Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning.

PubMed

Arribas-Bel, Daniel; Patino, Jorge E; Duque, Juan C

2017-01-01

This paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gathering socioeconomic information in urban settlements. We use land cover, spectral, structure and texture features extracted from a Google Earth image of Liverpool (UK) to evaluate their potential to predict Living Environment Deprivation at a small statistical area level. We also contribute to the methodological literature on the estimation of socioeconomic indices with remote-sensing data by introducing elements from modern machine learning. In addition to classical approaches such as Ordinary Least Squares (OLS) regression and a spatial lag model, we explore the potential of the Gradient Boost Regressor and Random Forests to improve predictive performance and accuracy. In addition to novel predicting methods, we also introduce tools for model interpretation and evaluation such as feature importance and partial dependence plots, or cross-validation. Our results show that Random Forest proved to be the best model with an R2 of around 0.54, followed by Gradient Boost Regressor with 0.5. Both the spatial lag model and the OLS fall behind with significantly lower performances of 0.43 and 0.3, respectively.

Short-Term Effects of Climatic Variables on Hand, Foot, and Mouth Disease in Mainland China, 2008–2013: A Multilevel Spatial Poisson Regression Model Accounting for Overdispersion

PubMed Central

Yang, Fang; Yang, Min; Hu, Yuehua; Zhang, Juying

2016-01-01

Background Hand, Foot, and Mouth Disease (HFMD) is a worldwide infectious disease. In China, many provinces have reported HFMD cases, especially the south and southwest provinces. Many studies have found a strong association between the incidence of HFMD and climatic factors such as temperature, rainfall, and relative humidity. However, few studies have analyzed cluster effects between various geographical units. Methods The nonlinear relationships and lag effects between weekly HFMD cases and climatic variables were estimated for the period of 2008–2013 using a polynomial distributed lag model. The extra-Poisson multilevel spatial polynomial model was used to model the exact relationship between weekly HFMD incidence and climatic variables after considering cluster effects, provincial correlated structure of HFMD incidence and overdispersion. The smoothing spline methods were used to detect threshold effects between climatic factors and HFMD incidence. Results The HFMD incidence spatial heterogeneity distributed among provinces, and the scale measurement of overdispersion was 548.077. After controlling for long-term trends, spatial heterogeneity and overdispersion, temperature was highly associated with HFMD incidence. Weekly average temperature and weekly temperature difference approximate inverse “V” shape and “V” shape relationships associated with HFMD incidence. The lag effects for weekly average temperature and weekly temperature difference were 3 weeks and 2 weeks. High spatial correlated HFMD incidence were detected in northern, central and southern province. Temperature can be used to explain most of variation of HFMD incidence in southern and northeastern provinces. After adjustment for temperature, eastern and Northern provinces still had high variation HFMD incidence. Conclusion We found a relatively strong association between weekly HFMD incidence and weekly average temperature. The association between the HFMD incidence and climatic variables spatial heterogeneity distributed across provinces. Future research should explore the risk factors that cause spatial correlated structure or high variation of HFMD incidence which can be explained by temperature. When analyzing association between HFMD incidence and climatic variables, spatial heterogeneity among provinces should be evaluated. Moreover, the extra-Poisson multilevel model was capable of modeling the association between overdispersion of HFMD incidence and climatic variables. PMID:26808311

Short-Term Effects of Climatic Variables on Hand, Foot, and Mouth Disease in Mainland China, 2008-2013: A Multilevel Spatial Poisson Regression Model Accounting for Overdispersion.

PubMed

Liao, Jiaqiang; Yu, Shicheng; Yang, Fang; Yang, Min; Hu, Yuehua; Zhang, Juying

2016-01-01

Hand, Foot, and Mouth Disease (HFMD) is a worldwide infectious disease. In China, many provinces have reported HFMD cases, especially the south and southwest provinces. Many studies have found a strong association between the incidence of HFMD and climatic factors such as temperature, rainfall, and relative humidity. However, few studies have analyzed cluster effects between various geographical units. The nonlinear relationships and lag effects between weekly HFMD cases and climatic variables were estimated for the period of 2008-2013 using a polynomial distributed lag model. The extra-Poisson multilevel spatial polynomial model was used to model the exact relationship between weekly HFMD incidence and climatic variables after considering cluster effects, provincial correlated structure of HFMD incidence and overdispersion. The smoothing spline methods were used to detect threshold effects between climatic factors and HFMD incidence. The HFMD incidence spatial heterogeneity distributed among provinces, and the scale measurement of overdispersion was 548.077. After controlling for long-term trends, spatial heterogeneity and overdispersion, temperature was highly associated with HFMD incidence. Weekly average temperature and weekly temperature difference approximate inverse "V" shape and "V" shape relationships associated with HFMD incidence. The lag effects for weekly average temperature and weekly temperature difference were 3 weeks and 2 weeks. High spatial correlated HFMD incidence were detected in northern, central and southern province. Temperature can be used to explain most of variation of HFMD incidence in southern and northeastern provinces. After adjustment for temperature, eastern and Northern provinces still had high variation HFMD incidence. We found a relatively strong association between weekly HFMD incidence and weekly average temperature. The association between the HFMD incidence and climatic variables spatial heterogeneity distributed across provinces. Future research should explore the risk factors that cause spatial correlated structure or high variation of HFMD incidence which can be explained by temperature. When analyzing association between HFMD incidence and climatic variables, spatial heterogeneity among provinces should be evaluated. Moreover, the extra-Poisson multilevel model was capable of modeling the association between overdispersion of HFMD incidence and climatic variables.

Developing GIS-based eastern equine encephalitis vector-host models in Tuskegee, Alabama.

PubMed

Jacob, Benjamin G; Burkett-Cadena, Nathan D; Luvall, Jeffrey C; Parcak, Sarah H; McClure, Christopher J W; Estep, Laura K; Hill, Geoffrey E; Cupp, Eddie W; Novak, Robert J; Unnasch, Thomas R

2010-02-24

A site near Tuskegee, Alabama was examined for vector-host activities of eastern equine encephalomyelitis virus (EEEV). Land cover maps of the study site were created in ArcInfo 9.2 from QuickBird data encompassing visible and near-infrared (NIR) band information (0.45 to 0.72 microm) acquired July 15, 2008. Georeferenced mosquito and bird sampling sites, and their associated land cover attributes from the study site, were overlaid onto the satellite data. SAS 9.1.4 was used to explore univariate statistics and to generate regression models using the field and remote-sampled mosquito and bird data. Regression models indicated that Culex erracticus and Northern Cardinals were the most abundant mosquito and bird species, respectively. Spatial linear prediction models were then generated in Geostatistical Analyst Extension of ArcGIS 9.2. Additionally, a model of the study site was generated, based on a Digital Elevation Model (DEM), using ArcScene extension of ArcGIS 9.2. For total mosquito count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 5.041 km, nugget of 6.325 km, lag size of 7.076 km, and range of 31.43 km, using 12 lags. For total adult Cx. erracticus count, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 5.764 km, nugget of 6.114 km, lag size of 7.472 km, and range of 32.62 km, using 12 lags. For the total bird count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 4.998 km, nugget of 5.413 km, lag size of 7.549 km and range of 35.27 km, using 12 lags. For the Northern Cardinal count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 6.387 km, nugget of 5.935 km, lag size of 8.549 km and a range of 41.38 km, using 12 lags. Results of the DEM analyses indicated a statistically significant inverse linear relationship between total sampled mosquito data and elevation (R2 = -.4262; p < .0001), with a standard deviation (SD) of 10.46, and total sampled bird data and elevation (R2 = -.5111; p < .0001), with a SD of 22.97. DEM statistics also indicated a significant inverse linear relationship between total sampled Cx. erracticus data and elevation (R2 = -.4711; p < .0001), with a SD of 11.16, and the total sampled Northern Cardinal data and elevation (R2 = -.5831; p < .0001), SD of 11.42. These data demonstrate that GIS/remote sensing models and spatial statistics can capture space-varying functional relationships between field-sampled mosquito and bird parameters for determining risk for EEEV transmission.

The spatial and temporal association of neighborhood drug markets and rates of sexually transmitted infections in an urban setting.

PubMed

Jennings, Jacky M; Woods, Stacy E; Curriero, Frank C

2013-09-01

This study examined temporal and spatial relationships between neighborhood drug markets and gonorrhea among census block groups from 2002 to 2005. This was a spatial, longitudinal ecologic study. Poisson regression was used with adjustment in final models for socioeconomic status, residential stability and vacant housing. Increased drug market arrests were significantly associated with a 11% increase gonorrhea (adjusted relative risk (ARR) 1.11; 95% CI 1.05, 1.16). Increased drug market arrests in adjacent neighborhoods were significantly associated with a 27% increase in gonorrhea (ARR 1.27; 95% CI 1.16, 1.36), independent of focal neighborhood drug markets. Increased drug market arrests in the previous year in focal neighborhoods were not associated with gonorrhea (ARR 1.04; 95% CI 0.98, 1.10), adjusting for focal and adjacent drug markets. While the temporal was not supported, our findings support an associative link between drug markets and gonorrhea. The findings suggest that drug markets and their associated sexual networks may extend beyond local neighborhood boundaries indicating the importance of including spatial lags in regression models investigating these associations. Copyright © 2013 Elsevier Ltd. All rights reserved.

The spatial and temporal association of neighborhood drug markets and rates of sexually transmitted infections in an urban setting

PubMed Central

Jennings, Jacky M.; Woods, Stacy E.; Curriero, Frank C.

2013-01-01

This study examined temporal and spatial relationships between neighborhood drug markets and gonorrhea among census block groups from 2002 to 2005. This was a spatial, longitudinal ecologic study. Poisson regression was used with adjustment in final models for socioeconomic status, residential stability and vacant housing. Increased drug market arrests were significantly associated with a 11% increase gonorrhea (Adjusted Relative Risk (ARR) 1.11; 95% CI 1.05, 1.16). Increased drug market arrests in adjacent neighborhoods were significantly associated with a 27% increase in gonorrhea (ARR 1.27; 95% CI 1.16, 1.36), independent of focal neighborhood drug markets. Increased drug market arrests in the previous year in focal neighborhoods were not associated with gonorrhea (ARR 1.04; 95% CI 0.98, 1.10), adjusting for focal and adjacent drug markets. While the temporal was not supported, our findings support an associative link between drug markets and gonorrhea. The findings suggest that drug markets and their associated sexual networks may extend beyond local neighborhood boundaries indicating the importance of including spatial lags in regression models investigating these associations. PMID:23872251

Spatial regression analysis of traffic crashes in Seoul.

PubMed

Rhee, Kyoung-Ah; Kim, Joon-Ki; Lee, Young-ihn; Ulfarsson, Gudmundur F

2016-06-01

Traffic crashes can be spatially correlated events and the analysis of the distribution of traffic crash frequency requires evaluation of parameters that reflect spatial properties and correlation. Typically this spatial aspect of crash data is not used in everyday practice by planning agencies and this contributes to a gap between research and practice. A database of traffic crashes in Seoul, Korea, in 2010 was developed at the traffic analysis zone (TAZ) level with a number of GIS developed spatial variables. Practical spatial models using available software were estimated. The spatial error model was determined to be better than the spatial lag model and an ordinary least squares baseline regression. A geographically weighted regression model provided useful insights about localization of effects. The results found that an increased length of roads with speed limit below 30 km/h and a higher ratio of residents below age of 15 were correlated with lower traffic crash frequency, while a higher ratio of residents who moved to the TAZ, more vehicle-kilometers traveled, and a greater number of access points with speed limit difference between side roads and mainline above 30 km/h all increased the number of traffic crashes. This suggests, for example, that better control or design for merging lower speed roads with higher speed roads is important. A key result is that the length of bus-only center lanes had the largest effect on increasing traffic crashes. This is important as bus-only center lanes with bus stop islands have been increasingly used to improve transit times. Hence the potential negative safety impacts of such systems need to be studied further and mitigated through improved design of pedestrian access to center bus stop islands. Copyright © 2016 Elsevier Ltd. All rights reserved.

Spatial analysis of highway incident durations in the context of Hurricane Sandy.

PubMed

Xie, Kun; Ozbay, Kaan; Yang, Hong

2015-01-01

The objectives of this study are (1) to develop an incident duration model which can account for the spatial dependence of duration observations, and (2) to investigate the impacts of a hurricane on incident duration. Highway incident data from New York City and its surrounding regions before and after Hurricane Sandy was used for the study. Moran's I statistics confirmed that durations of the neighboring incidents were spatially correlated. Moreover, Lagrange Multiplier tests suggested that the spatial dependence should be captured in a spatial lag specification. A spatial error model, a spatial lag model and a standard model without consideration of spatial effects were developed. The spatial lag model is found to outperform the others by capturing the spatial dependence of incident durations via a spatially lagged dependent variable. It was further used to assess the effects of hurricane-related variables on incident duration. The results show that the incidents during and post the hurricane are expected to have 116.3% and 79.8% longer durations than those that occurred in the regular time. However, no significant increase in incident duration is observed in the evacuation period before Sandy's landfall. Results of temporal stability tests further confirm the existence of the significant changes in incident duration patterns during and post the hurricane. Those findings can provide insights to aid in the development of hurricane evacuation plans and emergency management strategies. Copyright © 2014 Elsevier Ltd. All rights reserved.

Identifying Flood-Related Infectious Diseases in Anhui Province, China: A Spatial and Temporal Analysis

PubMed Central

Gao, Lu; Zhang, Ying; Ding, Guoyong; Liu, Qiyong; Jiang, Baofa

2016-01-01

The aim of this study was to explore infectious diseases related to the 2007 Huai River flood in Anhui Province, China. The study was based on the notified incidences of infectious diseases between June 29 and July 25 from 2004 to 2011. Daily incidences of notified diseases in 2007 were compared with the corresponding daily incidences during the same period in the other years (from 2004 to 2011, except 2007) by Poisson regression analysis. Spatial autocorrelation analysis was used to test the distribution pattern of the diseases. Spatial regression models were then performed to examine the association between the incidence of each disease and flood, considering lag effects and other confounders. After controlling the other meteorological and socioeconomic factors, malaria (odds ratio [OR] = 3.67, 95% confidence interval [CI] = 1.77–7.61), diarrhea (OR = 2.16, 95% CI = 1.24–3.78), and hepatitis A virus (HAV) infection (OR = 6.11, 95% CI = 1.04–35.84) were significantly related to the 2007 Huai River flood both from the spatial and temporal analyses. Special attention should be given to develop public health preparation and interventions with a focus on malaria, diarrhea, and HAV infection, in the study region. PMID:26903612

Effects of urban form on the urban heat island effect based on spatial regression model.

PubMed

Yin, Chaohui; Yuan, Man; Lu, Youpeng; Huang, Yaping; Liu, Yanfang

2018-09-01

The urban heat island (UHI) effect is becoming more of a concern with the accelerated process of urbanization. However, few studies have examined the effect of urban form on land surface temperature (LST) especially from an urban planning perspective. This paper used spatial regression model to investigate the effects of both land use composition and urban form on LST in Wuhan City, China, based on the regulatory planning management unit. Landsat ETM+ image data was used to estimate LST. Land use composition was calculated by impervious surface area proportion, vegetated area proportion, and water proportion, while urban form indicators included sky view factor (SVF), building density, and floor area ratio (FAR). We first tested for spatial autocorrelation of urban LST, which confirmed that a traditional regression method would be invalid. A spatial error model (SEM) was chosen because its parameters were better than a spatial lag model (SLM). The results showed that urban form metrics should be the focus for mitigation efforts of UHI effects. In addition, analysis of the relationship between urban form and UHI effect based on the regulatory planning management unit was helpful for promoting corresponding UHI effect mitigation rules in practice. Finally, the spatial regression model was recommended to be an appropriate method for dealing with problems related to the urban thermal environment. Results suggested that the impact of urbanization on the UHI effect can be mitigated not only by balancing various land use types, but also by optimizing urban form, which is even more effective. This research expands the scientific understanding of effects of urban form on UHI by explicitly analyzing indicators closely related to urban detailed planning at the level of regulatory planning management unit. In addition, it may provide important insights and effective regulation measures for urban planners to mitigate future UHI effects. Copyright © 2018 Elsevier B.V. All rights reserved.

Skunk and Raccoon Rabies in the Eastern United States: Temporal and Spatial Analysis

PubMed Central

Curns, Aaron T.; Rupprecht, Charles E.; Hanlon, Cathleen A.; Krebs, John W.; Childs, James E.

2003-01-01

Since 1981, an epizootic of raccoon rabies has spread throughout the eastern United States. A concomitant increase in reported rabies cases in skunks has raised concerns that an independent maintenance cycle of rabies virus in skunks could become established, affecting current strategies of wildlife rabies control programs. Rabies surveillance data from 1981 through 2000 obtained from the health departments of 11 eastern states were used to analyze temporal and spatial characteristics of rabies epizootics in each species. Spatial analysis indicated that epizootics in raccoons and skunks moved in a similar direction from 1990 to 2000. Temporal regression analysis showed that the number of rabid raccoons predicted the number of rabid skunks through time, with a 1-month lag. In areas where the raccoon rabies virus variant is enzootic, spatio-temporal analysis does not provide evidence that this rabies virus variant is currently cycling independently among skunks. PMID:14519253

Robust Short-Lag Spatial Coherence Imaging.

PubMed

Nair, Arun Asokan; Tran, Trac Duy; Bell, Muyinatu A Lediju

2018-03-01

Short-lag spatial coherence (SLSC) imaging displays the spatial coherence between backscattered ultrasound echoes instead of their signal amplitudes and is more robust to noise and clutter artifacts when compared with traditional delay-and-sum (DAS) B-mode imaging. However, SLSC imaging does not consider the content of images formed with different lags, and thus does not exploit the differences in tissue texture at each short-lag value. Our proposed method improves SLSC imaging by weighting the addition of lag values (i.e., M-weighting) and by applying robust principal component analysis (RPCA) to search for a low-dimensional subspace for projecting coherence images created with different lag values. The RPCA-based projections are considered to be denoised versions of the originals that are then weighted and added across lags to yield a final robust SLSC (R-SLSC) image. Our approach was tested on simulation, phantom, and in vivo liver data. Relative to DAS B-mode images, the mean contrast, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) improvements with R-SLSC images are 21.22 dB, 2.54, and 2.36, respectively, when averaged over simulated, phantom, and in vivo data and over all lags considered, which corresponds to mean improvements of 96.4%, 121.2%, and 120.5%, respectively. When compared with SLSC images, the corresponding mean improvements with R-SLSC images were 7.38 dB, 1.52, and 1.30, respectively (i.e., mean improvements of 14.5%, 50.5%, and 43.2%, respectively). Results show great promise for smoothing out the tissue texture of SLSC images and enhancing anechoic or hypoechoic target visibility at higher lag values, which could be useful in clinical tasks such as breast cyst visualization, liver vessel tracking, and obese patient imaging.

Spatial and temporal dynamics of deep percolation, lag time and recharge in an irrigated semi-arid region

NASA Astrophysics Data System (ADS)

Nazarieh, F.; Ansari, H.; Ziaei, A. N.; Izady, A.; Davari, K.; Brunner, P.

2018-05-01

The time required for deep percolating water to reach the water table can be considerable in areas with a thick vadose zone. Sustainable groundwater management, therefore, has to consider the spatial and temporal dynamics of groundwater recharge. The key parameters that control the lag time have been widely examined in soil physics using small-scale lysimeters and modeling studies. However, only a small number of studies have analyzed how deep-percolation rates affect groundwater recharge dynamics over large spatial scales. This study examined how the parameters influencing lag time affect groundwater recharge in a semi-arid catchment under irrigation (in northeastern Iran) using a numerical modeling approach. Flow simulations were performed by the MODFLOW-NWT code with the Vadose-Zone Flow (UZF) Package. Calibration of the groundwater model was based on data from 48 observation wells. Flow simulations showed that lag times vary from 1 to more than 100 months. A sensitivity analysis demonstrated that during drought conditions, the lag time was highly sensitive to the rate of deep percolation. The study illustrated two critical points: (1) the importance of providing estimates of the lag time as a basis for sustainable groundwater management, and (2) lag time not only depends on factors such as soil hydraulic conductivity or vadose zone depth but also depends on the deep-percolation rates and the antecedent soil-moisture condition. Therefore, estimates of the lag time have to be associated with specific percolation rates, in addition to depth to groundwater and soil properties.

Geographic Distribution of Trauma Centers and Injury Related Mortality in the United States

PubMed Central

Brown, Joshua B.; Rosengart, Matthew R.; Billiar, Timothy R.; Peitzman, Andrew B.; Sperry, Jason L.

2015-01-01

Background Regionalized trauma care improves outcomes; however access to care is not uniform across the US. The objective was to evaluate whether geographic distribution of trauma centers correlates with injury mortality across state trauma systems. Methods Level I/II trauma centers in the contiguous US were mapped. State-level age-adjusted injury fatality rates/100,000people were obtained and evaluated for spatial autocorrelation. Nearest neighbor ratios (NNR) were generated for each state. A NNR<1 indicates clustering, while NNR>1 indicates dispersion. NNR were tested for difference from random geographic distribution. Fatality rates and NNR were examined for correlation. Fatality rates were compared between states with trauma center clustering versus dispersion. Trauma center distribution and population density were evaluated. Spatial-lag regression determined the association between fatality rate and NNR, controlling for state-level demographics, population density, injury severity, trauma system resources, and socioeconomic factors. Results Fatality rates were spatially autocorrelated (Moran's I=0.35, p<0.01). Nine states had a clustered pattern (median NNR 0.55, IQR 0.48–0.60), 22 had a dispersed pattern (median NNR 2.00, IQR 1.68–3.99), and 10 had a random pattern (median NNR 0.90, IQR 0.85–1.00) of trauma center distribution. Fatality rate and NNR were correlated (ρ=0.34, p=0.03). Clustered states had a lower median injury fatality rate compared to dispersed states (56.9 [IQR 46.5–58.9] versus 64.9 [IQR 52.5–77.1], p=0.04). Dispersed compared to clustered states had more counties without a trauma center that had higher population density than counties with a trauma center (5.7% versus 1.2%, p<0.01). Spatial-lag regression demonstrated fatality rates increased 0.02/100,000persons for each unit increase in NNR (p<0.01). Conclusions Geographic distribution of trauma centers correlates with injury mortality, with more clustered state trauma centers associated with lower fatality rates. This may be a result of access relative to population density. These results may have implications for trauma system planning and requires further study to investigate underlying mechanisms PMID:26517780

Effects of environmental amenities and locational disamenities on home values in the Santa Cruz watershed: a hedonic analysis using census data

USGS Publications Warehouse

Arora, Gaurav; Frisvold, George; Norman, Laura

2014-01-01

For this study, we used the hedonic pricing method to measure the effects of natural amenities on home prices in the U.S-side of the Santa Cruz Watershed. We employed multivariate spatial regression techniques to estimate how difference factors affect median home values in 613 census block groups of the 2000 Census, accounting for spatial autocorrelation, spatial lags, and/or spatial heterogeneity in the data. Diagnostic tests suggest that failure to account for the hedonic model can be classified as (1) physical features of the housing stock, (2) neighborhood characteristics, and (3) environmental attributes. Census data was combined with GIS data for vegetation and land cover, land administration, measures of species richness and open space, and proximity to amenities and disamenities. Census block groups close to the US-Mexico border of airports/air bases were negative. Results suggest that policies to maintain biodiversity and open space provide economic benefits to homeowners, reflected in higher home values. Future research will quantify the marginal effects of regression explanatory variables on home values to assess their economic and policy significant. These marginal effects will be used as input indicators to discern potential economic impacts of various scenarios in the Santa Cruz Watershed Ecosystem Portfolio Model (SCWEPM). Future research will also expand this effort into the Mexican-portion of the watershed.

Aeromechanical stability of helicopters with composite rotor blades in forward flight

NASA Technical Reports Server (NTRS)

Smith, Edward C.; Chopra, Inderjit

1992-01-01

The aeromechanical stability, including air resonance in hover, air resonance in forward flight, and ground resonance, of a helicopter with elastically tailored composite rotor blades is investigated. Five soft-inplane hingeless rotor configurations, featuring elastic pitch-lag, pitch-flap and extension-torsion couplings, are analyzed. Elastic couplings introduced through tailored composite blade spars can have a powerful effect on both air and ground resonance behavior. Elastic pitch-flap couplings (positive and negative) strongly affect body, rotor and dynamic inflow modes. Air resonance stability is diminished by elastic pitch-flap couplings in hover and forward flight. Negative pitch-lag elastic coupling has a stabilizing effect on the regressive lag mode in hover and forward flight. The negative pitch-lag coupling has a detrimental effect on ground resonance stability. Extension-torsion elastic coupling (blade pitch decreases due to tension) decreases regressive lag mode stability in both airborne and ground contact conditions. Increasing thrust levels has a beneficial influence on ground resonance stability for rotors with pitch-flap and extension-torsion coupling and is only marginally effective in improving stability of rotors with pitch-lag coupling.

Air and ground resonance of helicopters with elastically tailored composite rotor blades

NASA Technical Reports Server (NTRS)

Smith, Edward C.; Chopra, Inderjit

1993-01-01

The aeromechanical stability, including air resonance in hover, air resonance in forward flight, and ground resonance, of a helicopter with elastically tailored composite rotor blades is investigated. Five soft-inplane hingeless rotor configurations, featuring elastic pitch-lag, pitch-flap and extension-torsion couplings, are analyzed. Elastic couplings introduced through tailored composite blade spars can have a powerful effect on both air and ground resonance behavior. Elastic pitch-flap couplings (positive and negative) strongly affect body, rotor and dynamic inflow modes. Air resonance stability is diminished by elastic pitch-flap couplings in hover and forwrad flight. Negative pitch-lag elastic coupling has a stabilizing effect on the regressive lag mode in hover and forward flight. The negative pitch-lag coupling has a detrimental effect on ground resonance stability. Extension-torsion elastic coupling (blade pitch decreases due to tension) decreases regressive lag mode stability in both airborne and ground contact conditions. Increasing thrust levels has a beneficial influence on ground resonance stability for rotors with pitch-flap and extension-torsion coupling and is only marginally effective in improving stability of rotors with pitch-lag coupling.

Performance and effects of land cover type on synthetic surface reflectance data and NDVI estimates for assessment and monitoring of semi-arid rangeland

USGS Publications Warehouse

Olexa, Edward M.; Lawrence, Rick L

2014-01-01

Federal land management agencies provide stewardship over much of the rangelands in the arid andsemi-arid western United States, but they often lack data of the proper spatiotemporal resolution andextent needed to assess range conditions and monitor trends. Recent advances in the blending of com-plementary, remotely sensed data could provide public lands managers with the needed information.We applied the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to five Landsat TMand concurrent Terra MODIS scenes, and used pixel-based regression and difference image analyses toevaluate the quality of synthetic reflectance and NDVI products associated with semi-arid rangeland. Pre-dicted red reflectance data consistently demonstrated higher accuracy, less bias, and stronger correlationwith observed data than did analogous near-infrared (NIR) data. The accuracy of both bands tended todecline as the lag between base and prediction dates increased; however, mean absolute errors (MAE)were typically ≤10%. The quality of area-wide NDVI estimates was less consistent than either spectra lband, although the MAE of estimates predicted using early season base pairs were ≤10% throughout the growing season. Correlation between known and predicted NDVI values and agreement with the 1:1regression line tended to decline as the prediction lag increased. Further analyses of NDVI predictions,based on a 22 June base pair and stratified by land cover/land use (LCLU), revealed accurate estimates through the growing season; however, inter-class performance varied. This work demonstrates the successful application of the STARFM algorithm to semi-arid rangeland; however, we encourage evaluation of STARFM’s performance on a per product basis, stratified by LCLU, with attention given to the influence of base pair selection and the impact of the time lag.

Exploring lag times between monthly atmospheric deposition and stream chemistry in Appalachian forests using cross-correlation

NASA Astrophysics Data System (ADS)

DeWalle, David R.; Boyer, Elizabeth W.; Buda, Anthony R.

2016-12-01

Forecasts of ecosystem changes due to variations in atmospheric emissions policies require a fundamental understanding of lag times between changes in chemical inputs and watershed response. Impacts of changes in atmospheric deposition in the United States have been documented using national and regional long-term environmental monitoring programs beginning several decades ago. Consequently, time series of weekly NADP atmospheric wet deposition and monthly EPA-Long Term Monitoring stream chemistry now exist for much of the Northeast which may provide insights into lag times. In this study of Appalachian forest basins, we estimated lag times for S, N and Cl by cross-correlating monthly data from four pairs of stream and deposition monitoring sites during the period from 1978 to 2012. A systems or impulse response function approach to cross-correlation was used to estimate lag times where the input deposition time series was pre-whitened using regression modeling and the stream response time series was filtered using the deposition regression model prior to cross-correlation. Cross-correlations for S were greatest at annual intervals over a relatively well-defined range of lags with the maximum correlations occurring at mean lags of 48 months. Chloride results were similar but more erratic with a mean lag of 57 months. Few high-correlation lags for N were indicated. Given the growing availability of atmospheric deposition and surface water chemistry monitoring data and our results for four Appalachian basins, further testing of cross-correlation as a method of estimating lag times on other basins appears justified.

Bayesian quantitative precipitation forecasts in terms of quantiles

NASA Astrophysics Data System (ADS)

Bentzien, Sabrina; Friederichs, Petra

2014-05-01

Ensemble prediction systems (EPS) for numerical weather predictions on the mesoscale are particularly developed to obtain probabilistic guidance for high impact weather. An EPS not only issues a deterministic future state of the atmosphere but a sample of possible future states. Ensemble postprocessing then translates such a sample of forecasts into probabilistic measures. This study focus on probabilistic quantitative precipitation forecasts in terms of quantiles. Quantiles are particular suitable to describe precipitation at various locations, since no assumption is required on the distribution of precipitation. The focus is on the prediction during high-impact events and related to the Volkswagen Stiftung funded project WEX-MOP (Mesoscale Weather Extremes - Theory, Spatial Modeling and Prediction). Quantile forecasts are derived from the raw ensemble and via quantile regression. Neighborhood method and time-lagging are effective tools to inexpensively increase the ensemble spread, which results in more reliable forecasts especially for extreme precipitation events. Since an EPS provides a large amount of potentially informative predictors, a variable selection is required in order to obtain a stable statistical model. A Bayesian formulation of quantile regression allows for inference about the selection of predictive covariates by the use of appropriate prior distributions. Moreover, the implementation of an additional process layer for the regression parameters accounts for spatial variations of the parameters. Bayesian quantile regression and its spatially adaptive extension is illustrated for the German-focused mesoscale weather prediction ensemble COSMO-DE-EPS, which runs (pre)operationally since December 2010 at the German Meteorological Service (DWD). Objective out-of-sample verification uses the quantile score (QS), a weighted absolute error between quantile forecasts and observations. The QS is a proper scoring function and can be decomposed into reliability, resolutions and uncertainty parts. A quantile reliability plot gives detailed insights in the predictive performance of the quantile forecasts.

Time series regression model for infectious disease and weather.

PubMed

Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro

2015-10-01

Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Selecting multiple features delays perception, but only when targets are horizontally arranged.

PubMed

Lo, Shih-Yu

2017-01-01

Based on the finding that perception is lagged by attention split on multiple features (Lo et al., 2012), this study investigated how the feature-based lag effect interacts with the target spatial arrangement. Participants were presented with gratings the spatial frequencies of which constantly changed. The task was to monitor two gratings of the same or different colors and report their spatial frequencies right before the stimulus offset. The results showed a perceptual lag wherein the reported value was closer to the physical value some time prior to the stimulus offset. This lag effect was larger when the two gratings were of different colors than when they were the same color. Furthermore, the feature-based lag effect was statistically significant when the two gratings were horizontally arranged but not when they were vertically or diagonally arranged. A model is proposed to explain the effect of target arrangement: When targets are horizontally arranged, selecting an additional feature delays perception. When targets are vertically or diagonally arranged, target selection for the lower field is prioritized. This prioritization on the lower target might prompt observers to only select the lower target and ignore the upper one, and this causes more perceptual errors without delaying perception. © 2017 Elsevier B.V. All rights reserved.

Development and validation of a short-lag spatial coherence theory for photoacoustic imaging

NASA Astrophysics Data System (ADS)

Graham, Michelle T.; Lediju Bell, Muyinatu A.

2018-02-01

We previously derived spatial coherence theory to be implemented for studying theoretical properties of ShortLag Spatial Coherence (SLSC) beamforming applied to photoacoustic images. In this paper, our newly derived theoretical equation is evaluated to generate SLSC images of a point target and a 1.2 mm diameter target and corresponding lateral profiles. We compared SLSC images simulated solely based on our theory to SLSC images created after beamforming acoustic channel data from k-Wave simulations of 1.2 mm-diameter disc target. This process was repeated for a point target and the full width at half the maximum signal amplitudes were measured to estimate the resolution of each imaging system. Resolution as a function of lag was comparable for the first 10% of the receive aperture (i.e., the short-lag region), after which resolution measurements diverged by a maximum of 1 mm between the two types of simulated images. These results indicate the potential for both simulation methods to be utilized as independent resources to study coherence-based photoacoustic beamformers when imaging point-like targets.

Modelling the perennial energy crop market: the role of spatial diffusion

PubMed Central

Alexander, Peter; Moran, Dominic; Rounsevell, Mark D. A.; Smith, Pete

2013-01-01

Biomass produced from energy crops, such as Miscanthus and short rotation coppice is expected to contribute to renewable energy targets, but the slower than anticipated development of the UK market implies the need for greater understanding of the factors that govern adoption. Here, we apply an agent-based model of the UK perennial energy crop market, including the contingent interaction of supply and demand, to understand the spatial and temporal dynamics of energy crop adoption. Results indicate that perennial energy crop supply will be between six and nine times lower than previously published, because of time lags in adoption arising from a spatial diffusion process. The model simulates time lags of at least 20 years, which is supported empirically by the analogue of oilseed rape adoption in the UK from the 1970s. This implies the need to account for time lags arising from spatial diffusion in evaluating land-use change, climate change (mitigation or adaptation) or the adoption of novel technologies. PMID:24026474

Modelling the perennial energy crop market: the role of spatial diffusion.

PubMed

Alexander, Peter; Moran, Dominic; Rounsevell, Mark D A; Smith, Pete

2013-11-06

Biomass produced from energy crops, such as Miscanthus and short rotation coppice is expected to contribute to renewable energy targets, but the slower than anticipated development of the UK market implies the need for greater understanding of the factors that govern adoption. Here, we apply an agent-based model of the UK perennial energy crop market, including the contingent interaction of supply and demand, to understand the spatial and temporal dynamics of energy crop adoption. Results indicate that perennial energy crop supply will be between six and nine times lower than previously published, because of time lags in adoption arising from a spatial diffusion process. The model simulates time lags of at least 20 years, which is supported empirically by the analogue of oilseed rape adoption in the UK from the 1970s. This implies the need to account for time lags arising from spatial diffusion in evaluating land-use change, climate change (mitigation or adaptation) or the adoption of novel technologies.

[Spatial pattern of land surface dead combustible fuel load in Huzhong forest area in Great Xing'an Mountains].

PubMed

Liu, Zhi-Hua; Chang, Yu; Chen, Hong-Wei; Zhou, Rui; Jing, Guo-Zhi; Zhang, Hong-Xin; Zhang, Chang-Meng

2008-03-01

By using geo-statistics and based on time-lag classification standard, a comparative study was made on the land surface dead combustible fuels in Huzhong forest area in Great Xing'an Mountains. The results indicated that the first level land surface dead combustible fuel, i. e., 1 h time-lag dead fuel, presented stronger spatial auto-correlation, with an average of 762.35 g x m(-2) and contributing to 55.54% of the total load. Its determining factors were species composition and stand age. The second and third levels land surface dead combustible fuel, i. e., 10 h and 100 h time-lag dead fuels, had a sum of 610.26 g x m(-2), and presented weaker spatial auto-correlation than 1 h time-lag dead fuel. Their determining factor was the disturbance history of forest stand. The complexity and heterogeneity of the factors determining the quality and quantity of forest land surface dead combustible fuels were the main reasons for the relatively inaccurate interpolation. However, the utilization of field survey data coupled with geo-statistics could easily and accurately interpolate the spatial pattern of forest land surface dead combustible fuel loads, and indirectly provide a practical basis for forest management.

Considering spatial heterogeneity in the distributed lag non-linear model when analyzing spatiotemporal data.

PubMed

Chien, Lung-Chang; Guo, Yuming; Li, Xiao; Yu, Hwa-Lung

2018-01-01

The distributed lag non-linear (DLNM) model has been frequently used in time series environmental health research. However, its functionality for assessing spatial heterogeneity is still restricted, especially in analyzing spatiotemporal data. This study proposed a solution to take a spatial function into account in the DLNM, and compared the influence with and without considering spatial heterogeneity in a case study. This research applied the DLNM to investigate non-linear lag effect up to 7 days in a case study about the spatiotemporal impact of fine particulate matter (PM 2.5 ) on preschool children's acute respiratory infection in 41 districts of northern Taiwan during 2005 to 2007. We applied two spatiotemporal methods to impute missing air pollutant data, and included the Markov random fields to analyze district boundary data in the DLNM. When analyzing the original data without a spatial function, the overall PM 2.5 effect accumulated from all lag-specific effects had a slight variation at smaller PM 2.5 measurements, but eventually decreased to relative risk significantly <1 when PM 2.5 increased. While analyzing spatiotemporal imputed data without a spatial function, the overall PM 2.5 effect did not decrease but increased in monotone as PM 2.5 increased over 20 μg/m 3 . After adding a spatial function in the DLNM, spatiotemporal imputed data conducted similar results compared with the overall effect from the original data. Moreover, the spatial function showed a clear and uneven pattern in Taipei, revealing that preschool children living in 31 districts of Taipei were vulnerable to acute respiratory infection. Our findings suggest the necessity of including a spatial function in the DLNM to make a spatiotemporal analysis available and to conduct more reliable and explainable research. This study also revealed the analytical impact if spatial heterogeneity is ignored.

Asian Dust Storm Elevates Children’s Respiratory Health Risks: A Spatiotemporal Analysis of Children’s Clinic Visits across Taipei (Taiwan)

PubMed Central

Yu, Hwa-Lung; Chien, Lung-Chang; Yang, Chiang-Hsing

2012-01-01

Concerns have been raised about the adverse impact of Asian dust storms (ADS) on human health; however, few studies have examined the effect of these events on children’s health. Using databases from the Taiwan National Health Insurance and Taiwan Environmental Protection Agency, this study investigates the documented daily visits of children to respiratory clinics during and after ADS that occurred from 1997 to 2007 among 12 districts across Taipei City by applying a Bayesian structural additive regressive model controlled for spatial and temporal patterns. This study finds that the significantly impact of elevated children’s respiratory clinic visits happened after ADS. Five of the seven lagged days had increasing percentages of relative rate, which was consecutively elevated from a 2-day to a 5-day lag by 0.63%∼2.19% for preschool children (i.e., 0∼6 years of age) and 0.72%∼3.17% for school children (i.e., 7∼14 years of age). The spatial pattern of clinic visits indicated that geographical heterogeneity was possibly associated with the clinic’s location and accessibility. Moreover, day-of-week effects were elevated on Monday, Friday, and Saturday. We concluded that ADS may significantly increase the risks of respiratory diseases consecutively in the week after exposure, especially in school children. PMID:22848461

Fine-scale landscape genetics of the American badger (Taxidea taxus): disentangling landscape effects and sampling artifacts in a poorly understood species

PubMed Central

Kierepka, E M; Latch, E K

2016-01-01

Landscape genetics is a powerful tool for conservation because it identifies landscape features that are important for maintaining genetic connectivity between populations within heterogeneous landscapes. However, using landscape genetics in poorly understood species presents a number of challenges, namely, limited life history information for the focal population and spatially biased sampling. Both obstacles can reduce power in statistics, particularly in individual-based studies. In this study, we genotyped 233 American badgers in Wisconsin at 12 microsatellite loci to identify alternative statistical approaches that can be applied to poorly understood species in an individual-based framework. Badgers are protected in Wisconsin owing to an overall lack in life history information, so our study utilized partial redundancy analysis (RDA) and spatially lagged regressions to quantify how three landscape factors (Wisconsin River, Ecoregions and land cover) impacted gene flow. We also performed simulations to quantify errors created by spatially biased sampling. Statistical analyses first found that geographic distance was an important influence on gene flow, mainly driven by fine-scale positive spatial autocorrelations. After controlling for geographic distance, both RDA and regressions found that Wisconsin River and Agriculture were correlated with genetic differentiation. However, only Agriculture had an acceptable type I error rate (3–5%) to be considered biologically relevant. Collectively, this study highlights the benefits of combining robust statistics and error assessment via simulations and provides a method for hypothesis testing in individual-based landscape genetics. PMID:26243136

Alcohol outlets, social disorganization, and robberies: accounting for neighborhood characteristics and alcohol outlet types.

PubMed

Snowden, Aleksandra J; Freiburger, Tina L

2015-05-01

We estimated spatially lagged regression and spatial regime models to determine if the variation in total, on-premise, and off-premise alcohol outlet(1) density is related to robbery density, while controlling for direct and moderating effects of social disorganization.(2) Results suggest that the relationship between alcohol outlet density and robbery density is sensitive to the measurement of social disorganization levels. Total alcohol outlet density and off-premise alcohol outlet density were significantly associated with robbery density when social disorganization variables were included separately in the models. However, when social disorganization levels were captured as a four item index, only the association between off-premise alcohol outlets and robbery density remained significant. More work is warranted in identifying the role of off-premise alcohol outlets and their characteristics in robbery incidents. Copyright © 2015 Elsevier Inc. All rights reserved.

Lag and seasonality considerations in evaluating AVHRR NDVI response to precipitation

USGS Publications Warehouse

Ji, Lei; Peters, Albert J.

2005-01-01

Assessment of the relationship between the normalized difference vegetation index (NDVI) and precipitation is important in understanding vegetation and climate interaction at a large scale. NDVI response to precipitation, however, is difficult to quantify due to the lag and seasonality effects, which will vary due to vegetation cover type, soils and climate. A time series analysis was performed on biweekly NDVI and precipitation around weather stations in the northern and central U.S. Great Plains. Regression models that incorporate lag and seasonality effects were used to quantify the relationship between NDVI and lagged precipitation in grasslands and croplands. It was found that the time lag was shorter in the early growing season, but longer in the mid- to late-growing season for most locations. The regression models with seasonal adjustment indicate that the relationship between NDVI and precipitation over the entire growing season was strong, with R2 values of 0.69 and 0.72 for grasslands and croplands, respectively. We conclude that vegetation greenness can be predicted using current and antecedent precipitation, if seasonal effects are taken into account.

Accounting for and predicting the influence of spatial autocorrelation in water quality modeling

NASA Astrophysics Data System (ADS)

Miralha, L.; Kim, D.

2017-12-01

Although many studies have attempted to investigate the spatial trends of water quality, more attention is yet to be paid to the consequences of considering and ignoring the spatial autocorrelation (SAC) that exists in water quality parameters. Several studies have mentioned the importance of accounting for SAC in water quality modeling, as well as the differences in outcomes between models that account for and ignore SAC. However, the capacity to predict the magnitude of such differences is still ambiguous. In this study, we hypothesized that SAC inherently possessed by a response variable (i.e., water quality parameter) influences the outcomes of spatial modeling. We evaluated whether the level of inherent SAC is associated with changes in R-Squared, Akaike Information Criterion (AIC), and residual SAC (rSAC), after accounting for SAC during modeling procedure. The main objective was to analyze if water quality parameters with higher Moran's I values (inherent SAC measure) undergo a greater increase in R² and a greater reduction in both AIC and rSAC. We compared a non-spatial model (OLS) to two spatial regression approaches (spatial lag and error models). Predictor variables were the principal components of topographic (elevation and slope), land cover, and hydrological soil group variables. We acquired these data from federal online sources (e.g. USGS). Ten watersheds were selected, each in a different state of the USA. Results revealed that water quality parameters with higher inherent SAC showed substantial increase in R² and decrease in rSAC after performing spatial regressions. However, AIC values did not show significant changes. Overall, the higher the level of inherent SAC in water quality variables, the greater improvement of model performance. This indicates a linear and direct relationship between the spatial model outcomes (R² and rSAC) and the degree of SAC in each water quality variable. Therefore, our study suggests that the inherent level of SAC in response variables can predict improvements in models even before performing spatial regression approaches. We also recognize the constraints of this research and suggest that further studies focus on better ways of defining spatial neighborhoods, considering the differences among stations set in tributaries near to each other and in upstream areas.

Geographic distribution of trauma centers and injury-related mortality in the United States.

PubMed

Brown, Joshua B; Rosengart, Matthew R; Billiar, Timothy R; Peitzman, Andrew B; Sperry, Jason L

2016-01-01

Regionalized trauma care improves outcomes; however, access to care is not uniform across the United States. The objective was to evaluate whether geographic distribution of trauma centers correlates with injury mortality across state trauma systems. Level I or II trauma centers in the contiguous United States were mapped. State-level age-adjusted injury fatality rates per 100,000 people were obtained and evaluated for spatial autocorrelation. Nearest neighbor ratios (NNRs) were generated for each state. A NNR less than 1 indicates clustering, while a NNR greater than 1 indicates dispersion. NNRs were tested for difference from random geographic distribution. Fatality rates and NNRs were examined for correlation. Fatality rates were compared between states with trauma center clustering versus dispersion. Trauma center distribution and population density were evaluated. Spatial-lag regression determined the association between fatality rate and NNR, controlling for state-level demographics, population density, injury severity, trauma system resources, and socioeconomic factors. Fatality rates were spatially autocorrelated (Moran's I = 0.35, p < 0.01). Nine states had a clustered pattern (median NNR, 0.55; interquartile range [IQR], 0.48-0.60), 22 had a dispersed pattern (median NNR, 2.00; IQR, 1.68-3.99), and 10 had a random pattern (median NNR, 0.90; IQR, 0.85-1.00) of trauma center distribution. Fatality rate and NNR were correlated (ρ = 0.34, p = 0.03). Clustered states had a lower median injury fatality rate compared with dispersed states (56.9 [IQR, 46.5-58.9] vs. 64.9 [IQR, 52.5-77.1]; p = 0.04). Dispersed compared with clustered states had more counties without a trauma center that had higher population density than counties with a trauma center (5.7% vs. 1.2%, p < 0.01). Spatial-lag regression demonstrated that fatality rates increased by 0.02 per 100,000 persons for each unit increase in NNR (p < 0.01). Geographic distribution of trauma centers correlates with injury mortality, with more clustered state trauma centers associated with lower fatality rates. This may be a result of access relative to population density. These results may have implications for trauma system planning and require further study to investigate underlying mechanisms. Therapeutic/care management study, level IV.

Using Time-Series Regression to Predict Academic Library Circulations.

ERIC Educational Resources Information Center

Brooks, Terrence A.

1984-01-01

Four methods were used to forecast monthly circulation totals in 15 midwestern academic libraries: dummy time-series regression, lagged time-series regression, simple average (straight-line forecasting), monthly average (naive forecasting). In tests of forecasting accuracy, dummy regression method and monthly mean method exhibited smallest average…

Spatial Dynamics and Determinants of County-Level Education Expenditure in China

ERIC Educational Resources Information Center

Gu, Jiafeng

2012-01-01

In this paper, a multivariate spatial autoregressive model of local public education expenditure determination with autoregressive disturbance is developed and estimated. The existence of spatial interdependence is tested using Moran's I statistic and Lagrange multiplier test statistics for both the spatial error and spatial lag models. The full…

The time-course of recovery from interruption during reading: eye movement evidence for the role of interruption lag and spatial memory.

PubMed

Cane, James E; Cauchard, Fabrice; Weger, Ulrich W

2012-01-01

Two experiments examined how interruptions impact reading and how interruption lags and the reader's spatial memory affect the recovery from such interruptions. Participants read paragraphs of text and were interrupted unpredictably by a spoken news story while their eye movements were monitored. Time made available for consolidation prior to responding to the interruption did not aid reading resumption. However, providing readers with a visual cue that indicated the interruption location did aid task resumption substantially in Experiment 2. Taken together, the findings show that the recovery from interruptions during reading draws on spatial memory resources and can be aided by processes that support spatial memory. Practical implications are discussed.

A Time Series Analysis: Weather Factors, Human Migration and Malaria Cases in Endemic Area of Purworejo, Indonesia, 2005–2014

PubMed Central

REJEKI, Dwi Sarwani Sri; NURHAYATI, Nunung; AJI, Budi; MURHANDARWATI, E. Elsa Herdiana; KUSNANTO, Hari

2018-01-01

Background: Climatic and weather factors become important determinants of vector-borne diseases transmission like malaria. This study aimed to prove relationships between weather factors with considering human migration and previous case findings and malaria cases in endemic areas in Purworejo during 2005–2014. Methods: This study employed ecological time series analysis by using monthly data. The independent variables were the maximum temperature, minimum temperature, maximum humidity, minimum humidity, precipitation, human migration, and previous malaria cases, while the dependent variable was positive malaria cases. Three models of count data regression analysis i.e. Poisson model, quasi-Poisson model, and negative binomial model were applied to measure the relationship. The least Akaike Information Criteria (AIC) value was also performed to find the best model. Negative binomial regression analysis was considered as the best model. Results: The model showed that humidity (lag 2), precipitation (lag 3), precipitation (lag 12), migration (lag1) and previous malaria cases (lag 12) had a significant relationship with malaria cases. Conclusion: Weather, migration and previous malaria cases factors need to be considered as prominent indicators for the increase of malaria case projection. PMID:29900134

Response of spectral vegetation indices to soil moisture in grasslands and shrublands

USGS Publications Warehouse

Zhang, Li; Ji, Lei; Wylie, Bruce K.

2011-01-01

The relationships between satellite-derived vegetation indices (VIs) and soil moisture are complicated because of the time lag of the vegetation response to soil moisture. In this study, we used a distributed lag regression model to evaluate the lag responses of VIs to soil moisture for grasslands and shrublands at Soil Climate Analysis Network sites in the central and western United States. We examined the relationships between Moderate Resolution Imaging Spectroradiometer (MODIS)-derived VIs and soil moisture measurements. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) showed significant lag responses to soil moisture. The lag length varies from 8 to 56 days for NDVI and from 16 to 56 days for NDWI. However, the lag response of NDVI and NDWI to soil moisture varied among the sites. Our study suggests that the lag effect needs to be taken into consideration when the VIs are used to estimate soil moisture.

The lag effects and vulnerabilities of temperature effects on cardiovascular disease mortality in a subtropical climate zone in China.

PubMed

Huang, Jixia; Wang, Jinfeng; Yu, Weiwei

2014-04-11

This research quantifies the lag effects and vulnerabilities of temperature effects on cardiovascular disease in Changsha--a subtropical climate zone of China. A Poisson regression model within a distributed lag nonlinear models framework was used to examine the lag effects of cold- and heat-related CVD mortality. The lag effect for heat-related CVD mortality was just 0-3 days. In contrast, we observed a statistically significant association with 10-25 lag days for cold-related CVD mortality. Low temperatures with 0-2 lag days increased the mortality risk for those ≥65 years and females. For all ages, the cumulative effects of cold-related CVD mortality was 6.6% (95% CI: 5.2%-8.2%) for 30 lag days while that of heat-related CVD mortality was 4.9% (95% CI: 2.0%-7.9%) for 3 lag days. We found that in Changsha city, the lag effect of hot temperatures is short while the lag effect of cold temperatures is long. Females and older people were more sensitive to extreme hot and cold temperatures than males and younger people.

Temporal and micro-spatial heterogeneity in the distribution of Anopheles vectors of malaria along the Kenyan coast

PubMed Central

2013-01-01

Background The distribution of anopheline mosquitoes is determined by temporally dynamic environmental and human-associated variables, operating over a range of spatial scales. Macro-spatial short-term trends are driven predominantly by prior (lagged) seasonal changes in climate, which regulate the abundance of suitable aquatic larval habitats. Micro-spatial distribution is determined by the location of these habitats, proximity and abundance of available human bloodmeals and prevailing micro-climatic conditions. The challenge of analysing—in a single coherent statistical framework—the lagged and distributed effect of seasonal climate changes simultaneously with the effects of an underlying hierarchy of spatial factors has hitherto not been addressed. Methods Data on Anopheles gambiae sensu stricto and A. funestus collected from households in Kilifi district, Kenya, were analysed using polynomial distributed lag generalized linear mixed models (PDL GLMMs). Results Anopheline density was positively and significantly associated with amount of rainfall between 4 to 47 days, negatively and significantly associated with maximum daily temperature between 5 and 35 days, and positively and significantly associated with maximum daily temperature between 29 and 48 days in the past (depending on Anopheles species). Multiple-occupancy households harboured greater mosquito numbers than single-occupancy households. A significant degree of mosquito clustering within households was identified. Conclusions The PDL GLMMs developed here represent a generalizable framework for analysing hierarchically-structured data in combination with explanatory variables which elicit lagged effects. The framework is a valuable tool for facilitating detailed understanding of determinants of the spatio-temporal distribution of Anopheles. Such understanding facilitates delivery of targeted, cost-effective and, in certain circumstances, preventative antivectorial interventions against malaria. PMID:24330615

Managed care and the diffusion of endoscopy in fee-for-service Medicare.

PubMed

Mobley, Lee Rivers; Subramanian, Sujha; Koschinsky, Julia; Frech, H E; Trantham, Laurel Clayton; Anselin, Luc

2011-12-01

To determine whether Medicare managed care penetration impacted the diffusion of endoscopy services (sigmoidoscopy, colonoscopy) among the fee-for-service (FFS) Medicare population during 2001-2006. We model utilization rates for colonoscopy or sigmoidoscopy as impacted by both market supply and demand factors. We use spatial regression to perform ecological analysis of county-area utilization rates over two time intervals (2001-2003, 2004-2006) following Medicare benefits expansion in 2001 to cover colonoscopy for persons of average risk. We examine each technology in separate cross-sectional regressions estimated over early and later periods to assess differential effects on diffusion over time. We discuss selection factors in managed care markets and how failure to control perfectly for market selection might impact our managed care spillover estimates. Areas with worse socioeconomic conditions have lower utilization rates, especially for colonoscopy. Holding constant statistically the socioeconomic factors, we find that managed care spillover effects onto FFS Medicare utilization rates are negative for colonoscopy and positive for sigmoidoscopy. The spatial lag estimates are conservative and interpreted as a lower bound on true effects. Our findings suggest that managed care presence fostered persistence of the older technology during a time when it was rapidly being replaced by the newer technology. © Health Research and Educational Trust.

Population dynamics of pond zooplankton, I. Diaptomus pallidus Herrick

USGS Publications Warehouse

Armitage, K.B.; Saxena, B.; Angino, E.E.

1973-01-01

The simultaneous and lag relationships between 27 environmental variables and seven population components of a perennial calanoid copepod were examined by simple and partial correlations and stepwise regression. The analyses consistently explained more than 70% of the variation of a population component. The multiple correlation coefficient (R) usually was highest in no lag or in 3-week or 4-week lag except for clutch size in which R was highest in 1-week lag. Population control, egg-bearing, and clutch size were affected primarily by environmental components categorized as weather; food apparently was relatively minor in affecting population control or reproduction. ?? 1973 Dr. W. Junk B.V. Publishers.

Developing 3D Spatial Skills for K-12 Students

ERIC Educational Resources Information Center

Sorby, Sheryl A.

2006-01-01

Three-dimensional spatial skills have been shown to be critical to success in engineering and other technological fields. Well-developed 3D spatial skills are particularly important for success in engineering graphics courses. Further, 3D spatial skills of women lag significantly behind those of their male counterparts, which could hinder their…

The impact of anthropogenic emissions and meteorological conditions on the spatial variation of ambient SO2 concentrations: A panel study of 113 Chinese cities.

PubMed

Yang, Xue; Wang, Shaojian; Zhang, Wenzhong; Zhan, Dongsheng; Li, Jiaming

2017-04-15

China has received increased international criticism in recent years in relation to its air pollution levels, both in terms of the transmission of pollutants across international borders and the attendant adverse health effects being witnessed. Whilst existing research has examined the factors influencing ambient air pollutant concentrations, previous studies have failed to adequately explore the determinants of such concentrations from either a source or diffusion perspective. This study addressed both source (specifically, anthropogenic emissions) and diffusion (namely, meteorological conditions) indicators, in order to detect their respective impacts on the spatial variations seen in the distribution of air pollution. Spatial panel data for 113 major cities in China was processed using a range of global regression models-the ordinary least square model, the spatial lag model, and the spatial error model-as well as a local, geographic weighted regression (GWR) model. Results from the study suggest that in 2014, average SO 2 concentrations exceeded China's first-level target. The most polluted cities were found to be predominantly located in northern China, while less polluted cities were located in southern China. Global regression results indicated that precipitation exerts a significant effect on SO 2 reduction (p<0.001) and that a regional increase of 1mm in precipitation can reduce SO 2 concentrations by 0.026μg/m 3 . Both emission and temperature factors were found to aggravate SO 2 concentrations, although no such significant correlation was found in relation to wind speed. GWR results suggest that the association between SO 2 and its factors varied over space. Increased emissions were found to be able to produce more pollution in the northwest than in other parts of the country. Higher wind speeds and temperatures in northwestern areas were shown to reinforce SO 2 pollution, while in southern regions, they had the opposite effect. Further, increased precipitation was found to exert a greater inhibitory effect on SO 2 pollution in the country's northeast than that in other areas. Our findings could provide a detailed reference for formulating regionally specific emission reduction policies in China. Copyright © 2016 Elsevier B.V. All rights reserved.

Homozygous diploid deletion strains of Saccharomyces cerevisiae that determine lag phase and dehydration tolerance.

PubMed

D'Elia, Riccardo; Allen, Patricia L; Johanson, Kelly; Nickerson, Cheryl A; Hammond, Timothy G

2005-06-01

This study identifies genes that determine length of lag phase, using the model eukaryotic organism, Saccharomyces cerevisiae. We report growth of a yeast deletion series following variations in the lag phase induced by variable storage times after drying-down yeast on filters. Using a homozygous diploid deletion pool, lag times ranging from 0 h to 90 h were associated with increased drop-out of mitochondrial genes and increased survival of nuclear genes. Simple linear regression (R2 analysis) shows that there are over 500 genes for which > 70% of the variation can be explained by lag alone. In the genes with a positive correlation, such that the gene abundance increases with lag and hence the deletion strain is suitable for survival during prolonged storage, there is a strong predominance of nucleonic genes. In the genes with a negative correlation, such that the gene abundance decreases with lag and hence the strain may be critical for getting yeast out of the lag phase, there is a strong predominance of glycoproteins and transmembrane proteins. This study identifies yeast deletion strains with survival advantage on prolonged storage and amplifies our understanding of the genes critical for getting out of the lag phase.

Homozygous diploid deletion strains of Saccharomyces cerevisiae that determine lag phase and dehydration tolerance

NASA Technical Reports Server (NTRS)

D'Elia, Riccardo; Allen, Patricia L.; Johanson, Kelly; Nickerson, Cheryl A.; Hammond, Timothy G.

2005-01-01

This study identifies genes that determine length of lag phase, using the model eukaryotic organism, Saccharomyces cerevisiae. We report growth of a yeast deletion series following variations in the lag phase induced by variable storage times after drying-down yeast on filters. Using a homozygous diploid deletion pool, lag times ranging from 0 h to 90 h were associated with increased drop-out of mitochondrial genes and increased survival of nuclear genes. Simple linear regression (R2 analysis) shows that there are over 500 genes for which > 70% of the variation can be explained by lag alone. In the genes with a positive correlation, such that the gene abundance increases with lag and hence the deletion strain is suitable for survival during prolonged storage, there is a strong predominance of nucleonic genes. In the genes with a negative correlation, such that the gene abundance decreases with lag and hence the strain may be critical for getting yeast out of the lag phase, there is a strong predominance of glycoproteins and transmembrane proteins. This study identifies yeast deletion strains with survival advantage on prolonged storage and amplifies our understanding of the genes critical for getting out of the lag phase.

A Spatial Analysis of County-level Variation in Syphilis and Gonorrhea in Guangdong Province, China

PubMed Central

Tan, Nicholas X.; Messina, Jane P.; Yang, Li-Gang; Yang, Bin; Emch, Michael; Chen, Xiang-Sheng; Cohen, Myron S.; Tucker, Joseph D.

2011-01-01

Background Sexually transmitted infections (STI) have made a resurgence in many rapidly developing regions of southern China, but there is little understanding of the social changes that contribute to this spatial distribution of STI. This study examines county-level socio-demographic characteristics associated with syphilis and gonorrhea in Guangdong Province. Methods/Principal Findings This study uses linear regression and spatial lag regression to determine county-level (n = 97) socio-demographic characteristics associated with a greater burden of syphilis, gonorrhea, and a combined syphilis/gonorrhea index. Data were obtained from the 2005 China Population Census and published public health data. A range of socio-demographic variables including gross domestic product, the Gender Empowerment Measure, standard of living, education level, migrant population and employment are examined. Reported syphilis and gonorrhea cases are disproportionately clustered in the Pearl River Delta, the central region of Guangdong Province. A higher fraction of employed men among the adult population, higher fraction of divorced men among the adult population, and higher standard of living (based on water availability and people per room) are significantly associated with higher STI cases across all three models. Gross domestic product and gender inequality measures are not significant predictors of reported STI in these models. Conclusions/Significance Although many ecological studies of STIs have found poverty to be associated with higher reported STI, this analysis found a greater number of reported syphilis cases in counties with a higher standard of living. Spatially targeted syphilis screening measures in regions with a higher standard of living may facilitate successful control efforts. This analysis also reinforces the importance of changing male sexual behaviors as part of a comprehensive response to syphilis control in China. PMID:21573127

Geographic variations of ecosystem service intensity in Fuzhou City, China.

PubMed

Hu, Xisheng; Hong, Wei; Qiu, Rongzu; Hong, Tao; Chen, Can; Wu, Chengzhen

2015-04-15

Ecosystem services are strongly influenced by the landscape configuration of natural and human systems. So they are heterogeneous across landscapes. However lack of the knowledge of spatial variations of ecosystem services constrains the effective management and conservation of ecosystems. We presented a spatially explicit and quantitative assessment of the geographic variations in ecosystem services for the Fuzhou City in 2009 using exploratory spatial data analysis (ESDA) and semivariance analysis. Results confirmed a significant and positive spatial autocorrelation, and revealed several hot-spots and cold-spots for the spatial distribution of ecosystem service intensity (ESI) in the study area. Also the trend surface analysis indicated that the level of ESI tended to be reduced gradually from north to south and from west to east, with a trough in the urban central area, which was quite in accordance with land-use structure. A more precise cluster map was then developed using the range of lag distance, deriving from semivariance analysis, as neighborhood size instead of default value in the software of ESRI ArcGIS 10.0, and geographical clusters where population growth and land-use pressure varied significantly and positively with ESI across the city were also created by geographically weighted regression (GWR). This study has good policy implications applicable to prioritize areas for conservation or construction, and design ecological corridor to improve ecosystem service delivery to benefiting areas. Copyright © 2015 Elsevier B.V. All rights reserved.

Spatiotemporal variation in heat-related out-of-hospital cardiac arrest during the summer in Japan.

PubMed

Onozuka, Daisuke; Hagihara, Akihito

2017-04-01

Although several studies have reported the impacts of extremely high temperature on cardiovascular diseases, few studies have investigated the spatiotemporal variation in the incidence of out-of-hospital cardiac arrest (OHCA) due to extremely high temperature in Japan. Daily OHCA data from 2005 to 2014 were acquired from all 47 prefectures of Japan. We used time-series Poisson regression analysis combined with a distributed lag non-linear model to assess the temporal variability in the effects of extremely high temperature on OHCA incidence in each prefecture, adjusted for time trends. Spatial variability in the relationships between extremely high temperature and OHCA between prefectures was estimated using a multivariate random-effects meta-analysis. We analyzed 166,496 OHCA cases of presumed cardiac origin occurring during the summer (June to September) that met the inclusion criteria. The minimum morbidity percentile (MMP) was the 51st percentile of temperature during the summer in Japan. The overall cumulative relative risk at the 99th percentile vs. the MMP over lags 0-10days was 1.21 (95% CI: 1.12-1.31). There was also a strong low temperature effect during the summer periods. No substantial difference in spatial or temporal variability was observed over the study period. Our study demonstrated spatiotemporal homogeneity in the risk of OHCA during periods of extremely high temperature between 2005 and 2014 in Japan. Our findings suggest that public health strategies for OHCA due to extremely high temperatures should be finely adjusted and should particularly account for the unchanging risk during the summer. Copyright © 2017 Elsevier B.V. All rights reserved.

A new model of strabismic amblyopia: Loss of spatial acuity due to increased temporal dispersion of geniculate X-cell afferents on to cortical neurons.

PubMed

Crewther, D P; Crewther, S G

2015-09-01

Although the neural locus of strabismic amblyopia has been shown to lie at the first site of binocular integration, first in cat and then in primate, an adequate mechanism is still lacking. Here we hypothesise that increased temporal dispersion of LGN X-cell afferents driven by the deviating eye onto single cortical neurons may provide a neural mechanism for strabismic amblyopia. This idea was investigated via single cell extracellular recordings of 93 X and 50 Y type LGN neurons from strabismic and normal cats. Both X and Y neurons driven by the non-deviating eye showed shorter latencies than those driven by either the strabismic or normal eyes. Also the mean latency difference between X and Y neurons was much greater for the strabismic cells compared with the other two groups. The incidence of lagged X-cells driven by the deviating eye of the strabismic cats was higher than that of LGN X-cells from normal animals. Remarkably, none of the cells recorded from the laminae driven by the non-deviating eye were of the lagged class. A simple computational model was constructed in which a mixture of lagged and non-lagged afferents converge on to single cortical neurons. Model cut-off spatial frequencies to a moving grating stimulus were sensitive to the temporal dispersion of the geniculate afferents. Thus strabismic amblyopia could be viewed as a lack of developmental tuning of geniculate lags for neurons driven by the amblyopic eye. Monocular control of fixation by the non-deviating eye is associated with reduced incidence of lagged neurons, suggesting that in normal vision, lagged neurons might play a role in maintaining binocular connections for cortical neurons. Copyright © 2014 Elsevier Ltd. All rights reserved.

The effect of rising vs. falling glucose level on amperometric glucose sensor lag and accuracy in Type 1 diabetes.

PubMed

Ward, W K; Engle, J M; Branigan, D; El Youssef, J; Massoud, R G; Castle, J R

2012-08-01

Because declining glucose levels should be detected quickly in persons with Type 1 diabetes, a lag between blood glucose and subcutaneous sensor glucose can be problematic. It is unclear whether the magnitude of sensor lag is lower during falling glucose than during rising glucose. Initially, we analysed 95 data segments during which glucose changed and during which very frequent reference blood glucose monitoring was performed. However, to minimize confounding effects of noise and calibration error, we excluded data segments in which there was substantial sensor error. After these exclusions, and combination of data from duplicate sensors, there were 72 analysable data segments (36 for rising glucose, 36 for falling). We measured lag in two ways: (1) the time delay at the vertical mid-point of the glucose change (regression delay); and (2) determination of the optimal time shift required to minimize the difference between glucose sensor signals and blood glucose values drawn concurrently. Using the regression delay method, the mean sensor lag for rising vs. falling glucose segments was 8.9 min (95%CI 6.1-11.6) vs. 1.5 min (95%CI -2.6 to 5.5, P<0.005). Using the time shift optimization method, results were similar, with a lag that was higher for rising than for falling segments [8.3 (95%CI 5.8-10.7) vs. 1.5 min (95% CI -2.2 to 5.2), P<0.001]. Commensurate with the lag results, sensor accuracy was greater during falling than during rising glucose segments. In Type 1 diabetes, when noise and calibration error are minimized to reduce effects that confound delay measurement, subcutaneous glucose sensors demonstrate a shorter lag duration and greater accuracy when glucose is falling than when rising. © 2011 The Authors. Diabetic Medicine © 2011 Diabetes UK.

Using Baidu Search Index to Predict Dengue Outbreak in China

NASA Astrophysics Data System (ADS)

Liu, Kangkang; Wang, Tao; Yang, Zhicong; Huang, Xiaodong; Milinovich, Gabriel J.; Lu, Yi; Jing, Qinlong; Xia, Yao; Zhao, Zhengyang; Yang, Yang; Tong, Shilu; Hu, Wenbiao; Lu, Jiahai

2016-12-01

This study identified the possible threshold to predict dengue fever (DF) outbreaks using Baidu Search Index (BSI). Time-series classification and regression tree models based on BSI were used to develop a predictive model for DF outbreak in Guangzhou and Zhongshan, China. In the regression tree models, the mean autochthonous DF incidence rate increased approximately 30-fold in Guangzhou when the weekly BSI for DF at the lagged moving average of 1-3 weeks was more than 382. When the weekly BSI for DF at the lagged moving average of 1-5 weeks was more than 91.8, there was approximately 9-fold increase of the mean autochthonous DF incidence rate in Zhongshan. In the classification tree models, the results showed that when the weekly BSI for DF at the lagged moving average of 1-3 weeks was more than 99.3, there was 89.28% chance of DF outbreak in Guangzhou, while, in Zhongshan, when the weekly BSI for DF at the lagged moving average of 1-5 weeks was more than 68.1, the chance of DF outbreak rose up to 100%. The study indicated that less cost internet-based surveillance systems can be the valuable complement to traditional DF surveillance in China.

Echolocation versus echo suppression in humans

PubMed Central

Wallmeier, Ludwig; Geßele, Nikodemus; Wiegrebe, Lutz

2013-01-01

Several studies have shown that blind humans can gather spatial information through echolocation. However, when localizing sound sources, the precedence effect suppresses spatial information of echoes, and thereby conflicts with effective echolocation. This study investigates the interaction of echolocation and echo suppression in terms of discrimination suppression in virtual acoustic space. In the ‘Listening’ experiment, sighted subjects discriminated between positions of a single sound source, the leading or the lagging of two sources, respectively. In the ‘Echolocation’ experiment, the sources were replaced by reflectors. Here, the same subjects evaluated echoes generated in real time from self-produced vocalizations and thereby discriminated between positions of a single reflector, the leading or the lagging of two reflectors, respectively. Two key results were observed. First, sighted subjects can learn to discriminate positions of reflective surfaces echo-acoustically with accuracy comparable to sound source discrimination. Second, in the Listening experiment, the presence of the leading source affected discrimination of lagging sources much more than vice versa. In the Echolocation experiment, however, the presence of both the lead and the lag strongly affected discrimination. These data show that the classically described asymmetry in the perception of leading and lagging sounds is strongly diminished in an echolocation task. Additional control experiments showed that the effect is owing to both the direct sound of the vocalization that precedes the echoes and owing to the fact that the subjects actively vocalize in the echolocation task. PMID:23986105

Enhancement of Spatial Ability in Girls in a Single-Sex Environment through Spatial Experience and the Impact on Information Seeking

ERIC Educational Resources Information Center

Swarlis, Linda L.

2008-01-01

The test scores of spatial ability for women lag behind those of men in many spatial tests. On the Mental Rotations Test (MRT), a significant gender gap has existed for over 20 years and continues to exist. High spatial ability has been linked to efficiencies in typical computing tasks including Web and database searching, text editing, and…

An experimental and analytical investigation of stall effects on flap-lag stability in forward flight

NASA Technical Reports Server (NTRS)

Nagabhushanam, J.; Gaonkar, Gopal H.; Mcnulty, Michael J.

1987-01-01

Experiments have been performed with a 1.62 m diameter hingeless rotor in a wind tunnel to investigate flap-lag stability of isolated rotors in forward flight. The three-bladed rotor model closely approaches the simple theoretical concept of a hingeless rotor as a set of rigid, articulated flap-lag blades with offset and spring restrained flap and lag hinges. Lag regressing mode stability data was obtained for advance ratios as high as 0.55 for various combinations of collective pitch and shaft angle. The prediction includes quasi-steady stall effects on rotor trim and Floquet stability analyses. Correlation between data and prediction is presented and is compared with that of an earlier study based on a linear theory without stall effects. While the results with stall effects show marked differences from the linear theory results, the stall theory still falls short of adequate agreement with the experimental data.

Sample selection and spatial models of housing price indexes, and, A disequilibrium analysis of the U.S. gasoline market using panel data

NASA Astrophysics Data System (ADS)

Hu, Haixin

This dissertation consists of two parts. The first part studies the sample selection and spatial models of housing price index using transaction data on detached single-family houses of two California metropolitan areas from 1990 through 2008. House prices are often spatially correlated due to shared amenities, or when the properties are viewed as close substitutes in a housing submarket. There have been many studies that address spatial correlation in the context of housing markets. However, none has used spatial models to construct housing price indexes at zip code level for the entire time period analyzed in this dissertation to the best of my knowledge. In this paper, I study a first-order autoregressive spatial model with four different weighing matrix schemes. Four sets of housing price indexes are constructed accordingly. Gatzlaff and Haurin (1997, 1998) study the sample selection problem in housing index by using Heckman's two-step method. This method, however, is generally inefficient and can cause multicollinearity problem. Also, it requires data on unsold houses in order to carry out the first-step probit regression. Maximum likelihood (ML) method can be used to estimate a truncated incidental model which allows one to correct for sample selection based on transaction data only. However, convergence problem is very prevalent in practice. In this paper I adopt Lewbel's (2007) sample selection correction method which does not require one to model or estimate the selection model, except for some very general assumptions. I then extend this method to correct for spatial correlation. In the second part, I analyze the U.S. gasoline market with a disequilibrium model that allows lagged-latent variables, endogenous prices, and panel data with fixed effects. Most existing studies (see the survey of Espey, 1998, Energy Economics) of the gasoline market assume equilibrium. In practice, however, prices do not always adjust fast enough to clear the market. Equilibrium assumptions greatly simplify statistical inference, but are very restrictive and can produce conflicting estimates. For example, econometric models of markets that assume equilibrium often produce more elastic demand price elasticity than their disequilibrium counterparts (Holt and Johnson, 1989, Review of Economics and Statistics, Oczkowski, 1998, Economics Letters). The few studies that allow disequilibrium, however, have been limited to macroeconomic time-series data without lagged-latent variables. While time series data allows one to investigate national trends, it cannot be used to identify and analyze regional differences and the role of local markets. Exclusion of the lagged-latent variables is also undesirable because such variables capture adjustment costs and inter-temporal spillovers. Simulation methods offer tractable solutions to dynamic and panel data disequilibrium models (Lee, 1997, Journal of Econometrics), but assume normally distributed errors. This paper compares estimates of price/income elasticity and excess supply/demand across time periods, regions, and model specifications, using both equilibrium and disequilibrium methods. In the equilibrium model, I compare the within group estimator with Anderson and Hsiao's first-difference 2SLS estimator. In the disequilibrium model, I extend Amemiya's 2SLS by using Newey's efficient estimator with optimal instruments.

The inequitable distribution of tobacco outlet density: the role of income in two Black Mid-Atlantic geopolitical areas.

PubMed

Fakunle, D O; Milam, A J; Furr-Holden, C D; Butler, J; Thorpe, R J; LaVeist, T A

2016-07-01

Studies have shown that communities with higher concentrations of low-income racial and ethnic minorities correlate with a greater presence of tobacco outlets. Community-level income has consistently been among the strongest predictors of tobacco outlet density. This study analyzes two Maryland geopolitical areas with similar racial concentrations yet differing income levels in an attempt to disentangle the race-income relationship with tobacco outlet density. In this cross-sectional examination of tobacco outlet and census tract-level sociodemographic data, Baltimore City, Maryland, and Prince George's County, Maryland, were geocoded to determine tobacco outlet density. Tobacco outlet density was defined as the mean number of tobacco outlets per 1000 persons per census tract. Comparisons of tobacco outlet density and sociodemographic variables were analysed via two-sample t-tests, and the direct effect of sociodemographic variables on tobacco outlet density for each area was analysed via spatial lag regressions. Prince George's County, the area with the higher income level ($77,190 vs $43,571), has a significantly lower tobacco outlet density than Baltimore City (P < 0.001). Prince George's County has a 67.5% Black population and an average of 3.94 tobacco outlets per 1000 persons per tract. By contrast, Baltimore City has a 65.3% Black population and an average of 7.95 tobacco outlets per 1000 persons per tract. Spatial lag regression model results indicate an inverse relationship between income and tobacco outlet density in Baltimore City and Prince George's County (β = -0.03, P < 0.01 &β = -0.01, P = 0.02, respectively), and a significant interaction term indicating a greater magnitude in the relationship between income and tobacco outlet density in Baltimore City (β = -0.05, P < 0.01). Results suggest that higher socio-economic status, even in primarily underrepresented racial and ethnic geopolitical areas, is linked to lower tobacco outlet density. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Modeling of Engine Parameters for Condition-Based Maintenance of the MTU Series 2000 Diesel Engine

DTIC Science & Technology

2016-09-01

are suitable. To model the behavior of the engine, an autoregressive distributed lag (ARDL) time series model of engine speed and exhaust gas... time series model of engine speed and exhaust gas temperature is derived. The lag length for ARDL is determined by whitening of residuals using the...15 B. REGRESSION ANALYSIS ....................................................................15 1. Time Series Analysis

Chromatic induction in space and time.

PubMed

Coia, Andrew J; Shevell, Steven K

2018-04-01

The color appearance of a light depends on variation in the complete visual field over both space and time. In the spatial domain, a chromatic stimulus within a patterned chromatic surround can appear a different hue than the same stimulus within a uniform surround. In the temporal domain, a stimulus presented as an element of a continuously changing chromaticity can appear a different color compared to the identical stimulus, presented simultaneously but viewed alone. This is the flash-lag effect for color, which has an analog in the domain of motion: a pulsed object seen alone can appear to lag behind an identical pulsed object that is an element of a motion sequence. Studies of the flash-lag effect for motion have considered whether it is mediated by a neural representation for the moving physical stimulus or, alternatively, for the perceived motion. The current study addresses this question for the flash-lag effect for color by testing whether the color flash lag depends on a representation of only the changing chromatic stimulus or, alternatively, its color percept, which can be altered by chromatic induction. baseline measurements for spatial chromatic induction determined the chromaticity of a flashed ring within a uniform surround that matched a flashed ring within a patterned surround. Baseline measurements for the color flash-lag effect determined the chromaticity of a pulsed ring presented alone (within a uniform surround) that matched a pulsed ring presented in a sequence of changing chromaticity over time (also within a uniform surround). Finally, the main experiments combined chromatic induction from a patterned surround and the flash-lag effect, in three conditions: (1) both the changing and pulsed rings were within a patterned chromatic surround; (2) the changing ring was within a patterned surround and the pulsed ring within a uniform surround; and (3) the changing ring was within a uniform surround and the pulsed ring within a patterned surround. the flash-lag measurements for a changing chromaticity were affected by perceptual changes induced by the surrounding chromatic pattern. Thus, the color shifts induced by a chromatic surround are incorporated in the neural representation mediating the flash-lag effect for color.

Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

PubMed

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

Effects of Air Pollutant Exposure on Acute Myocardial Infarction, According to Gender.

PubMed

Tuan, Tássia Soldi; Venâncio, Taís Siqueira; Nascimento, Luiz Fernando Costa

2016-09-01

There is evidence of the effects of air pollution on hospital admissions due to cardiovascular diseases, including myocardial infarction. To estimate the association between exposure to air pollutants and hospital admissions due to myocardial infarction according to gender, between January 1st 2012 and December 31st 2013, in São Jose dos Campos-SP. An ecological time series study was carried out with daily data of admissions due to AMI, pollutants CO, O3, PM10, SO2, and NO2, according to gender. We used the Poisson regression generalized linear model to estimate the relative risks of hospital admissions with lags of 0-5 days, adjusted for temperature, humidity, seasonality and days of the week. There were 1837 admissions for ischemic heart diseases, with 636 women and 1201 men. For females, the risks were significant for CO in lag 0 (RR = 1,09), lag1 (RR = 1,08) and lag 5 (RR = 1,10) and SO2 in lag 0 (RR = 1,10) and 3 (RR = 1,09). For men there was significance of the CO in, lag 3 and lag 5 (RR = 1,05). There was significance, regardless of gender, for CO at lag 1 (RR = 1,05) and lag 5 (RR = 1,07) and lag 0 for SO2 (RR = 1,06). The data presented show the important role of CO and SO2 in the genesis of myocardial infarction admissions, and responses to pollutant exposure are different if analyzed by gender and together - hence the importance of a stratified analyses. Existem evidências sobre os efeitos da poluição do ar nas internações por doenças cardiovasculares, entre elas o infarto do miocárdio. Estimar a associação entre exposição a poluentes do ar e internações por infarto segundo gêneros, entre 01 de Janeiro de 2012 e 31 de Dezembro de 2013, em São José dos Campos - SP. Estudo ecológico de série temporal com dados diários de internações por IAM dos poluentes CO, O3, PM10, SO2, NO2, segundo gêneros. Utilizou-se modelo linear generalizado da Regressão de Poisson para estimar os riscos relativos para internações com defasagens de 0 a 5 dias, ajustados por temperatura, umidade, sazonalidade e dias da semana. Foram 1837 internações por doenças isquêmicas do coração, sendo 636 mulheres e 1201 homens. Para o gênero feminino, os riscos foram significativos para o CO nos lag 0 (RR = 1,09), lag1 (RR = 1,08) e lag 5 (RR = 1,10) e para o SO2 no lag 0 (RR = 1,10) e 3 (RR = 1,09). Para o gênero masculino houve significância para o CO no lag 3 e lag 5 (RR = 1,05). Sem distinção de gênero houve significância para o CO no lag 1 (RR = 1,05) e lag 5 (RR = 1,07) e no lag 0 para o SO2 (RR = 1,06). Os dados apresentados mostram o importante papel do CO e do SO2 na gênese das internações por infarto e que as respostas à exposição aos poluentes são diferentes se analisadas por sexo e em conjunto, daí a importância de se estratificarem as análises.

Spatial-temporal clustering of tornadoes

NASA Astrophysics Data System (ADS)

Malamud, Bruce D.; Turcotte, Donald L.; Brooks, Harold E.

2016-12-01

The standard measure of the intensity of a tornado is the Enhanced Fujita scale, which is based qualitatively on the damage caused by a tornado. An alternative measure of tornado intensity is the tornado path length, L. Here we examine the spatial-temporal clustering of severe tornadoes, which we define as having path lengths L ≥ 10 km. Of particular concern are tornado outbreaks, when a large number of severe tornadoes occur in a day in a restricted region. We apply a spatial-temporal clustering analysis developed for earthquakes. We take all pairs of severe tornadoes in observed and modelled outbreaks, and for each pair plot the spatial lag (distance between touchdown points) against the temporal lag (time between touchdown points). We apply our spatial-temporal lag methodology to the intense tornado outbreaks in the central United States on 26 and 27 April 2011, which resulted in over 300 fatalities and produced 109 severe (L ≥ 10 km) tornadoes. The patterns of spatial-temporal lag correlations that we obtain for the 2 days are strikingly different. On 26 April 2011, there were 45 severe tornadoes and our clustering analysis is dominated by a complex sequence of linear features. We associate the linear patterns with the tornadoes generated in either a single cell thunderstorm or a closely spaced cluster of single cell thunderstorms moving at a near-constant velocity. Our study of a derecho tornado outbreak of six severe tornadoes on 4 April 2011 along with modelled outbreak scenarios confirms this association. On 27 April 2011, there were 64 severe tornadoes and our clustering analysis is predominantly random with virtually no embedded linear patterns. We associate this pattern with a large number of interacting supercell thunderstorms generating tornadoes randomly in space and time. In order to better understand these associations, we also applied our approach to the Great Plains tornado outbreak of 3 May 1999. Careful studies by others have associated individual tornadoes with specified supercell thunderstorms. Our analysis of the 3 May 1999 tornado outbreak directly associated linear features in the largely random spatial-temporal analysis with several supercell thunderstorms, which we then confirmed using model scenarios of synthetic tornado outbreaks. We suggest that it may be possible to develop a semi-automated modelling of tornado touchdowns to match the type of observations made on the 3 May 1999 outbreak.

Spatial-Temporal Clustering of Tornadoes

NASA Astrophysics Data System (ADS)

Malamud, Bruce D.; Turcotte, Donald L.; Brooks, Harold E.

