sensors, high-precision mapping and time-series sampling. Several research expeditions have taken place at the Endeavour ISS in the past year. In June 2003, an NSF-sponsored cruise with R.V. al T.G.Thompson/ROV al Jason2 installed microbial incubators in drill-holes in the sides of active sulfide chimneys and sampled rocks, fluids, and microbes in the Mothra and Main Endeavour Field (MEF). In July 2003, with al Thompson/Jason2, an NSF-LEXEN project at Baby Bare on Endeavour east flank conducted sampling through seafloor-penetrating probes, plus time-series sampling of fluids, microbes, and rocks at the MEF. In September 2003, with al Thompson/ROV al ROPOS, the Keck Proto-Neptune project installed a seismic network consisting of 1 broadband and 7 short-period seismometers, installation of chemical/physical sensors and time-series samplers for chemistry and microbiology in the MEF and Clam Bed sites, collection of rocks, fluids, animals, and microbes. In May/June 2004, an NSF-sponsored al Atlantis/Alvin cruise recovered sulfide incubators installed in 2003, redeployed a sulfide incubator, mapped MEF and Mothra vent fields with high-resolution Imagenix sonar, sampled fluids from MEF, Mothra, and Clam Bed, recovered year-long time-series fluid and microbial samplers from MEF and Clam Bed, recovered and installed hot vent temperature-resistivity monitors, cleaned up the MEF and deployed new markers at major sulfide structures. In August 2004, there were two MBARI/Keck-sponsored cruises with R.V. al Western Flyer/ROV al Tiburon. The first cruise completed the seismic network with addition of two more broadband seismometers and serviced all 7 short-period seismometers. al Tiburon then performed microbial and chemical investigations at MEF, Mothra, Sasquatch, and Middle Valley, collecting fluid, particle, and animal samples for culture and phylogenetic analysis. al Tiburon continued in late August/September with detailed petrological sampling. A Keck-sponsored al Thompson/ROPOS cruise in September continued work on chemical/physical sensor deployments and time-series chemical and microbial sampling. A graduate student workshop at Friday Harbor beginning October 2004 will analyze the first year of data from the seismic network and begin to correlate seismic activity with hydrothermal activity. The Endeavour ISS is still in a phase of data collection and sensor development, but moving toward data integration.

Assessment of water chemistry, habitat, and benthic macroinvertebrates at selected stream-quality monitoring sites in Chester County, Pennsylvania, 1998-2000

USGS Publications Warehouse

Reif, Andrew G.

2004-01-01

Biological, chemical, and habitat data have been collected from a network of sites in Chester County, Pa., from 1970 to 2003 to assess stream quality. Forty sites in 6 major stream basins were sampled between 1998 and 2000. Biological data were used to determine levels of impairment in the benthic-macroinvertebrate community in Chester County streams and relate the impairment, in conjunction with chemical and habitat data, to overall stream quality. Biological data consisted of benthic-macroinvertebrate samples that were collected annually in the fall. Water-chemistry samples were collected and instream habitat was assessed in support of the biological sampling.Most sites in the network were designated as nonimpacted or slightly impacted by human activities or extreme climatic conditions on the basis of biological-metric analysis of benthic-macroinvertebrate data. Impacted sites were affected by factors, such as nutrient enrichment, erosion and sedimentation, point discharges, and droughts and floods. Streams in the Schuylkill River, Delaware River, and East Branch Brandywine Creek Basins in Chester County generally had low nutrient concentrations, except in areas affected by wastewater-treatment discharges, and stream habitat that was affected by erosion. Streams in the West Branch Brandywine, Christina, Big Elk, and Octoraro Creek Basins in Chester County generally had elevated nutrient concentrations and streambottom habitat that was affected by sediment deposition.Macroinvertebrate communities identified in samples from French Creek, Pigeon Creek (Schuylkill River Basin), and East Branch Brandywine Creek at Glenmoore consistently indicate good stream conditions and were the best conditions measured in the network. Macroinvertebrate communities identified in samples from Trout Creek (site 61), West Branch Red Clay Creek (site 55) (Christina River Basin), and Valley Creek near Atglen (site 34) (Octoraro Creek Basin) indicated fair to poor stream conditions and were the worst conditions measured in the network. Trout Creek is heavily impacted due to erosion, and Valley Creek near Atglen and West Branch Red Clay Creek are influenced by wastewater discharges. Hydrologic conditions in 1999, including a prolonged drought and a flood, influenced chemical concentrations and macroinvertebrate community structure throughout the county. Concentrations of nutrients and ions were lower in 1999 when compared to 1998 and 2000 concentrations. Macroinvertebrate communities identified in samples from 1999 contained lower numbers of individuals when compared to 1998 and 2000 but had similar community structure. Results from chemical and biological sampling in 2000 indicated that the benthic-macroinvertebrate community structure and the concentrations of nutrients and ions recovered to pre-1999 levels.

Scalable population estimates using spatial-stream-network (SSN) models, fish density surveys, and national geospatial database frameworks for streams

Treesearch

Daniel J. Isaak; Jay M. Ver Hoef; Erin E. Peterson; Dona L. Horan; David E. Nagel

2017-01-01

Population size estimates for stream fishes are important for conservation and management, but sampling costs limit the extent of most estimates to small portions of river networks that encompass 100s–10 000s of linear kilometres. However, the advent of large fish density data sets, spatial-stream-network (SSN) models that benefit from nonindependence among samples,...

Optimization of monitoring networks based on uncertainty quantification of model predictions of contaminant transport

NASA Astrophysics Data System (ADS)

Vesselinov, V. V.; Harp, D.

2010-12-01

The process of decision making to protect groundwater resources requires a detailed estimation of uncertainties in model predictions. Various uncertainties associated with modeling a natural system, such as: (1) measurement and computational errors; (2) uncertainties in the conceptual model and model-parameter estimates; (3) simplifications in model setup and numerical representation of governing processes, contribute to the uncertainties in the model predictions. Due to this combination of factors, the sources of predictive uncertainties are generally difficult to quantify individually. Decision support related to optimal design of monitoring networks requires (1) detailed analyses of existing uncertainties related to model predictions of groundwater flow and contaminant transport, (2) optimization of the proposed monitoring network locations in terms of their efficiency to detect contaminants and provide early warning. We apply existing and newly-proposed methods to quantify predictive uncertainties and to optimize well locations. An important aspect of the analysis is the application of newly-developed optimization technique based on coupling of Particle Swarm and Levenberg-Marquardt optimization methods which proved to be robust and computationally efficient. These techniques and algorithms are bundled in a software package called MADS. MADS (Model Analyses for Decision Support) is an object-oriented code that is capable of performing various types of model analyses and supporting model-based decision making. The code can be executed under different computational modes, which include (1) sensitivity analyses (global and local), (2) Monte Carlo analysis, (3) model calibration, (4) parameter estimation, (5) uncertainty quantification, and (6) model selection. The code can be externally coupled with any existing model simulator through integrated modules that read/write input and output files using a set of template and instruction files (consistent with the PEST I/O protocol). MADS can also be internally coupled with a series of built-in analytical simulators. MADS provides functionality to work directly with existing control files developed for the code PEST (Doherty 2009). To perform the computational modes mentioned above, the code utilizes (1) advanced Latin-Hypercube sampling techniques (including Improved Distributed Sampling), (2) various gradient-based Levenberg-Marquardt optimization methods, (3) advanced global optimization methods (including Particle Swarm Optimization), and (4) a selection of alternative objective functions. The code has been successfully applied to perform various model analyses related to environmental management of real contamination sites. Examples include source identification problems, quantification of uncertainty, model calibration, and optimization of monitoring networks. The methodology and software codes are demonstrated using synthetic and real case studies where monitoring networks are optimized taking into account the uncertainty in model predictions of contaminant transport.

