Science.gov

Sample records for advanced regional prediction

  1. A Global and Regional Multi-scale Advanced Prediction System

    NASA Astrophysics Data System (ADS)

    Chen, D.; Xue, J.; Yang, X.; Zhang, H.; Liu, J.; Jin, Z.; Huang, L.; Wu, X.

    With social progress and economic development, the requirement for providing much longer, more detailed and more accurate meteorological forecasting services with higher resolution, including climate, synoptic and meso-scale weather forecasts, and air pollution as well as forest fire warning is increased significantly. On the other hand, to meet all needs of services, the numerical weather prediction models will be- come more and more complicated, and more and more ?huge?. The costs for improve- ment and maintenance will be expensive if several NWP systems are to be developed, improved and maintained at the same time and at the same center! In this paper, a Global and Regional multi-scale Advanced Prediction model System (GRAPS) was designed to meet all needs of short, medium and long range weather forecasts as well as environmental predictions. The main features of the GRAPS model include (1) full latitude-longitude grid points; (2) multi-scale in an unified model; (3) hydrostatic or non hydrostatic hypotheses optionally(4) variable or uniform resolution in option (5) possibility to run in regional or global mode(6) finite difference in the vertical dis- cretization in option (7) semi-implicit and semi-Lagrangian scheme; (8) height terrain- following coordinate; (9) Arakawa-C variable staggering; (10) Cascade-interpolation; (11) quasi-conservation of semi-Lagrangian advection scheme combined Staniforth (1992) and Preistley (1993).

  2. Drought Prediction Site Specific and Regional up to Three Years in Advance

    NASA Astrophysics Data System (ADS)

    Suhler, G.; O'Brien, D. P.

    2002-12-01

    Dynamic Predictables has developed proprietary software that analyzes and predicts future climatic behavior based on past data. The programs employ both a regional thermodynamic model together with a unique predictive algorithm to achieve a high degree of prediction accuracy up to 36 months. The thermodynamic model was developed initially to explain the results of a study on global circulation models done at SUNY-Stony Brook by S. Hameed, R.G. Currie, and H. LaGrone (Int. Jour. Climatology, 15, pp.852-871, 1995). The authors pointed out that on a time scale of 2-70 months the spectrum of sea level pressure is dominated by the harmonics and subharmonics of the seasonal cycle and their combination tones. These oscillations are fundamental to an understanding of climatic variations on a sub-regional to continental basis. The oscillatory nature of these variations allows them to be used as broad based climate predictors. In addition, they can be subtracted from the data to yield residuals. The residuals are then analyzed to determine components that are predictable. The program then combines both the thermodynamic model results (the primary predictive model) with those from the residual data (the secondary model) to yield an estimate of the future behavior of the climatic variable. Spatial resolution is site specific or aggregated regional based upon appropriate length (45 years or more monthly data) and reasonable quality weather observation records. Most climate analysis has been based on monthly time-step data, but time scales on the order of days can be used. Oregon Climate Division 1 (Coastal) precipitation provides an example relating DynaPred's method to nature's observed elements in the early 2000s. The prediction's leading dynamic factors are the strong seasonal in the primary model combined with high secondary model contributions from planet Earth's Chandler Wobble (near 15 months) and what has been called the Quasi-Triennial Oscillation (QTO, near 36 months

  3. A computational framework to advance hydrometeorological prediction capabilities in cold regions

    NASA Astrophysics Data System (ADS)

    Clark, M. P.; Kavetski, D.; Slater, A. G.; Lundquist, J. D.; Wood, A. W.; Gochis, D. J.; Gutmann, E. D.; Rasmussen, R.

    2012-12-01

    Many different modeling groups recognize the need for new computational frameworks for use as both (i) a model development tool to evaluate competing process representations; and (ii) a predictive tool to reliably represent model uncertainty. Here we describe a computational framework to explore different approaches for modeling the hydrology and thermodynamics of snow and partially frozen soils. The framework has two main features: it has a "numerically agile" structural core to support evaluating the impact of different numerical approximations (e.g., vertical discretization, linearizations, etc.), and it has the modularity to support experimenting with different constitutive functions and boundary conditions. The broad flexibility of the framework facilitates constructing multiple equally plausible model realizations - these realizations can be used either as ensembles to represent model uncertainty, or examined in a systematic way to isolate the impact of individual model components on model predictions and hence facilitate a controlled approach to hypothesis testing. Application of the framework in different snow environments emphasizes the impact of (and interactions among) different modeling decisions. The approaches used to parameterize turbulent heat fluxes, parameters controlling the storage of liquid water in the snowpack, and the lower boundary conditions for hydrology were especially important in the case studies examined. More generally, results show that the impacts of differences in model structure are often overwhelmed by uncertainty in a-priori estimates of model parameters, and suggest that careful specification of probability distributions of model parameters can be used to represent model uncertainty.

  4. COLLABORATIVE RESEARCH: TOWARDS ADVANCED UNDERSTANDING AND PREDICTIVE CAPABILITY OF CLIMATE CHANGE IN THE ARCTIC USING A HIGH-RESOLUTION REGIONAL ARCTIC CLIMATE SYSTEM MODEL

    SciTech Connect

    Gutowski, William J.