2017-04-01

The standard measure of the intensity of a tornado is the Enhanced Fujita scale, which is based qualitatively on the damage caused by a tornado. An alternative measure of tornado intensity is the tornado path length, L. Here we examine the spatial-temporal clustering of severe tornadoes, which we define as having path lengths L ≥ 10 km. Of particular concern are tornado outbreaks, when a large number of severe tornadoes occur in a day in a restricted region. We apply a spatial-temporal clustering analysis developed for earthquakes. We take all pairs of severe tornadoes in observed and modelled outbreaks, and for each pair plot the spatial lag (distance between touchdown points) against the temporal lag (time between touchdown points). We apply our spatial-temporal lag methodology to the intense tornado outbreaks in the central United States on 26 and 27 April 2011, which resulted in over 300 fatalities and produced 109 severe (L ≥ 10 km) tornadoes. The patterns of spatial-temporal lag correlations that we obtain for the 2 days are strikingly different. On 26 April 2011, there were 45 severe tornadoes and our clustering analysis is dominated by a complex sequence of linear features. We associate the linear patterns with the tornadoes generated in either a single cell thunderstorm or a closely spaced cluster of single cell thunderstorms moving at a near-constant velocity. Our study of a derecho tornado outbreak of six severe tornadoes on 4 April 2011 along with modelled outbreak scenarios confirms this association. On 27 April 2011, there were 64 severe tornadoes and our clustering analysis is predominantly random with virtually no embedded linear patterns. We associate this pattern with a large number of interacting supercell thunderstorms generating tornadoes randomly in space and time. In order to better understand these associations, we also applied our approach to the Great Plains tornado outbreak of 3 May 1999. Careful studies by others have associated individual tornadoes with specified supercell thunderstorms. Our analysis of the 3 May 1999 tornado outbreak directly associated linear features in the largely random spatial-temporal analysis with several supercell thunderstorms, which we then confirmed using model scenarios of synthetic tornado outbreaks. We suggest that it may be possible to develop a semi-automated modelling of tornado touchdowns to match the type of observations made on the 3 May 1999 outbreak.

Measuring attention using flash-lag effect.

PubMed

Shioiri, Satoshi; Yamamoto, Ken; Oshida, Hiroki; Matsubara, Kazuya; Yaguchi, Hirohisa

2010-08-13

We investigated the effect of attention on the flash-lag effect (FLE) in order to determine whether the FLE can be used to estimate the effect of visual attention. The FLE is the effect that a flash aligned with a moving object is perceived to lag the moving object, and several studies have shown that attention reduces its magnitude. We measured the FLE as a function of the number or speed of moving objects. The results showed that the effect of cueing, which we attributed the effect of attention, on the FLE increased monotonically with the number or the speed of the objects. This suggests that the amount of attention can be estimated by measuring the FLE, assuming that more amount of attention is required for a larger number or faster speed of objects to attend. On the basis of this presumption, we attempted to measure the spatial spread of visual attention by FLE measurements. The estimated spatial spreads were similar to those estimated by other experimental methods.

Short-term effects of floods on Japanese encephalitis in Nanchong, China, 2007-2012: A time-stratified case-crossover study.

PubMed

Zhang, Feifei; Liu, Zhidong; Zhang, Caixia; Jiang, Baofa

2016-09-01

This time-stratified case-crossover study aimed to quantify the impact of floods on daily Japanese encephalitis (JE) cases from 2007 to 2012 in Nanchong city of Sichuan Province, China. Using conditional logistic regression analysis, we calculated the odds ratios (ORs) and 95% confidence intervals (CIs) at different lagged days, adjusting for daily average temperature (AT) and daily average relative humidity (ARH). A total of 370 JE cases were notified during the study period, with the median patient age being 4.2years. The seasonal pattern of JE cases clustered in July and August during the study period. Floods were significantly associated with an increased number of JE cases from lag 23 to lag 24, with the strongest lag effect at lag 23 (OR=2.00, 95% CI: 1.14-3.52). Similarly, AT and ARH were positively associated with daily JE cases from lag 0 to lag 8 and from lag 0 to lag 9, respectively. Floods, with AT and ARH, can be used to forecast JE outbreaks in the study area. Based on the results of this study, recommendations include undertaking control measures before the number of cases increases, especially for regions with similar geographic, climatic, and socio-economic conditions as those in the study area. Copyright © 2016 Elsevier B.V. All rights reserved.

Local Spatial and Temporal Processes of Influenza in Pennsylvania, USA: 2003–2009

PubMed Central

Stark, James H.; Sharma, Ravi; Ostroff, Stephen; Cummings, Derek A. T.; Ermentrout, Bard; Stebbins, Samuel; Burke, Donald S.; Wisniewski, Stephen R.

2012-01-01

Background Influenza is a contagious respiratory disease responsible for annual seasonal epidemics in temperate climates. An understanding of how influenza spreads geographically and temporally within regions could result in improved public health prevention programs. The purpose of this study was to summarize the spatial and temporal spread of influenza using data obtained from the Pennsylvania Department of Health's influenza surveillance system. Methodology and Findings We evaluated the spatial and temporal patterns of laboratory-confirmed influenza cases in Pennsylvania, United States from six influenza seasons (2003–2009). Using a test of spatial autocorrelation, local clusters of elevated risk were identified in the South Central region of the state. Multivariable logistic regression indicated that lower monthly precipitation levels during the influenza season (OR = 0.52, 95% CI: 0.28, 0.94), fewer residents over age 64 (OR = 0.27, 95% CI: 0.10, 0.73) and fewer residents with more than a high school education (OR = 0.76, 95% CI: 0.61, 0.95) were significantly associated with membership in this cluster. In addition, time series analysis revealed a temporal lag in the peak timing of the influenza B epidemic compared to the influenza A epidemic. Conclusions These findings illustrate a distinct spatial cluster of cases in the South Central region of Pennsylvania. Further examination of the regional transmission dynamics within these clusters may be useful in planning public health influenza prevention programs. PMID:22470544

Hybrid ARIMAX quantile regression method for forecasting short term electricity consumption in east java

NASA Astrophysics Data System (ADS)

Prastuti, M.; Suhartono; Salehah, NA

2018-04-01

The need for energy supply, especially for electricity in Indonesia has been increasing in the last past years. Furthermore, the high electricity usage by people at different times leads to the occurrence of heteroscedasticity issue. Estimate the electricity supply that could fulfilled the community’s need is very important, but the heteroscedasticity issue often made electricity forecasting hard to be done. An accurate forecast of electricity consumptions is one of the key challenges for energy provider to make better resources and service planning and also take control actions in order to balance the electricity supply and demand for community. In this paper, hybrid ARIMAX Quantile Regression (ARIMAX-QR) approach was proposed to predict the short-term electricity consumption in East Java. This method will also be compared to time series regression using RMSE, MAPE, and MdAPE criteria. The data used in this research was the electricity consumption per half-an-hour data during the period of September 2015 to April 2016. The results show that the proposed approach can be a competitive alternative to forecast short-term electricity in East Java. ARIMAX-QR using lag values and dummy variables as predictors yield more accurate prediction in both in-sample and out-sample data. Moreover, both time series regression and ARIMAX-QR methods with addition of lag values as predictor could capture accurately the patterns in the data. Hence, it produces better predictions compared to the models that not use additional lag variables.

Catchment Legacies and Time Lags: A Parsimonious Watershed Model to Predict the Effects of Legacy Storage on Nitrogen Export

PubMed Central

Van Meter, Kimberly J.; Basu, Nandita B.

2015-01-01

Nutrient legacies in anthropogenic landscapes, accumulated over decades of fertilizer application, lead to time lags between implementation of conservation measures and improvements in water quality. Quantification of such time lags has remained difficult, however, due to an incomplete understanding of controls on nutrient depletion trajectories after changes in land-use or management practices. In this study, we have developed a parsimonious watershed model for quantifying catchment-scale time lags based on both soil nutrient accumulations (biogeochemical legacy) and groundwater travel time distributions (hydrologic legacy). The model accurately predicted the time lags observed in an Iowa watershed that had undergone a 41% conversion of area from row crop to native prairie. We explored the time scales of change for stream nutrient concentrations as a function of both natural and anthropogenic controls, from topography to spatial patterns of land-use change. Our results demonstrate that the existence of biogeochemical nutrient legacies increases time lags beyond those due to hydrologic legacy alone. In addition, we show that the maximum concentration reduction benefits vary according to the spatial pattern of intervention, with preferential conversion of land parcels having the shortest catchment-scale travel times providing proportionally greater concentration reductions as well as faster response times. In contrast, a random pattern of conversion results in a 1:1 relationship between percent land conversion and percent concentration reduction, irrespective of denitrification rates within the landscape. Our modeling framework allows for the quantification of tradeoffs between costs associated with implementation of conservation measures and the time needed to see the desired concentration reductions, making it of great value to decision makers regarding optimal implementation of watershed conservation measures. PMID:25985290

Catchment legacies and time lags: a parsimonious watershed model to predict the effects of legacy storage on nitrogen export.

PubMed

Van Meter, Kimberly J; Basu, Nandita B

2015-01-01

Nutrient legacies in anthropogenic landscapes, accumulated over decades of fertilizer application, lead to time lags between implementation of conservation measures and improvements in water quality. Quantification of such time lags has remained difficult, however, due to an incomplete understanding of controls on nutrient depletion trajectories after changes in land-use or management practices. In this study, we have developed a parsimonious watershed model for quantifying catchment-scale time lags based on both soil nutrient accumulations (biogeochemical legacy) and groundwater travel time distributions (hydrologic legacy). The model accurately predicted the time lags observed in an Iowa watershed that had undergone a 41% conversion of area from row crop to native prairie. We explored the time scales of change for stream nutrient concentrations as a function of both natural and anthropogenic controls, from topography to spatial patterns of land-use change. Our results demonstrate that the existence of biogeochemical nutrient legacies increases time lags beyond those due to hydrologic legacy alone. In addition, we show that the maximum concentration reduction benefits vary according to the spatial pattern of intervention, with preferential conversion of land parcels having the shortest catchment-scale travel times providing proportionally greater concentration reductions as well as faster response times. In contrast, a random pattern of conversion results in a 1:1 relationship between percent land conversion and percent concentration reduction, irrespective of denitrification rates within the landscape. Our modeling framework allows for the quantification of tradeoffs between costs associated with implementation of conservation measures and the time needed to see the desired concentration reductions, making it of great value to decision makers regarding optimal implementation of watershed conservation measures.

Short-term association between air pollution and emergency room admissions for chronic obstructive pulmonary disease in Nis, Serbia.

PubMed

Milutinović, Suzana; Nikić, Dragana; Stosić, Ljiljana; Stanković, Aleksandra; Bogdanović, Dragan

2009-03-01

The present study assesses the short-term association between black smoke (BS) and sulphur dioxide (SO2) levels in urban air and the daily number of emergency room admissions for chronic obstructive pulmonary disease (COPD) in Nis, Serbia. Generalised linear models extending Poisson regression were fitted controlling for time trend, seasonal variations, days of the week, temperature, relative humidity, air pressure, precipitation, rainfall, snowfall, overcast, and wind velocity. The emergency room admissions for all ages for COPD were significantly associated with previous-day level of BS and lag 0-2 (1,60% and 2,26% increase per 10 microg/m3, respectively). After controlling for SO2, single lagged (lag 1 and lag 2) as well as mean lagged values of BS (up to lag 0-3) were significantly associated with COPD emergencies. No effect was found for SO2, even after controlling for black smoke. The present findings support the conclusion that current levels of ambient BS may have an effect on the respiratory health of susceptible persons.

Identifying some determinants of "jet lag" and its symptoms: a study of athletes and other travellers.

PubMed

Waterhouse, J; Edwards, B; Nevill, A; Carvalho, S; Atkinson, G; Buckley, P; Reilly, T; Godfrey, R; Ramsay, R

2002-02-01

Travelling across multiple time zones disrupts normal circadian rhythms and induces "jet lag". Possible effects of this on training and performance in athletes were concerns before the Sydney Olympic Games. To identify some determinants of jet lag and its symptoms. A mixture of athletes, their coaches, and academics attending a conference (n = 85) was studied during their flights from the United Kingdom to Australia (two flights with a one hour stopover in Singapore), and for the first six days in Australia. Subjects differed in age, sex, chronotype, flexibility of sleeping habits, feelings of languor, fitness, time of arrival in Australia, and whether or not they had previous experience of travel to Australia. These variables and whether the body clock adjusted to new local time by phase advance or delay were tested as predictors for jet lag and some of its symptoms by stepwise multiple regression analyses. The amount of sleep in the first flight was significantly greater in those who had left the United Kingdom in the evening than the morning (medians of 5.5 hours and 1.5 hours respectively; p = 0.0002, Mann-Whitney), whereas there was no significant difference on the second flight (2.5 hours v 2.8 hours; p = 0.72). Only the severity of jet lag and assessments of sleep and fatigue were commonly predicted significantly (p<0.05) by regression analysis, and then by only some of the variables. Thus increasing age and a later time of arrival in Australia were associated with less jet lag and fatigue, and previous experience of travel to Australia was associated with an earlier time of getting to sleep. Subjects who had adjusted by phase advance suffered worse jet lag during the 5th and 6th days in Australia. These results indicate the importance of an appropriate choice of itinerary and lifestyle for reducing the negative effects of jet lag in athletes and others who wish to perform optimally in the new time zone.

Spatial clustering of toxic trace elements in adolescents around the Torreón, Mexico lead-zinc smelter.

PubMed

Garcia-Vargas, Gonzalo G; Rothenberg, Stephen J; Silbergeld, Ellen K; Weaver, Virginia; Zamoiski, Rachel; Resnick, Carol; Rubio-Andrade, Marisela; Parsons, Patrick J; Steuerwald, Amy J; Navas-Acién, Ana; Guallar, Eliseo

2014-11-01

High blood lead (BPb) levels in children and elevated soil and dust arsenic, cadmium, and lead were previously found in Torreón, northern Mexico, host to the world's fourth largest lead-zinc metal smelter. The objectives of this study were to determine spatial distributions of adolescents with higher BPb and creatinine-corrected urine total arsenic, cadmium, molybdenum, thallium, and uranium around the smelter. Cross-sectional study of 512 male and female subjects 12-15 years of age was conducted. We measured BPb by graphite furnace atomic absorption spectrometry and urine trace elements by inductively coupled plasma-mass spectrometry, with dynamic reaction cell mode for arsenic. We constructed multiple regression models including sociodemographic variables and adjusted for subject residence spatial correlation with spatial lag or error terms. We applied local indicators of spatial association statistics to model residuals to identify hot spots of significant spatial clusters of subjects with higher trace elements. We found spatial clusters of subjects with elevated BPb (range 3.6-14.7 μg/dl) and urine cadmium (0.18-1.14 μg/g creatinine) adjacent to and downwind of the smelter and elevated urine thallium (0.28-0.93 μg/g creatinine) and uranium (0.07-0.13 μg/g creatinine) near ore transport routes, former waste, and industrial discharge sites. The conclusion derived from this study was that spatial clustering of adolescents with high BPb and urine cadmium adjacent to and downwind of the smelter and residual waste pile, areas identified over a decade ago with high lead and cadmium in soil and dust, suggests that past and/or present plant operations continue to present health risks to children in those neighborhoods.

Spatial clustering of toxic trace elements in adolescents around the Torreón, Mexico lead–zinc smelter

PubMed Central

Garcia-Vargas, Gonzalo G.; Rothenberg, Stephen J.; Silbergeld, Ellen K.; Weaver, Virginia; Zamoiski, Rachel; Resnick, Carol; Rubio-Andrade, Marisela; Parsons, Patrick J.; Steuerwald, Amy J.; Navas-Acién, Ana; Guallar, Eliseo

2016-01-01

High blood lead (BPb) levels in children and elevated soil and dust arsenic, cadmium, and lead were previously found in Torreón, northern Mexico, host to the world’s fourth largest lead–zinc metal smelter. The objectives of this study were to determine spatial distributions of adolescents with higher BPb and creatinine-corrected urine total arsenic, cadmium, molybdenum, thallium, and uranium around the smelter. Cross-sectional study of 512 male and female subjects 12–15 years of age was conducted. We measured BPb by graphite furnace atomic absorption spectrometry and urine trace elements by inductively coupled plasma-mass spectrometry, with dynamic reaction cell mode for arsenic. We constructed multiple regression models including sociodemographic variables and adjusted for subject residence spatial correlation with spatial lag or error terms. We applied local indicators of spatial association statistics to model residuals to identify hot spots of significant spatial clusters of subjects with higher trace elements. We found spatial clusters of subjects with elevated BPb (range 3.6–14.7 µg/dl) and urine cadmium (0.18–1.14 µg/g creatinine) adjacent to and downwind of the smelter and elevated urine thallium (0.28–0.93 µg/g creatinine) and uranium (0.07–0.13 µg/g creatinine) near ore transport routes, former waste, and industrial discharge sites. The conclusion derived from this study was that spatial clustering of adolescents with high BPb and urine cadmium adjacent to and downwind of the smelter and residual waste pile, areas identified over a decade ago with high lead and cadmium in soil and dust, suggests that past and/or present plant operations continue to present health risks to children in those neighborhoods. PMID:24549228

Signatures of Steady Heating in Time Lag Analysis of Coronal Emission

NASA Technical Reports Server (NTRS)

Viall, Nicholeen M.; Klimchuk, James A.

2016-01-01

Among the multitude of methods used to investigate coronal heating, the time lag method of Viall Klimchuk is becoming increasingly prevalent as an analysis technique that is complementary to those that are traditionally used.The time lag method cross correlates light curves at a given spatial location obtained in spectral bands that sample different temperature plasmas. It has been used most extensively with data from the Atmospheric Imaging Assembly on the Solar Dynamics Observatory. We have previously applied the time lag method to entire active regions and surrounding the quiet Sun and created maps of the results. We find that the majority of time lags are consistent with the cooling of coronal plasma that has been impulsively heated. Additionally, a significant fraction of the map area has a time lag of zero. This does not indicate a lack of variability. Rather, strong variability must be present, and it must occur in phase between the different channels. We have previously shown that these zero time lags are consistent with the transition region response to coronal nanoflares, although other explanations are possible. A common misconception is that the zero time lag indicates steady emission resulting from steady heating. Using simulated and observed light curves, we demonstrate here that highly correlated light curves at zero time lag are not compatible with equilibrium solutions. Such light curves can only be created by evolution

Exploring geographic variation in US mortality rates using a spatial Durbin approach

PubMed Central

Yang, Tse-Chuan; Noah, Aggie; Shoff, Carla

2015-01-01

Previous studies focused on identifying the determinants of mortality in US counties have examined the relationships between mortality and explanatory covariates within a county only, and have ignored the well-documented spatial dependence of mortality. We challenge earlier literature by arguing that the mortality rate of a certain county may also be associated with the features of its neighboring counties beyond its own features. Drawing from both the spillover (i.e., same direction effect) and social relativity (i.e., opposite direction effect) perspectives, our spatial Durbin modeling results indicate that both theoretical perspectives provide valuable frameworks to guide the modeling of mortality variation in US counties. Our empirical findings support that mortality rate of a certain county is associated with the features of its neighbors beyond its own features. Specifically, we found support for the spillover perspective in which the percentage of the Hispanic population, concentrated disadvantage, and the social capital of a specific county are negatively associated with the mortality rate in the specific county and also in neighboring counties. On the other hand, the following covariates fit the social relativity process: health insurance coverage, percentage of non-Hispanic other races, and income inequality. Their direction of the associations with mortality in the specific county is opposite to that of the relationships with mortality in neighboring counties. Methodologically, spatial Durbin modeling addresses the shortcomings of traditional analytic approaches used in ecological mortality research such as ordinary least squares, spatial error, and spatial lag regression. Our results produce new insights drawn from unbiased estimates. PMID:25642156

Association between exposure to ambient air pollution before conception date and likelihood of giving birth to girls in Guangzhou, China

NASA Astrophysics Data System (ADS)

Lin, Hualiang; Liang, Zhijiang; Liu, Tao; Di, Qian; Qian, Zhengmin; Zeng, Weilin; Xiao, Jianpeng; Li, Xing; Guo, Lingchuan; Ma, Wenjun; Zhao, Qingguo

2015-12-01

A few studies have linked ambient air pollution with sex ratio at birth. Most of these studies examined the long-term effects using spatial or temporal comparison approaches. This study aimed to investigate whether parental exposure to air pollution before conception date could affect the likelihood of the offspring being male or female. We used the information collected in a major maternal hospital in Guangzhou, China. The parental exposure to air pollution was assessed using the air pollution concentration before the conception date. Logistic regression models were used to assess the association between air pollution exposure and birth sex with adjustment for potential confounding factors, such as maternal age, parental education levels, long-term trend, season, and weather condition (mean temperature and relative humidity). The analysis revealed that higher air pollution was associated with higher probability of female newborns, with the effective exposure around one week prior to conception date. In the one-pollutant models, PM10, SO2 and NO2 had significant effects. For example, the excess risk was 0.61% (95% confidence interval (95% CI): 0.36%, 0.86%) for a 10 ug/m3 increase in lag 2 day's PM10, 0.42% (95% CI: 0.21%, 0.64%) for lag 3 day's SO2 and 0.97% (95% CI: 0.44%, 1.50%) for lag 3 day's NO2; and in two-pollutant models, PM10 remained statistically significant. These results suggest that parental exposure to ambient air pollution a few days prior to conception might be a contributing factor to higher probability of giving birth to female offspring in Guangzhou.

Spatial eigenmodes and synchronous oscillation: co-incidence detection in simulated cerebral cortex.

PubMed

Chapman, Clare L; Wright, James J; Bourke, Paul D

2002-07-01

Zero-lag synchronisation arises between points on the cerebral cortex receiving concurrent independent inputs; an observation generally ascribed to nonlinear mechanisms. Using simulations of cerebral cortex and Principal Component Analysis (PCA) we show patterns of zero-lag synchronisation (associated with empirically realistic spectral content) can arise from both linear and nonlinear mechanisms. For low levels of activation, we show the synchronous field is described by the eigenmodes of the resultant damped wave activity. The first and second spatial eigenmodes (which capture most of the signal variance) arise from the even and odd components of the independent input signals. The pattern of zero-lag synchronisation can be accounted for by the relative dominance of the first mode over the second, in the near-field of the inputs. The simulated cortical surface can act as a few millisecond response coincidence detector for concurrent, but uncorrelated, inputs. As cortical activation levels are increased, local damped oscillations in the gamma band undergo a transition to highly nonlinear undamped activity with 40 Hz dominant frequency. This is associated with "locking" between active sites and spatially segregated phase patterns. The damped wave synchronisation and the locked nonlinear oscillations may combine to permit fast representation of multiple patterns of activity within the same field of neurons.

Population dynamics of pond zooplankton II Daphnia ambigua Scourfield

USGS Publications Warehouse

Angino, E.E.; Armitage, K.B.; Saxena, B.

1973-01-01

Calcium was the most important of 27 environmental components affecting density for a 50 week period. Simultaneous stepwise regression accounted for more variability in total number/1 and in the number of ovigerous females/1 than did any of the lag analyses; 1-week lag accounted for the greatest amount of variability in clutch size. Total number and clutch size were little affected by measures of food. ?? 1973 Dr. W. Junk b.v. Publishers.

A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting

NASA Astrophysics Data System (ADS)

Luk, K. C.; Ball, J. E.; Sharma, A.

2000-01-01

Artificial neural networks (ANNs), which emulate the parallel distributed processing of the human nervous system, have proven to be very successful in dealing with complicated problems, such as function approximation and pattern recognition. Due to their powerful capability and functionality, ANNs provide an alternative approach for many engineering problems that are difficult to solve by conventional approaches. Rainfall forecasting has been a difficult subject in hydrology due to the complexity of the physical processes involved and the variability of rainfall in space and time. In this study, ANNs were adopted to forecast short-term rainfall for an urban catchment. The ANNs were trained to recognise historical rainfall patterns as recorded from a number of gauges in the study catchment for reproduction of relevant patterns for new rainstorm events. The primary objective of this paper is to investigate the effect of temporal and spatial information on short-term rainfall forecasting. To achieve this aim, a comparison test on the forecast accuracy was made among the ANNs configured with different orders of lag and different numbers of spatial inputs. In developing the ANNs with alternative configurations, the ANNs were trained to an optimal level to achieve good generalisation of data. It was found in this study that the ANNs provided the most accurate predictions when an optimum number of spatial inputs was included into the network, and that the network with lower lag consistently produced better performance.

Recent trends for drug lag in clinical development of oncology drugs in Japan: does the oncology drug lag still exist in Japan?

PubMed

Maeda, Hideki; Kurokawa, Tatsuo

2015-12-01

This study exhaustively and historically investigated the status of drug lag for oncology drugs approved in Japan. We comprehensively investigated oncology drugs approved in Japan between April 2001 and July 2014, using publicly available information. We also examined changes in the status of drug lag between Japan and the United States, as well as factors influencing drug lag. This study included 120 applications for approval of oncology drugs in Japan. The median difference over a 13-year period in the approval date between the United States and Japan was 875 days (29.2 months). This figure peaked in 2002, and showed a tendency to decline gradually each year thereafter. In 2014, the median approval lag was 281 days (9.4 months). Multiple regression analysis identified the following potential factors that reduce drug lag: "Japan's participation in global clinical trials"; "bridging strategies"; "designation of priority review in Japan"; and "molecularly targeted drugs". From 2001 to 2014, molecularly targeted drugs emerged as the predominant oncology drug, and the method of development has changed from full development in Japan or bridging strategy to global simultaneous development by Japan's taking part in global clinical trials. In line with these changes, the drug lag between the United States and Japan has significantly reduced to less than 1 year.

Anticipated and zero-lag synchronization in motifs of delay-coupled systems

NASA Astrophysics Data System (ADS)

Mirasso, Claudio R.; Carelli, Pedro V.; Pereira, Tiago; Matias, Fernanda S.; Copelli, Mauro

2017-11-01

Anticipated and zero-lag synchronization have been observed in different scientific fields. In the brain, they might play a fundamental role in information processing, temporal coding and spatial attention. Recent numerical work on anticipated and zero-lag synchronization studied the role of delays. However, an analytical understanding of the conditions for these phenomena remains elusive. In this paper, we study both phenomena in systems with small delays. By performing a phase reduction and studying phase locked solutions, we uncover the functional relation between the delay, excitation and inhibition for the onset of anticipated synchronization in a sender-receiver-interneuron motif. In the case of zero-lag synchronization in a chain motif, we determine the stability conditions. These analytical solutions provide an excellent prediction of the phase-locked regimes of Hodgkin-Huxley models and Roessler oscillators.

Extending the Distributed Lag Model framework to handle chemical mixtures.

PubMed

Bello, Ghalib A; Arora, Manish; Austin, Christine; Horton, Megan K; Wright, Robert O; Gennings, Chris

2017-07-01

Distributed Lag Models (DLMs) are used in environmental health studies to analyze the time-delayed effect of an exposure on an outcome of interest. Given the increasing need for analytical tools for evaluation of the effects of exposure to multi-pollutant mixtures, this study attempts to extend the classical DLM framework to accommodate and evaluate multiple longitudinally observed exposures. We introduce 2 techniques for quantifying the time-varying mixture effect of multiple exposures on an outcome of interest. Lagged WQS, the first technique, is based on Weighted Quantile Sum (WQS) regression, a penalized regression method that estimates mixture effects using a weighted index. We also introduce Tree-based DLMs, a nonparametric alternative for assessment of lagged mixture effects. This technique is based on the Random Forest (RF) algorithm, a nonparametric, tree-based estimation technique that has shown excellent performance in a wide variety of domains. In a simulation study, we tested the feasibility of these techniques and evaluated their performance in comparison to standard methodology. Both methods exhibited relatively robust performance, accurately capturing pre-defined non-linear functional relationships in different simulation settings. Further, we applied these techniques to data on perinatal exposure to environmental metal toxicants, with the goal of evaluating the effects of exposure on neurodevelopment. Our methods identified critical neurodevelopmental windows showing significant sensitivity to metal mixtures. Copyright © 2017 Elsevier Inc. All rights reserved.

The dynamic relationship between social support and HIV-related stigma in rural Uganda.

PubMed

Takada, Sae; Weiser, Sheri D; Kumbakumba, Elias; Muzoora, Conrad; Martin, Jeffrey N; Hunt, Peter W; Haberer, Jessica E; Kawuma, Annet; Bangsberg, David R; Tsai, Alexander C

2014-08-01

Cross-sectional studies show that human immunodeficiency virus (HIV) stigma is negatively correlated with social support. The purpose of this study is to examine the bidirectional relationship between social support and HIV stigma. We collected quarterly data from a cohort of 422 people living with HIV in Uganda, followed for a median of 2.1 years. We used multilevel regression to model the contemporaneous and 3-month-lagged associations between social support and both enacted and internalized stigma. Lagged enacted stigma was negatively correlated with emotional and instrumental social support, and lagged instrumental social support was negatively correlated with enacted stigma. Internalized stigma and emotional social support had reciprocal lagged associations. Interventions to reduce enacted stigma may strengthen social support for people living with HIV. Improved social support may in turn have a protective influence against future enacted and internalized stigma.

The Dynamic Relationship Between Social Support and HIV-Related Stigma in Rural Uganda

PubMed Central

Weiser, Sheri D.; Kumbakumba, Elias; Muzoora, Conrad; Martin, Jeffrey N.; Hunt, Peter W.; Haberer, Jessica E.; Kawuma, Annet; Bangsberg, David R.; Tsai, Alexander C.

2014-01-01

Background Cross-sectional studies show that human immunodeficiency virus (HIV) stigma is negatively correlated with social support. Purpose The purpose of this study is to examine the bidirectional relationship between social support and HIV stigma. Methods We collected quarterly data from a cohort of 422 people living with HIV in Uganda, followed for a median of 2.1 years. We used multilevel regression to model the contemporaneous and 3-month-lagged associations between social support and both enacted and internalized stigma. Results Lagged enacted stigma was negatively correlated with emotional and instrumental social support, and lagged instrumental social support was negatively correlated with enacted stigma. Internalized stigma and emotional social support had reciprocal lagged associations. Conclusions Interventions to reduce enacted stigma may strengthen social support for people living with HIV. Improved social support may in turn have a protective influence against future enacted and internalized stigma. PMID:24500077

Stochastic calculus of protein filament formation under spatial confinement

NASA Astrophysics Data System (ADS)

Michaels, Thomas C. T.; Dear, Alexander J.; Knowles, Tuomas P. J.

2018-05-01

The growth of filamentous aggregates from precursor proteins is a process of central importance to both normal and aberrant biology, for instance as the driver of devastating human disorders such as Alzheimer's and Parkinson's diseases. The conventional theoretical framework for describing this class of phenomena in bulk is based upon the mean-field limit of the law of mass action, which implicitly assumes deterministic dynamics. However, protein filament formation processes under spatial confinement, such as in microdroplets or in the cellular environment, show intrinsic variability due to the molecular noise associated with small-volume effects. To account for this effect, in this paper we introduce a stochastic differential equation approach for investigating protein filament formation processes under spatial confinement. Using this framework, we study the statistical properties of stochastic aggregation curves, as well as the distribution of reaction lag-times. Moreover, we establish the gradual breakdown of the correlation between lag-time and normalized growth rate under spatial confinement. Our results establish the key role of spatial confinement in determining the onset of stochasticity in protein filament formation and offer a formalism for studying protein aggregation kinetics in small volumes in terms of the kinetic parameters describing the aggregation dynamics in bulk.

Quantifying the lag time to detect barriers in landscape genetics

Treesearch

E. L. Landguth; S. A Cushman; M. K. Schwartz; K. S. McKelvey; M. Murphy; G. Luikart

2010-01-01

Understanding how spatial genetic patterns respond to landscape change is crucial for advancing the emerging field of landscape genetics. We quantified the number of generations for new landscape barrier signatures to become detectable and for old signatures to disappear after barrier removal. We used spatially explicit, individualbased simulations to examine the...

Spatial modeling on the upperstream of the Citarum watershed: An application of geoinformatics

NASA Astrophysics Data System (ADS)

Ningrum, Windy Setia; Widyaningsih, Yekti; Indra, Tito Latif

2017-03-01

The Citarum watershed is the longest and the largest watershed in West Java, Indonesia, located at 106°51'36''-107°51' E and 7°19'-6°24'S across 10 districts, and serves as the water supply for over 15 million people. In this area, the water criticality index is concerned to reach the balance between water supply and water demand, so that in the dry season, the watershed is still able to meet the water needs of the society along the Citarum river. The objective of this research is to evaluate the water criticality index of Citarum watershed area using spatial model to overcome the spatial dependencies in the data. The result of Lagrange multiplier diagnostics for spatial dependence results are LM-err = 34.6 (p-value = 4.1e-09) and LM-lag = 8.05 (p-value = 0.005), then modeling using Spatial Lag Model (SLM) and Spatial Error Model (SEM) were conducted. The likelihood ratio test show that both of SLM dan SEM model is better than OLS model in modeling water criticality index in Citarum watershed. The AIC value of SLM and SEM model are 78.9 and 51.4, then the SEM model is better than SLM model in predicting water criticality index in Citarum watershed.

Determination of the Time-Space Magnetic Correlation Functions in the Solar Wind

NASA Astrophysics Data System (ADS)

Weygand, J. M.; Matthaeus, W. H.; Kivelson, M.; Dasso, S.

2013-12-01

Magnetic field data from many different intervals and 7 different solar wind spacecraft are employed to estimate the scale-dependent time decorrelation function in the interplanetary magnetic field in both the slow and fast solar wind. This estimation requires correlations varying with both space and time lags. The two point correlation function with no time lag is determined by correlating time series data from multiple spacecraft separated in space and for complete coverage of length scales relies on many intervals with different spacecraft spatial separations. In addition we employ single spacecraft time-lagged correlations, and two spacecraft time lagged correlations to access different spatial and temporal correlation data. Combining these data sets gives estimates of the scale-dependent time decorrelation function, which in principle tells us how rapidly time decorrelation occurs at a given wavelength. For static fields the scale-dependent time decorrelation function is trivially unity, but in turbulence the nonlinear cascade process induces time-decorrelation at a given length scale that occurs more rapidly with decreasing scale. The scale-dependent time decorrelation function is valuable input to theories as well as various applications such as scattering, transport, and study of predictability. It is also a fundamental element of formal turbulence theory. Our results are extension of the Eulerian correlation functions estimated in Matthaeus et al. [2010], Weygand et al [2012; 2013].

[A clinical study on the relationship of the tail femur distance and the lag screw migration or cutting-out after the third generation of Gamma nail fixation of intertrochanteric fracture].

PubMed

Hou, Yu; Yao, Qi; Zhang, Gen'ai; Ding, Lixiang

2018-01-01

To confirm the association between tail femur distance (TFD) and lag screw migration or cutting-out in the treatment of intertrochanteric fracture with the third generation of Gamma nail (TGN). The clinical data of 124 cases of intertrochanteric fracture treated with TGN internal fixation and followed up more than 18 months between January 2012 and December 2015 were reviewed and analyzed. There were 52 males and 72 females, with an age of 46-93 years (mean, 78.5 years). According to AO/Association for the Study of Internal Fixation (AO/ASIF) classification, 43 cases were type 31-A1, 69 cases were type 31-A2, and 12 cases were type 31-A3. The time from injury to operation was 1-10 days (mean, 2.9 days). According to the fracture healing of the patients, the patients were divided into the healing group and failure group. The age, gender, height, bone mineral density (BMD), fracture AO/ASIF classification, the time from injury to operation, and the TFD value at 1 day after operation were recorded and compared. The risk factors for the migration or cutting-out of lag screw were analyzed by logistic regression. There were 111 cases in healing group, the healing time was 80-110 days (mean, 95.5 days). There were 13 cases in failure group, including 2 cases of lag screw cutting-out and 11 cases of significant migration. Except for the TFD value at 1 day after operation in failure group was significantly higher than that in the healing group( t =5.14, P =0.00), there was no significant difference in gender, age, height, BMD, fracture of AO/ASIF classification, and the time from injury to operation ( P >0.05) between 2 groups. logistic regression analysis showed that TFD value was a risk factor for the migration or cutting-out of lag screw (B=1.22, standardized coefficient=0.32, Wald χ 2 =14.66, P =0.00, OR=3.37). The patients with higher TFD value had higher risk of postoperative lag screw migration or cutting-out. This result indicates that the appropriate length of the lag screw is helpful to reduce TFD value and prevent postoperative lag screw migration or cutting-out.

[Influence of humidex on incidence of bacillary dysentery in Hefei: a time-series study].

PubMed

Zhang, H; Zhao, K F; He, R X; Zhao, D S; Xie, M Y; Wang, S S; Bai, L J; Cheng, Q; Zhang, Y W; Su, H

2017-11-10

Objective: To investigate the effect of humidex combined with mean temperature and relative humidity on the incidence of bacillary dysentery in Hefei. Methods: Daily counts of bacillary dysentery cases and weather data in Hefei were collected from January 1, 2006 to December 31, 2013. Then, the humidex was calculated from temperature and relative humidity. A Poisson generalized linear regression combined with distributed lag non-linear model was applied to analyze the relationship between humidex and the incidence of bacillary dysentery, after adjusting for long-term and seasonal trends, day of week and other weather confounders. Stratified analyses by gender, age and address were also conducted. Results: The risk of bacillary dysentery increased with the rise of humidex. The adverse effect of high humidex (90 percentile of humidex) appeared in 2-days lag and it was the largest at 4-days lag ( RR =1.063, 95 %CI : 1.037-1.090). Subgroup analyses indicated that all groups were affected by high humidex at lag 2-5 days. Conclusion: High humidex could significantly increase the risk of bacillary dysentery, and the lagged effects were observed.

Correspondence between the distribution of hydrodynamic time parameters and the distribution of biological and chemical variables in a semi-enclosed coral reef lagoon

NASA Astrophysics Data System (ADS)

Torréton, Jean-Pascal; Rochelle-Newall, Emma; Jouon, Aymeric; Faure, Vincent; Jacquet, Séverine; Douillet, Pascal

2007-09-01

Hydrodynamic modeling can be used to spatially characterize water renewal rates in coastal ecosystems. Using a hydrodynamic model implemented over the semi-enclosed Southwest coral lagoon of New Caledonia, a recent study computed the flushing lag as the minimum time required for a particle coming from outside the lagoon (open ocean) to reach a specific station [Jouon, A., Douillet, P., Ouillon, S., Fraunié, P., 2006. Calculations of hydrodynamic time parameters in a semi-opened coastal zone using a 3D hydrodynamic model. Continental Shelf Research 26, 1395-1415]. Local e -flushing time was calculated as the time requested to reach a local grid mesh concentration of 1/e from the precedent step. Here we present an attempt to connect physical forcing to biogeochemical functioning of this coastal ecosystem. An array of stations, located in the lagoonal channel as well as in several bays under anthropogenic influence, was sampled during three cruises. We then tested the statistical relationships between the distribution of flushing indices and those of biological and chemical variables. Among the variables tested, silicate, chlorophyll a and bacterial biomass production present the highest correlations with flushing indices. Correlations are higher with local e-flushing times than with flushing lags or the sum of these two indices. In the bays, these variables often deviate from the relationships determined in the main lagoon channel. In the three bays receiving significant riverine inputs, silicate is well above the regression line, whereas data from the bay receiving almost insignificant freshwater inputs generally fit the lagoon channel regressions. Moreover, in the three bays receiving important urban and industrial effluents, chlorophyll a and bacterial production of biomass generally display values exceeding the lagoon channel regression trends whereas in the bay under moderate anthropogenic influence values follow the regressions obtained in the lagoon channel. The South West lagoon of New Caledonia can hence be viewed as a coastal mesotrophic ecosystem that is flushed by oligotrophic oceanic waters which subsequently replace the lagoonal waters with water considerably impoverished in resources for microbial growth. This flushing was high enough during the periods of study to influence the distribution of phytoplankton biomass, bacterial production of biomass and silicate concentrations in the lagoon channel as well as in some of the bay areas.

Spatial epidemiology of suspected clinical leptospirosis in Sri Lanka.

PubMed

Robertson, C; Nelson, T A; Stephen, C

2012-04-01

Leptospirosis is one of the most widespread zoonoses in the world. A large outbreak of suspected human leptospirosis began in Sri Lanka during 2008. This study investigated spatial variables associated with suspected leptospirosis risk during endemic and outbreak periods. Data were obtained for monthly numbers of reported cases of suspected clinical leptospirosis for 2005-2009 for all of Sri Lanka. Space-time scan statistics were combined with regression modelling to test associations during endemic and outbreak periods. The cross-correlation function was used to test association between rainfall and leptospirosis at four locations. During the endemic period (2005-2007), leptospirosis risk was positively associated with shorter average distance to rivers and with higher percentage of agriculture made up of farms <0·20 hectares. Temporal correlation analysis of suspected leptospirosis cases and rainfall revealed a 2-month lag in rainfall-case association during the baseline period. Outbreak locations in 2008 were characterized by shorter distance to rivers and higher population density. The analysis suggests the possibility of household transmission in densely populated semi-urban villages as a defining characteristic of the outbreak. The role of rainfall in the outbreak remains to be investigated, although analysis here suggests a more complex relationship than simple correlation.

[Complexity and its integrative effects of the time lags of environment factors affecting Larix gmelinii stem sap flow].

PubMed

Wang, Hui-Mei; Sun, Wei; Zu, Yuan-Gang; Wang, Wen-Jie

2011-12-01

Based on the one-year (2005) observations with a frequency of half hour on the stem sap flow of Larix gmelinii plantation trees planted in 1969 and the related environmental factors air humidity (RH), air temperature (T(air)), photosynthetic components active radiation (PAR), soil temperature (T(soil)), and soil moisture (TDR), principal analysis (PCA) and correction analysis were made on the time lag effect of the stem flow in different seasons (26 days of each season) and in a year via dislocation analysis, with the complexity and its integrative effects of the time lags of environment factors affecting the stem sap flow approached. The results showed that in different seasons and for different environmental factors, the time lag effect varied obviously. In general, the time lag of PAR was 0.5-1 hour ahead of sap flow, that of T(air) and RH was 0-2 hours ahead of or behind the sap flow, and the time lags of T(soil) and TDR were much longer or sometimes undetectable. Because of the complexity of the time lags, no evident improvements were observed in the linear correlations (R2, slope, and intercept) when the time lags based on short-term (20 days) data were used to correct the time lags based on whole year data. However, obvious improvements were found in the standardized and non-standardized correlation coefficients in stepwise multiple regressions, i.e., the time lag corrections could improve the effects of RH, but decreased the effects of PAR, T(air), and T(soil). PCA could be used to simplify the complexity. The first and the second principal components could stand for over 75% information of all the environmental factors in different seasons and in whole year. The time lags of both the first and the second principal components were 1-1.5 hours in advance of the sap flow, except in winter (no time lag effect).

Air pollutants and atmospheric pressure increased risk of ED visit for spontaneous pneumothorax.

PubMed

Park, Joo Hyung; Lee, Sun Hwa; Yun, Seong Jong; Ryu, Seokyong; Choi, Seung Woon; Kim, Hye Jin; Kang, Tae Kyung; Oh, Sung Chan; Cho, Suk Jin

2018-04-14

To investigate the impact of short-term exposure to air pollutants and meteorological variation on ED visits for primary spontaneous pneumothorax (PSP). We retrospectively identified PSP cases that presented at the ED of our tertiary center between January 2015 and September 2016. We classified the days into three types: no PSP day (0 case/day), sporadic days (1-2 cases/day), and cluster days (PSP, ≥3 cases/day). Association between the daily incidence of PSP with air pollutants and meteorological data were determined using Poisson generalized-linear-model to calculate incidence rate ratio (IRRs) and the use of time-series (lag-1 [the cumulative air pollution level on the previous day of PSP], lag-2 [two days ago], and lag-3 [three days ago]). Using multivariate logistic regression analysis, O 3 (p = 0.010), NO 2 (p = 0.047), particulate matters (PM) 10 (p = 0.021), and PM 2.5 (p = 0.008) were significant factors of PSP occurrence. When the concentration of O 3 , NO 2 , PM 10 , and PM 2.5 were increased, PSP IRRs increased approximately 15, 16, 3, and 5-fold, respectively. With the time-series analyses, atmospheric pressure in lag-3 was significantly lower and in lag-2, was significantly higher in PSP days compared with no PSP days. Among air pollutant concentrations, O 3 in lag-1 (p = 0.017) and lag-2 (p = 0.038), NO 2 in lag-1 (p = 0.015) and lag-2 (p = 0.009), PM 10 in lag-1 (p = 0.012), and PM 2.5 in lag-1 (p = 0.021) and lag-2 (p = 0.032) were significantly different between no PSP and PSP days. Increased concentrations of air pollutants and abrupt change in atmospheric pressure were significantly associated with increased IRR of PSP. Copyright © 2018 Elsevier Inc. All rights reserved.

Lagging skills contribute to challenging behaviors in children with autism spectrum disorder without intellectual disability.

PubMed

Maddox, Brenna B; Cleary, Patrick; Kuschner, Emily S; Miller, Judith S; Armour, Anna Chelsea; Guy, Lisa; Kenworthy, Lauren; Schultz, Robert T; Yerys, Benjamin E

2017-08-01

Many children with autism spectrum disorder display challenging behaviors. These behaviors are not limited to those with cognitive and/or language impairments. The Collaborative and Proactive Solutions framework proposes that challenging behaviors result from an incompatibility between environmental demands and a child's "lagging skills." The primary Collaborative and Proactive Solutions lagging skills-executive function, emotion regulation, language, and social skills-are often areas of weakness for individuals with autism spectrum disorder. The purpose of this study was to evaluate whether these lagging skills are associated with challenging behaviors in youth with autism spectrum disorder without intellectual disability. Parents of 182 youth with autism spectrum disorder (6-15 years) completed measures of their children's challenging behaviors, executive function, language, emotion regulation, and social skills. We tested whether the Collaborative and Proactive Solutions lagging skills predicted challenging behaviors using multiple linear regression. The Collaborative and Proactive Solutions lagging skills explained significant variance in participants' challenging behaviors. The Depression (emotion regulation), Inhibit (executive function), and Sameness (executive function) scales emerged as significant predictors. Impairments in emotion regulation and executive function may contribute substantially to aggressive and oppositional behaviors in school-age youth with autism spectrum disorder without intellectual disability. Treatment for challenging behaviors in this group may consider targeting the incompatibility between environmental demands and a child's lagging skills.

Impact of climate variability on Plasmodium vivax and Plasmodium falciparum malaria in Yunnan Province, China.

PubMed

Bi, Yan; Yu, Weiwei; Hu, Wenbiao; Lin, Hualiang; Guo, Yuming; Zhou, Xiao-Nong; Tong, Shilu

2013-12-17

Malaria remains a public health problem in the remote and poor area of Yunnan Province, China. Yunnan faces an increasing risk of imported malaria infections from Mekong river neighboring countries. This study aimed to identify the high risk area of malaria transmission in Yunnan Province, and to estimate the effects of climatic variability on the transmission of Plasmodium vivax and Plasmodium falciparum in the identified area. We identified spatial clusters of malaria cases using spatial cluster analysis at a county level in Yunnan Province, 2005-2010, and estimated the weekly effects of climatic factors on P. vivax and P. falciparum based on a dataset of daily malaria cases and climatic variables. A distributed lag nonlinear model was used to estimate the impact of temperature, relative humidity and rainfall up to 10-week lags on both types of malaria parasite after adjusting for seasonal and long-term effects. The primary cluster area was identified along the China-Myanmar border in western Yunnan. A 1°C increase in minimum temperature was associated with a lag 4 to 9 weeks relative risk (RR), with the highest effect at lag 7 weeks for P. vivax (RR = 1.03; 95% CI, 1.01, 1.05) and 6 weeks for P. falciparum (RR = 1.07; 95% CI, 1.04, 1.11); a 10-mm increment in rainfall was associated with RRs of lags 2-4 weeks and 9-10 weeks, with the highest effect at 3 weeks for both P. vivax (RR = 1.03; 95% CI, 1.01, 1.04) and P. falciparum (RR = 1.04; 95% CI, 1.01, 1.06); and the RRs with a 10% rise in relative humidity were significant from lag 3 to 8 weeks with the highest RR of 1.24 (95% CI, 1.10, 1.41) for P. vivax at 5-week lag. Our findings suggest that the China-Myanmar border is a high risk area for malaria transmission. Climatic factors appeared to be among major determinants of malaria transmission in this area. The estimated lag effects for the association between temperature and malaria are consistent with the life cycles of both mosquito vector and malaria parasite. These findings will be useful for malaria surveillance-response systems in the Mekong river region.

Constraints of philanthropy on determining the distribution of biodiversity conservation funding.

PubMed

Larson, Eric R; Howell, Stephen; Kareiva, Peter; Armsworth, Paul R

2016-02-01

Caught between ongoing habitat destruction and funding shortfalls, conservation organizations are using systematic planning approaches to identify places that offer the highest biodiversity return per dollar invested. However, available tools do not account for the landscape of funding for conservation or quantify the constraints this landscape imposes on conservation outcomes. Using state-level data on philanthropic giving to and investments in land conservation by a large nonprofit organization, we applied linear regression to evaluate whether the spatial distribution of conservation philanthropy better explained expenditures on conservation than maps of biodiversity priorities, which were derived from a planning process internal to the organization and return on investment (ROI) analyses based on data on species richness, land costs, and existing protected areas. Philanthropic fund raising accounted for considerably more spatial variation in conservation spending (r(2) = 0.64) than either of the 2 systematic conservation planning approaches (r(2) = 0.08-0.21). We used results of one of the ROI analyses to evaluate whether increases in flexibility to reallocate funding across space provides conservation gains. Small but plausible "tax" increments of 1-10% on states redistributed to the optimal funding allocation from the ROI analysis could result in gains in endemic species protected of 8.5-80.2%. When such increases in spatial flexibility are not possible, conservation organizations should seek to cultivate increased support for conservation in priority locations. We used lagged correlations of giving to and spending by the organization to evaluate whether investments in habitat protection stimulate future giving to conservation. The most common outcome at the state level was that conservation spending quarters correlated significantly and positively with lagged fund raising quarters. In effect, periods of high fund raising for biodiversity followed (rather than preceded) periods of high expenditure on land conservation projects, identifying one mechanism conservation organizations could explore to seed greater activity in priority locations. Our results demonstrate how limitations on the ability of conservation organizations to reallocate their funding across space can impede organizational effectiveness and elucidate ways conservation planning tools could be more useful if they quantified and incorporated these constraints. © 2015 Society for Conservation Biology.

Combining geostatistics with Moran's I analysis for mapping soil heavy metals in Beijing, China.

PubMed

Huo, Xiao-Ni; Li, Hong; Sun, Dan-Feng; Zhou, Lian-Di; Li, Bao-Guo

2012-03-01

Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran's I analysis was used to supplement the traditional geostatistics. According to Moran's I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran's I and the standardized Moran's I, Z(I) reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran's I analysis was better than traditional geostatistics. Thus, Moran's I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals.

Combining Geostatistics with Moran’s I Analysis for Mapping Soil Heavy Metals in Beijing, China

PubMed Central

Huo, Xiao-Ni; Li, Hong; Sun, Dan-Feng; Zhou, Lian-Di; Li, Bao-Guo

2012-01-01

Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran’s I analysis was used to supplement the traditional geostatistics. According to Moran’s I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran’s I and the standardized Moran’s I, Z(I) reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran’s I analysis was better than traditional geostatistics. Thus, Moran’s I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals. PMID:22690179

Application of satellite precipitation data to analyse and model arbovirus activity in the tropics

PubMed Central

2011-01-01

Background Murray Valley encephalitis virus (MVEV) is a mosquito-borne Flavivirus (Flaviviridae: Flavivirus) which is closely related to Japanese encephalitis virus, West Nile virus and St. Louis encephalitis virus. MVEV is enzootic in northern Australia and Papua New Guinea and epizootic in other parts of Australia. Activity of MVEV in Western Australia (WA) is monitored by detection of seroconversions in flocks of sentinel chickens at selected sample sites throughout WA. Rainfall is a major environmental factor influencing MVEV activity. Utilising data on rainfall and seroconversions, statistical relationships between MVEV occurrence and rainfall can be determined. These relationships can be used to predict MVEV activity which, in turn, provides the general public with important information about disease transmission risk. Since ground measurements of rainfall are sparse and irregularly distributed, especially in north WA where rainfall is spatially and temporally highly variable, alternative data sources such as remote sensing (RS) data represent an attractive alternative to ground measurements. However, a number of competing alternatives are available and careful evaluation is essential to determine the most appropriate product for a given problem. Results The Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 product was chosen from a range of RS rainfall products to develop rainfall-based predictor variables and build logistic regression models for the prediction of MVEV activity in the Kimberley and Pilbara regions of WA. Two models employing monthly time-lagged rainfall variables showed the strongest discriminatory ability of 0.74 and 0.80 as measured by the Receiver Operating Characteristics area under the curve (ROC AUC). Conclusions TMPA data provide a state-of-the-art data source for the development of rainfall-based predictive models for Flavivirus activity in tropical WA. Compared to ground measurements these data have the advantage of being collected spatially regularly, irrespective of remoteness. We found that increases in monthly rainfall and monthly number of days above average rainfall increased the risk of MVEV activity in the Pilbara at a time-lag of two months. Increases in monthly rainfall and monthly number of days above average rainfall increased the risk of MVEV activity in the Kimberley at a lag of three months. PMID:21255449

Predicting depressed patients with suicidal ideation from ECG recordings.

PubMed

Khandoker, A H; Luthra, V; Abouallaban, Y; Saha, S; Ahmed, K I; Mostafa, R; Chowdhury, N; Jelinek, H F

2017-05-01

Globally suicidal behavior is the third most common cause of death among patients with major depressive disorder (MDD). This study presents multi-lag tone-entropy (T-E) analysis of heart rate variability (HRV) as a screening tool for identifying MDD patients with suicidal ideation. Sixty-one ECG recordings (10 min) were acquired and analyzed from control subjects (29 CONT), 16 MDD subjects with (MDDSI+) and 16 without suicidal ideation (MDDSI-). After ECG preprocessing, tone and entropy values were calculated for multiple lags (m: 1-10). The MDDSI+ group was found to have a higher mean tone value compared to that of the MDDSI- group for lags 1-8, whereas the mean entropy value was lower in MDDSI+ than that in CONT group at all lags (1-10). Leave-one-out cross-validation tests, using a classification and regression tree (CART), obtained 94.83 % accuracy in predicting MDDSI+ subjects by using a combination of tone and entropy values at all lags and including demographic factors (age, BMI and waist circumference) compared to results with time and frequency domain HRV analysis. The results of this pilot study demonstrate the usefulness of multi-lag T-E analysis in identifying MDD patients with suicidal ideation and highlight the change in autonomic nervous system modulation of the heart rate associated with depression and suicidal ideation.

School Attendance Problems and Youth Psychopathology: Structural Cross-Lagged Regression Models in Three Longitudinal Datasets

PubMed Central

Wood, Jeffrey J.; Lynne, Sarah D.; Langer, David A.; Wood, Patricia A.; Clark, Shaunna L.; Eddy, J. Mark; Ialongo, Nicholas

2011-01-01

This study tests a model of reciprocal influences between absenteeism and youth psychopathology using three longitudinal datasets (Ns= 20745, 2311, and 671). Participants in 1st through 12th grades were interviewed annually or bi-annually. Measures of psychopathology include self-, parent-, and teacher-report questionnaires. Structural cross-lagged regression models were tested. In a nationally representative dataset (Add Health), middle school students with relatively greater absenteeism at study year 1 tended towards increased depression and conduct problems in study year 2, over and above the effects of autoregressive associations and demographic covariates. The opposite direction of effects was found for both middle and high school students. Analyses with two regionally representative datasets were also partially supportive. Longitudinal links were more evident in adolescence than in childhood. PMID:22188462

Comparison of long-term results between laparoscopy-assisted gastrectomy and open gastrectomy with D2 lymph node dissection for advanced gastric cancer.

PubMed

Hamabe, Atsushi; Omori, Takeshi; Tanaka, Koji; Nishida, Toshirou

2012-06-01

Laparoscopy-assisted gastrectomy (LAG) has been established as a low-invasive surgery for early gastric cancer. However, it remains unknown whether it is applicable also for advanced gastric cancer, mainly because the long-term results of LAG with D2 lymph node dissection for advanced gastric cancer have not been well validated compared with open gastrectomy (OG). A retrospective cohort study was performed to compare LAG and OG with D2 lymph node dissection. For this study, 167 patients (66 LAG and 101 OG patients) who underwent gastrectomy with D2 lymph node dissection for advanced gastric cancer were reviewed. Recurrence-free survival and overall survival time were estimated using Kaplan-Meier curves. Stratified log-rank statistical evaluation was used to compare the difference between the LAG and OG groups stratified by histologic type, pathologic T status, N status, and postoperative adjuvant chemotherapy. The adjusted Cox proportional hazards regression models were used to calculate the hazard ratios (HRs) of LAG. The 5-year recurrence-free survival rate was 89.6% in the LAG group and 75.8% in the OG group (nonsignificant difference; stratified log-rank statistic, 3.11; P = 0.0777). The adjusted HR of recurrence for LAG compared with OG was 0.389 [95% confidence interval (CI) 0.131-1.151]. The 5-year overall survival rate was 94.4% in the LAG group and 78.5% in the OG group (nonsignificant difference; stratified log-rank statistic, 0.4817; P = 0.4877). The adjusted HR of death for LAG compared with OG was 0.633 (95% CI 0.172-2.325). The findings show that LAG with D2 lymph node dissection is acceptable in terms of long-term results for advanced gastric cancer cases and may be applicable for advanced gastric cancer treatment.

Radiographic morphology of intrabony defects in the first molars of patients with localized aggressive periodontitis: Comparison with health and chronic periodontitis.

PubMed

Nibali, L; Tomlins, P; Akcalı, A

2018-04-16

The aim of this study was to describe the radiographic features of the first molars of patients with localized aggressive periodontitis (LAgP) and of their associated intrabony defects and to compare them with a control sample of chronic periodontitis cases and healthy subjects. Data from a total of 93 patients were included in this analysis. First, dental panoramic tomograms of 34 patients with LAgP (131 first molars) and 30 periodontally healthy patients (110 first molars) were compared. Then, periapical radiographs of the first molars of the same patients with LAgP and of 29 patients with chronic periodontitis affected by intrabony defects were analysed. Shorter root trunks were associated with the presence of intrabony defects in patients with LAgP (P = .002 at multilevel logistic regression), also when LAgP molars were compared with healthy subjects (P = .036). Although no difference in defect depth and angle was noted between LAgP and chronic periodontitis intrabony defects, LAgP intrabony defects appeared to be more frequently symmetrical and arch-shaped than in chronic periodontitis (P = .008), with positive predictive value and negative predictive value of for 'wide arch' defect of 87.3% (95% CI = 77.2%-93.3%) and 32.3% (95% CI = 27.7%-37.2%) respectively. First molars of patients with LAgP affected by intrabony defects may have some distinct radiographic anatomical characteristics to those of healthy subjects. The shape of intrabony defects seems to differ between LAgP and chronic periodontitis cases. Further studies need to confirm these features and investigate if they are related to the initiation and progression of periodontitis. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Failure of the precedence effect with a noise-band vocoder

PubMed Central

Seeber, Bernhard U.; Hafter, Ervin R.

2011-01-01

The precedence effect (PE) describes the ability to localize a direct, leading sound correctly when its delayed copy (lag) is present, though not separately audible. The relative contribution of binaural cues in the temporal fine structure (TFS) of lead–lag signals was compared to that of interaural level differences (ILDs) and interaural time differences (ITDs) carried in the envelope. In a localization dominance paradigm participants indicated the spatial location of lead–lag stimuli processed with a binaural noise-band vocoder whose noise carriers introduced random TFS. The PE appeared for noise bursts of 10 ms duration, indicating dominance of envelope information. However, for three test words the PE often failed even at short lead–lag delays, producing two images, one toward the lead and one toward the lag. When interaural correlation in the carrier was increased, the images appeared more centered, but often remained split. Although previous studies suggest dominance of TFS cues, no image is lateralized in accord with the ITD in the TFS. An interpretation in the context of auditory scene analysis is proposed: By replacing the TFS with that of noise the auditory system loses the ability to fuse lead and lag into one object, and thus to show the PE. PMID:21428515

The effects of preceding lead-alone and lag-alone click trains on the buildup of echo suppression.

PubMed

Bishop, Christopher W; Yadav, Deepak; London, Sam; Miller, Lee M

2014-08-01

Spatial perception in echoic environments is influenced by recent acoustic history. For instance, echo suppression becomes more effective or "builds up" with repeated exposure to echoes having a consistent acoustic relationship to a temporally leading sound. Four experiments were conducted to investigate how buildup is affected by prior exposure to unpaired lead-alone or lag-alone click trains. Unpaired trains preceded lead-lag click trains designed to evoke and assay buildup. Listeners reported how many sounds they heard from the echo hemifield during the lead-lag trains. Stimuli were presented in free field (experiments 1 and 4) or dichotically through earphones (experiments 2 and 3). In experiment 1, listeners reported more echoes following a lead-alone train compared to a period of silence. In contrast, listeners reported fewer echoes following a lag-alone train; similar results were observed with earphones. Interestingly, the effects of lag-alone click trains on buildup were qualitatively different when compared to a no-conditioner trial type in experiment 4. Finally, experiment 3 demonstrated that the effects of preceding click trains on buildup cannot be explained by a change in counting strategy or perceived click salience. Together, these findings demonstrate that echo suppression is affected by prior exposure to unpaired stimuli.

Early biometric lag in the prediction of small for gestational age neonates and preeclampsia.

PubMed

Schwartz, Nadav; Pessel, Cara; Coletta, Jaclyn; Krieger, Abba M; Timor-Tritsch, Ilan E

2011-01-01

An early fetal growth lag may be a marker of future complications. We sought to determine the utility of early biometric variables in predicting adverse pregnancy outcomes. In this retrospective cohort study, the crown-rump length at 11 to 14 weeks and the head circumference, biparietal diameter, abdominal circumference, femur length, humerus length, transverse cerebellar diameter, and estimated fetal weight at 18 to 24 weeks were converted to an estimated gestational age using published regression formulas. Sonographic fetal growth (difference between each biometric gestational age and the crown-rump length gestational age) minus expected fetal growth (number of days elapsed between the two scans) yielded the biometric growth lag. These lags were tested as predictors of small for gestational age (SGA) neonates (≤10th percentile) and preeclampsia. A total of 245 patients were included. Thirty-two (13.1%) delivered an SGA neonate, and 43 (17.6%) had the composite outcome. The head circumference, biparietal diameter, abdominal circumference, and estimated fetal weight lags were identified as significant predictors of SGA neonates after adjusted analyses (P < .05). The addition of either the estimated fetal weight or abdominal circumference lag to maternal characteristics alone significantly improved the performance of the predictive model, achieving areas under the curve of 0.72 and 0.74, respectively. No significant association was found between the biometric lag variables and the development of preeclampsia. Routinely available biometric data can be used to improve the prediction of adverse outcomes such as SGA. These biometric lags should be considered in efforts to develop screening algorithms for adverse outcomes.

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

Wang, Jiali; Swati, F. N. U.; Stein, Michael L.

Regional climate models (RCMs) are a standard tool for downscaling climate forecasts to finer spatial scales. The evaluation of RCMs against observational data is an important step in building confidence in the use of RCMs for future prediction. In addition to model performance in climatological means and marginal distributions, a model’s ability to capture spatio-temporal relationships is important. This study develops two approaches: (1) spatial correlation/variogram for a range of spatial lags, with total monthly precipitation and non-seasonal precipitation components used to assess the spatial variations of precipitation; and (2) spatio-temporal correlation for a wide range of distances, directions, andmore » time lags, with daily precipitation occurrence used to detect the dynamic features of precipitation. These measures of spatial and spatio-temporal dependence are applied to a high-resolution RCM run and to the National Center for Environmental Prediction (NCEP)-U.S. Department of Energy (DOE) AMIP II reanalysis data (NCEP-R2), which provides initial and lateral boundary conditions for the RCM. The RCM performs better than NCEP-R2 in capturing both the spatial variations of total and non-seasonal precipitation components and the spatio-temporal correlations of daily precipitation occurrences, which are related to dynamic behaviors of precipitating systems. The improvements are apparent not just at resolutions finer than that of NCEP-R2, but also when the RCM and observational data are aggregated to the resolution of NCEP-R2.« less

Linear Changes in the Spatial Extent of the Focus of Attention across Time

ERIC Educational Resources Information Center

Jefferies, Lisa N.; Di Lollo, Vincent

2009-01-01

This research examined changes in the spatial extent of focal attention over time. The Attentional Blink (impaired perception of the second of two targets) and Lag-1 sparing (the seemingly paradoxical finding that second-target accuracy is high when the second target immediately follows the first) were employed in a dual-stream paradigm to index…

Surveillance on the endemic of Zika virus infection by meteorological factors in Colombia: a population-based spatial and temporal study.

PubMed

Chien, Lung-Chang; Lin, Ro-Ting; Liao, Yunqi; Sy, Francisco S; Pérez, Adriana

2018-04-17

Zika virus (ZIKV) infection is a pandemic and a public health emergency. It is transmitted by mosquitoes, primarily the Aedes genus. In light of no treatment currently, it is crucial to develop effective vector control programs to prevent the spread of ZIKV infection earlier when observing possible risk factors, such as weather conditions enhancing mosquito breeding and surviving. This study collected daily meteorological measurements and weekly ZIKV infectious cases among 32 departments of Colombia from January 2015-December 2016. This study applied the distributed lag nonlinear model to estimate the association between the number of ZIKA virus infection and meteorological measurements, controlling for spatial and temporal variations. We examined at most three meteorological factors with 20 lags in weeks in the model. Average humidity, total rainfall, and maximum temperature were more predictable of ZIKV infection outbreaks than other meteorological factors. Our models can detect significantly lagged effects of average humidity, total rainfall, and maximum temperature on outbreaks up to 15, 14, and 20 weeks, respectively. The spatial analysis identified 12 departments with a significant threat of ZIKV, and eight of those high-risk departments were located between the Equator and 6°N. The outbreak prediction also performed well in identified high-risk departments. Our results demonstrate that meteorological factors could be used for predicting ZIKV epidemics. Building an early warning surveillance system is important for preventing ZIKV infection, particularly in endemic areas.

A dynamic regression analysis tool for quantitative assessment of bacterial growth written in Python.

PubMed

Hoeflinger, Jennifer L; Hoeflinger, Daniel E; Miller, Michael J

2017-01-01

Herein, an open-source method to generate quantitative bacterial growth data from high-throughput microplate assays is described. The bacterial lag time, maximum specific growth rate, doubling time and delta OD are reported. Our method was validated by carbohydrate utilization of lactobacilli, and visual inspection revealed 94% of regressions were deemed excellent. Copyright © 2016 Elsevier B.V. All rights reserved.

[Time lag effect between poplar' s sap flow velocity and microclimate factors in agroforestry system in West Liaoning Province].

PubMed

Di, Sun; Guan, De-xin; Yuan, Feng-hui; Wang, An-zhi; Wu, Jia-bing

2010-11-01

By using Granier's thermal dissipation probe, the sap flow velocity of the poplars in agroforestry system in west Liaoning was continuously measured, and the microclimate factors were measured synchronously. Dislocation contrast method was applied to analyze the sap flow velocity and corresponding air temperature, air humidity, net radiation, and vapor pressure deficit to discuss the time lag effect between poplar' s sap flow velocity and microclimate factors on sunny days. It was found that the poplar's sap flow velocity advanced of air temperature, air humidity, and vapor pressure deficit, and lagged behind net radiation. The sap flow velocity in June, July, August, and September was advanced of 70, 30, 50, and 90 min to air temperature, of 80, 30, 40, and 90 min to air humidity, and of 90, 50, 70, and 120 min to vapor pressure deficit, but lagged behind 10, 10, 40, and 40 min to net radiation, respectively. The time lag time of net radiation was shorter than that of air temperature, air humidity, and vapor pressure. The regression analysis showed that in the cases the time lag effect was contained and not, the determination coefficients between comprehensive microclimate factor and poplar's sap flow velocity were 0.903 and 0.855, respectively, indicating that when the time lag effect was contained, the determination coefficient was ascended by 2.04%, and thus, the simulation accuracy of poplar's sap flow velocity was improved.

Adding In-Plane Flexibility to the Equations of Motion of a Single Rotor Helicopter

NASA Technical Reports Server (NTRS)

Curtiss, H. C., Jr.

2000-01-01

This report describes a way to add the effects of main rotor blade flexibility in the in- plane or lead-lag direction to a large set of non-linear equations of motion for a single rotor helicopter with rigid blades(l). Differences between the frequency of the regressing lag mode predicted by the equations of (1) and that measured in flight (2) for a UH-60 helicopter indicate that some element is missing from the analytical model of (1) which assumes rigid blades. A previous study (3) noted a similar discrepancy for the CH-53 helicopter. Using a relatively simple analytical model in (3), compared to (1), it was shown that a mechanical lag damper increases significantly the coupling between the rigid lag mode and the first flexible mode. This increased coupling due to a powerful lag damper produces an increase in the lowest lag frequency when viewed in a frame rotating with the blade. Flight test measurements normally indicate the frequency of this mode in a non-rotating or fixed frame. This report presents the additions necessary to the full equations of motion, to include main rotor blade lag flexibility. Since these additions are made to a very complex nonlinear dynamic model, in order to provide physical insight, a discussion of the results obtained from a simplified set of equations of motion is included. The reduced model illustrates the physics involved in the coupling and should indicate trends in the full model.

Protein Z efficiently depletes thrombin generation in disseminated intravascular coagulation with poor prognosis.

PubMed

Lee, Nuri; Kim, Ji-Eun; Gu, Ja-Yoon; Yoo, Hyun Ju; Kim, Inho; Yoon, Sung-Soo; Park, Seonyang; Han, Kyou-Sup; Kim, Hyun Kyung

2016-01-01

Disseminated intravascular coagulation (DIC) is characterized by consumption of coagulation factors and anticoagulants. Thrombin generation assay (TGA) gives useful information about global hemostatic status. We developed a new TGA system that anticoagulant addition can deplete thrombin generation in plasma, which may reflect defective anticoagulant system in DIC. TGAs were measured on the calibrated automated thrombogram with and without thrombomodulin or protein Z in 152 patients who were suspected of having DIC, yielding four parameters including lag time, endogenous thrombin potential, peak thrombin and time-to-peak in each experiment. Nonsurvivors showed significantly prolonged lag time and time-to-peak in TGA-protein Z system, which was performed with added protein Z. In multivariate Cox regression analysis, lag time and time-to-peak in TGA system were significant independent prognostic factors. In TGA-protein Z system, lag time and time-to-peak were revealed as independent prognostic factors of DIC. Protein Z addition could potentiate its anticoagulant effect in DIC with poor prognosis, suggesting the presence of defective protein Z system. The prolonged lag time and time-to-peak in both TGA and TGA-protein Z systems are expected to be used as independent prognostic factors of DIC.

Toward comparing experiment and theory for corroborative research on hingeless rotor stability in forward flight (an experimental and analytical investigation of isolated rotor-flap-lag stability in forward flight)

NASA Technical Reports Server (NTRS)

Gaonkar, G.

1986-01-01

For flap-lag stability of isolated rotors, experimental and analytical investigations are conducted in hover and forward flight on the adequacy of a linear quasisteady aerodynamics theory with dynamic inflow. Forward flight effects on lag regressing mode are emphasized. Accordingly, a soft inplane hingeless rotor with three blades is tested at advance ratios as high as 0.55 and at shaft angles as high as 20 degrees. The 1.62 m model rotor is untrimmed with an essentially unrestricted tilt of the tip path plane. In combination with lag natural frequencies, collective pitch settings and flap-lag coupling parameters, the data base comprises nearly 1200 test points (damping and frequency) in forward flight and 200 test points in hover. By computerized symbolic manipulation, a linear analytical model is developed in substall to predict stability margins with mode identificaton. To help explain the correlation between theory and data it also predicts substall and stall regions of the rotor disk from equilibrium values. The correlation shows both the strengthts and weaknesses of the theory in substall.

Source-reconstruction of the sensorimotor network from resting-state macaque electrocorticography.

PubMed

Hindriks, R; Micheli, C; Bosman, C A; Oostenveld, R; Lewis, C; Mantini, D; Fries, P; Deco, G

2018-06-07

The discovery of hemodynamic (BOLD-fMRI) resting-state networks (RSNs) has brought about a fundamental shift in our thinking about the role of intrinsic brain activity. The electrophysiological underpinnings of RSNs remain largely elusive and it has been shown only recently that electric cortical rhythms are organized into the same RSNs as hemodynamic signals. Most electrophysiological studies into RSNs use magnetoencephalography (MEG) or scalp electroencephalography (EEG), which limits the spatial resolution with which electrophysiological RSNs can be observed. Due to their close proximity to the cortical surface, electrocorticographic (ECoG) recordings can potentially provide a more detailed picture of the functional organization of resting-state cortical rhythms, albeit at the expense of spatial coverage. In this study we propose using source-space spatial independent component analysis (spatial ICA) for identifying generators of resting-state cortical rhythms as recorded with ECoG and for reconstructing their functional connectivity. Network structure is assessed by two kinds of connectivity measures: instantaneous correlations between band-limited amplitude envelopes and oscillatory phase-locking. By simulating rhythmic cortical generators, we find that the reconstruction of oscillatory phase-locking is more challenging than that of amplitude correlations, particularly for low signal-to-noise levels. Specifically, phase-lags can both be over- and underestimated, which troubles the interpretation of lag-based connectivity measures. We illustrate the methodology on somatosensory beta rhythms recorded from a macaque monkey using ECoG. The methodology decomposes the resting-state sensorimotor network into three cortical generators, distributed across primary somatosensory and primary and higher-order motor areas. The generators display significant and reproducible amplitude correlations and phase-locking values with non-zero lags. Our findings illustrate the level of spatial detail attainable with source-projected ECoG and motivates wider use of the methodology for studying resting-state as well as event-related cortical dynamics in macaque and human. Copyright © 2018. Published by Elsevier Inc.

Modeling Dynamics of South American Rangelands to Climate Variability and Human Impact

NASA Astrophysics Data System (ADS)

Stanimirova, R.; Arevalo, P. A.; Kaufmann, R.; Maus, V.; Lesiv, M.; Havlik, P.; Friedl, M. A.

2017-12-01

The combined pressures of climate change and shifting dietary preferences are creating an urgent need to improve understanding of how climate and land management are jointly affecting the sustainability of rangelands. In particular, our ability to effectively manage rangelands in a manner that satisfies increasing demand for meat and dairy while reducing environmental impact depends on the sensitivity of rangelands to perturbations from both climate (e.g., drought) and land use (e.g., grazing). To characterize the sensitivity of rangeland vegetation to variation in climate, we analyzed gridded time series of satellite and climate data at 0.5-degree spatial resolution from 2003 to 2016 for rangeland ecosystems in South America. We used panel regression and canonical correlation to analyze the relationship between time series of enhanced vegetation index (EVI) derived from NASA's Moderate Spatial Resolution Imaging Spectroradiometer (MODIS) and gridded precipitation and air temperature data from the University of East Anglia's Climate Research Unit. To quantify the degree to which livestock management explains geographic variation of EVI, we used global livestock distribution (FAO) and feed requirements data from the Global Biosphere Management Model (GLOBIOM). Because rangeland ecosystems are sensitive to changes in meteorological variables at different time scales, we evaluated the strength of coupling between anomalies in EVI and anomalies in temperature and standardized precipitation index (SPI) data at 1-6 month lags. Our results show statistically significant relationships between EVI and precipitation during summer, fall, and winter in both tropical and subtropical agroecological zones of South America. Further, lagged precipitation effects, which reflect memory in the system, explain significant variance in winter EVI anomalies. While precipitation emerges as the dominant driver of variability in rangeland greenness, we find evidence of a management-induced signal as well. Our modeling framework integrates satellite observation, meteorological data sets, and land use/cover change information to improve our capability to monitor and manage the long-term sustainability of rangelands.

Variability in lateral carbon export from four major tributaries in the Upper Mississippi River Basin

NASA Astrophysics Data System (ADS)

Stanimirova, R.; Arevalo, P. A.; Kaufmann, R.; Maus, V.; Lesiv, M.; Havlik, P.; Friedl, M. A.

2016-12-01

The combined pressures of climate change and shifting dietary preferences are creating an urgent need to improve understanding of how climate and land management are jointly affecting the sustainability of rangelands. In particular, our ability to effectively manage rangelands in a manner that satisfies increasing demand for meat and dairy while reducing environmental impact depends on the sensitivity of rangelands to perturbations from both climate (e.g., drought) and land use (e.g., grazing). To characterize the sensitivity of rangeland vegetation to variation in climate, we analyzed gridded time series of satellite and climate data at 0.5-degree spatial resolution from 2003 to 2016 for rangeland ecosystems in South America. We used panel regression and canonical correlation to analyze the relationship between time series of enhanced vegetation index (EVI) derived from NASA's Moderate Spatial Resolution Imaging Spectroradiometer (MODIS) and gridded precipitation and air temperature data from the University of East Anglia's Climate Research Unit. To quantify the degree to which livestock management explains geographic variation of EVI, we used global livestock distribution (FAO) and feed requirements data from the Global Biosphere Management Model (GLOBIOM). Because rangeland ecosystems are sensitive to changes in meteorological variables at different time scales, we evaluated the strength of coupling between anomalies in EVI and anomalies in temperature and standardized precipitation index (SPI) data at 1-6 month lags. Our results show statistically significant relationships between EVI and precipitation during summer, fall, and winter in both tropical and subtropical agroecological zones of South America. Further, lagged precipitation effects, which reflect memory in the system, explain significant variance in winter EVI anomalies. While precipitation emerges as the dominant driver of variability in rangeland greenness, we find evidence of a management-induced signal as well. Our modeling framework integrates satellite observation, meteorological data sets, and land use/cover change information to improve our capability to monitor and manage the long-term sustainability of rangelands.

Resolving uncertainty in the spatial relationships between passive benzene exposure and risk of non-Hodgkin lymphoma.

PubMed

Switchenko, Jeffrey M; Bulka, Catherine; Ward, Kevin; Koff, Jean L; Bayakly, A Rana; Ryan, P Barry; Waller, Lance A; Flowers, Christopher R

2016-04-01

Benzene is a known occupational carcinogen associated with increased risk of hematologic cancers, but the relationships between quantity of passive benzene exposure through residential proximity to toxic release sites, duration of exposure, lag time from exposure to cancer development, and lymphoma risk remain unclear. We collected release data through the Environmental Protection Agency's Toxics Release Inventory (TRI) from 1989 to 2003, which included location of benzene release sites, years when release occurred, and amount of release. We also collected data on incident cases of non-Hodgkin lymphoma (NHL) from the Georgia Comprehensive Cancer Registry (GCCR) for the years 1999-2008. We constructed distance-decay surrogate exposure metrics and Poisson and negative binomial regression models of NHL incidence to quantify associations between passive exposure to benzene and NHL risk and examined the impact of amount, duration of exposure, and lag time on cancer development. Akaike's information criteria (AIC) were used to determine the scaling factors for benzene dispersion and exposure periods that best predicted NHL risk. Using a range of scaling factors and exposure periods, we found that increased levels of passive benzene exposure were associated with higher risk of NHL. The best fitting model, with a scaling factor of 4 kilometers (km) and exposure period of 1989-1993, showed that higher exposure levels were associated with increased NHL risk (Level 4 (1.1-160kilograms (kg)) vs. Level 1: risk ratio 1.56 [1.44-1.68], Level 5 (>160kg) vs. Level 1: 1.60 [1.48-1.74]). Higher levels of passive benzene exposure are associated with increased NHL risk across various lag periods. Additional epidemiological studies are needed to refine these models and better quantify the expected total passive benzene exposure in areas surrounding release sites. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Instantaneous global spatial interaction? Exploring the Gaussian inequality, distance and Internet pings in a global network

NASA Astrophysics Data System (ADS)

Baker, R. G. V.

2005-12-01

The Internet has been publicly portrayed as a new technological horizon yielding instantaneous interaction to a point where geography no longer matters. This research aims to dispel this impression by applying a dynamic form of trip modelling to investigate pings in a global computer network compiled by the Stanford Linear Accelerator Centre (SLAC) from 1998 to 2004. Internet flows have been predicted to have the same mathematical operators as trips to a supermarket, since they are both periodic and constrained by a distance metric. Both actual and virtual trips are part of a spectrum of origin-destination pairs in the time-space convergence of trip time-lines. Internet interaction is very near to the convergence of these time-lines (at a very small time scale in milliseconds, but with interactions over thousands of kilometres). There is a lag effect and this is formalised by the derivation of Gaussian and gravity inequalities between the time taken (Δ t) and the partitioning of distance (Δ x). This inequality seems to be robust for a regression of Δ t to Δ x in the SLAC data set for each year (1998 to 2004). There is a constant ‘forbidden zone’ in the interaction, underpinned by the fact that pings do not travel faster than the speed of light. Superimposed upon this zone is the network capacity where a linear regression of Δ t to Δ x is a proxy summarising global Internet connectivity for that year. The results suggest that there has been a substantial improvement in connectivity over the period with R 2 increasing steadily from 0.39 to 0.65 from less Gaussian spreading of the ping latencies. Further, the regression line shifts towards the inequality boundary from 1998 to 2004, where the increased slope shows a greater proportional rise in local connectivity over global connectivity. A conclusion is that national geography still does matter in spatial interaction modelling of the Internet.

Deconstructing Building Blocks: Preschoolers' Spatial Assembly Performance Relates to Early Mathematics Skills

PubMed Central

Verdine, Brian N.; Golinkoff, Roberta Michnick; Hirsh-Pasek, Kathryn; Newcombe, Nora S.; Filipowicz, Andrew T.; Chang, Alicia

2013-01-01

This study focuses on three main goals: First, 3-year-olds' spatial assembly skills are probed using interlocking block constructions (N = 102). A detailed scoring scheme provides insight into early spatial processing and offers information beyond a basic accuracy score. Second, the relation of spatial assembly to early mathematics skills was evaluated. Spatial skill independently predicted a significant amount of the variability in concurrent mathematics performance. Finally, the relationship between spatial assembly skill and socioeconomic status, gender, and parent-reported spatial language was examined. While children's performance did not differ by gender, lower-SES children were already lagging behind higher-SES children in block assembly. Furthermore, lower-SES parents reported using significantly fewer spatial words with their children. PMID:24112041

Association with meteo-climatological factors and daily emergency visits for renal colic and urinary calculi in Cuneo, Italy. A retrospective observational study, 2007-2010

NASA Astrophysics Data System (ADS)

Condemi, Vincenzo; Gestro, Massimo; Dozio, Elena; Tartaglino, Bruno; Corsi Romanelli, Massimiliano Marco; Solimene, Umberto; Meco, Roberto

2015-03-01

The incidence of nephrolithiasis is rising worldwide, especially in women and with increasing age. Incidence and prevalence of kidney stones are affected by genetic, nutritional, and environmental factors. The aim of this study is to investigate the link between various meteorological factors (independent variables) and the daily number of visits to the Emergency Department (ED of the S. Croce and Carle Hospital of Cuneo for renal colic (RC) and urinary stones (UC) as the dependent variable over the years 2007-2010. The Poisson generalized regression models (PGAMs) have been used in different progressive ways. The results of PGAMs (stage 1) adjusted for seasonal and calendar factors confirmed a significant correlation ( p < 0.03) with the thermal parameter. Evaluation of the dose-response effect [PGAMs combined with distributed lags nonlinear models (DLNMs)—stage 2], expressed in terms of relative risk (RR) and cumulative relative risk (RRC), indicated a relative significant effect up to 15 lag days of lag (RR > 1), with a first peak after 5 days (lag ranges 0-1, 0-3, and 0-5) and a second weak peak observed along the 5-15 lag range days. The estimated RR for females was significant, mainly in the second and fourth age group considered (19-44 and >65 years): RR for total ED visits 1.27, confidence interval (CI) 1.11-1.46 (lag 0-5 days); RR 1.42, CI 1.01-2.01 (lag 0-10 days); and RR 1.35, CI 1.09-1.68 (lag 0-15 days). The research also indicated a moderate involvement of the thermal factor in the onset of RC caused by UC, exclusively in the female sex. Further studies will be necessary to confirm these results.