Using complex networks to characterize international business cycles.

PubMed

Caraiani, Petre

2013-01-01

There is a rapidly expanding literature on the application of complex networks in economics that focused mostly on stock markets. In this paper, we discuss an application of complex networks to study international business cycles. We construct complex networks based on GDP data from two data sets on G7 and OECD economies. Besides the well-known correlation-based networks, we also use a specific tool for presenting causality in economics, the Granger causality. We consider different filtering methods to derive the stationary component of the GDP series for each of the countries in the samples. The networks were found to be sensitive to the detrending method. While the correlation networks provide information on comovement between the national economies, the Granger causality networks can better predict fluctuations in countries' GDP. By using them, we can obtain directed networks allows us to determine the relative influence of different countries on the global economy network. The US appears as the key player for both the G7 and OECD samples. The use of complex networks is valuable for understanding the business cycle comovements at an international level.

The relation between social network site usage and loneliness and mental health in community-dwelling older adults.

PubMed

Aarts, S; Peek, S T M; Wouters, E J M

2015-09-01

Loneliness is expected to become an even bigger social problem in the upcoming decades, because of the growing number of older adults. It has been argued that the use of social network sites can aid in decreasing loneliness and improving mental health. The purpose of this study was to examine whether and how social network sites usage is related to loneliness and mental health in community-dwelling older adults. The study population included community-dwelling older adults aged 60 and over residing in the Netherlands (n = 626) collected through the LISS panel (www.lissdata.nl). Univariate and multivariate linear regression analyses, adjusted for potentially important confounders, were conducted in order to investigate the relation between social network sites usage and (emotional and social) loneliness and mental health. More than half of the individuals (56.2%) reported to use social network sites at least several times per week. Social network sites usage appeared unrelated to loneliness in general, and to emotional and social loneliness in particular. Social network sites usage also appeared unrelated to mental health. Several significant associations between related factors and the outcomes at hand were detected. In this sample, which was representative for the Dutch population, social network sites usage was unrelated to loneliness and/or mental health. The results indicate that a simple association between social network site usage and loneliness and mental health as such, cannot automatically be assumed in community-dwelling older adults. Copyright © 2014 John Wiley & Sons, Ltd.

Selective Narrowing of Social Networks Across Adulthood is Associated With Improved Emotional Experience in Daily Life

PubMed Central

English, Tammy; Carstensen, Laura L.

2014-01-01

Past research has documented age differences in the size and composition of social networks that suggest that networks grow smaller with age and include an increasingly greater proportion of well-known social partners. According to socioemotional selectivity theory, such changes in social network composition serve an antecedent emotion regulatory function that supports an age-related increase in the priority that people place on emotional well-being. The present study employed a longitudinal design with a sample that spanned the full adult age range to examine whether there is evidence of within-individual (developmental) change in social networks and whether the characteristics of relationships predict emotional experiences in daily life. Using growth curve analyses, social networks were found to increase in size in young adulthood and then decline steadily throughout later life. As postulated by socioemotional selectivity theory, reductions were observed primarily in the number of peripheral partners; the number of close partners was relatively stable over time. In addition, cross-sectional analyses revealed that older adults reported that social network members elicited less negative emotion and more positive emotion. The emotional tone of social networks, particularly when negative emotions were associated with network members, also predicted experienced emotion of participants. Overall, findings were robust after taking into account demographic variables and physical health. The implications of these findings are discussed in the context of socioemotional selectivity theory and related theoretical models. PMID:24910483

Detection and assessment of flood susceptible irrigation networks in Licab, Nueva Ecija, Philippines using LiDAR DTM

NASA Astrophysics Data System (ADS)

Alberto, R. T.; Hernando, P. J. C.; Tagaca, R. C.; Celestino, A. B.; Palado, G. C.; Camaso, E. E.; Damian, G. B.

2017-09-01

Climate change has wide-ranging effects on the environment and socio-economic and related sectors which includes water resources, agriculture and food security, human health, terrestrial ecosystems, coastal zones and biodiversity. Farmers are under pressure to the changing weather and increasing unpredictable water supply. Because of rainfall deficiencies, artificial application of water has been made through irrigation. Irrigation is a basic determinant of agriculture because its inadequacies are the most powerful constraints on the increase of agricultural production. Irrigation networks are permanent and temporary conduits that supply water to agricultural areas from an irrigation source. Detection of irrigation networks using LiDAR DTM, and flood susceptible assessment of irrigation networks could give baseline information on the development and management of sustainable agriculture. Map Gully Depth (MGD) in Whitebox GAT was used to generate the potential irrigation networks. The extracted MGD was overlaid in ArcGIS as guide in the digitization of potential irrigation networks. A flood hazard map was also used to identify the flood susceptible irrigation networks in the study area. The study was assessed through field validation of points which were generated using random sampling method. Results of the study showed that most of the detected irrigation networks have low to moderate susceptibility to flooding while the rest have high susceptibility to flooding which is due to shifting weather. These irrigation networks may cause flood when it overflows that could also bring huge damage to rice and other agricultural areas.

Changing Social Networks Among Homeless Individuals: A Prospective Evaluation of a Job- and Life-Skills Training Program.

PubMed

Gray, Heather M; Shaffer, Paige M; Nelson, Sarah E; Shaffer, Howard J

2016-10-01

Social networks play important roles in mental and physical health among the general population. Building healthier social networks might contribute to the development of self-sufficiency among people struggling to overcome homelessness and substance use disorders. In this study of homeless adults completing a job- and life-skills program (i.e., the Moving Ahead Program at St. Francis House, Boston), we prospectively examined changes in social network quality, size, and composition. Among the sample of participants (n = 150), we observed positive changes in social network quality over time. However, social network size and composition did not change among the full sample. The subset of participants who reported abstaining from alcohol during the months before starting the program reported healthy changes in their social networks; specifically, while completing the program, they re-structured their social networks such that fewer members of their network used alcohol to intoxication. We discuss practical implications of these findings.

Long-term Monitoring Program Optimization for Chlorinated Volatile Organic Compound Plume, Naval Air Station Brunswick, Maine

NASA Astrophysics Data System (ADS)

Calderone, G. M.

2006-12-01

A long-term monitoring program was initiated in 1995 at 6 sites at NAS Brunswick, including 3 National Priorities List (Superfund) sites. Primary contaminants of concern include chlorinated volatile organic compounds, including tetrachloroethane, trichloroethene, and vinyl chloride, in addition to metals. More than 80 submersible pumping systems were installed to facilitate sample collection utilizing the low-flow sampling technique. Long-term monitoring of the groundwater is conducted to assess the effectiveness of remedial measures, and monitor changes in contaminant concentrations in the Eastern Plume Operable Unit. Long-term monitoring program activities include quarterly groundwater sampling and analysis at more than 90 wells across 6 sites; surface water, sediment, seep, and leachate sampling and analysis at 3 sites; landfill gas monitoring; well maintenance; engineering inspections of landfill covers and other sites or evidence of stressed vegetation; water level gauging; and treatment plant sampling and analysis. Significant cost savings were achieved by optimizing the sampling network and reducing sampling frequency from quarterly to semi- annual or annual sampling. As part of an ongoing optimization effort, a geostatistical assessment of the Eastern Plume was conducted at the Naval Air Station, Brunswick, Maine. The geostatistical assessment used 40 monitoring points and analytical data collected over 3 years. For this geostatistical assessment, EA developed and utilized a database of analytical results generated during 3 years of long-term monitoring which was linked to a Geographic Information System to enhance data visualization capacity. The Geographic Information System included themes for groundwater volatile organic compound concentration, groundwater flow directions, shallow and deep wells, and immediate access to point-specific analytical results. This statistical analysis has been used by the site decision-maker and its conclusions supported a significant reduction in the Long-Term Monitoring Program.