    2013-02-07

    The motivation for this project was to advance the science of climate change and prediction in the Arctic region. Its primary goals were to (i) develop a state-of-the-art Regional Arctic Climate system Model (RACM) including high-resolution atmosphere, land, ocean, sea ice and land hydrology components and (ii) to perform extended numerical experiments using high performance computers to minimize uncertainties and fundamentally improve current predictions of climate change in the northern polar regions. These goals were realized first through evaluation studies of climate system components via one-way coupling experiments. Simulations were then used to examine the effects of advancements in climate component systems on their representation of main physics, time-mean fields and to understand variability signals at scales over many years. As such this research directly addressed some of the major science objectives of the BER Climate Change Research Division (CCRD) regarding the advancement of long-term climate prediction.

  5. Requirements for an Advanced Low Earth Orbit (LEO) Sounder (ALS) for improved regional weather prediction and monitoring of greenhouse gases

    NASA Astrophysics Data System (ADS)

    Pagano, Thomas S.; Chahine, Moustafa T.; Susskind, Joel

    2008-12-01

    Hyperspectral infrared atmospheric sounders (e.g. the Atmospheric Infrared Sounder (AIRS) on Aqua and the Infrared Atmospheric Sounding Interferometer (IASI) on MetOp) provide highly accurate temperature and water vapor profiles in the lower to upper troposphere. These systems are vital operational components of our National Weather Prediction system and the AIRS has demonstrated over 6 hrs of forecast improvement on the 5 day operational forecast1. Despite the success in the mid troposphere to lower stratosphere, a reduction in sensitivity and accuracy has been seen in these systems in the boundary layer over land. In this paper we demonstrate the potential improvement associated with higher spatial resolution (1km vs currently 13.5 km) on the accuracy of boundary layer products with an added consequence of higher yield of cloud free scenes. This latter feature is related to the number of samples that can be assimilated and has also shown to have a significant impact on improving forecast accuracy. We also present a set of frequencies and resolutions that will improve vertical resolution of temperature and water vapor and trace gas species throughout the atmosphere. Development of an Advanced Low Earth Orbit (LEO) Sounder (ALS) with these improvements will improve weather forecast at the regional scale and of tropical storms and hurricanes. Improvements are also expected in the accuracy of the water vapor and cloud properties products, enhancing process studies and providing a better match to the resolution of future climate models. The improvements of technology required for the ALS are consistent with the current state of technology as demonstrated in NASA Instrument Incubator Program and NOAA's Hyperspectral Environmental Suite (HES) formulation phase development programs.

  6. Requirements for an Advanced Low Earth Orbit (LEO) Sounder (ALS) for Improved Regional Weather Prediction and Monitoring of Greenhouse Gases

    NASA Technical Reports Server (NTRS)

    Pagano, Thomas S.; Chahine, Moustafa T.; Susskind, Joel

    2008-01-01

    Hyperspectral infrared atmospheric sounders (e.g., the Atmospheric Infrared Sounder (AIRS) on Aqua and the Infrared Atmospheric Sounding Interferometer (IASI) on Met Op) provide highly accurate temperature and water vapor profiles in the lower to upper troposphere. These systems are vital operational components of our National Weather Prediction system and the AIRS has demonstrated over 6 hrs of forecast improvement on the 5 day operational forecast. Despite the success in the mid troposphere to lower stratosphere, a reduction in sensitivity and accuracy has been seen in these systems in the boundary layer over land. In this paper we demonstrate the potential improvement associated with higher spatial resolution (1 km vs currently 13.5 km) on the accuracy of boundary layer products with an added consequence of higher yield of cloud free scenes. This latter feature is related to the number of samples that can be assimilated and has also shown to have a significant impact on improving forecast accuracy. We also present a set of frequencies and resolutions that will improve vertical resolution of temperature and water vapor and trace gas species throughout the atmosphere. Development of an Advanced Low Earth Orbit (LEO) Sounder (ALS) with these improvements will improve weather forecast at the regional scale and of tropical storms and hurricanes. Improvements are also expected in the accuracy of the water vapor and cloud properties products, enhancing process studies and providing a better match to the resolution of future climate models. The improvements of technology required for the ALS are consistent with the current state of technology as demonstrated in NASA Instrument Incubator Program and NOAA's Hyperspectral Environmental Suite (HES) formulation phase development programs.

  7. Predicting Epileptic Seizures in Advance

    PubMed Central

    Moghim, Negin; Corne, David W.

    2014-01-01

    Epilepsy is the second most common neurological disorder, affecting 0.6–0.8% of the world's population. In this neurological disorder, abnormal activity of the brain causes seizures, the nature of which tend to be sudden. Antiepileptic Drugs (AEDs) are used as long-term therapeutic solutions that control the condition. Of those treated with AEDs, 35% become resistant to medication. The unpredictable nature of seizures poses risks for the individual with epilepsy. It is clearly desirable to find more effective ways of preventing seizures for such patients. The automatic detection of oncoming seizures, before their actual onset, can facilitate timely intervention and hence minimize these risks. In addition, advance prediction of seizures can enrich our understanding of the epileptic brain. In this study, drawing on the body of work behind automatic seizure detection and prediction from digitised Invasive Electroencephalography (EEG) data, a prediction algorithm, ASPPR (Advance Seizure Prediction via Pre-ictal Relabeling), is described. ASPPR facilitates the learning of predictive models targeted at recognizing patterns in EEG activity that are in a specific time window in advance of a seizure. It then exploits advanced machine learning coupled with the design and selection of appropriate features from EEG signals. Results, from evaluating ASPPR independently on 21 different patients, suggest that seizures for many patients can be predicted up to 20 minutes in advance of their onset. Compared to benchmark performance represented by a mean S1-Score (harmonic mean of Sensitivity and Specificity) of 90.6% for predicting seizure onset between 0 and 5 minutes in advance, ASPPR achieves mean S1-Scores of: 96.30% for prediction between 1 and 6 minutes in advance, 96.13% for prediction between 8 and 13 minutes in advance, 94.5% for prediction between 14 and 19 minutes in advance, and 94.2% for prediction between 20 and 25 minutes in advance. PMID:24911316