Evaluating Alcoholics Anonymous's effect on drinking in Project MATCH using cross-lagged regression panel analysis.

PubMed

Magura, Stephen; Cleland, Charles M; Tonigan, J Scott

2013-05-01

The objective of the study is to determine whether Alcoholics Anonymous (AA) participation leads to reduced drinking and problems related to drinking within Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity), an existing national alcoholism treatment data set. The method used is structural equation modeling of panel data with cross-lagged partial regression coefficients. The main advantage of this technique for the analysis of AA outcomes is that potential reciprocal causation between AA participation and drinking behavior can be explicitly modeled through the specification of finite causal lags. For the outpatient subsample (n = 952), the results strongly support the hypothesis that AA attendance leads to increases in alcohol abstinence and reduces drinking/ problems, whereas a causal effect in the reverse direction is unsupported. For the aftercare subsample (n = 774), the results are not as clear but also suggest that AA attendance leads to better outcomes. Although randomized controlled trials are the surest means of establishing causal relations between interventions and outcomes, such trials are rare in AA research for practical reasons. The current study successfully exploited the multiple data waves in Project MATCH to examine evidence of causality between AA participation and drinking outcomes. The study obtained unique statistical results supporting the effectiveness of AA primarily in the context of primary outpatient treatment for alcoholism.

Mapping the Asymmetric Thick Disk. III. The Kinematics and Interaction with the Galactic Bar

NASA Astrophysics Data System (ADS)

Humphreys, Roberta M.; Beers, Timothy C.; Cabanela, Juan E.; Grammer, Skyler; Davidson, Kris; Lee, Young Sun; Larsen, Jeffrey A.

2011-04-01

In the first two papers of this series, Larsen et al. describe our faint CCD survey in the inner Galaxy and map the overdensity of thick disk stars in Quadrant 1 (Q1) to 5 kpc or more along the line of sight. The regions showing the strongest excess are above the density contours of the bar in the Galactic disk. In this third paper on the asymmetric thick disk, we report on radial velocities and derived metallicity parameters for over 4000 stars in Q1, above and below the plane, and in Quadrant 4 (Q4) above the plane. We confirm the corresponding kinematic asymmetry first reported by Parker et al., extended to greater distances and with more spatial coverage. The thick disk stars in Q1 have a rotational lag of 60-70 km s-1 relative to circular rotation, and the metal-weak thick disk stars have an even greater lag of 100 km s-1. Both lag their corresponding populations in Q4 by ≈30 km s-1. Interestingly, the disk stars in Q1 also appear to participate in the rotational lag by about 30 km s-1. The enhanced rotational lag for the thick disk in Q1 extends to 4 kpc or more from the Sun. At 3-4 kpc, our sight lines extend above the density contours on the near side of the bar, and as our lines of sight pass directly over the bar the rotational lag appears to decrease. This is consistent with a "gravitational wake" induced by the rotating bar in the disk which would trap and pile up stars behind it. We conclude that a dynamical interaction with the stellar bar is the most probable explanation for the observed kinematic and spatial asymmetries. Based on observations obtained at the MMT Observatory, a joint facility of the Smithsonian Institution and the University of Arizona, and at the Cerro Tololo Inter-American Observatory (NOAO) operated by the Association of Universities for Research in Astronomy (AURA).

Making a Place for Space: Spatial Thinking in Social Science

PubMed Central

Logan, John R.

2013-01-01

New technologies and multilevel data sets that include geographic identifiers have heightened sociologists’ interest in spatial analysis. I review several of the key concepts, measures, and methods that are brought into play in this work, and offer examples of their application in a variety of substantive fields. I argue that the most effective use of the new tools requires greater emphasis on spatial thinking. A device as simple as an illustrative map requires some understanding of how people respond to visual cues; models as complex as HLM with spatial lags require thoughtful measurement decisions and raise questions about what a spatial effect represents. PMID:24273374

White matter tract abnormalities are associated with cognitive dysfunction in secondary progressive multiple sclerosis.

PubMed

Meijer, Kim A; Muhlert, Nils; Cercignani, Mara; Sethi, Varun; Ron, Maria A; Thompson, Alan J; Miller, David H; Chard, Declan; Geurts, Jeroen Jg; Ciccarelli, Olga

2016-10-01

While our knowledge of white matter (WM) pathology underlying cognitive impairment in relapsing remitting multiple sclerosis (MS) is increasing, equivalent understanding in those with secondary progressive (SP) MS lags behind. The aim of this study is to examine whether the extent and severity of WM tract damage differ between cognitively impaired (CI) and cognitively preserved (CP) secondary progressive multiple sclerosis (SPMS) patients. Conventional magnetic resonance imaging (MRI) and diffusion MRI were acquired from 30 SPMS patients and 32 healthy controls (HC). Cognitive domains commonly affected in MS patients were assessed. Linear regression was used to predict cognition. Diffusion measures were compared between groups using tract-based spatial statistics (TBSS). A total of 12 patients were classified as CI, and processing speed was the most commonly affected domain. The final regression model including demographic variables and radial diffusivity explained the greatest variance of cognitive performance (R 2  = 0.48, p = 0.002). SPMS patients showed widespread loss of WM integrity throughout the WM skeleton when compared with HC. When compared with CP patients, CI patients showed more extensive and severe damage of several WM tracts, including the fornix, superior longitudinal fasciculus and forceps major. Loss of WM integrity assessed using TBSS helps to explain cognitive decline in SPMS patients. © The Author(s), 2016.

Effect of Asian dust storms on mortality in three Asian cities

NASA Astrophysics Data System (ADS)

Lee, Hyewon; Honda, Yasushi; Lim, Youn-Hee; Guo, Yue Leon; Hashizume, Masahiro; Kim, Ho

2014-06-01

Asian dust storms (ADS) have affected several Asian countries and have been a major concern due to adverse effects on public health. The occurrence of ADS differs in each country based on geographical features and distance from the storms' origin. Many studies have reported significant associations between ADS and morbidity. However, regarding the association between ADS and mortality, only a few studies have found statistically significant ADS effects in Korea, Taiwan and Japan. Accordingly, this study aimed to examine the effects of ADS on daily mortality in three Asian cities (Seoul, South Korea; Taipei, Taiwan; and Kitakyushu, Japan) and to explore the differences in the extent of effects in each city. We performed time-series analyses using a generalized additive model (GAM) with Quasi-Poisson regressions. Deaths due to accidents or external causes were excluded. We used a dummy variable as an indicator of ADS and considered lag effects of ADS. Stratified analyses by disease and age and sensitivity analyses controlling for NO2, SO2, and PM10 were also conducted respectively. Additionally, influenza epidemics were adjusted for considering seasonal patterns, and a meta-analysis was performed. We reported results as excess mortality by percentage due to Asian dust storms. We found significant excess mortality in Seoul and Kitakyushu as follows. In Seoul, ADS showed adverse effects on mortality under 65 years old (lag 2: 4.44%, lag 3: 5%, lag 4: 4.39%). In Kitakyushu, ADS had adverse effects on respiratory mortality (lag 2: 18.82%). Contradictory to results in Seoul and Kitakyushu, ADS seemed to have a protective effect in Taipei: total non-accidental mortality (lag 0: -2.77%, lag 1: -3.24%), mortality over 65 years old (lag 0: -3.35%, lag 1: -3.29%) and respiratory mortality (lag 0: -10.62%, lag 1: -9.67%). Sensitivity analyses showed similar findings as the main results. Our findings suggest that ADS may affect mortality in several Asian cities, and that a dust storm warning system could help protect people from dust storms.

Utilization of the Bridging Strategy for the Development of New Drugs in Oncology to Avoid Drug Lag.

PubMed

Kogure, Seiji; Koyama, Nobuyuki; Hidaka, Shinji

2017-11-01

Global trial (GT) strategy and bridging (BG) strategy are currently the main clinical development strategies of oncology drugs in Japan, but the relationship between development style and drug lag and how the bridging strategy has contributed to the solution of drug lag have not been clear. We investigated the potential factors that influenced submission lag (SL), and also compared the differences in SL among early-initiation BG strategy, late-initiation BG strategy, and GT strategy. A stepwise linear regression analysis identified the potential factors that shorten SL: development start lag and development style. Comparison of the differences in SL among the strategies also indicated that the SL in the GT strategy and that in the early-initiation BG strategy were significantly shorter than that in the late-initiation BG strategy. The findings in our study suggest that the late-initiation BG strategy may not contribute to shortening drug lag. Because the number of late-initiation BG studies has not decreased, we propose first that pharmaceutical companies should initiate clinical development as early as possible in Japan so that they can choose the GT strategy as a first option at the next step, and second when they cannot choose the GT strategy after investigating differences in exposure between Japanese and non-Japanese in a phase 1 study, they should select the early BG strategy to avoid future drug lag. It is also important for the regulatory authorities to provide reasonable guidance to have a positive impact on strategic decisions, even for foreign-capital companies. © 2017, The American College of Clinical Pharmacology.

Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection.

PubMed

Yang, Su; Shi, Shixiong; Hu, Xiaobing; Wang, Minjie

2015-01-01

Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1) Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2) The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3) The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks.

Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection

PubMed Central

Yang, Su; Shi, Shixiong; Hu, Xiaobing; Wang, Minjie

2015-01-01

Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1) Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2) The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3) The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks. PMID:26496370

Toward comparing experiment and theory for corroborative research on hingeless rotor stability in forward flight

NASA Technical Reports Server (NTRS)

Gaonkar, G.

1987-01-01

For flap lag stability of isolated rotors, experimental and analytical investigations were conducted in hover and forward flight on the adequacy of a linear quasisteady aerodynamics theory with dynamic flow. Forward flight effects on lag regressing mode were emphasized. A soft inplane hingeless rotor with three blades was tested at advance ratios as high as 0.55 and at shaft angles as high as 20 deg. The 1.62 m model rotor was untrimmed with an essentially unrestricted tilt of the tip path plane. In combination with lag natural frequencies, collective pitch settings and flap lag coupling parameters, the data base comprises nearly 1200 test points (damping and frequency) in forward flight and 200 test points in hover. By computerized symbolic manipulation, a linear model was developed in substall to predict stability margins with mode identification. To help explain the correlation between theory and data it also predicted substall and stall regions of the rotor disk from equilibrium values. The correlation showed both the strengths and weaknesses of the theory in substall ((angle of attack) equal to or less than 12 deg).

Alcohol and drug treatment involvement, 12-step attendance and abstinence: 9-year cross-lagged analysis of adults in an integrated health plan.

PubMed

Witbrodt, Jane; Ye, Yu; Bond, Jason; Chi, Felicia; Weisner, Constance; Mertens, Jennifer

2014-04-01

This study explored causal relationships between post-treatment 12-step attendance and abstinence at multiple data waves and examined indirect paths leading from treatment initiation to abstinence 9-years later. Adults (N = 1945) seeking help for alcohol or drug use disorders from integrated healthcare organization outpatient treatment programs were followed at 1-, 5-, 7- and 9-years. Path modeling with cross-lagged partial regression coefficients was used to test causal relationships. Cross-lagged paths indicated greater 12-step attendance during years 1 and 5 and were casually related to past-30-day abstinence at years 5 and 7 respectfully, suggesting 12-step attendance leads to abstinence (but not vice versa) well into the post-treatment period. Some gender differences were found in these relationships. Three significant time-lagged, indirect paths emerged linking treatment duration to year-9 abstinence. Conclusions are discussed in the context of other studies using longitudinal designs. For outpatient clients, results reinforce the value of lengthier treatment duration and 12-step attendance in year 1. Copyright © 2014 Elsevier Inc. All rights reserved.

Precipitation Nowcast using Deep Recurrent Neural Network

NASA Astrophysics Data System (ADS)

Akbari Asanjan, A.; Yang, T.; Gao, X.; Hsu, K. L.; Sorooshian, S.

2016-12-01

An accurate precipitation nowcast (0-6 hours) with a fine temporal and spatial resolution has always been an important prerequisite for flood warning, streamflow prediction and risk management. Most of the popular approaches used for forecasting precipitation can be categorized into two groups. One type of precipitation forecast relies on numerical modeling of the physical dynamics of atmosphere and another is based on empirical and statistical regression models derived by local hydrologists or meteorologists. Given the recent advances in artificial intelligence, in this study a powerful Deep Recurrent Neural Network, termed as Long Short-Term Memory (LSTM) model, is creatively used to extract the patterns and forecast the spatial and temporal variability of Cloud Top Brightness Temperature (CTBT) observed from GOES satellite. Then, a 0-6 hours precipitation nowcast is produced using a Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) algorithm, in which the CTBT nowcast is used as the PERSIANN algorithm's raw inputs. Two case studies over the continental U.S. have been conducted that demonstrate the improvement of proposed approach as compared to a classical Feed Forward Neural Network and a couple simple regression models. The advantages and disadvantages of the proposed method are summarized with regard to its capability of pattern recognition through time, handling of vanishing gradient during model learning, and working with sparse data. The studies show that the LSTM model performs better than other methods, and it is able to learn the temporal evolution of the precipitation events through over 1000 time lags. The uniqueness of PERSIANN's algorithm enables an alternative precipitation nowcast approach as demonstrated in this study, in which the CTBT prediction is produced and used as the inputs for generating precipitation nowcast.

Associations between ambient air pollution and daily mortality in a cohort of congestive heart failure: Case-crossover and nested case-control analyses using a distributed lag nonlinear model.

PubMed

Buteau, Stephane; Goldberg, Mark S; Burnett, Richard T; Gasparrini, Antonio; Valois, Marie-France; Brophy, James M; Crouse, Dan L; Hatzopoulou, Marianne

2018-04-01

Persons with congestive heart failure may be at higher risk of the acute effects related to daily fluctuations in ambient air pollution. To meet some of the limitations of previous studies using grouped-analysis, we developed a cohort study of persons with congestive heart failure to estimate whether daily non-accidental mortality were associated with spatially-resolved, daily exposures to ambient nitrogen dioxide (NO 2 ) and ozone (O 3 ), and whether these associations were modified according to a series of indicators potentially reflecting complications or worsening of health. We constructed the cohort from the linkage of administrative health databases. Daily exposure was assigned from different methods we developed previously to predict spatially-resolved, time-dependent concentrations of ambient NO 2 (all year) and O 3 (warm season) at participants' residences. We performed two distinct types of analyses: a case-crossover that contrasts the same person at different times, and a nested case-control that contrasts different persons at similar times. We modelled the effects of air pollution and weather (case-crossover only) on mortality using distributed lag nonlinear models over lags 0 to 3 days. We developed from administrative health data a series of indicators that may reflect the underlying construct of "declining health", and used interactions between these indicators and the cross-basis function for air pollutant to assess potential effect modification. The magnitude of the cumulative as well as the lag-specific estimates of association differed in many instances according to the metric of exposure. Using the back-extrapolation method, which is our preferred exposure model, we found for the case-crossover design a cumulative mean percentage changes (MPC) in daily mortality per interquartile increment in NO 2 (8.8 ppb) of 3.0% (95% CI: -0.9, 6.9%) and for O 3 (16.5 ppb) 3.5% (95% CI: -4.5, 12.1). For O 3 there was strong confounding by weather (unadjusted MPC = 7.1%; 95% CI: 1.7, 12.7%). For the nested case-control approach the cumulative MPC for NO 2 in daily mortality was 2.9% (95% CI: -0.9, 6.9%) and for O 3 7.3% (95% CI: 3.0, 11.9%). We found evidence of effect modification between daily mortality and cumulative NO 2 and O 3 according to the prescribed dose of furosemide in the nested case-control analysis, but not in the case-crossover analysis. Mortality in congestive heart failure was associated with exposure to daily ambient NO 2 and O 3 predicted from a back-extrapolation method using a land use regression model from dense sampling surveys. The methods used to assess exposure can have considerable influence on the estimated acute health effects of the two air pollutants. Copyright © 2018 Elsevier Ltd. All rights reserved.

Characteristics and Impact of Imperviousness From a GIS-based Hydrological Perspective

NASA Astrophysics Data System (ADS)

Moglen, G. E.; Kim, S.

2005-12-01

With the concern that imperviousness can be differently quantified depending on data sources and methods, this study assessed imperviousness estimates using two different data sources: land use and land cover. Year 2000 land use developed by the Maryland Department of Planning was utilized to estimate imperviousness by assigning imperviousness coefficients to unique land use categories. These estimates were compared with imperviousness estimates based on satellite-derived land cover from the 2001 National Land Cover Dataset. Our study developed the relationships between these two estimates in the form of regression equations to convert imperviousness derived from one data source to the other. The regression equations are considered reliable, based on goodness-of-fit measures. Furthermore, this study examined how quantitatively different imperviousness estimates affect the prediction of hydrological response both in the flow regime and in the thermal regime. We assessed the relationships between indicators of hydrological response and imperviousness-descriptors. As indicators of flow variability, coefficient of variance, lag-one autocorrelation, and mean daily flow change were calculated based on measured mean daily stream flow from the water year 1997 to 2003. For thermal variability, indicators such as percent-days of surge, degree-day, and mean daily temperature difference were calculated base on measured stream temperature over several basins in Maryland. To describe imperviousness through the hydrological process, GIS-based spatially distributed hydrological models were developed based on a water-balance method and the SCS-CN method. Imperviousness estimates from land use and land cover were used as predictors in these models to examine the effect of imperviousness using different data sources on the prediction of hydrological response. Indicators of hydrological response were also regressed on aggregate imperviousness. This allowed for identifying if hydrological response is more sensitive to spatially distributed imperviousness or aggregate (lumped) imperviousness. The regressions between indicators of hydrological response and imperviousness-descriptors were evaluated by examining goodness-of-fit measures such as explained variance or relative standard error. The results show that imperviousness estimates using land use are better predictors of flow variability and thermal variability than imperviousness estimates using land cover. Also, this study reveals that flow variability is more sensitive to spatially distributed models than lumped models, while thermal variability is equally responsive to both models. The findings from this study can be further examined from a policy perspective with regard to policies that are based on a threshold concept for imperviousness impacts on the ecological and hydrological system.

Evolution of genuine cross-correlation strength of focal onset seizures.

PubMed

Müller, Markus F; Baier, Gerold; Jiménez, Yurytzy López; Marín García, Arlex O; Rummel, Christian; Schindler, Kaspar

2011-10-01

To quantify the evolution of genuine zero-lag cross-correlations of focal onset seizures, we apply a recently introduced multivariate measure to broad band and to narrow-band EEG data. For frequency components below 12.5 Hz, the strength of genuine cross-correlations decreases significantly during the seizure and the immediate postseizure period, while higher frequency bands show a tendency of elevated cross-correlations during the same period. We conclude that in terms of genuine zero-lag cross-correlations, the electrical brain activity as assessed by scalp electrodes shows a significant spatial fragmentation, which might promote seizure offset.

Do subjective symptoms predict our perception of jet-lag?

PubMed

Waterhouse, J; Edwards, B; Nevill, A; Atkinson, G; Reilly, T; Davies, P; Godfrey, R

2000-10-01

A total of 39 subjects were studied after a flight from the UK to either Sydney or Brisbane (10 time-zones to the east). Subjects varied widely in their age, their athletic ability, whether or not they were taking melatonin, and in their objectives when in Australia. For the first 6 days after arrival, subjects scored their jet-lag five times per day and other subjective variables up to five times per day, using visual analogue scales. For jet-lag, the scale was labelled 0 = no jet-lag to 10 = very bad jet-lag; the extremes of the other scales were labelled - 5 and + 5, indicating marked changes compared with normal, and the centrepoint was labelled 0 indicating 'normal'. Mean daily values for jet-lag and fatigue were initially high (+ 3.65 +/- 0.35 and + 1.55 +/- 0.22 on day 1, respectively) and fell progressively on subsequent days, but were still raised significantly (p < 0.05) on day 5 (fatigue) or day 6 (jet-lag). In addition, times of waking were earlier on all days. By contrast, falls in concentration and motivation, and rises in irritability and nocturnal wakings, had recovered by day 4 or earlier, and bowel activity was less frequent, with harder stools, on days 1 and 2 only. Also, on day 1, there was a decrease in the ease of getting to sleep (- 1.33 +/- 0.55), but this changed to an increase from day 2 onwards (for example, + 0.75 +/- 0.25 on day 6). Stepwise regression analysis was used to investigate predictors of jet-lag. The severity of jet-lag at all the times that were measured was strongly predicted by fatigue ratings made at the same time. Its severity at 08:00 h was predicted by an earlier time of waking, by feeling less alert 30 min after waking and, marginally, by the number of waking episodes. Jet-lag at 12:00 and 16:00 h was strongly predicted by a fall of concentration at these times; jet-lag at mealtimes (12:00, 16:00 and 20:00 h) was predicted by the amount of feeling bloated. Such results complicate an exact interpretation that can be placed on an assessment of a global term such as jet-lag, particularly if the assessment is made only once per day.

An experimental and analytical investigation of isolated rotor flap-lag stability in forward flight

NASA Technical Reports Server (NTRS)

Gaonkar, Gopal H.; Mcnulty, Michael J.

1985-01-01

For flap-lag stability of isolated rotors, experimental and analytical investigations are conducted in hover and forward flight on the adequacy of a linear quasi-steady aerodynamics theory with dynamic inflow. Forward flight effects on lag regressing mode are emphasized. Accordingly, a soft inplane hingeless rotor with three blades is tested at advance ratios as high as 0.55 and at shaft angles as high as 20 deg. The 1.62-m model rotor is untrimmed with an essentially unrestricted tilt of the tip path plane. By computerized symbolic manipulation, an analytical model is developed in substall to predict stability margins with mode indentification. It also predicts substall and stall regions to help explain the correlation between theory and data. The correlation shows both the strengths and weaknesses of the data and theory, and promotes further insights into areas in which further study is needed in substall and stall.

INVASIVE SPECIES: PREDICTING GEOGRAPHIC DISTRIBUTIONS USING ECOLOGICAL NICHE MODELING

EPA Science Inventory

Present approaches to species invasions are reactive in nature. This scenario results in management that perpetually lags behind the most recent invasion and makes control much more difficult. In contrast, spatially explicit ecological niche modeling provides an effective solut...

Spatial and temporal variation in emergency transport during periods of extreme heat in Japan: A nationwide study.

PubMed

Onozuka, Daisuke; Hagihara, Akihito

2016-02-15

Several studies have reported the burden of climate change on extreme heat-related mortality or morbidity. However, few studies have investigated the spatial and temporal variation in emergency transport during periods of extreme heat on a national scale. Daily emergency ambulance dispatch data from 2007 to 2010 were acquired from all 47 prefectures of Japan. The temporal variability in the relationship between heat and morbidity in each prefecture was estimated using Poisson regression combined with a distributed lag non-linear model and adjusted for time trends. The spatial variability in the heat-morbidity relationships between prefectures was estimated using a multivariate meta-analysis. A total of 5,289,660 emergency transports were reported during the summer months (June through September) within the study period. The overall cumulative relative risk (RR) at the 99th percentile vs. the minimum morbidity percentile was 1.292 (95% CI: 1.251-1.333) for all causes, 1.039 (95% CI: 0.989-1.091) for cardiovascular diseases, and 1.287 (95% CI: 1.210-1.368) for respiratory diseases. Temporal variation in the estimated effects indicated a non-linear relationship, and there were differences in the temporal variations between heat and all-cause and cause-specific morbidity. Spatial variation between prefectures was observed for all causes (Cochran Q test, p<0.001; I(2)=45.8%); however, there was no significant spatial heterogeneity for cardiovascular (Cochran Q test, p=0.054; I(2)=15.1%) and respiratory (Cochran Q test, p=0.681; I(2)=1.0%) diseases. Our nationwide study demonstrated differences in the spatial and temporal variations in the relative risk for all-cause and cause-specific emergency transport during periods of extreme heat in Japan between 2007 and 2010. Our results suggest that public health strategies aimed at controlling heat-related morbidity should be tailored according to region-specific weather conditions. Copyright © 2015 Elsevier B.V. All rights reserved.

Global Autocorrelation Scales of the Partial Pressure of Oceanic CO2

NASA Technical Reports Server (NTRS)

Li, Zhen; Adamec, David; Takahashi, Taro; Sutherland, Stewart C.

2004-01-01

A global database of approximately 1.7 million observations of the partial pressure of carbon dioxide in surface ocean waters (pCO2) collected between 1970 and 2003 is used to estimate its spatial autocorrelation structure. The patterns of the lag distance where the autocorrelation exceeds 0.8 is similar to patterns in the spatial distribution of the first baroclinic Rossby radius of deformation indicating that ocean circulation processes play a significant role in determining the spatial variability of pCO2. For example, the global maximum of the distance at which autocorrelations exceed 0.8 averages about 140 km in the equatorial Pacific. Also, the lag distance at which the autocorrelation exceed 0.8 is greater in the vicinity of the Gulf Stream than it is near the Kuroshio, approximately 50 km near the Gulf Stream as opposed to 20 km near the Kuroshio. Separate calculations for times when the sun is north and south of the equator revealed no obvious seasonal dependence of the spatial autocorrelation scales. The pCO2 measurements at Ocean Weather Station (OWS) 'P', in the eastern subarctic Pacific (50 N, 145 W) is the only fixed location where an uninterrupted time series of sufficient length exists to calculate a meaningful temporal autocorrelation function for lags greater than a few days. The estimated temporal autocorrelation function at OWS 'P', is highly variable. A spectral analysis of the longest four pCO2 time series indicates a high level of variability occurring over periods from the atmospheric synoptic to the maximum length of the time series, in this case 42 days. It is likely that a relative peak in variability with a period of 3-6 days is related to atmospheric synoptic period variability and ocean mixing events due to wind stirring. However, the short length of available time series makes identifying temporal relationships between pCO2 and atmospheric or ocean processes problematic.

Mapping the Distribution of Anthrax in Mainland China, 2005-2013.

PubMed

Chen, Wan-Jun; Lai, Sheng-Jie; Yang, Yang; Liu, Kun; Li, Xin-Lou; Yao, Hong-Wu; Li, Yu; Zhou, Hang; Wang, Li-Ping; Mu, Di; Yin, Wen-Wu; Fang, Li-Qun; Yu, Hong-Jie; Cao, Wu-Chun

2016-04-01

Anthrax, a global re-emerging zoonotic disease in recent years is enzootic in mainland China. Despite its significance to the public health, spatiotemporal distributions of the disease in human and livestock and its potential driving factors remain poorly understood. Using the national surveillance data of human and livestock anthrax from 2005 to 2013, we conducted a retrospective epidemiological study and risk assessment of anthrax in mainland China. The potential determinants for the temporal and spatial distributions of human anthrax were also explored. We found that the majority of human anthrax cases were located in six provinces in western and northeastern China, and five clustering areas with higher incidences were identified. The disease mostly peaked in July or August, and males aged 30-49 years had higher incidence than other subgroups. Monthly incidence of human anthrax was positively correlated with monthly average temperature, relative humidity and monthly accumulative rainfall with lags of 0-2 months. A boosted regression trees (BRT) model at the county level reveals that densities of cattle, sheep and human, coverage of meadow, coverage of typical grassland, elevation, coverage of topsoil with pH > 6.1, concentration of organic carbon in topsoil, and the meteorological factors have contributed substantially to the spatial distribution of the disease. The model-predicted probability of occurrence of human cases in mainland China was mapped at the county level. Anthrax in China was characterized by significant seasonality and spatial clustering. The spatial distribution of human anthrax was largely driven by livestock husbandry, human density, land cover, elevation, topsoil features and climate. Enhanced surveillance and intervention for livestock and human anthrax in the high-risk regions, particularly on the Qinghai-Tibetan Plateau, is the key to the prevention of human infections.

Mapping the Distribution of Anthrax in Mainland China, 2005–2013

PubMed Central

Yang, Yang; Liu, Kun; Li, Xin-Lou; Yao, Hong-Wu; Li, Yu; Zhou, Hang; Wang, Li-Ping; Mu, Di; Yin, Wen-Wu; Fang, Li-Qun; Yu, Hong-Jie; Cao, Wu-Chun

2016-01-01

Background Anthrax, a global re-emerging zoonotic disease in recent years is enzootic in mainland China. Despite its significance to the public health, spatiotemporal distributions of the disease in human and livestock and its potential driving factors remain poorly understood. Methodology/Principal Findings Using the national surveillance data of human and livestock anthrax from 2005 to 2013, we conducted a retrospective epidemiological study and risk assessment of anthrax in mainland China. The potential determinants for the temporal and spatial distributions of human anthrax were also explored. We found that the majority of human anthrax cases were located in six provinces in western and northeastern China, and five clustering areas with higher incidences were identified. The disease mostly peaked in July or August, and males aged 30–49 years had higher incidence than other subgroups. Monthly incidence of human anthrax was positively correlated with monthly average temperature, relative humidity and monthly accumulative rainfall with lags of 0–2 months. A boosted regression trees (BRT) model at the county level reveals that densities of cattle, sheep and human, coverage of meadow, coverage of typical grassland, elevation, coverage of topsoil with pH > 6.1, concentration of organic carbon in topsoil, and the meteorological factors have contributed substantially to the spatial distribution of the disease. The model-predicted probability of occurrence of human cases in mainland China was mapped at the county level. Conclusions/Significance Anthrax in China was characterized by significant seasonality and spatial clustering. The spatial distribution of human anthrax was largely driven by livestock husbandry, human density, land cover, elevation, topsoil features and climate. Enhanced surveillance and intervention for livestock and human anthrax in the high-risk regions, particularly on the Qinghai-Tibetan Plateau, is the key to the prevention of human infections. PMID:27097318

FIA data and species diversity—successes and failures using multivariate analysis techniques, spatial lag and error models and hot-spot analysis

Treesearch

Andrew J. Hartsell

2015-01-01

This study will investigate how global and local predictors differ with varying spatial scale in relation to species evenness and richness in the gulf coastal plain. Particularly, all-live trees >= one-inch d.b.h. Forest Inventory and Analysis (FIA) data was used as the basis for the study. Watersheds are defined by the USGS 12 digit hydrologic units. The...

[The warning model and influence of climatic changes on hemorrhagic fever with renal syndrome in Changsha city].

PubMed

Xiao, Hong; Tian, Huai-yu; Zhang, Xi-xing; Zhao, Jian; Zhu, Pei-juan; Liu, Ru-chun; Chen, Tian-mu; Dai, Xiang-yu; Lin, Xiao-ling

2011-10-01

To realize the influence of climatic changes on the transmission of hemorrhagic fever with renal syndrome (HFRS), and to explore the adoption of climatic factors in warning HFRS. A total of 2171 cases of HFRS and the synchronous climatic data in Changsha from 2000 to 2009 were collected to a climate-based forecasting model for HFRS transmission. The Cochran-Armitage trend test was employed to explore the variation trend of the annual incidence of HFRS. Cross-correlations analysis was then adopted to assess the time-lag period between the climatic factors, including monthly average temperature, relative humidity, rainfall and Multivariate Elño-Southern Oscillation Index (MEI) and the monthly HFRS cases. Finally the time-series Poisson regression model was constructed to analyze the influence of different climatic factors on the HFRS transmission. The annual incidence of HFRS in Changsha between 2000 - 2009 was 13.09/100 000 (755 cases), 9.92/100 000 (578 cases), 5.02/100 000 (294 cases), 2.55/100 000 (150 cases), 1.13/100 000 (67 cases), 1.16/100 000 (70 cases), 0.95/100 000 (58 cases), 1.40/100 000 (87 cases), 0.75/100 000 (47 cases) and 1.02/100 000 (65 cases), respectively. The incidence showed a decline during these years (Z = -5.78, P < 0.01). The results of Poisson regression model indicated that the monthly average temperature (18.00°C, r = 0.26, P < 0.01, 1-month lag period; IRR = 1.02, 95%CI: 1.00 - 1.03, P < 0.01), relative humidity (75.50%, r = 0.62, P < 0.01, 3-month lag period; IRR = 1.03, 95%CI: 1.02 - 1.04, P < 0.01), rainfall (112.40 mm, r = 0.25, P < 0.01, 6-month lag period; IRR = 1.01, 95CI: 1.01 - 1.02, P = 0.02), and MEI (r = 0.31, P < 0.01, 3-month lag period; IRR = 0.77, 95CI: 0.67 - 0.88, P < 0.01) were closely associated with monthly HFRS cases (18.10 cases). Climate factors significantly influence the incidence of HFRS. If the influence of variable-autocorrelation, seasonality, and long-term trend were controlled, the accuracy of forecasting by the time-series Poisson regression model in Changsha would be comparatively high, and we could forecast the incidence of HFRS in advance.

Need for and Access to Supportive Services in the Child Welfare System

PubMed Central

Freisthler, Bridget

2011-01-01

Objective The purpose of this paper is to examine how geographical availability of social services is related to foster care entry rates and referrals for child maltreatment investigations. The primary concerns are to (1) determine locations across Los Angeles County where the availability of social services is low but display a high need for those services and (2) begin to examine how the geographic distribution of social services is related to rates of referrals and foster care entries in child maltreatment. Methods Archival data for all 288 zip codes within Los Angeles County were collected on rates of referrals, foster care entries, location and types of social service agencies, and zip code demographics. Data were analyzed using point process models and spatial regressions. Results Higher densities of child welfare services in local areas (for referrals) and lagged areas (for referrals and foster care entries) were related to lower rates of child maltreatment. The density of housing and housing-related services was negatively related to referrals in local areas and foster care entry rates in lagged areas. Areas with higher densities of substance abuse and domestic violence service agencies had significantly higher rates of both Child Protective Services (CPS) referrals and entries into foster care in local areas. Conclusions While the total density of child welfare services within and surrounding zip code areas is related to lower rates of referrals and foster care entries, the findings are less clear about what those specific services are. Living in and around “resource rich” zip codes may reduce rates of child maltreatment. PMID:23788827

Spatial and Temporal Variation in the Effects of Climatic Variables on Dugong Calf Production.

PubMed

Fuentes, Mariana M P B; Delean, Steven; Grayson, Jillian; Lavender, Sally; Logan, Murray; Marsh, Helene

2016-01-01

Knowledge of the relationships between environmental forcing and demographic parameters is important for predicting responses from climatic changes and to manage populations effectively. We explore the relationships between the proportion of sea cows (Dugong dugon) classified as calves and four climatic drivers (rainfall anomaly, Southern Oscillation El Niño Index [SOI], NINO 3.4 sea surface temperature index, and number of tropical cyclones) at a range of spatially distinct locations in Queensland, Australia, a region with relatively high dugong density. Dugong and calf data were obtained from standardized aerial surveys conducted along the study region. A range of lagged versions of each of the focal climatic drivers (1 to 4 years) were included in a global model containing the proportion of calves in each population crossed with each of the lagged versions of the climatic drivers to explore relationships. The relative influence of each predictor was estimated via Gibbs variable selection. The relationships between the proportion of dependent calves and the climatic drivers varied spatially and temporally, with climatic drivers influencing calf counts at sub-regional scales. Thus we recommend that the assessment of and management response to indirect climatic threats on dugongs should also occur at sub-regional scales.

Disentangling endogenous versus exogenous pattern formation in spatial ecology: a case study of the ant Azteca sericeasur in southern Mexico.

PubMed

Li, Kevin; Vandermeer, John H; Perfecto, Ivette

2016-05-01

Spatial patterns in ecology can be described as reflective of environmental heterogeneity (exogenous), or emergent from dynamic relationships between interacting species (endogenous), but few empirical studies focus on the combination. The spatial distribution of the nests of Azteca sericeasur, a keystone tropical arboreal ant, is thought to form endogenous spatial patterns among the shade trees of a coffee plantation through self-regulating interactions with controlling agents (i.e. natural enemies). Using inhomogeneous point process models, we found evidence for both types of processes in the spatial distribution of A. sericeasur. Each year's nest distribution was determined mainly by a density-dependent relationship with the previous year's lagged nest density; but using a novel application of a Thomas cluster process to account for the effects of nest clustering, we found that nest distribution also correlated significantly with tree density in the later years of the study. This coincided with the initiation of agricultural intensification and tree felling on the coffee farm. The emergence of this significant exogenous effect, along with the changing character of the density-dependent effect of lagged nest density, provides clues to the mechanism behind a unique phenomenon observed in the plot, that of an increase in nest population despite resource limitation in nest sites. Our results have implications in coffee agroecological management, as this system provides important biocontrol ecosystem services. Further research is needed, however, to understand the effective scales at which these relationships occur.

Continuous recognition of spatial and nonspatial stimuli in hippocampal-lesioned rats.

PubMed

Jackson-Smith, P; Kesner, R P; Chiba, A A

1993-03-01

The present experiments compared the performance of hippocampal-lesioned rats to control rats on a spatial continuous recognition task and an analogous nonspatial task with similar processing demands. Daily sessions for Experiment 1 involved sequential presentation of individual arms on a 12-arm radial maze. Each arm contained a Froot Loop reinforcement the first time it was presented, and latency to traverse the arm was measured. A subset of the arms were repeated, but did not contain reinforcement. Repeated arms were presented with lags ranging from 0 to 6 (0 to 6 different arm presentations occurred between the first and the repeated presentation). Difference scores were computed by subtracting the latency on first presentations from the latency on repeated presentations, and these scores were high in all rats prior to surgery, with a decreasing function across lag. There were no differences in performance following cortical control or sham surgery. However, there was a total deficit in performance following large electrolytic lesions of the hippocampus. The second experiment employed the same continuous recognition memory procedure, but used three-dimensional visual objects (toys, junk items, etc., in various shapes, sizes, and textures) as stimuli on a flat runway. As in Experiment 1, the stimuli were presented successively and latency to run to and move the object was measured. Objects were repeated with lags ranging from 0 to 4. Performance on this task following surgery did not differ from performance prior to surgery for either the control group or the hippocampal lesion group. These results provide support for Kesner's attribute model of hippocampal function in that the hippocampus is assumed to mediate data-based memory for spatial locations, but not three-dimensional visual objects.

Association with meteo-climatological factors and daily emergency visits for renal colic and urinary calculi in Cuneo, Italy. A retrospective observational study, 2007-2010.

PubMed

Condemi, Vincenzo; Gestro, Massimo; Dozio, Elena; Tartaglino, Bruno; Corsi Romanelli, Massimiliano Marco; Solimene, Umberto; Meco, Roberto

2015-03-01

The incidence of nephrolithiasis is rising worldwide, especially in women and with increasing age. Incidence and prevalence of kidney stones are affected by genetic, nutritional, and environmental factors. The aim of this study is to investigate the link between various meteorological factors (independent variables) and the daily number of visits to the Emergency Department (ED of the S. Croce and Carle Hospital of Cuneo for renal colic (RC) and urinary stones (UC) as the dependent variable over the years 2007-2010.The Poisson generalized regression models (PGAMs) have been used in different progressive ways. The results of PGAMs (stage 1) adjusted for seasonal and calendar factors confirmed a significant correlation (p < 0.03) with the thermal parameter. Evaluation of the dose-response effect [PGAMs combined with distributed lags nonlinear models (DLNMs)-stage 2], expressed in terms of relative risk (RR) and cumulative relative risk (RRC), indicated a relative significant effect up to 15 lag days of lag (RR > 1), with a first peak after 5 days (lag ranges 0-1, 0-3, and 0-5) and a second weak peak observed along the 5-15 lag range days. The estimated RR for females was significant, mainly in the second and fourth age group considered (19-44 and >65 years): RR for total ED visits 1.27, confidence interval (CI) 1.11-1.46 (lag 0-5 days); RR 1.42, CI 1.01-2.01 (lag 0-10 days); and RR 1.35, CI 1.09-1.68 (lag 0-15 days). The research also indicated a moderate involvement of the thermal factor in the onset of RC caused by UC, exclusively in the female sex. Further studies will be necessary to confirm these results.

Influence of Solar Variability on the North Atlantic / European Sector.

NASA Astrophysics Data System (ADS)

Gray, L. J.

2016-12-01

The 11year solar cycle signal in December-January-February averaged mean-sea-level pressure and Atlantic/European blocking frequency is examined using multilinear regression with indices to represent variability associated with the solar cycle, volcanic eruptions, the El Nino - Southern Oscillation (ENSO) and the Atlantic Multidecadal Oscillation (AMO). Results from a previous 11-year solar cycle signal study of the period 1870-2010 (140 years; 13 solar cycles) that suggested a 3-4 year lagged signal in SLP over the Atlantic are confirmed by analysis of a much longer reconstructed dataset for the period 1660-2010 (350 years; 32 solar cycles). Apparent discrepancies between earlier studies are resolved and stem primarily from the lagged nature of the response and differences between early- and late-winter responses. Analysis of the separate winter months provide supporting evidence for two mechanisms of influence, one operating via the atmosphere that maximises in late winter at 0-2 year lags and one via the mixd-layer ocean that maximises in early winter at 3-4 year lags. Corresponding analysis of DJF-averaged Atlantic / European blocking frequency shows a highly statistically significant signal at 1-year lag that originates promarily from the late winter response. The 11-year solar signal in DJF blocking frequency is compared with other known influences from ENSO and the AMO and found to be as large in amplitude and have a larger region of statistical significance.

Effect of Air Pollution on Exacerbations of Bronchiectasis in Badalona, Spain, 2008-2016.

PubMed

Garcia-Olivé, Ignasi; Stojanovic, Zoran; Radua, Joaquim; Rodriguez-Pons, Laura; Martinez-Rivera, Carlos; Ruiz Manzano, Juan

2018-05-17

Air pollution has been widely associated with respiratory diseases. Nevertheless, the association between air pollution and exacerbations of bronchiectasis has been less studied. To analyze the effect of air pollution on exacerbations of bronchiectasis. This was a retrospective observational study conducted in Badalona. The number of daily hospital admissions and emergency room visits related to exacerbation of bronchiectasis (ICD-9 code 494.1) between 2008 and 2016 was obtained. We used simple Poisson regressions to test the effects of daily mean temperature, SO2, NO2, CO, and PM10 levels on bronchiectasis-related emergencies and hospitalizations on the same day and 1-4 days after. All p values were corrected for multiple comparisons. SO2 was significantly associated with an increase in the number of hospitalizations (lags 0, 1, 2, and 3). None of these associations remained significant after correcting for multiple comparisons. The number of emergency room visits was associated with higher levels of SO2 (lags 0-4). After correcting for multiple comparisons, the association between emergency room visits and SO2 levels was statistically significant for lag 0 (p = 0.043), lag 1 (p = 0.018), and lag 3 (p = 0.050). The number of emergency room visits for exacerbation of bronchiectasis is associated with higher levels of SO2. © 2018 S. Karger AG, Basel.

Spatiotemporal modeling of ecological and sociological ...

EPA Pesticide Factsheets

Suffolk County, New York, is a locus for West Nile virus (WNV) infection in the American northeast that includes the majority of Long Island to the east of New York City. The county has a system of light and gravid traps used for mosquito collection and disease monitoring. In order to identify predictors of WNV incidence in mosquitoes and predict future occurrence of WNV, we have developed a spatiotemporal Bayesian model, beginning with over 40 ecological, meteorological, and built-environment covariates. A mixed-effects model including spatially and temporally correlated errors was fit to WNV surveillance data from 2008 to 2014 using the R package “R-INLA,” which allows for Bayesian modeling using the stochastic partial differential equation (SPDE) approach. The integrated nested Laplace approximation (INLA) SPDE allows for simultaneous fitting of a temporal parameter and a spatial covariance, while incorporating a variety of likelihood functions and running in R statistical software on a home computer. We found that land cover classified as open water and woody wetlands had a negative association with WNV incidence in mosquitoes, and the count of septic systems was associated with an increase in WNV. Mean temperature at two-week lag was associated with a strong positive impact, while mean precipitation at no lag and one-week lag was associated with positive and negative impacts on WNV, respectively. Incorporation of spatiotemporal factors resulted in a mar

Is a matrix exponential specification suitable for the modeling of spatial correlation structures?

PubMed Central

Strauß, Magdalena E.; Mezzetti, Maura; Leorato, Samantha

2018-01-01

This paper investigates the adequacy of the matrix exponential spatial specifications (MESS) as an alternative to the widely used spatial autoregressive models (SAR). To provide as complete a picture as possible, we extend the analysis to all the main spatial models governed by matrix exponentials comparing them with their spatial autoregressive counterparts. We propose a new implementation of Bayesian parameter estimation for the MESS model with vague prior distributions, which is shown to be precise and computationally efficient. Our implementations also account for spatially lagged regressors. We further allow for location-specific heterogeneity, which we model by including spatial splines. We conclude by comparing the performances of the different model specifications in applications to a real data set and by running simulations. Both the applications and the simulations suggest that the spatial splines are a flexible and efficient way to account for spatial heterogeneities governed by unknown mechanisms. PMID:29492375

The study of frequency-scan photothermal reflectance technique for thermal diffusivity measurement

DOE PAGES

Hua, Zilong; Ban, Heng; Hurley, David H.

2015-05-05

A frequency scan photothermal reflectance technique to measure thermal diffusivity of bulk samples is studied in this manuscript. Similar to general photothermal reflectance methods, an intensity-modulated heating laser and a constant intensity probe laser are used to determine the surface temperature response under sinusoidal heating. The approach involves fixing the distance between the heating and probe laser spots, recording the phase lag of reflected probe laser intensity with respect to the heating laser frequency modulation, and extracting thermal diffusivity using the phase lag – (frequency) 1/2 relation. The experimental validation is performed on three samples (SiO 2, CaF 2 andmore » Ge), which have a wide range of thermal diffusivities. The measured thermal diffusivity values agree closely with literature values. Lastly, compared to the commonly used spatial scan method, the experimental setup and operation of the frequency scan method are simplified, and the uncertainty level is equal to or smaller than that of the spatial scan method.« less

Timing of population peaks of Norway lemming in relation to atmospheric pressure: A hypothesis to explain the spatial synchrony.

PubMed

Selås, Vidar

2016-06-01

Herbivore cycles are often synchronized over larger areas than what could be explained by dispersal. In Norway, the 3-4 year lemming cycle usually show no more than a one-year time lag between different regions, despite distances of up to 1000 km. If important food plants are forced to reallocate defensive proteins in years with high seed production, spatially synchronized herbivore outbreaks may be due to climate-synchronized peaks in flowering. Because lemming peaks are expected to occur one year after a flowering peak, and the formation of flower buds is induced in the year before flowering, a two-year time lag between flower-inducing climate events and lemming peaks is predicted. At Hardangervidda, South Norway, the probability that a year was a population peak year of lemming during 1920-2014 increased with increasing midsummer atmospheric pressure two years earlier, even when the number of years since the previous peak was accounted for.

The study of frequency-scan photothermal reflectance technique for thermal diffusivity measurement

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

Hua, Zilong; Ban, Heng; Hurley, David H.

A frequency scan photothermal reflectance technique to measure thermal diffusivity of bulk samples is studied in this manuscript. Similar to general photothermal reflectance methods, an intensity-modulated heating laser and a constant intensity probe laser are used to determine the surface temperature response under sinusoidal heating. The approach involves fixing the distance between the heating and probe laser spots, recording the phase lag of reflected probe laser intensity with respect to the heating laser frequency modulation, and extracting thermal diffusivity using the phase lag – (frequency) 1/2 relation. The experimental validation is performed on three samples (SiO 2, CaF 2 andmore » Ge), which have a wide range of thermal diffusivities. The measured thermal diffusivity values agree closely with literature values. Lastly, compared to the commonly used spatial scan method, the experimental setup and operation of the frequency scan method are simplified, and the uncertainty level is equal to or smaller than that of the spatial scan method.« less

Climate variation and incidence of Ross river virus in Cairns, Australia: a time-series analysis.

PubMed Central

Tong, S; Hu, W

2001-01-01

In this study we assessed the impact of climate variability on the Ross River virus (RRv) transmission and validated an epidemic-forecasting model in Cairns, Australia. Data on the RRv cases recorded between 1985 and 1996 were obtained from the Queensland Department of Health. Climate and population data were supplied by the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. The cross-correlation function (CCF) showed that maximum temperature in the current month and rainfall and relative humidity at a lag of 2 months were positively and significantly associated with the monthly incidence of RRv, whereas relative humidity at a lag of 5 months was inversely associated with the RRv transmission. We developed autoregressive integrated moving average (ARIMA) models on the data collected between 1985 to 1994, and then validated the models using the data collected between 1995 and 1996. The results show that the relative humidity at a lag of 5 months (p < 0.001) and the rainfall at a lag of 2 months (p < 0.05) appeared to play significant roles in the transmission of RRv disease in Cairns. Furthermore, the regressive forecast curves were consistent with the pattern of actual values. PMID:11748035

Modelling the influence of climate on malaria occurrence in Chimoio Municipality, Mozambique.

PubMed

Ferrão, João Luís; Mendes, Jorge M; Painho, Marco

2017-05-25

Mozambique was recently ranked fifth in the African continent for the number of cases of malaria. In Chimoio municipality cases of malaria are increasing annually, contrary to the decreasing trend in Africa. As malaria transmission is influenced to a large extent by climatic conditions, modelling this relationship can provide useful insights for designing precision health measures for malaria control. There is a scarcity of information on the association between climatic variability and malaria transmission risk in Mozambique in general, and in Chimoio in particular. Therefore, the aim of this study is to model the association between climatic variables and malaria cases on a weekly basis, to help policy makers find adequate measures for malaria control and eradication. Time series analysis was conducted using data on weekly climatic variables and weekly malaria cases (counts) in Chimoio municipality, from 2006 to 2014. All data were analysed using SPSS-20, R 3.3.2 and BioEstat 5.0. Cross-correlation analysis, linear processes, namely ARIMA models and regression modelling, were used to develop the final model. Between 2006 and 2014, 490,561 cases of malaria were recorded in Chimoio. Both malaria and climatic data exhibit weekly and yearly systematic fluctuations. Cross-correlation analysis showed that mean temperature and precipitation present significantly lagged correlations with malaria cases. An ARIMA model (2,1,0) (2,1,1) 52 , and a regression model for a Box-Cox transformed number of malaria cases with lags 1, 2 and 3 of weekly malaria cases and lags 6 and 7 of weekly mean temperature and lags 12 of precipitation were fitted. Although, both produced similar widths for prediction intervals, the last was able to anticipate malaria outbreak more accurately. The Chimoio climate seems ideal for malaria occurrence. Malaria occurrence peaks during January to March in Chimoio. As the lag effect between climatic events and malaria occurrence is important for the prediction of malaria cases, this can be used for designing public precision health measures. The model can be used for planning specific measures for Chimoio municipality. Prospective and multidisciplinary research involving researchers from different fields is welcomed to improve the effect of climatic factors and other factors in malaria cases.

[The short-term effects of air pollution on mortality: the results of the EMECAM project in the city of Pamplona, 1991-95. Estudio Multicéntrico Español sobre la Relación entre la Contaminación Atmosférica y la Mortalidad].

PubMed

Aguinaga Ontoso, I; Guillén Grima, F; Oviedo de Sola, P J; Floristan Floristan, M Y; Laborda Santesteban, M S; Martínez Ramírez, M T; Martínez González, M A

1999-01-01

To assess the short-term impact of air pollution on the daily death rate in the city of Pamplona. Ecological study with a population of 212,000 inhabitants. A time series data analysis is conducted by means of multiple linear regression and Poisson regression, with the daily death rate data, air pollution levels for Particles and SO2, weather parameters of average relative humidity and temperature daily and number of cases weekly of flu for the 1991-1995 period. The average number of deaths daily for non-external causes is that of 4.15 deaths, with a range from zero to 13 deaths. The city of Pamplona has a mean annual temperature of 12.7 degrees C (-2.3 degrees C to 31.6 degrees C) and a relative humidity of 68.5%. In the model, the temperature (with a one-day time lag and a six-day time lag temperature squared) and the humidity (with a one-day time lag) is related to the death rate for all causes. But the death rate for non-external causes is only related in the model with the temperature (one-day time lag, P: 0.035) and five-day time lag with temperature squared (p: 0.028). The timely estimates of the relative particle-related risk show that the highest risk of dying stems from respiratory causes with a relative risk of 1.13. However, none of these relationships is statistically significant. In the case of Sulfur Dioxide, the estimates closely near the zero figure, and none of them is significant. The Temperature has an impact of the death rate for all causes, both external and non-external, and the relative humidity solely has an impact on the death rate for non-external causes. It has not been possible to prove any influence of the daily environmental pollution levels on the daily death rate.

Drought prediction using co-active neuro-fuzzy inference system, validation, and uncertainty analysis (case study: Birjand, Iran)

NASA Astrophysics Data System (ADS)

Memarian, Hadi; Pourreza Bilondi, Mohsen; Rezaei, Majid

2016-08-01

This work aims to assess the capability of co-active neuro-fuzzy inference system (CANFIS) for drought forecasting of Birjand, Iran through the combination of global climatic signals with rainfall and lagged values of Standardized Precipitation Index (SPI) index. Using stepwise regression and correlation analyses, the signals NINO 1 + 2, NINO 3, Multivariate Enso Index, Tropical Southern Atlantic index, Atlantic Multi-decadal Oscillation index, and NINO 3.4 were recognized as the effective signals on the drought event in Birjand. Based on the results from stepwise regression analysis and regarding the processor limitations, eight models were extracted for further processing by CANFIS. The metrics P-factor and D-factor were utilized for uncertainty analysis, based on the sequential uncertainty fitting algorithm. Sensitivity analysis showed that for all models, NINO indices and rainfall variable had the largest impact on network performance. In model 4 (as the model with the lowest error during training and testing processes), NINO 1 + 2(t-5) with an average sensitivity of 0.7 showed the highest impact on network performance. Next, the variables rainfall, NINO 1 + 2(t), and NINO 3(t-6) with the average sensitivity of 0.59, 0.28, and 0.28, respectively, could have the highest effect on network performance. The findings based on network performance metrics indicated that the global indices with a time lag represented a better correlation with El Niño Southern Oscillation (ENSO). Uncertainty analysis of the model 4 demonstrated that 68 % of the observed data were bracketed by the 95PPU and D-Factor value (0.79) was also within a reasonable range. Therefore, the fourth model with a combination of the input variables NINO 1 + 2 (with 5 months of lag and without any lag), monthly rainfall, and NINO 3 (with 6 months of lag) and correlation coefficient of 0.903 (between observed and simulated SPI) was selected as the most accurate model for drought forecasting using CANFIS in the climatic region of Birjand.

Toxic releases and risk disparity: a spatiotemporal model of industrial ecology and social empowerment.

PubMed

Aoyagi, Hannah; Ogunseitan, Oladele A

2015-06-02

Information-based regulations (IBRs) are founded on the theoretical premise that public participation in accomplishing policy goals is empowered by open access to information. Since its inception in 1988, the Toxics Release Inventory (TRI) has provided the framework and regulatory impetus for the compilation and distribution of data on toxic releases associated with industrial development, following the tenets of IBR. As TRI emissions are reputed to disproportionately affect low-income communities, we investigated how demographic characteristics are related to change in TRI emissions and toxicity risks between 1989 and 2002, and we sought to identify factors that predict these changes. We used local indicators of spatial association (LISA) maps and spatial regression techniques to study risk disparity in the Los Angeles urban area. We also surveyed 203 individuals in eight communities in the same region to measure the levels of awareness of TRI, attitudes towards air pollution, and general environmental risk. We discovered, through spatial lag models, that changes in gross and toxic emissions are related to community ethnic composition, poverty level, home ownership, and base 1989 emissions (R-square=0.034-0.083). We generated a structural equation model to explain the determinants of social empowerment to act on the basis of environmental information. Hierarchical confirmatory factor analysis (HCFA) supports the theoretical model that individual empowerment is predicted by risk perception, worry, and awareness (Chi-square=63.315, p=0.022, df=42). This study provides strong evidence that spatiotemporal changes in regional-scale environmental risks are influenced by individual-scale empowerment mediated by IBRs.

Toxic Releases and Risk Disparity: A Spatiotemporal Model of Industrial Ecology and Social Empowerment

PubMed Central

Aoyagi, Hannah; Ogunseitan, Oladele A.

2015-01-01

Information-based regulations (IBRs) are founded on the theoretical premise that public participation in accomplishing policy goals is empowered by open access to information. Since its inception in 1988, the Toxics Release Inventory (TRI) has provided the framework and regulatory impetus for the compilation and distribution of data on toxic releases associated with industrial development, following the tenets of IBR. As TRI emissions are reputed to disproportionately affect low-income communities, we investigated how demographic characteristics are related to change in TRI emissions and toxicity risks between 1989 and 2002, and we sought to identify factors that predict these changes. We used local indicators of spatial association (LISA) maps and spatial regression techniques to study risk disparity in the Los Angeles urban area. We also surveyed 203 individuals in eight communities in the same region to measure the levels of awareness of TRI, attitudes towards air pollution, and general environmental risk. We discovered, through spatial lag models, that changes in gross and toxic emissions are related to community ethnic composition, poverty level, home ownership, and base 1989 emissions (R-square = 0.034–0.083). We generated a structural equation model to explain the determinants of social empowerment to act on the basis of environmental information. Hierarchical confirmatory factor analysis (HCFA) supports the theoretical model that individual empowerment is predicted by risk perception, worry, and awareness (Chi-square = 63.315, p = 0.022, df = 42). This study provides strong evidence that spatiotemporal changes in regional-scale environmental risks are influenced by individual-scale empowerment mediated by IBRs. PMID:26042368

Calibration of Watershed Lag Time Equation for Philippine Hydrology using RADARSAT Digital Elevation Models

NASA Astrophysics Data System (ADS)

Cipriano, F. R.; Lagmay, A. M. A.; Horritt, M.; Mendoza, J.; Sabio, G.; Punay, K. N.; Taniza, H. J.; Uichanco, C.

2015-12-01

Widespread flooding is a major problem in the Philippines. The country experiences heavy amount of rainfall throughout the year and several areas are prone to flood hazards because of its unique topography. Human casualties and destruction of infrastructure are just some of the damages caused by flooding and the Philippine government has undertaken various efforts to mitigate these hazards. One of the solutions was to create flood hazard maps of different floodplains and use them to predict the possible catastrophic results of different rain scenarios. To produce these maps with accurate output, different input parameters were needed and one of those is calculating hydrological components from topographical data. This paper presents how a calibrated lag time (TL) equation was obtained using measurable catchment parameters. Lag time is an essential input in flood mapping and is defined as the duration between the peak rainfall and peak discharge of the watershed. The lag time equation involves three measurable parameters, namely, watershed length (L), maximum potential retention (S) derived from the curve number, and watershed slope (Y), all of which were available from RADARSAT Digital Elevation Models (DEM). This approach was based on a similar method developed by CH2M Hill and Horritt for Taiwan, which has a similar set of meteorological and hydrological parameters with the Philippines. Rainfall data from fourteen water level sensors covering 67 storms from all the regions in the country were used to estimate the actual lag time. These sensors were chosen by using a screening process that considers the distance of the sensors from the sea, the availability of recorded data, and the catchment size. The actual lag time values were plotted against the values obtained from the Natural Resource Conservation Management handbook lag time equation. Regression analysis was used to obtain the final calibrated equation that would be used to calculate the lag time specifically for rivers in the Philippine setting. The calculated lag time values could then be used as a parameter for modeling different flood scenarios in the country.

The Association between Dust Storms and Daily Non-Accidental Mortality in the United States, 1993-2005.

PubMed

Crooks, James Lewis; Cascio, Wayne E; Percy, Madelyn S; Reyes, Jeanette; Neas, Lucas M; Hilborn, Elizabeth D

2016-11-01

The impact of dust storms on human health has been studied in the context of Asian, Saharan, Arabian, and Australian storms, but there has been no recent population-level epidemiological research on the dust storms in North America. The relevance of dust storms to public health is likely to increase as extreme weather events are predicted to become more frequent with anticipated changes in climate through the 21st century. We examined the association between dust storms and county-level non-accidental mortality in the United States from 1993 through 2005. Dust storm incidence data, including date and approximate location, are taken from the U.S. National Weather Service storm database. County-level mortality data for the years 1993-2005 were acquired from the National Center for Health Statistics. Distributed lag conditional logistic regression models under a time-stratified case-crossover design were used to study the relationship between dust storms and daily mortality counts over the whole United States and in Arizona and California specifically. End points included total non-accidental mortality and three mortality subgroups (cardiovascular, respiratory, and other non-accidental). We estimated that for the United States as a whole, total non-accidental mortality increased by 7.4% (95% CI: 1.6, 13.5; p = 0.011) and 6.7% (95% CI: 1.1, 12.6; p = 0.018) at 2- and 3-day lags, respectively, and by an average of 2.7% (95% CI: 0.4, 5.1; p = 0.023) over lags 0-5 compared with referent days. Significant associations with non-accidental mortality were estimated for California (lag 2 and 0-5 day) and Arizona (lag 3), for cardiovascular mortality in the United States (lag 2) and Arizona (lag 3), and for other non-accidental mortality in California (lags 1-3 and 0-5). Dust storms are associated with increases in lagged non-accidental and cardiovascular mortality. Citation: Crooks JL, Cascio WE, Percy MS, Reyes J, Neas LM, Hilborn ED. 2016. The association between dust storms and daily non-accidental mortality in the United States, 1993-2005. Environ Health Perspect 124:1735-1743; http://dx.doi.org/10.1289/EHP216.

Time-dependent Electron Acceleration in Blazar Transients: X-Ray Time Lags and Spectral Formation

NASA Astrophysics Data System (ADS)

Lewis, Tiffany R.; Becker, Peter A.; Finke, Justin D.

2016-06-01

Electromagnetic radiation from blazar jets often displays strong variability, extending from radio to γ-ray frequencies. In a few cases, this variability has been characterized using Fourier time lags, such as those detected in the X-rays from Mrk 421 using BeppoSAX. The lack of a theoretical framework to interpret the data has motivated us to develop a new model for the formation of the X-ray spectrum and the time lags in blazar jets based on a transport equation including terms describing stochastic Fermi acceleration, synchrotron losses, shock acceleration, adiabatic expansion, and spatial diffusion. We derive the exact solution for the Fourier transform of the electron distribution and use it to compute the Fourier transform of the synchrotron radiation spectrum and the associated X-ray time lags. The same theoretical framework is also used to compute the peak flare X-ray spectrum, assuming that a steady-state electron distribution is achieved during the peak of the flare. The model parameters are constrained by comparing the theoretical predictions with the observational data for Mrk 421. The resulting integrated model yields, for the first time, a complete first-principles physical explanation for both the formation of the observed time lags and the shape of the peak flare X-ray spectrum. It also yields direct estimates of the strength of the shock and the stochastic magnetohydrodynamical wave acceleration components in the Mrk 421 jet.

Climate Prediction Center - Seasonal Outlook

Science.gov Websites

SEASONAL CLIMATE VARIABILITY, INCLUDING ENSO, SOIL MOISTURE, AND VARIOUS STATE-OF-THE-ART DYNAMICAL MODEL ACROSS PARTS OF THE EAST-CENTRAL CONUS CENTERED ON THE MISSISSIPPI RIVER. THIS IS DUE TO VERY HIGH SOIL TRENDS, NEGATIVE SOIL MOISTURE ANOMALIES, LAGGED ENSO REGRESSIONS, AND DYNAMICAL MODEL GUIDANCE ARE ALL

The Protective Role of Supportive Friends against Bullying Perpetration and Victimization

ERIC Educational Resources Information Center

Kendrick, Kristin; Jutengren, Goran; Stattin, Hakan

2012-01-01

A crossed-lagged regression model was tested to investigate relationships between friendship support, bullying involvement, and its consequences during adolescence. Students, 12-16 years (N = 880), were administered questionnaires twice, one year apart. Using structural equation modeling, a model was specified and higher levels of support from…

40 CFR 86.1341-90 - Test cycle validation criteria.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 19 2011-07-01 2011-07-01 false Test cycle validation criteria. 86... Procedures § 86.1341-90 Test cycle validation criteria. (a) To minimize the biasing effect of the time lag... brake horsepower-hour. (c) Regression line analysis to calculate validation statistics. (1) Linear...

40 CFR 86.1341-90 - Test cycle validation criteria.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 20 2013-07-01 2013-07-01 false Test cycle validation criteria. 86... Procedures § 86.1341-90 Test cycle validation criteria. (a) To minimize the biasing effect of the time lag... brake horsepower-hour. (c) Regression line analysis to calculate validation statistics. (1) Linear...

40 CFR 86.1341-90 - Test cycle validation criteria.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 20 2012-07-01 2012-07-01 false Test cycle validation criteria. 86... Procedures § 86.1341-90 Test cycle validation criteria. (a) To minimize the biasing effect of the time lag... brake horsepower-hour. (c) Regression line analysis to calculate validation statistics. (1) Linear...

A multivariate auto-regressive combined-harmonics analysis and its application to ozone time series data

NASA Astrophysics Data System (ADS)

Yang, Eun-Su

2001-07-01

A new statistical approach is used to analyze Dobson Umkehr layer-ozone measurements at Arosa for 1979-1996 and Total Ozone Mapping Spectrometer (TOMS) Version 7 zonal mean ozone for 1979-1993, accounting for stratospheric aerosol optical depth (SAOD), quasi-biennial oscillation (QBO), and solar flux effects. A stepwise regression scheme selects statistically significant periodicities caused by season, SAOD, QBO, and solar variations and filters them out. Auto-regressive (AR) terms are included in ozone residuals and time lags are assumed for the residuals of exogenous variables. Then, the magnitudes of responses of ozone to the SAOD, QBO, and solar index (SI) series are derived from the stationary time series of the residuals. These Multivariate Auto-Regressive Combined Harmonics (MARCH) processes possess the following significant advantages: (1)the ozone trends are estimated more precisely than the previous methods; (2)the influences of the exogenous SAOD, QBO, and solar variations are clearly separated at various time lags; (3)the collinearity of the exogenous variables in the regression is significantly reduced; and (4)the probability of obtaining misleading correlations between ozone and exogenous times series is reduced. The MARCH results indicate that the Umkehr ozone response to SAOD (not a real ozone response but rather an optical interference effect), QBO, and solar effects is driven by combined dynamical radiative-chemical processes. These results are independently confirmed using the revised Standard models that include aerosol and solar forcing mechanisms with all possible time lags but not by the Standard model when restricted to a zero time lag in aerosol and solar ozone forcings. As for Dobson Umkehr ozone measurements at Arosa, the aerosol effects are most significant in layers 8, 7, and 6 with no time lag, as is to be expected due to the optical contamination of Umkehr measurements by SAOD. The QBO and solar UV effects appear in all layers 4-8, and in total ozone. In order to account for annual modulation of the equatorial winds that affects ozone at midlatitudes, a new QBO proxy is selected and applied to the Dobson Umkehr measurements at Arosa. The QBO proxy turns out to be more effective to filter the modulated ozone signals at midlatitudes than the mostly used QBO proxy, the Singapore winds at 30 mb. A statistically significant negative phase relationship is found between solar UV variation and ozone response, especially in layer 4, implying dynamical effects of solar variations on ozone at midlatitudes. Linear negative trends in ozone of -7.8 +/- 1.1 and -5.2 +/- 1.4 [%/decade +/- 2σ] are calculated in layers 7 (~35 km) and 8 (~40 km), respectively, for the period of 1979-1996, with smaller trends of -2.2 +/- 1.0, 1.8 +/- 0.9, and -1.4 +/- 1.1 in layers 6 (~30 km), 5 (~25 km), and 4 (~20 km), respectively. A trend in total ozone (layers 1 through 10) of -2.9 +/- 1.2 [%/decade +/- 2σ] is found over this same period. The aerosol effects obtained from the TOMS zonal means become significant at midlatitudes. QBO ozone contributes to the TOMS zonal means by +/-2 to 4% of their means. The negative solar ozone responses are also found at midlatitudes from the TOMS measurements. The most negative trends from TOMS zonal means are about -6.3 +/- 0.6%/decade at 40-50°N.

Using lagged dependence to identify (de)coupled surface and subsurface soil moisture values

NASA Astrophysics Data System (ADS)

Carranza, Coleen D. U.; van der Ploeg, Martine J.; Torfs, Paul J. J. F.

2018-04-01

Recent advances in radar remote sensing popularized the mapping of surface soil moisture at different spatial scales. Surface soil moisture measurements are used in combination with hydrological models to determine subsurface soil moisture values. However, variability of soil moisture across the soil column is important for estimating depth-integrated values, as decoupling between surface and subsurface can occur. In this study, we employ new methods to investigate the occurrence of (de)coupling between surface and subsurface soil moisture. Using time series datasets, lagged dependence was incorporated in assessing (de)coupling with the idea that surface soil moisture conditions will be reflected at the subsurface after a certain delay. The main approach involves the application of a distributed-lag nonlinear model (DLNM) to simultaneously represent both the functional relation and the lag structure in the time series. The results of an exploratory analysis using residuals from a fitted loess function serve as a posteriori information to determine (de)coupled values. Both methods allow for a range of (de)coupled soil moisture values to be quantified. Results provide new insights into the decoupled range as its occurrence among the sites investigated is not limited to dry conditions.

Aerosol Plume Detection Algorithm Based on Image Segmentation of Scanning Atmospheric Lidar Data

DOE PAGES

Weekley, R. Andrew; Goodrich, R. Kent; Cornman, Larry B.

2016-04-06

An image-processing algorithm has been developed to identify aerosol plumes in scanning lidar backscatter data. The images in this case consist of lidar data in a polar coordinate system. Each full lidar scan is taken as a fixed image in time, and sequences of such scans are considered functions of time. The data are analyzed in both the original backscatter polar coordinate system and a lagged coordinate system. The lagged coordinate system is a scatterplot of two datasets, such as subregions taken from the same lidar scan (spatial delay), or two sequential scans in time (time delay). The lagged coordinatemore » system processing allows for finding and classifying clusters of data. The classification step is important in determining which clusters are valid aerosol plumes and which are from artifacts such as noise, hard targets, or background fields. These cluster classification techniques have skill since both local and global properties are used. Furthermore, more information is available since both the original data and the lag data are used. Performance statistics are presented for a limited set of data processed by the algorithm, where results from the algorithm were compared to subjective truth data identified by a human.« less

Environmental injustice along the US-Mexico border: residential proximity to industrial parks in Tijuana, Mexico

NASA Astrophysics Data System (ADS)

Grineski, Sara E.; Collins, Timothy W.; de Lourdes Romo Aguilar, María

2015-09-01

Research in the Global North (e.g., US, Europe) has revealed robust patterns of environmental injustice whereby low income and minority residents face exposure to industrial hazards in their neighborhoods. A small body of research suggests that patterns of environmental injustice may diverge between the Global North and South due to differing urban development trajectories. This study uses quantitative environmental justice methods to examine spatial relationships between residential socio-demographics and industrial parks in Tijuana, Baja California Norte, Mexico using 2010 census data for Tijuana’s 401 neighborhoods and municipality-provided locations of industrial parks in the city. Results of spatial lag regression models reveal that formal development is significantly associated with industrial park density, and it accounts for the significant effect of higher socioeconomic status (measured using mean education) on greater industrial density. Higher proportions of female-headed households are also significantly associated with industrial park density, while higher proportions of children and recent migrants are not. The formal development findings align with other studies in Mexico and point to the importance of urban development trajectories in shaping patterns of environmental injustice. The risks for female-headed households are novel in the Mexican context. One potential explanation is that women factory workers live near their places of employment. A second, albeit counterintuitive explanation, is the relative economic advantage experienced by female-headed households in Mexico.

Forecasting carbon dioxide emissions based on a hybrid of mixed data sampling regression model and back propagation neural network in the USA.

PubMed

Zhao, Xin; Han, Meng; Ding, Lili; Calin, Adrian Cantemir

2018-01-01

The accurate forecast of carbon dioxide emissions is critical for policy makers to take proper measures to establish a low carbon society. This paper discusses a hybrid of the mixed data sampling (MIDAS) regression model and BP (back propagation) neural network (MIDAS-BP model) to forecast carbon dioxide emissions. Such analysis uses mixed frequency data to study the effects of quarterly economic growth on annual carbon dioxide emissions. The forecasting ability of MIDAS-BP is remarkably better than MIDAS, ordinary least square (OLS), polynomial distributed lags (PDL), autoregressive distributed lags (ADL), and auto-regressive moving average (ARMA) models. The MIDAS-BP model is suitable for forecasting carbon dioxide emissions for both the short and longer term. This research is expected to influence the methodology for forecasting carbon dioxide emissions by improving the forecast accuracy. Empirical results show that economic growth has both negative and positive effects on carbon dioxide emissions that last 15 quarters. Carbon dioxide emissions are also affected by their own change within 3 years. Therefore, there is a need for policy makers to explore an alternative way to develop the economy, especially applying new energy policies to establish a low carbon society.

Modeling optical and UV polarization of AGNs. IV. Polarization timing

NASA Astrophysics Data System (ADS)

Rojas Lobos, P. A.; Goosmann, R. W.; Marin, F.; Savić, D.

2018-03-01

Context. Optical observations cannot resolve the structure of active galactic nuclei (AGN), and a unified model for AGN was inferred mostly from indirect methods, such as spectroscopy and variability studies. Optical reverberation mapping allowed us to constrain the spatial dimension of the broad emission line region and thereby to measure the mass of supermassive black holes. Recently, reverberation was also applied to the polarized signal emerging from different AGN components. In principle, this should allow us to measure the spatial dimensions of the sub-parsec reprocessing media. Aim. We conduct numerical modeling of polarization reverberation and provide theoretical predictions for the polarization time lag induced by different AGN components. The model parameters are adjusted to the observational appearance of the Seyfert 1 galaxy NGC 4151. Methods: We modeled scattering-induced polarization and tested different geometries for the circumnuclear dust component. Our tests included the effects of clumpiness and different dust prescriptions. To further extend the model, we also explored the effects of additional ionized winds stretched along the polar direction, and of an equatorial scattering ring that is responsible for the polarization angle observed in pole-on AGN. The simulations were run using a time-dependent version of the STOKES code. Results: Our modeling confirms the previously found polarization characteristics as a function of the observer`s viewing angle. When the dust adopts a flared-disk geometry, the lags reveal a clear difference between type 1 and type 2 AGN. This distinction is less clear for a torus geometry where the time lag is more sensitive to the geometry and optical depth of the inner surface layers of the funnel. The presence of a scattering equatorial ring and ionized outflows increased the recorded polarization time lags, and the polar outflows smooths out dependence on viewing angle, especially for the higher optical depth of the wind (τ = 0.3). Conclusions: Together with other AGN observables, the polarization time lag places new, independent "seismological" constraints on the inner geometry of AGN. If we conduct time-dependent spectropolarimetric observing campaigns of AGN, this method has a high potential for a census of supermassive black holes.

Associations between ozone and morbidity using the Spatial Synoptic Classification system

PubMed Central

2011-01-01

Background Synoptic circulation patterns (large-scale tropospheric motion systems) affect air pollution and, potentially, air-pollution-morbidity associations. We evaluated the effect of synoptic circulation patterns (air masses) on the association between ozone and hospital admissions for asthma and myocardial infarction (MI) among adults in North Carolina. Methods Daily surface meteorology data (including precipitation, wind speed, and dew point) for five selected cities in North Carolina were obtained from the U.S. EPA Air Quality System (AQS), which were in turn based on data from the National Climatic Data Center of the National Oceanic and Atmospheric Administration. We used the Spatial Synoptic Classification system to classify each day of the 9-year period from 1996 through 2004 into one of seven different air mass types: dry polar, dry moderate, dry tropical, moist polar, moist moderate, moist tropical, or transitional. Daily 24-hour maximum 1-hour ambient concentrations of ozone were obtained from the AQS. Asthma and MI hospital admissions data for the 9-year period were obtained from the North Carolina Department of Health and Human Services. Generalized linear models were used to assess the association of the hospitalizations with ozone concentrations and specific air mass types, using pollutant lags of 0 to 5 days. We examined the effect across cities on days with the same air mass type. In all models we adjusted for dew point and day-of-the-week effects related to hospital admissions. Results Ozone was associated with asthma under dry tropical (1- to 5-day lags), transitional (3- and 4-day lags), and extreme moist tropical (0-day lag) air masses. Ozone was associated with MI only under the extreme moist tropical (5-day lag) air masses. Conclusions Elevated ozone levels are associated with dry tropical, dry moderate, and moist tropical air masses, with the highest ozone levels being associated with the dry tropical air mass. Certain synoptic circulation patterns/air masses in conjunction with ambient ozone levels were associated with increased asthma and MI hospitalizations. PMID:21609456

Molecular detection of influenza A(H1N1)pdm09 viruses with M genes from human pandemic strains among Nigerian pigs, 2013-2015: implications and associated risk factors.

PubMed

Adeola, O A; Olugasa, B O; Emikpe, B O

2017-12-01

In the post-pandemic period, influenza A(H1N1)pdm09 virus has been detected in swine populations in different parts of the world. This study was conducted to determine the presence and spatial patterns of this human pandemic virus among Nigerian pigs and identify associated risk factors. Using a two-stage stratified random sampling method, nasal swab specimens were obtained from pigs in Ibadan, Nigeria during the 2013-2014 and 2014-2015 influenza seasons, and the virus was detected by reverse transcriptase-polymerase chain reaction (RT-PCR). Purified RT-PCR products were sequenced in both directions, and sequences were aligned using MUSCLE. Phylogenetic analysis was conducted in MEGA6. Purely spatial scan statistics and a spatial lag regression model were used to identify spatial clusters and associated risk factors. The virus was detected in both seasons, with an overall prevalence of 8·7%. Phylogenetic analyses revealed that the M genes were similar to those of pandemic strains which circulated in humans prior to and during the study. Cluster analysis revealed a significant primary spatial cluster (RR = 4·71, LLR = 5·66, P = 0·0046), while 'hours spent with pigs (R 2 = 0·90, P = 0·0018)' and 'hours spent with pigs from different farms (R 2 = 0·91, P = 0·0001)' were identified as significant risk factors (P < 0·05). These findings reveal that there is considerable risk of transmission of the pandemic virus, either directly from pig handlers or through fomites, to swine herds in Ibadan, Nigeria. Active circulation of the virus among Nigerian pigs could enhance its reassortment with endemic swine influenza viruses. Campaigns for adoption of biosecurity measures in West African piggeries and abattoirs should be introduced and sustained in order to prevent the emergence of a new influenza epicentre in the sub-region.

How spatial and temporal rainfall variability affect runoff across basin scales: insights from field observations in the (semi-)urbanised Charlotte watershed

NASA Astrophysics Data System (ADS)

Ten Veldhuis, M. C.; Smith, J. A.; Zhou, Z.

2017-12-01

Impacts of rainfall variability on runoff response are highly scale-dependent. Sensitivity analyses based on hydrological model simulations have shown that impacts are likely to depend on combinations of storm type, basin versus storm scale, temporal versus spatial rainfall variability. So far, few of these conclusions have been confirmed on observational grounds, since high quality datasets of spatially variable rainfall and runoff over prolonged periods are rare. Here we investigate relationships between rainfall variability and runoff response based on 30 years of radar-rainfall datasets and flow measurements for 16 hydrological basins ranging from 7 to 111 km2. Basins vary not only in scale, but also in their degree of urbanisation. We investigated temporal and spatial variability characteristics of rainfall fields across a range of spatial and temporal scales to identify main drivers for variability in runoff response. We identified 3 ranges of basin size with different temporal versus spatial rainfall variability characteristics. Total rainfall volume proved to be the dominant agent determining runoff response at all basin scales, independent of their degree of urbanisation. Peak rainfall intensity and storm core volume are of secondary importance. This applies to all runoff parameters, including runoff volume, runoff peak, volume-to-peak and lag time. Position and movement of the storm with respect to the basin have a negligible influence on runoff response, with the exception of lag times in some of the larger basins. This highlights the importance of accuracy in rainfall estimation: getting the position right but the volume wrong will inevitably lead to large errors in runoff prediction. Our study helps to identify conditions where rainfall variability matters for correct estimation of the rainfall volume as well as the associated runoff response.

Forecasting vegetation greenness with satellite and climate data

USGS Publications Warehouse

Ji, Lei; Peters, Albert J.

2004-01-01

A new and unique vegetation greenness forecast (VGF) model was designed to predict future vegetation conditions to three months through the use of current and historical climate data and satellite imagery. The VGF model is implemented through a seasonality-adjusted autoregressive distributed-lag function, based on our finding that the normalized difference vegetation index is highly correlated with lagged precipitation and temperature. Accurate forecasts were obtained from the VGF model in Nebraska grassland and cropland. The regression R2 values range from 0.97-0.80 for 2-12 week forecasts, with higher R2 associated with a shorter prediction. An important application would be to produce real-time forecasts of greenness images.

Improving models of democracy: the example of lagged effects of economic development, education, and gender equality.

PubMed

Balaev, Mikhail

2014-07-01

The author examines how time delayed effects of economic development, education, and gender equality influence political democracy. Literature review shows inadequate understanding of lagged effects, which raises methodological and theoretical issues with the current quantitative studies of democracy. Using country-years as a unit of analysis, the author estimates a series of OLS PCSE models for each predictor with a systematic analysis of the distributions of the lagged effects. The second set of multiple OLS PCSE regressions are estimated including all three independent variables. The results show that economic development, education, and gender have three unique trajectories of the time-delayed effects: Economic development has long-term effects, education produces continuous effects regardless of the timing, and gender equality has the most prominent immediate and short term effects. The results call for the reassessment of model specifications and theoretical setups in the quantitative studies of democracy. Copyright © 2014 Elsevier Inc. All rights reserved.

Transient combustion in hybrid rockets

NASA Astrophysics Data System (ADS)

Karabeyoglu, Mustafa Arif

1998-09-01

Hybrid rockets regained interest recently as an alternative chemical propulsion system due to their advantages over the solid and liquid systems that are currently in use. Development efforts on hybrids revealed two important problem areas: (1) low frequency instabilities and (2) slow transient response. Both of these are closely related to the transient behavior which is a poorly understood aspect of hybrid operation. This thesis is mainly involved with a theoretical study of transient combustion in hybrid rockets. We follow the methodology of identifying and modeling the subsystems of the motor such as the thermal lags in the solid, boundary layer combustion and chamber gasdynamics from a dynamic point of view. We begin with the thermal lag in the solid which yield the regression rate for any given wall heat flux variation. Interesting phenomena such as overshooting during throttling and the amplification and phase lead regions in the frequency domain are discovered. Later we develop a quasi-steady transient hybrid combustion model supported with time delays for the boundary layer processes. This is integrated with the thermal lag system to obtain the thermal combustion (TC) coupled response. The TC coupled system with positive delays generated low frequency instabilities. The scaling of the instabilities are in good agreement with actual motor test data. Finally, we formulate a gasdynamic model for the hybrid chamber which successfully resolves the filling/emptying and longitudinal acoustic behavior of the motor. The TC coupled system is later integrated to the gasdynamic model to obtain the overall response (TCG coupled system) of gaseous oxidizer motors with stiff feed systems. Low frequency instabilities were also encountered for the TCG coupled system. Apart from the transient investigations, the regression rate behavior of liquefying hybrid propellants such as solid cryogenic materials are also studied. The theory is based on the possibility of enhancement of regression rate by the entrainment mass transfer from a liquid layer formed on the fuel surface. The predicted regression rates are in good agreement with the cryogenic experimental findings obtained recently at Edwards Airforce Base with a frozen pentane and gaseous oxygen system.

Predicting redox conditions in groundwater at a regional scale

USGS Publications Warehouse

Tesoriero, Anthony J.; Terziotti, Silvia; Abrams, Daniel B.

2015-01-01

Defining the oxic-suboxic interface is often critical for determining pathways for nitrate transport in groundwater and to streams at the local scale. Defining this interface on a regional scale is complicated by the spatial variability of reaction rates. The probability of oxic groundwater in the Chesapeake Bay watershed was predicted by relating dissolved O2 concentrations in groundwater samples to indicators of residence time and/or electron donor availability using logistic regression. Variables that describe surficial geology, position in the flow system, and soil drainage were important predictors of oxic water. The probability of encountering oxic groundwater at a 30 m depth and the depth to the bottom of the oxic layer were predicted for the Chesapeake Bay watershed. The influence of depth to the bottom of the oxic layer on stream nitrate concentrations and time lags (i.e., time period between land application of nitrogen and its effect on streams) are illustrated using model simulations for hypothetical basins. Regional maps of the probability of oxic groundwater should prove useful as indicators of groundwater susceptibility and stream susceptibility to contaminant sources derived from groundwater.