Sampling design optimization for spatial functions

USGS Publications Warehouse

Olea, R.A.

1984-01-01

A new procedure is presented for minimizing the sampling requirements necessary to estimate a mappable spatial function at a specified level of accuracy. The technique is based on universal kriging, an estimation method within the theory of regionalized variables. Neither actual implementation of the sampling nor universal kriging estimations are necessary to make an optimal design. The average standard error and maximum standard error of estimation over the sampling domain are used as global indices of sampling efficiency. The procedure optimally selects those parameters controlling the magnitude of the indices, including the density and spatial pattern of the sample elements and the number of nearest sample elements used in the estimation. As an illustration, the network of observation wells used to monitor the water table in the Equus Beds of Kansas is analyzed and an improved sampling pattern suggested. This example demonstrates the practical utility of the procedure, which can be applied equally well to other spatial sampling problems, as the procedure is not limited by the nature of the spatial function. ?? 1984 Plenum Publishing Corporation.

An Analysis of Respondent Driven Sampling with Injection Drug Users (IDU) in Albania and the Russian Federation

PubMed Central

Stormer, Ame; Tun, Waimar; Harxhi, Arjan; Bodanovskaia, Zinaida; Yakovleva, Anna; Rusakova, Maia; Levina, Olga; Bani, Roland; Rjepaj, Klodian; Bino, Silva

2006-01-01

Injection drug users in Tirana, Albania and St. Petersburg, Russia were recruited into a study assessing HIV-related behaviors and HIV serostatus using Respondent Driven Sampling (RDS), a peer-driven recruitment sampling strategy that results in a probability sample. (Salganik M, Heckathorn DD. Sampling and estimation in hidden populations using respondent-driven sampling. Sociol Method. 2004;34:193–239). This paper presents a comparison of RDS implementation, findings on network and recruitment characteristics, and lessons learned. Initiated with 13 to 15 seeds, approximately 200 IDUs were recruited within 8 weeks. Information resulting from RDS indicates that social network patterns from the two studies differ greatly. Female IDUs in Tirana had smaller network sizes than male IDUs, unlike in St. Petersburg where female IDUs had larger network sizes than male IDUs. Recruitment patterns in each country also differed by demographic categories. Recruitment analyses indicate that IDUs form socially distinct groups by sex in Tirana, whereas there was a greater degree of gender mixing patterns in St. Petersburg. RDS proved to be an effective means of surveying these hard-to-reach populations. PMID:17075727

Electromagnetic scattering from microwave absorbers - Laboratory verification of the coupled wave theory

NASA Technical Reports Server (NTRS)

Gasiewski, A. J.; Jackson, D. M.

1992-01-01

W-band measurements of the bistatic scattering function of some common microwave absorbing structures, including periodic wedge-type and pyramid-type iron-epoxy calibration loads and flat carbon-foam 'Echosorb' samples, were made using a network analyzer interface to a focused-lens scattering range. Swept frequency measurements over the 75-100 GHz band revealed specular and Bragg reflection characteristics in the measured data.

Future Naval Use of COTS Networking Infrastructure

DTIC Science & Technology

2009-07-01

user to benefit from Google’s vast databases and computational resources. Obviously, the ability to harness the full power of the Cloud could be... Computing Impact Findings Action Items Take-Aways Appendices: Pages 54-68 A. Terms of Reference Document B. Sample Definitions of Cloud ...and definition of Cloud Computing . While Cloud Computing is developing in many variations – including Infrastructure as a Service (IaaS), Platform as

Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning

PubMed Central

2013-01-01

Background Osteoarthritis (OA) is an inflammatory disease of synovial joints involving the loss and degeneration of articular cartilage. The gold standard for evaluating cartilage loss in OA is the measurement of joint space width on standard radiographs. However, in most cases the diagnosis is made well after the onset of the disease, when the symptoms are well established. Identification of early biomarkers of OA can facilitate earlier diagnosis, improve disease monitoring and predict responses to therapeutic interventions. Methods This study describes the bioinformatic analysis of data generated from high throughput proteomics for identification of potential biomarkers of OA. The mass spectrometry data was generated using a canine explant model of articular cartilage treated with the pro-inflammatory cytokine interleukin 1 β (IL-1β). The bioinformatics analysis involved the application of machine learning and network analysis to the proteomic mass spectrometry data. A rule based machine learning technique, BioHEL, was used to create a model that classified the samples into their relevant treatment groups by identifying those proteins that separated samples into their respective groups. The proteins identified were considered to be potential biomarkers. Protein networks were also generated; from these networks, proteins pivotal to the classification were identified. Results BioHEL correctly classified eighteen out of twenty-three samples, giving a classification accuracy of 78.3% for the dataset. The dataset included the four classes of control, IL-1β, carprofen, and IL-1β and carprofen together. This exceeded the other machine learners that were used for a comparison, on the same dataset, with the exception of another rule-based method, JRip, which performed equally well. The proteins that were most frequently used in rules generated by BioHEL were found to include a number of relevant proteins including matrix metalloproteinase 3, interleukin 8 and matrix gla protein. Conclusions Using this protocol, combining an in vitro model of OA with bioinformatics analysis, a number of relevant extracellular matrix proteins were identified, thereby supporting the application of these bioinformatics tools for analysis of proteomic data from in vitro models of cartilage degradation. PMID:24330474

Laboratory surveillance for wild and vaccine-derived polioviruses, January 2004-June 2005.

PubMed

2005-09-30

A global network of 145 virology laboratories has been established by the World Health Organization (WHO) to support surveillance activities of the Polio Eradication Initiative (PEI). The Global Polio Laboratory Network analyzes stool specimens from patients with acute flaccid paralysis (AFP) and environmental samples for the presence of polioviruses. Surveillance systems detect at least one AFP case per 100,000 persons aged <15 years, collect adequate stool samples from patients, and send the samples to network laboratories for analysis. Laboratory data are used to identify locations where wild polioviruses (WPVs) or vaccine-derived polioviruses (VDPVs) are circulating, target supplementary immunization activities (SIAs) to interrupt transmission chains, and investigate genetic relationships among viral isolates. This report updates previous publications and describes the laboratory network's performance during the period January 2004-June 2005.

Objective Classification of Radar Profile Types, and Their Relationship to Lightning Occurrence

NASA Technical Reports Server (NTRS)

Boccippio, Dennis

2003-01-01

A cluster analysis technique is used to identify 16 "archetypal" vertical radar profile types from a large, globally representative sample of profiles from the TRMM Precipitation Radar. These include nine convective types (7 of these deep convective) and seven stratiform types (5 of these clearly glaciated). Radar profile classification provides an alternative to conventional deep convective storm metrics, such as 30 dBZ echo height, maximum reflectivity or VIL. As expected, the global frequency of occurrence of deep convective profile types matches satellite-observed total lightning production, including to very small scall local features. Each location's "mix" of profile types provides an objective description of the local convective spectrum, and in turn, is a first step in objectively classifying convective regimes. These classifiers are tested as inputs to a neural network which attempts to predict lightning occurrence based on radar-only storm observations, and performance is compared with networks using traditional radar metrics as inputs.