  8. The GEO Water Strategy: Advances in Monitoring, Modeling, and Predicting Groundwater Variations at Regional to Local Scales

    NASA Astrophysics Data System (ADS)

    Miller, N. L.; Heinrich, L.; Kukuri, N.; Plag, H.; Famiglietti, J. S.; Rodell, M.

    2012-12-01

    Groundwater remains one of the most important freshwater resources, especially during droughts and as global warming increases. For informed decisions on managing these resources sustainably, it is important to have sound assessments of the current state of groundwater resources as well as future predictions. This requires reliable groundwater quantity and quality data. However global access to this data is limited. As part of the GEOSS Water Strategy, the International Groundwater Assessment Centre (IGRAC) is therefore implementing the Global Groundwater Monitoring Network (GGMN). The GGMN facilitates periodic assessments of changes in groundwater quantity and quality by aggregating data and information from existing groundwater monitoring networks and regional hydrogeological knowledge (Fig. 1). The GGMN is a participatory process that relies upon contributions from regional and national networks of groundwater experts. Such observation data, along with local well data, surface displacements observed by and GPS data and InSAR, and local in situ gravity data, are necessary for evaluation and simulation of groundwater, leading to improved understanding and prediction of groundwater variations. In conjunction with these observations, regional scale groundwater variations are derived as a residual from land surface-groundwater models through extraction of the total mass of water using geo-rectified Gravity Recovery and Climate Experiment (GRACE) data. Such model-based studies have quantified overdraft and regions at risk of groundwater depletion in parts of Asia, US, and Africa (Fig. 2).We provide an overview of these systems, planned missions, and new model-based approaches toward local-scale methods for assimilation of well data for several regions.igure 1. Example of GGMN (Example of Botswana with fictitious data, with local precipitation map) igure 2. GRACE-derived groundwater storage in northwestern India for 2002 - 2008, relative to the mean. Deviations from

  9. Advanced Regional and Decadal Predictions of Coastal Inundation for the U.S. Atlantic and Gulf Coasts

    NASA Astrophysics Data System (ADS)

    Horton, B. P.; Donnelly, J. P.; Corbett, D. R.; Kemp, A.; Lindeman, K.; Mann, M. E.; Peltier, W. R.; Rahmstorf, S.

    2012-12-01

    Future inundation of the US Atlantic and Gulf coasts will depend upon both sea-level rise and the intensity and frequency of tropical cyclones, each of which will be affected by climate change. In this proposal, we will employ new interdisciplinary approaches to bring about a step change in the reliability of predictions of such inundation. The rate of sea-level rise along the US Atlantic and Gulf coasts has increased throughout the 20th century. Whilst there is widespread agreement that it continue to accelerate during the 21st century, great uncertainty surrounds its magnitude and geographic distribution. Key uncertainties include the role of continental ice sheets, mountain glaciers and ocean density changes. Insufficient understanding of these complex physical processes precludes accurate prediction of sea-level rise. New approaches using semi-empirical models that relate instrumental records of climate and sea-level rise have projected up to 2 m of sea-level rise by AD 2100. But the time span of instrumental sea-level records is insufficient to adequately constrain the climate:sea-level relationship. Here, we produce new high resolution proxy data of sea-level and temperature to provide crucial additional constraints to such semi-empirical models. Our dataset will span the alternation between the "Medieval Climate Anomaly" and "Little Ice Age". Before the models can provide appropriate data for coastal management and planning, they must be complemented with regional estimates of sea-level rise. Therefore, the proxy sea-level data has been collected from six study areas (Massachusetts, New Jersey, North Carolina, Georgia and Atlantic and Gulf coasts of Florida) to accommodate the required extent of regional variability. In the case of inundation arising from tropical cyclones, the historical and observational records are insufficient for predicting their nature and recurrence, because they are such extreme and rare events. Moreover, in the future, the resultant

  10. Advanced Regional and Decadal Predictions of Coastal Inundation for the U.S. Atlantic and Gulf Coasts (Invited)

    NASA Astrophysics Data System (ADS)

    Horton, B.; Corbett, D. R.; Donnelly, J. P.; Kemp, A.; Lin, N.; Lindeman, K.; Mann, M. E.; Peltier, W. R.; Rahmstorf, S.