Accounting for spatial effects in land use regression for urban air pollution modeling.

PubMed

Bertazzon, Stefania; Johnson, Markey; Eccles, Kristin; Kaplan, Gilaad G

2015-01-01

In order to accurately assess air pollution risks, health studies require spatially resolved pollution concentrations. Land-use regression (LUR) models estimate ambient concentrations at a fine spatial scale. However, spatial effects such as spatial non-stationarity and spatial autocorrelation can reduce the accuracy of LUR estimates by increasing regression errors and uncertainty; and statistical methods for resolving these effects--e.g., spatially autoregressive (SAR) and geographically weighted regression (GWR) models--may be difficult to apply simultaneously. We used an alternate approach to address spatial non-stationarity and spatial autocorrelation in LUR models for nitrogen dioxide. Traditional models were re-specified to include a variable capturing wind speed and direction, and re-fit as GWR models. Mean R(2) values for the resulting GWR-wind models (summer: 0.86, winter: 0.73) showed a 10-20% improvement over traditional LUR models. GWR-wind models effectively addressed both spatial effects and produced meaningful predictive models. These results suggest a useful method for improving spatially explicit models. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

First unitary, then divided: the temporal dynamics of dividing attention.

PubMed

Jefferies, Lisa N; Witt, Joseph B

2018-04-24

Whether focused visual attention can be divided has been the topic of much investigation, and there is a compelling body of evidence showing that, at least under certain conditions, attention can be divided and deployed as two independent foci. Three experiments were conducted to examine whether attention can be deployed in divided form from the outset, or whether it is first deployed as a unitary focus before being divided. To test this, we adapted the methodology of Jefferies, Enns, and Di Lollo (Journal of Experimental Psychology: Human Perception and Performance 40: 465, 2014), who used a dual-stream Attentional Blink paradigm and two letter-pair targets. One aspect of the AB, Lag-1 sparing, has been shown to occur only if the second target pair appears within the focus of attention. By presenting the second target pair at various spatial locations and assessing the magnitude of Lag-1 sparing, we probed the spatial distribution of attention. By systematically manipulating the stimulus-onset-asynchrony between the targets, we also tracked changes to the spatial distribution of attention over time. The results showed that even under conditions which encourage the division of attention, the attentional focus is first deployed in unitary form before being divided. It is then maintained in divided form only briefly before settling on a single location.

Spatial and Temporal Variation in the Effects of Climatic Variables on Dugong Calf Production

PubMed Central

Fuentes, Mariana M. P. B.; Delean, Steven; Grayson, Jillian; Lavender, Sally; Logan, Murray; Marsh, Helene

2016-01-01

Knowledge of the relationships between environmental forcing and demographic parameters is important for predicting responses from climatic changes and to manage populations effectively. We explore the relationships between the proportion of sea cows (Dugong dugon) classified as calves and four climatic drivers (rainfall anomaly, Southern Oscillation El Niño Index [SOI], NINO 3.4 sea surface temperature index, and number of tropical cyclones) at a range of spatially distinct locations in Queensland, Australia, a region with relatively high dugong density. Dugong and calf data were obtained from standardized aerial surveys conducted along the study region. A range of lagged versions of each of the focal climatic drivers (1 to 4 years) were included in a global model containing the proportion of calves in each population crossed with each of the lagged versions of the climatic drivers to explore relationships. The relative influence of each predictor was estimated via Gibbs variable selection. The relationships between the proportion of dependent calves and the climatic drivers varied spatially and temporally, with climatic drivers influencing calf counts at sub-regional scales. Thus we recommend that the assessment of and management response to indirect climatic threats on dugongs should also occur at sub-regional scales. PMID:27355367

Fine particulate matter estimated by mathematical model and hospitalizations for pneumonia and asthma in children

PubMed Central

César, Ana Cristina Gobbo; Nascimento, Luiz Fernando Costa; Mantovani, Katia Cristina Cota; Vieira, Luciana Cristina Pompeo

2016-01-01

Abstract Objective: To estimate the association between exposure to fine particulate matter with an aerodynamic diameter <2.5 microns (PM2.5) and hospitalizations for pneumonia and asthma in children. Methods: An ecological study of time series was performed, with daily indicators of hospitalization for pneumonia and asthma in children up to 10 years of age, living in Taubaté (SP) and estimated concentrations of PM2.5, between August 2011 and July 2012. A generalized additive model of Poisson regression was used to estimate the relative risk, with lag zero up to five days after exposure; the single pollutant model was adjusted by the apparent temperature, as defined from the temperature and relative air humidity, seasonality and weekday. Results: The values of the relative risks for hospitalization for pneumonia and asthma were significant for lag 0 (RR=1.051, 95%CI; 1.016 to 1.088); lag 2 (RR=1.066, 95%CI: 1.023 to 1.113); lag 3 (RR=1.053, 95%CI: 1.015 to 1.092); lag 4 (RR=1.043, 95%CI: 1.004 to 1.088) and lag 5 (RR=1.061, 95%CI: 1.018 to 1.106). The increase of 5mcg/m3 in PM2.5 contributes to increase the relative risk for hospitalization from 20.3 to 38.4 percentage points; however, the reduction of 5µg/m3 in PM2.5 concentration results in 38 fewer hospital admissions. Conclusions: Exposure to PM2.5 was associated with hospitalizations for pneumonia and asthma in children younger than 10 years of age, showing the role of fine particulate matter in child health and providing subsidies for the implementation of preventive measures to decrease these outcomes. PMID:26522821

Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. IX. 10 New Observations of Reverberation Mapping and Shortened Hβ Lags

NASA Astrophysics Data System (ADS)

Du, Pu; Zhang, Zhi-Xiang; Wang, Kai; Huang, Ying-Ke; Zhang, Yue; Lu, Kai-Xing; Hu, Chen; Li, Yan-Rong; Bai, Jin-Ming; Bian, Wei-Hao; Yuan, Ye-Fei; Ho, Luis C.; Wang, Jian-Min; SEAMBH collaboration

2018-03-01

As one paper in a series reporting on a large reverberation mapping campaign of super-Eddington accreting massive black holes (SEAMBHs) in active galactic nuclei (AGNs), we present the results of 10 SEAMBHs monitored spectroscopically during 2015–2017. Six of them are observed for the first time, and have generally higher 5100 Å luminosities than the SEAMBHs monitored in our campaign from 2012 to 2015; the remaining four are repeat observations to check if their previous lags change. Similar to the previous SEAMBHs, the Hβ time lags of the newly observed objects are shorter than the values predicted by the canonical R Hβ –L 5100 relation of sub-Eddington AGNs, by factors of ∼2–6, depending on the accretion rate. The four previously observed objects have lags consistent with previous measurements. We provide linear regressions for the R Hβ –L 5100 relation, solely for the SEAMBH sample and for low-accretion AGNs. We find that the relative strength of Fe II and the profile of the Hβ emission line can be used as proxies of accretion rate, showing that the shortening of Hβ lags depends on accretion rates. The recent SDSS-RM discovery of shortened Hβ lags in AGNs with low accretion rates provides compelling evidence for retrograde accretion onto the black hole. These evidences show that the canonical R Hβ –L 5100 relation holds only in AGNs with moderate accretion rates. At low accretion rates, it should be revised to include the effects of black hole spin, whereas the accretion rate itself becomes a key factor in the regime of high accretion rates.

Examining lag effects between industrial land development and regional economic changes: The Netherlands experience.

PubMed

Ustaoglu, Eda; Lavalle, Carlo

2017-01-01

In most empirical applications, forecasting models for the analysis of industrial land focus on the relationship between current values of economic parameters and industrial land use. This paper aims to test this assumption by focusing on the dynamic relationship between current and lagged values of the 'economic fundamentals' and industrial land development. Not much effort has yet been attributed to develop land forecasting models to predict the demand for industrial land except those applying static regressions or other statistical measures. In this research, we estimated a dynamic panel data model across 40 regions from 2000 to 2008 for the Netherlands to uncover the relationship between current and lagged values of economic parameters and industrial land development. Land-use regulations such as land zoning policies, and other land-use restrictions like natural protection areas, geographical limitations in the form of water bodies or sludge areas are expected to affect supply of land, which will in turn be reflected in industrial land market outcomes. Our results suggest that gross domestic product (GDP), industrial employment, gross value added (GVA), property price, and other parameters representing demand and supply conditions in the industrial market explain industrial land developments with high significance levels. It is also shown that contrary to the current values, lagged values of the economic parameters have more sound relationships with the industrial developments in the Netherlands. The findings suggest use of lags between selected economic parameters and industrial land use in land forecasting applications.

Mean air temperature as a risk factor for stroke mortality in São Paulo, Brazil

NASA Astrophysics Data System (ADS)

Ikefuti, Priscilla V.; Barrozo, Ligia V.; Braga, Alfésio L. F.

2018-05-01

In Brazil, chronic diseases account for the largest percentage of all deaths among men and women. Among the cardiovascular diseases, stroke is the leading cause of death, accounting for 10% of all deaths. We evaluated associations between stroke and mean air temperature using recorded mortality data and meteorological station data from 2002 to 2011. A time series analysis was applied to 55,633 mortality cases. Ischemic and hemorrhagic strokes (IS and HS, respectively) were divided to test different impact on which subgroup. Poisson regression with distributed lag non-linear model was used and adjusted for seasonality, pollutants, humidity, and days of the week. HS mortality was associated with low mean temperatures for men relative risk (RR) = 2.43 (95% CI, 1.12-5.28) and women RR = 1.39 (95% CI, 1.03-1.86). RR of IS mortality was not significant using a 21-day lag window. Analyzing the lag response separately, we observed that the effect of temperature is acute in stroke mortality (higher risk among lags 0-5). However, for IS, higher mean temperatures were significant for this subtype with more than 15-day lag. Our findings showed that mean air temperature is associated with stroke mortality in the city of São Paulo for men and women and IS and HS may have different triggers. Further studies are needed to evaluate physiologic differences between these two subtypes of stroke.

Examining lag effects between industrial land development and regional economic changes: The Netherlands experience

PubMed Central

Ustaoglu, Eda; Lavalle, Carlo

2017-01-01

In most empirical applications, forecasting models for the analysis of industrial land focus on the relationship between current values of economic parameters and industrial land use. This paper aims to test this assumption by focusing on the dynamic relationship between current and lagged values of the ‘economic fundamentals’ and industrial land development. Not much effort has yet been attributed to develop land forecasting models to predict the demand for industrial land except those applying static regressions or other statistical measures. In this research, we estimated a dynamic panel data model across 40 regions from 2000 to 2008 for the Netherlands to uncover the relationship between current and lagged values of economic parameters and industrial land development. Land-use regulations such as land zoning policies, and other land-use restrictions like natural protection areas, geographical limitations in the form of water bodies or sludge areas are expected to affect supply of land, which will in turn be reflected in industrial land market outcomes. Our results suggest that gross domestic product (GDP), industrial employment, gross value added (GVA), property price, and other parameters representing demand and supply conditions in the industrial market explain industrial land developments with high significance levels. It is also shown that contrary to the current values, lagged values of the economic parameters have more sound relationships with the industrial developments in the Netherlands. The findings suggest use of lags between selected economic parameters and industrial land use in land forecasting applications. PMID:28877204

Fine Particulate air Pollution is Associated with Higher Vulnerability to Atrial Fibrillation—The APACR Study

PubMed Central

Liao, Duanping; Shaffer, Michele L.; He, Fan; Rodriguez-Colon, Sol; Wu, Rongling; Whitsel, Eric A.; Bixler, Edward O.; Cascio, Wayne E.

2011-01-01

The acute effects and the time course of fine particulate pollution (PM2.5) on atrial fibrillation/flutter (AF) predictors, including P-wave duration, PR interval duration, and P-wave complexity, were investigated in a community-dwelling sample of 106 nonsmokers. Individual-level 24-h beat-to-beat electrocardiogram (ECG) data were visually examined. After identifying and removing artifacts and arrhythmic beats, the 30-min averages of the AF predictors were calculated. A personal PM2.5 monitor was used to measure individual-level, real-time PM2.5 exposures during the same 24-h period, and corresponding 30-min average PM2.5 concentration were calculated. Under a linear mixed-effects modeling framework, distributed lag models were used to estimate regression coefficients (βs) associating PM2.5 with AF predictors. Most of the adverse effects on AF predictors occurred within 1.5–2 h after PM2.5 exposure. The multivariable adjusted βs per 10-µg/m3 rise in PM2.5 at lag 1 and lag 2 were significantly associated with P-wave complexity. PM2.5 exposure was also significantly associated with prolonged PR duration at lag 3 and lag 4. Higher PM2.5 was found to be associated with increases in P-wave complexity and PR duration. Maximal effects were observed within 2 h. These findings suggest that PM2.5 adversely affects AF predictors; thus, PM2.5 may be indicative of greater susceptibility to AF. PMID:21480044

Reconstitution of a eukaryotic replisome reveals suppression mechanisms that define leading/lagging strand operation

PubMed Central

Georgescu, Roxana E; Schauer, Grant D; Yao, Nina Y; Langston, Lance D; Yurieva, Olga; Zhang, Dan; Finkelstein, Jeff; O'Donnell, Mike E

2015-01-01

We have reconstituted a eukaryotic leading/lagging strand replisome comprising 31 distinct polypeptides. This study identifies a process unprecedented in bacterial replisomes. While bacteria and phage simply recruit polymerases to the fork, we find that suppression mechanisms are used to position the distinct eukaryotic polymerases on their respective strands. Hence, Pol ε is active with CMG on the leading strand, but it is unable to function on the lagging strand, even when Pol δ is not present. Conversely, Pol δ-PCNA is the only enzyme capable of extending Okazaki fragments in the presence of Pols ε and α. We have shown earlier that Pol δ-PCNA is suppressed on the leading strand with CMG (Georgescu et al., 2014). We propose that CMG, the 11-subunit helicase, is responsible for one or both of these suppression mechanisms that spatially control polymerase occupancy at the fork. DOI: http://dx.doi.org/10.7554/eLife.04988.001 PMID:25871847

Antarctic Sea ice variations and seasonal air temperature relationships

NASA Technical Reports Server (NTRS)

Weatherly, John W.; Walsh, John E.; Zwally, H. J.

1991-01-01

Data through 1987 are used to determine the regional and seasonal dependencies of recent trends of Antarctic temperature and sea ice. Lead-lag relationships involving regional sea ice and air temperature are systematically evaluated, with an eye toward the ice-temperature feedbacks that may influence climatic change. Over the 1958-1087 period the temperature trends are positive in all seasons. For the 15 years (l973-l987) for which ice data are available, the trends are predominantly positive only in winter and summer, and are most strongly positive over the Antarctic Peninsula. The spatially aggregated trend of temperature for this latter period is small but positive, while the corresponding trend of ice coverage is small but negative. Lag correlations between seasonal anomalies of the two variables are generally stronger with ice lagging the summer temperatures and with ice leading the winter temperatures. The implication is that summer temperatures predispose the near-surface waters to above-or below-normal ice coverage in the following fall and winter.

Association between Atmospheric Fine Particulate Matter and Hospital Admissions for Chronic Obstructive Pulmonary Disease in Southwestern Taiwan: A Population-Based Study

PubMed Central

Hwang, Su-Lun; Guo, Su-Er; Chi, Miao-Ching; Chou, Chiang-Ting; Lin, Yu-Ching; Lin, Chieh-Mo; Chou, Yen-Li

2016-01-01

Objectives: This paper reports on the findings of a population-based study to evaluate the relationship between atmospheric fine particulate matter (PM2.5) levels and hospital admissions for chronic obstructive pulmonary disease (COPD) in southwestern Taiwan over a three-year period, 2008–2010. Methods: Data on hospital admissions for COPD and PM2.5 levels were obtained from the National Health Insurance Research database (NHIRD) and the Environmental Protection Administration from 2008 to 2010, respectively. The lag structure of relative risks (RRs) of hospital admissions for COPD was estimated using a Poisson regression model. Results: During the study period, the overall average hospitalization rate of COPD and mean 24-h average level of PM2.5 was 0.18% and 39.37 μg/m3, respectively. There were seasonal variations in PM2.5 concentrations in southwestern Taiwan, with higher PM2.5 concentrations in both spring (average: 48.54 μg/m3) and winter (49.96 μg/m3) than in summer (25.89 μg/m3) and autumn (33.37 μg/m3). Increased COPD admissions were significantly associated with PM2.5 in both spring (February–April) and winter (October–January), with the relative risks (RRs) for every 10 μg/m3 increase in PM2.5 being 1.25 (95% CI = 1.22–1.27) and 1.24 (95% CI = 1.23–1.26), respectively, at a lag zero days (i.e., no lag days). Lag effects on COPD admissions were observed for PM2.5, with the elevated RRs beginning at lag zero days and larger RRs estimates tending to occur at longer lags (up to six days, i.e., lag 0–5 days). Conclusions: In general, findings reveal an association between atmospheric fine particulate matter (PM2.5) and hospital admissions for COPD in southwestern Taiwan, especially during both spring and winter seasons. PMID:27023589

CD4+ T Cells Expressing PD-1, TIGIT and LAG-3 Contribute to HIV Persistence during ART

PubMed Central

Fromentin, Rémi; Bakeman, Wendy; Lawani, Mariam B.; Khoury, Gabriela; Hartogensis, Wendy; DaFonseca, Sandrina; Killian, Marisela; Epling, Lorrie; Hoh, Rebecca; Sinclair, Elizabeth; Hecht, Frederick M.; Bacchetti, Peter; Deeks, Steven G.; Lewin, Sharon R.; Sékaly, Rafick-Pierre; Chomont, Nicolas

2016-01-01

HIV persists in a small pool of latently infected cells despite antiretroviral therapy (ART). Identifying cellular markers expressed at the surface of these cells may lead to novel therapeutic strategies to reduce the size of the HIV reservoir. We hypothesized that CD4+ T cells expressing immune checkpoint molecules would be enriched in HIV-infected cells in individuals receiving suppressive ART. Expression levels of 7 immune checkpoint molecules (PD-1, CTLA-4, LAG-3, TIGIT, TIM-3, CD160 and 2B4) as well as 4 markers of HIV persistence (integrated and total HIV DNA, 2-LTR circles and cell-associated unspliced HIV RNA) were measured in PBMCs from 48 virally suppressed individuals. Using negative binomial regression models, we identified PD-1, TIGIT and LAG-3 as immune checkpoint molecules positively associated with the frequency of CD4+ T cells harboring integrated HIV DNA. The frequency of CD4+ T cells co-expressing PD-1, TIGIT and LAG-3 independently predicted the frequency of cells harboring integrated HIV DNA. Quantification of HIV genomes in highly purified cell subsets from blood further revealed that expressions of PD-1, TIGIT and LAG-3 were associated with HIV-infected cells in distinct memory CD4+ T cell subsets. CD4+ T cells co-expressing the three markers were highly enriched for integrated viral genomes (median of 8.2 fold compared to total CD4+ T cells). Importantly, most cells carrying inducible HIV genomes expressed at least one of these markers (median contribution of cells expressing LAG-3, PD-1 or TIGIT to the inducible reservoir = 76%). Our data provide evidence that CD4+ T cells expressing PD-1, TIGIT and LAG-3 alone or in combination are enriched for persistent HIV during ART and suggest that immune checkpoint blockers directed against these receptors may represent valuable tools to target latently infected cells in virally suppressed individuals. PMID:27415008

Predicting Culex pipiens/restuans population dynamics by interval lagged weather data

PubMed Central

2013-01-01

Background Culex pipiens/restuans mosquitoes are important vectors for a variety of arthropod borne viral infections. In this study, the associations between 20 years of mosquito capture data and the time lagged environmental quantities daytime length, temperature, precipitation, relative humidity and wind speed were used to generate a predictive model for the population dynamics of this vector species. Methods Mosquito population in the study area was represented by averaged time series of mosquitos counts captured at 6 sites in Cook County (Illinois, USA). Cross-correlation maps (CCMs) were compiled to investigate the association between mosquito abundances and environmental quantities. The results obtained from the CCMs were incorporated into a Poisson regression to generate a predictive model. To optimize the predictive model the time lags obtained from the CCMs were adjusted using a genetic algorithm. Results CCMs for weekly data showed a highly positive correlation of mosquito abundances with daytime length 4 to 5 weeks prior to capture (quantified by a Spearman rank order correlation of rS = 0.898) and with temperature during 2 weeks prior to capture (rS = 0.870). Maximal negative correlations were found for wind speed averaged over 3 week prior to capture (rS = −0.621). Cx. pipiens/restuans population dynamics was predicted by integrating the CCM results in Poisson regression models. They were used to simulate the average seasonal cycle of the mosquito abundance. Verification with observations resulted in a correlation of rS = 0.899 for daily and rS = 0.917 for weekly data. Applying the optimized models to the entire 20-years time series also resulted in a suitable fit with rS = 0.876 for daily and rS = 0.899 for weekly data. Conclusions The study demonstrates the application of interval lagged weather data to predict mosquito abundances with a feasible accuracy, especially when related to weekly Cx. pipiens/restuans populations. PMID:23634763

Using non-systematic surveys to investigate effects of regional climate variability on Australasian gannets in the Hauraki Gulf, New Zealand

NASA Astrophysics Data System (ADS)

Srinivasan, Mridula; Dassis, Mariela; Benn, Emily; Stockin, Karen A.; Martinez, Emmanuelle; Machovsky-Capuska, Gabriel E.

2015-05-01

Few studies have investigated regional and natural climate variability on seabird populations using ocean reanalysis datasets (e.g. Simple Ocean Data Assimilation (SODA)) that integrate atmospheric information to supplement ocean observations and provide improved estimates of ocean conditions. Herein we use a non-systematic dataset on Australasian gannets (Morus serrator) from 2001 to 2009 to identify potential connections between Gannet Sightings Per Unit Effort (GSPUE) and climate and oceanographic variability in a region of known importance for breeding seabirds, the Hauraki Gulf (HG), New Zealand. While no statistically significant relationships between GSPUE and global climate indices were determined, there was a significant correlation between GSPUE and regional SST anomaly for HG. Also, there appears to be a strong link between global climate indices and regional climate in the HG. Further, based on cross-correlation function coefficients and lagged multiple regression models, we identified potential leading and lagging climate variables, and climate variables but with limited predictive capacity in forecasting future GSPUE. Despite significant inter-annual variability and marginally cooler SSTs since 2001, gannet sightings appear to be increasing. We hypothesize that at present underlying physical changes in the marine ecosystem may be insufficient to affect supply of preferred gannet main prey (pilchard Sardinops spp.), which tolerate a wide thermal range. Our study showcases the potential scientific value of lengthy non-systematic data streams and when designed properly (i.e., contain abundance, flock size, and spatial data), can yield useful information in climate impact studies on seabirds and other marine fauna. Such information can be invaluable for enhancing conservation measures for protected species in fiscally constrained research environments.

A spatially explicit approach to the study of socio-demographic inequality in the spatial distribution of trees across Boston neighborhoods.

PubMed

Duncan, Dustin T; Kawachi, Ichiro; Kum, Susan; Aldstadt, Jared; Piras, Gianfranco; Matthews, Stephen A; Arbia, Giuseppe; Castro, Marcia C; White, Kellee; Williams, David R

2014-04-01

The racial/ethnic and income composition of neighborhoods often influences local amenities, including the potential spatial distribution of trees, which are important for population health and community wellbeing, particularly in urban areas. This ecological study used spatial analytical methods to assess the relationship between neighborhood socio-demographic characteristics (i.e. minority racial/ethnic composition and poverty) and tree density at the census tact level in Boston, Massachusetts (US). We examined spatial autocorrelation with the Global Moran's I for all study variables and in the ordinary least squares (OLS) regression residuals as well as computed Spearman correlations non-adjusted and adjusted for spatial autocorrelation between socio-demographic characteristics and tree density. Next, we fit traditional regressions (i.e. OLS regression models) and spatial regressions (i.e. spatial simultaneous autoregressive models), as appropriate. We found significant positive spatial autocorrelation for all neighborhood socio-demographic characteristics (Global Moran's I range from 0.24 to 0.86, all P =0.001), for tree density (Global Moran's I =0.452, P =0.001), and in the OLS regression residuals (Global Moran's I range from 0.32 to 0.38, all P <0.001). Therefore, we fit the spatial simultaneous autoregressive models. There was a negative correlation between neighborhood percent non-Hispanic Black and tree density (r S =-0.19; conventional P -value=0.016; spatially adjusted P -value=0.299) as well as a negative correlation between predominantly non-Hispanic Black (over 60% Black) neighborhoods and tree density (r S =-0.18; conventional P -value=0.019; spatially adjusted P -value=0.180). While the conventional OLS regression model found a marginally significant inverse relationship between Black neighborhoods and tree density, we found no statistically significant relationship between neighborhood socio-demographic composition and tree density in the spatial regression models. Methodologically, our study suggests the need to take into account spatial autocorrelation as findings/conclusions can change when the spatial autocorrelation is ignored. Substantively, our findings suggest no need for policy intervention vis-à-vis trees in Boston, though we hasten to add that replication studies, and more nuanced data on tree quality, age and diversity are needed.

Radio pulsar glitches as a state-dependent Poisson process

NASA Astrophysics Data System (ADS)

Fulgenzi, W.; Melatos, A.; Hughes, B. D.

2017-10-01

Gross-Pitaevskii simulations of vortex avalanches in a neutron star superfluid are limited computationally to ≲102 vortices and ≲102 avalanches, making it hard to study the long-term statistics of radio pulsar glitches in realistically sized systems. Here, an idealized, mean-field model of the observed Gross-Pitaevskii dynamics is presented, in which vortex unpinning is approximated as a state-dependent, compound Poisson process in a single random variable, the spatially averaged crust-superfluid lag. Both the lag-dependent Poisson rate and the conditional distribution of avalanche-driven lag decrements are inputs into the model, which is solved numerically (via Monte Carlo simulations) and analytically (via a master equation). The output statistics are controlled by two dimensionless free parameters: α, the glitch rate at a reference lag, multiplied by the critical lag for unpinning, divided by the spin-down rate; and β, the minimum fraction of the lag that can be restored by a glitch. The system evolves naturally to a self-regulated stationary state, whose properties are determined by α/αc(β), where αc(β) ≈ β-1/2 is a transition value. In the regime α ≳ αc(β), one recovers qualitatively the power-law size and exponential waiting-time distributions observed in many radio pulsars and Gross-Pitaevskii simulations. For α ≪ αc(β), the size and waiting-time distributions are both power-law-like, and a correlation emerges between size and waiting time until the next glitch, contrary to what is observed in most pulsars. Comparisons with astrophysical data are restricted by the small sample sizes available at present, with ≤35 events observed per pulsar.

A climate-based prediction model in the high-risk clusters of the Mekong Delta region, Vietnam: towards improving dengue prevention and control.

PubMed

Phung, Dung; Talukder, Mohammad Radwanur Rahman; Rutherford, Shannon; Chu, Cordia

2016-10-01

To develop a prediction score scheme useful for prevention practitioners and authorities to implement dengue preparedness and controls in the Mekong Delta region (MDR). We applied a spatial scan statistic to identify high-risk dengue clusters in the MDR and used generalised linear-distributed lag models to examine climate-dengue associations using dengue case records and meteorological data from 2003 to 2013. The significant predictors were collapsed into categorical scales, and the β-coefficients of predictors were converted to prediction scores. The score scheme was validated for predicting dengue outbreaks using ROC analysis. The north-eastern MDR was identified as the high-risk cluster. A 1 °C increase in temperature at lag 1-4 and 5-8 weeks increased the dengue risk 11% (95% CI, 9-13) and 7% (95% CI, 6-8), respectively. A 1% rise in humidity increased dengue risk 0.9% (95% CI, 0.2-1.4) at lag 1-4 and 0.8% (95% CI, 0.2-1.4) at lag 5-8 weeks. Similarly, a 1-mm increase in rainfall increased dengue risk 0.1% (95% CI, 0.05-0.16) at lag 1-4 and 0.11% (95% CI, 0.07-0.16) at lag 5-8 weeks. The predicted scores performed with high accuracy in diagnosing the dengue outbreaks (96.3%). This study demonstrates the potential usefulness of a dengue prediction score scheme derived from complex statistical models for high-risk dengue clusters. We recommend a further study to examine the possibility of incorporating such a score scheme into the dengue early warning system in similar climate settings. © 2016 John Wiley & Sons Ltd.

EUCLIA—Exploring the UV/Optical Continuum Lag in Active Galactic Nuclei. I. A Model without Light Echoing

NASA Astrophysics Data System (ADS)

Cai, Zhen-Yi; Wang, Jun-Xian; Zhu, Fei-Fan; Sun, Mou-Yuan; Gu, Wei-Min; Cao, Xin-Wu; Yuan, Feng

2018-03-01

The tight interband correlation and the lag–wavelength relation among UV/optical continua of active galactic nuclei have been firmly established. They are usually understood within the widespread reprocessing scenario; however, the implied interband lags are generally too small. Furthermore, it is challenged by new evidence, such as that the X-ray reprocessing yields too much high-frequency UV/optical variation and that it fails to reproduce the observed timescale-dependent color variations among the Swift light curves of NGC 5548. In a different manner, we demonstrate that an upgraded inhomogeneous accretion disk model, whose local independent temperature fluctuations are subject to a speculated common large-scale temperature fluctuation, can intrinsically generate the tight interband correlation and lag across the UV/optical and be in nice agreement with several observational properties of NGC 5548, including the timescale-dependent color variation. The emergent lag is a result of the differential regression capability of local temperature fluctuations when responding to the large-scale fluctuation. An average speed of propagations as large as ≳15% of the speed of light may be required by this common fluctuation. Several potential physical mechanisms for such propagations are discussed. Our interesting phenomenological scenario may shed new light on comprehending the UV/optical continuum variations of active galactic nuclei.

Concurrent and lagged effects of registered nurse turnover and staffing on unit-acquired pressure ulcers.

PubMed

Park, Shin Hye; Boyle, Diane K; Bergquist-Beringer, Sandra; Staggs, Vincent S; Dunton, Nancy E

2014-08-01

We examined the concurrent and lagged effects of registered nurse (RN) turnover on unit-acquired pressure ulcer rates and whether RN staffing mediated the effects. Quarterly unit-level data were obtained from the National Database of Nursing Quality Indicators for 2008 to 2010. A total of 10,935 unit-quarter observations (2,294 units, 465 hospitals) were analyzed. This longitudinal study used multilevel regressions and tested time-lagged effects of study variables on outcomes. The lagged effect of RN turnover on unit-acquired pressure ulcers was significant, while there was no concurrent effect. For every 10 percentage-point increase in RN turnover in a quarter, the odds of a patient having a pressure ulcer increased by 4 percent in the next quarter. Higher RN turnover in a quarter was associated with lower RN staffing in the current and subsequent quarters. Higher RN staffing was associated with lower pressure ulcer rates, but it did not mediate the relationship between turnover and pressure ulcers. We suggest that RN turnover is an important factor that affects pressure ulcer rates and RN staffing needed for high-quality patient care. Given the high RN turnover rates, hospital and nursing administrators should prepare for its negative effect on patient outcomes. © Health Research and Educational Trust.

Concurrent and Lagged Effects of Registered Nurse Turnover and Staffing on Unit-Acquired Pressure Ulcers

PubMed Central

Park, Shin Hye; Boyle, Diane K; Bergquist-Beringer, Sandra; Staggs, Vincent S; Dunton, Nancy E

2014-01-01

Objective We examined the concurrent and lagged effects of registered nurse (RN) turnover on unit-acquired pressure ulcer rates and whether RN staffing mediated the effects. Data Sources/Setting Quarterly unit-level data were obtained from the National Database of Nursing Quality Indicators for 2008 to 2010. A total of 10,935 unit-quarter observations (2,294 units, 465 hospitals) were analyzed. Methods This longitudinal study used multilevel regressions and tested time-lagged effects of study variables on outcomes. Findings The lagged effect of RN turnover on unit-acquired pressure ulcers was significant, while there was no concurrent effect. For every 10 percentage-point increase in RN turnover in a quarter, the odds of a patient having a pressure ulcer increased by 4 percent in the next quarter. Higher RN turnover in a quarter was associated with lower RN staffing in the current and subsequent quarters. Higher RN staffing was associated with lower pressure ulcer rates, but it did not mediate the relationship between turnover and pressure ulcers. Conclusions We suggest that RN turnover is an important factor that affects pressure ulcer rates and RN staffing needed for high-quality patient care. Given the high RN turnover rates, hospital and nursing administrators should prepare for its negative effect on patient outcomes. PMID:24476194

TIME-DEPENDENT ELECTRON ACCELERATION IN BLAZAR TRANSIENTS: X-RAY TIME LAGS AND SPECTRAL FORMATION

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

Lewis, Tiffany R.; Becker, Peter A.; Finke, Justin D., E-mail: pbecker@gmu.edu, E-mail: tlewis13@gmu.edu, E-mail: justin.finke@nrl.navy.mil

2016-06-20

Electromagnetic radiation from blazar jets often displays strong variability, extending from radio to γ -ray frequencies. In a few cases, this variability has been characterized using Fourier time lags, such as those detected in the X-rays from Mrk 421 using Beppo SAX. The lack of a theoretical framework to interpret the data has motivated us to develop a new model for the formation of the X-ray spectrum and the time lags in blazar jets based on a transport equation including terms describing stochastic Fermi acceleration, synchrotron losses, shock acceleration, adiabatic expansion, and spatial diffusion. We derive the exact solution formore » the Fourier transform of the electron distribution and use it to compute the Fourier transform of the synchrotron radiation spectrum and the associated X-ray time lags. The same theoretical framework is also used to compute the peak flare X-ray spectrum, assuming that a steady-state electron distribution is achieved during the peak of the flare. The model parameters are constrained by comparing the theoretical predictions with the observational data for Mrk 421. The resulting integrated model yields, for the first time, a complete first-principles physical explanation for both the formation of the observed time lags and the shape of the peak flare X-ray spectrum. It also yields direct estimates of the strength of the shock and the stochastic magnetohydrodynamical wave acceleration components in the Mrk 421 jet.« less

A Study on the Response of Non-photosynthetic Vegetation (NPV) towards the Anomalies of Climate in the Southwestern U.S.

NASA Astrophysics Data System (ADS)

Jia, S.; Okin, G. S.

2014-12-01

Non-photosynthetic vegetation (NPV), including the standing dead leaves and trunks of plants, is not only a crucial component of aboveground biomass in the dry ecosystems but also an effective indicator of drought, since the photosynthetic plants (PV) usually degrade to NPV after drought. With the multiple-endmember spectral mixture (MESMA) analysis, it is possible to extract the NPV coverage to a selected baseline date from MODIS MOD43 NBAR data. In this study, we used a baseline image derived based on JHU Spectral Library to obtain the NPV, PV and bare soil of southwest U.S. from 2000 to 2012. To investigate the response of NPV, we then calculated the lagged and non-lagged correlation between the anomalies of land cover and climate variables. The major land use categories are also employed to investigate the spatial pattern of the response. The more significant correlation between NPV or PV and precipitation than temperature indicates the importance of moisture in the study site. In addition, there is an asymmetric response between NPV to the drought and the increased precipitation. In drier inland, the most significant response of NPV after a deficit of precipitation occurs later than after an increase. The increase of temperature, especially under the deficits of moisture, facilitates the presence of NPV with lag, which is due to the response time of detectable withering of PV. The response of NPV also varies between the different land cover categories. In southwest U.S., the NPV from shrubs and grassland have more sensitive feedbacks on the dynamics of climate than wetter region. The nature of the ecosystems can partly explain the difference, but finer scale studies are necessary for further investigation of specific regions. Considering the increase of drought in southwest U.S., obtaining a better understanding on the response of vegetation is crucial to further evaluate its impacts on the dry ecosystems. This study provides a perspective by examining NPV, another direct indicator of drought. For further studies, temporal and spatial patterns of NPV response to the climate need more scrutiny, such as the spatial pattern of the lags, hotspots of change, and regional-specific feedbacks. Different indicators of extreme events, such as the U.S. Drought Monitor may also be employed to provide more direct evaluation.

Importance of spatial autocorrelation in modeling bird distributions at a continental scale

USGS Publications Warehouse

Bahn, V.; O'Connor, R.J.; Krohn, W.B.

2006-01-01

Spatial autocorrelation in species' distributions has been recognized as inflating the probability of a type I error in hypotheses tests, causing biases in variable selection, and violating the assumption of independence of error terms in models such as correlation or regression. However, it remains unclear whether these problems occur at all spatial resolutions and extents, and under which conditions spatially explicit modeling techniques are superior. Our goal was to determine whether spatial models were superior at large extents and across many different species. In addition, we investigated the importance of purely spatial effects in distribution patterns relative to the variation that could be explained through environmental conditions. We studied distribution patterns of 108 bird species in the conterminous United States using ten years of data from the Breeding Bird Survey. We compared the performance of spatially explicit regression models with non-spatial regression models using Akaike's information criterion. In addition, we partitioned the variance in species distributions into an environmental, a pure spatial and a shared component. The spatially-explicit conditional autoregressive regression models strongly outperformed the ordinary least squares regression models. In addition, partialling out the spatial component underlying the species' distributions showed that an average of 17% of the explained variation could be attributed to purely spatial effects independent of the spatial autocorrelation induced by the underlying environmental variables. We concluded that location in the range and neighborhood play an important role in the distribution of species. Spatially explicit models are expected to yield better predictions especially for mobile species such as birds, even in coarse-grained models with a large extent. ?? Ecography.

Tracking of climatic niche boundaries under recent climate change.

PubMed

La Sorte, Frank A; Jetz, Walter

2012-07-01

1. Global climate has changed significantly during the past 30 years and especially in northern temperate regions which have experienced poleward shifts in temperature regimes. While there is evidence that some species have responded by moving their distributions to higher latitudes, the efficiency of this response in tracking species' climatic niche boundaries over time has yet to be addressed. 2. Here, we provide a continental assessment of the temporal structure of species responses to recent spatial shifts in climatic conditions. We examined geographic associations with minimum winter temperature for 59 species of winter avifauna at 476 Christmas Bird Count circles in North America from 1975 to 2009 under three sampling schemes that account for spatial and temporal sampling effects. 3. Minimum winter temperature associated with species occurrences showed an overall increase with a weakening trend after 1998. Species displayed highly variable responses that, on average and across sampling schemes, contained a strong lag effect that weakened in strength over time. In general, the conservation of minimum winter temperature was relevant when all species were considered together but only after an initial lag period (c. 35 years) was overcome. The delayed niche tracking observed at the combined species level was likely supported by the post1998 lull in the warming trend. 4. There are limited geographic and ecological explanations for the observed variability, suggesting that the efficiency of species' responses under climate change is likely to be highly idiosyncratic and difficult to predict. This outcome is likely to be even more pronounced and time lags more persistent for less vagile taxa, particularly during the periods of consistent or accelerating warming. Current modelling efforts and conservation strategies need to better appreciate the variation, strength and duration of lag effects and their association with climatic variability. Conservation strategies in particular will benefit through identifying and maintaining dispersal corridors that accommodate diverging dispersal strategies and timetables. © 2012 The Authors. Journal of Animal Ecology © 2012 British Ecological Society.

The assembly of ecological communities inferred from taxonomic and functional composition

Treesearch

Eric R. Sokol; E.F. Benfield; Lisa K. Belden; H. Maurice. Valett

2011-01-01

Among-site variation in metacommunities (beta diversity) is typically correlated with the distance separating the sites (spatial lag). This distance decay in similarity pattern has been linked to both niche-based and dispersal-based community assembly hypotheses. Here we show that beta diversity patterns in community composition, when supplemented with functional-trait...

The Association between Dust Storms and Daily Non-Accidental Mortality in the United States, 1993–2005

PubMed Central

Crooks, James Lewis; Cascio, Wayne E.; Percy, Madelyn S.; Reyes, Jeanette; Neas, Lucas M.; Hilborn, Elizabeth D.

2016-01-01

Background: The impact of dust storms on human health has been studied in the context of Asian, Saharan, Arabian, and Australian storms, but there has been no recent population-level epidemiological research on the dust storms in North America. The relevance of dust storms to public health is likely to increase as extreme weather events are predicted to become more frequent with anticipated changes in climate through the 21st century. Objectives: We examined the association between dust storms and county-level non-accidental mortality in the United States from 1993 through 2005. Methods: Dust storm incidence data, including date and approximate location, are taken from the U.S. National Weather Service storm database. County-level mortality data for the years 1993–2005 were acquired from the National Center for Health Statistics. Distributed lag conditional logistic regression models under a time-stratified case-crossover design were used to study the relationship between dust storms and daily mortality counts over the whole United States and in Arizona and California specifically. End points included total non-accidental mortality and three mortality subgroups (cardiovascular, respiratory, and other non-accidental). Results: We estimated that for the United States as a whole, total non-accidental mortality increased by 7.4% (95% CI: 1.6, 13.5; p = 0.011) and 6.7% (95% CI: 1.1, 12.6; p = 0.018) at 2- and 3-day lags, respectively, and by an average of 2.7% (95% CI: 0.4, 5.1; p = 0.023) over lags 0–5 compared with referent days. Significant associations with non-accidental mortality were estimated for California (lag 2 and 0–5 day) and Arizona (lag 3), for cardiovascular mortality in the United States (lag 2) and Arizona (lag 3), and for other non-accidental mortality in California (lags 1–3 and 0–5). Conclusions: Dust storms are associated with increases in lagged non-accidental and cardiovascular mortality. Citation: Crooks JL, Cascio WE, Percy MS, Reyes J, Neas LM, Hilborn ED. 2016. The association between dust storms and daily non-accidental mortality in the United States, 1993–2005. Environ Health Perspect 124:1735–1743; http://dx.doi.org/10.1289/EHP216 PMID:27128449

When Deriving the Spatial QRS-T Angle from the 12-lead ECG, which Transform is More Frank: Regression or Inverse Dower?

NASA Technical Reports Server (NTRS)

Schlegel, Todd T.; Cortez, Daniel

2010-01-01

Our primary objective was to ascertain which commonly used 12-to-Frank-lead transformation yields spatial QRS-T angle values closest to those obtained from simultaneously collected true Frank-lead recordings. Simultaneous 12-lead and Frank XYZ-lead recordings were analyzed for 100 post-myocardial infarction patients and 50 controls. Relative agreement, with true Frank-lead results, of 12-to-Frank-lead transformed results for the spatial QRS-T angle using Kors regression versus inverse Dower was assessed via ANOVA, Lin s concordance and Bland-Altman plots. Spatial QRS-T angles from the true Frank leads were not significantly different than those derived from the Kors regression-related transformation but were significantly smaller than those derived from the inverse Dower-related transformation (P less than 0.001). Independent of method, spatial mean QRS-T angles were also always significantly larger than spatial maximum (peaks) QRS-T angles. Spatial QRS-T angles are best approximated by regression-related transforms. Spatial mean and spatial peaks QRS-T angles should also not be used interchangeably.

Spatial Double Generalized Beta Regression Models: Extensions and Application to Study Quality of Education in Colombia

ERIC Educational Resources Information Center

Cepeda-Cuervo, Edilberto; Núñez-Antón, Vicente

2013-01-01

In this article, a proposed Bayesian extension of the generalized beta spatial regression models is applied to the analysis of the quality of education in Colombia. We briefly revise the beta distribution and describe the joint modeling approach for the mean and dispersion parameters in the spatial regression models' setting. Finally, we motivate…

Forecasting peak asthma admissions in London: an application of quantile regression models.

PubMed

Soyiri, Ireneous N; Reidpath, Daniel D; Sarran, Christophe

2013-07-01

Asthma is a chronic condition of great public health concern globally. The associated morbidity, mortality and healthcare utilisation place an enormous burden on healthcare infrastructure and services. This study demonstrates a multistage quantile regression approach to predicting excess demand for health care services in the form of asthma daily admissions in London, using retrospective data from the Hospital Episode Statistics, weather and air quality. Trivariate quantile regression models (QRM) of asthma daily admissions were fitted to a 14-day range of lags of environmental factors, accounting for seasonality in a hold-in sample of the data. Representative lags were pooled to form multivariate predictive models, selected through a systematic backward stepwise reduction approach. Models were cross-validated using a hold-out sample of the data, and their respective root mean square error measures, sensitivity, specificity and predictive values compared. Two of the predictive models were able to detect extreme number of daily asthma admissions at sensitivity levels of 76 % and 62 %, as well as specificities of 66 % and 76 %. Their positive predictive values were slightly higher for the hold-out sample (29 % and 28 %) than for the hold-in model development sample (16 % and 18 %). QRMs can be used in multistage to select suitable variables to forecast extreme asthma events. The associations between asthma and environmental factors, including temperature, ozone and carbon monoxide can be exploited in predicting future events using QRMs.

Forecasting peak asthma admissions in London: an application of quantile regression models

NASA Astrophysics Data System (ADS)

Soyiri, Ireneous N.; Reidpath, Daniel D.; Sarran, Christophe

2013-07-01

Asthma is a chronic condition of great public health concern globally. The associated morbidity, mortality and healthcare utilisation place an enormous burden on healthcare infrastructure and services. This study demonstrates a multistage quantile regression approach to predicting excess demand for health care services in the form of asthma daily admissions in London, using retrospective data from the Hospital Episode Statistics, weather and air quality. Trivariate quantile regression models (QRM) of asthma daily admissions were fitted to a 14-day range of lags of environmental factors, accounting for seasonality in a hold-in sample of the data. Representative lags were pooled to form multivariate predictive models, selected through a systematic backward stepwise reduction approach. Models were cross-validated using a hold-out sample of the data, and their respective root mean square error measures, sensitivity, specificity and predictive values compared. Two of the predictive models were able to detect extreme number of daily asthma admissions at sensitivity levels of 76 % and 62 %, as well as specificities of 66 % and 76 %. Their positive predictive values were slightly higher for the hold-out sample (29 % and 28 %) than for the hold-in model development sample (16 % and 18 %). QRMs can be used in multistage to select suitable variables to forecast extreme asthma events. The associations between asthma and environmental factors, including temperature, ozone and carbon monoxide can be exploited in predicting future events using QRMs.

Determinants of nutritional status of pre-school children in India.

PubMed

Bharati, Susmita; Pal, Manoranjan; Bharati, Premananda

2008-11-01

The aim of this paper is to assess the spatial distribution of nutritional status of children of less than three years through Z-scores of weight-for-age, height-for-age and weight-for-height using data collected by the National Family Health Survey (NFHS-2, 1998-99), India. The nutritional status of pre-school children was regressed on different socio-demographic factors after eliminating the effect of age. The data show that there are gender differences and spatial variations in the nutritional status of children in India. Gender difference is not very pronounced and almost disappears when the effects of age and socio-demographic variables are removed. The spatial difference, especially the rural-urban difference, was found to be very large and decreased substantially when the effects of age and socioeconomic variables were removed. However, the differences were not close to zero. All the variables were found to affect significantly the nutritional status of children. However, the literacy of mothers did not affect height-for-age significantly. The weight-for-age and height-for-age scores showed a dismal picture of the health condition of children in almost all states in India. The worst affected states are Bihar, Madhya Pradesh, Orissa and Uttar Pradesh. Assam and Rajasthans are also lagging behind. Weight-for-height scores do not give a clear picture of state-wise variation. Goa, Kerala and Punjab are the three most developed states in India and also have the lowest percentages of underweight children according to the Z-scores. Along with these three states come the north-eastern states where women are well educated. Thus overall development, enhancement of level of education and low gender inequality are the key factors for improvement in the health status of Indian children.

Spatial variability of biotic and abiotic tree establishment constraints across a treeline ecotone in the Alaska range.

PubMed

Stueve, Kirk M; Isaacs, Rachel E; Tyrrell, Lucy E; Densmore, Roseann V

2011-02-01

Throughout interior Alaska (U.S.A.), a gradual warming trend in mean monthly temperatures occurred over the last few decades (approximatlely 2-4 degrees C). The accompanying increases in woody vegetation at many alpine treeline (hereafter treeline) locations provided an opportunity to examine how biotic and abiotic local site conditions interact to control tree establishment patterns during warming. We devised a landscape ecological approach to investigate these relationships at an undisturbed treeline in the Alaska Range. We identified treeline changes between 1953 (aerial photography) and 2005 (satellite imagery) in a geographic information system (GIS) and linked them with corresponding local site conditions derived from digital terrain data, ancillary climate data, and distance to 1953 trees. Logistic regressions enabled us to rank the importance of local site conditions in controlling tree establishment. We discovered a spatial transition in the importance of tree establishment controls. The biotic variable (proximity to 1953 trees) was the most important tree establishment predictor below the upper tree limit, providing evidence of response lags with the abiotic setting and suggesting that tree establishment is rarely in equilibrium with the physical environment or responding directly to warming. Elevation and winter sun exposure were important predictors of tree establishment at the upper tree limit, but proximity to trees persisted as an important tertiary predictor, indicating that tree establishment may achieve equilibrium with the physical environment. However, even here, influences from the biotic variable may obscure unequivocal correlations with the abiotic setting (including temperature). Future treeline expansion will likely be patchy and challenging to predict without considering the spatial variability of influences from biotic and abiotic local site conditions.

Spatial variability of biotic and abiotic tree establishment constraints across a treeline ecotone in the Alaska Range

USGS Publications Warehouse

Stueve, K.M.; Isaacs, R.E.; Tyrrell, L.E.; Densmore, R.V.

2011-01-01

Throughout interior Alaska (USA), a gradual warming trend in mean monthly temperatures occurred over the last few decades (;2-48C). The accompanying increases in woody vegetation at many alpine treeline (hereafter treeline) locations provided an opportunity to examine how biotic and abiotic local site conditions interact to control tree establishment patterns during warming. We devised a landscape ecological approach to investigate these relationships at an undisturbed treeline in the Alaska Range. We identified treeline changes between 1953 (aerial photography) and 2005 (satellite imagery) in a geographic information system (GIS) and linked them with corresponding local site conditions derived from digital terrain data, ancillary climate data, and distance to 1953 trees. Logistic regressions enabled us to rank the importance of local site conditions in controlling tree establishment. We discovered a spatial transition in the importance of tree establishment controls. The biotic variable (proximity to 1953 trees) was the most important tree establishment predictor below the upper tree limit, providing evidence of response lags with the abiotic setting and suggesting that tree establishment is rarely in equilibrium with the physical environment or responding directly to warming. Elevation and winter sun exposure were important predictors of tree establishment at the upper tree limit, but proximity to trees persisted as an important tertiary predictor, indicating that tree establishment may achieve equilibrium with the physical environment. However, even here, influences from the biotic variable may obscure unequivocal correlations with the abiotic setting (including temperature). Future treeline expansion will likely be patchy and challenging to predict without considering the spatial variability of influences from biotic and abiotic local site conditions. ?? 2011 by the Ecological Society of America.

Early Warning Signals of Ecological Transitions: Methods for Spatial Patterns

PubMed Central

Brock, William A.; Carpenter, Stephen R.; Ellison, Aaron M.; Livina, Valerie N.; Seekell, David A.; Scheffer, Marten; van Nes, Egbert H.; Dakos, Vasilis

2014-01-01

A number of ecosystems can exhibit abrupt shifts between alternative stable states. Because of their important ecological and economic consequences, recent research has focused on devising early warning signals for anticipating such abrupt ecological transitions. In particular, theoretical studies show that changes in spatial characteristics of the system could provide early warnings of approaching transitions. However, the empirical validation of these indicators lag behind their theoretical developments. Here, we summarize a range of currently available spatial early warning signals, suggest potential null models to interpret their trends, and apply them to three simulated spatial data sets of systems undergoing an abrupt transition. In addition to providing a step-by-step methodology for applying these signals to spatial data sets, we propose a statistical toolbox that may be used to help detect approaching transitions in a wide range of spatial data. We hope that our methodology together with the computer codes will stimulate the application and testing of spatial early warning signals on real spatial data. PMID:24658137

Aeromechanical stability augmentation using semi-active friction-based lead-lag damper

NASA Astrophysics Data System (ADS)

Agarwal, Sandeep

2005-11-01

Lead-lag dampers are present in most rotors to provide the required level of damping in all flight conditions. These dampers are a critical component of the rotor system, but they also represent a major source of maintenance cost. In present rotor systems, both hydraulic and elastomeric lead-lag dampers have been used. Hydraulic dampers are complex mechanical components that require hydraulic fluids and have high associated maintenance costs. Elastomeric dampers are conceptually simpler and provide a "dry" rotor, but are rather costly. Furthermore, their damping characteristics can degrade with time without showing external signs of failure. Hence, the dampers must be replaced on a regular basis. A semi-active friction based lead-lag damper is proposed as a replacement for hydraulic and elastomeric dampers. Damping is provided by optimized energy dissipation due to frictional forces in semi-active joints. An actuator in the joint modulates the normal force that controls energy dissipation at the frictional interfaces, resulting in large hysteretic loops. Various selective damping strategies are developed and tested for a simple system containing two different frequency modes in its response, one of which needs to be damped out. The system reflects the situation encountered in rotor response where 1P excitation is present along with the potentially unstable regressive lag motion. Simulation of the system response is obtained to compare their effectiveness. Next, a control law governing the actuation in the lag damper is designed to generate the desired level of damping for performing adaptive selective damping of individual blade lag motion. Further, conceptual design of a piezoelectric friction based lag damper for a full-scale rotor is presented and various factors affecting size, design and maintenance cost, damping capacity, and power requirements of the damper are discussed. The selective semi-active damping strategy is then studied in the context of classical ground resonance problem. In view of the inherent nonlinearity in the system due to friction phenomena, multiblade transformation from rotating frame to nonrotating frame is not useful. Stability analysis of the system is performed in the rotating frame to gain an understanding of the dynamic characteristics of rotor system with attached semi-active friction based lag dampers. This investigation is extended to the ground resonance stability analysis of a comprehensive UH-60 model within the framework of finite element based multibody dynamics formulations. Simulations are conducted to study the performance of several integrated lag dampers ranging from passive to semi-active ones with varying levels of selectivity. Stability analysis is performed for a nominal range of rotor speeds using Prony's method.

Developing a Soil Moisture Index for California Grasslands from Airborne Hyperspectral Imagery

NASA Astrophysics Data System (ADS)

Flamme, H. E.; Roberts, D. A.; Miller, D. L.

2016-12-01

Soil moisture is a key environmental factor controlling vegetation diversity and productivity, evaporation, transpiration, and rainfall runoff. Despite the contribution of soil moisture to ecological productivity, the hydrologic cycle, and erosion, it is currently not being monitored as accurately or as frequently as other environmental factors. Traditional soil moisture monitoring techniques rely on in situ measurements, which become costly when evaluating areas of unevenly distributed soil characteristics and varying topography. Alternatively, satellite remote sensing, such as passive microwave from SMAP, can provide soil moisture but only at very coarse spatial resolutions. Imagery from the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) has the potential to allow better spatial and temporal monitoring of soil moisture. This study established a relationship between plant available water and hyperspectral reflectance via linear regressions of data from 2013-2015 for two grassland field sites: 1) near Santa Barbara, California, at Coal Oil Point Reserve (COPR) and 2) Airstrip station (AIRS) at UC Santa Barbara's Sedgwick Reserve near Santa Ynez, California. Volumetric soil moisture measurements at 10 cm and 20 cm depths were provided by meteorological stations situated in COPR and AIRS while reflectance data were extracted from AVIRIS. We found strong correlations between plant available water and bands centered at wavelengths 704 nm and 831 nm, which we used to create Hyperspectral Soil Moisture Index (HSMI): 0.38((ρ831-ρ704)/(ρ831+ρ704))-0.02. HSMI demonstrated a coefficient of determination (R2) of 0.71 for linear regressions of reflectance versus plant available water with a lag time of 28 days. We applied HSMI to the AIRS and COPR grasslands for 2011 AVIRIS scenes. Plant available water values predicted by HSMI were 0.039 higher at AIRS and 0.048 higher at COPR than the field measurements at the sites. Differences in grass species, soil composition, and climate between COPR and AIRS likely contributed to the errors in the soil moisture predicted by HSMI.

Dynamic regression modeling of daily nitrate-nitrogen concentrations in a large agricultural watershed.

PubMed

Feng, Zhujing; Schilling, Keith E; Chan, Kung-Sik

2013-06-01

Nitrate-nitrogen concentrations in rivers represent challenges for water supplies that use surface water sources. Nitrate concentrations are often modeled using time-series approaches, but previous efforts have typically relied on monthly time steps. In this study, we developed a dynamic regression model of daily nitrate concentrations in the Raccoon River, Iowa, that incorporated contemporaneous and lags of precipitation and discharge occurring at several locations around the basin. Results suggested that 95 % of the variation in daily nitrate concentrations measured at the outlet of a large agricultural watershed can be explained by time-series patterns of precipitation and discharge occurring in the basin. Discharge was found to be a more important regression variable than precipitation in our model but both regression parameters were strongly correlated with nitrate concentrations. The time-series model was consistent with known patterns of nitrate behavior in the watershed, successfully identifying contemporaneous dilution mechanisms from higher relief and urban areas of the basin while incorporating the delayed contribution of nitrate from tile-drained regions in a lagged response. The first difference of the model errors were modeled as an AR(16) process and suggest that daily nitrate concentration changes remain temporally correlated for more than 2 weeks although temporal correlation was stronger in the first few days before tapering off. Consequently, daily nitrate concentrations are non-stationary, i.e. of strong memory. Using time-series models to reliably forecast daily nitrate concentrations in a river based on patterns of precipitation and discharge occurring in its basin may be of great interest to water suppliers.

School Attendance Problems and Youth Psychopathology: Structural Cross-Lagged Regression Models in Three Longitudinal Data Sets

ERIC Educational Resources Information Center

Wood, Jeffrey J.; Lynne-Landsman, Sarah D.; Langer, David A.; Wood, Patricia A.; Clark, Shaunna L.; Eddy, J. Mark; Ialongo, Nick

2012-01-01

This study tests a model of reciprocal influences between absenteeism and youth psychopathology using 3 longitudinal datasets (Ns = 20,745, 2,311, and 671). Participants in 1st through 12th grades were interviewed annually or biannually. Measures of psychopathology include self-, parent-, and teacher-report questionnaires. Structural cross-lagged…

Snow depth spatial structure from hillslope to basin scale

NASA Astrophysics Data System (ADS)

Deems, J. S.

2017-12-01

Knowledge of spatial patterns of snow accumulation is required for understanding the hydrology, climatology, and ecology of mountain regions. Spatial structure in snow accumulation patterns changes with the scale of observation, a feature that has been characterized using fractal dimensions calculated from lidar-derived snow depth maps: fractal scaling structure at short length scales, with a `scale break' transition to more stochastic patterns at longer separation distances. Previous work has shown that this fractal structure of snow depth distributions differs between sites with different vegetation and terrain characteristics. Forested areas showed a transition to a nearly random spatial distribution at a much shorter lag distance than do unforested sites, enabling a statistical characterization. Alpine areas, however, showed strong spatial structure for a much wider scale range, and were the source of the dominant spatial pattern observable over a wider area. These spatial structure characteristics suggest that the choice of measurement or model resolution (satellite sensor, DEM, field survey point spacing, etc.) will strongly affect the estimates of snow volume or mass, as well as the magnitude of spatial variability. These prior efforts used data sets that were high resolution ( 1 m laser point spacing) but of limited extent ( 1 km2), constraining detection of scale features such as fractal dimension or scale breaks to areas of relatively similar characteristics and to lag distances of under 500 m. New datasets available from the NASA JPL Airborne Snow Observatory (ASO) provide similar resolution but over large areas, enabling assessment of snow spatial structure across an entire watershed, or in similar vegetation or physiography but in different parts of the basin. Additionally, the multi-year ASO time series allows an investigation into the temporal stability of these scale characteristics, within a single snow season and between seasons of strongly varying accumulation totals and patterns. This presentation will explore initial results from this study, using data from the Tuolumne River Basin in California, USA. Fractal scaling characteristics derived from ASO lidar snow depth measurements are examined at the basin scale, as well as in varying topographic and forest cover environments.

Predicting recreational water quality advisories: A comparison of statistical methods

USGS Publications Warehouse

Brooks, Wesley R.; Corsi, Steven R.; Fienen, Michael N.; Carvin, Rebecca B.

2016-01-01

Epidemiological studies indicate that fecal indicator bacteria (FIB) in beach water are associated with illnesses among people having contact with the water. In order to mitigate public health impacts, many beaches are posted with an advisory when the concentration of FIB exceeds a beach action value. The most commonly used method of measuring FIB concentration takes 18–24 h before returning a result. In order to avoid the 24 h lag, it has become common to ”nowcast” the FIB concentration using statistical regressions on environmental surrogate variables. Most commonly, nowcast models are estimated using ordinary least squares regression, but other regression methods from the statistical and machine learning literature are sometimes used. This study compares 14 regression methods across 7 Wisconsin beaches to identify which consistently produces the most accurate predictions. A random forest model is identified as the most accurate, followed by multiple regression fit using the adaptive LASSO.

High temperature effects on out-patient visits and hospital admissions in Chiang Mai, Thailand.

PubMed

Pudpong, Nareerut; Hajat, Shakoor

2011-11-15

This study investigated the short-term effects of temperature on out-patient visits and hospital admissions in Chiang Mai, Thailand. While mortality outcomes in the literature have been reported, there is less evidence of morbidity effects with very few studies conducted in developing countries with subtropical or tropical climate. Time-series regression analysis was employed using generalized negative binomial regression to model the short-term relationships between temperature and morbidity after controlling for seasonal patterns and other potential confounders. Lag effects up to 13 days and effect modification by age (0-14 years, 15-64 years, ≥65 years) were examined. Temperature effects with wide confidence intervals were found, with an increase in diabetic visits of 26.3% (95% CI: 7.1%-49.0%), and circulatory visits of 19.2% (95% CI: 7.0%-32.8%) per 1 °C increase in temperature above an identified threshold of 29 °C. Additionally, there was a rise of both visits (3.7% increase, 95% CI: 1.5%-5.9%) and admissions (5.8% increase, 95% CI: 2.3%-9.3%) due to intestinal infectious disease in association with each 1 °C increase across the whole temperature range. The effects of temperature were stronger in the elderly though not statistically significant. Daily morbidity in Chiang Mai was positively associated with temperature with a lag effect of up to 2 weeks, which was longer than lag effects previously reported. Public health preparedness and interventions should be considered to minimise possible increased hospital visits and admissions during hot weather. Copyright © 2011 Elsevier B.V. All rights reserved.

A spatially explicit approach to the study of socio-demographic inequality in the spatial distribution of trees across Boston neighborhoods

PubMed Central

Duncan, Dustin T.; Kawachi, Ichiro; Kum, Susan; Aldstadt, Jared; Piras, Gianfranco; Matthews, Stephen A.; Arbia, Giuseppe; Castro, Marcia C.; White, Kellee; Williams, David R.

2017-01-01

The racial/ethnic and income composition of neighborhoods often influences local amenities, including the potential spatial distribution of trees, which are important for population health and community wellbeing, particularly in urban areas. This ecological study used spatial analytical methods to assess the relationship between neighborhood socio-demographic characteristics (i.e. minority racial/ethnic composition and poverty) and tree density at the census tact level in Boston, Massachusetts (US). We examined spatial autocorrelation with the Global Moran’s I for all study variables and in the ordinary least squares (OLS) regression residuals as well as computed Spearman correlations non-adjusted and adjusted for spatial autocorrelation between socio-demographic characteristics and tree density. Next, we fit traditional regressions (i.e. OLS regression models) and spatial regressions (i.e. spatial simultaneous autoregressive models), as appropriate. We found significant positive spatial autocorrelation for all neighborhood socio-demographic characteristics (Global Moran’s I range from 0.24 to 0.86, all P=0.001), for tree density (Global Moran’s I=0.452, P=0.001), and in the OLS regression residuals (Global Moran’s I range from 0.32 to 0.38, all P<0.001). Therefore, we fit the spatial simultaneous autoregressive models. There was a negative correlation between neighborhood percent non-Hispanic Black and tree density (rS=−0.19; conventional P-value=0.016; spatially adjusted P-value=0.299) as well as a negative correlation between predominantly non-Hispanic Black (over 60% Black) neighborhoods and tree density (rS=−0.18; conventional P-value=0.019; spatially adjusted P-value=0.180). While the conventional OLS regression model found a marginally significant inverse relationship between Black neighborhoods and tree density, we found no statistically significant relationship between neighborhood socio-demographic composition and tree density in the spatial regression models. Methodologically, our study suggests the need to take into account spatial autocorrelation as findings/conclusions can change when the spatial autocorrelation is ignored. Substantively, our findings suggest no need for policy intervention vis-à-vis trees in Boston, though we hasten to add that replication studies, and more nuanced data on tree quality, age and diversity are needed. PMID:29354668

Dynamical behavior of the correlation between meteorological factors

NASA Astrophysics Data System (ADS)

You, Cheol-Hwan; Chang, Ki-Ho; Lee, Jun-Ho; Kim, Kyungsik

2017-12-01

We study the temporal and spatial variation characteristics of meteorological factors (temperature, humidity, and wind velocity) at a meteorological tower located on Bosung-gun of South Korea. We employ the detrended cross-correlation analysis (DCCA) method to extract the overall tendency of the hourly variation from data of meteorological factors. The relationships between meteorological factors are identified and quantified by using DCCA coefficients. From our results, we ascertain that the DCCA coefficient between temperature and humidity at time lag m = 24 has the smallest value at the height of 10 m of the measuring tower. Particularly, the DCCA coefficient between temperature and wind speed at time lag m = 24 has the largest value at a height of 10 m of the measuring tower

Changing response of the North Atlantic/European winter climate to the 11 year solar cycle

NASA Astrophysics Data System (ADS)

Ma, Hedi; Chen, Haishan; Gray, Lesley; Zhou, Liming; Li, Xing; Wang, Ruili; Zhu, Siguang

2018-03-01

Recent studies have presented conflicting results regarding the 11 year solar cycle (SC) influences on winter climate over the North Atlantic/European region. Analyses of only the most recent decades suggest a synchronized North Atlantic Oscillation (NAO)-like response pattern to the SC. Analyses of long-term climate data sets dating back to the late 19th century, however, suggest a mean sea level pressure (mslp) response that lags the SC by 2-4 years in the southern node of the NAO (i.e. Azores region). To understand the conflicting nature and cause of these time dependencies in the SC surface response, the present study employs a lead/lag multi-linear regression technique with a sliding window of 44 years over the period 1751-2016. Results confirm previous analyses, in which the average response for the whole time period features a statistically significant 2-4 year lagged mslp response centered over the Azores region. Overall, the lagged nature of Azores mslp response is generally consistent in time. Stronger and statistically significant SC signals tend to appear in the periods when the SC forcing amplitudes are relatively larger. Individual month analysis indicates the consistent lagged response in December-January-February average arises primarily from early winter months (i.e. December and January), which has been associated with ocean feedback processes that involve reinforcement by anomalies from the previous winter. Additional analysis suggests that the synchronous NAO-like response in recent decades arises primarily from late winter (February), possibly reflecting a result of strong internal noise.

Short-term association between road traffic noise and healthcare demand generated by Parkinson's disease in Madrid, Spain.

PubMed

Díaz, Julio; Martínez-Martín, Pablo; Rodríguez-Blázquez, Carmen; Vázquez, Blanca; Forjaz, Maria João; Ortiz, Cristina; Carmona, Rocío; Linares, Cristina

2017-03-23

To analyse whether there is a short-term association between road traffic noise in the city of Madrid and Parkinson's disease (PD)-related demand for healthcare. Time-series analysis (2008-2009) using variables of analysis linked to emergency and daily PD-related demand for healthcare (ICD-10: G20-G21), namely, PD-hospital admissions (HAs), PD-outpatient visits (OVs) and PD-emergency medical calls in Madrid. The noise pollution measurements used were Leqd, equivalent sound level for the daytime hours (from 8 a.m. to 10 p.m.), and Leqn, equivalent sound level for night time hours (from 10 p.m. to 8 a.m.) in dB(A). We controlled for temperature, pollution, trends and seasons, and used the Poisson regression model to calculate relative risk (RR). The association between Leqd and HAs was found to be linear. Leqd and Leqn at lag 0.1 and temperature at lags 1 and 5 were the only environmental variables associated with increased PD-related healthcare demand. The RR (lag 0) for Leqd and HA was 1.07 (1.04-1.09), the RR (lag 0) for Leqd and OV was 1.28 (1.12-1.45), and the RR (lags 0.1) for Leqn and emergency medical calls was 1.46 (1.06-2.01). The above results indicate that road traffic noise is a risk factor for PD exacerbation. Measures to reduce noise-exposure levels could result in a lower PD-related healthcare demand. Copyright © 2017 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

A Linkage of Recent Arctic Summer Sea Ice and Snowfall Variability of Japan

NASA Astrophysics Data System (ADS)

Iwamoto, K.; Honda, M.; Ukita, J.

2014-12-01

In spite of its mid-latitude location, Japan has a markedly high amount of snowfall, which owes much to the presence of cold air-break from Siberia and thus depends on the strength of the Siberian high and the Aleutian low. With this background this study examines the relationship between interannual variability and spatial patterns of snowfall in Japan with large-scale atmospheric and sea ice variations. The lag regression map of the winter snowfall in Japan on the time series of the Arctic SIE from the preceding summer shows a seesaw pattern in the snowfall, suggesting an Arctic teleconnection to regional weather. From the EOF analyses conducted on the snowfall distribution in Japan, we identify two modes with physical significance. The NH SIC and SLP regressed on PC1 show a sea ice reduction in the Barents and Kara Seas and anomalous strength of the Siberia high as discussed in Honda et al. (2009) and other studies, which support the above notion that the snowfall variability of Japan is influenced by Arctic sea ice conditions. Another mode is related to the AO/NAO and the hemispheric scale double sea-ice seesaw centered over the sub-Arctic region: one between the Labrador and Nordic Seas in the Atlantic and the other between the Okhotsk and Bering Seas from the Pacific as discussed in Ukita et al. (2007). Together, observations point to a significant role of the sea-ice in determining mid-latitude regional climate and weather patterns.

Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques.

PubMed

Faisal, Kamil; Shaker, Ahmed

2017-03-07

Urban Environmental Quality (UEQ) can be treated as a generic indicator that objectively represents the physical and socio-economic condition of the urban and built environment. The value of UEQ illustrates a sense of satisfaction to its population through assessing different environmental, urban and socio-economic parameters. This paper elucidates the use of the Geographic Information System (GIS), Principal Component Analysis (PCA) and Geographically-Weighted Regression (GWR) techniques to integrate various parameters and estimate the UEQ of two major cities in Ontario, Canada. Remote sensing, GIS and census data were first obtained to derive various environmental, urban and socio-economic parameters. The aforementioned techniques were used to integrate all of these environmental, urban and socio-economic parameters. Three key indicators, including family income, higher level of education and land value, were used as a reference to validate the outcomes derived from the integration techniques. The results were evaluated by assessing the relationship between the extracted UEQ results and the reference layers. Initial findings showed that the GWR with the spatial lag model represents an improved precision and accuracy by up to 20% with respect to those derived by using GIS overlay and PCA techniques for the City of Toronto and the City of Ottawa. The findings of the research can help the authorities and decision makers to understand the empirical relationships among environmental factors, urban morphology and real estate and decide for more environmental justice.

Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques

PubMed Central

Faisal, Kamil; Shaker, Ahmed

2017-01-01

Urban Environmental Quality (UEQ) can be treated as a generic indicator that objectively represents the physical and socio-economic condition of the urban and built environment. The value of UEQ illustrates a sense of satisfaction to its population through assessing different environmental, urban and socio-economic parameters. This paper elucidates the use of the Geographic Information System (GIS), Principal Component Analysis (PCA) and Geographically-Weighted Regression (GWR) techniques to integrate various parameters and estimate the UEQ of two major cities in Ontario, Canada. Remote sensing, GIS and census data were first obtained to derive various environmental, urban and socio-economic parameters. The aforementioned techniques were used to integrate all of these environmental, urban and socio-economic parameters. Three key indicators, including family income, higher level of education and land value, were used as a reference to validate the outcomes derived from the integration techniques. The results were evaluated by assessing the relationship between the extracted UEQ results and the reference layers. Initial findings showed that the GWR with the spatial lag model represents an improved precision and accuracy by up to 20% with respect to those derived by using GIS overlay and PCA techniques for the City of Toronto and the City of Ottawa. The findings of the research can help the authorities and decision makers to understand the empirical relationships among environmental factors, urban morphology and real estate and decide for more environmental justice. PMID:28272334

Bayesian hierarchical modelling of continuous non‐negative longitudinal data with a spike at zero: An application to a study of birds visiting gardens in winter

PubMed Central

Buckland, Stephen T.; King, Ruth; Toms, Mike P.

2015-01-01

The development of methods for dealing with continuous data with a spike at zero has lagged behind those for overdispersed or zero‐inflated count data. We consider longitudinal ecological data corresponding to an annual average of 26 weekly maximum counts of birds, and are hence effectively continuous, bounded below by zero but also with a discrete mass at zero. We develop a Bayesian hierarchical Tweedie regression model that can directly accommodate the excess number of zeros common to this type of data, whilst accounting for both spatial and temporal correlation. Implementation of the model is conducted in a Markov chain Monte Carlo (MCMC) framework, using reversible jump MCMC to explore uncertainty across both parameter and model spaces. This regression modelling framework is very flexible and removes the need to make strong assumptions about mean‐variance relationships a priori. It can also directly account for the spike at zero, whilst being easily applicable to other types of data and other model formulations. Whilst a correlative study such as this cannot prove causation, our results suggest that an increase in an avian predator may have led to an overall decrease in the number of one of its prey species visiting garden feeding stations in the United Kingdom. This may reflect a change in behaviour of house sparrows to avoid feeding stations frequented by sparrowhawks, or a reduction in house sparrow population size as a result of sparrowhawk increase. PMID:25737026

Meteorological Factors for Dengue Fever Control and Prevention in South China.

PubMed

Gu, Haogao; Leung, Ross Ka-Kit; Jing, Qinlong; Zhang, Wangjian; Yang, Zhicong; Lu, Jiahai; Hao, Yuantao; Zhang, Dingmei

2016-08-31

Dengue fever (DF) is endemic in Guangzhou and has been circulating for decades, causing significant economic loss. DF prevention mainly relies on mosquito control and change in lifestyle. However, alert fatigue may partially limit the success of these countermeasures. This study investigated the delayed effect of meteorological factors, as well as the relationships between five climatic variables and the risk for DF by boosted regression trees (BRT) over the period of 2005-2011, to determine the best timing and strategy for adapting such preventive measures. The most important meteorological factor was daily average temperature. We used BRT to investigate the lagged relationship between dengue clinical burden and climatic variables, with the 58 and 62 day lag models attaining the largest area under the curve. The climatic factors presented similar patterns between these two lag models, which can be used as references for DF prevention in the early stage. Our results facilitate the development of the Mosquito Breeding Risk Index for early warning systems. The availability of meteorological data and modeling methods enables the extension of the application to other vector-borne diseases endemic in tropical and subtropical countries.

Ventricular Repolarization Adaptation to Abrupt Changes in Heart Rate after Microgravity Simulation by 5-Day Head-Down Bed Rest

NASA Astrophysics Data System (ADS)

Bolea, J.; Almeida, R.; Pueyo, E.; Laguna, P.; Caiani, E. G.

2013-02-01

Microgravity exposure for long periods of time leads to body deconditioning and it increases the risk of experiencing life-threatening arrhythmias. The study of ventricular repolarization dependence on heart rate has been used to stratify patients according to their arrhythmic risk. The QTp adaptation to HR changes is characterized by M90 (90% of the adaptation). The QTp=HR , after compensation for the adaptation lag, is modeled using a set of regression functions (a , kind of slope of relationship). Subjects with lower orthostatic tolerance time showed a non significant decrease in the adaptation lag (M90 from 148 to 108 beats), which may be due to an extra deconditioning in the sympathovagal response. Nevertheless an increase in the QTp=HR adaptation lag (M90 from 108 to 117 beats with a p = 0.06) and a significant reduction in the slope (a from 0.53 to 0.35 with a p < 0.005), which in previous studies have been correlated with an increased arrhytimic risk, were observed for subjects with higher orthostatic tolerance time.

Spatial Patterns of Heat-Related Cardiovascular Mortality in the Czech Republic

PubMed Central

Urban, Aleš; Burkart, Katrin; Kyselý, Jan; Schuster, Christian; Plavcová, Eva; Hanzlíková, Hana; Štěpánek, Petr; Lakes, Tobia

2016-01-01

The study examines spatial patterns of effects of high temperature extremes on cardiovascular mortality in the Czech Republic at a district level during 1994–2009. Daily baseline mortality for each district was determined using a single location-stratified generalized additive model. Mean relative deviations of mortality from the baseline were calculated on days exceeding the 90th percentile of mean daily temperature in summer, and they were correlated with selected demographic, socioeconomic, and physical-environmental variables for the districts. Groups of districts with similar characteristics were identified according to socioeconomic status and urbanization level in order to provide a more general picture than possible on the district level. We evaluated lagged patterns of excess mortality after hot spell occurrences in: (i) urban areas vs. predominantly rural areas; and (ii) regions with different overall socioeconomic level. Our findings suggest that climatic conditions, altitude, and urbanization generally affect the spatial distribution of districts with the highest excess cardiovascular mortality, while socioeconomic status did not show a significant effect in the analysis across the Czech Republic as a whole. Only within deprived populations, socioeconomic status played a relevant role as well. After taking into account lagged effects of temperature on excess mortality, we found that the effect of hot spells was significant in highly urbanized regions, while most excess deaths in rural districts may be attributed to harvesting effects. PMID:26959044

Spatial dynamics of two oriental fruit fly (Diptera: Tephritidae) parasitoids, Fopius arisanus and Diachasmimorpha longicaudata (Hymenoptera: Braconidae), in a Guava orchard in Hawaii.

PubMed

Vargas, Roger I; Stark, John D; Banks, John; Leblanc, Luc; Manoukis, Nicholas C; Peck, Steven

2013-10-01

We examined spatial patterns of both sexes of oriental fruit fly, Bactrocera dorsalis (Hendel), and its two most abundant parasitoids, Fopius arisanus (Sonan) and Diachasmimorpha longicaudata (Ashmead) in a commercial guava (Psidium guajava L.) orchard. Oriental fruit fly spatial patterns were initially random, but became highly aggregated with host fruit ripening and the subsequent colonization of, first, F. arisanus (egg-pupal parasitoid) and, second, D. longicaudata (larval-pupal parasitoid). There was a significant positive relationship between populations of oriental fruit fly and F. arisanus during each of the F. arisanus increases, a pattern not exhibited between oriental fruit fly and D. longicaudata. Generally, highest total numbers of males and females (oriental fruit fly, F. arisanus, and D. longicaudata) occurred on or about the same date. There was a significant positive correlation between male and female populations of all three species; we measured a lag of 2-4 wk between increases of female F. arisanus and conspecific males. There was a similar trend in one of the two years for the second most abundant species, D. longicaudata, but no sign of a time lag between the sexes for oriental fruit fly. Spatially, we found a significant positive relationship between numbers of F. arisanus in blocks and the average number in adjoining blocks. We did not find the same effect for oriental fruit fly and D. longicaudata, possibly a result of lower overall numbers of the latter two species or less movement of F. arisanus within the field.

Climate variability and increase in intensity and magnitude of dengue incidence in Singapore.

PubMed

Hii, Yien Ling; Rocklöv, Joacim; Ng, Nawi; Tang, Choon Siang; Pang, Fung Yin; Sauerborn, Rainer

2009-11-11

Dengue is currently a major public health burden in Asia Pacific Region. This study aims to establish an association between dengue incidence, mean temperature and precipitation, and further discuss how weather predictors influence the increase in intensity and magnitude of dengue in Singapore during the period 2000-2007. Weekly dengue incidence data, daily mean temperature and precipitation and the midyear population data in Singapore during 2000-2007 were retrieved and analysed. We employed a time series Poisson regression model including time factors such as time trends, lagged terms of weather predictors, considered autocorrelation, and accounted for changes in population size by offsetting. The weekly mean temperature and cumulative precipitation were statistically significant related to the increases of dengue incidence in Singapore. Our findings showed that dengue incidence increased linearly at time lag of 5-16 and 5-20 weeks succeeding elevated temperature and precipitation, respectively. However, negative association occurred at lag week 17-20 with low weekly mean temperature as well as lag week 1-4 and 17-20 with low cumulative precipitation. As Singapore experienced higher weekly mean temperature and cumulative precipitation in the years 2004-2007, our results signified hazardous impacts of climate factors on the increase in intensity and magnitude of dengue cases. The ongoing global climate change might potentially increase the burden of dengue fever infection in near future.

Relations between Precipitation and Shallow Groundwater in Illinois.

NASA Astrophysics Data System (ADS)

Changnon, Stanley A.; Huff, Floyd A.; Hsu, Chin-Fei

1988-12-01

The statistical relationships between monthly precipitation (P) and shallow groundwater levels (GW) in 20 wells scattered across Illinois with data for 1960-84 were defined using autoregressive integrated moving average (ARIMA) modeling. A lag of 1 month between P to GW was the strongest temporal relationship found across Illinois, followed by no (0) lag in the northern two-thirds of Illinois where mollisols predominate, and a lag of 2 months in the alfisols of southern Illinois. Spatial comparison of the 20 P-GW correlations with several physical conditions (aquifer types, soils, and physiography) revealed that the parent soil materials of outwash alluvium, glacial till, thick loess (2.1 m), and thin loess (>2.1) best defined regional relationships for drought assessment.Equations developed from ARTMA using 1960-79 data for each region were used to estimate GW levels during the 1980-81 drought, and estimates averaged between 25 to 45 cm of actual levels. These estimates are considered adequate to allow a useful assessment of drought onset, severity, and termination in other parts of the state. The techniques and equations should be transferrable to regions of comparable soils and climate.

[The gravity field of the Earth: geophysical factor of gerontology (The Vorobeichikov effect)].

PubMed

Shapovalov, S N

2016-01-01

The results of investigations of the growth in vitro of Escherichia coli M-17, obtained in the processing of V. M. Vorobeichikov observational data during the movement of the scientific expedition ship «Akademik Fedorov» from St. Petersburg to Antarctica and back, in the period from 13.11.2002 on 26.05.2003 (48th Russian Antarctic expedition). The findings based on the growth in vitro of Escherichia coli from changes in geographical location on a planetary scale, that doesn't eliminate the dependence of other species of microorganisms from the spatial position in the gravity field of the Earth. It is established that the duration of the lag phase of Escherichia coli in the Equatorial zone close to its duration in the high-latitude zone and Antarctic, however, the duration of the lag phase at the equator and the Antarctic corresponds to the time of the lag phase at the time of the Central phase of the lunar Eclipse. The conclusion about high sensitivity in vitro of Escherichia coli to the field of gravity of the Earth, and to syzigium events.

A Temporal Association between Accumulated Petrol (Gasoline) Lead Emissions and Motor Neuron Disease in Australia.

PubMed

Laidlaw, Mark A S; Rowe, Dominic B; Ball, Andrew S; Mielke, Howard W

2015-12-19

The age standardised death rate from motor neuron disease (MND) has increased from 1.29 to 2.74 per 100,000, an increase of 112.4% between 1959 and 2013. It is clear that genetics could not have played a causal role in the increased rate of MND deaths over such a short time span. We postulate that environmental factors are responsible for this rate increase. We focus on lead additives in Australian petrol as a possible contributing environmental factor. The associations between historical petrol lead emissions and MND death trends in Australia between 1962 and 2013 were examined using linear regressions. Regression results indicate best fit correlations between a 20 year lag of petrol lead emissions and age-standardised female death rate (R² = 0.86, p = 4.88 × 10(-23)), male age standardised death rate (R² = 0.86, p = 9.4 × 10(-23)) and percent all cause death attributed to MND (R² = 0.98, p = 2.6 × 10(-44)). Legacy petrol lead emissions are associated with increased MND death trends in Australia. Further examination of the 20 year lag between exposure to petrol lead and the onset of MND is warranted.

Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression.

PubMed

Chen, Yanguang

2016-01-01

In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson's statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran's index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China's regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test.

[Fine particulate matter estimated by mathematical model and hospitalizations for pneumonia and asthma in children].

PubMed

César, Ana Cristina Gobbo; Nascimento, Luiz Fernando Costa; Mantovani, Katia Cristina Cota; Pompeo Vieira, Luciana Cristina

2016-01-01

To estimate the association between exposure to fine particulate matter with an aerodynamic diameter <2.5 microns (PM2.5) and hospitalizations for pneumonia and asthma in children. An ecological study of time series was performed, with daily indicators of hospitalization for pneumonia and asthma in children up to 10 years of age, living in Taubaté (SP) and estimated concentrations of PM2.5, between August 2011 and July 2012. A generalized additive model of Poisson regression was used to estimate the relative risk, with lag zero up to five days after exposure; the single pollutant model was adjusted by the apparent temperature, as defined from the temperature and relative air humidity, seasonality and weekday. The values of the relative risks for hospitalization for pneumonia and asthma were significant for lag 0 (RR=1.051, 95%CI; 1.016 to 1.088); lag 2 (RR=1.066, 95%CI: 1.023 to 1.113); lag 3 (RR=1.053, 95%CI: 1.015 to 1.092); lag 4 (RR=1.043, 95%CI: 1.004 to 1.088) and lag 5 (RR=1.061, 95%CI: 1.018 to 1.106). The increase of 5mcg/m(3) in PM2.5 contributes to increase the relative risk for hospitalization from 20.3 to 38.4 percentage points; however, the reduction of 5μg/m(3) in PM2.5 concentration results in 38 fewer hospital admissions. Exposure to PM2.5 was associated with hospitalizations for pneumonia and asthma in children younger than 10 years of age, showing the role of fine particulate matter in child health and providing subsidies for the implementation of preventive measures to decrease these outcomes. Copyright © 2015 Sociedade de Pediatria de São Paulo. Publicado por Elsevier Editora Ltda. All rights reserved.

Lagged correlations between the NAO and the 11-year solar cycle: forced response or internal variability?

NASA Astrophysics Data System (ADS)

Oehrlein, J.; Chiodo, G.; Polvani, L. M.; Smith, A. K.

2017-12-01

Recently, the North Atlantic Oscillation has been suggested to respond to the 11-year solar cycle with a lag of a few years. The solar/NAO relationship provides a potential pathway for solar activity to modulate surface climate. However, a short observational record paired with the strong internal variability of the NAO raises questions about the robustness of the claimed solar/NAO relationship. For the first time, we investigate the robustness of the solar/NAO signal in four different reanalysis data sets and long integrations from an ocean-coupled chemistry-climate model forced with the 11-year solar cycle. The signal appears to be robust in the different reanalysis datasets. We also show, for the first time, that many features of the observed signal, such as amplitude, spatial pattern, and lag of 2/3 years, can be accurately reproduced in our model simulations. However, in both the reanalysis and model simulations, we find that this signal is non-stationary. A lagged NAO/solar signal can also be reproduced in two sets of model integrations without the 11-year solar cycle. This suggests that the correlation found in observational data could be the result of internal decadal variability in the NAO and not a response to the solar cycle. This has wide implications towards the interpretation of solar signals in observational data.

Statistical scaling of pore-scale Lagrangian velocities in natural porous media.

PubMed

Siena, M; Guadagnini, A; Riva, M; Bijeljic, B; Pereira Nunes, J P; Blunt, M J

2014-08-01

We investigate the scaling behavior of sample statistics of pore-scale Lagrangian velocities in two different rock samples, Bentheimer sandstone and Estaillades limestone. The samples are imaged using x-ray computer tomography with micron-scale resolution. The scaling analysis relies on the study of the way qth-order sample structure functions (statistical moments of order q of absolute increments) of Lagrangian velocities depend on separation distances, or lags, traveled along the mean flow direction. In the sandstone block, sample structure functions of all orders exhibit a power-law scaling within a clearly identifiable intermediate range of lags. Sample structure functions associated with the limestone block display two diverse power-law regimes, which we infer to be related to two overlapping spatially correlated structures. In both rocks and for all orders q, we observe linear relationships between logarithmic structure functions of successive orders at all lags (a phenomenon that is typically known as extended power scaling, or extended self-similarity). The scaling behavior of Lagrangian velocities is compared with the one exhibited by porosity and specific surface area, which constitute two key pore-scale geometric observables. The statistical scaling of the local velocity field reflects the behavior of these geometric observables, with the occurrence of power-law-scaling regimes within the same range of lags for sample structure functions of Lagrangian velocity, porosity, and specific surface area.

Schistosomiasis Breeding Environment Situation Analysis in Dongting Lake Area

NASA Astrophysics Data System (ADS)

Li, Chuanrong; Jia, Yuanyuan; Ma, Lingling; Liu, Zhaoyan; Qian, Yonggang

2013-01-01

Monitoring environmental characteristics, such as vegetation, soil moisture et al., of Oncomelania hupensis (O. hupensis)’ spatial/temporal distribution is of vital importance to the schistosomiasis prevention and control. In this study, the relationship between environmental factors derived from remotely sensed data and the density of O. hupensis was analyzed by a multiple linear regression model. Secondly, spatial analysis of the regression residual was investigated by the semi-variogram method. Thirdly, spatial analysis of the regression residual and the multiple linear regression model were both employed to estimate the spatial variation of O. hupensis density. Finally, the approach was used to monitor and predict the spatial and temporal variations of oncomelania of Dongting Lake region, China. And the areas of potential O. hupensis habitats were predicted and the influence of Three Gorges Dam (TGB)project on the density of O. hupensis was analyzed.

Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes

NASA Astrophysics Data System (ADS)

Mekanik, F.; Imteaz, M. A.; Gato-Trinidad, S.; Elmahdi, A.

2013-10-01

In this study, the application of Artificial Neural Networks (ANN) and Multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO-IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg-Marquardt algorithm. Both MR and ANN modelling were assessed statistically using mean square error (MSE), mean absolute error (MAE), Pearson correlation (r) and Willmott index of agreement (d). The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed very poor generalisation ability for east Victoria with correlation coefficients of -0.99 to -0.90 compared to ANN with correlation coefficients of 0.42-0.93; ANN models also showed better generalisation ability for central and west Victoria with correlation coefficients of 0.68-0.85 and 0.58-0.97 respectively. The ability of multiple regression models to forecast out-of-sample sets is compatible with ANN for Daylesford in central Victoria and Kaniva in west Victoria (r = 0.92 and 0.67 respectively). The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggest the potential of ANN over MR models for rainfall forecasting using large scale climate modes.

Relation between germination and mycelium growth of individual fungal spores.

PubMed

Gougouli, Maria; Koutsoumanis, Konstantinos P

2013-02-15

The relation between germination time and lag time of mycelium growth of individual spores was studied by combining microscopic and macroscopic techniques. The radial growth of a large number (100-200) of Penicillium expansum and Aspergillus niger mycelia originating from single spores was monitored macroscopically at isothermal conditions ranging from 0 to 30°C and 10 to 41.5°C, respectively. The radial growth curve for each mycelium was fitted to a linear model for the estimation of mycelium lag time. The results showed that the lag time varied significantly among single spores. The cumulative frequency distributions of the lag times were fitted to the modified Gompertz model and compared with the respective distributions for the germination time, which were obtained microscopically. The distributions of the measured mycelium lag time were found to be similar to the germination time distributions under the same conditions but shifted in time with the lag times showing a significant delay compared to germination times. A numerical comparison was also performed based on the distribution parameters λ(m) and λ(g), which indicate the time required from the spores to start the germination process and the completion of the lag phase, respectively. The relative differences %(λ(m)-λ(g))/λ(m) were not found to be significantly affected by temperatures tested with mean values of 72.5±5.1 and 60.7±2.1 for P. expansum for A. niger, respectively. In order to investigate the source of the above difference, a time-lapse microscopy method was developed providing videos with the behavior of single fungal spore from germination until mycelium formation. The distances of the apexes of the first germ tubes that emerged from the swollen spore were measured in each frame of the videos and these data were expressed as a function of time. The results showed that in the early hyphal development, the measured radii appear to increase exponentially, until a certain time, where growth becomes linear. The two phases of hyphal development can explain the difference between germination and lag time. Since the lag time is estimated from the extrapolation of the regression line of the linear part of the graph only, its value is significantly higher than the germination time, t(G). The relation of germination and lag time was further investigated by comparing their temperature dependence using the Cardinal Model with Inflection. The estimated values of the cardinal parameters (T(min), T(opt), and T(max)) for 1/λ(g) were found to be very close to the respective values for 1/λ(m), indicating similar temperature dependence between them. Copyright © 2012 Elsevier B.V. All rights reserved.

Is short-term exposure to ambient fine particles associated with measles incidence in China? A multi-city study.

PubMed

Chen, Gongbo; Zhang, Wenyi; Li, Shanshan; Williams, Gail; Liu, Chao; Morgan, Geoffrey G; Jaakkola, Jouni J K; Guo, Yuming

2017-07-01

China's rapid economic development has resulted in severe particulate matter (PM) air pollution and the control and prevention of infectious disease is an ongoing priority. This study examined the relationships between short-term exposure to ambient particles with aerodynamic diameter ≤2.5µm (PM 2.5 ) and measles incidence in China. Data on daily numbers of new measles cases and concentrations of ambient PM 2.5 were collected from 21 cities in China during Oct 2013 and Dec 2014. Poisson regression was used to examine city-specific associations of PM 2.5 and measles, with a constrained distributed lag model, after adjusting for seasonality, day of the week, and weather conditions. Then, the effects at the national scale were pooled with a random-effect meta-analysis. A 10µg/m 3 increase in PM 2.5 at lag 1day, lag 2day and lag 3day was significantly associated with increased measles incidence [relative risk (RR) and 95% confidence interval (CI) were 1.010 (1.003, 1.018), 1.010 (1.003, 1.016) and 1.006 (1.000, 1.012), respectively]. The cumulative relative risk of measles associated with PM 2.5 at lag 1-3 days was 1.029 (95% CI: 1.010, 1.048). Stratified analyses by meteorological factors showed that the PM 2.5 and measles associations were stronger on days with high temperature, low humidity, and high wind speed. We provide new evidence that measles incidence is associated with exposure to ambient PM 2.5 in China. Effective policies to reduce air pollution may also reduce measles incidence. Copyright © 2017 Elsevier Inc. All rights reserved.

Cardiovascular and Cerebrovascular Emergency Department Visits Associated With Wildfire Smoke Exposure in California in 2015.

PubMed

Wettstein, Zachary S; Hoshiko, Sumi; Fahimi, Jahan; Harrison, Robert J; Cascio, Wayne E; Rappold, Ana G

2018-04-11

Wildfire smoke is known to exacerbate respiratory conditions; however, evidence for cardiovascular and cerebrovascular events has been inconsistent, despite biological plausibility. A population-based epidemiologic analysis was conducted for daily cardiovascular and cerebrovascular emergency department (ED) visits and wildfire smoke exposure in 2015 among adults in 8 California air basins. A quasi-Poisson regression model was used for zip code-level counts of ED visits, adjusting for heat index, day of week, seasonality, and population. Satellite-imaged smoke plumes were classified as light, medium, or dense based on model-estimated concentrations of fine particulate matter. Relative risk was determined for smoky days for lag days 0 to 4. Rates of ED visits by age- and sex-stratified groups were also examined. Rates of all-cause cardiovascular ED visits were elevated across all lags, with the greatest increase on dense smoke days and among those aged ≥65 years at lag 0 (relative risk 1.15, 95% confidence interval [1.09, 1.22]). All-cause cerebrovascular visits were associated with smoke, especially among those 65 years and older, (1.22 [1.00, 1.49], dense smoke, lag 1). Respiratory conditions were also increased, as anticipated (1.18 [1.08, 1.28], adults >65 years, dense smoke, lag 1). No association was found for the control condition, acute appendicitis. Elevated risks for individual diagnoses included myocardial infarction, ischemic heart disease, heart failure, dysrhythmia, pulmonary embolism, ischemic stroke, and transient ischemic attack. Analysis of an extensive wildfire season found smoke exposure to be associated with cardiovascular and cerebrovascular ED visits for all adults, particularly for those over aged 65 years. © 2018 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Is Exposure to Income Inequality a Public Health Concern? Lagged Effects of Income Inequality on Individual and Population Health

PubMed Central

Mellor, Jennifer M; Milyo, Jeffrey

2003-01-01

Objective To examine the health consequences of exposure to income inequality. Data Sources Secondary analysis employing data from several publicly available sources. Measures of individual health status and other individual characteristics are obtained from the March Current Population Survey (CPS). State-level income inequality is measured by the Gini coefficient based on family income, as reported by the U.S. Census Bureau and Al-Samarrie and Miller (1967). State-level mortality rates are from the Vital Statistics of the United States; other state-level characteristics are from U.S. census data as reported in the Statistical Abstract of the United States. Study Design We examine the effects of state-level income inequality lagged from 5 to 29 years on individual health by estimating probit models of poor/fair health status for samples of adults aged 25–74 in the 1995 through 1999 March CPS. We control for several individual characteristics, including educational attainment and household income, as well as regional fixed effects. We use multivariate regression to estimate the effects of income inequality lagged 10 and 20 years on state-level mortality rates for 1990, 1980, 1970, and 1960. Principal Findings Lagged income inequality is not significantly associated with individual health status after controlling for regional fixed effects. Lagged income inequality is not associated with all cause mortality, but associated with reduced mortality from cardiovascular disease and malignant neoplasms, after controlling for state fixed-effects. Conclusions In contrast to previous studies that fail to control for regional variations in health outcomes, we find little support for the contention that exposure to income inequality is detrimental to either individual or population health. PMID:12650385

Standard operating procedures for antibiotic therapy and the occurrence of acute kidney injury: a prospective, clinical, non-interventional, observational study.

PubMed

Nachtigall, Irit; Tafelski, Sascha; Günzel, Karsten; Uhrig, Alexander; Powollik, Robert; Tamarkin, Andrey; Wernecke, Klaus D; Spies, Claudia

2014-06-12

Acute kidney injury (AKI) occurs in 7% of hospitalized and 66% of Intensive Care Unit (ICU) patients. It increases mortality, hospital length of stay, and costs. The aim of this study was to investigate, whether there is an association between adherence to guidelines (standard operating procedures (SOP)) for potentially nephrotoxic antibiotics and the occurrence of AKI. This study was carried out as a prospective, clinical, non-interventional, observational study. Data collection was performed over a total of 170 days in three ICUs at Charité - Universitaetsmedizin Berlin. A total of 675 patients were included; 163 of these had therapy with vancomycin, gentamicin, or tobramycin; were >18 years; and treated in the ICU for >24 hours. Patients with an adherence to SOP >70% were classified into the high adherence group (HAG) and patients with an adherence of <70% into the low adherence group (LAG). AKI was defined according to RIFLE criteria. Adherence to SOPs was evaluated by retrospective expert audit. Development of AKI was compared between groups with exact Chi2-test and multivariate logistic regression analysis (two-sided P <0.05). LAG consisted of 75 patients (46%) versus 88 HAG patients (54%). AKI occurred significantly more often in LAG with 36% versus 21% in HAG (P = 0.035). Basic characteristics were comparable, except an increased rate of soft tissue infections in LAG. Multivariate analysis revealed an odds ratio of 2.5-fold for LAG to develop AKI compared with HAG (95% confidence interval 1.195 to 5.124, P = 0.039). Low adherence to SOPs for potentially nephrotoxic antibiotics was associated with a higher occurrence of AKI. Current Controlled Trials ISRCTN54598675. Registered 17 August 2007.

Standard operating procedures for antibiotic therapy and the occurrence of acute kidney injury: a prospective, clinical, non-interventional, observational study

PubMed Central

2014-01-01

Introduction Acute kidney injury (AKI) occurs in 7% of hospitalized and 66% of Intensive Care Unit (ICU) patients. It increases mortality, hospital length of stay, and costs. The aim of this study was to investigate, whether there is an association between adherence to guidelines (standard operating procedures (SOP)) for potentially nephrotoxic antibiotics and the occurrence of AKI. Methods This study was carried out as a prospective, clinical, non-interventional, observational study. Data collection was performed over a total of 170 days in three ICUs at Charité – Universitaetsmedizin Berlin. A total of 675 patients were included; 163 of these had therapy with vancomycin, gentamicin, or tobramycin; were >18 years; and treated in the ICU for >24 hours. Patients with an adherence to SOP >70% were classified into the high adherence group (HAG) and patients with an adherence of <70% into the low adherence group (LAG). AKI was defined according to RIFLE criteria. Adherence to SOPs was evaluated by retrospective expert audit. Development of AKI was compared between groups with exact Chi2-test and multivariate logistic regression analysis (two-sided P <0.05). Results LAG consisted of 75 patients (46%) versus 88 HAG patients (54%). AKI occurred significantly more often in LAG with 36% versus 21% in HAG (P = 0.035). Basic characteristics were comparable, except an increased rate of soft tissue infections in LAG. Multivariate analysis revealed an odds ratio of 2.5-fold for LAG to develop AKI compared with HAG (95% confidence interval 1.195 to 5.124, P = 0.039). Conclusion Low adherence to SOPs for potentially nephrotoxic antibiotics was associated with a higher occurrence of AKI. Trial registration Current Controlled Trials ISRCTN54598675. Registered 17 August 2007. PMID:24923469

Comparison of short-term associations with meteorological variables between COPD and pneumonia hospitalization among the elderly in Hong Kong—a time-series study

NASA Astrophysics Data System (ADS)

Lam, Holly Ching-yu; Chan, Emily Ying-yang; Goggins, William Bernard

2018-05-01

Pneumonia and chronic obstructive pulmonary diseases (COPD) are the commonest causes of respiratory hospitalization among older adults. Both diseases have been reported to be associated with ambient temperature, but the associations have not been compared between the diseases. Their associations with other meteorological variables have also not been well studied. This study aimed to evaluate the associations between meteorological variables, pneumonia, and COPD hospitalization among adults over 60 and to compare these associations between the diseases. Daily cause-specific hospitalization counts in Hong Kong during 2004-2011 were regressed on daily meteorological variables using distributed lag nonlinear models. Associations were compared between diseases by ratio of relative risks. Analyses were stratified by season and age group (60-74 vs. ≥ 75). In hot season, high temperature (> 28 °C) and high relative humidity (> 82%) were statistically significantly associated with more pneumonia in lagged 0-2 and lagged 0-10 days, respectively. Pneumonia hospitalizations among the elderly (≥ 75) also increased with high solar radiation and high wind speed. During the cold season, consistent hockey-stick associations with temperature and relative humidity were found for both admissions and both age groups. The minimum morbidity temperature and relative humidity were at about 21-22 °C and 82%. The lagged effects of low temperature were comparable for both diseases (lagged 0-20 days). The low-temperature-admissions associations with COPD were stronger and were strongest among the elderly. This study found elevated pneumonia and COPD admissions risks among adults ≥ 60 during periods of extreme weather conditions, and the associations varied by season and age group. Vulnerable groups should be advised to avoid exposures, such as staying indoor and maintaining satisfactory indoor conditions, to minimize risks.

Geographical variations and contextual effects on age of initiation of sexual intercourse among women in Nigeria: a multilevel and spatial analysis.

PubMed

Uthman, Olalekan A

2008-05-30

The age of initiation of sexual intercourse is an increasingly important issue to study given that sexually active young women are at risk of multiple outcomes including early pregnancies, vesico-vaginal fistula, and sexually transmitted infections. Much research has focused on the demographic, familial, and social factors associated with sexual initiation and reasons adolescents begin having consensual intercourse. Less is known, however, about the geographical and contextual factors associated with age of initiation of sexual intercourse. Therefore, the purpose of this study was to examine the extent of regional and state disparities in age of initiation of sexual intercourse and to examine individual- and community-level predictors of early sexual debut. Multilevel logistic regression models were applied to data on 5531 ever or currently married women who had participated in 2003 Nigeria Demographic and Health Survey. Coital debut at 15 years or younger was used to define early sexual debut. Exploratory spatial data analysis methods were used to study geographic variation in age at first sexual intercourse. The median age at first sexual intercourse for all women included in the study was 15 years (range; 14 - 19). North West and North East had the highest proportion of women who had reported early sexual debut (61% - 78%). The spatial distribution of age of initiation of sexual intercourse was nonrandom and clustered with a Moran's I = 0.635 (p = .001). There was significant positive spatial relationship between median age of marriage and spatial lag of median age of sexual debut (Bivariate Moran's I = 0.646, (p = .001). After adjusting for both individual-level and contextual factors, the probability of starting sex at an earlier age was associated with respondents' current age, education attainment, ethnicity, region, and community median age of marriage. The study found that individual-level and community contextual characteristics were independently associated with early sexual debut, suggesting that interventions to reduce adolescent high-risk sexual behaviour should focus on high-risk places as well as high-risk groups of people.

Atmospheric precursors of and response to anomalous Arctic sea ice in CMIP5 models

NASA Astrophysics Data System (ADS)

Kelleher, Michael; Screen, James

2018-01-01

This study examines pre-industrial control simulations from CMIP5 climate models in an effort to better understand the complex relationships between Arctic sea ice and the stratosphere, and between Arctic sea ice and cold winter temperatures over Eurasia. We present normalized regressions of Arctic sea-ice area against several atmospheric variables at extended lead and lag times. Statistically significant regressions are found at leads and lags, suggesting both atmospheric precursors of, and responses to, low sea ice; but generally, the regressions are stronger when the atmosphere leads sea ice, including a weaker polar stratospheric vortex indicated by positive polar cap height anomalies. Significant positive midlatitude eddy heat flux anomalies are also found to precede low sea ice. We argue that low sea ice and raised polar cap height are both a response to this enhanced midlatitude eddy heat flux. The so-called "warm Arctic, cold continents" anomaly pattern is present one to two months before low sea ice, but is absent in the months following low sea ice, suggesting that the Eurasian cooling and low sea ice are driven by similar processes. Lastly, our results suggest a dependence on the geographic region of low sea ice, with low Barents-Kara Sea ice correlated with a weakened polar stratospheric vortex, whilst low Sea of Okhotsk ice is correlated with a strengthened polar vortex. Overall, the results support a notion that the sea ice, polar stratospheric vortex and Eurasian surface temperatures collectively respond to large-scale changes in tropospheric circulation.

Dynamic linear models using the Kalman filter for early detection and early warning of malaria outbreaks

NASA Astrophysics Data System (ADS)

Merkord, C. L.; Liu, Y.; DeVos, M.; Wimberly, M. C.

2015-12-01

Malaria early detection and early warning systems are important tools for public health decision makers in regions where malaria transmission is seasonal and varies from year to year with fluctuations in rainfall and temperature. Here we present a new data-driven dynamic linear model based on the Kalman filter with time-varying coefficients that are used to identify malaria outbreaks as they occur (early detection) and predict the location and timing of future outbreaks (early warning). We fit linear models of malaria incidence with trend and Fourier form seasonal components using three years of weekly malaria case data from 30 districts in the Amhara Region of Ethiopia. We identified past outbreaks by comparing the modeled prediction envelopes with observed case data. Preliminary results demonstrated the potential for improved accuracy and timeliness over commonly-used methods in which thresholds are based on simpler summary statistics of historical data. Other benefits of the dynamic linear modeling approach include robustness to missing data and the ability to fit models with relatively few years of training data. To predict future outbreaks, we started with the early detection model for each district and added a regression component based on satellite-derived environmental predictor variables including precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and land surface temperature (LST) and spectral indices from the Moderate Resolution Imaging Spectroradiometer (MODIS). We included lagged environmental predictors in the regression component of the model, with lags chosen based on cross-correlation of the one-step-ahead forecast errors from the first model. Our results suggest that predictions of future malaria outbreaks can be improved by incorporating lagged environmental predictors.

Short term effects of particulate matter on cause specific mortality: effects of lags and modification by city characteristics.

PubMed

Zeka, A; Zanobetti, A; Schwartz, J

2005-10-01

Consistent evidence has shown increased all-cause mortality, and mortality from broad categories of causes associated with airborne particles. Less is known about associations with specific causes of death, and modifiers of those associations. To examine these questions in 20 US cities, between 1989 and 2000. Mortality files were obtained from the National Center for Health Statistics. Air pollution data were obtained from the Environmental Protection Agency website. The associations between daily concentrations of particulate matter of aero-diameter < or =10 microm (PM10) and daily mortality from all-cause and selected causes of death, were examined using a case-crossover design. Temporal effects of PM10 were examined using lag models, in first stage regressions. City specific modifiers of these associations were examined in second stage regressions. All-cause mortality increased with PM10 exposures occurring both one and two days prior the event. Deaths from heart disease were primarily associated with PM10 on the two days before, while respiratory deaths were associated with PM10 exposure on all three days. Analyses using only one lag underestimated the effects for all-cause, heart, and respiratory deaths. Several city characteristics modified the effects of PM10 on daily mortality. Important findings were seen for population density, percentage of primary PM10 from traffic, variance of summer temperature, and mean of winter temperature. There was overall evidence of increased daily mortality from increased concentrations of PM10 that persisted across several days, and matching for temperature did not affect these associations. Heterogeneity in the city specific PM10 effects could be explained by differences in certain city characteristics.

Wavelet-based functional linear mixed models: an application to measurement error-corrected distributed lag models.

PubMed

Malloy, Elizabeth J; Morris, Jeffrey S; Adar, Sara D; Suh, Helen; Gold, Diane R; Coull, Brent A

2010-07-01

Frequently, exposure data are measured over time on a grid of discrete values that collectively define a functional observation. In many applications, researchers are interested in using these measurements as covariates to predict a scalar response in a regression setting, with interest focusing on the most biologically relevant time window of exposure. One example is in panel studies of the health effects of particulate matter (PM), where particle levels are measured over time. In such studies, there are many more values of the functional data than observations in the data set so that regularization of the corresponding functional regression coefficient is necessary for estimation. Additional issues in this setting are the possibility of exposure measurement error and the need to incorporate additional potential confounders, such as meteorological or co-pollutant measures, that themselves may have effects that vary over time. To accommodate all these features, we develop wavelet-based linear mixed distributed lag models that incorporate repeated measures of functional data as covariates into a linear mixed model. A Bayesian approach to model fitting uses wavelet shrinkage to regularize functional coefficients. We show that, as long as the exposure error induces fine-scale variability in the functional exposure profile and the distributed lag function representing the exposure effect varies smoothly in time, the model corrects for the exposure measurement error without further adjustment. Both these conditions are likely to hold in the environmental applications we consider. We examine properties of the method using simulations and apply the method to data from a study examining the association between PM, measured as hourly averages for 1-7 days, and markers of acute systemic inflammation. We use the method to fully control for the effects of confounding by other time-varying predictors, such as temperature and co-pollutants.

Comparison of published and unpublished phase I clinical cancer trials: an analysis of the CliniclTrials.gov database.

PubMed

Shepshelovich, D; Goldvaser, H; Wang, L; Abdul Razak, A R

2017-12-13

Introduction The role of phase I cancer trials is constantly evolving and they are increasingly being used in 'go/no' decisions in drug development. As a result, there is a growing need to ensure trials are published when completed. There are limited data on the publication rate and the factors associated with publication in phase I trials. Methods The ClinicalTrials.gov database was searched for completed adult phase I cancer trials with reported results. PubMed was searched for matching publications published prior to April 1, 2017. Logistic regression was used to identify factors associated with unpublished trials. Linear regression was used to explore factors associated with time lag from study database lock to publication for published trials. Results The study cohort included 319 trials. 95 (30%) trials had no matching publication. Thirty (9%) trials were not published in abstract form as well. On multivariable analysis, the most significant factor associated with unpublished trials was industry funding (odds ratio 3.3, 95% confidence interval 1.7-6.6, p=0.019). For published trials, time lag between database lock and publication was longer by 10.9 months (standard error 3.6, p<0.001) for industry funded trials compared with medical center funded trials. Conclusions Timely publishing of early cancer clinical trials results remains unsatisfactory. Industry funded phase I cancer trials were more likely to remain unpublished, and were associated with a longer time lag from database lock to publication. Policies that promote transparency and data sharing in clinical trial research might improve accountability among industry and investigators and improve timely results publication.

Temporal Responses of NDVI to Climate Factors in Different Climatic Regions

NASA Astrophysics Data System (ADS)

Zare, H.

2015-12-01

The satellite-derived Normalized Difference Vegetation Index (NDVI) has been widely used to investigate the impact of climate factors on vegetation changes. However, a few studies have concentrated on comparing the relationship of climate factors and vegetation in different climatic regions. To enhance the understanding of these relationship, a temporal analysis was carried out on time series of 16-day NDVI from MODIS (2000-2014) during the growing season in ten protected areas of different regions of Iran. The correlation analyses between climate factors and NDVI was classified into two sub-periods. First from February to April and second from May to September. In the first sub-period, NDVI was more correlated to temperature than precipitation, all the areas had positive correlation with temperature. Slope of regression in arid region was less than others. In contrast, precipitation had different impact on NDVI among the locations from February to April. The negative correlation was found between precipitation and woody lands (humid regions), whereas precipitation in Bafgh and Turan in which annual plants are dominant (arid regions), had positive impact on NDVI. In the second sub-period, temperature showed negative significant influence on NDVI; however, the slope of regression was not identical across the locations. Woody lands had more strong correlation with temperature. NDVI sensitivity to temperature had a time lag of 30 days in most of areas, whereas arid regions did not show time lag. Positive correlation was found between precipitation and NDVI during warm period in all the locations. The areas covered by perennial plant had 1-2 months lag to respond to precipitation. Overall, no significant trend in NDVI changes was shown during the study period. We concluded that NDVI sensitivity to climate factors relies on vegetation type and time of year.

Income convergence in a rural, majority African American region

Treesearch

Buddhi Gyawali; Rory Fraser; James Bukenya; John Schelhas

2008-01-01

This paper revisits the issue of income convergence by examining the question of whether poorer Census Block Groups have been catching up with wealthier Census Block Groups over the 1980-2000 period. The dataset consists of 161 Census Block Groups in Alabama’s west-central Black Belt region. Estimates of a spatial lag model provide support for the conditional...

Educational Progression in Ghana: Gender and Spatial Variations in Longitudinal Trajectories of Junior High School Completion Rate

ERIC Educational Resources Information Center

Ansong, David; Alhassan, Mustapha

2016-01-01

Completion of junior high school is a critical milestone in every Ghanaian child's educational trajectory and a critical step toward the transition to higher education. However, the rate of children completing junior high school still lags behind most educational indicators in Ghana. Far more attention is paid to ensuring that students enroll in…

Analysis of classical Fourier, SPL and DPL heat transfer model in biological tissues in presence of metabolic and external heat source

NASA Astrophysics Data System (ADS)

Kumar, Dinesh; Singh, Surjan; Rai, K. N.

2016-06-01

In this paper, the temperature distribution in a finite biological tissue in presence of metabolic and external heat source when the surface subjected to different type of boundary conditions is studied. Classical Fourier, single-phase-lag (SPL) and dual-phase-lag (DPL) models were developed for bio-heat transfer in biological tissues. The analytical solution obtained for all the three models using Laplace transform technique and results are compared. The effect of the variability of different parameters such as relaxation time, metabolic heat source, spatial heat source, different type boundary conditions on temperature distribution in different type of the tissues like muscle, tumor, fat, dermis and subcutaneous based on three models are analyzed and discussed in detail. The result obtained in three models is compared with experimental observation of Stolwijk and Hardy (Pflug Arch 291:129-162, 1966). It has been observe that the DPL bio-heat transfer model provides better result in comparison of other two models. The value of metabolic and spatial heat source in boundary condition of first, second and third kind for different type of thermal therapies are evaluated.

The small G-protein MglA connects to the MreB actin cytoskeleton at bacterial focal adhesions.

PubMed

Treuner-Lange, Anke; Macia, Eric; Guzzo, Mathilde; Hot, Edina; Faure, Laura M; Jakobczak, Beata; Espinosa, Leon; Alcor, Damien; Ducret, Adrien; Keilberg, Daniela; Castaing, Jean Philippe; Lacas Gervais, Sandra; Franco, Michel; Søgaard-Andersen, Lotte; Mignot, Tâm

2015-07-20

In Myxococcus xanthus the gliding motility machinery is assembled at the leading cell pole to form focal adhesions, translocated rearward to propel the cell, and disassembled at the lagging pole. We show that MglA, a Ras-like small G-protein, is an integral part of this machinery. In this function, MglA stimulates the assembly of the motility complex by directly connecting it to the MreB actin cytoskeleton. Because the nucleotide state of MglA is regulated spatially and MglA only binds MreB in the guanosine triphosphate-bound form, the motility complexes are assembled at the leading pole and dispersed at the lagging pole where the guanosine triphosphatase activating protein MglB disrupts the MglA-MreB interaction. Thus, MglA acts as a nucleotide-dependent molecular switch to regulate the motility machinery spatially. The function of MreB in motility is independent of its function in peptidoglycan synthesis, representing a coopted function. Our findings highlight a new function for the MreB cytoskeleton and suggest that G-protein-cytoskeleton interactions are a universally conserved feature. © 2015 Treuner-Lange et al.

The small G-protein MglA connects to the MreB actin cytoskeleton at bacterial focal adhesions

PubMed Central

Treuner-Lange, Anke; Macia, Eric; Guzzo, Mathilde; Hot, Edina; Faure, Laura M.; Jakobczak, Beata; Espinosa, Leon; Alcor, Damien; Ducret, Adrien; Keilberg, Daniela; Castaing, Jean Philippe; Lacas Gervais, Sandra; Franco, Michel

2015-01-01

In Myxococcus xanthus the gliding motility machinery is assembled at the leading cell pole to form focal adhesions, translocated rearward to propel the cell, and disassembled at the lagging pole. We show that MglA, a Ras-like small G-protein, is an integral part of this machinery. In this function, MglA stimulates the assembly of the motility complex by directly connecting it to the MreB actin cytoskeleton. Because the nucleotide state of MglA is regulated spatially and MglA only binds MreB in the guanosine triphosphate–bound form, the motility complexes are assembled at the leading pole and dispersed at the lagging pole where the guanosine triphosphatase activating protein MglB disrupts the MglA–MreB interaction. Thus, MglA acts as a nucleotide-dependent molecular switch to regulate the motility machinery spatially. The function of MreB in motility is independent of its function in peptidoglycan synthesis, representing a coopted function. Our findings highlight a new function for the MreB cytoskeleton and suggest that G-protein–cytoskeleton interactions are a universally conserved feature. PMID:26169353

The flexible focus: whether spatial attention is unitary or divided depends on observer goals.

PubMed

Jefferies, Lisa N; Enns, James T; Di Lollo, Vincent

2014-04-01

The distribution of visual attention has been the topic of much investigation, and various theories have posited that attention is allocated either as a single unitary focus or as multiple independent foci. In the present experiment, we demonstrate that attention can be flexibly deployed as either a unitary or a divided focus in the same experimental task, depending on the observer's goals. To assess the distribution of attention, we used a dual-stream Attentional Blink (AB) paradigm and 2 target pairs. One component of the AB, Lag-1 sparing, occurs only if the second target pair appears within the focus of attention. By varying whether the first-target-pair could be expected in a predictable location (always in-stream) or not (unpredictably in-stream or between-streams), observers were encouraged to deploy a divided or a unitary focus, respectively. When the second-target-pair appeared between the streams, Lag-1 sparing occurred for the Unpredictable group (consistent with a unitary focus) but not for the Predictable group (consistent with a divided focus). Thus, diametrically different outcomes occurred for physically identical displays, depending on the expectations of the observer about where spatial attention would be required.

Children at risk: A comparison of child pedestrian traffic collisions in Santiago, Chile, and Seoul, South Korea.

PubMed

Blazquez, Carola; Lee, Jae Seung; Zegras, Christopher

2016-01-01

We examine and compare pedestrian-vehicle collisions and injury outcomes involving school-age children between 5 and 18 years of age in the capital cities of Santiago, Chile, and Seoul, South Korea. We conduct descriptive analysis of the child pedestrian-vehicle collision (P-VC) data (904 collisions for Santiago and 3,505 for Seoul) reported by the police between 2010 and 2011. We also statistically analyze factors associated with child P-VCs, by both incident severity and age group, using 3 regression models: negative binomial, probit, and spatial lag models. Descriptive statistics suggest that child pedestrians in Seoul have a higher risk of being involved in traffic crashes than their counterparts in Santiago. However, in Seoul a greater proportion of children are unharmed as a result of these incidents, whereas more child pedestrians are killed in Santiago. Younger children in Seoul suffer more injuries from P-VCs than in Santiago. The majority of P-VCs in both cities tend to occur in the afternoon and evening, at intersections in Santiago and at midblock locations in Seoul. Our model results suggest that the resident population of children is positively associated with P-VCs in both cities, and school concentrations apparently increase P-VC risk among older children in Santiago. Bus stops are associated with higher P-VCs in Seoul, and subway stations relate to higher P-VCs among older children in Santiago. Zone-level land use mix was negatively related to child P-VCs in Seoul but not in Santiago. Arterial roads are associated with fewer P-VCs, especially for younger children in both cities. A share of collector roads is associated with increased P-VCs in Seoul but fewer P-VCs in Santiago. Hilliness is related to fewer P-VCs in both cities. Differences in these model results for Santiago and Seoul warrant additional analysis, as do the differences in results across model type (negative binomial versus spatial lag models). To reduce child P-VCs, this study suggests the need to assess subway station and bus stop area conditions in Santiago and Seoul, respectively; areas with high density of schools in Santiago; areas with greater concentrations of children in both cities; and collector roads in Seoul.

Simulating land-use changes by incorporating spatial autocorrelation and self-organization in CLUE-S modeling: a case study in Zengcheng District, Guangzhou, China

NASA Astrophysics Data System (ADS)

Mei, Zhixiong; Wu, Hao; Li, Shiyun

2018-06-01

The Conversion of Land Use and its Effects at Small regional extent (CLUE-S), which is a widely used model for land-use simulation, utilizes logistic regression to estimate the relationships between land use and its drivers, and thus, predict land-use change probabilities. However, logistic regression disregards possible spatial autocorrelation and self-organization in land-use data. Autologistic regression can depict spatial autocorrelation but cannot address self-organization, while logistic regression by considering only self-organization (NElogistic regression) fails to capture spatial autocorrelation. Therefore, this study developed a regression (NE-autologistic regression) method, which incorporated both spatial autocorrelation and self-organization, to improve CLUE-S. The Zengcheng District of Guangzhou, China was selected as the study area. The land-use data of 2001, 2005, and 2009, as well as 10 typical driving factors, were used to validate the proposed regression method and the improved CLUE-S model. Then, three future land-use scenarios in 2020: the natural growth scenario, ecological protection scenario, and economic development scenario, were simulated using the improved model. Validation results showed that NE-autologistic regression performed better than logistic regression, autologistic regression, and NE-logistic regression in predicting land-use change probabilities. The spatial allocation accuracy and kappa values of NE-autologistic-CLUE-S were higher than those of logistic-CLUE-S, autologistic-CLUE-S, and NE-logistic-CLUE-S for the simulations of two periods, 2001-2009 and 2005-2009, which proved that the improved CLUE-S model achieved the best simulation and was thereby effective to a certain extent. The scenario simulation results indicated that under all three scenarios, traffic land and residential/industrial land would increase, whereas arable land and unused land would decrease during 2009-2020. Apparent differences also existed in the simulated change sizes and locations of each land-use type under different scenarios. The results not only demonstrate the validity of the improved model but also provide a valuable reference for relevant policy-makers.

Lymphocyte activation gene 3 and coronary artery disease

PubMed Central

Golden, Diana; Kolmakova, Antonina; Sura, Sunitha; Vella, Anthony T.; Manichaikul, Ani; Wang, Xin-Qun; Bielinski, Suzette J.; Taylor, Kent D.; Chen, Yii-Der Ida; Rich, Stephen S.

2016-01-01

BACKGROUND: The lipoprotein scavenger receptor BI (SCARB1) rs10846744 noncoding variant is significantly associated with atherosclerotic disease independently of traditional cardiovascular risk factors. We identified a potentially novel connection between rs10846744, the immune checkpoint inhibitor lymphocyte activation gene 3 (LAG3), and atherosclerosis. METHODS: In vitro approaches included flow cytometry, lipid raft isolation, phosphosignaling, cytokine measurements, and overexpressing and silencing LAG3 protein. Fasting plasma LAG3 protein was measured in hyperalphalipoproteinemic (HALP) and Multi-Ethnic Study of Atherosclerosis (MESA) participants. RESULTS: In comparison with rs10846744 reference (GG homozygous) cells, LAG3 protein levels by flow cytometry (P < 0.001), in lipid rafts stimulated and unstimulated (P = 0.03), and phosphosignaling downstream of B cell receptor engagement of CD79A (P = 0.04), CD19 (P = 0.04), and LYN (P = 0.001) were lower in rs10846744 risk (CC homozygous) cells. Overexpressing LAG3 protein in risk cells and silencing LAG3 in reference cells confirmed its importance in phosphosignaling. Secretion of TNF-α was higher (P = 0.04) and IL-10 was lower (P = 0.04) in risk cells. Plasma LAG3 levels were lower in HALP carriers of the CC allele (P < 0.0001) and by race (P = 0.004). In MESA, race (P = 0.0005), age (P = 0.003), lipid medications (P = 0.03), smoking history (P < 0.0001), and rs10846744 genotype (P = 0.002) were independent predictors of plasma LAG3. In multivariable regression models, plasma LAG3 was significantly associated with HDL-cholesterol (HDL-C) (P = 0.007), plasma IL-10 (P < 0.0001), and provided additional predictive value above the Framingham risk score (P = 0.04). In MESA, when stratified by high HDL-C, plasma LAG3 was associated with coronary heart disease (CHD) (odds ratio 1.45, P = 0.004). CONCLUSION: Plasma LAG3 is a potentially novel independent predictor of HDL-C levels and CHD risk. FUNDING: This work was supported by an NIH RO1 grant (HL075646), the endowed Linda and David Roth Chair for Cardiovascular Research, and the Harold S. Geneen Charitable Trust Coronary Heart Disease Research award to Annabelle Rodriguez. MESA is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-001079, UL1-TR-000040, and DK063491. Cardiometabochip genotyping data for the MESA samples was supported in part by grants and contracts R01HL98077, N02-HL-64278, HL071205, UL1TR000124, DK063491, RD831697, and P50 ES015915. PMID:27777974

Time series regression studies in environmental epidemiology.

PubMed

Bhaskaran, Krishnan; Gasparrini, Antonio; Hajat, Shakoor; Smeeth, Liam; Armstrong, Ben

2013-08-01

Time series regression studies have been widely used in environmental epidemiology, notably in investigating the short-term associations between exposures such as air pollution, weather variables or pollen, and health outcomes such as mortality, myocardial infarction or disease-specific hospital admissions. Typically, for both exposure and outcome, data are available at regular time intervals (e.g. daily pollution levels and daily mortality counts) and the aim is to explore short-term associations between them. In this article, we describe the general features of time series data, and we outline the analysis process, beginning with descriptive analysis, then focusing on issues in time series regression that differ from other regression methods: modelling short-term fluctuations in the presence of seasonal and long-term patterns, dealing with time varying confounding factors and modelling delayed ('lagged') associations between exposure and outcome. We finish with advice on model checking and sensitivity analysis, and some common extensions to the basic model.

Space-Time Modelling of Groundwater Level Using Spartan Covariance Function

NASA Astrophysics Data System (ADS)

Varouchakis, Emmanouil; Hristopulos, Dionissios

2014-05-01

Geostatistical models often need to handle variables that change in space and in time, such as the groundwater level of aquifers. A major advantage of space-time observations is that a higher number of data supports parameter estimation and prediction. In a statistical context, space-time data can be considered as realizations of random fields that are spatially extended and evolve in time. The combination of spatial and temporal measurements in sparsely monitored watersheds can provide very useful information by incorporating spatiotemporal correlations. Spatiotemporal interpolation is usually performed by applying the standard Kriging algorithms extended in a space-time framework. Spatiotemoral covariance functions for groundwater level modelling, however, have not been widely developed. We present a new non-separable theoretical spatiotemporal variogram function which is based on the Spartan covariance family and evaluate its performance in spatiotemporal Kriging (STRK) interpolation. The original spatial expression (Hristopulos and Elogne 2007) that has been successfully used for the spatial interpolation of groundwater level (Varouchakis and Hristopulos 2013) is modified by defining the following space-time normalized distance h = °h2r-+-α h2τ, hr=r- ξr, hτ=τ- ξτ; where r is the spatial lag vector, τ the temporal lag vector, ξr is the correlation length in position space (r) and ξτ in time (τ), h the normalized space-time lag vector, h = |h| is its Euclidean norm of the normalized space-time lag and α the coefficient that determines the relative weight of the time lag. The space-time experimental semivariogram is determined from the biannual (wet and dry period) time series of groundwater level residuals (obtained from the original series after trend removal) between the years 1981 and 2003 at ten sampling stations located in the Mires hydrological basin in the island of Crete (Greece). After the hydrological year 2002-2003 there is a significant groundwater level increase during the wet period of 2003-2004 and a considerable drop during the dry period of 2005-2006. Both periods are associated with significant annual changes in the precipitation compared to the basin average, i.e., a 40% increase and 65% decrease, respectively. We use STRK to 'predict' the groundwater level for the two selected hydrological periods (wet period of 2003-2004 and dry period of 2005-2006) at each sampling station. The predictions are validated using the respective measured values. The novel Spartan spatiotemporal covariance function gives a mean absolute relative prediction error of 12%. This is 45% lower than the respective value obtained with the commonly used product-sum covariance function, and 31% lower than the respective value obtained with a non-separable function based on the diffusion equation (Kolovos et al. 2010). The advantage of the Spartan space-time covariance model is confirmed with statistical measures such as the root mean square standardized error (RMSSE), the modified coefficient of model efficiency, E' (Legates and McCabe, 1999) and the modified Index of Agreement, IoA'(Janssen and Heuberger, 1995). Hristopulos, D. T. and Elogne, S. N. 2007. Analytic properties and covariance functions for a new class of generalized Gibbs random fields. IEEE Transactions on Information Theory, 53, 4667-4467. Janssen, P.H.M. and Heuberger P.S.C. 1995. Calibration of process-oriented models. Ecological Modelling, 83, 55-66. Kolovos, A., Christakos, G., Hristopulos, D. T. and Serre, M. L. 2004. Methods for generating non-separable spatiotemporal covariance models with potential environmental applications. Advances in Water Resources, 27 (8), 815-830. Legates, D.R. and McCabe Jr., G.J. 1999. Evaluating the use of 'goodness-of-fit' measures in hydrologic and hydro climatic model validation. Water Resources Research, 35, 233-241. Varouchakis, E. A. and Hristopulos, D. T. 2013. Improvement of groundwater level prediction in sparsely gauged basins using physical laws and local geographic features as auxiliary variables. Advances in Water Resources, 52, 34-49.

GIS-based spatial regression and prediction of water quality in river networks: A case study in Iowa

USGS Publications Warehouse

Yang, X.; Jin, W.

2010-01-01

Nonpoint source pollution is the leading cause of the U.S.'s water quality problems. One important component of nonpoint source pollution control is an understanding of what and how watershed-scale conditions influence ambient water quality. This paper investigated the use of spatial regression to evaluate the impacts of watershed characteristics on stream NO3NO2-N concentration in the Cedar River Watershed, Iowa. An Arc Hydro geodatabase was constructed to organize various datasets on the watershed. Spatial regression models were developed to evaluate the impacts of watershed characteristics on stream NO3NO2-N concentration and predict NO3NO2-N concentration at unmonitored locations. Unlike the traditional ordinary least square (OLS) method, the spatial regression method incorporates the potential spatial correlation among the observations in its coefficient estimation. Study results show that NO3NO2-N observations in the Cedar River Watershed are spatially correlated, and by ignoring the spatial correlation, the OLS method tends to over-estimate the impacts of watershed characteristics on stream NO3NO2-N concentration. In conjunction with kriging, the spatial regression method not only makes better stream NO3NO2-N concentration predictions than the OLS method, but also gives estimates of the uncertainty of the predictions, which provides useful information for optimizing the design of stream monitoring network. It is a promising tool for better managing and controlling nonpoint source pollution. ?? 2010 Elsevier Ltd.

Spatial and temporal analyses of citrus sudden death as a tool to generate hypotheses concerning its etiology.

PubMed

Bassanezi, Renato B; Bergamin Filho, Armando; Amorim, Lilian; Gimenes-Fernandes, Nelson; Gottwald, Tim R; Bové, Joseph M

2003-04-01

ABSTRACT Citrus sudden death (CSD), a new disease of unknown etiology that affects sweet orange grafted on Rangpur lime, was visually monitored for 14 months in 41 groves in Brazil. Ordinary runs analysis of CSD-symptomatic trees indicated a departure from randomness of symptomatic trees status among immediately adjacent trees mainly within rows. The binomial index of dispersion (D) and the intraclass correlation (k) for various quadrat sizes suggested aggregation of CSD-symptomatic trees for almost all plots within the quadrat sizes tested. Estimated parameters of the binary form of Taylor's power law provided an overall measure of aggregation of CSD-symptomatic trees for all quadrat sizes tested. Aggregation in each plot was dependent on disease incidence. Spatial autocorrelation analysis of proximity patterns suggested that aggregation often existed among quadrats of various sizes up to three lag distances; however, significant lag positions discontinuous from main proximity patterns were rare, indicating a lack of spatial association among discrete foci. Some asymmetry was also detected for some spatial autocorrelation proximity patterns, indicating that within-row versus across-row distributions are not necessarily equivalent. These results were interpreted to mean that the cause of the disease was most likely biotic and its dissemination was common within a local area of influence that extended to approximately six trees in all directions, including adjacent trees. Where asymmetry was indicated, this area of influence was somewhat elliptical. Longer-distance patterns were not detected within the confines of the plot sizes tested. Annual rates of CSD progress based on the Gompertz model ranged from 0.37 to 2.02. Numerous similarities were found between the spatial patterns of CSD and Citrus tristeza virus (CTV) described in the literature, both in the presence of the aphid vector, Toxoptera citricida. CSD differs from CTV in that symptoms occur in sweet orange grafted on Rangpur lime. Based on the symptoms of CSD and on its spatial and temporal patterns, our hypothesis is that CSD may be caused by a similar but undescribed pathogen such as a virus and probably vectored by insects such as aphids by similar spatial processes to those affecting CTV.

Spatial Assessment of Model Errors from Four Regression Techniques

Treesearch

Lianjun Zhang; Jeffrey H. Gove; Jeffrey H. Gove

2005-01-01

Fomst modelers have attempted to account for the spatial autocorrelations among trees in growth and yield models by applying alternative regression techniques such as linear mixed models (LMM), generalized additive models (GAM), and geographicalIy weighted regression (GWR). However, the model errors are commonly assessed using average errors across the entire study...

Forecasting cyanobacteria dominance in Canadian temperate lakes.

PubMed

Persaud, Anurani D; Paterson, Andrew M; Dillon, Peter J; Winter, Jennifer G; Palmer, Michelle; Somers, Keith M

2015-03-15

Predictive models based on broad scale, spatial surveys typically identify nutrients and climate as the most important predictors of cyanobacteria abundance; however these models generally have low predictive power because at smaller geographic scales numerous other factors may be equally or more important. At the lake level, for example, the ability to forecast cyanobacteria dominance is of tremendous value to lake managers as they can use such models to communicate exposure risks associated with recreational and drinking water use, and possible exposure to algal toxins, in advance of bloom occurrence. We used detailed algal, limnological and meteorological data from two temperate lakes in south-central Ontario, Canada to determine the factors that are closely linked to cyanobacteria dominance, and to develop easy to use models to forecast cyanobacteria biovolume. For Brandy Lake (BL), the strongest and most parsimonious model for forecasting % cyanobacteria biovolume (% CB) included water column stability, hypolimnetic TP, and % cyanobacteria biovolume two weeks prior. For Three Mile Lake (TML), the best model for forecasting % CB included water column stability, hypolimnetic TP concentration, and 7-d mean wind speed. The models for forecasting % CB in BL and TML are fundamentally different in their lag periods (BL = lag 1 model and TML = lag 2 model) and in some predictor variables despite the close proximity of the study lakes. We speculate that three main factors (nutrient concentrations, water transparency and lake morphometry) may have contributed to differences in the models developed, and may account for variation observed in models derived from large spatial surveys. Our results illustrate that while forecast models can be developed to determine when cyanobacteria will dominate within two temperate lakes, the models require detailed, lake-specific calibration to be effective as risk-management tools. Copyright © 2015 Elsevier Ltd. All rights reserved.

Time-lagged response of carabid species richness and composition to past management practices and landscape context of semi-natural field margins.

PubMed

Alignier, Audrey; Aviron, Stéphanie

2017-12-15

Field margins are key features for the maintenance of biodiversity and associated ecosystem services in agricultural landscapes. Little is known about the effects of management practices of old semi-natural field margins, and their historical dimension regarding past management practices and landscape context is rarely considered. In this paper, the relative influence of recent and past management practices and landscape context (during the last five years) were assessed on the local biodiversity (species richness and composition) of carabid assemblages of field margins in agricultural landscapes of northwestern France. The results showed that recent patterns of carabid species richness and composition were best explained by management practices and landscape context measured four or five years ago. It suggests the existence of a time lag in the response of carabid assemblages to past environmental conditions of field margins. The relative contribution of past management practices and past landscape context varied depending on the spatial scale at which landscape context was taken into account. Carabid species richness was higher in grazed or sprayed field margins probably due to increased heterogeneity in habitat conditions. Field margins surrounded by grasslands and crops harbored species associated with open habitats whilst forest species dominated field margins surrounded by woodland. Landscape effect was higher at fine spatial scale, within 50 m around field margins. The present study highlights the importance of considering time-lagged responses of biodiversity when managing environment. It also suggests that old semi-natural field margins should not be considered as undisturbed habitats but more as management units being part of farming activities in agricultural landscapes, as for arable fields. Copyright © 2017 Elsevier Ltd. All rights reserved.

Runoff Response at Three Spatial Scale from a Burned Watershed

NASA Astrophysics Data System (ADS)

Moody, J. A.; Kinner, D. A.

2007-12-01

The hypothesis that the magnitude and timing of runoff from burned watersheds are functions of the properties of flow paths at multiple scales was investigated at three nested spatial scales within an area burned by the 2005 Harvard Fire near Burbank, California. Water depths were measured using pressure sensors: at the outlet of a subwatershed (10000 m2); in 3-inch Parshall flumes near the outlets of three mini-watersheds (820-1780 m2) within the subwatershed; and by 12 overland-flow detectors in 6 micro-watersheds (~11-15 m2) within one of the mini-watersheds. Rainfall intensities were measured using recording raingages deployed around the perimeter of the mini-watersheds and at the subwatershed outlet. Time-to-concentration, TC, and lag time, TL, were computed for the 15 largest of 30 rainstorms (maximum 30- minute intensities were 3.3-13.0 mm/h) between December 2005 and April 2006. TC , elapsed time from the beginning of the rain until the first increase in water depth, averaged 1.0 hours at the micro-scale, 1.7 hours at the mini-scale, and 1.5 hours at the subwatershed scale. TL is the lag time that produced the maximum cross- correlation coefficient between the time series of rainfall intensities and the series of water depths. TL averaged 0.15 hours at the micro-scale, 0.35 hours at the mini-scale, and 0.39 hours at the subwatershed scale. The coefficient was >0.50 for 43% (N=168) of the measurements at the micro-scale, for 61% (N=54) at the mini- scale, and for 67% (N=6) at the subwatershed scale indicating the runoff response lagged but was often well correlated with the time-varying rainfall intensity.

Response of Vegetation Dynamics to Projected Climate Change based on NDVI Simulations using Stepwise Cluster Analysis in the Three-River Headwaters Region of China

NASA Astrophysics Data System (ADS)

Zheng, Y.; Lv, E.; Huang, Y.

2016-12-01

Located in the hinterland of the Qinghai-Tibetan Plateau, the Three-River Headwaters region (THR) features unique eco-environmental conditions and fragile ecosystems, and is very vulnerable to climate change. To investigate the effects of climate change on the ecosystem, the Normalized Difference Vegetation Index (NDVI) was employed as an indicator to reflect the vegetation dynamics in response to climate change. This study proposed a model based on Stepwise-cluster analysis to predict the temporal and spatial distributions of NDVI values for five future years according to Global Circulation Models (GCMs) climate projections under the RCP4.5 scenario. The obtained spatial results showed very good agreements between simulations and remote sensing observations of the NDVI value for both training and validation, and the developed model demonstrated its capability of predicting the monthly changes of NDVI through representing the relationships between it and various climatic factors, including remote sensed precipitation and temperature with no, 1 and 2-month lag period. The monthly average precipitation with one-month lag period was further found to be the most important climatic factor that drives the changes of NDVI in the THR. Compared with the values of NDVI in 2000 - 2013, the predicting results indicate the values of NDVI for the THR in growing season (May to October) will decrease by 15.74% in the next 100 years, suggesting that the THR is going to experience an environmental degradation. The results also show that precipitation is the primary driving factor relative to temperature, especially the one-month-lag precipitation. Findings from this study would help policy makers draw up effective water resource and eco-environmental management strategies for adapting to climate change in the THR.

Predictive coding of visual object position ahead of moving objects revealed by time-resolved EEG decoding.

PubMed

Hogendoorn, Hinze; Burkitt, Anthony N

2018-05-01

Due to the delays inherent in neuronal transmission, our awareness of sensory events necessarily lags behind the occurrence of those events in the world. If the visual system did not compensate for these delays, we would consistently mislocalize moving objects behind their actual position. Anticipatory mechanisms that might compensate for these delays have been reported in animals, and such mechanisms have also been hypothesized to underlie perceptual effects in humans such as the Flash-Lag Effect. However, to date no direct physiological evidence for anticipatory mechanisms has been found in humans. Here, we apply multivariate pattern classification to time-resolved EEG data to investigate anticipatory coding of object position in humans. By comparing the time-course of neural position representation for objects in both random and predictable apparent motion, we isolated anticipatory mechanisms that could compensate for neural delays when motion trajectories were predictable. As well as revealing an early neural position representation (lag 80-90 ms) that was unaffected by the predictability of the object's trajectory, we demonstrate a second neural position representation at 140-150 ms that was distinct from the first, and that was pre-activated ahead of the moving object when it moved on a predictable trajectory. The latency advantage for predictable motion was approximately 16 ± 2 ms. To our knowledge, this provides the first direct experimental neurophysiological evidence of anticipatory coding in human vision, revealing the time-course of predictive mechanisms without using a spatial proxy for time. The results are numerically consistent with earlier animal work, and suggest that current models of spatial predictive coding in visual cortex can be effectively extended into the temporal domain. Copyright © 2018 Elsevier Inc. All rights reserved.

Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression

PubMed Central

Chen, Yanguang

2016-01-01

In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson’s statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran’s index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China’s regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test. PMID:26800271

International Trade, Pollution Accumulation and Sustainable Growth: A VAR Estimation from the Pearl River Delta Region

NASA Astrophysics Data System (ADS)

Zuo, Hui; Tian, Lu

2018-03-01

In order to investigate international trade influence in the regional environment. This paper constructs a vector auto-regression (VAR) model and estimates the equations with the environment and trade data of the Pearl River Delta Region. The major mechanisms to the lag are discussed and the fit simulation of the environmental change by the international impulse is given. The result shows that impulse of pollution-intensive export deteriorates the environment continuously and impulse of such import improves it. These effects on the environment are insignificantly correlated with contemporary regional income but significantly correlative to early-stage trade feature. To a typical trade-dependent economy, both export and import have hysteresis influence in the regional environment. The lagged impulse will change environmental development in the turning point, maximal pollution level and convergence.

Modelling malaria incidence by an autoregressive distributed lag model with spatial component.

PubMed

Laguna, Francisco; Grillet, María Eugenia; León, José R; Ludeña, Carenne

2017-08-01

The influence of climatic variables on the dynamics of human malaria has been widely highlighted. Also, it is known that this mosquito-borne infection varies in space and time. However, when the data is spatially incomplete most popular spatio-temporal methods of analysis cannot be applied directly. In this paper, we develop a two step methodology to model the spatio-temporal dependence of malaria incidence on local rainfall, temperature, and humidity as well as the regional sea surface temperatures (SST) in the northern coast of Venezuela. First, we fit an autoregressive distributed lag model (ARDL) to the weekly data, and then, we adjust a linear separable spacial vectorial autoregressive model (VAR) to the residuals of the ARDL. Finally, the model parameters are tuned using a Markov Chain Monte Carlo (MCMC) procedure derived from the Metropolis-Hastings algorithm. Our results show that the best model to account for the variations of malaria incidence from 2001 to 2008 in 10 endemic Municipalities in North-Eastern Venezuela is a logit model that included the accumulated local precipitation in combination with the local maximum temperature of the preceding month as positive regressors. Additionally, we show that although malaria dynamics is highly heterogeneous in space, a detailed analysis of the estimated spatial parameters in our model yield important insights regarding the joint behavior of the disease incidence across the different counties in our study. Copyright © 2017 Elsevier Ltd. All rights reserved.

Longitudinal Associations Among Pain, Posttraumatic Stress Disorder Symptoms, and Stress Appraisals.

PubMed

Vaughan, Christine A; Miles, Jeremy N V; Eisenman, David P; Meredith, Lisa S

2016-04-01

Comorbidity of posttraumatic stress disorder (PTSD) and pain is well documented, but the mechanisms underlying their comorbidity are not well understood. Cross-lagged regression models were estimated with 3 waves of longitudinal data to examine the reciprocal associations between PTSD symptom severity, as measured by the Clinician-Administered PTSD Scale (CAPS), and pain, as measured by a brief self-report measure of pain called the PEG (pain intensity [P], interference with enjoyment of life [E], and interference with general activity [G]). We evaluated stress appraisals as a mediator of these associations in a sample of low-income, underserved patients with PTSD (N = 355) at federally qualified health centers in a northeastern metropolitan area. Increases in PTSD symptom severity between baseline and 6-month and 6- and 12-month assessments were independently predicted by higher levels of pain (β = .14 for both lags) and appraisals of life stress as uncontrollable (β = .15 for both lags). Stress appraisals, however, did not mediate these associations, and PTSD symptom severity did not predict change in pain. Thus, the results did not support the role of stress appraisals as a mechanism underlying the associations between pain and PTSD. Copyright © 2016 International Society for Traumatic Stress Studies.

Modeling exposure–lag–response associations with distributed lag non-linear models

PubMed Central

Gasparrini, Antonio

2014-01-01

In biomedical research, a health effect is frequently associated with protracted exposures of varying intensity sustained in the past. The main complexity of modeling and interpreting such phenomena lies in the additional temporal dimension needed to express the association, as the risk depends on both intensity and timing of past exposures. This type of dependency is defined here as exposure–lag–response association. In this contribution, I illustrate a general statistical framework for such associations, established through the extension of distributed lag non-linear models, originally developed in time series analysis. This modeling class is based on the definition of a cross-basis, obtained by the combination of two functions to flexibly model linear or nonlinear exposure-responses and the lag structure of the relationship, respectively. The methodology is illustrated with an example application to cohort data and validated through a simulation study. This modeling framework generalizes to various study designs and regression models, and can be applied to study the health effects of protracted exposures to environmental factors, drugs or carcinogenic agents, among others. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:24027094

Climate variability and increase in intensity and magnitude of dengue incidence in Singapore

PubMed Central

Hii, Yien Ling; Rocklöv, Joacim; Ng, Nawi; Tang, Choon Siang; Pang, Fung Yin; Sauerborn, Rainer

2009-01-01

Introduction Dengue is currently a major public health burden in Asia Pacific Region. This study aims to establish an association between dengue incidence, mean temperature and precipitation, and further discuss how weather predictors influence the increase in intensity and magnitude of dengue in Singapore during the period 2000–2007. Materials and methods Weekly dengue incidence data, daily mean temperature and precipitation and the midyear population data in Singapore during 2000–2007 were retrieved and analysed. We employed a time series Poisson regression model including time factors such as time trends, lagged terms of weather predictors, considered autocorrelation, and accounted for changes in population size by offsetting. Results The weekly mean temperature and cumulative precipitation were statistically significant related to the increases of dengue incidence in Singapore. Our findings showed that dengue incidence increased linearly at time lag of 5–16 and 5–20 weeks succeeding elevated temperature and precipitation, respectively. However, negative association occurred at lag week 17–20 with low weekly mean temperature as well as lag week 1–4 and 17–20 with low cumulative precipitation. Discussion As Singapore experienced higher weekly mean temperature and cumulative precipitation in the years 2004–2007, our results signified hazardous impacts of climate factors on the increase in intensity and magnitude of dengue cases. The ongoing global climate change might potentially increase the burden of dengue fever infection in near future. PMID:20052380

Incidence and risk factors of AIDS-defining cancers in a cohort of HIV-positive adults: Importance of the definition of incident cases.

PubMed

Suárez-García, Inés; Jarrín, Inmaculada; Iribarren, José Antonio; López-Cortés, Luis Fernando; Lacruz-Rodrigo, José; Masiá, Mar; Gómez-Sirvent, Juan Luis; Hernández-Quero, José; Vidal, Francesc; Alejos-Ferreras, Belén; Moreno, Santiago; Del Amo, Julia

2013-05-01

The aim of this study was to investigate the incidence and risk factors for the development of AIDS-defining cancers (ADCs); and to investigate the effect of making different assumptions on the definition of incident cases. A multicentre cohort study was designed. Poisson regression was used to assess incidence and risk factors. To account for misclassification, incident cases were defined using lag-times of 0, 14 and 30 days after enrolment. A total of 6393 HIV-positive subjects were included in the study. The incidences of ADCs changed as the lag periods were varied from 0 to 30 days. Different risk factors emerged as the definition of incident cases was changed. For a lag time of 0, the risk of Kaposi sarcoma [KS] and non-Hodgkin lymphoma [NHL] increased at CD4 counts <200/ml. HAART was associated with lower risk of NHL and KS. Men who had sex with men had a higher risk of KS. KS and NHL were not associated with viral load, gender, or hepatitis B or C. The results were similar for a lag-time of 14 and 30 days; however, hepatitis C was significantly associated with NHL. This analysis shows the importance of the definition of incident cases in cohort studies. Alternative definitions gave different incidence estimates, and may have implications for the analysis of risk factors. Copyright © 2011 Elsevier España, S.L. All rights reserved.

Physiological Equivalent Temperature Index and mortality in Tabriz (The northwest of Iran).

PubMed

Sharafkhani, Rahim; Khanjani, Narges; Bakhtiari, Bahram; Jahani, Yunes; Sadegh Tabrizi, Jafar

2018-01-01

There are few epidemiological studies about climate change and the effect of temperature variation on health using human thermal indices such as the Physiological Equivalent Temperature (PET) Index in Iran. This study was conducted in Tabriz, the northwest of Iran and Distributed Lag Non-linear Models (DLNM) combined with quasi-Poisson regression models were used to assess the impacts of PET on mortality by using the DLNM Package in R Software. The effect of air pollutants, time trend, day of the week and holidays were controlled as confounders. There was a significant relation between high (30°C, 27°C) and low (-0.8°C, -9.2°C and -14.2°C) PET and total (non-accidental) mortality; and a significant increase in respiratory and cardiovascular deaths in high PET values. Heat stress increased Cumulative Relative Risk (CRR) for total (non-accidental), respiratory and cardiovascular mortality significantly (CRR Non Accidental Death, PET=30°C, lag 0-30 =1.67, 95%CI: 1.31-2.13; CRR Respiratory Death, PET=30°C, lag 0-13 =1.88, 95%CI: 1.30-2.72; CRR Cardiovascular Death, PET=30°C, lag0-30 =1.67 95%CI: 1.16-2.40). Heat stress increases the risk of total (non-accidental), respiratory mortality, but cold stress decreases the risk of total (non-accidental) mortality in Tabriz which is one of the cold cities of Iran. Copyright © 2017 Elsevier Ltd. All rights reserved.

Association between malaria incidence and meteorological factors: a multi-location study in China, 2005-2012.

PubMed

Xiang, J; Hansen, A; Liu, Q; Tong, M X; Liu, X; Sun, Y; Cameron, S; Hanson-Easey, S; Han, G S; Williams, C; Weinstein, P; Bi, P

2018-01-01

This study aims to investigate the climate-malaria associations in nine cities selected from malaria high-risk areas in China. Daily reports of malaria cases in Anhui, Henan, and Yunnan Provinces for 2005-2012 were obtained from the Chinese Center for Disease Control and Prevention. Generalized estimating equation models were used to quantify the city-specific climate-malaria associations. Multivariate random-effects meta-regression analyses were used to pool the city-specific effects. An inverted-U-shaped curve relationship was observed between temperatures, average relative humidity, and malaria. A 1 °C increase of maximum temperature (T max) resulted in 6·7% (95% CI 4·6-8·8%) to 15·8% (95% CI 14·1-17·4%) increase of malaria, with corresponding lags ranging from 7 to 45 days. For minimum temperature (T min), the effect estimates peaked at lag 0 to 40 days, ranging from 5·3% (95% CI 4·4-6·2%) to 17·9% (95% CI 15·6-20·1%). Malaria is more sensitive to T min in cool climates and T max in warm climates. The duration of lag effect in a cool climate zone is longer than that in a warm climate zone. Lagged effects did not vanish after an epidemic season but waned gradually in the following 2-3 warm seasons. A warming climate may potentially increase the risk of malaria resurgence in China.

Deciphering factors controlling groundwater arsenic spatial variability in Bangladesh

NASA Astrophysics Data System (ADS)

Tan, Z.; Yang, Q.; Zheng, C.; Zheng, Y.

2017-12-01

Elevated concentrations of geogenic arsenic in groundwater have been found in many countries to exceed 10 μg/L, the WHO's guideline value for drinking water. A common yet unexplained characteristic of groundwater arsenic spatial distribution is the extensive variability at various spatial scales. This study investigates factors influencing the spatial variability of groundwater arsenic in Bangladesh to improve the accuracy of models predicting arsenic exceedance rate spatially. A novel boosted regression tree method is used to establish a weak-learning ensemble model, which is compared to a linear model using a conventional stepwise logistic regression method. The boosted regression tree models offer the advantage of parametric interaction when big datasets are analyzed in comparison to the logistic regression. The point data set (n=3,538) of groundwater hydrochemistry with 19 parameters was obtained by the British Geological Survey in 2001. The spatial data sets of geological parameters (n=13) were from the Consortium for Spatial Information, Technical University of Denmark, University of East Anglia and the FAO, while the soil parameters (n=42) were from the Harmonized World Soil Database. The aforementioned parameters were regressed to categorical groundwater arsenic concentrations below or above three thresholds: 5 μg/L, 10 μg/L and 50 μg/L to identify respective controlling factors. Boosted regression tree method outperformed logistic regression methods in all three threshold levels in terms of accuracy, specificity and sensitivity, resulting in an improvement of spatial distribution map of probability of groundwater arsenic exceeding all three thresholds when compared to disjunctive-kriging interpolated spatial arsenic map using the same groundwater arsenic dataset. Boosted regression tree models also show that the most important controlling factors of groundwater arsenic distribution include groundwater iron content and well depth for all three thresholds. The probability of a well with iron content higher than 5mg/L to contain greater than 5 μg/L, 10 μg/L and 50 μg/L As is estimated to be more than 91%, 85% and 51%, respectively, while the probability of a well from depth more than 160m to contain more than 5 μg/L, 10 μg/L and 50 μg/L As is estimated to be less than 38%, 25% and 14%, respectively.

Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Suparti; Wahyu Utami, Tiani

2018-03-01

Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.

Bayesian hierarchical modelling of continuous non-negative longitudinal data with a spike at zero: An application to a study of birds visiting gardens in winter.

PubMed

Swallow, Ben; Buckland, Stephen T; King, Ruth; Toms, Mike P

2016-03-01

The development of methods for dealing with continuous data with a spike at zero has lagged behind those for overdispersed or zero-inflated count data. We consider longitudinal ecological data corresponding to an annual average of 26 weekly maximum counts of birds, and are hence effectively continuous, bounded below by zero but also with a discrete mass at zero. We develop a Bayesian hierarchical Tweedie regression model that can directly accommodate the excess number of zeros common to this type of data, whilst accounting for both spatial and temporal correlation. Implementation of the model is conducted in a Markov chain Monte Carlo (MCMC) framework, using reversible jump MCMC to explore uncertainty across both parameter and model spaces. This regression modelling framework is very flexible and removes the need to make strong assumptions about mean-variance relationships a priori. It can also directly account for the spike at zero, whilst being easily applicable to other types of data and other model formulations. Whilst a correlative study such as this cannot prove causation, our results suggest that an increase in an avian predator may have led to an overall decrease in the number of one of its prey species visiting garden feeding stations in the United Kingdom. This may reflect a change in behaviour of house sparrows to avoid feeding stations frequented by sparrowhawks, or a reduction in house sparrow population size as a result of sparrowhawk increase. © 2015 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Application of spatial synoptic classification in evaluating links between heat stress and cardiovascular mortality and morbidity in Prague, Czech Republic

NASA Astrophysics Data System (ADS)

Urban, Aleš; Kyselý, Jan

2018-01-01

Spatial synoptic classification (SSC) is here first employed in assessing heat-related mortality and morbidity in Central Europe. It is applied for examining links between weather patterns and cardiovascular (CVD) mortality and morbidity in an extended summer season (16 May-15 September) during 1994-2009. As in previous studies, two SSC air masses (AMs)—dry tropical (DT) and moist tropical (MT)—are associated with significant excess CVD mortality in Prague, while effects on CVD hospital admissions are small and insignificant. Excess mortality for ischaemic heart diseases is more strongly associated with DT, while MT has adverse effect especially on cerebrovascular mortality. Links between the oppressive AMs and excess mortality relate also to conditions on previous days, as DT and MT occur in typical sequences. The highest CVD mortality deviations are found 1 day after a hot spell's onset, when temperature as well as frequency of the oppressive AMs are highest. Following this peak is typically DT- to MT-like weather transition, characterized by decrease in temperature and increase in humidity. The transition between upward (DT) and downward (MT) phases is associated with the largest excess CVD mortality, and the change contributes to the increased and more lagged effects on cerebrovascular mortality. The study highlights the importance of critically evaluating SSC's applicability and benefits within warning systems relative to other synoptic and epidemiological approaches. Only a subset of days with the oppressive AMs is associated with excess mortality, and regression models accounting for possible meteorological and other factors explain little of the mortality variance.

Environmental Factors Influencing Antarctic Krill Recruitment along the Western Antarctic Peninsula

NASA Astrophysics Data System (ADS)

Cope, J. S.; Steinberg, D. K.; Thanassekos, S.

2016-02-01

Climate warming in the Western Antarctic Peninsula (WAP) is impacting pelagic food web structure. Antarctic krill, Euphausia superba, are a critical food-web link between primary producers and higher trophic levels such as penguins, seals, and whales. Climate-induced changes in krill recruitment are thus an important consideration when evaluating future WAP ecosystem trends. We examined long-term (1993 to 2015) and spatial (north/south) changes in summer krill recruitment. Krill were collected within the epipelagic zone during the Palmer Antarctica Long-Term Ecological Research (PAL LTER) cruises within a 700 x 260 km sampling grid along the WAP. Krill from each tow were enumerated and their lengths were measured. A simple recruitment index based on the proportion of krill smaller than 40 mm (F40) was used in our analyses. There was a significant 5-6-year cyclical trend in F40. In the last 5 years, the southern population has begun to deviate from this cycle. To investigate potential environmental factors leading to this pattern in recruitment success, F40 was regressed with environmental factors and climatological indices for both the whole PAL LTER grid and north/south sub-regions. Over the whole grid, F40 was positively correlated with chlorophyll a and primary production, both with a 1-year lag. Spatially, these trends were strongest for chlorophyll in the north, and primary production in the south. Krill recruitment in the south was also correlated to climatological indices such as the Multivariate El Niño/Southern Oscillation Index (MEI). These correlations could be used to forecast future krill population changes.

HydroClimATe: hydrologic and climatic analysis toolkit

USGS Publications Warehouse

Dickinson, Jesse; Hanson, Randall T.; Predmore, Steven K.

2014-01-01

The potential consequences of climate variability and climate change have been identified as major issues for the sustainability and availability of the worldwide water resources. Unlike global climate change, climate variability represents deviations from the long-term state of the climate over periods of a few years to several decades. Currently, rich hydrologic time-series data are available, but the combination of data preparation and statistical methods developed by the U.S. Geological Survey as part of the Groundwater Resources Program is relatively unavailable to hydrologists and engineers who could benefit from estimates of climate variability and its effects on periodic recharge and water-resource availability. This report documents HydroClimATe, a computer program for assessing the relations between variable climatic and hydrologic time-series data. HydroClimATe was developed for a Windows operating system. The software includes statistical tools for (1) time-series preprocessing, (2) spectral analysis, (3) spatial and temporal analysis, (4) correlation analysis, and (5) projections. The time-series preprocessing tools include spline fitting, standardization using a normal or gamma distribution, and transformation by a cumulative departure. The spectral analysis tools include discrete Fourier transform, maximum entropy method, and singular spectrum analysis. The spatial and temporal analysis tool is empirical orthogonal function analysis. The correlation analysis tools are linear regression and lag correlation. The projection tools include autoregressive time-series modeling and generation of many realizations. These tools are demonstrated in four examples that use stream-flow discharge data, groundwater-level records, gridded time series of precipitation data, and the Multivariate ENSO Index.

Mortality related to extreme temperature for 15 cities in northeast Asia.

PubMed

Chung, Yeonseung; Lim, Youn-Hee; Honda, Yasushi; Guo, Yue-Liang Leon; Hashizume, Masahiro; Bell, Michelle L; Chen, Bing-Yu; Kim, Ho

2015-03-01

Multisite time-series studies for temperature-related mortality have been conducted mainly in the United States and Europe, but are lacking in Asia. This multisite time-series study examined mortality related to extreme temperatures (both cold and hot) in Northeast Asia, focusing on 15 cities of 3 high-income countries. This study includes 3 cities in Taiwan for 1994-2007, 6 cities in Korea for 1992-2010, and 6 cities in Japan for 1972-2009. We used 2-stage Bayesian hierarchical Poisson semiparametric regression to model the nonlinear relationship between temperature and mortality, providing city-specific and country-wide estimates for cold and heat effects. Various exposure time frames, age groups, and causes of death were considered. Cold effects had longer time lags (5-11 days) than heat effects, which were immediate (1-3 days). Cold effects were larger for cities in Taiwan, whereas heat effects were larger for cities in Korea and Japan. Patterns of increasing effects with age were observed in both cold and heat effects. Both cold and heat effects were larger for cardiorespiratory mortality than for other causes of death. Several city characteristics related to weather or air pollution were associated with both cold and heat effects. Mortality increased with either cold or hot temperature in urban populations of high-income countries in Northeast Asia, with spatial variations of effects among cities and countries. Findings suggest that climate factors are major contributors to the spatial heterogeneity of effects in this region, although further research is merited to identify other factors as determinants of variability.

[Spatial-temporal evolution characteristic of coordination between urbanization and eco-environment in Jilin Province, Northeast China].

PubMed

Tan, Jun-tao; Zhang, Ping-yu; Li, Jing; Liu, Shi-wei

2015-12-01

By building urbanization and eco-environment evaluation index systems, the levels of urbanization and eco-environment, and the degree of their coupling coordination of Jilin Province from 2000 to 2012 were evaluated. The level of comprehensive urbanization showed a continued growth trend, and the economic urbanization contributed the largest share. The eco-environment comprehensive level fluctuated upward. The eco-environment state, response and pressure increased faster since the implementation of the strategy of revitalizing Northeast China and other old industrial regions. Coupling coordination degree between urbanization and eco-environment increased continuously, from uncoordinated status to advanced coordinated status, changing from eco-environment lagged to urbanization lagged. The level of urbanization in central region was higher than east and west regions in Jilin Province, but its eco-environment level was low. Coupling coordination degree in Changchun was the highest, and that of Baishan was the lowest. Coupling coordination degree of Chang-Ji integrated region was always at the leading level, but the level of eco-environment lagged behind was growing since 2000. Coupling coordination degree of Siping, Liaoyuan, Songyuan and Yanbian increased, but that of Baicheng decreased.

Environmental Variability and Fluctuation of Coccidioidomycosis (Valley Fever) In California: Based on a New Framework Involving Fungal Life Cycle

NASA Astrophysics Data System (ADS)

Jia, S.; Okin, G. S.; Shafir, S. C.

2013-12-01

Coccidioidomycosis (valley fever), caused by inhalation of spores from pathogenic fungus includingCoccidiodes immitis (C. immitis) and Coccidioides posadasii (C. posadasii), is a disease endemic to arid regions in the southwest US, as well as parts of Central and South America. With a projected increase of drought in this region, an improved understanding of environmental factors behind the outbreaks of coccidioidomycosis will enable the prediction of coccidioidomycosis in a changing climate regime. Previous research shows the infections correlate with climate conditions including precipitation, temperature, and dust. However, most studies focus only on the environmental conditions of fungus growth, which is the first stage in the fungal life cycle. In contrast, we extend the analysis to the following two stages in the life cycle, arthrospore formation and dispersal, to form a better model to predict the disease outbreaks. Besides climate conditions, we use relative spectral mixture analysis (RSMA) based on MODIS MOD43 nadir BRDF adjusted reflectance (NBAR) data to derive the relative dynamics of green vegetation, non-photosynthetic vegetation and bare soil coverage as better indicators of soil moisture, which is important for arthospore formation and dispersal. After detecting the hotspots of disease outbreaks, we correlate seasonal incidence from 2000 to 2010 with the environmental variables zero to eight seasons before to obtain candidates for stepwise regression. Regression result shows a seasonal difference in the leading explanatory variables. Such difference indicates the different seasonal main influential process from fungal life cycle. C. immitis (fungus responsible for coccidioidomycosis outbreaks in California) growth explains outbreaks in winter and fall better than other two stages in the life cycle, while arthospore formation is more responsible for spring and summer outbreaks. As the driest season, summer has the largest area related with arthospore dispersal. The seasonal difference of main influential process relates to the length of lags between the outbreaks and stages in fungal life cycle. During wet seasons of California including winter and fall, outbreaks are less correlated with the short-lag process such as dispersal of arthospores because of high soil moisture. In contrast, the long-lag process like C.immitis growth is influential on outbreaks in wet seasons. The arthospore formation, especially during the latest dry season (with a lag less than one year), is more responsible for outbreaks in spring and summer, when the influence of C. immitis growth is dampened by time. However, arthospores formed and preserved years ago may introduce uncertainty to the seasonal lag patterns. The long lags also exist in outbreaks related to arthospore formation. By including all three stages of fungal life cycle, we formed a more comprehensive framework in explaining the relationship between environmental conditions and disease outbreaks. Such analysis can be extended to a finer temporal resolution (e.g. per month) to obtain a clearer picture between environmental variability and coccidioidomycosis fluctuation.

Influence of sound source location on the behavior and physiology of the precedence effect in cats.

PubMed

Dent, Micheal L; Tollin, Daniel J; Yin, Tom C T

2009-08-01

Psychophysical experiments on the precedence effect (PE) in cats have shown that they localize pairs of auditory stimuli presented from different locations in space based on the spatial position of the stimuli and the interstimulus delay (ISD) between the stimuli in a manner similar to humans. Cats exhibit localization dominance for pairs of transient stimuli with |ISDs| from approximately 0.4 to 10 ms, summing localization for |ISDs| < 0.4 ms and breakdown of fusion for |ISDs| > 10 ms, which is the approximate echo threshold. The neural correlates to the PE have been described in both anesthetized and unanesthetized animals at many levels from auditory nerve to cortex. Single-unit recordings from the inferior colliculus (IC) and auditory cortex of cats demonstrate that neurons respond to both lead and lag sounds at ISDs above behavioral echo thresholds, but the response to the lag is reduced at shorter ISDs, consistent with localization dominance. Here the influence of the relative locations of the leading and lagging sources on the PE was measured behaviorally in a psychophysical task and physiologically in the IC of awake behaving cats. At all configurations of lead-lag stimulus locations, the cats behaviorally exhibited summing localization, localization dominance, and breakdown of fusion. Recordings from the IC of awake behaving cats show neural responses paralleling behavioral measurements. Both behavioral and physiological results suggest systematically shorter echo thresholds when stimuli are further apart in space.