Increasing Ethnic Minority Participation in Substance Abuse Clinical Trials: Lessons Learned in the National Institute on Drug Abuse’s Clinical Trials Network

PubMed Central

Burlew, Kathleen; Larios, Sandra; Suarez-Morales, Lourdes; Holmes, Beverly; Venner, Kamilla; Chavez, Roberta

2012-01-01

Underrepresentation in clinical trials limits the extent to which ethnic minorities benefit from advances in substance abuse treatment. The objective of this article is to share the knowledge gained within the Clinical Trials Network (CTN) of the National Institute on Drug Abuse and other research on recruiting and retaining ethnic minorities into substance abuse clinical trials. The article includes a discussion of two broad areas for improving inclusion— community involvement and cultural adaptation. CTN case studies are included to illustrate three promising strategies for improving ethnic minority inclusion: respondent-driven sampling, community-based participatory research, and the cultural adaptation of the recruitment and retention procedures. The article concludes with two sections describing a number of methodological concerns in the current research base and our proposed research agenda for improving ethnic minority inclusion that builds on the CTN experience. PMID:21988575

Increasing ethnic minority participation in substance abuse clinical trials: lessons learned in the National Institute on Drug Abuse's Clinical Trials Network.

PubMed

Burlew, Kathleen; Larios, Sandra; Suarez-Morales, Lourdes; Holmes, Beverly; Venner, Kamilla; Chavez, Roberta

2011-10-01

Underrepresentation in clinical trials limits the extent to which ethnic minorities benefit from advances in substance abuse treatment. The objective of this article is to share the knowledge gained within the Clinical Trials Network (CTN) of the National Institute on Drug Abuse and other research on recruiting and retaining ethnic minorities into substance abuse clinical trials. The article includes a discussion of two broad areas for improving inclusion-community involvement and cultural adaptation. CTN case studies are included to illustrate three promising strategies for improving ethnic minority inclusion: respondent-driven sampling, community-based participatory research, and the cultural adaptation of the recruitment and retention procedures. The article concludes with two sections describing a number of methodological concerns in the current research base and our proposed research agenda for improving ethnic minority inclusion that builds on the CTN experience.

A Method of DTM Construction Based on Quadrangular Irregular Networks and Related Error Analysis

PubMed Central

Kang, Mengjun

2015-01-01

A new method of DTM construction based on quadrangular irregular networks (QINs) that considers all the original data points and has a topological matrix is presented. A numerical test and a real-world example are used to comparatively analyse the accuracy of QINs against classical interpolation methods and other DTM representation methods, including SPLINE, KRIGING and triangulated irregular networks (TINs). The numerical test finds that the QIN method is the second-most accurate of the four methods. In the real-world example, DTMs are constructed using QINs and the three classical interpolation methods. The results indicate that the QIN method is the most accurate method tested. The difference in accuracy rank seems to be caused by the locations of the data points sampled. Although the QIN method has drawbacks, it is an alternative method for DTM construction. PMID:25996691

Automatic Camera Calibration Using Multiple Sets of Pairwise Correspondences.

PubMed

Vasconcelos, Francisco; Barreto, Joao P; Boyer, Edmond

2018-04-01

We propose a new method to add an uncalibrated node into a network of calibrated cameras using only pairwise point correspondences. While previous methods perform this task using triple correspondences, these are often difficult to establish when there is limited overlap between different views. In such challenging cases we must rely on pairwise correspondences and our solution becomes more advantageous. Our method includes an 11-point minimal solution for the intrinsic and extrinsic calibration of a camera from pairwise correspondences with other two calibrated cameras, and a new inlier selection framework that extends the traditional RANSAC family of algorithms to sampling across multiple datasets. Our method is validated on different application scenarios where a lack of triple correspondences might occur: addition of a new node to a camera network; calibration and motion estimation of a moving camera inside a camera network; and addition of views with limited overlap to a Structure-from-Motion model.

A developmental neuroimaging investigation of the change paradigm.

PubMed

Thomas, Laura A; Hall, Julie M; Skup, Martha; Jenkins, Sarah E; Pine, Daniel S; Leibenluft, Ellen

2011-01-01

This neuroimaging study examines the development of cognitive flexibility using the Change task in a sample of youths and adults. The Change task requires subjects to inhibit a prepotent response and substitute an alternative response, and the task incorporates an algorithm that adjusts task difficulty in response to subject performance. Data from both groups combined show a network of prefrontal and parietal areas that are active during the task. For adults vs. youths, a distributed network was more active for successful change trials versus go, baseline, or unsuccessful change trials. This network included areas involved in rule representation, retrieval (lateral PFC), and switching (medial PFC and parietal regions). These results are consistent with data from previous task-switching experiments and inform developmental understandings of cognitive flexibility. Published 2010. This article is a US Government work and is in the public domain in the USA.

Estimating the Size of the Methamphetamine-Using Population in New York City Using Network Sampling Techniques.

PubMed

Dombrowski, Kirk; Khan, Bilal; Wendel, Travis; McLean, Katherine; Misshula, Evan; Curtis, Ric

2012-12-01

As part of a recent study of the dynamics of the retail market for methamphetamine use in New York City, we used network sampling methods to estimate the size of the total networked population. This process involved sampling from respondents' list of co-use contacts, which in turn became the basis for capture-recapture estimation. Recapture sampling was based on links to other respondents derived from demographic and "telefunken" matching procedures-the latter being an anonymized version of telephone number matching. This paper describes the matching process used to discover the links between the solicited contacts and project respondents, the capture-recapture calculation, the estimation of "false matches", and the development of confidence intervals for the final population estimates. A final population of 12,229 was estimated, with a range of 8235 - 23,750. The techniques described here have the special virtue of deriving an estimate for a hidden population while retaining respondent anonymity and the anonymity of network alters, but likely require larger sample size than the 132 persons interviewed to attain acceptable confidence levels for the estimate.

Multi-Objective Design Of Optimal Greenhouse Gas Observation Networks

NASA Astrophysics Data System (ADS)

Lucas, D. D.; Bergmann, D. J.; Cameron-Smith, P. J.; Gard, E.; Guilderson, T. P.; Rotman, D.; Stolaroff, J. K.