    2013-12-01

    Future inundation of the U.S. Atlantic and Gulf coasts will depend upon sea-level rise and the intensity and frequency of tropical cyclones, each of which will be affected by climate change. Through ongoing, collaborative research we are employing new interdisciplinary approaches to bring about a step change in the reliability of predictions of such inundation. The rate of sea level rise along the U.S. Atlantic and Gulf coasts increased throughout the 20th century. Whilst there is widespread agreement that it continue to accelerate during the 21st century, great uncertainty surrounds its magnitude and geographic variability. Key uncertainties include the role of continental ice sheets, mountain glaciers, and ocean density changes. Insufficient understanding of these complex physical processes precludes accurate prediction of sea-level rise. New approaches using semi-empirical models that relate instrumental records of climate and sea-level rise have projected up to 2 m of sea-level rise by AD 2100. But the time span of instrumental sea-level records is insufficient to adequately constrain the climate:sea-level relationship. We produced new, high-resolution proxy sea-level reconstructions to provide crucial additional constraints to such semi-empirical models. Our dataset spans the alternation between the 'Medieval Climate Anomaly' and 'Little Ice Age'. Before the models can provide appropriate data for coastal management and planning, they must be complemented with regional estimates of sea-level rise. Therefore, the proxy sea-level data has been collected from four study areas (Connecticut, New Jersey, North Carolina and Florida) to accommodate the required extent of regional variability. In the case of inundation arising from tropical cyclones, the historical and observational records are insufficient for predicting their nature and recurrence, because they are such extreme and rare events. Moreover, future storm surges will be superimposed on background sea

  11. A Global and Regional Multi-scale Advanced Prediction Model System(graps) Part I: A Scientific Design of A Nh/h Multi-scale Dynamic Model

    NASA Astrophysics Data System (ADS)

    Chen, Dehui; Xue Xuesheng Yang, Jishan; Zhang, Hongliang; Hu, Jianglin; Jin, Zhiyan; Huang, Liping; Wu, Xiangjun

    With social progress and economic development, the requirement for providing much longer, more detailed and more accurate meteorological forecasting services with higher resolution, including climate, synoptic and meso-scale weather forecasts, and air pollution as well as forest fire warning is increased significantly. On the other hand, to meet all needs of services, the numerical weather prediction models will become more and more complicated, and more and more "huge". The costs for improvement and maintenance will be expensive if several NWP systems are to be developed, improved and maintained at the same time and at the same center! In this paper, a Global and Regional multi-scale Advanced Prediction model System (GRAPS) was designed to meet all needs of short, medium and long range weather forecasts as well as environmental predictions. The main features of the GRAPS model include (1) full latitude-longitude grid points; (2) multi-scale in an unified model; (3) hydrostatic or non hydrostatic hypotheses optionally; (4) variable or uniform resolution in option; (5) possibility to run in regional or global mode; (6) finite difference in the vertical discretization in option; (7) semi-implicit and semi - Lagrangian scheme; (8) height terrain-following coordinate; (9) Arakawa-C variable staggering; (10) Cascade-interpolation; (11) quasi-conservation of semi-Lagrangian advection scheme combined Staniforth (1992) and Preistley (1993). Key Words: numerical weather prediction, grid point model, non-hydrostatic, variable resolution, vertical spectral formulation

  12. Regional Arctic System Model (RASM): A Tool to Address the U.S. Priorities and Advance Capabilities for Arctic Climate Modeling and Prediction

    NASA Astrophysics Data System (ADS)

    Maslowski, W.; Roberts, A.; Cassano, J. J.; Gutowski, W. J., Jr.; Nijssen, B.; Osinski, R.; Zeng, X.; Brunke, M.; Duvivier, A.; Hamman, J.; Hossainzadeh, S.; Hughes, M.; Seefeldt, M. W.

    2015-12-01

    The Arctic is undergoing some of the most coordinated rapid climatic changes currently occurring anywhere on Earth, including the retreat of the perennial sea ice cover, which integrates forcing by, exchanges with and feedbacks between atmosphere, ocean and land. While historical reconstructions from Earth System Models (ESMs) are in broad agreement with these changes, the rate of change in ESMs generally remains outpaced by observations. Reasons for that relate to a combination of coarse resolution, inadequate parameterizations, under-represented processes and a limited knowledge of physical interactions. We demonstrate the capability of the Regional Arctic System Model (RASM) in addressing some of the ESM limitations in simulating observed variability and trends in arctic surface climate. RASM is a high resolution, pan-Arctic coupled climate model with the sea ice and ocean model components configured at an eddy-permitting resolution of 1/12o and the atmosphere and land hydrology model components at 50 km resolution, which are all coupled at 20-minute intervals. RASM is an example of limited-area, process-resolving, fully coupled ESM, which due to the constraints from boundary conditions facilitates detailed comparisons with observational statistics that are not possible with ESMs. The overall goal of RASM is to address key requirements published in the Navy Arctic Roadmap: 2014-2030 and in the Implementation Plan for the National Strategy for the Arctic Region, regarding the need for advanced modeling capabilities for operational forecasting and strategic climate predictions through 2030. The main science objectives of RASM are to advance understanding and model representation of critical physical processes and feedbacks of importance to sea ice thickness and area distribution. RASM results are presented to quantify relative contributions by (i) resolved processes and feedbacks as well as (ii) sensitivity to space dependent sub-grid parameterizations to better

  13. Regional Arctic System Model (RASM): A Tool to Advance Understanding and Prediction of Arctic Climate Change at Process Scales

    NASA Astrophysics Data System (ADS)

    Maslowski, W.; Roberts, A.; Osinski, R.; Brunke, M.; Cassano, J. J.; Clement Kinney, J. L.; Craig, A.; Duvivier, A.; Fisel, B. J.; Gutowski, W. J., Jr.; Hamman, J.; Hughes, M.; Nijssen, B.; Zeng, X.