Influence of Sound Source Location on the Behavior and Physiology of the Precedence Effect in Cats

PubMed Central

Dent, Micheal L.; Tollin, Daniel J.; Yin, Tom C. T.

2009-01-01

Psychophysical experiments on the precedence effect (PE) in cats have shown that they localize pairs of auditory stimuli presented from different locations in space based on the spatial position of the stimuli and the interstimulus delay (ISD) between the stimuli in a manner similar to humans. Cats exhibit localization dominance for pairs of transient stimuli with |ISDs| from ∼0.4 to 10 ms, summing localization for |ISDs| < 0.4 ms and breakdown of fusion for |ISDs| > 10 ms, which is the approximate echo threshold. The neural correlates to the PE have been described in both anesthetized and unanesthetized animals at many levels from auditory nerve to cortex. Single-unit recordings from the inferior colliculus (IC) and auditory cortex of cats demonstrate that neurons respond to both lead and lag sounds at ISDs above behavioral echo thresholds, but the response to the lag is reduced at shorter ISDs, consistent with localization dominance. Here the influence of the relative locations of the leading and lagging sources on the PE was measured behaviorally in a psychophysical task and physiologically in the IC of awake behaving cats. At all configurations of lead-lag stimulus locations, the cats behaviorally exhibited summing localization, localization dominance, and breakdown of fusion. Recordings from the IC of awake behaving cats show neural responses paralleling behavioral measurements. Both behavioral and physiological results suggest systematically shorter echo thresholds when stimuli are further apart in space. PMID:19439668

Using an autologistic regression model to identify spatial risk factors and spatial risk patterns of hand, foot and mouth disease (HFMD) in Mainland China

PubMed Central

2014-01-01

Background There have been large-scale outbreaks of hand, foot and mouth disease (HFMD) in Mainland China over the last decade. These events varied greatly across the country. It is necessary to identify the spatial risk factors and spatial distribution patterns of HFMD for public health control and prevention. Climate risk factors associated with HFMD occurrence have been recognized. However, few studies discussed the socio-economic determinants of HFMD risk at a space scale. Methods HFMD records in Mainland China in May 2008 were collected. Both climate and socio-economic factors were selected as potential risk exposures of HFMD. Odds ratio (OR) was used to identify the spatial risk factors. A spatial autologistic regression model was employed to get OR values of each exposures and model the spatial distribution patterns of HFMD risk. Results Results showed that both climate and socio-economic variables were spatial risk factors for HFMD transmission in Mainland China. The statistically significant risk factors are monthly average precipitation (OR = 1.4354), monthly average temperature (OR = 1.379), monthly average wind speed (OR = 1.186), the number of industrial enterprises above designated size (OR = 17.699), the population density (OR = 1.953), and the proportion of student population (OR = 1.286). The spatial autologistic regression model has a good goodness of fit (ROC = 0.817) and prediction accuracy (Correct ratio = 78.45%) of HFMD occurrence. The autologistic regression model also reduces the contribution of the residual term in the ordinary logistic regression model significantly, from 17.25 to 1.25 for the odds ratio. Based on the prediction results of the spatial model, we obtained a map of the probability of HFMD occurrence that shows the spatial distribution pattern and local epidemic risk over Mainland China. Conclusions The autologistic regression model was used to identify spatial risk factors and model spatial risk patterns of HFMD. HFMD occurrences were found to be spatially heterogeneous over the Mainland China, which is related to both the climate and socio-economic variables. The combination of socio-economic and climate exposures can explain the HFMD occurrences more comprehensively and objectively than those with only climate exposures. The modeled probability of HFMD occurrence at the county level reveals not only the spatial trends, but also the local details of epidemic risk, even in the regions where there were no HFMD case records. PMID:24731248

100 years of mortality due to chronic obstructive pulmonary disease in Australia: the role of tobacco consumption.

PubMed

Adair, T; Hoy, D; Dettrick, Z; Lopez, A D

2012-12-01

Global studies of the long-term association between tobacco consumption and chronic obstructive pulmonary disease (COPD) have relied upon descriptions of trends. To statistically analyse the relationship of tobacco consumption with data on mortality due to COPD over the past 100 years in Australia. Tobacco consumption was reconstructed back to 1887. Log-linear Poisson regression models were used to analyse cumulative cohort and lagged time-specific smoking data and its relationship with COPD mortality. Age-standardised COPD mortality, although likely misclassified with other diseases, decreased for males and females from 1907 until the start of the Second World War in contrast to steadily rising tobacco consumption. Thereafter, COPD mortality rose sharply in line with trends in smoking, peaking in the early 1970s for males and over 20 years later for females, before falling again. Regression models revealed both cumulative and time-specific tobacco consumption to be strongly predictive of COPD mortality, with a time lag of 15 years for males and 20 years for females. Sharp falls in COPD mortality before the Second World War were unrelated to tobacco consumption. Smoking was the primary driver of post-War trends, and the success of anti-smoking campaigns has sharply reduced COPD mortality levels.

A Temporal Association between Accumulated Petrol (Gasoline) Lead Emissions and Motor Neuron Disease in Australia

PubMed Central

Laidlaw, Mark A. S.; Rowe, Dominic B.; Ball, Andrew S.; Mielke, Howard W.

2015-01-01

Background: The age standardised death rate from motor neuron disease (MND) has increased from 1.29 to 2.74 per 100,000, an increase of 112.4% between 1959 and 2013. It is clear that genetics could not have played a causal role in the increased rate of MND deaths over such a short time span. We postulate that environmental factors are responsible for this rate increase. We focus on lead additives in Australian petrol as a possible contributing environmental factor. Methods: The associations between historical petrol lead emissions and MND death trends in Australia between 1962 and 2013 were examined using linear regressions. Results: Regression results indicate best fit correlations between a 20 year lag of petrol lead emissions and age-standardised female death rate (R2 = 0.86, p = 4.88 × 10−23), male age standardised death rate (R2 = 0.86, p = 9.4 × 10−23) and percent all cause death attributed to MND (R2 = 0.98, p = 2.6 × 10−44). Conclusion: Legacy petrol lead emissions are associated with increased MND death trends in Australia. Further examination of the 20 year lag between exposure to petrol lead and the onset of MND is warranted. PMID:26703636

Sr-87/Sr-86 isotopic age determination of upper Cretaceous Santonian, Campanian and Maastrichtian chondrichthyan teeth of the Atlantic and Eastern Gulf Coastal Plains: Implications for sea level cyclicity and macrofossil time-averaging in depositional sequence lag deposits

NASA Astrophysics Data System (ADS)

Becker, Martin Andrew

1997-11-01

Unconformities and fossil rich layers are common elements in the stratigraphic architecture of upper Cretaceous sediments exposed on both the Atlantic and Eastern Gulf Coastal Plains. Contacts between the Eutaw Formation and Tombigbee Sands Member in Alabama, the Blufftown Formation and Cusseta Sands in Georgia and the Wenonah- Mt. Laurel and Navesink Formations in New Jersey are marked by erosional surfaces with overlying blankets and lenses of macrofossil residuum. These contacts correspond to bounding unconformities and transgressive lags separating Santonian-Campanian, lower Campanian-upper Campanian and Campanian-Maastrichtian depositional sequences. Regression and subsequent transgression of sea level at the top of these depositional sequences resulted in hydrodynamic sorting of sediments and fossils that had previously accumulated in shelf and lower shoreface paleoenvironments. Remobilization of sediments by shoreface retreat reworked fossil hard-parts which became concentrated above erosional surfaces as sea level rose. Because of the abundance of chondrichthyan, pelecypod and ammonite fossils, these lags have great biostratigraphic significance and provide a basis for examining time averaging in macrofossil zonation. Chondrichthyan teeth are composed of extremely durable and highly insoluble, biogenic apatite. This tooth apatite accurately records the Sr87/Sr86 isotopic signature of seawater, from which the numerical age of the teeth can be calculated using published age/concentration data. Teeth (e.g. Squalicorax kaupi, Scapanorhynchus texanus) from Santonian-Campanian lag deposits at the contact of the Eutaw Formation and Tombigbee Sands Member in Alabama yield approximate ages of 85-81 Ma. Teeth from lower-upper Campanian lag deposits at the contact of the Blufftown Formation and Cusseta Sands in Georgia yield approximate ages of 83-75 Ma. Teeth from Campanian-Maastrichtian lag deposits at the contact of the Wenonah-Mt. Laurel and Navesink Formations in New Jersey yield approximate ages of 80-76 Ma. Isotopic age determination from these chondrichthyan teeth indicate average hiatus of approximately 3-7 million years occur during the development of lag accumulations and transgressive unconformities. Santonian, Campanian and Maastrichtian macrofossils analyzed in this study are hydrodynamically stable components representing time-averaged fossil assemblages sorted together by physical processes and are not life cohorts. Abrupt appearance and disappearance of organisms found in upper Cretaceous lag deposits of the Atlantic and Eastern Gulf Coastal Plains are artifacts of a physical sorting processes associated with sea-level cyclicity.

Exploration of walking behavior in Vermont using spatial regression.

DOT National Transportation Integrated Search

2015-06-01

This report focuses on the relationship between walking and its contributing factors by : applying spatial regression methods. Using the Vermont data from the New England : Transportation Survey (NETS), walking variables as well as 170 independent va...

Spatial quantile regression using INLA with applications to childhood overweight in Malawi.

PubMed

Mtambo, Owen P L; Masangwi, Salule J; Kazembe, Lawrence N M

2015-04-01

Analyses of childhood overweight have mainly used mean regression. However, using quantile regression is more appropriate as it provides flexibility to analyse the determinants of overweight corresponding to quantiles of interest. The main objective of this study was to fit a Bayesian additive quantile regression model with structured spatial effects for childhood overweight in Malawi using the 2010 Malawi DHS data. Inference was fully Bayesian using R-INLA package. The significant determinants of childhood overweight ranged from socio-demographic factors such as type of residence to child and maternal factors such as child age and maternal BMI. We observed significant positive structured spatial effects on childhood overweight in some districts of Malawi. We recommended that the childhood malnutrition policy makers should consider timely interventions based on risk factors as identified in this paper including spatial targets of interventions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Implementations of geographically weighted lasso in spatial data with multicollinearity (Case study: Poverty modeling of Java Island)

NASA Astrophysics Data System (ADS)

Setiyorini, Anis; Suprijadi, Jadi; Handoko, Budhi

2017-03-01

Geographically Weighted Regression (GWR) is a regression model that takes into account the spatial heterogeneity effect. In the application of the GWR, inference on regression coefficients is often of interest, as is estimation and prediction of the response variable. Empirical research and studies have demonstrated that local correlation between explanatory variables can lead to estimated regression coefficients in GWR that are strongly correlated, a condition named multicollinearity. It later results on a large standard error on estimated regression coefficients, and, hence, problematic for inference on relationships between variables. Geographically Weighted Lasso (GWL) is a method which capable to deal with spatial heterogeneity and local multicollinearity in spatial data sets. GWL is a further development of GWR method, which adds a LASSO (Least Absolute Shrinkage and Selection Operator) constraint in parameter estimation. In this study, GWL will be applied by using fixed exponential kernel weights matrix to establish a poverty modeling of Java Island, Indonesia. The results of applying the GWL to poverty datasets show that this method stabilizes regression coefficients in the presence of multicollinearity and produces lower prediction and estimation error of the response variable than GWR does.

Identification of the prediction model for dengue incidence in Can Tho city, a Mekong Delta area in Vietnam.

PubMed

Phung, Dung; Huang, Cunrui; Rutherford, Shannon; Chu, Cordia; Wang, Xiaoming; Nguyen, Minh; Nguyen, Nga Huy; Manh, Cuong Do

2015-01-01

The Mekong Delta is highly vulnerable to climate change and a dengue endemic area in Vietnam. This study aims to examine the association between climate factors and dengue incidence and to identify the best climate prediction model for dengue incidence in Can Tho city, the Mekong Delta area in Vietnam. We used three different regression models comprising: standard multiple regression model (SMR), seasonal autoregressive integrated moving average model (SARIMA), and Poisson distributed lag model (PDLM) to examine the association between climate factors and dengue incidence over the period 2003-2010. We validated the models by forecasting dengue cases for the period of January-December, 2011 using the mean absolute percentage error (MAPE). Receiver operating characteristics curves were used to analyze the sensitivity of the forecast of a dengue outbreak. The results indicate that temperature and relative humidity are significantly associated with changes in dengue incidence consistently across the model methods used, but not cumulative rainfall. The Poisson distributed lag model (PDLM) performs the best prediction of dengue incidence for a 6, 9, and 12-month period and diagnosis of an outbreak however the SARIMA model performs a better prediction of dengue incidence for a 3-month period. The simple or standard multiple regression performed highly imprecise prediction of dengue incidence. We recommend a follow-up study to validate the model on a larger scale in the Mekong Delta region and to analyze the possibility of incorporating a climate-based dengue early warning method into the national dengue surveillance system. Copyright © 2014 Elsevier B.V. All rights reserved.

Biophysical characterization of a swimmer with a unilateral arm amputation: a case study.

PubMed

Figueiredo, Pedro; Willig, Renata; Alves, Francisco; Vilas-Boas, João Paulo; Fernandes, Ricardo J

2014-11-01

To examine the effect of swimming speed (v) on the biomechanical and physiological responses of a trained front-crawl swimmer with a unilateral arm amputation. A 13-y-old girl with a unilateral arm amputation (level of the elbow) was tested for stroke length (SL, horizontal displacement cover with each stroke cycle), stroke frequency (SF, inverse of the time to complete each stroke cycle), adapted index of coordination (IdCadapt, lag time between propulsive phases), intracycle velocity variation (IVV, coefficient of variation of the instantaneous velocity-time data), active drag (D, hydrodynamic resistance), and energy cost (C, ratio of metabolic power to speed) during trials of increasing v. Swimmer data showed a positive relationship between v and SF (R² = 1, P < .001), IVV (R² = .98, P = .002), D (R² = .98, P < .001), and C (R² = .95, P = .001) and a negative relationship with the SL (R² = .99, P = .001). No relation was found between v and IdCadapt (R² = .35, P = .22). A quadratic regression best fitted the relationship between v and general kinematical parameters (SL and SF); a cubic relationship fit the IVV best. The relationship between v and D was best expressed by a power regression, and the linear regression fit the C and IdCadapt best. The subject's adaptation to increased v was different from able-bodied swimmers, mainly on interarm coordination, maintaining the lag time between propulsive phases, which influence the magnitude of the other parameters. These results might be useful to develop specific training and enhance swimming performance in swimmers with amputations.

[Application of regression tree in analyzing the effects of climate factors on NDVI in loess hilly area of Shaanxi Province].

PubMed

Liu, Yang; Lü, Yi-he; Zheng, Hai-feng; Chen, Li-ding

2010-05-01

Based on the 10-day SPOT VEGETATION NDVI data and the daily meteorological data from 1998 to 2007 in Yan' an City, the main meteorological variables affecting the annual and interannual variations of NDVI were determined by using regression tree. It was found that the effects of test meteorological variables on the variability of NDVI differed with seasons and time lags. Temperature and precipitation were the most important meteorological variables affecting the annual variation of NDVI, and the average highest temperature was the most important meteorological variable affecting the inter-annual variation of NDVI. Regression tree was very powerful in determining the key meteorological variables affecting NDVI variation, but could not build quantitative relations between NDVI and meteorological variables, which limited its further and wider application.

Determinants of corporate dividend policy in Indonesia

NASA Astrophysics Data System (ADS)

Lestari, H. S.

2018-01-01

This study aims to investigate the determinants factors that effect the dividend policy. The sample used in this research is manufacture companies listed in Indonesia Stock Exchange (IDX) and the period 2011 - 2015. There are independent variables such as earning, cash flow, free cash flow, debt, growth opportunities, investment opportunities, firm size, largest shareholder, firm risk, lagged dividend and dividend policy used as dependent variable. The study examines a total of 32 manufacture companies. After analyzing the data using the program software Eviews 9.0 by multiples regression analysis reveal that earning, cash flow, free cash flow, firm size, and lagged dividend have significant effect on dividend policy, whereas debt, growth opportunities, investment opportunities, largest shareholder, and firm risk have no significant effect on dividend policy. The results of this study are expected to be implemented by the financial managers in improving corporate profits and basic information as return on investment decisions.

Online Soft Sensor of Humidity in PEM Fuel Cell Based on Dynamic Partial Least Squares

PubMed Central

Long, Rong; Chen, Qihong; Zhang, Liyan; Ma, Longhua; Quan, Shuhai

2013-01-01

Online monitoring humidity in the proton exchange membrane (PEM) fuel cell is an important issue in maintaining proper membrane humidity. The cost and size of existing sensors for monitoring humidity are prohibitive for online measurements. Online prediction of humidity using readily available measured data would be beneficial to water management. In this paper, a novel soft sensor method based on dynamic partial least squares (DPLS) regression is proposed and applied to humidity prediction in PEM fuel cell. In order to obtain data of humidity and test the feasibility of the proposed DPLS-based soft sensor a hardware-in-the-loop (HIL) test system is constructed. The time lag of the DPLS-based soft sensor is selected as 30 by comparing the root-mean-square error in different time lag. The performance of the proposed DPLS-based soft sensor is demonstrated by experimental results. PMID:24453923

Stressful work environment and wellbeing: What comes first?

PubMed

Elovainio, Marko; Heponiemi, Tarja; Jokela, Markus; Hakulinen, Christian; Presseau, Justin; Aalto, Anna-Mari; Kivimäki, Mika

2015-07-01

The association between the psychosocial work environment, including job demands, job control, and organizational justice, and employee wellbeing has been well established. However, the exposure to adverse work environments is typically measured only using self-reported measures that are vulnerable to reporting bias, and thus any associations found may be explained by reverse causality. Using linear regression models and cross-lagged structural equation modeling (SEM), we tested the direction of the association between established job stress models (job demand control and organizational justice models) and 3 wellbeing indicators (psychological distress, sleeping problems, and job satisfaction) among 1524 physicians in a 4-year follow-up. Results from the longitudinal cross-lagged analyses showed that the direction of the association was from low justice to decreasing wellbeing rather than the reverse. Although the pattern was similar in job demands and job control, a reciprocal association was found between job control and psychological distress. (c) 2015 APA, all rights reserved).

Relations between productivity, climate, and normalized difference vegetation index in the central Great Plains

NASA Astrophysics Data System (ADS)

Wang, Jue

Understanding the influences of climate on productivity remains a major challenge in landscape ecology. Satellite remote sensing of normalized difference vegetation index (NDVI) provides a useful tool to study landscape patterns, based on generalization of local measurements, and to examine relations between climate and variation in productivity. This dissertation examines temporal and spatial relations between NDVI, productivity, and climatic factors over the course of nine years in the central Great Plains. Two general findings emerge: (1) integrated NDVI is a reliable measure of production, as validated with ground-based productivity measurements; and (2) precipitation is the primary factor that determines spatial and temporal patterns of NDVI. NDVI, integrated over appropriate time intervals, is strongly correlated with ground productivity measurements in forests, grasslands, and croplands. Most tree productivity measurements (tree ring size, tree diameter growth, and seed production) are strongly correlated with NDVI integrated for a period during the early growing season; foliage production is most strongly correlated with NDVI integrated over the entire growing season; and tree height growth corresponds with NDVI integrate during the previous growing season. Similarly, productivity measurements for herbaceous plants (grassland biomass and crop yield) are strongly correlated with NDVI. Within the growing season, the temporal pattern of grassland biomass production covaries with NDVI, with a four-week lag time. Across years, grassland biomass production covaries with NDVI integrated from part to all of the current growing season. Corn and wheat yield are most strongly related to NDVI integrated from late June to early August and from late April to mid-May, respectively. Precipitation strongly influences both temporal and spatial patterns of NDVI, while temperature influences NDVI only during the early and late growing season. In terms of temporal patterns, NDVI integrated over the growing season is strongly correlated with precipitation received during the current growing season plus the seven preceding months (fifteen month period); NDVI within the growing season responds to changes in precipitation with a four to eight week lag time; and major precipitation events lead to changes in NDVI with a two to four week lag time. Temperature has a positive correlation with NDVI during the early and late growing season, and a weak negative correlation during the middle of the growing season. In terms of spatial patterns, average precipitation is a strong predictor of the major east-west gradient of NDVI. Deviation from average precipitation explains most of the year-to-year variation in spatial patterns. NDVI and precipitation deviations from average covary (both positive or both negative) for 60--95% of the total land area in Kansas. Minimum and average temperatures are positively correlated with NDVI, but temperature deviation from average is generally not correlated with NDVI deviation from average. The strong relationships between NDVI and productivity, and between precipitation and NDVI, along with detailed analysis of the temporal and spatial patterns for our study region, provides the basis for prediction of productivity at landscape scales under different climate regimes.

Risk of Cardiovascular Hospitalizations from Exposure to Coarse Particulate Matter (PM10) Below the European Union Safety Threshold.

PubMed

Vaduganathan, Muthiah; De Palma, Giuseppe; Manerba, Alessandra; Goldoni, Matteo; Triggiani, Marco; Apostoli, Pietro; Dei Cas, Livio; Nodari, Savina

2016-04-15

The association between exposure to air pollution and acute cardiovascular (CV) events is well documented; however, limited data are available evaluating the public health safety of various "doses" of particular matter (PM) below currently accepted safety thresholds. We explored the cross-sectional association between PM with aerodynamic diameter <10 μm (PM10) and daily CV hospitalizations in Brescia, Italy, using Poisson regression models adjusted for age, gender, and meteorologic indices. Average daily exposure to PM10 obtained from arithmetic means of air pollution data were captured by 4 selected monitoring stations. PM10 data were expressed as daily means (lag 0-day) or 3-day moving averages (lag 3-day) and categorized according to the European Union daily limit value of 50 μg/m(3). From September 2004 to September 2007, data from 6,000 acute CV admissions to a tertiary referral center were collected. An increase of 1 μg/m(3) PM10 at lag 0-day was independently associated with higher rates of acute hospitalizations for composite CV-related events (relative risk [RR] 1.004, 95% confidence interval [CI] 1.002 to 1.006), acute heart failure (RR 1.004, 95% CI 1.001 to 1.008), acute coronary syndromes (RR 1.002, 95% CI 0.999 to 1.005), malignant ventricular arrhythmias (RR 1.004, 95% CI 0.999 to 1.010), and atrial fibrillation (RR 1.008, 95% CI 1.003 to 1.012). Similar results were obtained using PM10 lag 3-day data. The excess PM10 CV hospitalization risk (by lag 0-day and lag 3-day) did not vary significantly above and below the 50 μg/m(3) safety threshold or by age and gender. In conclusion, increased levels of PM10, even below the current limits set by the European Union, were associated with excess risk for admissions for acute CV events. Copyright © 2016 Elsevier Inc. All rights reserved.

Effect of Environmental Factors on Low Weight in Non-Premature Births: A Time Series Analysis.

PubMed

Díaz, Julio; Arroyo, Virginia; Ortiz, Cristina; Carmona, Rocío; Linares, Cristina

2016-01-01

Exposure to pollutants during pregnancy has been related to adverse birth outcomes. LBW can give rise to lifelong impairments. Prematurity is the leading cause of LBW, yet few studies have attempted to analyse how environmental factors can influence LBW in infants who are not premature. This study therefore sought to analyse the influence of air pollution, noise levels and temperature on LBW in non-premature births in Madrid during the period 2001-2009. Ecological time-series study to assess the impact of PM2.5, NO2 and O3 concentrations, noise levels, and temperatures on LBW among non-premature infants across the period 2001-2009. Our analysis extended to infants having birth weights of 1,500 g to 2,500 g (VLBW) and less than 1,500 g (ELBW). Environmental variables were lagged until 37 weeks with respect to the date of birth, and cross-correlation functions were used to identify explaining lags. Results were quantified using Poisson regression models. Across the study period 298,705 births were registered in Madrid, 3,290 of which had LBW; of this latter total, 1,492 were non-premature. PM2.5 was the only pollutant to show an association with the three variables of LBW in non-premature births. This association occurred at around the third month of gestation for LBW and VLBW (LBW: lag 23 and VLBW: lag 25), and at around the eighth month of gestation for ELBW (lag 6). Leqd was linked to LBW at lag zero. The RR of PM2.5 on LBW was 1.01 (1.00 1.03). The RR of Leqd on LBW was 1.09 (0.99 1.19)(p<0.1). The results obtained indicate that PM2.5 had influence on LBW. The adoption of measures aimed at reducing the number of vehicles would serve to lower pregnant women's exposure. In the case of noise should be limited the exposure to high levels during the final weeks of pregnancy.

The influence of temperature on mortality and its Lag effect: a study in four Chinese cities with different latitudes.

PubMed

Bao, Junzhe; Wang, Zhenkun; Yu, Chuanhua; Li, Xudong

2016-05-04

Global climate change is one of the most serious environmental issues faced by humanity, and the resultant change in frequency and intensity of heat waves and cold spells could increase mortality. The influence of temperature on human health could be immediate or delayed. Latitude, relative humidity, and air pollution may influence the temperature-mortality relationship. We studied the influence of temperature on mortality and its lag effect in four Chinese cities with a range of latitudes over 2008-2011, adjusting for relative humidity and air pollution. We recorded the city-specific distributions of temperature and mortality by month and adopted a Poisson regression model combined with a distributed lag nonlinear model to investigate the lag effect of temperature on mortality. We found that the coldest months in the study area are December through March and the hottest months are June through September. The ratios of deaths during cold months to hot months were 1.43, 1.54, 1.37 and 1.12 for the cities of Wuhan, Changsha, Guilin and Haikou, respectively. The effects of extremely high temperatures generally persisted for 3 days, whereas the risk of extremely low temperatures could persist for 21 days. Compared with the optimum temperature of each city, at a lag of 21 days, the relative risks (95 % confidence interval) of extreme cold temperatures were 4.78 (3.63, 6.29), 2.38 (1.35, 4.19), 2.62 (1.15, 5.95) and 2.62 (1.44, 4.79) for Wuhan, Changsha, Guilin and Haikou, respectively. The respective risks were 1.35 (1.18, 1.55), 1.19 (0.96, 1.48), 1.22 (0.82, 1.82) and 2.47 (1.61, 3.78) for extreme hot temperatures, at a lag of 3 days. Temperature-mortality relationships vary among cities at different latitudes. Local governments should establish regional prevention and protection measures to more effectively confront and adapt to local climate change. The effects of hot temperatures predominantly occur over the short term, whereas those of cold temperatures can persist for an extended number of days.

Impacts of temperature change on ambulance dispatches and seasonal effect modification.

PubMed

Cheng, Jian; Xu, Zhiwei; Zhao, Desheng; Xie, Mingyu; Yang, Huihui; Wen, Liying; Li, Kesheng; Su, Hong

2016-12-01

Ambulance dispatch is a proxy of acute health outcomes, and growing epidemiological evidence documented its relation to extreme temperature events. Research, however, on short-term temperature change and ambulance dispatches is scarce. We aimed to investigate the effect of short-term temperature change on ambulance dispatches and potential modification by season. Daily data on ambulance dispatch and weather factors were collected in Huainan, a Chinese inland city from December 2011 through December 2013. A Poison generalized linear regression model combined with distributed lag nonlinear model was constructed to examine the association of temperature change between neighboring days (TCN) with ambulance dispatches. The effect modification by season was also examined. There were 48,700 ambulance attendances during the study period. A statistically significant association of TCN with ambulance dispatches was observed. Temperature rise between neighboring days (TCN > 0) was associated with elevated adverse risk of ambulance dispatches, and the effects appeared to be acute (lag0, on the current day) and could last for at least a week, while temperature drop between neighboring days (TCN < 0) had a protective effect. For a 1 °C increase of TCN at lag0 and lag06 (on the 7-day moving average), the risk of ambulance dispatches increased by 2 % (95 % CI 1-3 %) and 7 (95 % CI 1-13 %), respectively. Extreme TCN increase (95th percentile, 3.3 °C vs. 0 °C) at lag0 and lag05 was accompanied by 6 (95 % CI 3-8 %) and 27 % (95 % CI 12-44 %) increase in ambulance dispatches. Ambulance dispatches were more vulnerable to extremely great temperature rise in summer and autumn. TCN was adopted for the first time to quantify the impact of short-term temperature change on ambulance dispatches. Temperature drop between neighboring days (TCN < 0) had a protective effect on ambulance dispatches, while temperature rise between neighboring days (TCN > 0) could acutely trigger the increase in ambulance dispatches, and TCN effect differs by season.

Advances in nonmarket valuation econometrics: Spatial heterogeneity in hedonic pricing models and preference heterogeneity in stated preference models

NASA Astrophysics Data System (ADS)

Yoo, Jin Woo

In my 1st essay, the study explores Pennsylvania residents. willingness to pay for development of renewable energy technologies such as solar power, wind power, biomass electricity, and other renewable energy using a choice experiment method. Principle component analysis identified 3 independent attitude components that affect the variation of preference, a desire for renewable energy and environmental quality and concern over cost. The results show that urban residents have a higher desire for environmental quality and concern less about cost than rural residents and consequently have a higher willingness to pay to increase renewable energy production. The results of sub-sample analysis show that a representative respondent in rural (urban) Pennsylvania is willing to pay 3.8(5.9) and 4.1(5.7)/month for increasing the share of Pennsylvania electricity generated from wind power and other renewable energy by 1 percent point, respectively. Mean WTP for solar and biomass electricity was not significantly different from zero. In my second essay, heterogeneity of individual WTP for various renewable energy technologies is investigated using several different variants of the multinomial logit model: a simple MNL with interaction terms, a latent class choice model, a random parameter mixed logit choice model, and a random parameter-latent class choice model. The results of all models consistently show that respondents. preference for individual renewable technology is heterogeneous, but the degree of heterogeneity differs for different renewable technologies. In general, the random parameter logit model with interactions and a hybrid random parameter logit-latent class model fit better than other models and better capture respondents. heterogeneity of preference for renewable energy. The impact of the land under agricultural conservation easement (ACE) contract on the values of nearby residential properties is investigated using housing sales data in two Pennsylvania Counties. The spatial-lag (SLM), the spatial error (SEM) and the spatial error component (SEC) models were compared. A geographically weighted regression (GWR) model is estimated to study the spatial heterogeneity of the marginal implicit prices of ACE impact within each county. New hybrid spatial hedonic models, the GWR-SEC and a modified GWR-SEM, are estimated such that both spatial autocorrelation and heterogeneity are accounted. The results show that the coefficient of land under easement contract varies spatially within one county, but not within the other county studied. Also, ACE's are found to have both positive and negative impacts on the values of nearby residential properties. Among global spatial models, the SEM fit better than the SLM and the SEC. Statistical goodness of fit measures showed that the GWR-SEC model fit better than the GWR or the GWR-SEC model. Finally, the GWR-SEC showed spatial autocorrelation is stronger in one county than in the other county.

Area-to-point regression kriging for pan-sharpening

NASA Astrophysics Data System (ADS)

Wang, Qunming; Shi, Wenzhong; Atkinson, Peter M.

2016-04-01

Pan-sharpening is a technique to combine the fine spatial resolution panchromatic (PAN) band with the coarse spatial resolution multispectral bands of the same satellite to create a fine spatial resolution multispectral image. In this paper, area-to-point regression kriging (ATPRK) is proposed for pan-sharpening. ATPRK considers the PAN band as the covariate. Moreover, ATPRK is extended with a local approach, called adaptive ATPRK (AATPRK), which fits a regression model using a local, non-stationary scheme such that the regression coefficients change across the image. The two geostatistical approaches, ATPRK and AATPRK, were compared to the 13 state-of-the-art pan-sharpening approaches summarized in Vivone et al. (2015) in experiments on three separate datasets. ATPRK and AATPRK produced more accurate pan-sharpened images than the 13 benchmark algorithms in all three experiments. Unlike the benchmark algorithms, the two geostatistical solutions precisely preserved the spectral properties of the original coarse data. Furthermore, ATPRK can be enhanced by a local scheme in AATRPK, in cases where the residuals from a global regression model are such that their spatial character varies locally.

Experimental investigation of compliant wall surface deformation in a turbulent channel flow

NASA Astrophysics Data System (ADS)

Zhang, Cao; Wang, Jin; Katz, Joseph

2016-11-01

The dynamic response of a compliant wall under a turbulent channel flow is investigated by simultaneously measuring the time-resolved, 3D flow field (using tomographic PIV) and the 2D surface deformation (using interferometry). The pressure distributions are calculated by spatially integrating the material acceleration field. The Reynolds number is Reτ = 2300, and the centerline velocity (U0) is 15% of the material shear speed. The wavenumber-frequency spectra of the wall deformation contain a non-advected low-frequency component and advected modes, some traveling downstream at U0 and others at 0.72U0. Trends in the wall dynamics are elucidated by correlating the deformation with flow variables. The spatial pressure-deformation correlations peak at y/ h 0.12 (h is half channel height), the elevation of Reynolds shear stress maximum in the log-layer. Streamwise lagging of the deformation behind the pressure is caused in part by phase-lag of the pressure with decreasing distance from the wall, and in part by material damping. Positive deformations (bumps) are preferentially associated with ejections, which involve spanwise vortices located downstream and quasi-streamwise vortices with spanwise offset, consistent with hairpin-like structures. The negative deformations (dents) are preferentially associated with pressure maxima at the transition between an upstream sweep to a downstream ejection. Sponsored by ONR.

Gaussian Process Regression Model in Spatial Logistic Regression

NASA Astrophysics Data System (ADS)

Sofro, A.; Oktaviarina, A.

2018-01-01

Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.

Spatial heterogeneity in response of male greater sage-grouse lek attendance to energy development.

PubMed

Gregory, Andrew J; Beck, Jeffrey L

2014-01-01

Landscape modification due to rapidly expanding energy development, in particular oil and gas, in the westernUSA, have prompted concerns over how such developments may impact wildlife. One species of conservation concern across much of the Intermountain West is the greater sage-grouse (Centrocercusurophasianus). Sage-grouse have been petitioned for listing under provisions of the Endangered Species Act 7 times and the state of Wyoming alone represents 64% of the extant sage-grouse population in the eastern portion of their range. Consequently, the relationship between sage-grouse populations and oil and gas development in Wyoming is an important component to managing the long-term viability of this species. We used 814 leks from the Wyoming Game and Fish Department's lek survey database and well pad data from the Wyoming Oil and Gas Conservation Commission to evaluate changes in sage-grouse lek counts as a function of oil and gas development since 1991.From 1991-2011 we found that oil and gas well-pad density increased 3.6-fold across the state and was associated with a 24% decline in the number of male sage-grouse. Using a spatial and temporally structured analysis via Geographically Weighted Regression, we found a 1-to-4 year time lag between development density and lek decline. Sage-grouse also responded to development densities at multiple spatial neighborhoods surrounding leks, including broad scales of 10 km. However, sage-grouse lek counts do not always decline as a result of oil and gas development. We found similar development densities resulting in different sage-grouse lek count responses, suggesting that development density alone is insufficient to predict the impacts that oil and gas development have on sage-grouse. Finally, our analysis suggests a maximum development density of 1 well-pad within 2 km of leks to avoid measurable impacts within 1 year, and <6 well-pads within 10 km of leks to avoid delayed impacts.

Air pollution and respiratory diseases: ecological time series.

PubMed

Nascimento, Luiz Fernando Costa; Vieira, Luciana Cristina Pompeo Ferreira; Mantovani, Kátia Cristina Cota; Moreira, Demerval Soares

2016-01-01

Exposure to air pollutants is one of the factors responsible for hospitalizations due to respiratory diseases. The objective here was to estimate the effect of exposure to particulate matter (such as PM2.5) on hospitalizations due to certain respiratory diseases among residents in Volta Redonda (RJ). Ecological time series study using data from Volta Redonda (RJ). Data on hospital admissions among residents of Volta Redonda (RJ), between January 1, 2012, and December 31, 2012, due to pneumonia, acute bronchitis, bronchiolitis and asthma, were analyzed. Daily data on PM2.5 concentrations were estimated through the CCATT-BRAMS model. The generalized additive Poisson regression model was used, taking the daily number of hospitalizations to be the dependent variable and the PM2.5 concentration to be the independent variable, with adjustment for temperature, relative humidity, seasonality and day of the week, and using lags of zero to seven days. Excess hospitalization and its cost were calculated in accordance with increases in PM2.5 concentration of 5 µg/m3. There were 752 hospitalizations in 2012; the average concentration of PM2.5 was 17.2 µg/m3; the effects of exposure were significant at lag 2 (RR = 1.017), lag 5 (RR = 1.022) and lag 7 (RR = 1,020). A decrease in PM2.5 concentration of 5 µg/m3 could reduce admissions by up to 76 cases, with a decrease in spending of R$ 84,000 a year. The findings from this study provide support for implementing public health policies in this municipality, which is an important steelmaking center.

Effect of climatic variability on malaria trends in Baringo County, Kenya.

PubMed

Kipruto, Edwin K; Ochieng, Alfred O; Anyona, Douglas N; Mbalanya, Macrae; Mutua, Edna N; Onguru, Daniel; Nyamongo, Isaac K; Estambale, Benson B A

2017-05-25

Malaria transmission in arid and semi-arid regions of Kenya such as Baringo County, is seasonal and often influenced by climatic factors. Unravelling the relationship between climate variables and malaria transmission dynamics is therefore instrumental in developing effective malaria control strategies. The main aim of this study was to describe the effects of variability of rainfall, maximum temperature and vegetation indices on seasonal trends of malaria in selected health facilities within Baringo County, Kenya. Climate variables sourced from the International Research Institute (IRI)/Lamont-Doherty Earth Observatory (LDEO) climate database and malaria cases reported in 10 health facilities spread across four ecological zones (riverine, lowland, mid-altitude and highland) between 2004 and 2014 were subjected to a time series analysis. A negative binomial regression model with lagged climate variables was used to model long-term monthly malaria cases. The seasonal Mann-Kendall trend test was then used to detect overall monotonic trends in malaria cases. Malaria cases increased significantly in the highland and midland zones over the study period. Changes in malaria prevalence corresponded to variations in rainfall and maximum temperature. Rainfall at a time lag of 2 months resulted in an increase in malaria transmission across the four zones while an increase in temperature at time lags of 0 and 1 month resulted in an increase in malaria cases in the riverine and highland zones, respectively. Given the existence of a time lag between climatic variables more so rainfall and peak malaria transmission, appropriate control measures can be initiated at the onset of short and after long rains seasons.

Air pollution and mortality in Latin America: the role of education.

PubMed

O'Neill, Marie S; Bell, Michelle L; Ranjit, Nalini; Cifuentes, Luis A; Loomis, Dana; Gouveia, Nelson; Borja-Aburto, Victor H

2008-11-01

People with less education in Europe, Asia, and the United States are at higher risk of mortality associated with daily and longer-term air pollution exposure. We examined whether educational level modified associations between mortality and ambient particulate pollution (PM10) in Latin America, using several timescales. The study population included people who died during 1998-2002 in Mexico City, Mexico; Santiago, Chile; and São Paulo, Brazil. We fit city-specific robust Poisson regressions to daily deaths for nonexternal-cause mortality, and then stratified by age, sex, and educational attainment among adults older than age 21 years (none, some primary, some secondary, and high school degree or more). Predictor variables included a natural spline for temporal trend, linear PM10 and apparent temperature at matching lags, and day-of-week indicators. We evaluated PM10 for lags 0 and 1 day, and fit an unconstrained distributed lag model for cumulative 6-day effects. The effects of a 10-microg/m increment in lag 1 PM10 on all nonexternal-cause adult mortality were for Mexico City 0.39% (95% confidence interval = 0.13%-0.65%); São Paulo 1.04% (0.71%-1.38%); and for Santiago 0.61% (0.40%-0.83%). We found cumulative 6-day effects for adult mortality in Santiago (0.86% [0.48%-1.23%]) and São Paulo (1.38% [0.85%-1.91%]), but no consistent gradients by educational status. PM10 had important short- and intermediate-term effects on mortality in these Latin American cities, but associations did not differ consistently by educational level.

The effects of the Asselin time filter on numerical solutions to the linearized shallow-water wave equations

NASA Technical Reports Server (NTRS)

Schlesinger, R. E.; Johnson, D. R.; Uccellini, L. W.

1983-01-01

In the present investigation, a one-dimensional linearized analysis is used to determine the effect of Asselin's (1972) time filter on both the computational stability and phase error of numerical solutions for the shallow water wave equations, in cases with diffusion but without rotation. An attempt has been made to establish the approximate optimal values of the filtering parameter nu for each of the 'lagged', Dufort-Frankel, and Crank-Nicholson diffusion schemes, suppressing the computational wave mode without materially altering the physical wave mode. It is determined that in the presence of diffusion, the optimum filter length depends on whether waves are undergoing significant propagation. When moderate propagation is present, with or without diffusion, the Asselin filter has little effect on the spatial phase lag of the physical mode for the leapfrog advection scheme of the three diffusion schemes considered.

Rice evapotranspiration at the field and canopy scales under water-saving irrigation

NASA Astrophysics Data System (ADS)

Liu, Xiaoyin; Xu, Junzeng; Yang, Shihong; Zhang, Jiangang

2018-04-01

Evapotranspiration (ET) is an important process of land surface water and thermal cycling, with large temporal and spatial variability. To reveal the effect of water-saving irrigation (WSI) on rice ET at different spatial scales and understand the cross spatial scale difference, rice ET under WSI condition at canopy (ETCML) and field scale (ETEC) were measured simultaneously by mini-lysimeter and eddy covariance (EC) in the rice season of 2014. To overcome the shortage of energy balance deficit by EC system, and evaluate the influence of energy balance closure degree on ETEC, ETEC was corrected as {ET}_{EC}^{*} by energy balance closure correction according to the evaporative fraction. Seasonal average daily ETEC, {ET}_{EC}^{*} and ETCML of rice under WSI practice were estimated as 3.12, 4.03 and 4.35 mm day-1, smaller than the values reported in flooded paddy fields. Daily ETEC, {ET}_{EC}^{*} and ETCML varied in a similar trends and reached the maximum in late tillering, then decreased along with the crop growth in late season. The value of ETEC was much lower than ETCML, and was frequently 1-2 h lagged behind ETCML. It indicated that the energy balance deficit resulted in underestimation of crop ET by EC system. The corrected value of {ET}_{EC}^{*} matched ETCML much better than ETEC, with a smaller RMSE (0.086 mm h-1) and higher R 2 (0.843) and IOA (0.961). The time lapse between {ET}_{EC}^{*} and ETCML was mostly shortened to less than 0.5 h. The multiple stepwise regression analysis indicated that net radiation ( R n) is the dominant factor for rice ET, and soil moisture ( θ) is another significant factor ( p < 0.01) in WSI rice fields. The difference between ETCML and {ET}_{EC}^{*} ({ET}_{CML} - {ET}_{EC}^{*}) were significantly ( p < 0.05) correlated with R n, air temperature ( T a), and air vapor pressure deficit ( D), and its partial correlation coefficients to R n and T a were slightly greater than D. Thus, R n, T a and D are important variables for understanding the spatial scale effect of rice ET in WSI fields, and for its cross scale conversion.

Prediction of hourly PM2.5 using a space-time support vector regression model

NASA Astrophysics Data System (ADS)

Yang, Wentao; Deng, Min; Xu, Feng; Wang, Hang

2018-05-01

Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

USGS Publications Warehouse

Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall K.; Birdsey, Richard A.; Ryan, Michael G.

2015-01-01

Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.

Comparison of short-term associations with meteorological variables between COPD and pneumonia hospitalization among the elderly in Hong Kong-a time-series study.

PubMed

Lam, Holly Ching-Yu; Chan, Emily Ying-Yang; Goggins, William Bernard

2018-05-05

Pneumonia and chronic obstructive pulmonary diseases (COPD) are the commonest causes of respiratory hospitalization among older adults. Both diseases have been reported to be associated with ambient temperature, but the associations have not been compared between the diseases. Their associations with other meteorological variables have also not been well studied. This study aimed to evaluate the associations between meteorological variables, pneumonia, and COPD hospitalization among adults over 60 and to compare these associations between the diseases. Daily cause-specific hospitalization counts in Hong Kong during 2004-2011 were regressed on daily meteorological variables using distributed lag nonlinear models. Associations were compared between diseases by ratio of relative risks. Analyses were stratified by season and age group (60-74 vs. ≥ 75). In hot season, high temperature (> 28 °C) and high relative humidity (> 82%) were statistically significantly associated with more pneumonia in lagged 0-2 and lagged 0-10 days, respectively. Pneumonia hospitalizations among the elderly (≥ 75) also increased with high solar radiation and high wind speed. During the cold season, consistent hockey-stick associations with temperature and relative humidity were found for both admissions and both age groups. The minimum morbidity temperature and relative humidity were at about 21-22 °C and 82%. The lagged effects of low temperature were comparable for both diseases (lagged 0-20 days). The low-temperature-admissions associations with COPD were stronger and were strongest among the elderly. This study found elevated pneumonia and COPD admissions risks among adults ≥ 60 during periods of extreme weather conditions, and the associations varied by season and age group. Vulnerable groups should be advised to avoid exposures, such as staying indoor and maintaining satisfactory indoor conditions, to minimize risks.

Higher fine particulate matter and temperature levels impair exercise capacity in cardiac patients.

PubMed

Giorgini, Paolo; Rubenfire, Melvyn; Das, Ritabrata; Gracik, Theresa; Wang, Lu; Morishita, Masako; Bard, Robert L; Jackson, Elizabeth A; Fitzner, Craig A; Ferri, Claudio; Brook, Robert D

2015-08-01

Fine particulate matter (PM2.5) air pollution and variations in ambient temperature have been linked to increased cardiovascular morbidity and mortality. However, no large-scale study has assessed their effects on directly measured aerobic functional capacity among high-risk patients. Using a cross-sectional observational design, we evaluated the effects of ambient PM2.5 and temperature levels over 7 days on cardiopulmonary exercise test results performed among 2078 patients enrolling into a cardiac rehabilitation programme at the University of Michigan (from January 2003 to August 2011) using multiple linear regression analyses (controlling for age, sex, body mass index). Peak exercise oxygen consumption was significantly decreased by approximately 14.9% per 10 μg/m(3) increase in ambient PM2.5 levels (median 10.7 μg/m(3), IQR 10.1 μg/m(3)) (lag days 6-7). Elevations in PM2.5 were also related to decreases in ventilatory threshold (lag days 5-7) and peak heart rate (lag days 2-3) and increases in peak systolic blood pressure (lag days 4-5). A 10°C increase in temperature (median 10.5°C, IQR 17.5°C) was associated with reductions in peak exercise oxygen consumption (20.6-27.3%) and ventilatory threshold (22.9-29.2%) during all 7 lag days. In models including both factors, the outcome associations with PM2.5 were attenuated whereas the effects of temperature remained significant. Short-term elevations in ambient PM2.5, even at low concentrations within current air quality standards, and/or higher temperatures were associated with detrimental changes in aerobic exercise capacity, which can be linked to a worse quality of life and cardiovascular prognosis among cardiac rehabilitation patients. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

The lagged effect of cold temperature and wind chill on cardiorespiratory mortality in Scotland

PubMed Central

Carder, M; McNamee, R; Beverland, I; Elton, R; Cohen, G; Boyd, J; Agius, R

2005-01-01

Aims: To investigate the lagged effects of cold temperature on cardiorespiratory mortality and to determine whether "wind chill" is a better predictor of these effects than "dry bulb" temperature. Methods: Generalised linear Poisson regression models were used to investigate the relation between mortality and "dry bulb" and "wind chill" temperatures in the three largest Scottish cities (Glasgow, Edinburgh, and Aberdeen) between January 1981 and December 2001. Effects of temperature on mortality (lags up to one month) were quantified. Analyses were conducted for the whole year and by season (cool and warm seasons). Main results: Temperature was a significant predictor of mortality with the strongest association observed between temperature and respiratory mortality. There was a non-linear association between mortality and temperature. Mortality increased as temperatures fell throughout the range, but the rate of increase was steeper at temperatures below 11°C. The association between temperature and mortality persisted at lag periods beyond two weeks but the effect size generally decreased with increasing lag. For temperatures below 11°C, a 1°C drop in the daytime mean temperature on any one day was associated with an increase in mortality of 2.9% (95% CI 2.5 to 3.4), 3.4% (95% CI 2.6 to 4.1), 4.8% (95% CI 3.5 to 6.2) and 1.7% (95% CI 1.0 to 2.4) over the following month for all cause, cardiovascular, respiratory, and "other" cause mortality respectively. The effect of temperature on mortality was not observed to be significantly modified by season. There was little indication that "wind chill" temperature was a better predictor of mortality than "dry bulb" temperature. Conclusions: Exposure to cold temperature is an important public health problem in Scotland, particularly for those dying from respiratory disease. PMID:16169916

Effects of Extreme Temperatures on Cause-Specific Cardiovascular Mortality in China

PubMed Central

Wang, Xuying; Li, Guoxing; Liu, Liqun; Westerdahl, Dane; Jin, Xiaobin; Pan, Xiaochuan

2015-01-01

Objective: Limited evidence is available for the effects of extreme temperatures on cause-specific cardiovascular mortality in China. Methods: We collected data from Beijing and Shanghai, China, during 2007–2009, including the daily mortality of cardiovascular disease, cerebrovascular disease, ischemic heart disease and hypertensive disease, as well as air pollution concentrations and weather conditions. We used Poisson regression with a distributed lag non-linear model to examine the effects of extremely high and low ambient temperatures on cause-specific cardiovascular mortality. Results: For all cause-specific cardiovascular mortality, Beijing had stronger cold and hot effects than those in Shanghai. The cold effects on cause-specific cardiovascular mortality reached the strongest at lag 0–27, while the hot effects reached the strongest at lag 0–14. The effects of extremely low and high temperatures differed by mortality types in the two cities. Hypertensive disease in Beijing was particularly susceptible to both extremely high and low temperatures; while for Shanghai, people with ischemic heart disease showed the greatest relative risk (RRs = 1.16, 95% CI: 1.03, 1.34) to extremely low temperature. Conclusion: People with hypertensive disease were particularly susceptible to extremely low and high temperatures in Beijing. People with ischemic heart disease in Shanghai showed greater susceptibility to extremely cold days. PMID:26703637

Effects of Extreme Temperatures on Cause-Specific Cardiovascular Mortality in China.

PubMed

Wang, Xuying; Li, Guoxing; Liu, Liqun; Westerdahl, Dane; Jin, Xiaobin; Pan, Xiaochuan

2015-12-21

Limited evidence is available for the effects of extreme temperatures on cause-specific cardiovascular mortality in China. We collected data from Beijing and Shanghai, China, during 2007-2009, including the daily mortality of cardiovascular disease, cerebrovascular disease, ischemic heart disease and hypertensive disease, as well as air pollution concentrations and weather conditions. We used Poisson regression with a distributed lag non-linear model to examine the effects of extremely high and low ambient temperatures on cause-specific cardiovascular mortality. For all cause-specific cardiovascular mortality, Beijing had stronger cold and hot effects than those in Shanghai. The cold effects on cause-specific cardiovascular mortality reached the strongest at lag 0-27, while the hot effects reached the strongest at lag 0-14. The effects of extremely low and high temperatures differed by mortality types in the two cities. Hypertensive disease in Beijing was particularly susceptible to both extremely high and low temperatures; while for Shanghai, people with ischemic heart disease showed the greatest relative risk (RRs = 1.16, 95% CI: 1.03, 1.34) to extremely low temperature. People with hypertensive disease were particularly susceptible to extremely low and high temperatures in Beijing. People with ischemic heart disease in Shanghai showed greater susceptibility to extremely cold days.

Amyotrophic lateral sclerosis and exposure to diesel exhaust in a Danish cohort.

PubMed

Dickerson, Aisha S; Hansen, Johnni; Gredal, Ole; Weisskopf, Marc G

2018-03-24

Previous studies have suggested an increased risk of amyotrophic lateral sclerosis (ALS) and other motor neuron diseases for those in occupations commonly exposed to diesel exhaust (DE). In this study, we investigated the association between occupational exposures to DE and odds of ALS. ALS cases were identified from the Danish National Patient Registry 1982 to 2013 and individually matched to 100 controls per case based on birth year and sex. Using occupational history since 1964 from the Danish Pension Fund, Cumulative DE exposures were estimated using a job exposure matrix. Associations were evaluated using conditional logistic regression analyses and stratified by sex. DE exposure at 10-year lag periods was positively associated with ALS in men ever exposed (aOR: 1.20; 95% CI: 1.05, 1.38). For men with > 50% probability of DE exposure, we observed a positive association with ALS and the highest quartile exposures during the 5-year lag period (aOR: 1.40; 95% CI: 1.11, 1.78) and 10-year lag period (aOR: 1.41; 95% CI: 1.11, 1.79). Our study suggests an association between consistently higher exposures to DE and ALS in men but not in women. These findings support those of previously reported associations between ALS and commonly DE exposed occupations.

Short-term exposure to ambient ozone and stroke hospital admission: A case-crossover analysis.

PubMed

Montresor-López, Jessica A; Yanosky, Jeff D; Mittleman, Murray A; Sapkota, Amir; He, Xin; Hibbert, James D; Wirth, Michael D; Puett, Robin C

2016-01-01

We evaluated the association between short-term exposure to ambient ozone air pollution and stroke hospital admissions among adult residents of South Carolina (SC). Data on all incident stroke hospitalizations from 2002 to 2006 were obtained from the SC Office of Research and Statistics. Ozone exposure data were obtained from the US Environmental Protection Agency's Hierarchical Bayesian Model. A semi-symmetric bidirectional case-crossover design was used to examine the association between ozone exposure on lag days 0-2 (0 to 2 days before admission) and stroke hospitalization. Conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). No significant associations were observed between short-term ozone exposure and hospitalization for all stroke (e.g., lag day 0: OR=0.98; 95% CI=0.96, 1.00) or ischemic stroke (lag day 0: OR=0.98; 95% CI=0.96, 1.01). Risk of hospitalization for hemorrhagic stroke appeared to be higher among African Americans than European Americans; however, the majority of these associations did not reach statistical significance. Among adults in SC from 2002 to 2006, there was no evidence of an association between ozone exposure and risk of hospitalization for all stroke or ischemic stroke; however, African Americans may have an increased risk of hemorrhagic stroke.

[Prediction and spatial distribution of recruitment trees of natural secondary forest based on geographically weighted Poisson model].

PubMed

Zhang, Ling Yu; Liu, Zhao Gang

2017-12-01

Based on the data collected from 108 permanent plots of the forest resources survey in Maoershan Experimental Forest Farm during 2004-2016, this study investigated the spatial distribution of recruitment trees in natural secondary forest by global Poisson regression and geographically weighted Poisson regression (GWPR) with four bandwidths of 2.5, 5, 10 and 15 km. The simulation effects of the 5 regressions and the factors influencing the recruitment trees in stands were analyzed, a description was given to the spatial autocorrelation of the regression residuals on global and local levels using Moran's I. The results showed that the spatial distribution of the number of natural secondary forest recruitment was significantly influenced by stands and topographic factors, especially average DBH. The GWPR model with small scale (2.5 km) had high accuracy of model fitting, a large range of model parameter estimates was generated, and the localized spatial distribution effect of the model parameters was obtained. The GWPR model at small scale (2.5 and 5 km) had produced a small range of model residuals, and the stability of the model was improved. The global spatial auto-correlation of the GWPR model residual at the small scale (2.5 km) was the lowe-st, and the local spatial auto-correlation was significantly reduced, in which an ideal spatial distribution pattern of small clusters with different observations was formed. The local model at small scale (2.5 km) was much better than the global model in the simulation effect on the spatial distribution of recruitment tree number.

From neurons to circuits: linear estimation of local field potentials.

PubMed

Rasch, Malte; Logothetis, Nikos K; Kreiman, Gabriel

2009-11-04

Extracellular physiological recordings are typically separated into two frequency bands: local field potentials (LFPs) (a circuit property) and spiking multiunit activity (MUA). Recently, there has been increased interest in LFPs because of their correlation with functional magnetic resonance imaging blood oxygenation level-dependent measurements and the possibility of studying local processing and neuronal synchrony. To further understand the biophysical origin of LFPs, we asked whether it is possible to estimate their time course based on the spiking activity from the same electrode or nearby electrodes. We used "signal estimation theory" to show that a linear filter operation on the activity of one or a few neurons can explain a significant fraction of the LFP time course in the macaque monkey primary visual cortex. The linear filter used to estimate the LFPs had a stereotypical shape characterized by a sharp downstroke at negative time lags and a slower positive upstroke for positive time lags. The filter was similar across different neocortical regions and behavioral conditions, including spontaneous activity and visual stimulation. The estimations had a spatial resolution of approximately 1 mm and a temporal resolution of approximately 200 ms. By considering a causal filter, we observed a temporal asymmetry such that the positive time lags in the filter contributed more to the LFP estimation than the negative time lags. Additionally, we showed that spikes occurring within approximately 10 ms of spikes from nearby neurons yielded better estimation accuracies than nonsynchronous spikes. In summary, our results suggest that at least some circuit-level local properties of the field potentials can be predicted from the activity of one or a few neurons.

From neurons to circuits: linear estimation of local field potentials

PubMed Central

Rasch, Malte; Logthetis, Nikos K.; Kreiman, Gabriel

2010-01-01

Extracellular physiological recordings are typically separated into two frequency bands: local field potentials (LFPs, a circuit property) and spiking multi-unit activity (MUA). There has been increased interest in LFPs due to their correlation with fMRI measurements and the possibility of studying local processing and neuronal synchrony. To further understand the biophysical origin of LFPs, we asked whether it is possible to estimate their time course based on the spiking activity from the same or nearby electrodes. We used Signal Estimation Theory to show that a linear filter operation on the activity of one/few neurons can explain a significant fraction of the LFP time course in the macaque primary visual cortex. The linear filter used to estimate the LFPs had a stereotypical shape characterized by a sharp downstroke at negative time lags and a slower positive upstroke for positve time lags. The filter was similar across neocortical regions and behavioral conditions including spontaneous activity and visual stimulation. The estimations had a spatial resolution of ~1 mm and a temporal resolution of ~200 ms. By considering a causal filter, we observed a temporal asymmetry such that the positive time lags in the filter contributed more to the LFP estimation than negative time lags. Additionally, we showed that spikes occurring within ~10 ms of spikes from nearby neurons yielded better estimation accuracies than nonsynchronous spikes. In sum, our results suggest that at least some circuit-level local properties of the field potentials can be predicted from the activity of one or a few neurons. PMID:19889990

Comparing spatial regression to random forests for large environmental data sets

EPA Science Inventory

Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates, whereas spatial regression, when using reduced rank methods, has a reputatio...

Measuring ocean coherence time with dual-baseline interferometry

NASA Technical Reports Server (NTRS)

Carande, Richard E.

1992-01-01

Using the Jet Propulsion Laboratory (JPL) Airborne Synthetic Aperture Radar (AIRSAR) interferometer, measurements of the ocean coherence time at L and C band can be made at high spatial resolution. Fundamental to this measurement is the ability to image the ocean interferometrically at two different time-lags, or baselines. By modifying the operating procedure of the existing two antenna interferometer, a technique was developed make these measurements. L band coherence times are measured and presented.

Crystalline silica exposure and lung cancer mortality in diatomaceous earth industry workers: a quantitative risk assessment.

PubMed

Rice, F L; Park, R; Stayner, L; Smith, R; Gilbert, S; Checkoway, H

2001-01-01

To use various exposure-response models to estimate the risk of mortality from lung cancer due to occupational exposure to respirable crystalline silica dust. Data from a cohort mortality study of 2342 white male California diatomaceous earth mining and processing workers exposed to crystalline silica dust (mainly cristobalite) were reanalyzed with Poisson regression and Cox's proportional hazards models. Internal and external adjustments were used to control for potential confounding from the effects of time since first observation, calendar time, age, and Hispanic ethnicity. Cubic smoothing spline models were used to assess the fit of the models. Exposures were lagged by 10 years. Evaluations of the fit of the models were performed by comparing their deviances. Lifetime risks of lung cancer were estimated up to age 85 with an actuarial approach that accounted for competing causes of death. Exposure to respirable crystalline silica dust was a significant predictor (p<0.05) in nearly all of the models evaluated and the linear relative rate model with a 10 year exposure lag seemed to give the best fit in the Poisson regression analysis. For those who died of lung cancer the linear relative rate model predicted rate ratios for mortality from lung cancer of about 1.6 for the mean cumulative exposure to respirable silica compared with no exposure. The excess lifetime risk (to age 85) of mortality from lung cancer for white men exposed for 45 years and with a 10 year lag period at the current Occupational Safety and Health Administration (OSHA) standard of about 0.05 mg/m(3) for respirable cristobalite dust is 19/1000 (95% confidence interval (95% CI) 5/1000 to 46/1000). There was a significant risk of mortality from lung cancer that increased with cumulative exposure to respirable crystalline silica dust. The predicted number of deaths from lung cancer suggests that current occupational health standards may not be adequately protecting workers from the risk of lung cancer.

Predictions of Control Inputs, Periodic Responses and Damping Levels of an Isolated Experimental Rotor in Trimmed Flight

NASA Technical Reports Server (NTRS)

Gaonkar, G. H.; Subramanian, S.

1996-01-01

Since the early 1990s the Aeroflightdynamics Directorate at the Ames Research Center has been conducting tests on isolated hingeless rotors in hover and forward flight. The primary objective is to generate a database on aeroelastic stability in trimmed flight for torsionally soft rotors at realistic tip speeds. The rotor test model has four soft inplane blades of NACA 0012 airfoil section with low torsional stiffness. The collective pitch and shaft tilt are set prior to each test run, and then the rotor is trimmed in the following sense: the longitudinal and lateral cyclic pitch controls are adjusted through a swashplate to minimize the 1/rev flapping moment at the 12 percent radial station. In hover, the database comprises lag regressive-mode damping with pitch variations. In forward flight the database comprises cyclic pitch controls, root flap moment and lag regressive-mode damping with advance ratio, shaft angle and pitch variations. This report presents the predictions and their correlation with the database. A modal analysis is used, in which nonrotating modes in flap bending, lag bending and torsion are computed from the measured blade mass and stiffness distributions. The airfoil aerodynamics is represented by the ONERA dynamic stall models of lift, drag and pitching moment, and the wake dynamics is represented by a state-space wake model. The trim analysis of finding, the cyclic controls and the corresponding, periodic responses is based on periodic shooting with damped Newton iteration; the Floquet transition matrix (FTM) comes out as a byproduct. The stabillty analysis of finding the frequencies and damping levels is based on the eigenvalue-eigenvector analysis of the FTM. All the structural and aerodynamic states are included from modeling to trim analysis. A major finding is that dynamic wake dramatically improves the correlation for the lateral cyclic pitch control. Overall, the correlation is fairly good.

A geo-computational algorithm for exploring the structure of diffusion progression in time and space.

PubMed

Chin, Wei-Chien-Benny; Wen, Tzai-Hung; Sabel, Clive E; Wang, I-Hsiang

2017-10-03

A diffusion process can be considered as the movement of linked events through space and time. Therefore, space-time locations of events are key to identify any diffusion process. However, previous clustering analysis methods have focused only on space-time proximity characteristics, neglecting the temporal lag of the movement of events. We argue that the temporal lag between events is a key to understand the process of diffusion movement. Using the temporal lag could help to clarify the types of close relationships. This study aims to develop a data exploration algorithm, namely the TrAcking Progression In Time And Space (TaPiTaS) algorithm, for understanding diffusion processes. Based on the spatial distance and temporal interval between cases, TaPiTaS detects sub-clusters, a group of events that have high probability of having common sources, identifies progression links, the relationships between sub-clusters, and tracks progression chains, the connected components of sub-clusters. Dengue Fever cases data was used as an illustrative case study. The location and temporal range of sub-clusters are presented, along with the progression links. TaPiTaS algorithm contributes a more detailed and in-depth understanding of the development of progression chains, namely the geographic diffusion process.

Mapping the Climate of Puerto Rico, Vieques and Culebra.

Treesearch

CHRISTOPHER DALY; E. H. HELMER; MAYA QUINONES

2003-01-01

Spatially explicit climate data contribute to watershed resource management, mapping vegetation type with satellite imagery, mapping present and hypothetical future ecological zones, and predicting species distributions. The regression based Parameter-elevation Regressions on Independent Slopes Model (PRISM) uses spatial data sets, a knowledge base and expert...

Flood characteristics of urban watersheds in the United States

USGS Publications Warehouse

Sauer, Vernon B.; Thomas, W.O.; Stricker, V.A.; Wilson, K.V.

1983-01-01

A nationwide study of flood magnitude and frequency in urban areas was made for the purpose of reviewing available literature, compiling an urban flood data base, and developing methods of estimating urban floodflow characteristics in ungaged areas. The literature review contains synopses of 128 recent publications related to urban floodflow. A data base of 269 gaged basins in 56 cities and 31 States, including Hawaii, contains a wide variety of topographic and climatic characteristics, land-use variables, indices of urbanization, and flood-frequency estimates. Three sets of regression equations were developed to estimate flood discharges for ungaged sites for recurrence intervals of 2, 5, 10, 25, 50, 100, and 500 years. Two sets of regression equations are based on seven independent parameters and the third is based on three independent parameters. The only difference in the two sets of seven-parameter equations is the use of basin lag time in one and lake and reservoir storage in the other. Of primary importance in these equations is an independent estimate of the equivalent rural discharge for the ungaged basin. The equations adjust the equivalent rural discharge to an urban condition. The primary adjustment factor, or index of urbanization, is the basin development factor, a measure of the extent of development of the drainage system in the basin. This measure includes evaluations of storm drains (sewers), channel improvements, and curb-and-gutter streets. The basin development factor is statistically very significant and offers a simple and effective way of accounting for drainage development and runoff response in urban areas. Percentage of impervious area is also included in the seven-parameter equations as an additional measure of urbanization and apparently accounts for increased runoff volumes. This factor is not highly significant for large floods, which supports the generally held concept that imperviousness is not a dominant factor when soils become more saturated during large storms. Other parameters in the seven-parameter equations include drainage area size, channel slope, rainfall intensity, lake and reservoir storage, and basin lag time. These factors are all statistically significant and provide logical indices of basin conditions. The three-parameter equations include only the three most significant parameters: rural discharge, basin-development factor, and drainage area size. All three sets of regression equations provide unbiased estimates of urban flood frequency. The seven-parameter regression equations without basin lag time have average standard errors of regression varying from ? 37 percent for the 5-year flood to ? 44 percent for the 100-year flood and ? 49 percent for the 500-year flood. The other two sets of regression equations have similar accuracy. Several tests for bias, sensitivity, and hydrologic consistency are included which support the conclusion that the equations are useful throughout the United States. All estimating equations were developed from data collected on drainage basins where temporary in-channel storage, due to highway embankments, was not significant. Consequently, estimates made with these equations do not account for the reducing effect of this temporary detention storage.

Predicting Redox Conditions in Groundwater Using Statistical Techniques: Implications for Nitrate Transport in Groundwater and Streams

NASA Astrophysics Data System (ADS)

Tesoriero, A. J.; Terziotti, S.

2014-12-01

Nitrate trends in streams often do not match expectations based on recent nitrogen source loadings to the land surface. Groundwater discharge with long travel times has been suggested as the likely cause for these observations. The fate of nitrate in groundwater depends to a large extent on the occurrence of denitrification along flow paths. Because denitrification in groundwater is inhibited when dissolved oxygen (DO) concentrations are high, defining the oxic-suboxic interface has been critical in determining pathways for nitrate transport in groundwater and to streams at the local scale. Predicting redox conditions on a regional scale is complicated by the spatial variability of reaction rates. In this study, logistic regression and boosted classification tree analysis were used to predict the probability of oxic water in groundwater in the Chesapeake Bay watershed. The probability of oxic water (DO > 2 mg/L) was predicted by relating DO concentrations in over 3,000 groundwater samples to indicators of residence time and/or electron donor availability. Variables that describe position in the flow system (e.g., depth to top of the open interval), soil drainage and surficial geology were the most important predictors of oxic water. Logistic regression and boosted classification tree analysis correctly predicted the presence or absence of oxic conditions in over 75 % of the samples in both training and validation data sets. Predictions of the percentages of oxic wells in deciles of risk were very accurate (r2>0.9) in both the training and validation data sets. Depth to the bottom of the oxic layer was predicted and is being used to estimate the effect that groundwater denitrification has on stream nitrate concentrations and the time lag between the application of nitrogen at the land surface and its effect on streams.

Representation of limb kinematics in Purkinje cell simple spike discharge is conserved across multiple tasks

PubMed Central

Hewitt, Angela L.; Popa, Laurentiu S.; Pasalar, Siavash; Hendrix, Claudia M.

2011-01-01

Encoding of movement kinematics in Purkinje cell simple spike discharge has important implications for hypotheses of cerebellar cortical function. Several outstanding questions remain regarding representation of these kinematic signals. It is uncertain whether kinematic encoding occurs in unpredictable, feedback-dependent tasks or kinematic signals are conserved across tasks. Additionally, there is a need to understand the signals encoded in the instantaneous discharge of single cells without averaging across trials or time. To address these questions, this study recorded Purkinje cell firing in monkeys trained to perform a manual random tracking task in addition to circular tracking and center-out reach. Random tracking provides for extensive coverage of kinematic workspaces. Direction and speed errors are significantly greater during random than circular tracking. Cross-correlation analyses comparing hand and target velocity profiles show that hand velocity lags target velocity during random tracking. Correlations between simple spike firing from 120 Purkinje cells and hand position, velocity, and speed were evaluated with linear regression models including a time constant, τ, as a measure of the firing lead/lag relative to the kinematic parameters. Across the population, velocity accounts for the majority of simple spike firing variability (63 ± 30% of Radj2), followed by position (28 ± 24% of Radj2) and speed (11 ± 19% of Radj2). Simple spike firing often leads hand kinematics. Comparison of regression models based on averaged vs. nonaveraged firing and kinematics reveals lower Radj2 values for nonaveraged data; however, regression coefficients and τ values are highly similar. Finally, for most cells, model coefficients generated from random tracking accurately estimate simple spike firing in either circular tracking or center-out reach. These findings imply that the cerebellum controls movement kinematics, consistent with a forward internal model that predicts upcoming limb kinematics. PMID:21795616

Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures.

PubMed

Liu, Shelley H; Bobb, Jennifer F; Lee, Kyu Ha; Gennings, Chris; Claus Henn, Birgit; Bellinger, David; Austin, Christine; Schnaas, Lourdes; Tellez-Rojo, Martha M; Hu, Howard; Wright, Robert O; Arora, Manish; Coull, Brent A

2018-07-01

The impact of neurotoxic chemical mixtures on children's health is a critical public health concern. It is well known that during early life, toxic exposures may impact cognitive function during critical time intervals of increased vulnerability, known as windows of susceptibility. Knowledge on time windows of susceptibility can help inform treatment and prevention strategies, as chemical mixtures may affect a developmental process that is operating at a specific life phase. There are several statistical challenges in estimating the health effects of time-varying exposures to multi-pollutant mixtures, such as: multi-collinearity among the exposures both within time points and across time points, and complex exposure-response relationships. To address these concerns, we develop a flexible statistical method, called lagged kernel machine regression (LKMR). LKMR identifies critical exposure windows of chemical mixtures, and accounts for complex non-linear and non-additive effects of the mixture at any given exposure window. Specifically, LKMR estimates how the effects of a mixture of exposures change with the exposure time window using a Bayesian formulation of a grouped, fused lasso penalty within a kernel machine regression (KMR) framework. A simulation study demonstrates the performance of LKMR under realistic exposure-response scenarios, and demonstrates large gains over approaches that consider each time window separately, particularly when serial correlation among the time-varying exposures is high. Furthermore, LKMR demonstrates gains over another approach that inputs all time-specific chemical concentrations together into a single KMR. We apply LKMR to estimate associations between neurodevelopment and metal mixtures in Early Life Exposures in Mexico and Neurotoxicology, a prospective cohort study of child health in Mexico City.

Atmospheric pollutants and hospital admissions due to pneumonia in children

PubMed Central

Negrisoli, Juliana; Nascimento, Luiz Fernando C.

2013-01-01

OBJECTIVE: To analyze the relationship between exposure to air pollutants and hospitalizations due to pneumonia in children of Sorocaba, São Paulo, Brazil. METHODS: Time series ecological study, from 2007 to 2008. Daily data were obtained from the State Environmental Agency for Pollution Control for particulate matter, nitric oxide, nitrogen dioxide, ozone, besides air temperature and relative humidity. The data concerning pneumonia admissions were collected in the public health system of Sorocaba. Correlations between the variables of interest using Pearson cofficient were calculated. Models with lags from zero to five days after exposure to pollutants were performed to analyze the association between the exposure to environmental pollutants and hospital admissions. The analysis used the generalized linear model of Poisson regression, being significant p<0.05. RESULTS: There were 1,825 admissions for pneumonia, with a daily mean of 2.5±2.1. There was a strong correlation between pollutants and hospital admissions, except for ozone. Regarding the Poisson regression analysis with the multi-pollutant model, only nitrogen dioxide was statistically significant in the same day (relative risk - RR=1.016), as well as particulate matter with a lag of four days (RR=1.009) after exposure to pollutants. CONCLUSIONS: There was an acute effect of exposure to nitrogen dioxide and a later effect of exposure to particulate matter on children hospitalizations for pneumonia in Sorocaba. PMID:24473956

Spatial interpolation schemes of daily precipitation for hydrologic modeling

USGS Publications Warehouse

Hwang, Y.; Clark, M.R.; Rajagopalan, B.; Leavesley, G.

2012-01-01

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.

Seasonal Phosphorus Sources and Loads to Upper Klamath Lake, Oregon, as Determined by a Dynamic SPARROW Model

NASA Astrophysics Data System (ADS)

Saleh, D.; Domagalski, J. L.; Smith, R. A.

2016-12-01

The SPARROW (SPAtially-Referenced Regression On Watershed Attributes) model, developed by the U.S. Geological Survey, has been used to identify and quantify the sources of nitrogen and phosphorus in watersheds and to predict their fluxes and concentration at specified locations downstream. Existing SPARROW models use a hybrid statistical approach to describe an annual average ("steady-state") relationship between sources and stream conditions based on long-term water quality monitoring data and spatially-referenced explanatory information. Although these annual models are useful for some management purposes, many water quality issues stem from intra- and inter-annual changes in constituent sources, hydrologic forcing, or other environmental conditions, which cause a lag between watershed inputs and stream water quality. We are developing a seasonal dynamic SPARROW model of sources, fluxes, and yields of phosphorus for the watershed (approximately 9,700 square kilometers) draining to Upper Klamath Lake, Oregon. The lake is hyper-eutrophic and various options are being considered for water quality improvement. The model was calibrated with 11 years of water quality data (2000 to 2010) and simulates seasonal loads and yields for a total of 44 seasons. Phosphorus sources to the watershed include animal manure, farm fertilizer, discharges of treated wastewater, and natural sources (soil and streambed sediment). The model predicts that phosphorus delivery to the lake is strongly affected by intra- and inter-annual changes in precipitation and by temporary seasonal storage of phosphorus in the watershed. The model can be used to predict how different management actions for mitigating phosphorus sources might affect phosphorus loading to the lake as well as the time required for any changes in loading to occur following implementation of the action.

Habitat Fragmentation and Species Extirpation in Freshwater Ecosystems; Causes of Range Decline of the Indus River Dolphin (Platanista gangetica minor)

PubMed Central

Braulik, Gill T.; Arshad, Masood; Noureen, Uzma; Northridge, Simon P.

2014-01-01

Habitat fragmentation of freshwater ecosystems is increasing rapidly, however the understanding of extinction debt and species decline in riverine habitat fragments lags behind that in other ecosystems. The mighty rivers that drain the Himalaya - the Ganges, Brahmaputra, Indus, Mekong and Yangtze - are amongst the world’s most biodiverse freshwater ecosystems. Many hundreds of dams have been constructed, are under construction, or are planned on these rivers and large hydrological changes and losses of biodiversity have occurred and are expected to continue. This study examines the causes of range decline of the Indus dolphin, which inhabits one of the world’s most modified rivers, to demonstrate how we may expect other vertebrate populations to respond as planned dams and water developments come into operation. The historical range of the Indus dolphin has been fragmented into 17 river sections by diversion dams; dolphin sighting and interview surveys show that river dolphins have been extirpated from ten river sections, they persist in 6, and are of unknown status in one section. Seven potential factors influencing the temporal and spatial pattern of decline were considered in three regression model sets. Low dry-season river discharge, due to water abstraction at irrigation barrages, was the principal factor that explained the dolphin’s range decline, influencing 1) the spatial pattern of persistence, 2) the temporal pattern of subpopulation extirpation, and 3) the speed of extirpation after habitat fragmentation. Dolphins were more likely to persist in the core of the former range because water diversions are concentrated near the range periphery. Habitat fragmentation and degradation of the habitat were inextricably intertwined and in combination caused the catastrophic decline of the Indus dolphin. PMID:25029270

Long-term trends in atmospheric concentrations of α- and γ-HCH in the Arctic provide insight into the effects of legislation and climatic fluctuations on contaminant levels

NASA Astrophysics Data System (ADS)

Becker, S.; Halsall, C. J.; Tych, W.; Kallenborn, R.; Su, Y.; Hung, H.

Twelve year datasets of weekly atmospheric concentrations of α- and γ-HCH were compared between the two Arctic monitoring stations of Alert, Nunavut, Canada, and Zeppelin Mountain, Svalbard, Norway. Time-series analysis was conducted with the use of dynamic harmonic regression (DHR), which provided a very good model fit, to examine both the seasonal behaviour in these isomers and the longer-term, underlying trends. Strong spatial differences were not apparent between the two sites, although subtle differences in seasonal behaviour and composition were identified. For example, the composition of γ-HCH to total HCH (α + γ) was greater at Zeppelin compared to Alert, probably reflecting this site's proximity to major use regions of lindane. Pronounced seasonality in air concentrations for γ-HCH was marked by a 'spring maximum event' (SME), confirming earlier studies. For α-HCH, the SME was much weaker and only evident at Alert, whereas at Zeppelin, seasonal fluctuations for α-HCH were marked by elevated concentrations in summer and lower concentrations during winter, with this pattern most apparent for the years after 2000. We attribute this difference in spatial and temporal patterns to the Arctic oscillation. A similar climatic pattern was not evident at either site in the γ-HCH data. Seasonally adjusted, long-term trends revealed declining concentrations at both sites for α- and γ-HCH over the entire time-series. Recent legislation affecting lindane use appear to account for this decline in γ-HCH, with little evidence of a delay or 'lag' between the banning of lindane in Europe (a main source region) or Canada, and a decline in air concentrations observed at both Arctic sites.

Habitat fragmentation and species extirpation in freshwater ecosystems; causes of range decline of the Indus river dolphin (Platanista gangetica minor).

PubMed

Braulik, Gill T; Arshad, Masood; Noureen, Uzma; Northridge, Simon P

2014-01-01

Habitat fragmentation of freshwater ecosystems is increasing rapidly, however the understanding of extinction debt and species decline in riverine habitat fragments lags behind that in other ecosystems. The mighty rivers that drain the Himalaya - the Ganges, Brahmaputra, Indus, Mekong and Yangtze - are amongst the world's most biodiverse freshwater ecosystems. Many hundreds of dams have been constructed, are under construction, or are planned on these rivers and large hydrological changes and losses of biodiversity have occurred and are expected to continue. This study examines the causes of range decline of the Indus dolphin, which inhabits one of the world's most modified rivers, to demonstrate how we may expect other vertebrate populations to respond as planned dams and water developments come into operation. The historical range of the Indus dolphin has been fragmented into 17 river sections by diversion dams; dolphin sighting and interview surveys show that river dolphins have been extirpated from ten river sections, they persist in 6, and are of unknown status in one section. Seven potential factors influencing the temporal and spatial pattern of decline were considered in three regression model sets. Low dry-season river discharge, due to water abstraction at irrigation barrages, was the principal factor that explained the dolphin's range decline, influencing 1) the spatial pattern of persistence, 2) the temporal pattern of subpopulation extirpation, and 3) the speed of extirpation after habitat fragmentation. Dolphins were more likely to persist in the core of the former range because water diversions are concentrated near the range periphery. Habitat fragmentation and degradation of the habitat were inextricably intertwined and in combination caused the catastrophic decline of the Indus dolphin.

Quantitative characterization of the regressive ecological succession by fractal analysis of plant spatial patterns

USGS Publications Warehouse

Alados, C.L.; Pueyo, Y.; Giner, M.L.; Navarro, T.; Escos, J.; Barroso, F.; Cabezudo, B.; Emlen, J.M.

2003-01-01

We studied the effect of grazing on the degree of regression of successional vegetation dynamic in a semi-arid Mediterranean matorral. We quantified the spatial distribution patterns of the vegetation by fractal analyses, using the fractal information dimension and spatial autocorrelation measured by detrended fluctuation analyses (DFA). It is the first time that fractal analysis of plant spatial patterns has been used to characterize the regressive ecological succession. Plant spatial patterns were compared over a long-term grazing gradient (low, medium and heavy grazing pressure) and on ungrazed sites for two different plant communities: A middle dense matorral of Chamaerops and Periploca at Sabinar-Romeral and a middle dense matorral of Chamaerops, Rhamnus and Ulex at Requena-Montano. The two communities differed also in the microclimatic characteristics (sea oriented at the Sabinar-Romeral site and inland oriented at the Requena-Montano site). The information fractal dimension increased as we moved from a middle dense matorral to discontinuous and scattered matorral and, finally to the late regressive succession, at Stipa steppe stage. At this stage a drastic change in the fractal dimension revealed a change in the vegetation structure, accurately indicating end successional vegetation stages. Long-term correlation analysis (DFA) revealed that an increase in grazing pressure leads to unpredictability (randomness) in species distributions, a reduction in diversity, and an increase in cover of the regressive successional species, e.g. Stipa tenacissima L. These comparisons provide a quantitative characterization of the successional dynamic of plant spatial patterns in response to grazing perturbation gradient. ?? 2002 Elsevier Science B.V. All rights reserved.

What happens when catchments get excited? Exploring the link between hydrologic states and responses across spatial scales

NASA Astrophysics Data System (ADS)

Wrede, S.; Lyon, S. W.; Martinez-Carreras, N.; Pfister, L.; Uhlenbrook, S.

2010-12-01

Investigating relationships between dynamic hydrologic states and associated hydrologic responses of catchments is essential for a better understanding and conceptualization of hydrologic functioning and classification across spatial scales. Nevertheless, the question of “What happens when catchments get excited?” still remains unanswered for most catchments to date. This is especially true with regard to underlying landscape controls and how their relative importance can shift given the state of the various storages in a catchment. To help answering this question, we combined hydrometric and tracer approaches with landscape analysis in 24 nested catchments in Luxembourg, Europe with contrasting bedrock geology ranging from 0.5 to 1091 km2. In our study we discerned two major hydrological states (dry and wet) for each basin according to slope changes in double mass curves of cumulated discharge and precipitation. For each of these states the long-term (i.e. interannual) response of catchment behavior was characterized using conventional runoff signatures, such as master recession curves and average lag time between rainfall and runoff response. We found significantly different hydrologic responses for different hydrologic states of the catchments. These are typified by faster flow recessions, but longer average lag times during wet states and slower flow recessions, but shorter lag times during dry states. Dominating landscape controls on hydrological responses differed during these distinct hydrologic states and were identified as variables related to geology (percentage of impervious bedrock area) and soils (average soil depth), indicating different controls on hydrologic processes under different hydrologic states. Clustering of biweekly conductivity and silica stream water concentration data of the catchments further illustrated the dominant control of the geology on stream chemistry and revealed similar patterns during different hydrologic states. Our findings demonstrate that hydrologic response and their associated controls are closely linked to the dynamic hydrologic states of the catchments and hence should not be neglected in catchment modeling and classification approaches.