2010-12-01

One of the primary scientific functions of a Greenhouse Gas Information System (GHGIS) is to infer GHG source emission rates and their uncertainties by combining measurements from an observational network with atmospheric transport modeling. Certain features of the observational networks that serve as inputs to a GHGIS --for example, sampling location and frequency-- can greatly impact the accuracy of the retrieved GHG emissions. Observation System Simulation Experiments (OSSEs) provide a framework to characterize emission uncertainties associated with a given network configuration. By minimizing these uncertainties, OSSEs can be used to determine optimal sampling strategies. Designing a real-world GHGIS observing network, however, will involve multiple, conflicting objectives; there will be trade-offs between sampling density, coverage and measurement costs. To address these issues, we have added multi-objective optimization capabilities to OSSEs. We demonstrate these capabilities by quantifying the trade-offs between retrieval error and measurement costs for a prototype GHGIS, and deriving GHG observing networks that are Pareto optimal. [LLNL-ABS-452333: This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Tail-scope: Using friends to estimate heavy tails of degree distributions in large-scale complex networks

NASA Astrophysics Data System (ADS)

Eom, Young-Ho; Jo, Hang-Hyun

2015-05-01

Many complex networks in natural and social phenomena have often been characterized by heavy-tailed degree distributions. However, due to rapidly growing size of network data and concerns on privacy issues about using these data, it becomes more difficult to analyze complete data sets. Thus, it is crucial to devise effective and efficient estimation methods for heavy tails of degree distributions in large-scale networks only using local information of a small fraction of sampled nodes. Here we propose a tail-scope method based on local observational bias of the friendship paradox. We show that the tail-scope method outperforms the uniform node sampling for estimating heavy tails of degree distributions, while the opposite tendency is observed in the range of small degrees. In order to take advantages of both sampling methods, we devise the hybrid method that successfully recovers the whole range of degree distributions. Our tail-scope method shows how structural heterogeneities of large-scale complex networks can be used to effectively reveal the network structure only with limited local information.

High Definition Confocal Imaging Modalities for the Characterization of Tissue-Engineered Substitutes.

PubMed

Mayrand, Dominique; Fradette, Julie

2018-01-01

Optimal imaging methods are necessary in order to perform a detailed characterization of thick tissue samples from either native or engineered tissues. Tissue-engineered substitutes are featuring increasing complexity including multiple cell types and capillary-like networks. Therefore, technical approaches allowing the visualization of the inner structural organization and cellular composition of tissues are needed. This chapter describes an optical clearing technique which facilitates the detailed characterization of whole-mount samples from skin and adipose tissues (ex vivo tissues and in vitro tissue-engineered substitutes) when combined with spectral confocal microscopy and quantitative analysis on image renderings.

Water-quality data for the ground-water network in eastern Broward County, Florida, 1983-84

USGS Publications Warehouse

Waller, B.G.; Cannon, F.L.

1986-01-01

During 1983-84, groundwater from 63 wells located at 31 sites throughout eastern Broward County, Florida, was sampled and analyzed to determine baseline water quality conditions. The physical and chemical parameters analyzed included field measurements (pH and temperature), physical characteristics (color, turbidity, and specific conductance), major inorganic ions, nutrients, (nitrogen, phosphorus and carbon), selected metals, and total phenolic compounds. Groundwater samples were collected at the end of the dry season (April) and during the wet season (July and September). These data are tabulated, by well, in this report. (USGS)

The application of a network approach to Health-Related Quality of Life (HRQoL): introducing a new method for assessing HRQoL in healthy adults and cancer patients.

PubMed

Kossakowski, Jolanda J; Epskamp, Sacha; Kieffer, Jacobien M; van Borkulo, Claudia D; Rhemtulla, Mijke; Borsboom, Denny

2016-04-01

Health-Related Quality of Life (HRQoL) research has typically adopted either a formative approach, in which HRQoL is the common effect of its observables, or a reflective approach--defining HRQoL as a latent variable that determines observable characteristics of HRQoL. Both approaches, however, do not take into account the complex organization of these characteristics. The objective of this study was to introduce a new approach for analyzing HRQoL data, namely a network model (NM). An NM, as opposed to traditional research strategies, accounts for interactions among observables and offers a complementary analytic approach. We applied the NM to samples of Dutch cancer patients (N = 485) and Dutch healthy adults (N = 1742) who completed the 36-item Short Form Health Survey (SF-36). Networks were constructed for both samples separately and for a combined sample with diagnostic status added as an extra variable. We assessed the network structures and compared the structures of the two separate samples on the item and domain levels. The relative importance of individual items in the network structures was determined using centrality analyses. We found that the global structure of the SF-36 is dominant in all networks, supporting the validity of questionnaire's subscales. Furthermore, results suggest that the network structure of both samples was highly similar. Centrality analyses revealed that maintaining a daily routine despite one's physical health predicts HRQoL levels best. We concluded that the NM provides a fruitful alternative to classical approaches used in the psychometric analysis of HRQoL data.

Requirements management: A CSR's perspective

NASA Technical Reports Server (NTRS)

Thompson, Joanie

1991-01-01

The following subject areas are covered: customer service overview of network service request processing; Customer Service Representative (CSR) responsibility matrix; extract from a sample Memorandum of Understanding; Network Service Request Form and its instructions sample notification of receipt; and requirements management in the NASA Science Internet.

Electrical network method for the thermal or structural characterization of a conducting material sample or structure

DOEpatents

Ortiz, Marco G.

1993-01-01

A method for modeling a conducting material sample or structure system, as an electrical network of resistances in which each resistance of the network is representative of a specific physical region of the system. The method encompasses measuring a resistance between two external leads and using this measurement in a series of equations describing the network to solve for the network resistances for a specified region and temperature. A calibration system is then developed using the calculated resistances at specified temperatures. This allows for the translation of the calculated resistances to a region temperature. The method can also be used to detect and quantify structural defects in the system.

Electrical network method for the thermal or structural characterization of a conducting material sample or structure

DOEpatents

Ortiz, M.G.

1993-06-08

A method for modeling a conducting material sample or structure system, as an electrical network of resistances in which each resistance of the network is representative of a specific physical region of the system. The method encompasses measuring a resistance between two external leads and using this measurement in a series of equations describing the network to solve for the network resistances for a specified region and temperature. A calibration system is then developed using the calculated resistances at specified temperatures. This allows for the translation of the calculated resistances to a region temperature. The method can also be used to detect and quantify structural defects in the system.

Artificial odor discrimination system using electronic nose and neural networks for the identification of urinary tract infection.

PubMed

Kodogiannis, Vassilis S; Lygouras, John N; Tarczynski, Andrzej; Chowdrey, Hardial S

2008-11-01

Current clinical diagnostics are based on biochemical, immunological, or microbiological methods. However, these methods are operator dependent, time-consuming, expensive, and require special skills, and are therefore, not suitable for point-of-care testing. Recent developments in gas-sensing technology and pattern recognition methods make electronic nose technology an interesting alternative for medical point-of-care devices. An electronic nose has been used to detect urinary tract infection from 45 suspected cases that were sent for analysis in a U.K. Public Health Registry. These samples were analyzed by incubation in a volatile generation test tube system for 4-5 h. Two issues are being addressed, including the implementation of an advanced neural network, based on a modified expectation maximization scheme that incorporates a dynamic structure methodology and the concept of a fusion of multiple classifiers dedicated to specific feature parameters. This study has shown the potential for early detection of microbial contaminants in urine samples using electronic nose technology.

Integration, Networking, and Global Biobanking in the Age of New Biology.

PubMed

Karimi-Busheri, Feridoun; Rasouli-Nia, Aghdass

2015-01-01

Scientific revolution is changing the world forever. Many new disciplines and fields have emerged with unlimited possibilities and opportunities. Biobanking is one of many that is benefiting from revolutionary milestones in human genome, post-genomic, and computer and bioinformatics discoveries. The storage, management, and analysis of massive clinical and biological data sets cannot be achieved without a global collaboration and networking. At the same time, biobanking is facing many significant challenges that need to be addressed and solved including dealing with an ever increasing complexity of sample storage and retrieval, data management and integration, and establishing common platforms in a global context. The overall picture of the biobanking of the future, however, is promising. Many population-based biobanks have been formed, and more are under development. It is certain that amazing discoveries will emerge from this large-scale method of preserving and accessing human samples. Signs of a healthy collaboration between industry, academy, and government are encouraging.