    2014-12-01

    The Arctic is undergoing rapid climatic changes, which are some of the most coordinated changes currently occurring anywhere on Earth. They are exemplified by the retreat of the perennial sea ice cover, which integrates forcing by, exchanges with and feedbacks between atmosphere, ocean and land. While historical reconstructions from Global Climate and Global Earth System Models (GC/ESMs) are in broad agreement with these changes, the rate of change in the GC/ESMs remains outpaced by observations. Reasons for that stem from a combination of coarse model resolution, inadequate parameterizations, unrepresented processes and a limited knowledge of physical and other real world interactions. We demonstrate the capability of the Regional Arctic System Model (RASM) in addressing some of the GC/ESM limitations in simulating observed seasonal to decadal variability and trends in the sea ice cover and climate. RASM is a high resolution, fully coupled, pan-Arctic climate model that uses the Community Earth System Model (CESM) framework. It uses the Los Alamos Sea Ice Model (CICE) and Parallel Ocean Program (POP) configured at an eddy-permitting resolution of 1/12° as well as the Weather Research and Forecasting (WRF) and Variable Infiltration Capacity (VIC) models at 50 km resolution. All RASM components are coupled via the CESM flux coupler (CPL7) at 20-minute intervals. RASM is an example of limited-area, process-resolving, fully coupled earth system model, which due to the additional constraints from lateral boundary conditions and nudging within a regional model domain facilitates detailed comparisons with observational statistics that are not possible with GC/ESMs. In this talk, we will emphasize the utility of RASM to understand sensitivity to variable parameter space, importance of critical processes, coupled feedbacks and ultimately to reduce uncertainty in arctic climate change projections.

  14. Regional downscaling of decadal predictions

    NASA Astrophysics Data System (ADS)

    Feldmann, H.

    2014-12-01

    During the last years the research field of decadal predictions gained increased attention. Its intention is to exploit the predictability derived from slowly varying components of the climate system on inter-annual to decadal time-scales. Such predictions are mostly performed using ensembles of global earth system models. The prediction systems are able to achieve a relatively high predictive skill over some oceanic regions, like the North Atlantic sector. But potential users of decadal predictions are often interested in forecasts over land areas and require a higher resolution, too. Therefore, the German research program MiKlip develops a decadal ensemble predictions system with regional downscaling as an additional option. Dynamical downscaling and a statistical-dynamical downscaling approach are applied within the MiKlip regionalization module. The global prediction system consists of the MPI-ESM model. Different RCMs are used for the downscaling, e.g. CCLM and REMO. The focus regions are Europe and Western Africa. Hindcast experiments for the period 1960 - 2013 were performed to assess the general skill of the prediction system. Of special interest is the value added by the regional downscaling. For mean quantities, like annual mean temperature and precipitation, the predictive skill is comparable between the global and the downscaled systems. For extremes on the other hand there seems to be an improvement by the RCM ensemble. The skill strongly varies on sub-continental regions and with the season. The lead time up to which a positive predictive skill can be achieved depends on the parameter and season, too. A further goal is to assess the potential for valuable information, which can be derived from predicting long-term variations of the European climate. The leading mode of decadal variability in the European/Atlantic sector is the Atlantic Multidecadal Variation (AMV). The potential predictability from AMV teleconnections especially for extreme value

  15. Advancing Drought Understanding, Monitoring and Prediction

    NASA Technical Reports Server (NTRS)

    Mariotti, Annarita; Schubert, Siegfried D.; Mo, Kingtse; Peters-Lidard, Christa; Wood, Andy; Pulwarty, Roger; Huang, Jin; Barrie, Dan

    2013-01-01

    Having the capacity to monitor droughts in near-real time and providing accurate drought prediction from weeks to seasons in advance can greatly reduce the severity of social and economic damage caused by drought, a leading natural hazard for North America. The congressional mandate to establish the National Integrated Drought Information System (NIDIS; Public Law 109-430) in 2006 was a major impulse to develop, integrate, and provide drought information to meet the challenges posed by this hazard. Significant progress has been made on many fronts. On the research front, efforts by the broad scientific community have resulted in improved understanding of North American droughts and improved monitoring and forecasting tools. We now have a better understanding of the droughts of the twentieth century including the 1930s "Dust Bowl"; we have developed a broader array of tools and datasets that enhance the official North American Drought Monitor based on different methodologies such as state-of-the-art land surface modeling (e.g., the North American Land Data Assimilation System) and remote sensing (e.g., the evaporative stress index) to better characterize the occurrence and severity of drought in its multiple manifestations. In addition, we have new tools for drought prediction [including the new National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2, for operational prediction and an experimental National Multimodel Ensemble] and have explored diverse methodologies including ensemble hydrologic prediction approaches. Broad NIDIS-inspired progress is influencing the development of a Global Drought Information System (GDIS) under the auspices of the World Climate Research Program. Despite these advances, current drought monitoring and forecasting capabilities still fall short of users' needs, especially the need for skillful and reliable drought forecasts at regional and local scales. To tackle this outstanding challenging problem

  16. Advances in predicting acute GVHD

    PubMed Central

    Harris, Andrew C.; Ferrara, James L.M.; Levine, John E.