Physics of the Merging Clusters Cygnus A, A3667, and A2065

NASA Astrophysics Data System (ADS)

Markevitch, Maxim; Sarazin, Craig L.; Vikhlinin, Alexey

1999-08-01

We present ASCA gas temperature maps of the nearby merging galaxy clusters Cygnus A, A3667, and A2065. Cygnus A appears to have a particularly simple merger geometry that allows an estimate of the subcluster collision velocity from the observed temperature variations. We estimate it to be ~2000 km s-1. Interestingly, this is similar to the free-fall velocity that the two Cygnus A subclusters should have achieved at the observed separation, suggesting that the merger has been effective in dissipating the kinetic energy of gas halos into thermal energy, without channeling its major fraction elsewhere (e.g., into turbulence). In A3667 we may be observing a spatial lag between the shock front seen in the X-ray image and the corresponding rise of the electron temperature. A lag of the order of hundreds of kiloparsecs is possible because of the combination of thermal conduction and a finite electron-ion equilibration time. Forthcoming better spatial resolution data will allow a direct measurement of these phenomena in the cluster gas using such lags. A2065 has gas density peaks coincident with two central galaxies. A merger with the collision velocity estimated from the temperature map should have swept away such peaks if the subcluster total mass distributions had flat cores in the centers. The fact that the peaks have survived (or quickly reemerged) suggests that the gravitational potential is also strongly peaked. Finally, the observed specific entropy variations in A3667 and Cygnus A indicate that energy injection from a single major merger may be of the order of the full thermal energy of the gas. We hope that these order-of-magnitude estimates will encourage further work on hydrodynamic simulations, as well as a more quantitative representation of the simulation results, in anticipation of the Chandra and XMM data.

[Sociodemographic context of homicide in Mexico City: a spatial analysis].

PubMed

Fuentes Flores, César; Sánchez Salinas, Omar

2015-12-01

Investigate the spatial distribution pattern of the homicide rate and its relation to sociodemographic features in the Benito Juárez, Coyoacán, and Cuauhtémoc districts of Mexico City in 2010. Inferential cross-sectional study that uses spatial analysis methods to study the spatial association of the homicide rate and demographic features. Spatial association was determined through the location quotient, multiple regression analysis, and the use of geographically weighted regression. Homicides show a heterogeneous location pattern with high rates in areas with non-residential land use, low population density, and low marginalization. Spatial analysis tools are powerful instruments for the design of prevention- and recreation-focused public safety policies that aim to reduce mortality from external causes such as homicides.

Spatiotemporal modeling of ecological and sociological predictors of West Nile virus in Suffolk County, NY, mosquitoes

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

Myer, Mark H.; Campbell, Scott R.; Johnston, John M.

Suffolk County, New York, is a locus for West Nile virus (WNV) infection in the American northeast that includes the majority of Long Island to the east of New York City. The county has a system of light and gravid traps used for mosquito collection and disease monitoring. In order to identify predictors of WNV incidence in mosquitoes and predict future occurrence of WNV, we have developed a spatiotemporal Bayesian model, beginning with over 40 ecological, meteorological, and built-environment covariates. A mixed-effects model including spatially and temporally correlated errors was fit to WNV surveillance data from 2008 to 2014 usingmore » the R package “R-INLA,” which allows for Bayesian modeling using the stochastic partial differential equation (SPDE) approach. The integrated nested Laplace approximation (INLA) SPDE allows for simultaneous fitting of a temporal parameter and a spatial covariance, while incorporating a variety of likelihood functions and running in R statistical software on a home computer. We found that land cover classified as open water and woody wetlands had a negative association with WNV incidence in mosquitoes, and the count of septic systems was associated with an increase in WNV. Mean temperature at two-week lag was associated with a strong positive impact, while mean precipitation at no lag and one-week lag was associated with positive and negative impacts on WNV, respectively. Incorporation of spatiotemporal factors resulted in a marked increase in model goodness-of-fit. The predictive power of the model was evaluated on 2015 surveillance results, where the best model achieved a sensitivity of 80.9% and a specificity of 77.0%. The spatial covariate was mapped across the county, identifying a gradient of WNV prevalence increasing from east to west. The Bayesian spatiotemporal model improves upon previous approaches, and we recommend the INLA SPDE methodology as an efficient way to develop robust models from surveillance data to develop and enhance monitoring and control programs. Our study confirms previously found associations between weather conditions and WNV and suggests that wetland cover has a mitigating effect on WNV infection in mosquitoes, while high septic system density is associated with an increase in WNV infection.« less

Spatial coherence and large-scale drivers of drought

NASA Astrophysics Data System (ADS)

Svensson, Cecilia; Hannaford, Jamie

2017-04-01

Drought is a potentially widespread and generally multifaceted natural phenomenon affecting all aspects of the hydrological cycle. It mainly manifests itself at seasonal, or longer, time scales. Here, we use seasonal river flows across the climatologically and topographically diverse UK to investigate the spatial coherence of drought, and explore its oceanic and atmospheric drivers. A better understanding of the spatial characteristics and drivers will improve forecasting and help increase drought preparedness. The location of the UK in the mid-latitude belt of predominantly westerly winds, together with a pronounced topographical divide running roughly from north to south, produce strong windward and leeward effects. Weather fronts associated with storms tracking north-eastward between Scotland and Iceland typically lead to abundant precipitation in the mountainous north and west, while the south and east remain drier. In contrast, prolonged precipitation in eastern Britain tends to be associated with storms on a more southerly track, producing precipitation in onshore winds on the northern side of depressions. Persistence in the preferred storm tracks can therefore result in periods of wet/dry conditions across two main regions of the UK, a mountainous northwest region exposed to westerly winds and a more sheltered, lowland southeast region. This is reflected in cluster analyses of monthly river flow anomalies. A further division into three clusters separates out a region of highly permeable, slowly responding, catchments in the southeast. An expectation that the preferred storm tracks over seasonal time scales can be captured by atmospheric airflow indices, which in turn may be related to oceanic conditions, suggests that statistical methods may be used to describe the relationships between UK regional streamflows, and oceanic and atmospheric drivers. Such relationships may be concurrent or lagged, and the longer response time of the group of permeable catchments in the southeast also introduces lags in the statistical relationships. Three-month aggregations of the data were used to investigate potential oceanic and atmospheric drivers of streamflow drought in the three UK regions. Significant concurrent relationships were found for different parts of the year for several indices of northern hemisphere airflow patterns, including the North Atlantic Oscillation, the Arctic Oscillation, the East Atlantic, the East Atlantic/West Russia, and the Scandinavia patterns. Significant relationships with oceanic and atmospheric indices representing the El Niño/Southern Oscillation were found for both concurrent and lagged analyses.

Spatiotemporal modeling of ecological and sociological predictors of West Nile virus in Suffolk County, NY, mosquitoes

DOE PAGES

Myer, Mark H.; Campbell, Scott R.; Johnston, John M.

2017-06-15

Suffolk County, New York, is a locus for West Nile virus (WNV) infection in the American northeast that includes the majority of Long Island to the east of New York City. The county has a system of light and gravid traps used for mosquito collection and disease monitoring. In order to identify predictors of WNV incidence in mosquitoes and predict future occurrence of WNV, we have developed a spatiotemporal Bayesian model, beginning with over 40 ecological, meteorological, and built-environment covariates. A mixed-effects model including spatially and temporally correlated errors was fit to WNV surveillance data from 2008 to 2014 usingmore » the R package “R-INLA,” which allows for Bayesian modeling using the stochastic partial differential equation (SPDE) approach. The integrated nested Laplace approximation (INLA) SPDE allows for simultaneous fitting of a temporal parameter and a spatial covariance, while incorporating a variety of likelihood functions and running in R statistical software on a home computer. We found that land cover classified as open water and woody wetlands had a negative association with WNV incidence in mosquitoes, and the count of septic systems was associated with an increase in WNV. Mean temperature at two-week lag was associated with a strong positive impact, while mean precipitation at no lag and one-week lag was associated with positive and negative impacts on WNV, respectively. Incorporation of spatiotemporal factors resulted in a marked increase in model goodness-of-fit. The predictive power of the model was evaluated on 2015 surveillance results, where the best model achieved a sensitivity of 80.9% and a specificity of 77.0%. The spatial covariate was mapped across the county, identifying a gradient of WNV prevalence increasing from east to west. The Bayesian spatiotemporal model improves upon previous approaches, and we recommend the INLA SPDE methodology as an efficient way to develop robust models from surveillance data to develop and enhance monitoring and control programs. Our study confirms previously found associations between weather conditions and WNV and suggests that wetland cover has a mitigating effect on WNV infection in mosquitoes, while high septic system density is associated with an increase in WNV infection.« less

Restricted spatial regression in practice: Geostatistical models, confounding, and robustness under model misspecification

USGS Publications Warehouse

Hanks, Ephraim M.; Schliep, Erin M.; Hooten, Mevin B.; Hoeting, Jennifer A.

2015-01-01

In spatial generalized linear mixed models (SGLMMs), covariates that are spatially smooth are often collinear with spatially smooth random effects. This phenomenon is known as spatial confounding and has been studied primarily in the case where the spatial support of the process being studied is discrete (e.g., areal spatial data). In this case, the most common approach suggested is restricted spatial regression (RSR) in which the spatial random effects are constrained to be orthogonal to the fixed effects. We consider spatial confounding and RSR in the geostatistical (continuous spatial support) setting. We show that RSR provides computational benefits relative to the confounded SGLMM, but that Bayesian credible intervals under RSR can be inappropriately narrow under model misspecification. We propose a posterior predictive approach to alleviating this potential problem and discuss the appropriateness of RSR in a variety of situations. We illustrate RSR and SGLMM approaches through simulation studies and an analysis of malaria frequencies in The Gambia, Africa.

EMIC Wave Scale Size in the Inner Magnetosphere: Observations From the Dual Van Allen Probes

NASA Technical Reports Server (NTRS)

Blum, L. W.; Bonnell, J. W.; Agapitov, O.; Paulson, K.; Kletzing, C.

2017-01-01

Estimating the spatial scales of electromagnetic ion cyclotron (EMIC) waves is critical for quantifying their overall scattering efficiency and effects on thermal plasma, ring current, and radiation belt particles. Using measurements from the dual Van Allen Probes in 2013-2014, we characterize the spatial and temporal extents of regions of EMIC wave activity and how these depend on local time and radial distance within the inner magnetosphere. Observations are categorized into three types: waves observed by only one spacecraft, waves measured by both spacecraft simultaneously, and waves observed by both spacecraft with some time lag. Analysis reveals that dayside (and H+ band) EMIC waves more frequently span larger spatial areas, while nightside (and He+ band) waves are more often localized but can persist many hours. These investigations give insight into the nature of EMIC wave generation and support more accurate quantification of their effects on the ring current and outer radiation belt.

EMIC wave scale size in the inner magnetosphere: Observations from the dual Van Allen Probes

NASA Astrophysics Data System (ADS)

Blum, L. W.; Bonnell, J. W.; Agapitov, O.; Paulson, K.; Kletzing, C.

2017-02-01

Estimating the spatial scales of electromagnetic ion cyclotron (EMIC) waves is critical for quantifying their overall scattering efficiency and effects on thermal plasma, ring current, and radiation belt particles. Using measurements from the dual Van Allen Probes in 2013-2014, we characterize the spatial and temporal extents of regions of EMIC wave activity and how these depend on local time and radial distance within the inner magnetosphere. Observations are categorized into three types—waves observed by only one spacecraft, waves measured by both spacecraft simultaneously, and waves observed by both spacecraft with some time lag. Analysis reveals that dayside (and H+ band) EMIC waves more frequently span larger spatial areas, while nightside (and He+ band) waves are more often localized but can persist many hours. These investigations give insight into the nature of EMIC wave generation and support more accurate quantification of their effects on the ring current and outer radiation belt.

Air Pollution and Hospitalization for Acute Myocardial Infarction in China.

PubMed

Liu, Hui; Tian, Yaohua; Xiang, Xiao; Sun, Kexin; Juan, Juan; Song, Jing; Cao, Yaying; Xu, Beibei; Hu, Yonghua

2017-09-01

There is growing interest in the association between ambient air pollution and acute myocardial infarction (AMI). The objective of this study was to explore the association in 14 Chinese cities using a time-stratified case-crossover design. We identified 80,787 hospital admissions for AMI between January 1, 2014 and December 31, 2015 from electronic hospitalization summary reports. Conditional logistic regression was used to estimate the percent changes with 95% confidence intervals (CIs) in AMI admissions in relation to an interquartile range increase in ambient air pollutant concentrations. All analyzed air pollutants, with the exception of ozone, were positively associated with daily AMI admissions on lag2 and lag3 days. An interquartile range increase in particulate matter <10 µm in aerodynamic diameter, sulfur dioxide, nitrogen dioxide, and carbon monoxide concentrations on lag2 day was significantly associated with a 0.8% (95% CI 0.1%, 1.6%), 2.0% (95% CI 1.2%, 2.9%), 2.2% (95% CI 1.4%, 3.1%), and 1.1% (95% CI 0.4%, 1.8%) increase in AMI admissions, respectively. We also observed a significant association in relation to ozone on lag4 day (percent change: 1.3%; 95% CI 0.2%, 2.4%). Subgroup analyses indicated no effect modification of risk by age (≥65 years and <65 years) or gender. In conclusion, this is the first multicity study in China, or even in other developing countries, to report the short-term effects of air pollution on AMI morbidity. Our findings contribute to the limited scientific data on the effects of ambient air pollution on AMI in developing countries. Copyright © 2017 Elsevier Inc. All rights reserved.

Time-dependent oral absorption models

NASA Technical Reports Server (NTRS)

Higaki, K.; Yamashita, S.; Amidon, G. L.

2001-01-01

The plasma concentration-time profiles following oral administration of drugs are often irregular and cannot be interpreted easily with conventional models based on first- or zero-order absorption kinetics and lag time. Six new models were developed using a time-dependent absorption rate coefficient, ka(t), wherein the time dependency was varied to account for the dynamic processes such as changes in fluid absorption or secretion, in absorption surface area, and in motility with time, in the gastrointestinal tract. In the present study, the plasma concentration profiles of propranolol obtained in human subjects following oral dosing were analyzed using the newly derived models based on mass balance and compared with the conventional models. Nonlinear regression analysis indicated that the conventional compartment model including lag time (CLAG model) could not predict the rapid initial increase in plasma concentration after dosing and the predicted Cmax values were much lower than that observed. On the other hand, all models with the time-dependent absorption rate coefficient, ka(t), were superior to the CLAG model in predicting plasma concentration profiles. Based on Akaike's Information Criterion (AIC), the fluid absorption model without lag time (FA model) exhibited the best overall fit to the data. The two-phase model including lag time, TPLAG model was also found to be a good model judging from the values of sum of squares. This model also described the irregular profiles of plasma concentration with time and frequently predicted Cmax values satisfactorily. A comparison of the absorption rate profiles also suggested that the TPLAG model is better at prediction of irregular absorption kinetics than the FA model. In conclusion, the incorporation of a time-dependent absorption rate coefficient ka(t) allows the prediction of nonlinear absorption characteristics in a more reliable manner.

Impacts of Tropical Cyclones and Accompanying Precipitation on Infectious Diarrhea in Cyclone Landing Areas of Zhejiang Province, China

PubMed Central

Deng, Zhengyi; Xun, Huanmiao; Zhou, Maigeng; Jiang, Baofa; Wang, Songwang; Guo, Qing; Wang, Wei; Kang, Ruihua; Wang, Xin; Marley, Gifty; Ma, Wei

2015-01-01

Background: Zhejiang Province, located in southeastern China, is frequently hit by tropical cyclones. This study quantified the associations between infectious diarrhea and the seven tropical cyclones that landed in Zhejiang from 2005–2011 to assess the impacts of the accompanying precipitation on the studied diseases. Method: A unidirectional case-crossover study design was used to evaluate the impacts of tropical storms and typhoons on infectious diarrhea. Principal component analysis (PCA) was applied to eliminate multicollinearity. A multivariate logistic regression model was used to estimate the odds ratios (ORs) and the 95% confidence intervals (CIs). Results: For all typhoons studied, the greatest impacts on bacillary dysentery and other infectious diarrhea were identified on lag 6 days (OR = 2.30, 95% CI: 1.81–2.93) and lag 5 days (OR = 3.56, 95% CI: 2.98–4.25), respectively. For all tropical storms, impacts on these diseases were highest on lag 2 days (OR = 2.47, 95% CI: 1.41–4.33) and lag 6 days (OR = 2.46, 95% CI: 1.69–3.56), respectively. The tropical cyclone precipitation was a risk factor for both bacillary dysentery and other infectious diarrhea when daily precipitation reached 25 mm and 50 mm with the largest OR = 3.25 (95% CI: 1.45–7.27) and OR = 3.05 (95% CI: 2.20–4.23), respectively. Conclusions: Both typhoons and tropical storms could contribute to an increase in risk of bacillary dysentery and other infectious diarrhea in Zhejiang. Tropical cyclone precipitation may also be a risk factor for these diseases when it reaches or is above 25 mm and 50 mm, respectively. Public health preventive and intervention measures should consider the adverse health impacts from tropical cyclones. PMID:25622139

A Longitudinal Assessment of the Links Between Physical Activity and Self-Esteem in Early Adolescent Non-Hispanic Females

PubMed Central

Schmalz, Dorothy L.; Deane, Glenn D.; Birch, Leann L.; Davison, Kirsten Krahnstoever

2008-01-01

Purpose: For decades, researchers have proclaimed the positive psychosocial benefits of participation in physical activity. However, recent meta-analyses of the literature have found infrequent and inconclusive empirical support for the link between physical activity and psychosocial well-being. In this study, we use data from a longitudinal study to explore the links between participation in physical activity and global self-esteem among girls from childhood into early adolescence and the direction of this relationship. Methods: Participants included 197 non-Hispanic white girls. Girls' participation in physical activity and their global self-esteem were assessed when they were 9, 11, and 13 years old. Panel regression was used to assess the lagged effect of physical activity on self-esteem and the lagged effect of self-esteem on physical activity, controlling for family socioeconomic status (SES) and girls' body mass index (BMI). Results: A significant lagged effect of physical activity on self-esteem was identified. Specifically, higher physical activity at ages 9 and 11 years predicted higher self-esteem at ages 11 and 13 years respectively, controlling for covariates. Positive effects of physical activity on self-esteem were most apparent at age 11 and for girls with higher BMI. No support was gained for the lagged effect of self-esteem on physical activity. Conclusions: Results suggest that participating in physical activity can lead to positive self-esteem among adolescent girls, particularly for younger girls and those at greatest risk of overweight. These findings highlight the necessity of promoting physical activity among adolescent girls as a method of fostering positive self-worth. PMID:18023784

A new approach to modeling temperature-related mortality: Non-linear autoregressive models with exogenous input.

PubMed

Lee, Cameron C; Sheridan, Scott C

2018-07-01

Temperature-mortality relationships are nonlinear, time-lagged, and can vary depending on the time of year and geographic location, all of which limits the applicability of simple regression models in describing these associations. This research demonstrates the utility of an alternative method for modeling such complex relationships that has gained recent traction in other environmental fields: nonlinear autoregressive models with exogenous input (NARX models). All-cause mortality data and multiple temperature-based data sets were gathered from 41 different US cities, for the period 1975-2010, and subjected to ensemble NARX modeling. Models generally performed better in larger cities and during the winter season. Across the US, median absolute percentage errors were 10% (ranging from 4% to 15% in various cities), the average improvement in the r-squared over that of a simple persistence model was 17% (6-24%), and the hit rate for modeling spike days in mortality (>80th percentile) was 54% (34-71%). Mortality responded acutely to hot summer days, peaking at 0-2 days of lag before dropping precipitously, and there was an extended mortality response to cold winter days, peaking at 2-4 days of lag and dropping slowly and continuing for multiple weeks. Spring and autumn showed both of the aforementioned temperature-mortality relationships, but generally to a lesser magnitude than what was seen in summer or winter. When compared to distributed lag nonlinear models, NARX model output was nearly identical. These results highlight the applicability of NARX models for use in modeling complex and time-dependent relationships for various applications in epidemiology and environmental sciences. Copyright © 2018 Elsevier Inc. All rights reserved.

Modelling space of spread Dengue Hemorrhagic Fever (DHF) in Central Java use spatial durbin model

NASA Astrophysics Data System (ADS)

Ispriyanti, Dwi; Prahutama, Alan; Taryono, Arkadina PN

2018-05-01

Dengue Hemorrhagic Fever is one of the major public health problems in Indonesia. From year to year, DHF causes Extraordinary Event in most parts of Indonesia, especially Central Java. Central Java consists of 35 districts or cities where each region is close to each other. Spatial regression is an analysis that suspects the influence of independent variables on the dependent variables with the influences of the region inside. In spatial regression modeling, there are spatial autoregressive model (SAR), spatial error model (SEM) and spatial autoregressive moving average (SARMA). Spatial Durbin model is the development of SAR where the dependent and independent variable have spatial influence. In this research dependent variable used is number of DHF sufferers. The independent variables observed are population density, number of hospitals, residents and health centers, and mean years of schooling. From the multiple regression model test, the variables that significantly affect the spread of DHF disease are the population and mean years of schooling. By using queen contiguity and rook contiguity, the best model produced is the SDM model with queen contiguity because it has the smallest AIC value of 494,12. Factors that generally affect the spread of DHF in Central Java Province are the number of population and the average length of school.

Contrasting spatial structures of Atlantic Multidecadal Oscillation between observations and slab ocean model simulations

NASA Astrophysics Data System (ADS)

Sun, Cheng; Li, Jianping; Kucharski, Fred; Xue, Jiaqing; Li, Xiang

2018-04-01

The spatial structure of Atlantic multidecadal oscillation (AMO) is analyzed and compared between the observations and simulations from slab ocean models (SOMs) and fully coupled models. The observed sea surface temperature (SST) pattern of AMO is characterized by a basin-wide monopole structure, and there is a significantly high degree of spatial coherence of decadal SST variations across the entire North Atlantic basin. The observed SST anomalies share a common decadal-scale signal, corresponding to the basin-wide average (i. e., the AMO). In contrast, the simulated AMO in SOMs (AMOs) exhibits a tripole-like structure, with the mid-latitude North Atlantic SST showing an inverse relationship with other parts of the basin, and the SOMs fail to reproduce the observed strong spatial coherence of decadal SST variations associated with the AMO. The observed spatial coherence of AMO SST anomalies is identified as a key feature that can be used to distinguish the AMO mechanism. The tripole-like SST pattern of AMOs in SOMs can be largely explained by the atmosphere-forced thermodynamics mechanism due to the surface heat flux changes associated with the North Atlantic Oscillation (NAO). The thermodynamic forcing of AMOs by the NAO gives rise to a simultaneous inverse NAO-AMOs relationship at both interannual and decadal timescales and a seasonal phase locking of the AMOs variability to the cold season. However, the NAO-forced thermodynamics mechanism cannot explain the observed NAO-AMO relationship and the seasonal phase locking of observed AMO variability to the warm season. At decadal timescales, a strong lagged relationship between NAO and AMO is observed, with the NAO leading by up to two decades, while the simultaneous correlation of NAO with AMO is weak. This lagged relationship and the spatial coherence of AMO can be well understood from the view point of ocean dynamics. A time-integrated NAO index, which reflects the variations in Atlantic meridional overturning circulation (AMOC) and northward ocean heat transport caused by the accumulated effect of NAO forcing, reasonably well captures the observed multidecadal fluctuations in the AMO. Further analysis using the fully coupled model simulations provides direct modeling evidence that the observed spatial coherence of decadal SST variations across North Atlantic basin can be reproduced only by including the AMOC-related ocean dynamics, and the AMOC acts as a common forcing signal that results in a spatially coherent variation of North Atlantic SST.

A Geostatistical Scaling Approach for the Generation of Non Gaussian Random Variables and Increments

NASA Astrophysics Data System (ADS)

Guadagnini, Alberto; Neuman, Shlomo P.; Riva, Monica; Panzeri, Marco

2016-04-01

We address manifestations of non-Gaussian statistical scaling displayed by many variables, Y, and their (spatial or temporal) increments. Evidence of such behavior includes symmetry of increment distributions at all separation distances (or lags) with sharp peaks and heavy tails which tend to decay asymptotically as lag increases. Variables reported to exhibit such distributions include quantities of direct relevance to hydrogeological sciences, e.g. porosity, log permeability, electrical resistivity, soil and sediment texture, sediment transport rate, rainfall, measured and simulated turbulent fluid velocity, and other. No model known to us captures all of the documented statistical scaling behaviors in a unique and consistent manner. We recently proposed a generalized sub-Gaussian model (GSG) which reconciles within a unique theoretical framework the probability distributions of a target variable and its increments. We presented an algorithm to generate unconditional random realizations of statistically isotropic or anisotropic GSG functions and illustrated it in two dimensions. In this context, we demonstrated the feasibility of estimating all key parameters of a GSG model underlying a single realization of Y by analyzing jointly spatial moments of Y data and corresponding increments. Here, we extend our GSG model to account for noisy measurements of Y at a discrete set of points in space (or time), present an algorithm to generate conditional realizations of corresponding isotropic or anisotropic random field, and explore them on one- and two-dimensional synthetic test cases.

A spatiotemporal model of ecological and sociological ...

EPA Pesticide Factsheets

Background/Question/Methods Suffolk County, New York is a locus for West Nile virus (WNV) infection in the American northeast that includes the majority of Long Island to the east of New York City. The county has a robust system of light and gravid traps used for mosquito collection and disease monitoring. Since 2010, there have been 55 confirmed human cases of WNV in Suffolk County, resulting in 3 deaths. In order to identify predictors of WNV incidence in mosquitoes and predict future occurrence of WNV we developed a spatiotemporal Bayesian model, beginning with over 40 ecological, meteorological, and built-environment covariates. A mixed effects model including spatially and temporally correlated errors was fit to WNV surveillance data from 2008-2014 using the R package 'R-INLA' which allows for Bayesian modeling using the stochastic partial differential equation (SPDE) approach. The INLA SPDE allows for simultaneous fitting of temporal parameters and a spatial covariance matrix, while incorporating multiple likelihood functions and running in standard R statistical software on a typical home computer. Results/Conclusions We found that land cover classified as open water or woody wetlands had a negative association with WNV incidence in mosquitoes, and the count of septic systems was associated with an increase in WNV. Mean temperature at two weeks lag was associated with a strong positive impact, while mean precipitation at no lag and

Assessing metabolic heterogeneity in genetically homogeneous populations of bacteria using SIMS

NASA Astrophysics Data System (ADS)

McClelland, H. L. O.; Fike, D. A.; Jones, C.; Bradley, A. S.

2016-12-01

Biogeochemical cycles of elements are catalyzed by microbes, and can be assessed using a wide array of geochemical techniques. As the spatial resolution of these analytical techniques improves over time, it has become apparent that spatial heterogeneity of geochemical processes may impose noise on a range of geochemical signals. This spatial heterogeneity may reflect population structure, as well as metabolic heterogeneity among cells. New analytical approaches are required to understand, at the cellular level, differences in biogeochemical cycling of elements. We are developing such approaches by applying secondary-ion mass spectrometry (SIMS) techniques to populations of model organisms. In this work we report initial results from the analysis of genetically homogeneous cultures of Methylobacterium extorquens PA1, a facultative methylotrophic Alphaproteobacterium that has been extensively studied growing on both single carbon (e.g., methanol) and multi-carbon (e.g., succinate) substrates. PA1 cultures acclimated to succinate exhibited a more pronounced lag when grown on methanol compared with populations acclimated to methanol. However neither acclimation condition results in a pronounced lag during growth on succinate. When grown on a mixture of methanol and succinate, Methylobacterium co-utilize these substrates on a population level. We investigated the degree to which this apparent coutilisation is representative of individual cells, or whether it is a superposition of distinct metabolically specialized subpopulations. To explore this metabolic heterogeneity, we have grown populations of PA1 in liquid media containing a mixture of both methanol and succinate with one or the other substrate labelled with 13C. SIMS analysis of the isotopic composition of each cell allows us to infer the substrate, or mix of substrates, used for anabolic processes in each cell, along with cell-specfic growth rates via the exponential dilution of a 15N label.

Spatial and seasonal characterization of net primary productivity and climate variables in southeastern China using MODIS data*

PubMed Central

Peng, Dai-liang; Huang, Jing-feng; Huete, Alfredo R.; Yang, Tai-ming; Gao, Ping; Chen, Yan-chun; Chen, Hui; Li, Jun; Liu, Zhan-yu

2010-01-01

We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature, precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area, a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01), and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China, NPP showed 16-d, 48-d, and 96-d lagged correlation with air temperature, precipitation, and sunshine percentage, respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d, while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region, the spatial patterns of vegetation-climate relationship became complicated and diversiform, especially for precipitation influences on NPP. In the northern part of the study area, all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP. PMID:20349524

Functional CAR models for large spatially correlated functional datasets.

PubMed

Zhang, Lin; Baladandayuthapani, Veerabhadran; Zhu, Hongxiao; Baggerly, Keith A; Majewski, Tadeusz; Czerniak, Bogdan A; Morris, Jeffrey S

2016-01-01

We develop a functional conditional autoregressive (CAR) model for spatially correlated data for which functions are collected on areal units of a lattice. Our model performs functional response regression while accounting for spatial correlations with potentially nonseparable and nonstationary covariance structure, in both the space and functional domains. We show theoretically that our construction leads to a CAR model at each functional location, with spatial covariance parameters varying and borrowing strength across the functional domain. Using basis transformation strategies, the nonseparable spatial-functional model is computationally scalable to enormous functional datasets, generalizable to different basis functions, and can be used on functions defined on higher dimensional domains such as images. Through simulation studies, we demonstrate that accounting for the spatial correlation in our modeling leads to improved functional regression performance. Applied to a high-throughput spatially correlated copy number dataset, the model identifies genetic markers not identified by comparable methods that ignore spatial correlations.

Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?

NASA Astrophysics Data System (ADS)

Lin, Yingzhi; Deng, Xiangzheng; Li, Xing; Ma, Enjun

2014-12-01

Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.

A Predictive Logistic Regression Model of World Conflict Using Open Source Data

DTIC Science & Technology

2015-03-26

Added to the United Nations list are Palestine (West Bank and Gaza) and Kosovo. The total number of modeled nations is 182. Not all of these...The 26 variables are listed in Table 4. Also listed in Table 4 are the year the dataset was first collected, the data lag and the number of nation...state of violent conflict in 2015, seventeen of them are new to conflict since the last published list in 2013. A prediction tool is created to allow

Comparing spatial regression to random forests for large ...

EPA Pesticide Factsheets

Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates, whereas spatial regression, when using reduced rank methods, has a reputation for good predictive performance when using many records. In this study, we compare these two techniques using a data set containing the macroinvertebrate multimetric index (MMI) at 1859 stream sites with over 200 landscape covariates. Our primary goal is predicting MMI at over 1.1 million perennial stream reaches across the USA. For spatial regression modeling, we develop two new methods to accommodate large data: (1) a procedure that estimates optimal Box-Cox transformations to linearize covariate relationships; and (2) a computationally efficient covariate selection routine that takes into account spatial autocorrelation. We show that our new methods lead to cross-validated performance similar to random forests, but that there is an advantage for spatial regression when quantifying the uncertainty of the predictions. Simulations are used to clarify advantages for each method. This research investigates different approaches for modeling and mapping national stream condition. We use MMI data from the EPA's National Rivers and Streams Assessment and predictors from StreamCat (Hill et al., 2015). Previous studies have focused on modeling the MMI condition classes (i.e., good, fair, and po

Childbearing in crisis: war, migration and fertility in Angola.

PubMed

Avogo, Winfred; Agadjanian, Victor

2008-09-01

This study examines the short- and long-term effects of war-induced and war-unrelated migration on fertility outcomes using data from two peri-urban municipalities of Greater Luanda in Angola. In the short term, results from multi-level discrete-time logistic regression models indicate that net of other factors, war-unrelated migration is associated with a lower probability of birth than war-induced migration in a given year. Similar results are obtained when the effects of migration are lagged by a year. At the same time, the effects of war-triggered migration do not differ significantly from those of not migrating in a given year but are statistically significant when the effects of migration are lagged by a year. In the long term, the effects of migration experience on cumulative fertility are negligible and not statistically significant net of demographic and socioeconomic variables. Interpretations of the results are offered in the context of Angola and their broader implications are reflected on.

Simulation Analysis of Helicopter Ground Resonance Nonlinear Dynamics

NASA Astrophysics Data System (ADS)

Zhu, Yan; Lu, Yu-hui; Ling, Ai-min

2017-07-01

In order to accurately predict the dynamic instability of helicopter ground resonance, a modeling and simulation method of helicopter ground resonance considering nonlinear dynamic characteristics of components (rotor lead-lag damper, landing gear wheel and absorber) is presented. The numerical integral method is used to calculate the transient responses of the body and rotor, simulating some disturbance. To obtain quantitative instabilities, Fast Fourier Transform (FFT) is conducted to estimate the modal frequencies, and the mobile rectangular window method is employed in the predictions of the modal damping in terms of the response time history. Simulation results show that ground resonance simulation test can exactly lead up the blade lead-lag regressing mode frequency, and the modal damping obtained according to attenuation curves are close to the test results. The simulation test results are in accordance with the actual accident situation, and prove the correctness of the simulation method. This analysis method used for ground resonance simulation test can give out the results according with real helicopter engineering tests.

Association between outdoor ozone and compensated acute respiratory diseases among workers in Quebec (Canada).

PubMed

Adam-Poupart, Ariane; Labrèche, France; Busque, Marc-Antoine; Brand, Allan; Duguay, Patrice; Fournier, Michel; Zayed, Joseph; Smargiassi, Audrey

2015-01-01

Respiratory effects of ozone in the workplace have not been extensively studied. Our aim was to explore the relationship between daily average ozone levels and compensated acute respiratory problems among workers in Quebec between 2003 and 2010 using a time-stratified case-crossover design. Health data came from the Workers' Compensation Board. Daily concentrations of ozone were estimated using a spatiotemporal model. Conditional logistic regressions, with and without adjustment for temperature, were used to estimate odds ratios (ORs, per 1 ppb increase of ozone), and lag effects were assessed. Relationships with respiratory compensations in all industrial sectors were essentially null. Positive non-statistically significant associations were observed for outdoor sectors, and decreased after controlling for temperature (ORs of 0.98; 1.01 and 1.05 at Lags 0, 1 and 2 respectively). Considering the predicted increase of air pollutant concentrations in the context of climate change, closer investigation should be carried out on outdoor workers.

Global climate change: impact of diurnal temperature range on mortality in Guangzhou, China.

PubMed

Yang, Jun; Liu, Hua-Zhang; Ou, Chun-Quan; Lin, Guo-Zhen; Zhou, Qin; Shen, Gi-Chuan; Chen, Ping-Yan; Guo, Yuming

2013-04-01

Diurnal temperature range (DTR) is an important meteorological indicator associated with global climate change, but little is known about the effects of DTR on mortality. We examined the effects of DTR on cause-/age-/education-specific mortality in Guangzhou, a subtropical city in China during 2003-2010. A quasi-Poisson regression model combined with distributed lag non-linear model was used to examine the effects of DTR, after controlling for daily mean temperature, air pollutants, season and day of the week. A 1 °C increase in DTR at lag 0-4 days was associated with a 0.47% (95% confidence interval: 0.01%-0.93%) increase in non-accidental mortality. Stroke mortality was most sensitive to DTR. Female, the elderly and those with low education were more susceptible to DTR than male, the youth and those with high education, respectively. Our findings suggest that vulnerable subpopulations should pay more attention to protect themselves from unstable daily weather. Copyright © 2012 Elsevier Ltd. All rights reserved.

Study to eliminate ground resonance using active controls

NASA Technical Reports Server (NTRS)

Straub, F. K.

1984-01-01

The effectiveness of active control blade feathering in increasing rotor body damping and the possibility to eliminate ground resonance instabilities were investigated. An analytical model representing rotor flapping and lead-lag degrees of freedom and body pitch, roll, longitudinal and lateral motion is developed. Active control blade feathering is implemented as state variable feedback through a conventional swashplate. The influence of various feedback states, feedback gain, and weighting between the cyclic controls is studied through stability and response analyses. It is shown that blade cyclic inplane motion, roll rate and roll acceleration feedback can add considerable damping to the system and eliminate ground resonance instabilities, which the feedback phase is also a powerful parameter, if chosen properly, it maximizes augmentation of the inherent regressing lag mode damping. It is shown that rotor configuration parameters, like blade root hinge offset, flapping stiffness, and precone considerably influence the control effectiveness. It is found that active control is particularly powerful for hingeless and bearingless rotor systems.

Association between exposure to particulate matter and hospital admissions for respiratory disease in children

PubMed Central

Cesar, Ana Cristina Gobbo; Nascimento, Luiz Fernando C; de Carvalho, João Andrade

2013-01-01

The aim of this study was to estimate the association between exposure to particulate matter less than 2.5 microns in diameter and hospitalization for respiratory disease. It was an ecological time series study with daily indicators of hospitalization for respiratory diseases in children up to 10 years old, living in Piracicaba, SP, Southeastern Brazil, between August 1, 2011 and July 31, 2012. A generalized additive Poisson regression model was used. The relative risks were RR = 1.008; 95%CI 1.001;1.016 for lag 1 and RR = 1.009; 95%CI 1.001;1.017 for lag 3. The increment of 10 μg/m3in particulate matter less than 2.5 microns in diameter implies increase in relative risk of between 7.9 and 8.6 percentage points. In conclusion, exposure to particulate matter less than 2.5 microns in diameter was associated with hospitalization for respiratory disease in children. PMID:24626559

Role of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in local dengue epidemics in Taiwan.

PubMed

Tsai, Pui-Jen; Teng, Hwa-Jen

2016-11-09

Aedes mosquitoes in Taiwan mainly comprise Aedes albopictus and Ae. aegypti. However, the species contributing to autochthonous dengue spread and the extent at which it occurs remain unclear. Thus, in this study, we spatially analyzed real data to determine spatial features related to local dengue incidence and mosquito density, particularly that of Ae. albopictus and Ae. aegypti. We used bivariate Moran's I statistic and geographically weighted regression (GWR) spatial methods to analyze the globally spatial dependence and locally regressed relationship between (1) imported dengue incidences and Breteau indices (BIs) of Ae. albopictus, (2) imported dengue incidences and BI of Ae. aegypti, (3) autochthonous dengue incidences and BI of Ae. albopictus, (4) autochthonous dengue incidences and BI of Ae. aegypti, (5) all dengue incidences and BI of Ae. albopictus, (6) all dengue incidences and BI of Ae. aegypti, (7) BI of Ae. albopictus and human population density, and (8) BI of Ae. aegypti and human population density in 348 townships in Taiwan. In the GWR models, regression coefficients of spatially regressed relationships between the incidence of autochthonous dengue and vector density of Ae. aegypti were significant and positive in most townships in Taiwan. However, Ae. albopictus had significant but negative regression coefficients in clusters of dengue epidemics. In the global bivariate Moran's index, spatial dependence between the incidence of autochthonous dengue and vector density of Ae. aegypti was significant and exhibited positive correlation in Taiwan (bivariate Moran's index = 0.51). However, Ae. albopictus exhibited positively significant but low correlation (bivariate Moran's index = 0.06). Similar results were observed in the two spatial methods between all dengue incidences and Aedes mosquitoes (Ae. aegypti and Ae. albopictus). The regression coefficients of spatially regressed relationships between imported dengue cases and Aedes mosquitoes (Ae. aegypti and Ae. albopictus) were significant in 348 townships in Taiwan. The results indicated that local Aedes mosquitoes do not contribute to the dengue incidence of imported cases. The density of Ae. aegypti positively correlated with the density of human population. By contrast, the density of Ae. albopictus negatively correlated with the density of human population in the areas of southern Taiwan. The results indicated that Ae. aegypti has more opportunities for human-mosquito contact in dengue endemic areas in southern Taiwan. Ae. aegypti, but not Ae. albopictus, and human population density in southern Taiwan are closely associated with an increased risk of autochthonous dengue incidence.

Air pollution and associated respiratory morbidity in Delhi.

PubMed

Jayaraman, Girija; Nidhi

2008-06-01

As a rapidly expanding centre of government, trade, commerce and industry, Delhi, the Indian capital, presents an instructive location for studying the possible association between air pollution and adverse health effects. This study tries to determine the association, if any, between the air pollutants--sulphur dioxide, nitrogen dioxide, carbon monoxide, ozone, suspended particulate matter and respiratory suspended particulate matter--and daily variations in respiratory morbidity in Delhi during the years 2004--2005. Data analysis was based on the Generalized Additive Poisson regression model including a Lowess smoothing function for the entire patient population and subgroups defined by season. The best fitting lag period for each pollutant was found by testing its concentration at varying lags. The model demonstrated associations between daily visits and some of the pollutants (O3, NO2 and RSPM) but their strongest components were observed at varying lags. A single pollutant model showed that a 10 microg m(-3) rise in pollutant level led to statistically significant relative risks (RR): 1.033 for O3, 1.004 for NO2, 1.006 for RSPM. The effect of particulate was relatively low, presumably because unlike other pollutants, particulate matter is not a single pollutant but rather a class of pollutants. This study, continued on a long term basis, can provide guidelines for anticipation/preparedness in the management of health care and hospital admissions.

Comparing the influence of sunspot activity and geomagnetic activity on winter surface climate

NASA Astrophysics Data System (ADS)

Maliniemi, Ville; Mursula, Kalevi; Roy, Indrani; Asikainen, Timo

2017-04-01

We compare here the effect of geomagnetic activity (using the aa index) and sunspot activity on surface climate using sea level pressure dataset from Hadley centre during northern winter. Previous studies using the multiple linear regression method have been limited to using sunspots as a solar activity predictor. Sunspots and total solar irradiance indicate a robust positive influence around the Aleutian Low. This is valid up to a lag of one year. However, geomagnetic activity yields a positive NAM pattern at high to polar latitudes and a positive signal around Azores High pressure region. Interestingly, while there is a positive signal around Azores High for a 2-year lag in sunspots, the strongest signal in this region is found for aa index at 1-year lag. There is also a weak but significant negative signature present around central Pacific for both sunspots and aa index. The combined influence of geomagnetic activity and Quasi Biannual Oscillation (QBO 30 hPa) produces a particularly strong response at mid to polar latitudes, much stronger than the combined influence of sunspots and QBO, which was mostly studied in previous studies so far. This signal is robust and insensitive to the selected time period during the last century. Our results provide a useful way for improving the prediction of winter weather at middle to high latitudes of the northern hemisphere.

[Spatial differentiation and impact factors of Yutian Oasis's soil surface salt based on GWR model].

PubMed

Yuan, Yu Yun; Wahap, Halik; Guan, Jing Yun; Lu, Long Hui; Zhang, Qin Qin

2016-10-01

In this paper, topsoil salinity data gathered from 24 sampling sites in the Yutian Oasis were used, nine different kinds of environmental variables closely related to soil salinity were selec-ted as influencing factors, then, the spatial distribution characteristics of topsoil salinity and spatial heterogeneity of influencing factors were analyzed by combining the spatial autocorrelation with traditional regression analysis and geographically weighted regression model. Results showed that the topsoil salinity in Yutian Oasis was not of random distribution but had strong spatial dependence, and the spatial autocorrelation index for topsoil salinity was 0.479. Groundwater salinity, groundwater depth, elevation and temperature were the main factors influencing topsoil salt accumulation in arid land oases and they were spatially heterogeneous. The nine selected environmental variables except soil pH had significant influences on topsoil salinity with spatial disparity. GWR model was superior to the OLS model on interpretation and estimation of spatial non-stationary data, also had a remarkable advantage in visualization of modeling parameters.

The Bayesian group lasso for confounded spatial data

USGS Publications Warehouse

Hefley, Trevor J.; Hooten, Mevin B.; Hanks, Ephraim M.; Russell, Robin E.; Walsh, Daniel P.

2017-01-01

Generalized linear mixed models for spatial processes are widely used in applied statistics. In many applications of the spatial generalized linear mixed model (SGLMM), the goal is to obtain inference about regression coefficients while achieving optimal predictive ability. When implementing the SGLMM, multicollinearity among covariates and the spatial random effects can make computation challenging and influence inference. We present a Bayesian group lasso prior with a single tuning parameter that can be chosen to optimize predictive ability of the SGLMM and jointly regularize the regression coefficients and spatial random effect. We implement the group lasso SGLMM using efficient Markov chain Monte Carlo (MCMC) algorithms and demonstrate how multicollinearity among covariates and the spatial random effect can be monitored as a derived quantity. To test our method, we compared several parameterizations of the SGLMM using simulated data and two examples from plant ecology and disease ecology. In all examples, problematic levels multicollinearity occurred and influenced sampling efficiency and inference. We found that the group lasso prior resulted in roughly twice the effective sample size for MCMC samples of regression coefficients and can have higher and less variable predictive accuracy based on out-of-sample data when compared to the standard SGLMM.

Turbulence characteristics inferred from time-lagged satellite imagery of surface algae in a shallow tidal sea

NASA Astrophysics Data System (ADS)

Marmorino, George O.; Smith, Geoffrey B.; Miller, W. D.

2017-09-01

A pair of time-lagged satellite images of surface algae in the Great Barrier Reef lagoon is used to investigate characteristics of the horizontal velocity field at a spatial resolution as small as 4 m. A distinctive feature is the occurrence of surface patches that are relatively clear of algae and which grow in size. These patches are interpreted as resulting from the horizontally diverging motion associated with boils. The surface divergence in such boils can be as large as 0.01 s-1, as deduced directly from the imagery. Overall, root-mean-squared values of divergence, vorticity, and strain rate are 45, 58, and 170, respectively, when normalized by the Coriolis parameter. By observing the algae and its fluid environment simultaneously, the analysis thus provides a glimpse of how underlying hydrodynamic processes help shape the distribution of surface algae - under the calm winds that favor the formation of dense surface aggregations.

Meteorological Influence on the 2009 Influenza A (H1N1) Pandemic in Mainland China.

NASA Astrophysics Data System (ADS)

Zhao, X.; Cai, J.; Feng, D.; Bai, Y.; Xu, B.

2015-12-01

Since May 2009, a novel influenza A (H1N1) pandemic has spread rapidly in mainland China from Mexico. Although there has been substantial analysis of this influenza, reliable work estimating its spatial dynamics and determinants remain scarce. The survival and transmission of this pandemic virus not only depends on its biological properties, but also a correlation with external environmental factors. In this study, we collected daily influenza A (H1N1) cases and corresponding annual meteorological factors in mainland China from May 2009 to April 2010. By analyzing these data at county-level, a similarity index, which considered the spatio-temporal characteristics of the disease, was proposed to evaluate the role and lag time of meteorological factors in the influenza transmission. The results indicated that the influenza spanned a large geographical area, following an overall trend from east to west across the country. The spatio-temporal transmission of the disease was affected by a series of meteorological variables, especially absolute humidity with a 3-week lag. These findings confirmed that the absolute humidity and other meteorological variables contributed to the local occurrence and dispersal of influenza A (H1N1). The impact of meteorological variables and their lag effects could be involved in the improvement of effective strategies to control and prevent disease outbreaks.

Estimation of daily reference evapotranspiration (ETo) using artificial intelligence methods: Offering a new approach for lagged ETo data-based modeling

NASA Astrophysics Data System (ADS)

Mehdizadeh, Saeid

2018-04-01

Evapotranspiration (ET) is considered as a key factor in hydrological and climatological studies, agricultural water management, irrigation scheduling, etc. It can be directly measured using lysimeters. Moreover, other methods such as empirical equations and artificial intelligence methods can be used to model ET. In the recent years, artificial intelligence methods have been widely utilized to estimate reference evapotranspiration (ETo). In the present study, local and external performances of multivariate adaptive regression splines (MARS) and gene expression programming (GEP) were assessed for estimating daily ETo. For this aim, daily weather data of six stations with different climates in Iran, namely Urmia and Tabriz (semi-arid), Isfahan and Shiraz (arid), Yazd and Zahedan (hyper-arid) were employed during 2000-2014. Two types of input patterns consisting of weather data-based and lagged ETo data-based scenarios were considered to develop the models. Four statistical indicators including root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and mean absolute percentage error (MAPE) were used to check the accuracy of models. The local performance of models revealed that the MARS and GEP approaches have the capability to estimate daily ETo using the meteorological parameters and the lagged ETo data as inputs. Nevertheless, the MARS had the best performance in the weather data-based scenarios. On the other hand, considerable differences were not observed in the models' accuracy for the lagged ETo data-based scenarios. In the innovation of this study, novel hybrid models were proposed in the lagged ETo data-based scenarios through combination of MARS and GEP models with autoregressive conditional heteroscedasticity (ARCH) time series model. It was concluded that the proposed novel models named MARS-ARCH and GEP-ARCH improved the performance of ETo modeling compared to the single MARS and GEP. In addition, the external analysis of the performance of models at stations with similar climatic conditions denoted the applicability of nearby station' data for estimation of the daily ETo at target station.

Spatial variability of excess mortality during prolonged dust events in a high-density city: a time-stratified spatial regression approach.

PubMed

Wong, Man Sing; Ho, Hung Chak; Yang, Lin; Shi, Wenzhong; Yang, Jinxin; Chan, Ta-Chien

2017-07-24

Dust events have long been recognized to be associated with a higher mortality risk. However, no study has investigated how prolonged dust events affect the spatial variability of mortality across districts in a downwind city. In this study, we applied a spatial regression approach to estimate the district-level mortality during two extreme dust events in Hong Kong. We compared spatial and non-spatial models to evaluate the ability of each regression to estimate mortality. We also compared prolonged dust events with non-dust events to determine the influences of community factors on mortality across the city. The density of a built environment (estimated by the sky view factor) had positive association with excess mortality in each district, while socioeconomic deprivation contributed by lower income and lower education induced higher mortality impact in each territory planning unit during a prolonged dust event. Based on the model comparison, spatial error modelling with the 1st order of queen contiguity consistently outperformed other models. The high-risk areas with higher increase in mortality were located in an urban high-density environment with higher socioeconomic deprivation. Our model design shows the ability to predict spatial variability of mortality risk during an extreme weather event that is not able to be estimated based on traditional time-series analysis or ecological studies. Our spatial protocol can be used for public health surveillance, sustainable planning and disaster preparation when relevant data are available.

Lateralization of spatial rather than temporal attention underlies the left hemifield advantage in rapid serial visual presentation.

PubMed

Asanowicz, Dariusz; Kruse, Lena; Śmigasiewicz, Kamila; Verleger, Rolf

2017-11-01

In bilateral rapid serial visual presentation (RSVP), the second of two targets, T1 and T2, is better identified in the left visual field (LVF) than in the right visual field (RVF). This LVF advantage may reflect hemispheric asymmetry in temporal attention or/and in spatial orienting of attention. Participants performed two tasks: the "standard" bilateral RSVP task (Exp.1) and its unilateral variant (Exp.1 & 2). In the bilateral task, spatial location was uncertain, thus target identification involved stimulus-driven spatial orienting. In the unilateral task, the targets were presented block-wise in the LVF or RVF only, such that no spatial orienting was needed for target identification. Temporal attention was manipulated in both tasks by varying the T1-T2 lag. The results showed that the LVF advantage disappeared when involvement of stimulus-driven spatial orienting was eliminated, whereas the manipulation of temporal attention had no effect on the asymmetry. In conclusion, the results do not support the hypothesis of hemispheric asymmetry in temporal attention, and provide further evidence that the LVF advantage reflects right hemisphere predominance in stimulus-driven orienting of spatial attention. These conclusions fit evidence that temporal attention is implemented by bilateral parietal areas and spatial attention by the right-lateralized ventral frontoparietal network. Copyright © 2017 Elsevier Inc. All rights reserved.

Modified DTW for a quantitative estimation of the similarity between rainfall time series

NASA Astrophysics Data System (ADS)

Djallel Dilmi, Mohamed; Barthès, Laurent; Mallet, Cécile; Chazottes, Aymeric

2017-04-01

The Precipitations are due to complex meteorological phenomenon and can be described as intermittent process. The spatial and temporal variability of this phenomenon is significant and covers large scales. To analyze and model this variability and / or structure, several studies use a network of rain gauges providing several time series of precipitation measurements. To compare these different time series, the authors compute for each time series some parameters (PDF, rain peak intensity, occurrence, amount, duration, intensity …). However, and despite the calculation of these parameters, the comparison of the parameters between two series of measurements remains qualitative. Due to the advection processes, when different sensors of an observation network measure precipitation time series identical in terms of intermitency or intensities, there is a time lag between the different measured series. Analyzing and extracting relevant information on physical phenomena from these precipitation time series implies the development of automatic analytical methods capable of comparing two time series of precipitation measured by different sensors or at two different locations and thus quantifying the difference / similarity. The limits of the Euclidean distance to measure the similarity between the time series of precipitation have been well demonstrated and explained (eg the Euclidian distance is indeed very sensitive to the effects of phase shift : between two identical but slightly shifted time series, this distance is not negligible). To quantify and analysis these time lag, the correlation functions are well established, normalized and commonly used to measure the spatial dependences that are required by many applications. However, authors generally observed that there is always a considerable scatter of the inter-rain gauge correlation coefficients obtained from the individual pairs of rain gauges. Because of a substantial dispersion of estimated time lag, the interpretation of this inter-correlation is not straightforward. We propose here to use an improvement of the Euclidian distance which integrates the global complexity of the rainfall series. The Dynamic Time Wrapping (DTW) used in speech recognition allows matching two time series instantly different and provide the most probable time lag. However, the original formulation of the DTW suffers from some limitations. In particular, it is not adequate to the rain intermittency. In this study we present an adaptation of the DTW for the analysis of rainfall time series : we used time series from the "Météo France" rain gauge network observed between January 1st, 2007 and December 31st, 2015 on 25 stations located in the Île de France area. Then we analyze the results (eg. The distance, the relationship between the time lag detected by our methods and others measured parameters like speed and direction of the wind…) to show the ability of the proposed similarity to provide usefull information on the rain structure. The possibility of using this measure of similarity to define a quality indicator of a sensor integrated into an observation network is also envisaged.

Regression methods for spatially correlated data: an example using beetle attacks in a seed orchard

Treesearch

Preisler Haiganoush; Nancy G. Rappaport; David L. Wood

1997-01-01

We present a statistical procedure for studying the simultaneous effects of observed covariates and unmeasured spatial variables on responses of interest. The procedure uses regression type analyses that can be used with existing statistical software packages. An example using the rate of twig beetle attacks on Douglas-fir trees in a seed orchard illustrates the...

Logistic regression accuracy across different spatial and temporal scales for a wide-ranging species, the marbled murrelet

Treesearch

Carolyn B. Meyer; Sherri L. Miller; C. John Ralph

2004-01-01

The scale at which habitat variables are measured affects the accuracy of resource selection functions in predicting animal use of sites. We used logistic regression models for a wide-ranging species, the marbled murrelet, (Brachyramphus marmoratus) in a large region in California to address how much changing the spatial or temporal scale of...

Modeling vertebrate diversity in Oregon using satellite imagery

NASA Astrophysics Data System (ADS)

Cablk, Mary Elizabeth

Vertebrate diversity was modeled for the state of Oregon using a parametric approach to regression tree analysis. This exploratory data analysis effectively modeled the non-linear relationships between vertebrate richness and phenology, terrain, and climate. Phenology was derived from time-series NOAA-AVHRR satellite imagery for the year 1992 using two methods: principal component analysis and derivation of EROS data center greenness metrics. These two measures of spatial and temporal vegetation condition incorporated the critical temporal element in this analysis. The first three principal components were shown to contain spatial and temporal information about the landscape and discriminated phenologically distinct regions in Oregon. Principal components 2 and 3, 6 greenness metrics, elevation, slope, aspect, annual precipitation, and annual seasonal temperature difference were investigated as correlates to amphibians, birds, all vertebrates, reptiles, and mammals. Variation explained for each regression tree by taxa were: amphibians (91%), birds (67%), all vertebrates (66%), reptiles (57%), and mammals (55%). Spatial statistics were used to quantify the pattern of each taxa and assess validity of resulting predictions from regression tree models. Regression tree analysis was relatively robust against spatial autocorrelation in the response data and graphical results indicated models were well fit to the data.

Space, race, and poverty: Spatial inequalities in walkable neighborhood amenities?

PubMed Central

Aldstadt, Jared; Whalen, John; White, Kellee; Castro, Marcia C.; Williams, David R.

2017-01-01

BACKGROUND Multiple and varied benefits have been suggested for increased neighborhood walkability. However, spatial inequalities in neighborhood walkability likely exist and may be attributable, in part, to residential segregation. OBJECTIVE Utilizing a spatial demographic perspective, we evaluated potential spatial inequalities in walkable neighborhood amenities across census tracts in Boston, MA (US). METHODS The independent variables included minority racial/ethnic population percentages and percent of families in poverty. Walkable neighborhood amenities were assessed with a composite measure. Spatial autocorrelation in key study variables were first calculated with the Global Moran’s I statistic. Then, Spearman correlations between neighborhood socio-demographic characteristics and walkable neighborhood amenities were calculated as well as Spearman correlations accounting for spatial autocorrelation. We fit ordinary least squares (OLS) regression and spatial autoregressive models, when appropriate, as a final step. RESULTS Significant positive spatial autocorrelation was found in neighborhood socio-demographic characteristics (e.g. census tract percent Black), but not walkable neighborhood amenities or in the OLS regression residuals. Spearman correlations between neighborhood socio-demographic characteristics and walkable neighborhood amenities were not statistically significant, nor were neighborhood socio-demographic characteristics significantly associated with walkable neighborhood amenities in OLS regression models. CONCLUSIONS Our results suggest that there is residential segregation in Boston and that spatial inequalities do not necessarily show up using a composite measure. COMMENTS Future research in other geographic areas (including international contexts) and using different definitions of neighborhoods (including small-area definitions) should evaluate if spatial inequalities are found using composite measures but also should use measures of specific neighborhood amenities. PMID:29046612

Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach.

PubMed

Xu, Pengpeng; Huang, Helai; Dong, Ni; Wong, S C

2017-01-01

This study was performed to investigate the spatially varying relationships between crash frequency and related risk factors. A Bayesian spatially varying coefficients model was elaborately introduced as a methodological alternative to simultaneously account for the unstructured and spatially structured heterogeneity of the regression coefficients in predicting crash frequencies. The proposed method was appealing in that the parameters were modeled via a conditional autoregressive prior distribution, which involved a single set of random effects and a spatial correlation parameter with extreme values corresponding to pure unstructured or pure spatially correlated random effects. A case study using a three-year crash dataset from the Hillsborough County, Florida, was conducted to illustrate the proposed model. Empirical analysis confirmed the presence of both unstructured and spatially correlated variations in the effects of contributory factors on severe crash occurrences. The findings also suggested that ignoring spatially structured heterogeneity may result in biased parameter estimates and incorrect inferences, while assuming the regression coefficients to be spatially clustered only is probably subject to the issue of over-smoothness. Copyright © 2016 Elsevier Ltd. All rights reserved.

Spatial distribution of soil organic carbon and total nitrogen based on GIS and geostatistics in a small watershed in a hilly area of northern China.

PubMed

Peng, Gao; Bing, Wang; Guangpo, Geng; Guangcan, Zhang

2013-01-01

The spatial variability of soil organic carbon (SOC) and total nitrogen (STN) levels is important in both global carbon-nitrogen cycle and climate change research. There has been little research on the spatial distribution of SOC and STN at the watershed scale based on geographic information systems (GIS) and geostatistics. Ninety-seven soil samples taken at depths of 0-20 cm were collected during October 2010 and 2011 from the Matiyu small watershed (4.2 km(2)) of a hilly area in Shandong Province, northern China. The impacts of different land use types, elevation, vegetation coverage and other factors on SOC and STN spatial distributions were examined using GIS and a geostatistical method, regression-kriging. The results show that the concentration variations of SOC and STN in the Matiyu small watershed were moderate variation based on the mean, median, minimum and maximum, and the coefficients of variation (CV). Residual values of SOC and STN had moderate spatial autocorrelations, and the Nugget/Sill were 0.2% and 0.1%, respectively. Distribution maps of regression-kriging revealed that both SOC and STN concentrations in the Matiyu watershed decreased from southeast to northwest. This result was similar to the watershed DEM trend and significantly correlated with land use type, elevation and aspect. SOC and STN predictions with the regression-kriging method were more accurate than those obtained using ordinary kriging. This research indicates that geostatistical characteristics of SOC and STN concentrations in the watershed were closely related to both land-use type and spatial topographic structure and that regression-kriging is suitable for investigating the spatial distributions of SOC and STN in the complex topography of the watershed.

Spatial Distribution of Soil Organic Carbon and Total Nitrogen Based on GIS and Geostatistics in a Small Watershed in a Hilly Area of Northern China

PubMed Central

Peng, Gao; Bing, Wang; Guangpo, Geng; Guangcan, Zhang

2013-01-01

The spatial variability of soil organic carbon (SOC) and total nitrogen (STN) levels is important in both global carbon-nitrogen cycle and climate change research. There has been little research on the spatial distribution of SOC and STN at the watershed scale based on geographic information systems (GIS) and geostatistics. Ninety-seven soil samples taken at depths of 0–20 cm were collected during October 2010 and 2011 from the Matiyu small watershed (4.2 km2) of a hilly area in Shandong Province, northern China. The impacts of different land use types, elevation, vegetation coverage and other factors on SOC and STN spatial distributions were examined using GIS and a geostatistical method, regression-kriging. The results show that the concentration variations of SOC and STN in the Matiyu small watershed were moderate variation based on the mean, median, minimum and maximum, and the coefficients of variation (CV). Residual values of SOC and STN had moderate spatial autocorrelations, and the Nugget/Sill were 0.2% and 0.1%, respectively. Distribution maps of regression-kriging revealed that both SOC and STN concentrations in the Matiyu watershed decreased from southeast to northwest. This result was similar to the watershed DEM trend and significantly correlated with land use type, elevation and aspect. SOC and STN predictions with the regression-kriging method were more accurate than those obtained using ordinary kriging. This research indicates that geostatistical characteristics of SOC and STN concentrations in the watershed were closely related to both land-use type and spatial topographic structure and that regression-kriging is suitable for investigating the spatial distributions of SOC and STN in the complex topography of the watershed. PMID:24391791

Spatial distribution of ozone over Indonesia (Study case: Forest fire event 2015)

NASA Astrophysics Data System (ADS)

Muslimah, Sri; Buce Saleh, Muhamad; Hidayat, Rahmat

2018-05-01

Tropospheric ozone is known as surface ozone and caused several health impact. The objective of this study was to analysis spatial distribution of tropospheric ozone over Indonesia case study forest fire event in 2015. Monthly observation measured by Ozone Monitoring Instrument (OMI) have been analysed from January – December 2015 to study spatial distribution of tropospheric ozone related to forest fire event 2015. The study discovered high level of tropospheric column ozone (TCO) from October to November 2015. The result shows increasing average of TCO from September to October almost 6 DU. Meanwhile, monthly number of hotspot is higher in September 2015 with total number 257 hotspot which is acquired by Moderate Resolution Imaging Spectrometer (MODIS) Terra version 6.1 with confidence level same or more than 90%. The hotspot distribution compared with spatial TCO distribution and shows interesting time lag with respect to hotspot distribution, one month. Further study for daily comparison of TCO and forest fire event needed. This result suggested that the tropospheric ozone over the Indonesian region increases in 2015 were remarkable and corresponded to forest fire event.

Unique interrelationships between fiber composition, water-soluble carbohydrates, and in vitro gas production for fall-grown oat forages.

PubMed

Coblentz, W K; Nellis, S E; Hoffman, P C; Hall, M B; Weimer, P J; Esser, N M; Bertram, M G

2013-01-01

Sixty samples of 'ForagePlus' oat were selected from a previous plot study for analysis of in vitro gas production (IVGP) on the basis of 2 factors: (1) high (n=29) or low (n=31) neutral detergent fiber (NDF; 62.7±2.61 and 45.1±3.91%, respectively); and (2) the range of water-soluble carbohydrates (WSC) within the high- and low-NDF groups. For the WSC selection factor, concentrations ranged from 4.7 to 13.4% (mean=7.9±2.06%) and from 3.5 to 19.4% (mean=9.7±4.57%) within high- and low-NDF forages, respectively. Our objectives were to assess the relationships between IVGP and various agronomic or nutritional characteristics for high- and low-NDF fall-oat forages. Cumulative IVGP was fitted to a single-pool nonlinear regression model: Y=MAX × (1 - e ([-)(K)(× (t - lag)])), where Y=cumulative gas produced (mL), MAX=maximum cumulative gas produced with infinite incubation time (mL), K=rate constant, t=incubation time (h), and lag=discrete lag time (h). Generally, cumulative IVGP after 12, 24, 36, or 48h within high-NDF fall-oat forages was negatively correlated with NDF, hemicellulose, lignin, and ash, but positively correlated with WSC, nonfiber carbohydrate (NFC), and total digestible nutrients (TDN). For low-NDF fall-grown oat forages, IVGP was positively correlated with growth stage, canopy height, WSC, NFC, and TDN; negative correlations were observed with ash and crude protein (CP) but not generally with fiber components. These responses were also reflected in multiple regression analysis for high- and low-NDF forages. After 12, 24, or 36h of incubation, cumulative IVGP within high-NDF fall-oat forages was explained by complex regression equations utilizing (lignin:NDF)(2), lignin:NDF, hemicellulose, lignin, and TDN(2) as independent variables (R(2)≥0.43). Within low-NDF fall-grown oat forages, cumulative IVGP at these incubation intervals was explained by positive linear relationships with NFC that also exhibited high coefficients of determination (R(2)≥0.75). Gas production was accelerated at early incubation times within low-NDF forages, specifically in response to large pools of WSC that were most likely to be present as forages approached boot stage by late-fall. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Lagged cumulative spruce budworm defoliation affects the risk of fire ignition in Ontario, Canada.

PubMed

James, Patrick M A; Robert, Louis-Etienne; Wotton, B Mike; Martell, David L; Fleming, Richard A

2017-03-01

Detailed understanding of forest disturbance interactions is needed for effective forecasting, modelling, and management. Insect outbreaks are a significant forest disturbance that alters forest structure as well as the distribution and connectivity of combustible fuels at broad spatial scales. The effect of insect outbreaks on fire activity is an important but contentious issue with significant policy consequences. The eastern spruce budworm (Choristoneura fumiferana) is a native defoliating insect in eastern North America whose periodic outbreaks create large patches of dead fir and spruce trees. Of particular concern to fire and forest managers is whether these patches represent an increased fire risk, if so, for how long, and how the relationship between defoliation and fire risk varies through space and time. Previous work suggests a temporary increase in flammability in budworm-killed forests, but regional and seasonal variability in these relationships has not been examined. Using an extensive database on historical lightning-caused fire ignitions and spruce budworm defoliation between 1963 and 2000, we assess the relative importance of cumulative defoliation and fire weather on the probability of ignition in Ontario, Canada. We modeled fire ignition using a generalized additive logistic regression model that accounts for temporal autocorrelation in fire weather. We compared two ecoregions in eastern Ontario (Abitibi Plains) and western Ontario (Lake of the Woods) that differ in terms of climate, geomorphology, and forest composition. We found that defoliation has the potential to both increase and decrease the probability of ignition depending on the time scale, ecoregion, and season examined. Most importantly, we found that lagged spruce budworm defoliation (8-10 yr) increases the risk of fire ignition whereas recent defoliation (1 yr) can decrease this risk. We also found that historical defoliation has a greater influence on ignition risk during the spring than during the summer fire season. Given predicted increases in forest insect activity due to global change, these results represent important information for fire management agencies that can be used to refine existing models of fire risk. © 2016 by the Ecological Society of America.

Climate variability, weather and enteric disease incidence in New Zealand: time series analysis.

PubMed

Lal, Aparna; Ikeda, Takayoshi; French, Nigel; Baker, Michael G; Hales, Simon

2013-01-01

Evaluating the influence of climate variability on enteric disease incidence may improve our ability to predict how climate change may affect these diseases. To examine the associations between regional climate variability and enteric disease incidence in New Zealand. Associations between monthly climate and enteric diseases (campylobacteriosis, salmonellosis, cryptosporidiosis, giardiasis) were investigated using Seasonal Auto Regressive Integrated Moving Average (SARIMA) models. No climatic factors were significantly associated with campylobacteriosis and giardiasis, with similar predictive power for univariate and multivariate models. Cryptosporidiosis was positively associated with average temperature of the previous month (β =  0.130, SE =  0.060, p <0.01) and inversely related to the Southern Oscillation Index (SOI) two months previously (β =  -0.008, SE =  0.004, p <0.05). By contrast, salmonellosis was positively associated with temperature (β  = 0.110, SE = 0.020, p<0.001) of the current month and SOI of the current (β  = 0.005, SE = 0.002, p<0.050) and previous month (β  = 0.005, SE = 0.002, p<0.05). Forecasting accuracy of the multivariate models for cryptosporidiosis and salmonellosis were significantly higher. Although spatial heterogeneity in the observed patterns could not be assessed, these results suggest that temporally lagged relationships between climate variables and national communicable disease incidence data can contribute to disease prediction models and early warning systems.

Linking terrestrial P inputs to riverine export across the United ...

EPA Pesticide Factsheets

Human beings have greatly accelerated phosphorus (P) flows from land to aquatic ecosystems, often resulting in eutrophication, harmful algal blooms, and hypoxia. Although a variety of statistical and mechanistic models have been used to explore the relationship between terrestrial nutrient management and losses to waterways, our understanding of how natural and anthropogenic landscape characteristics mediate losses of P from watersheds lags behind that of nitrogen. The need for higher resolution data is often identified as an important barrier that limits our capacity to predict P loading. In order to address this gap, we constructed spatially explicit datasets of terrestrial P inputs and outputs (fertilizer, confined manure, crop harvest and sewage) across the continental U.S. for 2012. We then examined how these P sources, along with climate, hydrology, and land use, influenced P exports from 72 watersheds as total P (TP) and dissolved inorganic P (DIP) concentrations and yields, and TP fractional export. TP and DIP concentrations and TP yields were best correlated with runoff, but using simple linear regression, we were not able to explain more than 56% of the variance in any of the water quality variables (TP fractional export vs P manure inputs). The lack of clear and strong relationships between contemporary, high-resolution, anthropogenic, terrestrial P and riverine P export at the national scale highlights the fact that a complex suite of factors mediat

Flood inundation mapping in the Logone floodplain from multi temporal Landsat ETM+ imagery

NASA Astrophysics Data System (ADS)

Jung, H.; Alsdorf, D. E.; Moritz, M.; Lee, H.; Vassolo, S.

2011-12-01

Yearly flooding in the Logone floodplain makes an impact on agricultural, pastoral, and fishery systems in the Lake Chad Basin. Since the flooding extent and depth are highly variable, flood inundation mapping helps us make better use of water resources and prevent flood hazards in the Logone floodplain. The flood maps are generated from 33 multi temporal Landsat Enhanced Thematic Mapper Plus (ETM+) during three years 2006 to 2008. Flooded area is classified using a short-wave infrared band whereas open water is classified by Iterative Self-organizing Data Analysis (ISODATA) clustering. The maximum flooding extent in the study area increases up to ~5.8K km2 in late October 2008. The study also provides strong correlation of the flooding extents with water height variations in both the floodplain and the river based on a second polynomial regression model. The water heights are from ENIVSAT altimetry in the floodplain and gauge measurements in the river. Coefficients of determination between flooding extents and water height variations are greater than 0.91 with 4 to 36 days in phase lag. Floodwater drains back to the river and to the northeast during the recession period in December and January. The study supports understanding of the Logone floodplain dynamics in detail of spatial pattern and size of the flooding extent and assists the flood monitoring and prediction systems in the catchment.

Flood Inundation Mapping in the Logone Floodplain from Multi Temporal Landsat ETM+Imagery

NASA Technical Reports Server (NTRS)

Jung, Hahn Chul; Alsdorf, Douglas E.; Moritz, Mark; Lee, Hyongki; Vassolo, Sara

2011-01-01

Yearly flooding in the Logone floodplain makes an impact on agricultural, pastoral, and fishery systems in the Lake Chad Basin. Since the flooding extent and depth are highly variable, flood inundation mapping helps us make better use of water resources and prevent flood hazards in the Logone floodplain. The flood maps are generated from 33 multi temporal Landsat Enhanced Thematic Mapper Plus (ETM+) during three years 2006 to 2008. Flooded area is classified using a short-wave infrared band whereas open water is classified by Iterative Self-organizing Data Analysis (ISODATA) clustering. The maximum flooding extent in the study area increases up to approximately 5.8K km2 in late October 2008. The study also provides strong correlation of the flooding extents with water height variations in both the floodplain and the river based on a second polynomial regression model. The water heights are from ENIVSAT altimetry in the floodplain and gauge measurements in the river. Coefficients of determination between flooding extents and water height variations are greater than 0.91 with 4 to 36 days in phase lag. Floodwater drains back to the river and to the northeast during the recession period in December and January. The study supports understanding of the Logone floodplain dynamics in detail of spatial pattern and size of the flooding extent and assists the flood monitoring and prediction systems in the catchment.

Spatial and temporal drivers of wildfire occurrence in the context of rural development in northern Wisconsin, USA

Treesearch

Brian R Miranda; Brian R Sturtevant; Susan I Stewart; Roger B. Hammer

2012-01-01

Most drivers underlying wildfire are dynamic, but at different spatial and temporal scales. We quantified temporal and spatial trends in wildfire patterns over two spatial extents in northern Wisconsin to identify drivers and their change through time. We used spatial point pattern analysis to quantify the spatial pattern of wildfire occurrences, and linear regression...

Spatial structure, sampling design and scale in remotely-sensed imagery of a California savanna woodland

NASA Technical Reports Server (NTRS)

Mcgwire, K.; Friedl, M.; Estes, J. E.

1993-01-01

This article describes research related to sampling techniques for establishing linear relations between land surface parameters and remotely-sensed data. Predictive relations are estimated between percentage tree cover in a savanna environment and a normalized difference vegetation index (NDVI) derived from the Thematic Mapper sensor. Spatial autocorrelation in original measurements and regression residuals is examined using semi-variogram analysis at several spatial resolutions. Sampling schemes are then tested to examine the effects of autocorrelation on predictive linear models in cases of small sample sizes. Regression models between image and ground data are affected by the spatial resolution of analysis. Reducing the influence of spatial autocorrelation by enforcing minimum distances between samples may also improve empirical models which relate ground parameters to satellite data.

Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey

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

Keser, Saniye; Duzgun, Sebnem; Department of Geodetic and Geographic Information Technologies, Middle East Technical University, 06800 Ankara

Highlights: Black-Right-Pointing-Pointer Spatial autocorrelation exists in municipal solid waste generation rates for different provinces in Turkey. Black-Right-Pointing-Pointer Traditional non-spatial regression models may not provide sufficient information for better solid waste management. Black-Right-Pointing-Pointer Unemployment rate is a global variable that significantly impacts the waste generation rates in Turkey. Black-Right-Pointing-Pointer Significances of global parameters may diminish at local scale for some provinces. Black-Right-Pointing-Pointer GWR model can be used to create clusters of cities for solid waste management. - Abstract: In studies focusing on the factors that impact solid waste generation habits and rates, the potential spatial dependency in solid waste generation datamore » is not considered in relating the waste generation rates to its determinants. In this study, spatial dependency is taken into account in determination of the significant socio-economic and climatic factors that may be of importance for the municipal solid waste (MSW) generation rates in different provinces of Turkey. Simultaneous spatial autoregression (SAR) and geographically weighted regression (GWR) models are used for the spatial data analyses. Similar to ordinary least squares regression (OLSR), regression coefficients are global in SAR model. In other words, the effect of a given independent variable on a dependent variable is valid for the whole country. Unlike OLSR or SAR, GWR reveals the local impact of a given factor (or independent variable) on the waste generation rates of different provinces. Results show that provinces within closer neighborhoods have similar MSW generation rates. On the other hand, this spatial autocorrelation is not very high for the exploratory variables considered in the study. OLSR and SAR models have similar regression coefficients. GWR is useful to indicate the local determinants of MSW generation rates. GWR model can be utilized to plan waste management activities at local scale including waste minimization, collection, treatment, and disposal. At global scale, the MSW generation rates in Turkey are significantly related to unemployment rate and asphalt-paved roads ratio. Yet, significances of these variables may diminish at local scale for some provinces. At local scale, different factors may be important in affecting MSW generation rates.« less

Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey.

PubMed

Erdogan, Saffet

2009-10-01

The aim of the study is to describe the inter-province differences in traffic accidents and mortality on roads of Turkey. Two different risk indicators were used to evaluate the road safety performance of the provinces in Turkey. These indicators are the ratios between the number of persons killed in road traffic accidents (1) and the number of accidents (2) (nominators) and their exposure to traffic risk (denominator). Population and the number of registered motor vehicles in the provinces were used as denominators individually. Spatial analyses were performed to the mean annual rate of deaths and to the number of fatal accidents that were calculated for the period of 2001-2006. Empirical Bayes smoothing was used to remove background noise from the raw death and accident rates because of the sparsely populated provinces and small number of accident and death rates of provinces. Global and local spatial autocorrelation analyses were performed to show whether the provinces with high rates of deaths-accidents show clustering or are located closer by chance. The spatial distribution of provinces with high rates of deaths and accidents was nonrandom and detected as clustered with significance of P<0.05 with spatial autocorrelation analyses. Regions with high concentration of fatal accidents and deaths were located in the provinces that contain the roads connecting the Istanbul, Ankara, and Antalya provinces. Accident and death rates were also modeled with some independent variables such as number of motor vehicles, length of roads, and so forth using geographically weighted regression analysis with forward step-wise elimination. The level of statistical significance was taken as P<0.05. Large differences were found between the rates of deaths and accidents according to denominators in the provinces. The geographically weighted regression analyses did significantly better predictions for both accident rates and death rates than did ordinary least regressions, as indicated by adjusted R(2) values. Geographically weighted regression provided values of 0.89-0.99 adjusted R(2) for death and accident rates, compared with 0.88-0.95, respectively, by ordinary least regressions. Geographically weighted regression has the potential to reveal local patterns in the spatial distribution of rates, which would be ignored by the ordinary least regression approach. The application of spatial analysis and modeling of accident statistics and death rates at provincial level in Turkey will help to identification of provinces with outstandingly high accident and death rates. This could help more efficient road safety management in Turkey.

Psychophysical investigation of an auditory spatial illusion in cats: the precedence effect.

PubMed

Tollin, Daniel J; Yin, Tom C T

2003-10-01

The precedence effect (PE) describes several spatial perceptual phenomena that occur when similar sounds are presented from two different locations and separated by a delay. The mechanisms that produce the effect are thought to be responsible for the ability to localize sounds in reverberant environments. Although the physiological bases for the PE have been studied, little is known about how these sounds are localized by species other than humans. Here we used the search coil technique to measure the eye positions of cats trained to saccade to the apparent locations of sounds. To study the PE, brief broadband stimuli were presented from two locations, with a delay between their onsets; the delayed sound meant to simulate a single reflection. Although the cats accurately localized single sources, the apparent locations of the paired sources depended on the delay. First, the cats exhibited summing localization, the perception of a "phantom" sound located between the sources, for delays < +/-400 micros for sources positioned in azimuth along the horizontal plane, but not for sources positioned in elevation along the sagittal plane. Second, consistent with localization dominance, for delays from 400 micros to about 10 ms, the cats oriented toward the leading source location only, with little influence of the lagging source, both for horizontally and vertically placed sources. Finally, the echo threshold was reached for delays >10 ms, where the cats first began to orient to the lagging source on some trials. These data reveal that cats experience the PE phenomena similarly to humans.

State-Based Delay Representation and Its Transfer from a Game of Pong to Reaching and Tracking

PubMed Central

Leib, Raz; Pressman, Assaf; Simo, Lucia S.; Karniel, Amir

2017-01-01

Abstract To accurately estimate the state of the body, the nervous system needs to account for delays between signals from different sensory modalities. To investigate how such delays may be represented in the sensorimotor system, we asked human participants to play a virtual pong game in which the movement of the virtual paddle was delayed with respect to their hand movement. We tested the representation of this new mapping between the hand and the delayed paddle by examining transfer of adaptation to blind reaching and blind tracking tasks. These blind tasks enabled to capture the representation in feedforward mechanisms of movement control. A Time Representation of the delay is an estimation of the actual time lag between hand and paddle movements. A State Representation is a representation of delay using current state variables: the distance between the paddle and the ball originating from the delay may be considered as a spatial shift; the low sensitivity in the response of the paddle may be interpreted as a minifying gain; and the lag may be attributed to a mechanical resistance that influences paddle’s movement. We found that the effects of prolonged exposure to the delayed feedback transferred to blind reaching and tracking tasks and caused participants to exhibit hypermetric movements. These results, together with simulations of our representation models, suggest that delay is not represented based on time, but rather as a spatial gain change in visuomotor mapping. PMID:29379875

Application of QuickBird imagery in fuel load estimation in the Daxinganling region, China.

Treesearch

Sen Jin; Shyh-Chin Chen

2012-01-01

A high spatial resolution QuickBird satellite image and a low spatial but high spectral resolution Landsat Thermatic Mapper image were used to linearly regress fuel loads of 70 plots with size 30X30m over the Daxinganling region of north-east China. The results were compared with loads from field surveys and from regression estimations by surveyed stand characteristics...

Crystalline silica exposure and lung cancer mortality in diatomaceous earth industry workers: a quantitative risk assessment

PubMed Central

Rice, F; Park, R; Stayner, L; Smith, R; Gilbert, S; Checkoway, H

2001-01-01

OBJECTIVE—To use various exposure-response models to estimate the risk of mortality from lung cancer due to occupational exposure to respirable crystalline silica dust.
METHODS—Data from a cohort mortality study of 2342 white male California diatomaceous earth mining and processing workers exposed to crystalline silica dust (mainly cristobalite) were reanalyzed with Poisson regression and Cox's proportional hazards models. Internal and external adjustments were used to control for potential confounding from the effects of time since first observation, calendar time, age, and Hispanic ethnicity. Cubic smoothing spline models were used to assess the fit of the models. Exposures were lagged by 10 years. Evaluations of the fit of the models were performed by comparing their deviances. Lifetime risks of lung cancer were estimated up to age 85 with an actuarial approach that accounted for competing causes of death.
RESULTS—Exposure to respirable crystalline silica dust was a significant predictor (p<0.05) in nearly all of the models evaluated and the linear relative rate model with a 10 year exposure lag seemed to give the best fit in the Poisson regression analysis. For those who died of lung cancer the linear relative rate model predicted rate ratios for mortality from lung cancer of about 1.6 for the mean cumulative exposure to respirable silica compared with no exposure. The excess lifetime risk (to age 85) of mortality from lung cancer for white men exposed for 45 years and with a 10 year lag period at the current Occupational Safety and Health Administration (OSHA) standard of about 0.05 mg/m3 for respirable cristobalite dust is 19/1000 (95% confidence interval (95% CI) 5/1000 to 46/1000).
CONCLUSIONS—There was a significant risk of mortality from lung cancer that increased with cumulative exposure to respirable crystalline silica dust. The predicted number of deaths from lung cancer suggests that current occupational health standards may not be adequately protecting workers from the risk of lung cancer.


Keywords: crystalline silica; cristobalite; lung cancer PMID:11119633

Spatial Variability of Plant Available Water, Soil Organic Carbon, and Microbial Biomass under Divergent Land Uses: A Comparison among Regression-Kriging, Cokriging, and Regression-Cokriging

NASA Astrophysics Data System (ADS)

Kiani, M.; Hernandez Ramirez, G.; Quideau, S.