Comparing the characteristics of homeless adults in Poland and the United States.

PubMed

Toro, Paul A; Hobden, Karen L; Wyszacki Durham, Kathleen; Oko-Riebau, Marta; Bokszczanin, Anna

2014-03-01

This study compared the characteristics of probability samples of homeless adults in Poland (N = 200 from two cities) and the United States (N = 219 from one city), using measures with established reliability and validity in homeless populations. The same measures were used across nations and a systemic translation procedure assured comparability of measurement. The two samples were similar on some measures: In both nations, most homeless adults were male, many reported having dependent children and experiencing out-of-home placements when they themselves were children, and high levels of physical health problems were observed. Significant national differences were also found: Those in Poland were older, had been homeless for longer, showed lower rates on all psychiatric diagnoses assessed (including severe mental and substance abuse disorders), reported less contact with family and supportive network members, were less satisfied when they sought support from their networks, and reported fewer recent stressful life events and fewer risky sexual behaviors. Culturally-informed interpretations of these findings and their implications are presented.

Why do medical tourists travel to where they do? The role of networks in determining medical travel.

PubMed

Hanefeld, J; Lunt, N; Smith, R; Horsfall, D

2015-01-01

Evidence on medical tourism, including patient motivation, is increasing. Existing studies have focused on identifying push and pull factors across different types of treatment, for example cosmetic or bariatric surgery, or on groups, such as diaspora patients returning 'home' for treatment. Less attention has been on why individuals travel to specific locations or providers and on how this decision is made. The paper focused on the role of networks, defined as linkages - formal and informal - between individual providers, patients and facilitators to explain why and where patients travel. Findings are based on a recently completed, two year research project, which examined the effects of medical tourism on the UK NHS. Research included in-depth interviews with 77 returning medical tourists and over sixty managers, medical travel facilitators, clinicians and providers of medical tourism in recipient countries to understand the medical tourism industry. Interviews were conducted between 2011 and 2012, recorded and transcribed, or documented through note taking. Authors undertook a thematic analysis of interviews to identify treatment pathways by patients, and professional linkages between clinicians and facilitators to understand choice of treatment destination. The results highlight that across a large sample of patients travelling for a variety of conditions from dental treatment, cosmetic and bariatric surgery, through to specialist care the role of networks is critical to understand choice of treatment, provider and destination. While distance, costs, expertise and availability of treatment all were factors influencing patients' decision to travel, choice of destination and provider was largely the result of informal networks, including web fora, personal recommendations and support groups. Where patients were referred by UK clinicians or facilitators these followed informal networks. In conclusion, investigating medical travel through focus on networks of patients and providers opens up novel conception of medical tourism, deepening understanding of patterns of travel by combining investigation of industry with patient motivation. Copyright © 2014 Elsevier Ltd. All rights reserved.

The preBötzinger complex as a hub for network activity along the ventral respiratory column in the neonate rat.

PubMed

Gourévitch, Boris; Mellen, Nicholas

2014-09-01

In vertebrates, respiratory control is ascribed to heterogeneous respiration-modulated neurons along the Ventral Respiratory Column (VRC) in medulla, which includes the preBötzinger Complex (preBötC), the putative respiratory rhythm generator. Here, the functional anatomy of the VRC was characterized via optical recordings in the sagittaly sectioned neonate rat hindbrain, at sampling rates permitting coupling estimation between neuron pairs, so that each neuron was described using unitary, neuron-system, and coupling attributes. Structured coupling relations in local networks, significantly oriented coupling in the peri-inspiratory interval detected in pooled data, and significant correlations between firing rate and expiratory duration in subsets of neurons revealed network regulation at multiple timescales. Spatially averaged neuronal attributes, including coupling vectors, revealed a sharp boundary at the rostral margin of the preBötC, as well as other functional anatomical features congruent with identified structures, including the parafacial respiratory group and the nucleus ambiguus. Cluster analysis of attributes identified two spatially compact, homogenous groups: the first overlapped with the preBötC, and was characterized by strong respiratory modulation and dense bidirectional coupling with itself and other groups, consistent with a central role for the preBötC in respiratory control; the second lay between preBötC and the facial nucleus, and was characterized by weak respiratory modulation and weak coupling with other respiratory neurons, which is congruent with cardiovascular regulatory networks that are found in this region. Other groups identified using cluster analysis suggested that networks along VRC regulated expiratory duration, and the transition to and from inspiration, but these groups were heterogeneous and anatomically dispersed. Thus, by recording local networks in parallel, this study found evidence for respiratory regulation at multiple timescales along the VRC, as well as a role for the preBötC in the integration of functionally disparate respiratory neurons. Copyright © 2014 Elsevier Inc. All rights reserved.

Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex.

PubMed

Romero-Garcia, Rafael; Whitaker, Kirstie J; Váša, František; Seidlitz, Jakob; Shinn, Maxwell; Fonagy, Peter; Dolan, Raymond J; Jones, Peter B; Goodyer, Ian M; Bullmore, Edward T; Vértes, Petra E

2018-05-01

Complex network topology is characteristic of many biological systems, including anatomical and functional brain networks (connectomes). Here, we first constructed a structural covariance network from MRI measures of cortical thickness on 296 healthy volunteers, aged 14-24 years. Next, we designed a new algorithm for matching sample locations from the Allen Brain Atlas to the nodes of the SCN. Subsequently we used this to define, transcriptomic brain networks by estimating gene co-expression between pairs of cortical regions. Finally, we explored the hypothesis that transcriptional networks and structural MRI connectomes are coupled. A transcriptional brain network (TBN) and a structural covariance network (SCN) were correlated across connection weights and showed qualitatively similar complex topological properties: assortativity, small-worldness, modularity, and a rich-club. In both networks, the weight of an edge was inversely related to the anatomical (Euclidean) distance between regions. There were differences between networks in degree and distance distributions: the transcriptional network had a less fat-tailed degree distribution and a less positively skewed distance distribution than the SCN. However, cortical areas connected to each other within modules of the SCN had significantly higher levels of whole genome co-expression than expected by chance. Nodes connected in the SCN had especially high levels of expression and co-expression of a human supragranular enriched (HSE) gene set that has been specifically located to supragranular layers of human cerebral cortex and is known to be important for large-scale, long-distance cortico-cortical connectivity. This coupling of brain transcriptome and connectome topologies was largely but not entirely accounted for by the common constraint of physical distance on both networks. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Fabrication and Characterization of Surrogate Glasses Aimed to Validate Nuclear Forensic Techniques

DTIC Science & Technology

2017-12-01

sample is processed while submerged and produces fine sized particles the exposure levels and risk of contamination from the samples is also greatly...induced the partial collapses of the xerogel network strengthened the network while the sample sizes were reduced [22], [26]. As a result the wt...inhomogeneous, making it difficult to clearly determine which features were present in the sample before LDHP and which were caused by it. In this study

QADATA user's manual; an interactive computer program for the retrieval and analysis of the results from the external blind sample quality- assurance project of the U.S. Geological Survey

USGS Publications Warehouse

Lucey, K.J.