    2012-01-01

    Summary Acute graft-versus-host disease (GVHD) is a leading cause of non-relapse mortality following allogeneic haematopoietic cell transplantation. Attempts to improve treatment response in clinically-established GVHD have not improved overall survival, often due to the increased risk of infectious complications. Alternative approaches to decrease GVHD-related morbidity and mortality have focused on the ability to predict GVHD prior to clinical manifestation in an effort to provide an opportunity to abort GVHD development, and to gain new insights into GVHD pathophysiology. This review outlines the research efforts to date that have identified clinical and laboratory-based factors that are predictive of acute GVHD and describes future directions in developing algorithms that will improve the ability to predict the development of clinically relevant GVHD. PMID:23205489

  17. Observation simulation experiments with regional prediction models

    NASA Technical Reports Server (NTRS)

    Diak, George; Perkey, Donald J.; Kalb, Michael; Robertson, Franklin R.; Jedlovec, Gary

    1990-01-01

    Research efforts in FY 1990 included studies employing regional scale numerical models as aids in evaluating potential contributions of specific satellite observing systems (current and future) to numerical prediction. One study involves Observing System Simulation Experiments (OSSEs) which mimic operational initialization/forecast cycles but incorporate simulated Advanced Microwave Sounding Unit (AMSU) radiances as input data. The objective of this and related studies is to anticipate the potential value of data from these satellite systems, and develop applications of remotely sensed data for the benefit of short range forecasts. Techniques are also being used that rely on numerical model-based synthetic satellite radiances to interpret the information content of various types of remotely sensed image and sounding products. With this approach, evolution of simulated channel radiance image features can be directly interpreted in terms of the atmospheric dynamical processes depicted by a model. Progress is being made in a study using the internal consistency of a regional prediction model to simplify the assessment of forced diabatic heating and moisture initialization in reducing model spinup times. Techniques for model initialization are being examined, with focus on implications for potential applications of remote microwave observations, including AMSU and Special Sensor Microwave Imager (SSM/I), in shortening model spinup time for regional prediction.

  18. Predicting Career Advancement with Structural Equation Modelling

    ERIC Educational Resources Information Center

    Heimler, Ronald; Rosenberg, Stuart; Morote, Elsa-Sofia

    2012-01-01

    Purpose: The purpose of this paper is to use the authors' prior findings concerning basic employability skills in order to determine which skills best predict career advancement potential. Design/methodology/approach: Utilizing survey responses of human resource managers, the employability skills showing the largest relationships to career…

  19. Predictive Dynamic Security Assessment through Advanced Computing

    SciTech Connect

    Huang, Zhenyu; Diao, Ruisheng; Jin, Shuangshuang; Chen, Yousu

    2014-11-30

    Abstract— Traditional dynamic security assessment is limited by several factors and thus falls short in providing real-time information to be predictive for power system operation. These factors include the steady-state assumption of current operating points, static transfer limits, and low computational speed. This addresses these factors and frames predictive dynamic security assessment. The primary objective of predictive dynamic security assessment is to enhance the functionality and computational process of dynamic security assessment through the use of high-speed phasor measurements and the application of advanced computing technologies for faster-than-real-time simulation. This paper presents algorithms, computing platforms, and simulation frameworks that constitute the predictive dynamic security assessment capability. Examples of phasor application and fast computation for dynamic security assessment are included to demonstrate the feasibility and speed enhancement for real-time applications.

  20. Collaborative Research: Towards Advanced Understanding and Predictive Capability of Climate Change in the Arctic using a High-Resolution Regional Arctic Climate System Model

    SciTech Connect

    Lettenmaier, Dennis P

    2013-04-08

    Primary activities are reported in these areas: climate system component studies via one-way coupling experiments; development of the Regional Arctic Climate System Model (RACM); and physical feedback studies focusing on changes in Arctic sea ice using the fully coupled model.

  1. Advanced technology for future regional transport aircraft

    NASA Technical Reports Server (NTRS)

    Williams, L. J.

    1982-01-01

    In connection with a request for a report coming from a U.S. Senate committee, NASA formed a Small Transport Aircraft Technology (STAT) team in 1978. STAT was to obtain information concerning the technical improvements in commuter aircraft that would likely increase their public acceptance. Another area of study was related to questions regarding the help which could be provided by NASA's aeronautical research and development program to commuter aircraft manufacturers with respect to the solution of technical problems. Attention is given to commuter airline growth, current commuter/region aircraft and new aircraft in development, prospects for advanced technology commuter/regional transports, and potential benefits of advanced technology. A list is provided of a number of particular advances appropriate to small transport aircraft, taking into account small gas turbine engine component technology, propeller technology, three-dimensional wing-design technology, airframe aerodynamics/propulsion integration, and composite structure materials.