2016-12-01

Improved knowledge about the spatial variability of plant available water (PAW), soil organic carbon (SOC), and microbial biomass carbon (MBC) as affected by land-use systems can underpin the identification and inventory of beneficial ecosystem good and services in both agricultural and wild lands. Little research has been done that addresses the spatial patterns of PAW, SOC, and MBC under different land use types at a field scale. Therefore, we collected 56 soil samples (5-10 cm depth increment), using a nested cyclic sampling design within both a native grassland (NG) site and an irrigated cultivated (IC) site located near Brooks, Alberta. Using classical statistical and geostatistical methods, we characterized the spatial heterogeneities of PAW, SOC, and MBC under NG and IC using several geostatistical methods such as ordinary kriging (OK), regression-kriging (RK), cokriging (COK), and regression-cokriging (RCOK). Converting the native grassland to irrigated cultivated land altered soil pore distribution by reducing macroporosity which led to lower saturated water content and half hydraulic conductivity in IC compared to NG. This conversion also decreased the relative abundance of gram-negative bacteria, while increasing both the proportion of gram-positive bacteria and MBC concentration. At both studied sites, the best fitted spatial model was Gaussian based on lower RSS and higher R2 as criteria. The IC had stronger degree of spatial dependence and longer range of spatial auto-correlation revealing a homogenization of the spatial variability of soil properties as a result of intensive, recurrent agricultural activities. Comparison of OK, RK, COK, and RCOK approaches indicated that cokriging method had the best performance demonstrating a profound improvement in the accuracy of spatial estimations of PAW, SOC, and MBC. It seems that the combination of terrain covariates such as elevation and depth-to-water with kriging techniques offers more capability for incorporating explicit ancillary information in predictive soil mapping. Overall, identification of spatial patterns of soil properties in agricultural lands gives a bird's eye view to land owners to implement and improve management practices which lead to more sustainable production.

Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach?

USGS Publications Warehouse

Archfield, Stacey A.; Pugliese, Alessio; Castellarin, Attilio; Skøien, Jon O.; Kiang, Julie E.

2013-01-01

In the United States, estimation of flood frequency quantiles at ungauged locations has been largely based on regional regression techniques that relate measurable catchment descriptors to flood quantiles. More recently, spatial interpolation techniques of point data have been shown to be effective for predicting streamflow statistics (i.e., flood flows and low-flow indices) in ungauged catchments. Literature reports successful applications of two techniques, canonical kriging, CK (or physiographical-space-based interpolation, PSBI), and topological kriging, TK (or top-kriging). CK performs the spatial interpolation of the streamflow statistic of interest in the two-dimensional space of catchment descriptors. TK predicts the streamflow statistic along river networks taking both the catchment area and nested nature of catchments into account. It is of interest to understand how these spatial interpolation methods compare with generalized least squares (GLS) regression, one of the most common approaches to estimate flood quantiles at ungauged locations. By means of a leave-one-out cross-validation procedure, the performance of CK and TK was compared to GLS regression equations developed for the prediction of 10, 50, 100 and 500 yr floods for 61 streamgauges in the southeast United States. TK substantially outperforms GLS and CK for the study area, particularly for large catchments. The performance of TK over GLS highlights an important distinction between the treatments of spatial correlation when using regression-based or spatial interpolation methods to estimate flood quantiles at ungauged locations. The analysis also shows that coupling TK with CK slightly improves the performance of TK; however, the improvement is marginal when compared to the improvement in performance over GLS.

Influence of landscape-scale factors in limiting brook trout populations in Pennsylvania streams

USGS Publications Warehouse

Kocovsky, P.M.; Carline, R.F.

2006-01-01

Landscapes influence the capacity of streams to produce trout through their effect on water chemistry and other factors at the reach scale. Trout abundance also fluctuates over time; thus, to thoroughly understand how spatial factors at landscape scales affect trout populations, one must assess the changes in populations over time to provide a context for interpreting the importance of spatial factors. We used data from the Pennsylvania Fish and Boat Commission's fisheries management database to investigate spatial factors that affect the capacity of streams to support brook trout Salvelinus fontinalis and to provide models useful for their management. We assessed the relative importance of spatial and temporal variation by calculating variance components and comparing relative standard errors for spatial and temporal variation. We used binary logistic regression to predict the presence of harvestable-length brook trout and multiple linear regression to assess the mechanistic links between landscapes and trout populations and to predict population density. The variance in trout density among streams was equal to or greater than the temporal variation for several streams, indicating that differences among sites affect population density. Logistic regression models correctly predicted the absence of harvestable-length brook trout in 60% of validation samples. The r 2-value for the linear regression model predicting density was 0.3, indicating low predictive ability. Both logistic and linear regression models supported buffering capacity against acid episodes as an important mechanistic link between landscapes and trout populations. Although our models fail to predict trout densities precisely, their success at elucidating the mechanistic links between landscapes and trout populations, in concert with the importance of spatial variation, increases our understanding of factors affecting brook trout abundance and will help managers and private groups to protect and enhance populations of wild brook trout. ?? Copyright by the American Fisheries Society 2006.

Spatial-temporal-covariance-based modeling, analysis, and simulation of aero-optics wavefront aberrations.

PubMed

Vogel, Curtis R; Tyler, Glenn A; Wittich, Donald J

2014-07-01

We introduce a framework for modeling, analysis, and simulation of aero-optics wavefront aberrations that is based on spatial-temporal covariance matrices extracted from wavefront sensor measurements. Within this framework, we present a quasi-homogeneous structure function to analyze nonhomogeneous, mildly anisotropic spatial random processes, and we use this structure function to show that phase aberrations arising in aero-optics are, for an important range of operating parameters, locally Kolmogorov. This strongly suggests that the d5/3 power law for adaptive optics (AO) deformable mirror fitting error, where d denotes actuator separation, holds for certain important aero-optics scenarios. This framework also allows us to compute bounds on AO servo lag error and predictive control error. In addition, it provides us with the means to accurately simulate AO systems for the mitigation of aero-effects, and it may provide insight into underlying physical processes associated with turbulent flow. The techniques introduced here are demonstrated using data obtained from the Airborne Aero-Optics Laboratory.

Space-for-Time Substitution Works in Everglades Ecological Forecasting Models

PubMed Central

Banet, Amanda I.; Trexler, Joel C.

2013-01-01

Space-for-time substitution is often used in predictive models because long-term time-series data are not available. Critics of this method suggest factors other than the target driver may affect ecosystem response and could vary spatially, producing misleading results. Monitoring data from the Florida Everglades were used to test whether spatial data can be substituted for temporal data in forecasting models. Spatial models that predicted bluefin killifish (Lucania goodei) population response to a drying event performed comparably and sometimes better than temporal models. Models worked best when results were not extrapolated beyond the range of variation encompassed by the original dataset. These results were compared to other studies to determine whether ecosystem features influence whether space-for-time substitution is feasible. Taken in the context of other studies, these results suggest space-for-time substitution may work best in ecosystems with low beta-diversity, high connectivity between sites, and small lag in organismal response to the driver variable. PMID:24278368

Design and theoretical investigation of a digital x-ray detector with large area and high spatial resolution

NASA Astrophysics Data System (ADS)

Gui, Jianbao; Guo, Jinchuan; Yang, Qinlao; Liu, Xin; Niu, Hanben

2007-05-01

X-ray phase contrast imaging is a promising new technology today, but the requirements of a digital detector with large area, high spatial resolution and high sensitivity bring forward a large challenge to researchers. This paper is related to the design and theoretical investigation of an x-ray direct conversion digital detector based on mercuric iodide photoconductive layer with the latent charge image readout by photoinduced discharge (PID). Mercuric iodide has been verified having a good imaging performance (high sensitivity, low dark current, low voltage operation and good lag characteristics) compared with the other competitive materials (α-Se,PbI II,CdTe,CdZnTe) and can be easily deposited on large substrates in the manner of polycrystalline. By use of line scanning laser beam and parallel multi-electrode readout make the system have high spatial resolution and fast readout speed suitable for instant general radiography and even rapid sequence radiography.

Variations in the service quality of medical practices.

PubMed

Ly, Dan P; Glied, Sherry A

2013-11-01

To examine regional variation in the service quality of physician practices and to assess the association of this variation with the supply and organization of physicians. Secondary analyses of the Community Tracking Study (CTS) household and physician surveys. A total of 40,339 individuals who had seen a primary care physician because of an illness or injury and 17,345 generalist physicians across 4 survey time periods in 60 CTS sites were included. Service quality measures used were lag between making an appointment and seeing a physician, and wait time at the physician's office. Our supply measure was the physician-to-population ratio. Our organizational measure was the percentage of physicians in group practices. Multivariate regressions were performed to examine the relationship between service quality and the supply and organization of physicians. There was substantial variation in the service quality of physician visits across the country. For example, in 2003, the average wait time to see a doctor was 16 minutes in Milwaukee but more than 41 minutes in Miami; the average appointment lag for a sick visit in 2003 was 1.2 days in west-central Alabama but almost 6 days in Northwestern Washington. Service quality was not associated with the primary care physician-to-population ratio and had varying associations with the organization of practices. Cross-site variation in service quality of care in primary care has been large, persistent, and associated with the organization of practices. Areas with higher primary care physician-to-population ratios had longer, not shorter, appointment lags.

[Spatial patterns and influence factors of specialization in tea cultivation based on geographically weighted regression model: A case study of Anxi County of Fujian Province, China].

PubMed

Shui, Wei; DU, Yong; Chen, Yi Ping; Jian, Xiao Mei; Fan, Bing Xiong

2017-04-18

Anxi County, specializing in tea cultivation, was taken as a case in this research. Pearson correlation analysis, ordinary least squares model (OLS) and geographically weighted regression model (GWR) were used to select four primary influence factors of specialization in tea cultivation (i.e., the average elevation, net income per capita, proportion of agricultural population, and the distance from roads) by analyzing the specialization degree of each town of Anxi County. Meanwhile, the spatial patterns of specialization in tea cultivation of Anxi County were evaluated. The results indicated that specialization in tea cultivation of Anxi County showed an obvious spatial auto-correlation, and a spatial pattern with "low-middle-high" circle structure, which was similar to Von Thünen's circle structure model, appeared from the county town to its surrounding region. Meanwhile, GWR (0.624) had a better fitting degree than OLS (0.595), and GWR could reasonably expound the spatial data. Contrary to the agricultural location theory of Von Thünen's model, which indicated that distance from market was a determination factor, the specialization degree of tea cultivation in Anxi was mainly decided by natural conditions of mountain area, instead of the social factors. Specialization degree of tea cultivation was positively correlated with the average elevation, net income per capita and the proportion of agricultural population, while a negative correlation was found between the distance from roads and specialization degree of tea cultivation. Coefficients of regression between the specialization degree of tea cultivation and two factors (i.e., the average elevation and net income per capita) showed a spatial pattern of higher level in the north direction and lower level in the south direction. On the contrary, the regression coefficients for the proportion of agricultural population increased from south to north of Anxi County. Furthermore, regression coefficient for the distance from roads showed a spatial pattern of higher level in the northeast direction and lower level in the southwest direction of Anxi County.

The Use of High-Resolution Pléiades Images to Extract Volcanic-Cloud Top Heights and Plume Elevation Models: examples on Mount Etna (Italy) and Mount Ontake (Japan)

NASA Astrophysics Data System (ADS)

de Michele, Marcello; Raucoules, Daniel; Corradini, Stefano; Merucci, Luca; spinetti, claudia

2017-04-01

Accurate and spatially-detailed knowledge of Volcanic Cloud Top Height (VCTH) and velocity is crucial in volcanology. As an example, the ash/gas dispersion in the atmosphere, their impact and lifetime around the globe, greatly depends on the injection altitude. The VCTH is critical for ash dispersion modelling and air traffic security. Furthermore, the volcanic plume height during explosive volcanism is the primary parameter for estimating mass eruption rate. Satellite remote sensing offers a comprehensive and safe way to estimate VCTH. Recently, it has been shown that high spatial resolution optical imagery from Landsat-8 OLI sensor can be used to extract Volcanic Cloud Top Height with a precision of 250 meters and an accuracy or 300m (de Michele et al., 2016). This method allows to extract a Plume Elevation Model (PEM) by jointly measuring the parallax between two optical bands acquired with a time lag varying from 0.1 to 2.5 seconds depending on the bands chosen and the sensors employed. The measure of the parallax is biased because the volcanic cloud is moving between the two images acquisitions, even if the time lag is short. The precision of our measurements is enhanced by compensating the parallax by measuring the velocity of the volcanic cloud in the perpendicular-to-epipolar direction (which is height independent) and correcting the initial parallax measurement. In this study, we push this methodology forward. We apply it to the very high spatial resolution Pleiades data (1m pixel spacing) provided by the French Space Agency (CNES). We apply the method on Mount Etna, during the 05 September 2015 eruptive episode and on Mount Ontake eruption occurring on 30 September 2014. We are able to extract VCTH as a PEM with high spatial resolution and improved precision. Since Pléiades has an improved revisit time (1day), our method has potential for routine monitoring of volcanic plumes in clear sky conditions and when the VCTH is higher than meteo clouds.

[Spatial interpolation of soil organic matter using regression Kriging and geographically weighted regression Kriging].

PubMed

Yang, Shun-hua; Zhang, Hai-tao; Guo, Long; Ren, Yan

2015-06-01

Relative elevation and stream power index were selected as auxiliary variables based on correlation analysis for mapping soil organic matter. Geographically weighted regression Kriging (GWRK) and regression Kriging (RK) were used for spatial interpolation of soil organic matter and compared with ordinary Kriging (OK), which acts as a control. The results indicated that soil or- ganic matter was significantly positively correlated with relative elevation whilst it had a significantly negative correlation with stream power index. Semivariance analysis showed that both soil organic matter content and its residuals (including ordinary least square regression residual and GWR resi- dual) had strong spatial autocorrelation. Interpolation accuracies by different methods were esti- mated based on a data set of 98 validation samples. Results showed that the mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) of RK were respectively 39.2%, 17.7% and 20.6% lower than the corresponding values of OK, with a relative-improvement (RI) of 20.63. GWRK showed a similar tendency, having its ME, MAE and RMSE to be respectively 60.6%, 23.7% and 27.6% lower than those of OK, with a RI of 59.79. Therefore, both RK and GWRK significantly improved the accuracy of OK interpolation of soil organic matter due to their in- corporation of auxiliary variables. In addition, GWRK performed obviously better than RK did in this study, and its improved performance should be attributed to the consideration of sample spatial locations.

Economic Expansion Is a Major Determinant of Physician Supply and Utilization

PubMed Central

Cooper, Richard A; Getzen, Thomas E; Laud, Prakash

2003-01-01

Objective To assess the relationship between levels of economic development and the supply and utilization of physicians. Data Sources Data were obtained from the American Medical Association, American Osteopathic Association, Organization for Economic Cooperation and Development (OECD), Bureau of Health Professions, Bureau of Labor Statistics, Bureau of Economic Analysis, Census Bureau, Health Care Financing Administration, and historical sources. Study Design Economic development, expressed as real per capita gross domestic product (GDP) or personal income, was correlated with per capita health care labor and physician supply within countries and states over periods of time spanning 25–70 years and across countries, states, and metropolitan statistical areas (MSAs) at multiple points in time over periods of up to 30 years. Longitudinal data were analyzed in four complementary ways: (1) simple univariate regressions; (2) regressions in which temporal trends were partialled out; (3) time series comparing percentage differences across segments of time; and (4) a bivariate Granger causality test. Cross-sectional data were assessed at multiple time points by means of univariate regression analyses. Principal Findings Under each analytic scenario, physician supply correlated with differences in GDP or personal income. Longitudinal correlations were associated with temporal lags of approximately 5 years for health employment and 10 years for changes in physician supply. The magnitude of changes in per capita physician supply in the United States was equivalent to differences of approximately 0.75 percent for each 1.0 percent difference in GDP. The greatest effects of economic expansion were on the medical specialties, whereas the surgical and hospital-based specialties were affected to a lesser degree, and levels of economic expansion had little influence on family/general practice. Conclusions Economic expansion has a strong, lagged relationship with changes in physician supply. This suggests that economic projections could serve as a gauge for projecting the future utilization of physician services. PMID:12785567

Estimating radiative feedbacks from stochastic fluctuations in surface temperature and energy imbalance

NASA Astrophysics Data System (ADS)

Proistosescu, C.; Donohoe, A.; Armour, K.; Roe, G.; Stuecker, M. F.; Bitz, C. M.

2017-12-01

Joint observations of global surface temperature and energy imbalance provide for a unique opportunity to empirically constrain radiative feedbacks. However, the satellite record of Earth's radiative imbalance is relatively short and dominated by stochastic fluctuations. Estimates of radiative feedbacks obtained by regressing energy imbalance against surface temperature depend strongly on sampling choices and on assumptions about whether the stochastic fluctuations are primarily forced by atmospheric or oceanic variability (e.g. Murphy and Forster 2010, Dessler 2011, Spencer and Braswell 2011, Forster 2016). We develop a framework around a stochastic energy balance model that allows us to parse the different contributions of atmospheric and oceanic forcing based on their differing impacts on the covariance structure - or lagged regression - of temperature and radiative imbalance. We validate the framework in a hierarchy of general circulation models: the impact of atmospheric forcing is examined in unforced control simulations of fixed sea-surface temperature and slab ocean model versions; the impact of oceanic forcing is examined in coupled simulations with prescribed ENSO variability. With the impact of atmospheric and oceanic forcing constrained, we are able to predict the relationship between temperature and radiative imbalance in a fully coupled control simulation, finding that both forcing sources are needed to explain the structure of the lagged-regression. We further model the dependence of feedback estimates on sampling interval by considering the effects of a finite equilibration time for the atmosphere, and issues of smoothing and aliasing. Finally, we develop a method to fit the stochastic model to the short timeseries of temperature and radiative imbalance by performing a Bayesian inference based on a modified version of the spectral Whittle likelihood. We are thus able to place realistic joint uncertainty estimates on both stochastic forcing and radiative feedbacks derived from observational records. We find that these records are, as of yet, too short to be useful in constraining radiative feedbacks, and we provide estimates of how the uncertainty narrows as a function of record length.

Representation of limb kinematics in Purkinje cell simple spike discharge is conserved across multiple tasks.

PubMed

Hewitt, Angela L; Popa, Laurentiu S; Pasalar, Siavash; Hendrix, Claudia M; Ebner, Timothy J

2011-11-01

Encoding of movement kinematics in Purkinje cell simple spike discharge has important implications for hypotheses of cerebellar cortical function. Several outstanding questions remain regarding representation of these kinematic signals. It is uncertain whether kinematic encoding occurs in unpredictable, feedback-dependent tasks or kinematic signals are conserved across tasks. Additionally, there is a need to understand the signals encoded in the instantaneous discharge of single cells without averaging across trials or time. To address these questions, this study recorded Purkinje cell firing in monkeys trained to perform a manual random tracking task in addition to circular tracking and center-out reach. Random tracking provides for extensive coverage of kinematic workspaces. Direction and speed errors are significantly greater during random than circular tracking. Cross-correlation analyses comparing hand and target velocity profiles show that hand velocity lags target velocity during random tracking. Correlations between simple spike firing from 120 Purkinje cells and hand position, velocity, and speed were evaluated with linear regression models including a time constant, τ, as a measure of the firing lead/lag relative to the kinematic parameters. Across the population, velocity accounts for the majority of simple spike firing variability (63 ± 30% of R(adj)(2)), followed by position (28 ± 24% of R(adj)(2)) and speed (11 ± 19% of R(adj)(2)). Simple spike firing often leads hand kinematics. Comparison of regression models based on averaged vs. nonaveraged firing and kinematics reveals lower R(adj)(2) values for nonaveraged data; however, regression coefficients and τ values are highly similar. Finally, for most cells, model coefficients generated from random tracking accurately estimate simple spike firing in either circular tracking or center-out reach. These findings imply that the cerebellum controls movement kinematics, consistent with a forward internal model that predicts upcoming limb kinematics.

Publically accessible decision support system of the spatially referenced regressions on watershed attributes (SPARROW) model and model enhancements in South Carolina

Treesearch

Celeste Journey; Anne B. Hoos; David E. Ladd; John W. brakebill; Richard A. Smith

2016-01-01

The U.S. Geological Survey (USGS) National Water Quality Assessment program has developed a web-based decision support system (DSS) to provide free public access to the steady-stateSPAtially Referenced Regressions On Watershed attributes (SPARROW) model simulation results on nutrient conditions in streams and rivers and to offer scenario testing capabilities for...

Hyper-Spectral Image Analysis With Partially Latent Regression and Spatial Markov Dependencies

NASA Astrophysics Data System (ADS)

Deleforge, Antoine; Forbes, Florence; Ba, Sileye; Horaud, Radu

2015-09-01

Hyper-spectral data can be analyzed to recover physical properties at large planetary scales. This involves resolving inverse problems which can be addressed within machine learning, with the advantage that, once a relationship between physical parameters and spectra has been established in a data-driven fashion, the learned relationship can be used to estimate physical parameters for new hyper-spectral observations. Within this framework, we propose a spatially-constrained and partially-latent regression method which maps high-dimensional inputs (hyper-spectral images) onto low-dimensional responses (physical parameters such as the local chemical composition of the soil). The proposed regression model comprises two key features. Firstly, it combines a Gaussian mixture of locally-linear mappings (GLLiM) with a partially-latent response model. While the former makes high-dimensional regression tractable, the latter enables to deal with physical parameters that cannot be observed or, more generally, with data contaminated by experimental artifacts that cannot be explained with noise models. Secondly, spatial constraints are introduced in the model through a Markov random field (MRF) prior which provides a spatial structure to the Gaussian-mixture hidden variables. Experiments conducted on a database composed of remotely sensed observations collected from the Mars planet by the Mars Express orbiter demonstrate the effectiveness of the proposed model.

Spatial Shifts in Tidal-Fluvial Environments

NASA Astrophysics Data System (ADS)

Dykstra, S. L.; Dzwonkowski, B.

2017-12-01

Fresh water discharge damps tidal propagation and increases the phase lag, which has important impacts on system-wide sediment transport process and ecological structure. Here, the role of discharge on spatial variability in the dynamics of tidal rivers is investigated in Mobile Bay and Delta, a microtidal diurnal system where discharge ranges multiple orders of magnitude. Long-term observations at 7 velocity stations and 20 water level stations, ranging over 260km along the system, were analyzed. Observations of the tidal extinguishing point in both velocity and water level were highly variable with significant shifts in location covering a distance over 140km. The velocity stations also allowed for measuring the extent of flood (i.e. point where tidal flow is arrested by discharge) shifting 100km. With increased discharge, flow characteristics at station locations can transition from an estuary (i.e. bidirectional tidal flow) to a tidal river to a traditional fluvial environment. This revealed systematic discharge induced damping and an increase in phase lag. Interestingly, before damping occurs, the tide amplifies ( 15%) seaward of the extent of flood. Another consistent pattern is the higher sensitivity of the velocity signal to discharge than water level. This causes the velocity to lag more and create progressive tides. In a microtidal diurnal system, the signal propagates further inland than a semidiurnal tide due to its lower frequency but is easily damped due to the small amplitude, creating large shifts. Previous research has focused on environments dominated by semidiurnal tides with similar magnitudes to discharge using water level observations. For example, the well studied Columbia and the St. Lawrence rivers have small shifts in their tidal extinguishing point O(10km) (Jay 2016, Matte 2014). These shifts are not large enough to observe process like discharge-induced amplification and damping at the same site like in the Mobile system, but they may indicate a decoupling of the water level and velocity signal by discharge. Throughout the world, shifts in tidal rivers are created by seasonal discharge patterns, but large storms can quickly disrupt a system and move it over 140km in a few days.

EMD-regression for modelling multi-scale relationships, and application to weather-related cardiovascular mortality

NASA Astrophysics Data System (ADS)

Masselot, Pierre; Chebana, Fateh; Bélanger, Diane; St-Hilaire, André; Abdous, Belkacem; Gosselin, Pierre; Ouarda, Taha B. M. J.

2018-01-01

In a number of environmental studies, relationships between natural processes are often assessed through regression analyses, using time series data. Such data are often multi-scale and non-stationary, leading to a poor accuracy of the resulting regression models and therefore to results with moderate reliability. To deal with this issue, the present paper introduces the EMD-regression methodology consisting in applying the empirical mode decomposition (EMD) algorithm on data series and then using the resulting components in regression models. The proposed methodology presents a number of advantages. First, it accounts of the issues of non-stationarity associated to the data series. Second, this approach acts as a scan for the relationship between a response variable and the predictors at different time scales, providing new insights about this relationship. To illustrate the proposed methodology it is applied to study the relationship between weather and cardiovascular mortality in Montreal, Canada. The results shed new knowledge concerning the studied relationship. For instance, they show that the humidity can cause excess mortality at the monthly time scale, which is a scale not visible in classical models. A comparison is also conducted with state of the art methods which are the generalized additive models and distributed lag models, both widely used in weather-related health studies. The comparison shows that EMD-regression achieves better prediction performances and provides more details than classical models concerning the relationship.

Advantages of geographically weighted regression for modeling benthic substrate in two Greater Yellowstone Ecosystem streams

USGS Publications Warehouse

Sheehan, Kenneth R.; Strager, Michael P.; Welsh, Stuart A.

2013-01-01

Stream habitat assessments are commonplace in fish management, and often involve nonspatial analysis methods for quantifying or predicting habitat, such as ordinary least squares regression (OLS). Spatial relationships, however, often exist among stream habitat variables. For example, water depth, water velocity, and benthic substrate sizes within streams are often spatially correlated and may exhibit spatial nonstationarity or inconsistency in geographic space. Thus, analysis methods should address spatial relationships within habitat datasets. In this study, OLS and a recently developed method, geographically weighted regression (GWR), were used to model benthic substrate from water depth and water velocity data at two stream sites within the Greater Yellowstone Ecosystem. For data collection, each site was represented by a grid of 0.1 m2 cells, where actual values of water depth, water velocity, and benthic substrate class were measured for each cell. Accuracies of regressed substrate class data by OLS and GWR methods were calculated by comparing maps, parameter estimates, and determination coefficient r 2. For analysis of data from both sites, Akaike’s Information Criterion corrected for sample size indicated the best approximating model for the data resulted from GWR and not from OLS. Adjusted r 2 values also supported GWR as a better approach than OLS for prediction of substrate. This study supports GWR (a spatial analysis approach) over nonspatial OLS methods for prediction of habitat for stream habitat assessments.

A study on thermal damage during hyperthermia treatment based on DPL model for multilayer tissues using finite element Legendre wavelet Galerkin approach.

PubMed

Kumar, Dinesh; Rai, K N

2016-12-01

Hyperthermia is a process that uses heat from the spatial heat source to kill cancerous cells without damaging the surrounding healthy tissues. Efficacy of hyperthermia technique is related to achieve temperature at the infected cells during the treatment process. A mathematical model on heat transfer in multilayer tissues in finite domain is proposed to predict the control temperature profile at hyperthermia position. The treatment technique uses dual-phase-lag model of heat transfer in multilayer tissues with modified Gaussian distribution heat source subjected to the most generalized boundary condition and interface at the adjacent layers. The complete dual-phase-lag model of bioheat transfer is solved using finite element Legendre wavelet Galerkin approach. The present solution has been verified with exact solution in a specific case and provides a good accuracy. The effect of the variability of different parameters such as lagging times, external heat source, metabolic heat source and the most generalized boundary condition on temperature profile in multilayer tissues is analyzed and also discussed the effective approach of hyperthermia treatment. Furthermore, we studied the modified thermal damage model with regeneration of healthy tissues as well. For viewpoint of thermal damage, the least thermal damage has been observed in boundary condition of second kind. The article concludes with a discussion of better opportunities for future clinical application of hyperthermia treatment. Copyright © 2016 Elsevier Ltd. All rights reserved.

Marine mammal distribution in the open ocean: a comparison of ocean color data products and levant time scales

NASA Astrophysics Data System (ADS)

Ohern, J.

2016-02-01

Marine mammals are generally located in areas of enhanced surface primary productivity, though they may forage much deeper within the water column and higher on the food chain. Numerous studies over the past several decades have utilized ocean color data from remote sensing instruments (CZCS, MODIS, and others) to asses both the quantity and time scales over which surface primary productivity relates to marine mammal distribution. In areas of sustained upwelling, primary productivity may essentially grow in the secondary levels of productivity (the zooplankton and nektonic species on which marine mammals forage). However, in many open ocean habitats a simple trophic cascade does not explain relatively short time lags between enhanced surface productivity and marine mammal presence. Other dynamic features that entrain prey or attract marine mammals may be responsible for the correlations between marine mammals and ocean color. In order to investigate these features, two MODIS (moderate imaging spectroradiometer) data products, the concentration as well as the standard deviation of surface chlorophyll were used in conjunction with marine mammal sightings collected within Ecuadorian waters. Time lags between enhanced surface chlorophyll and marine mammal presence were on the order of 2-4 weeks, however correlations were much stronger when the standard deviation of spatially binned images was used, rather than the chlorophyll concentrations. Time lags also varied between Balaenopterid and Odontocete cetaceans. Overall, the standard deviation of surface chlorophyll proved a useful tool for assessing potential relationships between marine mammal sightings and surface chlorophyll.

Emergency department visits and "vog"-related air quality in Hilo, Hawai'i.

PubMed

Michaud, Jon-Pierre; Grove, John Sinclair; Krupitsky, Dmitry

2004-05-01

Emergency department (ED) visits in Hilo, Hawai'i, from January 1997 to May 2001, were examined for associations with volcanic fog, or "vog", measured as sulfur dioxide (SO(2)) and submicrometer particulate matter (PM(1)). Exponential regression models were used with robust standard errors. Four diagnostic groups were examined: asthma/COPD; cardiac; flu, cold, and pneumonia; and gastroenteritis. Before adjustments, highly significant associations with vog-related air quality were seen for all diagnostic groups except gastroenteritis. After adjusting for month, year, and day of the week, only asthma/COPD had consistently positive associations with air quality. The strongest associations were for SO(2) with a 3-day lag (6.8% per 10 ppb; P=0.001) and PM(1), with a 1-day lag (13.8% per 10 microg/m(3); P=0.011). The association of ED visits for asthma/COPD with month of the year was stronger than associations seen with air quality. Although vog appears influential, non-vog factors dominated associations with the frequency of asthma/COPD ED visits.

Cross correlation and time-lag between cosmic ray intensity and solar activity during solar cycles 21, 22 and 23

NASA Astrophysics Data System (ADS)

Sierra-Porta, D.

2018-07-01

In the present paper a systematic study is carried out to validate the similarity or co-variability between daily terrestrial cosmic-ray intensity and three parameters of the solar corona evolution, i.e., the number of sunspots and flare index observed in the solar corona and the Ap index for regular magnetic field variations caused by regular solar radiation changes. The study is made for a period including three solar cycles starting with cycle 21 (year 1976) and ending on cycle 23 (year 2008). A cross-correlation analysis was used to establish patterns and dependence of the variables. This study focused on the time lag calculation for these variables and found a maximum of negative correlation over CC1≈ 0.85, CC2≈ 0.75 and CC3≈ 0.63 with an estimation of 181, 156 and 2 days of deviation between maximum/minimum of peaks for the intensity of cosmic rays related with sunspot number, flare index and Ap index regression, respectively.

[Oxidative stress, lung function and exposure to air pollutants in Mexican schoolchildren with and without asthma].

PubMed

Romero-Calderón, Ana Teresa; Moreno-Macías, Hortensia; Manrique-Moreno, Joel David Francisco; Riojas-Rodríguez, Horacio; Torres-Ramos, Yessica Dorín; Montoya-Estrada, Araceli; Hicks-Gómez, Juan José; Linares-Segovia, Benigno; Cárdenas, Beatriz; Bárcenas, Claudia; Barraza-Villarreal, Albino

2017-01-01

To assess the association between the air pollutants exposure on markers of oxidative stress and lung function in schoolchildren with and without asthma from Salamanca and Leon Guanajuato, Mexico. We realized determinations of oxidative stress biomarkers and lung function tests in 314 schoolchildren. Information of air pollutants (O3, SO2, CO, PM2.5 and PM10) were obtained from monitoring stations and multiple linear regression models were run to assess the association. An increase of 0.09 pmol in conjugated dienes was observed by exposure to PM10 lag 1 in asthmatics from Salamanca (p<0.05). The exposure to O3 during the same day increased the concentration of Lipohydroperoxides in 4.38 nmol in asthmatics of Salamanca, as well as in 2.31 nmol by exposure to PM10 lag 2 (p<0.05). The forced vital capacity decreased by 138 and 203 ml in children without asthma, respectively, due to exposure to carbon monoxide (p<0.05). Exposure to air pollutants increase oxidative stress and decreased lung function in schoolchildren, with and without asthma.

The impact of ambient fine particles on influenza transmission and the modification effects of temperature in China: A multi-city study.

PubMed

Chen, Gongbo; Zhang, Wenyi; Li, Shanshan; Zhang, Yongming; Williams, Gail; Huxley, Rachel; Ren, Hongyan; Cao, Wei; Guo, Yuming

2017-01-01

There is good evidence that air pollution is a risk factor for adverse respiratory and vascular health outcomes. However, data are limited as to whether ambient fine particles contribute to the transmission of influenza and if so, how the association is modified by weather conditions. We examined the relationship between ambient PM 2.5 and influenza incidence at the national level in China and explored the associations at different temperatures. Daily data on concentrations of particulate matter with aerodynamic diameter<2.5μm (PM 2.5 ) and influenza incidence counts were collected in 47 Chinese cities. A Poisson regression model was used to estimate the city-specific PM 2.5 -influenza association, after controlling for potential confounders. Then, a random-effect meta-analysis was used to pool the effects at national level. In addition, stratified analyses were performed to examine modification effects of ambient temperature. For single lag models, the highest effect of ambient PM 2.5 on influenza incidence appeared at lag day 2, with relative risk (RR) of 1.015 (95% confidence interval (CI): 1.004, 1.025) associated with a 10μg/m 3 increase in PM 2.5 . For moving average lag models, the significant association was found at lag 2-3days, with RR of 1.020 (95% CI: 1.006, 1.034). The RR of influenza transmission associated with PM 2.5 was higher for cold compared with hot days. Overall, 10.7% of incident influenza cases may result from exposure to ambient PM 2.5 . Ambient PM 2.5 may increase the risk of exposure to influenza in China especially on cooler days. Control measures to reduce PM 2.5 concentrations could potentially also be of benefit in lowering the risk of exposure and subsequent transmission of influenza in China. Copyright © 2016 Elsevier Ltd. All rights reserved.

Does air pollution trigger suicide? A case-crossover analysis of suicide deaths over the life span.

PubMed

Casas, Lidia; Cox, Bianca; Bauwelinck, Mariska; Nemery, Benoit; Deboosere, Patrick; Nawrot, Tim Steve

2017-11-01

In addition to underlying health disorders and socio-economic or community factors, air pollution may trigger suicide mortality. This study evaluates the association between short-term variation in air pollution and 10 years of suicide mortality in Belgium. In a bidirectional time-stratified case-crossover design, 20,533 suicide deaths registered between January 1st 2002 and December 31st 2011 were matched by temperature with control days from the same month and year. We used municipality-level air pollution [particulate matter (PM 10 ) and O 3 concentrations] data and meteorology data. We applied conditional logistic regression models adjusted for duration of sunshine and day of the week to obtain odds ratios (OR) and their 95% CI for an increase of 10 µg/m 3 in pollutant concentrations over different lag periods (lag 0, 0-1, 0-2, 0-3, 0-4, 0-5, and 0-6 days). Effect modification by season and age was investigated by including interaction terms. We observed significant associations of PM 10 and O 3 with suicide during summer (OR ranging from 1.02 to 1.07, p-values <0.05). For O 3 , significant associations were also observed during spring and autumn. Age significantly modified the associations with PM 10 , with statistically significant associations observed only among 5-14 year old children (lag 0-6: OR = 1.45; 95% CI: 1.03-2.04) and ≥85 years old (e.g. lag 0-4: OR = 1.17; 95% CI: 1.06-1.29). Recent increases in outdoor air pollutants such as PM 10 or O 3 can trigger suicide, particularly during warm periods, even at concentrations below the European thresholds. Furthermore, PM 10 may have strong trigger effects among children and elderly population.

Assessing the impacts of Saskatchewan's minimum alcohol pricing regulations on alcohol-related crime.

PubMed

Stockwell, Tim; Zhao, Jinhui; Sherk, Adam; Callaghan, Russell C; Macdonald, Scott; Gatley, Jodi

2017-07-01

Saskatchewan's introduction in April 2010 of minimum prices graded by alcohol strength led to an average minimum price increase of 9.1% per Canadian standard drink (=13.45 g ethanol). This increase was shown to be associated with reduced consumption and switching to lower alcohol content beverages. Police also informally reported marked reductions in night-time alcohol-related crime. This study aims to assess the impacts of changes to Saskatchewan's minimum alcohol-pricing regulations between 2008 and 2012 on selected crime events often related to alcohol use. Data were obtained from Canada's Uniform Crime Reporting Survey. Auto-regressive integrated moving average time series models were used to test immediate and lagged associations between minimum price increases and rates of night-time and police identified alcohol-related crimes. Controls were included for simultaneous crime rates in the neighbouring province of Alberta, economic variables, linear trend, seasonality and autoregressive and/or moving-average effects. The introduction of increased minimum-alcohol prices was associated with an abrupt decrease in night-time alcohol-related traffic offences for men (-8.0%, P < 0.001), but not women. No significant immediate changes were observed for non-alcohol-related driving offences, disorderly conduct or violence. Significant monthly lagged effects were observed for violent offences (-19.7% at month 4 to -18.2% at month 6), which broadly corresponded to lagged effects in on-premise alcohol sales. Increased minimum alcohol prices may contribute to reductions in alcohol-related traffic-related and violent crimes perpetrated by men. Observed lagged effects for violent incidents may be due to a delay in bars passing on increased prices to their customers, perhaps because of inventory stockpiling. [Stockwell T, Zhao J, Sherk A, Callaghan RC, Macdonald S, Gatley J. Assessing the impacts of Saskatchewan's minimum alcohol pricing regulations on alcohol-related crime. Drug Alcohol Rev 2017;36:492-501]. © 2016 Australasian Professional Society on Alcohol and other Drugs.

[The EMECAM protocol: an analysis of the short-term effect of air pollution on mortality. Estudio Multicéntrico Español sobre la Relación entre la Contaminación Atmosférica u la Mortalidad].

PubMed

Pérez-Hoyos, S; Sáez Zafra, M; Barceló, M A; Cambra, C; Figueiras Guzmán, A; Ordóñez, J M; Guillén Grima, F; Ocaña, R; Bellido, J; Cirera Suárez, L; López, A A; Rodríguez, V; Alcalá Nalvaiz, T; Ballester Díez, F

1999-01-01

The aim of this study is to Mortality show the protocol of analysis which was set out as part of the EMECAM Project, illustrating the application thereof to the effect of pollution has on the mortality in the city of Valencia. The response variables considered will be the daily deaths rate resulting from all causes, except external ones. The explicative variables are the daily series of different pollutants (black smoke, SO2, NO2, CO, O3). As possible confusion variables, weather factors, structural factors and weekly cases of flu are taken into account. A Poisson regression model is built up for each one of the four deaths series in two stages. In the first stage, a baseline model is fitted using the possible confusion variables. In the second stage, the pollution variables or the time legs thereof are included, controlling the residual autocorrelation by including mortality time lags. The process of fitting the baseline model is as follows: 1) Include the significant sinusoidal terms up to the sixth order. 2) Include the significant temperature or temperature squared terms with the time lags thereof up to the 7th order. 3) Repeat this process with the relative humidity. 4) Add in the significant terms of calendar years, daily tendency and tendency squared. 5) The days of the week as dummy variables are always included in the model. 6) Include the holidays and the significant time lags of up to two weeks of flu. Following the reassessment of the model, each one of the pollutants and the time lags thereof up to the fifth order are proven out. The impact is analyzed by six-month periods, including interaction terms.

Occupational Vehicle-related Particulate Exposure and Inflammatory Markers in Trucking Industry Workers

PubMed Central

Chiu, Yueh-Hsiu Mathilda; Garshick, Eric; Hart, Jaime E.; Spiegelman, Donna; Dockery, Douglas W.; Smith, Thomas J.; Laden, Francine

2016-01-01

Background Previous studies have suggested an association between particulate air pollution and cardiovascular disease, but the mechanism is still unclear. Objective We examined the association between workplace exposure to vehicle-related particles and cardiovascular disease related systemic inflammatory markers, C-reactive protein (hs-CRP), soluble intercellular adhesion molecule-1 (sICAM-1), and interleukin-6 (IL-6) in 137 trucking terminal workers (non-drivers) in the U.S. trucking industry. Methods We visited two large trucking terminals in 2009 and measured vehicle-related elemental carbon (EC), organic carbon (OC), and particulate matter with aerodynamic diameter ≤2.5μm (PM2.5), for 5 days consecutively at the main work areas. Each participant provided a blood sample and completed a health questionnaire during the sampling period. Individual workplace exposure level was calculated by 12-hr time weighted moving averages based on work shift. The association between each blood marker and exposure to each pollutant during 0-12, 12-24, 24-36, and 36-48 hours before the blood draw was examined by multivariable regression analyses. Results In general, OC and EC had a positive association with sICAM-1, especially for exposure periods 12-24 (lag12-24) and 24-36 (lag24-36) hrs prior to blood draw [β=54.9 (95%CI: 12.3-97.5) for lag12-24 and β=46.5 (95%CI: 21.2-71.8) for lag12-24; change in sICAM-1 (in ng/mL) corresponding to an IQR increase in OC]. A similar pattern was found for EC and PM2.5. We did not find an association between measured pollutants up to 48 hours before blood draw and hs-CRP or IL-6. Conclusion In this group of healthy workers, short-term exposure to vehicle-related air pollutants may be associated with sICAM-1. Our findings may be dependent on the exposure period studied. PMID:27104805

National Income Inequality and Declining GDP Growth Rates Are Associated with Increases in HIV Diagnoses among People Who Inject Drugs in Europe: A Panel Data Analysis

PubMed Central

Nikolopoulos, Georgios K.; Fotiou, Anastasios; Kanavou, Eleftheria; Richardson, Clive; Detsis, Marios; Pharris, Anastasia; Suk, Jonathan E.; Semenza, Jan C.; Costa-Storti, Claudia; Paraskevis, Dimitrios; Sypsa, Vana; Malliori, Melpomeni-Minerva; Friedman, Samuel R.; Hatzakis, Angelos

2015-01-01

Background There is sparse evidence that demonstrates the association between macro-environmental processes and drug-related HIV epidemics. The present study explores the relationship between economic, socio-economic, policy and structural indicators, and increases in reported HIV infections among people who inject drugs (PWID) in the European Economic Area (EEA). Methods We used panel data (2003–2012) for 30 EEA countries. Statistical analyses included logistic regression models. The dependent variable was taking value 1 if there was an outbreak (significant increase in the national rate of HIV diagnoses in PWID) and 0 otherwise. Explanatory variables included the growth rate of Gross Domestic Product (GDP), the share of the population that is at risk for poverty, the unemployment rate, the Eurostat S80/S20 ratio, the Gini coefficient, the per capita government expenditure on health and social protection, and variables on drug control policy and drug-using population sizes. Lags of one to three years were investigated. Findings In multivariable analyses, using two-year lagged values, we found that a 1% increase of GDP was associated with approximately 30% reduction in the odds of an HIV outbreak. In GDP-adjusted analyses with three-year lagged values, the effect of the national income inequality on the likelihood of an HIV outbreak was significant [S80/S20 Odds Ratio (OR) = 3.89; 95% Confidence Interval (CI): 1.15 to 13.13]. Generally, the multivariable analyses produced similar results across three time lags tested. Interpretation Given the limitations of ecological research, we found that declining economic growth and increasing national income inequality were associated with an elevated probability of a large increase in the number of HIV diagnoses among PWID in EEA countries during the last decade. HIV prevention may be more effective if developed within national and European-level policy contexts that promote income equality, especially among vulnerable groups. PMID:25875598

National income inequality and declining GDP growth rates are associated with increases in HIV diagnoses among people who inject drugs in Europe: a panel data analysis.

PubMed

Nikolopoulos, Georgios K; Fotiou, Anastasios; Kanavou, Eleftheria; Richardson, Clive; Detsis, Marios; Pharris, Anastasia; Suk, Jonathan E; Semenza, Jan C; Costa-Storti, Claudia; Paraskevis, Dimitrios; Sypsa, Vana; Malliori, Melpomeni-Minerva; Friedman, Samuel R; Hatzakis, Angelos

2015-01-01

There is sparse evidence that demonstrates the association between macro-environmental processes and drug-related HIV epidemics. The present study explores the relationship between economic, socio-economic, policy and structural indicators, and increases in reported HIV infections among people who inject drugs (PWID) in the European Economic Area (EEA). We used panel data (2003-2012) for 30 EEA countries. Statistical analyses included logistic regression models. The dependent variable was taking value 1 if there was an outbreak (significant increase in the national rate of HIV diagnoses in PWID) and 0 otherwise. Explanatory variables included the growth rate of Gross Domestic Product (GDP), the share of the population that is at risk for poverty, the unemployment rate, the Eurostat S80/S20 ratio, the Gini coefficient, the per capita government expenditure on health and social protection, and variables on drug control policy and drug-using population sizes. Lags of one to three years were investigated. In multivariable analyses, using two-year lagged values, we found that a 1% increase of GDP was associated with approximately 30% reduction in the odds of an HIV outbreak. In GDP-adjusted analyses with three-year lagged values, the effect of the national income inequality on the likelihood of an HIV outbreak was significant [S80/S20 Odds Ratio (OR) = 3.89; 95% Confidence Interval (CI): 1.15 to 13.13]. Generally, the multivariable analyses produced similar results across three time lags tested. Given the limitations of ecological research, we found that declining economic growth and increasing national income inequality were associated with an elevated probability of a large increase in the number of HIV diagnoses among PWID in EEA countries during the last decade. HIV prevention may be more effective if developed within national and European-level policy contexts that promote income equality, especially among vulnerable groups.

Association between meteorological factors and hepatitis A in Spain 2010-2014.

PubMed

Gullón, Pedro; Varela, Carmen; Martínez, Elena Vanessa; Gómez-Barroso, Diana

2017-05-01

There is growing concern of how climate change could affect public health, due to the increase number of extreme climate events. Hence, the study of the role that climate events play on the distribution of waterborne diseases, like Hepatitis A, could be key for developing new prevention approaches. To investigate the association between climate factors and Hepatitis A in Spain between 2010 and 2014. Weekly Hepatitis A cases between 2010 and 2014 were obtained from the Spanish Epidemiology Surveillance Network. Climate variables (weekly cumulative rainfall, rainy days, storm days and snow days) were obtained from National Climatic Data Center (NOAA satellite and information Service of USA). Each municipality was assigned to the nearest weather station (N=73). A Mixed-Effects Poisson regression was performed to estimate Incidence Rate Ratios (IRR), including a time lag of 2, 3 and 4weeks (most probable incubation period for Hepatitis A). Rainfall higher than 90th percentile (extreme precipitation) was associated with increased number of Hepatitis A cases 2weeks (IRR=1.24 CI 95%=1.09-1.40) and 4weeks after the event (IRR=1.15 CI 95%=1.01-1.30). An extra rainy day increased the risk of Hepatitis A two weeks after (IRR=1.03 CI 95%=1.01-1.05). We found higher risk of Hepatitis A two weeks after each extra storm day (IRR=1.06 CI 95%=1.00-1.12), and lower risk with 3 and 4weeks' lag (IRR=0.93 CI 95%=0.88-0.99 for lag3; IRR=0.94 CI 95%=0.88-0.99 for lag 4). There is an increased risk of Hepatitis A 2weeks after water-related climate events. Including meteorological information in surveillance systems might improve to develop early prevention strategies for waterborne diseases. Copyright © 2016 Elsevier Ltd. All rights reserved.

Comparison of different spatial transformations applied to EEG data: A case study of error processing.

PubMed

Cohen, Michael X

2015-09-01

The purpose of this paper is to compare the effects of different spatial transformations applied to the same scalp-recorded EEG data. The spatial transformations applied are two referencing schemes (average and linked earlobes), the surface Laplacian, and beamforming (a distributed source localization procedure). EEG data were collected during a speeded reaction time task that provided a comparison of activity between error vs. correct responses. Analyses focused on time-frequency power, frequency band-specific inter-electrode connectivity, and within-subject cross-trial correlations between EEG activity and reaction time. Time-frequency power analyses showed similar patterns of midfrontal delta-theta power for errors compared to correct responses across all spatial transformations. Beamforming additionally revealed error-related anterior and lateral prefrontal beta-band activity. Within-subject brain-behavior correlations showed similar patterns of results across the spatial transformations, with the correlations being the weakest after beamforming. The most striking difference among the spatial transformations was seen in connectivity analyses: linked earlobe reference produced weak inter-site connectivity that was attributable to volume conduction (zero phase lag), while the average reference and Laplacian produced more interpretable connectivity results. Beamforming did not reveal any significant condition modulations of connectivity. Overall, these analyses show that some findings are robust to spatial transformations, while other findings, particularly those involving cross-trial analyses or connectivity, are more sensitive and may depend on the use of appropriate spatial transformations. Copyright © 2014 Elsevier B.V. All rights reserved.

Developing and testing a global-scale regression model to quantify mean annual streamflow

NASA Astrophysics Data System (ADS)

Barbarossa, Valerio; Huijbregts, Mark A. J.; Hendriks, A. Jan; Beusen, Arthur H. W.; Clavreul, Julie; King, Henry; Schipper, Aafke M.

2017-01-01

Quantifying mean annual flow of rivers (MAF) at ungauged sites is essential for assessments of global water supply, ecosystem integrity and water footprints. MAF can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict MAF based on climate and catchment characteristics. Yet, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. In this study, we developed a global-scale regression model for MAF based on a dataset unprecedented in size, using observations of discharge and catchment characteristics from 1885 catchments worldwide, measuring between 2 and 106 km2. In addition, we compared the performance of the regression model with the predictive ability of the spatially explicit global hydrological model PCR-GLOBWB by comparing results from both models to independent measurements. We obtained a regression model explaining 89% of the variance in MAF based on catchment area and catchment averaged mean annual precipitation and air temperature, slope and elevation. The regression model performed better than PCR-GLOBWB for the prediction of MAF, as root-mean-square error (RMSE) values were lower (0.29-0.38 compared to 0.49-0.57) and the modified index of agreement (d) was higher (0.80-0.83 compared to 0.72-0.75). Our regression model can be applied globally to estimate MAF at any point of the river network, thus providing a feasible alternative to spatially explicit process-based global hydrological models.

LAG-3 Represents a Marker of CD4+ T Cells with Regulatory Activity in Patients with Bone Fracture.

PubMed

Wang, Jun; Ti, Yunfan; Wang, Yicun; Guo, Guodong; Jiang, Hui; Chang, Menghan; Qian, Hongbo; Zhao, Jianning; Sun, Guojing

2018-04-19

The lymphocyte activation gene 3 (LAG-3) is a CD4 homolog with binding affinity to MHC class II molecules. It is thought that LAG-3 exerts a bimodal function, such that co-ligation of LAG-3 and CD3 could deliver an inhibitory signal in conventional T cells, whereas, on regulatory T cells, LAG-3 expression could promote their inhibitory function. In this study, we investigated the role of LAG-3 expression on CD4 + T cells in patients with long bone fracture. We found that LAG-3 + cells represented approximately 13% of peripheral blood CD4 + T cells on average. Compared to LAG-3 - CD4 + T cells, LAG-3 + CD4 + T cells presented significantly higher Foxp3 and CTLA-4 expression. Directly ex vivo or with TCR stimulation, LAG-3 + CD4 + T cells expressed significantly higher levels of IL-10 and TGF-β than LAG-3 - CD4 + T cells. Interestingly, blocking the LAG-3-MHC class II interaction actually increased the IL-10 expression by LAG-3 + CD4 + T cells. The frequency of LAG-3 + CD4 + T cell was positively correlated with restoration of healthy bone function in long bone fracture patients. These results together suggested that LAG-3 is a marker of CD4 + T cells with regulatory function; at the same time, LAG-3 might have limited the full suppressive potential of Treg cells.

Intramedullary nails with two lag screws.

PubMed

Brown, C J; Wang, C J; Yettram, A L; Procter, P

2004-06-01

To investigate the structural integrity of intramedullary nails with two lag screws, and to give guidance to orthopaedic surgeons in the choice of appropriate devices. Alternative designs of the construct are considered, and the use of a slotted upper lag screw insertion hole is analysed. Intramedullary fixation devices with a single lag screw have been known to fail at the lag screw insertion hole. Using two lag screws is considered. It has also been proposed to use a slot in the nail for the upper lag screw to prevent the upper lag screw from sticking. Bending and torsion load cases are analysed using finite element method. Consideration of both load conditions is essential. The results present the overall stiffness of the assembly, the load sharing between lag screws, and the possibility for cut-out to occur. While the slot for the upper lag screw might be advantageous with regard to the stresses in the lag screws, it could be detrimental for cut-out occurring adjacent to the lag screws. Comparative analyses demonstrate that two lag screws may be advantageous in patients whose cancellous bone quality is good and who impose large loads on the lag screw/nail interface. However, the use of two screws might pre-dispose to failure by cut-out of the lag screws. The addition of a slotted hole for the upper lag screw appears to do nothing significant to reduce the risk of such a failure. Copyright 2004 Elsevier Ltd.

The geography of recreational open space: influence of neighborhood racial composition and neighborhood poverty.

PubMed

Duncan, Dustin T; Kawachi, Ichiro; White, Kellee; Williams, David R

2013-08-01

The geography of recreational open space might be inequitable in terms of minority neighborhood racial/ethnic composition and neighborhood poverty, perhaps due in part to residential segregation. This study evaluated the association between minority neighborhood racial/ethnic composition, neighborhood poverty, and recreational open space in Boston, Massachusetts (US). Across Boston census tracts, we computed percent non-Hispanic Black, percent Hispanic, and percent families in poverty as well as recreational open space density. We evaluated spatial autocorrelation in study variables and in the ordinary least squares (OLS) regression residuals via the Global Moran's I. We then computed Spearman correlations between the census tract socio-demographic characteristics and recreational open space density, including correlations adjusted for spatial autocorrelation. After this, we computed OLS regressions or spatial regressions as appropriate. Significant positive spatial autocorrelation was found for neighborhood socio-demographic characteristics (all p value = 0.001). We found marginally significant positive spatial autocorrelation in recreational open space (Global Moran's I = 0.082; p value = 0.053). However, we found no spatial autocorrelation in the OLS regression residuals, which indicated that spatial models were not appropriate. There was a negative correlation between census tract percent non-Hispanic Black and recreational open space density (r S = -0.22; conventional p value = 0.005; spatially adjusted p value = 0.019) as well as a negative correlation between predominantly non-Hispanic Black census tracts (>60 % non-Hispanic Black in a census tract) and recreational open space density (r S = -0.23; conventional p value = 0.003; spatially adjusted p value = 0.007). In bivariate and multivariate OLS models, percent non-Hispanic Black in a census tract and predominantly Black census tracts were associated with decreased density of recreational open space (p value < 0.001). Consistent with several previous studies in other geographic locales, we found that Black neighborhoods in Boston were less likely to have recreational open spaces, indicating the need for policy interventions promoting equitable access. Such interventions may contribute to reductions and disparities in obesity.

Effect of long-term mechanical perturbation on intertidal soft-bottom meiofaunal community spatial structure

NASA Astrophysics Data System (ADS)

Boldina, Inna; Beninger, Peter G.; Le Coz, Maïwen

2014-01-01

Situated at the interface of the microbial and macrofaunal compartments, soft-bottom meiofauna accomplish important ecological functions. However, little is known of their spatial distribution in the benthic environment. To assess the effects of long-term mechanical disturbance on soft-bottom meiofaunal spatial distribution, we compared a site subjected to long-term clam digging to a nearby site untouched by such activities, in Bourgneuf Bay, on the Atlantic coast of France. Six patterned replicate samples were taken at 3, 6, 9, 12, 15, 18, 21 and 24 cm lags, all sampling stations being separated by 5 m. A combined correlogram-variogram approach was used to enhance interpretation of the meiofaunal spatial distribution; in particular, the definition of autocorrelation strength and its statistical significance, as well as the detailed characteristics of the periodic spatial structure of nematode assemblages, and the determination of the maximum distance of their spatial autocorrelation. At both sites, nematodes and copepods clearly exhibited aggregated spatial structure at the meso scale; this structure was attenuated at the impacted site. The nematode spatial distribution showed periodicity at the non-impacted site, but not at the impacted site. This is the first explicit report of a periodic process in meiofaunal spatial distribution. No such cyclic spatial process was observed for the more motile copepods at either site. This first study to indicate the impacts of long-term anthropogenic mechanical perturbation on meiofaunal spatial structure opens the door to a new dimension of mudflat ecology. Since macrofaunal predator search behaviour is known to be strongly influenced by prey spatial structure, the alteration of this structure may have important consequences for ecosystem functioning.

Spatial analysis and land use regression of VOCs and NO(2) from school-based urban air monitoring in Detroit/Dearborn, USA.

PubMed

Mukerjee, Shaibal; Smith, Luther A; Johnson, Mary M; Neas, Lucas M; Stallings, Casson A

2009-08-01

Passive ambient air sampling for nitrogen dioxide (NO(2)) and volatile organic compounds (VOCs) was conducted at 25 school and two compliance sites in Detroit and Dearborn, Michigan, USA during the summer of 2005. Geographic Information System (GIS) data were calculated at each of 116 schools. The 25 selected schools were monitored to assess and model intra-urban gradients of air pollutants to evaluate impact of traffic and urban emissions on pollutant levels. Schools were chosen to be statistically representative of urban land use variables such as distance to major roadways, traffic intensity around the schools, distance to nearest point sources, population density, and distance to nearest border crossing. Two approaches were used to investigate spatial variability. First, Kruskal-Wallis analyses and pairwise comparisons on data from the schools examined coarse spatial differences based on city section and distance from heavily trafficked roads. Secondly, spatial variation on a finer scale and as a response to multiple factors was evaluated through land use regression (LUR) models via multiple linear regression. For weeklong exposures, VOCs did not exhibit spatial variability by city section or distance from major roads; NO(2) was significantly elevated in a section dominated by traffic and industrial influence versus a residential section. Somewhat in contrast to coarse spatial analyses, LUR results revealed spatial gradients in NO(2) and selected VOCs across the area. The process used to select spatially representative sites for air sampling and the results of coarse and fine spatial variability of air pollutants provide insights that may guide future air quality studies in assessing intra-urban gradients.

Spending more money, saving more lives? The relationship between avoidable mortality and healthcare spending in 14 countries.

PubMed

Heijink, Richard; Koolman, Xander; Westert, Gert P

2013-06-01

Healthcare expenditures rise as a share of GDP in most countries, raising questions regarding the value of further spending increases. Against this backdrop, we assessed the value of healthcare spending growth in 14 western countries between 1996 and 2006. We estimated macro-level health production functions using avoidable mortality as outcome measure. Avoidable mortality comprises deaths from certain conditions "that should not occur in the presence of timely and effective healthcare". We investigated the relationship between total avoidable mortality and healthcare spending using descriptive analyses and multiple regression models, focussing on within-country variation and growth rates. We aimed to take into account the role of potential confounders and dynamic effects such as time lags. Additionally, we explored a method to estimate macro-level cost-effectiveness. We found an average yearly avoidable mortality decline of 2.6-5.3% across countries. Simultaneously, healthcare spending rose between 1.9 and 5.9% per year. Most countries with above-average spending growth demonstrated above-average reductions in avoidable mortality. The regression models showed a significant association between contemporaneous and lagged healthcare spending and avoidable mortality. The time-trend, representing an exogenous shift of the health production function, reduced the impact of healthcare spending. After controlling for this time-trend and other confounders, i.e. demographic and socioeconomic variables, a statistically significant relationship between healthcare spending and avoidable mortality remained. We tentatively conclude that macro-level healthcare spending increases provided value for money, at least for the disease groups, countries and years included in this study.

Organizational and environmental correlates of the adoption of a focus strategy in U.S. hospices.

PubMed

Apenteng, Bettye A; Nayar, Preethy; Yu, Fang; Adams, John; Opoku, Samuel T

2015-01-01

The hospice industry has experienced rapid growth in the last decade and has become a prominent component of the U.S. health care delivery system. In recent decades, the number of hospices serving nursing facility residents has increased. However, there is paucity of research on the organizational and environmental determinants of this strategic behavior. The aim of this study was to empirically identify the factors associated with the adoption of a nursing facility focus strategy in U.S. hospices. A nursing facility focus strategy was defined in this study as a strategic choice to target the provision of hospice services to skilled nursing facility or nursing home residents. This study employed a longitudinal study design with lagged independent variables in answering its research questions. Data for the study's dependent variables are obtained for the years 2005-2008, whereas data for the independent variables are obtained for the years 2004-2007, representing a 1-year lag. Mixed effects regression models were used in the multivariate regression analyses. Using a resource dependence framework, the findings from this study indicate that organizational size, community wealth, competition, and ownership type are important predictors of the adoption of a nursing facility focus strategy. Hospices may be adopting a nursing facility focus strategy in response to increasing competition. The decision to focus the provision of care to nursing facility residents may be driven by the need to secure stability in referrals. Further empirical exploration of the performance implications of adopting a nursing facility focus strategy is warranted.

A near-infrared, optical, and ultraviolet polarimetric and timing investigation of complex equatorial dusty structures

NASA Astrophysics Data System (ADS)

Marin, F.; Rojas Lobos, P. A.; Hameury, J. M.; Goosmann, R. W.

2018-05-01

Context. From stars to active galactic nuclei, many astrophysical systems are surrounded by an equatorial distribution of dusty material that is, in a number of cases, spatially unresolved even with cutting edge facilities. Aims: In this paper, we investigate if and how one can determine the unresolved and heterogeneous morphology of dust distribution around a central bright source using time-resolved polarimetric observations. Methods: We used polarized radiative transfer simulations to study a sample of circumnuclear dusty morphologies. We explored a grid of geometrically variable models that are uniform, fragmented, and density stratified in the near-infrared, optical, and ultraviolet bands, and we present their distinctive time-dependent polarimetric signatures. Results: As expected, varying the structure of the obscuring equatorial disk has a deep impact on the inclination-dependent flux, polarization degree and angle, and time lags we observe. We find that stratified media are distinguishable by time-resolved polarimetric observations, and that the expected polarization is much higher in the infrared band than in the ultraviolet. However, because of the physical scales imposed by dust sublimation, the average time lags of months to years between the total and polarized fluxes are important; these time lags lengthens the observational campaigns necessary to break more sophisticated, and therefore also more degenerated, models. In the ultraviolet band, time lags are slightly shorter than in the infrared or optical bands, and, coupled to lower diluting starlight fluxes, time-resolved polarimetry in the UV appears more promising for future campaigns. Conclusions: Equatorial dusty disks differ in terms of inclination-dependent photometric, polarimetric, and timing observables, but only the coupling of these different markers can lead to inclination-independent constraints on the unresolved structures. Even though it is complex and time consuming, polarized reverberation mapping in the ultraviolet-blue band is probably the best technique to rely on in this field.

Evidence for a physical linkage between galactic cosmic rays and regional climate time series

USGS Publications Warehouse

Perry, C.A.

2007-01-01

The effects of solar variability on regional climate time series were examined using a sequence of physical connections between total solar irradiance (TSI) modulated by galactic cosmic rays (GCRs), and ocean and atmospheric patterns that affect precipitation and streamflow. The solar energy reaching the Earth's surface and its oceans is thought to be controlled through an interaction between TSI and GCRs, which are theorized to ionize the atmosphere and increase cloud formation and its resultant albedo. High (low) GCR flux may promote cloudiness (clear skies) and higher (lower) albedo at the same time that TSI is lowest (highest) in the solar cycle which in turn creates cooler (warmer) ocean temperature anomalies. These anomalies have been shown to affect atmospheric flow patterns and ultimately affect precipitation over the Midwestern United States. This investigation identified a relation among TSI and geomagnetic index aa (GI-AA), and streamflow in the Mississippi River Basin for the period 1878-2004. The GI-AA was used as a proxy for GCRs. The lag time between the solar signal and streamflow in the Mississippi River at St. Louis, Missouri is approximately 34 years. The current drought (1999-2007) in the Mississippi River Basin appears to be caused by a period of lower solar activity that occurred between 1963 and 1977. There appears to be a solar "fingerprint" that can be detected in climatic time series in other regions of the world, with each series having a unique lag time between the solar signal and the hydroclimatic response. A progression of increasing lag times can be spatially linked to the ocean conveyor belt, which may transport the solar signal over a time span of several decades. The lag times for any one region vary slightly and may be linked to the fluctuations in the velocity of the ocean conveyor belt.

Investigation of the spatial variability and possible origins of wind-induced air pressure fluctuations responsible for pressure pumping

NASA Astrophysics Data System (ADS)

Mohr, Manuel; Laemmel, Thomas; Maier, Martin; Zeeman, Matthias; Longdoz, Bernard; Schindler, Dirk

2017-04-01

The exchange of greenhouse gases between the soil and the atmosphere is highly relevant for the climate of the Earth. Recent research suggests that wind-induced air pressure fluctuations can alter the soil gas transport and therefore soil gas efflux significantly. Using a newly developed method, we measured soil gas transport in situ in a well aerated forest soil. Results from these measurements showed that the commonly used soil gas diffusion coefficient is enhanced up to 30% during periods of strong wind-induced air pressure fluctuations. The air pressure fluctuations above the forest floor are only induced at high above-canopy wind speeds (> 5 m s-1) and lie in the frequency range 0.01-0.1 Hz. Moreover, the amplitudes of air pressure fluctuations in this frequency range show a clear quadratic dependence on mean above-canopy wind speed. However, the origin of these wind-induced pressure fluctuations is still unclear. Airflow measurements and high-precision air pressure measurements were conducted at three different vegetation-covered sites (conifer forest, deciduous forest, grassland) to investigate the spatial variability of dominant air pressure fluctuations, their origin and vegetation-dependent characteristics. At the conifer forest site, a vertical profile of air pressure fluctuations was measured and an array consisting of five pressure sensors were installed at the forest floor. At the grassland site, the air pressure measurements were compared with wind observations made by ground-based LIDAR and spatial temperature observations from a fibre-optic sensing network (ScaleX Campaign 2016). Preliminary results show that at all sites the amplitudes of relevant air pressure fluctuations increase with increasing wind speed. Data from the array measurements reveal that there are no time lags between the air pressure signals of different heights, but a time lag existed between the air pressure signals of the sensors distributed laterally on the forest floor, suggesting a horizontal propagation of the air pressure waves.

Hydrogeological characterisation of groundwater over Brazil using remotely sensed and model products.

PubMed

Hu, Kexiang; Awange, Joseph L; Khandu; Forootan, Ehsan; Goncalves, Rodrigo Mikosz; Fleming, Kevin

2017-12-01

For Brazil, a country frequented by droughts and whose rural inhabitants largely depend on groundwater, reliance on isotope for its monitoring, though accurate, is expensive and limited in spatial coverage. We exploit total water storage (TWS) derived from Gravity Recovery and Climate Experiment (GRACE) satellites to analyse spatial-temporal groundwater changes in relation to geological characteristics. Large-scale groundwater changes are estimated using GRACE-derived TWS and altimetry observations in addition to GLDAS and WGHM model outputs. Additionally, TRMM precipitation data are used to infer impacts of climate variability on groundwater fluctuations. The results indicate that climate variability mainly controls groundwater change trends while geological properties control change rates, spatial distribution, and storage capacity. Granular rocks in the Amazon and Guarani aquifers are found to influence larger storage capability, higher permeability (>10 -4 m/s) and faster response to rainfall (1 to 3months' lag) compared to fractured rocks (permeability <10 -7 m/s and lags > 3months) found only in Bambui aquifer. Groundwater in the Amazon region is found to rely not only on precipitation but also on inflow from other regions. Areas beyond the northern and southern Amazon basin depict a 'dam-like' pattern, with high inflow and slow outflow rates (recharge slope > 0.75, discharge slope < 0.45). This is due to two impermeable rock layer-like 'walls' (permeability <10 -8 m/s) along the northern and southern Alter do Chão aquifer that help retain groundwater. The largest groundwater storage capacity in Brazil is the Amazon aquifer (with annual amplitudes of > 30cm). Amazon's groundwater declined between 2002 and 2008 due to below normal precipitation (wet seasons lasted for about 36 to 47% of the time). The Guarani aquifer and adjacent coastline areas rank second in terms of storage capacity, while the northeast and southeast coastal regions indicate the smallest storage capacity due to lack of rainfall (annual average is rainfall <10cm). Copyright © 2017 Elsevier B.V. All rights reserved.

Classification and its scale analysis of Severe Haze recently observed in Korea

NASA Astrophysics Data System (ADS)

Lee, K. M.; Eun, S. H.; Kim, B. G.; Kim, S. W.; Park, J. S.

2015-12-01

Cloud-aerosol-precipitation interactions mechanism is heavily dependent upon scale problems, and thus the first thing to understand its mechanism is to quantify the time (or spatial) scale of forcing driver, aerosols. This study is focused on recently occurring dense haze episodes accompanied with severe visibility impairment from 2011 to 2013 at two adjacent monitoring stations (Baengnyeongdo and Seoul) in Korea. Baengnyeongdo is an island being located 200 km west from Seoul. First of all, we have tested various flow charts to classify the various categories of heavy haze events by making use of aerosol scattering coefficient, PM2.5, and time lag difference of PM2.5 increase time at both stations, backward trajectories, and the ratio of PM2.5 to PM10 specifically in the quantitative perspective. One of them is selected for this study. Long range transported haze (LH) and Yellow Sand (YS) show very distinctive time lags of both PM2.5 and PM10 between both stations, but much higher ratio of PM2.5 to PM10 for LH in comparison with YS. Meanwhile urban haze (UH) has a significant increase in PM2.5 only at Seoul as easily expected. Time scales (e-folding time) of aerosol light scattering coefficients for LH and UH are 6-12 hrs and 7-16 hrs, respectively calculated for several episodes according to the criteria developed, which eventually corresponds to spatial scale of 120 - 240 km, 140 - 320 km, respectively by assuming average boundary wind speed, 5.6 m/s (Anderson et al., 2003). In general, long-range transported hazes have larger temporal and spatial dimension (about meso-a scale) than domestic hazes, after carefully designed classification of haze episodes. These results can be an useful basis for the estimation of regional aerosol radiative forcings in East Asia.

Human onchocerciasis in the Amazonian area of southern Venezuela: spatial and temporal variations in biting and parity rates of black fly (Diptera: Simuliidae) vectors.

PubMed

Grillet, M E; Basáñez, M G; Vivas-Martínez, S; Villamizar, N; Frontado, H; Cortez, J; Coronel, P; Botto, C

2001-07-01

We investigated some entomological factors underlying altitudinal prevalence variation in the Venezuelan Amazonia human onchocerciasis focus. Spatial and temporal variation in relative abundance, daily biting rate, proportion of parous flies, and monthly parous biting rate were studied for the three main simuliid vectors (based on their vectorial competence: Simulium oyapockense s.l. Floch & Abonnenc approximately = S. incrustatum Lutz < S. guianense s.l. Wise). Yanomami villages were selected among sentinel communities of the ivermectin control program, representing hypo- to hyperendemicity conditions of infection. Spatial variation was explored via increasing village altitude on two river systems (A: Ocamo-Putaco and B: Orinoco-Orinoquito). Temporal variation was studied between 1995 and 1999 by sampling the biting population during dry and rainy mouths. Environmental variables included monthly rainfall and maximum river height. Simuliid species composition itself varied along the altitudinal and prevalence gradient. S. oyapockense s.l. prevailed below 150 m. Above this altitude and up to 240 m, S. incrustatum and S. guianense s.l. became more frequently and evenly collected along A but not along B, where S. incrustatum remained absent. The daily biting rate of S. oyapockense s.l. was higher during the dry season along A, whereas the converse took place along B. Daily biting rate of S. incrustatum was lowest during early rains. By contrast, the daily biting rate of S. guianense s.l. was highest during this period. There was a significant negative cross-correlation between proportion of parous of S. oyapockense s.l. and river height (2 and 3 mo lagged), whereas this variable (1 and 2 mo lagged) was positively correlated with the proportion of parous flies for S. incrustatum. Monthly parous biting rate values suggest that the months contributing most to onchocerciasis transmission in the area are likely to be the dry season and the transition periods between seasons.

Four spot laser anemometer and optical access techniques for turbine applications

NASA Astrophysics Data System (ADS)

Wernet, Mark P.

A time-of-flight anemometer (TOFA) system utilizing a spatial lead-lag filter for bipolar pulse generation has been constructed and tested. This system, called a four-spot laser anemometer, was specifically designed for use in high-speed, turbulent flows in the presence of walls or surfaces. The TOFA system uses elliptical spots to increase the flow acceptance angle to be comparable with that of a fringe-type anemometer. The tightly focused spots used in the four spot yield excellent flare light rejection capabilities. Good results have been obtained to 75 microns normal to a surface, with an f/2.5 collection lens. This system is being evaluated for use in a warm turbine facility. Results from both a particle-lag velocity experiment and boundary layer profiles will be discussed. In addition, an analysis of the use of curved windows in a turbine casing will be presented. Curved windows, matching the inner radius of the turbine casing, preserve the flow conditions, but introduce astigmatic aberrations. A correction optic was designed that virtually eliminates these astigmatic aberrations throughout the intrablade survey region for normal incidence.

Four spot laser anemometer and optical access techniques for turbine applications

NASA Astrophysics Data System (ADS)

Wernet, Mark P.

A time-of-flight anemometer (TOFA) system, utilizing a spatial lead-lag filter for bipolar pulse generation was constructed and tested. This system, called a Four Spot Laser Anemometer, was specifically designed for use in high speed, turbulent flows in the presence of walls or surfaces. The TOFA system uses elliptical spots to increase the flow acceptance angle to be comparable with that of a fringe type anemometer. The tightly focused spots used in the Four Spot yield excellent flare light rejection capabilities. Good results were obtained to 75 microns normal to a surface, with a f/2.5 collecting lens. This system is being evaluated for use in a warm turbine facility. Results from both a particle lag velocity experiment and boundary layer profiles will be discussed. In addition, an analysis of the use of curved windows in a turbine casing will be presented. Curved windows, matching the inner radius of the turbine casing, preserve the flow conditions, but introduce astigmatic aberrations. A correction optic was designed that virtually eliminates these astigmatic aberrations throughout the intrablade survey region for normal incidence.

Differential Variance Analysis: a direct method to quantify and visualize dynamic heterogeneities

NASA Astrophysics Data System (ADS)

Pastore, Raffaele; Pesce, Giuseppe; Caggioni, Marco

2017-03-01

Many amorphous materials show spatially heterogenous dynamics, as different regions of the same system relax at different rates. Such a signature, known as Dynamic Heterogeneity, has been crucial to understand the nature of the jamming transition in simple model systems and is currently considered very promising to characterize more complex fluids of industrial and biological relevance. Unfortunately, measurements of dynamic heterogeneities typically require sophisticated experimental set-ups and are performed by few specialized groups. It is now possible to quantitatively characterize the relaxation process and the emergence of dynamic heterogeneities using a straightforward method, here validated on video microscopy data of hard-sphere colloidal glasses. We call this method Differential Variance Analysis (DVA), since it focuses on the variance of the differential frames, obtained subtracting images at different time-lags. Moreover, direct visualization of dynamic heterogeneities naturally appears in the differential frames, when the time-lag is set to the one corresponding to the maximum dynamic susceptibility. This approach opens the way to effectively characterize and tailor a wide variety of soft materials, from complex formulated products to biological tissues.

Comb model for the anomalous diffusion with dual-phase-lag constitutive relation

NASA Astrophysics Data System (ADS)

Liu, Lin; Zheng, Liancun; Fan, Yu; Chen, Yanping; Liu, Fawang

2018-10-01

As a development of the Fick's model, the dual-phase-lag constitutive relationship with macroscopic and microscopic relaxation characteristics is introduced to describe the anomalous diffusion in comb model. The Dirac delta function in the formulated governing equation represents the special spatial structure of comb model that the horizontal current only exists on the x axis. Solutions are obtained by analytical method with Laplace transform and Fourier transform. The dependence of concentration field and mean square displacement on different parameters are presented and discussed. Results show that the macroscopic and microscopic relaxation parameters have opposite effects on the particle distribution and mean square displacement. Furthermore, four significant results with constant 1/2 are concluded, namely the product of the particle number and the mean square displacement on the x axis equals to 1/2, the exponent of mean square displacement is 1/2 at the special case τq= τP, an asymptotic form of mean square displacement (MSD∼t1/2 as t→0, ∞) is obtained as well at the short time behavior and the long time behavior.

Four spot laser anemometer and optical access techniques for turbine applications

NASA Technical Reports Server (NTRS)

Wernet, Mark P.

1987-01-01

A time-of-flight anemometer (TOFA) system utilizing a spatial lead-lag filter for bipolar pulse generation has been constructed and tested. This system, called a four-spot laser anemometer, was specifically designed for use in high-speed, turbulent flows in the presence of walls or surfaces. The TOFA system uses elliptical spots to increase the flow acceptance angle to be comparable with that of a fringe-type anemometer. The tightly focused spots used in the four spot yield excellent flare light rejection capabilities. Good results have been obtained to 75 microns normal to a surface, with an f/2.5 collection lens. This system is being evaluated for use in a warm turbine facility. Results from both a particle-lag velocity experiment and boundary layer profiles will be discussed. In addition, an analysis of the use of curved windows in a turbine casing will be presented. Curved windows, matching the inner radius of the turbine casing, preserve the flow conditions, but introduce astigmatic aberrations. A correction optic was designed that virtually eliminates these astigmatic aberrations throughout the intrablade survey region for normal incidence.

Four spot laser anemometer and optical access techniques for turbine applications

NASA Technical Reports Server (NTRS)

Wernet, Mark P.

1987-01-01

A time-of-flight anemometer (TOFA) system, utilizing a spatial lead-lag filter for bipolar pulse generation was constructed and tested. This system, called a Four Spot Laser Anemometer, was specifically designed for use in high speed, turbulent flows in the presence of walls or surfaces. The TOFA system uses elliptical spots to increase the flow acceptance angle to be comparable with that of a fringe type anemometer. The tightly focused spots used in the Four Spot yield excellent flare light rejection capabilities. Good results were obtained to 75 microns normal to a surface, with a f/2.5 collecting lens. This system is being evaluated for use in a warm turbine facility. Results from both a particle lag velocity experiment and boundary layer profiles will be discussed. In addition, an analysis of the use of curved windows in a turbine casing will be presented. Curved windows, matching the inner radius of the turbine casing, preserve the flow conditions, but introduce astigmatic aberrations. A correction optic was designed that virtually eliminates these astigmatic aberrations throughout the intrablade survey region for normal incidence.

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

Spatial analysis of lettuce downy mildew using geostatistics and geographic information systems.

PubMed

Wu, B M; van Bruggen, A H; Subbarao, K V; Pennings, G G

2001-02-01

ABSTRACT The epidemiology of lettuce downy mildew has been investigated extensively in coastal California. However, the spatial patterns of the disease and the distance that Bremia lactucae spores can be transported have not been determined. During 1995 to 1998, we conducted several field- and valley-scale surveys to determine spatial patterns of this disease in the Salinas valley. Geostatistical analyses of the survey data at both scales showed that the influence range of downy mildew incidence at one location on incidence at other locations was between 80 and 3,000 m. A linear relationship was detected between semivariance and lag distance at the field scale, although no single statistical model could fit the semi-variograms at the valley scale. Spatial interpolation by the inverse distance weighting method with a power of 2 resulted in plausible estimates of incidence throughout the valley. Cluster analysis in geographic information systems on the interpolated disease incidence from different dates demonstrated that the Salinas valley could be divided into two areas, north and south of Salinas City, with high and low disease pressure, respectively. Seasonal and spatial trends along the valley suggested that the distinction between the downy mildew conducive and nonconducive areas might be determined by environmental factors.

A Spatial Perspective of Droughts and Pluvials in the Tropics and their Relationships to ENSO in CMIP5 Model Simulations

NASA Astrophysics Data System (ADS)

Perez Arango, J. D.; Lintner, B. R.; Lyon, B.

2016-12-01

Although many aspects of the tropical response to ENSO are well-known, the spatial characteristics of the rainfall response to ENSO remain relatively unexplored. Moreover, in current generation climate models, the spatial signatures of the ENSO tropical teleconnection are more uncertain than other aspects of ENSO variability, such as the amplitude of rainfall anomalies. Following the approach of Lyon (2004) and Lyon and Barnston (2005), we analyze here integrated measures of the spatial extent of drought and pluvial conditions in the tropics and their relationship to ENSO in observations as well as simulations of Phase 5 of the Coupled Model Intercomparison Project (CMIP5) with prescribed SST forcing. We compute diagnostics including the model ensemble-means and standard deviations of moderate, intermediate, and severe droughts and pluvials and the lagged correlations with respect to ENSO-based SST indices like NINO3. Overall, in a tropics-wide sense, the models generally capture the areal extent of observed droughts and pluvials and their phasing with respect to ENSO. However, at more local scales, e.g., tropical South America, the simulated metrics agree less strongly with observations, underscoring the role of errors in the spatial patterns of ENSO-induced rainfall anomalies.

Spatial-Temporal Variations of Chlorophyll-a in the Adjacent Sea Area of the Yangtze River Estuary Influenced by Yangtze River Discharge.

PubMed

Wang, Ying; Jiang, Hong; Jin, Jiaxin; Zhang, Xiuying; Lu, Xuehe; Wang, Yueqi

2015-05-20

Carrying abundant nutrition, terrigenous freshwater has a great impact on the spatial and temporal heterogeneity of phytoplankton in coastal waters. The present study analyzed the spatial-temporal variations of Chlorophyll-a (Chl-a) concentration under the influence of discharge from the Yangtze River, based on remotely sensed Chl-a concentrations. The study area was initially zoned to quantitatively investigate the spatial variation patterns of Chl-a. Then, the temporal variation of Chl-a in each zone was simulated by a sinusoidal curve model. The results showed that in the inshore waters, the terrigenous discharge was the predominant driving force determining the pattern of Chl-a, which brings the risk of red tide disasters; while in the open sea areas, Chl-a was mainly affected by meteorological factors. Furthermore, a diversity of spatial and temporal variations of Chl-a existed based on the degree of influences from discharge. The diluted water extended from inshore to the east of Jeju Island. This process affected the Chl-a concentration flowing through the area, and had a potential impact on the marine environment. The Chl-a from September to November showed an obvious response to the discharge from July to September with a lag of 1 to 2 months.

Spatial-Temporal Variations of Chlorophyll-a in the Adjacent Sea Area of the Yangtze River Estuary Influenced by Yangtze River Discharge

PubMed Central

Wang, Ying; Jiang, Hong; Jin, Jiaxin; Zhang, Xiuying; Lu, Xuehe; Wang, Yueqi

2015-01-01

Carrying abundant nutrition, terrigenous freshwater has a great impact on the spatial and temporal heterogeneity of phytoplankton in coastal waters. The present study analyzed the spatial-temporal variations of Chlorophyll-a (Chl-a) concentration under the influence of discharge from the Yangtze River, based on remotely sensed Chl-a concentrations. The study area was initially zoned to quantitatively investigate the spatial variation patterns of Chl-a. Then, the temporal variation of Chl-a in each zone was simulated by a sinusoidal curve model. The results showed that in the inshore waters, the terrigenous discharge was the predominant driving force determining the pattern of Chl-a, which brings the risk of red tide disasters; while in the open sea areas, Chl-a was mainly affected by meteorological factors. Furthermore, a diversity of spatial and temporal variations of Chl-a existed based on the degree of influences from discharge. The diluted water extended from inshore to the east of Jeju Island. This process affected the Chl-a concentration flowing through the area, and had a potential impact on the marine environment. The Chl-a from September to November showed an obvious response to the discharge from July to September with a lag of 1 to 2 months. PMID:26006121

Neighborhood social capital and crime victimization: comparison of spatial regression analysis and hierarchical regression analysis.

PubMed

Takagi, Daisuke; Ikeda, Ken'ichi; Kawachi, Ichiro

2012-11-01

Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g., Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a spatial Durbin model with an inverse-distance weighting matrix that assigned each respondent a unique level of "exposure" to social capital based on all other residents' perceptions. The study is based on a postal questionnaire sent to 20-69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the spatial Durbin model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan. Copyright © 2012 Elsevier Ltd. All rights reserved.

Semiparametric regression during 2003–2007*

PubMed Central

Ruppert, David; Wand, M.P.; Carroll, Raymond J.

2010-01-01

Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application. PMID:20305800