1990-01-01

The U.S. Geological Survey conducts an external blind sample quality assurance project for its National Water Quality Laboratory in Denver, Colorado, based on the analysis of reference water samples. Reference samples containing selected inorganic and nutrient constituents are disguised as environmental samples at the Survey 's office in Ocala, Florida, and are sent periodically through other Survey offices to the laboratory. The results of this blind sample project indicate the quality of analytical data produced by the laboratory. This report provides instructions on the use of QADATA, an interactive, menu-driven program that allows users to retrieve the results of the blind sample quality- assurance project. The QADATA program, which is available on the U.S. Geological Survey 's national computer network, accesses a blind sample data base that contains more than 50,000 determinations from the last five water years for approximately 40 constituents at various concentrations. The data can be retrieved from the database for any user- defined time period and for any or all available constituents. After the user defines the retrieval, the program prepares statistical tables, control charts, and precision plots and generates a report which can be transferred to the user 's office through the computer network. A discussion of the interpretation of the program output is also included. This quality assurance information will permit users to document the quality of the analytical results received from the laboratory. The blind sample data is entered into the database within weeks after being produced by the laboratory and can be retrieved to meet the needs of specific projects or programs. (USGS)

Magnetoresistive immunosensor for the detection of Escherichia coli O157:H7 including a microfluidic network.

PubMed

Mujika, M; Arana, S; Castaño, E; Tijero, M; Vilares, R; Ruano-López, J M; Cruz, A; Sainz, L; Berganza, J

2009-01-01

A hand held device has been designed for the immunomagnetic detection and quantification of the pathogen Escherichia coli O157:H7 in food and clinical samples. In this work, a technology to manufacture a Lab on a Chip that integrates a 3D microfluidic network with a microfabricated biosensor has been developed. With this aim, the sensing film optimization, the design of the microfluidic circuitry, the development of the biological protocols involved in the measurements and, finally, the packaging needed to carry out the assays in a safe and straightforward way have been completed. The biosensor is designed to be capable to detect and quantify small magnetic field variations caused by the presence of superparamagnetic beads bound to the antigens previously immobilized on the sensor surface via an antibody-antigen reaction. The giant magnetoresistive multilayer structure implemented as sensing film consists of 20[Cu(5.10nm)/Co(2.47 nm)] with a magnetoresistance of 3.20% at 235Oe and a sensitivity up to 0.06 Omega/Oe between 150Oe and 230Oe. Silicon nitride has been selected as optimum sensor surface coating due to its suitability for antibody immobilization. In order to guide the biological samples towards the sensing area, a microfluidic network made of SU-8 photoresist has been included. Finally, a novel packaging design has been fabricated employing 3D stereolithographic techniques. The microchannels are connected to the outside using standard tubing. Hence, this packaging allows an easy replacement of the used devices.

Roles of Thermophiles and Fungi in Bitumen Degradation in Mostly Cold Oil Sands Outcrops

PubMed Central

Wong, Man-Ling; An, Dongshan; Caffrey, Sean M.; Soh, Jung; Dong, Xiaoli; Sensen, Christoph W.; Oldenburg, Thomas B. P.; Larter, Steve R.

2015-01-01

Oil sands are surface exposed in river valley outcrops in northeastern Alberta, where flat slabs (tablets) of weathered, bitumen-saturated sandstone can be retrieved from outcrop cliffs or from riverbeds. Although the average yearly surface temperature of this region is low (0.7°C), we found that the temperatures of the exposed surfaces of outcrop cliffs reached 55 to 60°C on sunny summer days, with daily maxima being 27 to 31°C. Analysis of the cooccurrence of taxa derived from pyrosequencing of 16S/18S rRNA genes indicated that an aerobic microbial network of fungi and hydrocarbon-, methane-, or acetate-oxidizing heterotrophic bacteria was present in all cliff tablets. Metagenomic analyses indicated an elevated presence of fungal cytochrome P450 monooxygenases in these samples. This network was distinct from the heterotrophic community found in riverbeds, which included fewer fungi. A subset of cliff tablets had a network of anaerobic and/or thermophilic taxa, including methanogens, Firmicutes, and Thermotogae, in the center. Long-term aerobic incubation of outcrop samples at 55°C gave a thermophilic microbial community. Analysis of residual bitumen with a Fourier transform ion cyclotron resonance mass spectrometer indicated that aerobic degradation proceeded at 55°C but not at 4°C. Little anaerobic degradation was observed. These results indicate that bitumen degradation on outcrop surfaces is a largely aerobic process with a minor anaerobic contribution and is catalyzed by a consortium of bacteria and fungi. Bitumen degradation is stimulated by periodic high temperatures on outcrop cliffs, which cause significant decreases in bitumen viscosity. PMID:26209669

3D quantitative phase imaging of neural networks using WDT

NASA Astrophysics Data System (ADS)

Kim, Taewoo; Liu, S. C.; Iyer, Raj; Gillette, Martha U.; Popescu, Gabriel

2015-03-01

White-light diffraction tomography (WDT) is a recently developed 3D imaging technique based on a quantitative phase imaging system called spatial light interference microscopy (SLIM). The technique has achieved a sub-micron resolution in all three directions with high sensitivity granted by the low-coherence of a white-light source. Demonstrations of the technique on single cell imaging have been presented previously; however, imaging on any larger sample, including a cluster of cells, has not been demonstrated using the technique. Neurons in an animal body form a highly complex and spatially organized 3D structure, which can be characterized by neuronal networks or circuits. Currently, the most common method of studying the 3D structure of neuron networks is by using a confocal fluorescence microscope, which requires fluorescence tagging with either transient membrane dyes or after fixation of the cells. Therefore, studies on neurons are often limited to samples that are chemically treated and/or dead. WDT presents a solution for imaging live neuron networks with a high spatial and temporal resolution, because it is a 3D imaging method that is label-free and non-invasive. Using this method, a mouse or rat hippocampal neuron culture and a mouse dorsal root ganglion (DRG) neuron culture have been imaged in order to see the extension of processes between the cells in 3D. Furthermore, the tomogram is compared with a confocal fluorescence image in order to investigate the 3D structure at synapses.

Effects of road network on diversiform forest cover changes in the highest coverage region in China: An analysis of sampling strategies.

PubMed

Hu, Xisheng; Wu, Zhilong; Wu, Chengzhen; Ye, Limin; Lan, Chaofeng; Tang, Kun; Xu, Lu; Qiu, Rongzu

2016-09-15

Forest cover changes are of global concern due to their roles in global warming and biodiversity. However, many previous studies have ignored the fact that forest loss and forest gain are different processes that may respond to distinct factors by stressing forest loss more than gain or viewing forest cover change as a whole. It behooves us to carefully examine the patterns and drivers of the change by subdividing it into several categories. Our study includes areas of forest loss (4.8% of the study area), forest gain (1.3% of the study area) and forest loss and gain (2.0% of the study area) from 2000 to 2012 in Fujian Province, China. In the study area, approximately 65% and 90% of these changes occurred within 2000m of the nearest road and under road densities of 0.6km/km(2), respectively. We compared two sampling techniques (systematic sampling and random sampling) and four intensities for each technique to investigate the driving patterns underlying the changes using multinomial logistic regression. The results indicated the lack of pronounced differences in the regressions between the two sampling designs, although the sample size had a great impact on the regression outcome. The application of multi-model inference indicated that the low level road density had a negative significant association with forest loss and forest loss and gain, the expressway density had a positive significant impact on forest loss, and the road network was insignificantly related to forest gain. The model including socioeconomic and biophysical variables illuminated potentially different predictors of the different forest change categories. Moreover, the multiple comparisons tested by Fisher's least significant difference (LSD) were a good compensation for the multinomial logistic model to enrich the interpretation of the regression results. Copyright © 2016 Elsevier B.V. All rights reserved.