  2. Advancements in predictive plasma formation modeling

    NASA Astrophysics Data System (ADS)

    Purvis, Michael A.; Schafgans, Alexander; Brown, Daniel J. W.; Fomenkov, Igor; Rafac, Rob; Brown, Josh; Tao, Yezheng; Rokitski, Slava; Abraham, Mathew; Vargas, Mike; Rich, Spencer; Taylor, Ted; Brandt, David; Pirati, Alberto; Fisher, Aaron; Scott, Howard; Koniges, Alice; Eder, David; Wilks, Scott; Link, Anthony; Langer, Steven

    2016-03-01

    We present highlights from plasma simulations performed in collaboration with Lawrence Livermore National Labs. This modeling is performed to advance the rate of learning about optimal EUV generation for laser produced plasmas and to provide insights where experimental results are not currently available. The goal is to identify key physical processes necessary for an accurate and predictive model capable of simulating a wide range of conditions. This modeling will help to drive source performance scaling in support of the EUV Lithography roadmap. The model simulates pre-pulse laser interaction with the tin droplet and follows the droplet expansion into the main pulse target zone. Next, the interaction of the expanded droplet with the main laser pulse is simulated. We demonstrate the predictive nature of the code and provide comparison with experimental results.

  3. Collaborative Research: Towards Advanced Understanding and Predictive Capability of Climate Change in the Arctic Using a High-Resolution Regional Arctic Climate Model

    SciTech Connect

    Cassano, John

    2013-06-30

    The primary research task completed for this project was the development of the Regional Arctic Climate Model (RACM). This involved coupling existing atmosphere, ocean, sea ice, and land models using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM) coupler (CPL7). RACM is based on the Weather Research and Forecasting (WRF) atmospheric model, the Parallel Ocean Program (POP) ocean model, the CICE sea ice model, and the Variable Infiltration Capacity (VIC) land model. A secondary research task for this project was testing and evaluation of WRF for climate-scale simulations on the large pan-Arctic model domain used in RACM. This involved identification of a preferred set of model physical parameterizations for use in our coupled RACM simulations and documenting any atmospheric biases present in RACM.

  4. Towards predictive understanding of regional climate change

    NASA Astrophysics Data System (ADS)

    Xie, Shang-Ping; Deser, Clara; Vecchi, Gabriel A.; Collins, Matthew; Delworth, Thomas L.; Hall, Alex; Hawkins, Ed; Johnson, Nathaniel C.; Cassou, Christophe; Giannini, Alessandra; Watanabe, Masahiro

    2015-10-01

    Regional information on climate change is urgently needed but often deemed unreliable. To achieve credible regional climate projections, it is essential to understand underlying physical processes, reduce model biases and evaluate their impact on projections, and adequately account for internal variability. In the tropics, where atmospheric internal variability is small compared with the forced change, advancing our understanding of the coupling between long-term changes in upper-ocean temperature and the atmospheric circulation will help most to narrow the uncertainty. In the extratropics, relatively large internal variability introduces substantial uncertainty, while exacerbating risks associated with extreme events. Large ensemble simulations are essential to estimate the probabilistic distribution of climate change on regional scales. Regional models inherit atmospheric circulation uncertainty from global models and do not automatically solve the problem of regional climate change. We conclude that the current priority is to understand and reduce uncertainties on scales greater than 100 km to aid assessments at finer scales.

  5. D region predictions. [effects on radio propagation

    NASA Technical Reports Server (NTRS)

    Thrane, E. V.; Chakrabarty, D. K.; Deshpande, S. D.; Doherty, R. H.; Gregory, J. B.; Hargreaves, J. K.; Lastovicka, J.; Morris, P.; Piggott, W. R.; Reagan, J. B.

    1979-01-01

    Present knowledge of D region phenomena is briefly reviewed and the status of current methods of predicting their effects on radio propagation considered. The ELF, VLF and LF navigational and timing systems depend on the stability of the lower part of the D layer where these waves are reflected, whereas MF and HF waves are absorbed as they penetrate the region, in most cases mainly in the upper part of the layer. Possible methods of improving predictions, warnings, and real time operations are considered with particular stress on those which can be implemented in the near future.

  6. Weather Prediction Improvement Using Advanced Satellite Technology

    NASA Technical Reports Server (NTRS)

    Einaudi, Franco; Uccellini, L.; Purdom, J.; Rogers, D.; Gelaro, R.; Dodge, J.; Atlas, R.; Lord, S.

    2001-01-01

    We discuss in this paper some of the problems that exist today in the fall utilization of satellite data to improve weather forecasts and we propose specific recommendations to solve them. This discussion can be viewed as an aspect of the general debate on how best to organize the transition from research to operational satellites and how to evaluate the impact of a research instrument on numerical weather predictions. A method for providing this transition is offered by the National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP). This mission will bridge the time between the present NOAA and Department of Defense (DOD) polar orbiting missions and the initiation of the converged NPOESS series and will evaluate some of the Earth Observing System (EOS) instruments as appropriate for operational missions. Thus, this mission can be viewed as an effort to meet the operational requirements of NOAA and DOD and the research requirements of NASA. More generally, however, it can be said that the process of going from the conception of new, more advanced instruments to their operational implementation and full utilization by the weather forecast communities is not optimal. Instruments developed for research purposes may have insufficient funding to explore their potential operational capabilities. Furthermore, instrument development programs designed for operational satellites typically have insufficient funding for assimilation algorithms needed to transform the satellite observations into data that can be used by sophisticated global weather forecast models. As a result, years often go by before satellite data are efficiently used for operational forecasts. NASA and NOAA each have unique expertise in the design of satellite instruments, their use for basic and applied research and their utilization in weather and climate research. At a time of limited resources, the two agencies must combine their efforts to work toward common

  7. Improved methodology for temperature predictions in advanced reactors

    SciTech Connect

    Ambrosek, R.G.; Chang, G.S.