Laboratory-based ROTEM(®) analysis: implementing pneumatic tube transport and real-time graphic transmission.

PubMed

Colucci, G; Giabbani, E; Barizzi, G; Urwyler, N; Alberio, L

2011-08-01

ROTEM(®) is considered a helpful point-of-care device to monitor blood coagulation. Centrally performed analysis is desirable but rapid transport of blood samples and real-time transmission of graphic results are an important prerequisite. The effect of sample transport through a pneumatic tube system on ROTEM(®) results is unknown. The aims of the present work were (i) to determine the influence of blood sample transport through a pneumatic tube system on ROTEM(®) parameters compared to manual transportation, and (ii) to verify whether graphic results can be transmitted on line via virtual network computing using local area network to the physician in charge of the patient. Single centre study with 30 normal volunteers. Two whole blood samples were transferred to the central haematology laboratory by either normal transport or pneumatic delivery. EXTEM, INTEM, FIBTEM and APTEM were analysed in parallel with two ROTEM(®) devices and compared. Connection between central laboratory, emergency and operating rooms was established using local area network. All collected ROTEM(®) parameters were within normal limits. No statistically significant differences between normal transport and pneumatic delivery were observed. Real-time transmission of the original ROTEM(®) curves using local area network is feasible and easy to establish. At our institution, transport of blood samples by pneumatic delivery does not influence ROTEM(®) parameters. Blood samples can be analysed centrally, and results transmitted live via virtual network computing to emergency or operating rooms. Prior to analyse blood samples centrally, the type of sample transport should be tested to exclude in vitro blood activation by local pneumatic transport system. © 2011 Blackwell Publishing Ltd.

Social networks and social support for healthy eating among Latina breast cancer survivors: Implications for social and behavioral interventions

PubMed Central

Crookes, Danielle M.; Shelton, Rachel C.; Tehranifar, Parisa; Aycinena, Corina; Gaffney, Ann Ogden; Koch, Pam; Contento, Isobel R.; Greenlee, Heather

2015-01-01

Purpose Little is known about Latina breast cancer survivors' social networks or their perceived social support to achieve and maintain a healthy diet. This paper describes the social networks and perceived support for healthy eating in a sample of breast cancer survivors of predominantly Dominican descent living in New York City. Methods Spanish-speaking Latina breast cancer survivors enrolled in a randomized controlled trial of a culturally-tailored dietary intervention. Social networks were assessed using Cohen's Social Network Index and a modified General Social Survey Social Networks Module that included assessments of shared health promoting behaviors. Perceived social support from family and friends for healthy, food-related behaviors was assessed. Results Participants' networks consisted predominantly of family and friends. Family members were more likely than other individuals to be identified as close network members. Participants were more likely to share food-related activities than exercise activities with close network members. Perceived social support for healthy eating was high, although perceived support from spouses and children was higher than support from friends. Despite high levels of perceived support, family was also identified as a barrier to eating healthy foods by nearly half of women. Conclusions Although friends are part of Latina breast cancer survivors' social networks, spouses and children may provide greater support for healthy eating than friends. Implications for Cancer Survivors Involving family members in dietary interventions for Latina breast cancer survivors may tap into positive sources of support for women, which could facilitate uptake and maintenance of healthy eating behaviors. PMID:26202538

Personal Network Correlates of Alcohol, Cigarette, and Marijuana Use Among Homeless Youth

PubMed Central

Wenzel, Suzanne L.; Tucker, Joan S.; Golinelli, Daniela; Green, Harold D.; Zhou, Annie

2013-01-01

Background Youth who are homeless and on their own are among the most marginalized individuals in the United States and face multiple risks, including use of substances. This study investigates how the use of alcohol, cigarettes, and marijuana among homeless youth may be influenced by characteristics of their social networks. Methods Homeless youth aged 13–24 were randomly sampled from 41 service and street sites in Los Angeles County (N = 419). Predictors of substance use were examined using linear regression analysis (for average number of drinks and average number of cigarettes per day) and negative binomal regression analysis (for frequency of past month marijuana use). Results Youth with more substance users in their networks reported greater alcohol, cigarette, and marijuana consumption regardless of whether these network members provided tangible or emotional support. Marijuana use was more frequent for youth who met more network members through homeless settings, but less frequent among those who met more network members through treatment or AA/NA. Greater alcohol use occurred among youth who met more network members through substance use-related activities. Youth having more adults in positions of responsibility in their networks consumed less alcohol, and those with more school attendees in their networks consumed less alcohol and cigarettes. Conclusions Findings highlight the importance of social context in understanding substance use among homeless youth. Results also support the relevance of network-based interventions to change social context for substance using youth, in terms of both enhancing pro-social influences and reducing exposure to substance use. PMID:20656423

Personal network correlates of alcohol, cigarette, and marijuana use among homeless youth.

PubMed

Wenzel, Suzanne L; Tucker, Joan S; Golinelli, Daniela; Green, Harold D; Zhou, Annie

2010-11-01

Youth who are homeless and on their own are among the most marginalized individuals in the United States and face multiple risks, including use of substances. This study investigates how the use of alcohol, cigarettes, and marijuana among homeless youth may be influenced by characteristics of their social networks. Homeless youth aged 13-24 were randomly sampled from 41 service and street sites in Los Angeles County (N=419). Predictors of substance use were examined using linear regression analysis (for average number of drinks and average number of cigarettes per day) and negative binomial regression analysis (for frequency of past month marijuana use). Youth with more substance users in their networks reported greater alcohol, cigarette, and marijuana consumption regardless of whether these network members provided tangible or emotional support. Marijuana use was more frequent for youth who met more network members through homeless settings, but less frequent among those who met more network members through treatment or AA/NA. Greater alcohol use occurred among youth who met more network members through substance use-related activities. Youth having more adults in positions of responsibility in their networks consumed less alcohol, and those with more school attendees in their networks consumed less alcohol and cigarettes. Findings highlight the importance of social context in understanding substance use among homeless youth. Results also support the relevance of network-based interventions to change social context for substance-using youth, in terms of both enhancing pro-social influences and reducing exposure to substance use. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Structural covariance networks across the life span, from 6 to 94 years of age.

PubMed

DuPre, Elizabeth; Spreng, R Nathan

2017-10-01

Structural covariance examines covariation of gray matter morphology between brain regions and across individuals. Despite significant interest in the influence of age on structural covariance patterns, no study to date has provided a complete life span perspective-bridging childhood with early, middle, and late adulthood-on the development of structural covariance networks. Here, we investigate the life span trajectories of structural covariance in six canonical neurocognitive networks: default, dorsal attention, frontoparietal control, somatomotor, ventral attention, and visual. By combining data from five open-access data sources, we examine the structural covariance trajectories of these networks from 6 to 94 years of age in a sample of 1,580 participants. Using partial least squares, we show that structural covariance patterns across the life span exhibit two significant, age-dependent trends. The first trend is a stable pattern whose integrity declines over the life span. The second trend is an inverted-U that differentiates young adulthood from other age groups. Hub regions, including posterior cingulate cortex and anterior insula, appear particularly influential in the expression of this second age-dependent trend. Overall, our results suggest that structural covariance provides a reliable definition of neurocognitive networks across the life span and reveal both shared and network-specific trajectories.