    1995-10-01

    Advanced nuclear reactors maximize power and/or flux levels for increased performance levels. One of the challenges is accurate prediction of temperatures in the structural components and experiments. An improved methodology utilizing the computer codes MCNP and ABAQUS has been demonstrated in instrumented experiments at the Advanced Test Reactor. The analytical predictions have shown excellent agreement with the measured results.

  8. Regional price targets appropriate for advanced coal extraction

    NASA Technical Reports Server (NTRS)

    Terasawa, K. L.; Whipple, D. M.

    1980-01-01

    A methodology is presented for predicting coal prices in regional markets for the target time frames 1985 and 2000 that could subsequently be used to guide the development of an advanced coal extraction system. The model constructed is a supply and demand model that focuses on underground mining since the advanced technology is expected to be developed for these reserves by the target years. Coal reserve data and the cost of operating a mine are used to obtain the minimum acceptable selling price that would induce the producer to bring the mine into production. Based on this information, market supply curves can be generated. Demand by region is calculated based on an EEA methodology that emphasizes demand by electric utilities and demand by industry. The demand and supply curves are then used to obtain the price targets. The results show a growth in the size of the markets for compliance and low sulphur coal regions. A significant rise in the real price of coal is not expected even by the year 2000. The model predicts heavy reliance on mines with thick seams, larger block size and deep overburden.

  9. Recent Advances in Predictive (Machine) Learning

    SciTech Connect

    Friedman, J

    2004-01-24

    Prediction involves estimating the unknown value of an attribute of a system under study given the values of other measured attributes. In prediction (machine) learning the prediction rule is derived from data consisting of previously solved cases. Most methods for predictive learning were originated many years ago at the dawn of the computer age. Recently two new techniques have emerged that have revitalized the field. These are support vector machines and boosted decision trees. This paper provides an introduction to these two new methods tracing their respective ancestral roots to standard kernel methods and ordinary decision trees.

  10. Density prediction in Mars' aerobraking region

    NASA Astrophysics Data System (ADS)

    Moudden, Y.; Forbes, J. M.

    2015-01-01

    Aerobraking at Mars is significantly impacted by the density variability at altitudes between about 100 and 140 km. Density changes can be quite substantial from orbit to orbit and from day to day. Much of this variability arises from tides propagating upward from the lower atmosphere. In this paper we present first results from a method developed to predict density variability in Mars aerobraking region due to this source. It consists of employing physics-based tidal functions to fit tidal temperatures between 60 and 80 km inferred from Mars Climate Sounder measurements on Mars Reconnaissance Orbiter and using these functions to predict the density variations at aerobraking altitudes due to vertical propagation of the fitted tidal components. Validation against densities measured by the Mars Global Surveyor accelerometer suggest that these initial results capture salient features sufficiently well that users may want to incorporate them into operational models.

  11. Advanced technology wind shear prediction system evaluation

    NASA Technical Reports Server (NTRS)

    Gering, Greg

    1992-01-01

    The program overviews: (1) American Airline (AA)/Turbulence Prediction Systems (TPS), which have installed forward looking infrared predictive windshear system on 3 MD-80 aircraft; (2) AA/TPS AWAS III evaluation, which is a joint effort and is installed in the noise landing gear (NLG) area and a data recorder installed in the E/E compartment.

  12. Predicting success on the Advanced Placement Biology Examination

    NASA Astrophysics Data System (ADS)

    Shepherd, Lesa Hanlin

    Four hundred sixty students in four public high schools were used as subjects to determine which of eleven academic and demographic factors studied were significant predictors of success for the Advanced Placement Biology Examination. Factors studied were attendance, class rank, gender, grade level at the time of the examination, grade point average, level of prerequisite biology course, number of Advanced Placement Examinations taken in the year prior to the Advanced Placement Biology Examination, number of Advanced Placement Examinations taken in the same year as the Advanced Placement Biology Examination, proposed major in college, race, and SAT mathematics, verbal, and combined score. Significant relationships were found to exist between the Advanced Placement Biology Examination score and attendance, class rank, gender, grade level at the time of the Advanced Placement Biology Examination, grade point average, number of Advanced Placement Examinations taken in the year prior to the Advanced Placement Biology Examination, number of Advanced Placement Examinations taken in the same year as the Advanced Placement Biology Examination, race, and SAT scores. Significant relationships were not found to exist between Advanced Placement Biology Examination score and level prerequisite biology course and Advanced Placement Biology Examination score and proposed major in college. A multiple regression showed the best combination of predictors to be attendance, SAT verbal score, and SAT mathematics score. Discriminant analysis showed the variables in this study to be good predictors of whether the student would pass the Advanced Placement Biology Examination (score a 3, 4, or 5) or fail the Advanced Placement Biology Examination (score a 1 or 2). These results demonstrated that significant predictors for the Advanced Placement Biology Examination do exist and can be used to assist in the prediction of scores, prediction of passing or failing, the identification of

  13. Predictable nonlinear dynamics: Advances and limitations

    SciTech Connect

    Anosov, L.A.; Butkovskii, O.Y.; Kravtsov, Y.A.; Surovyatkina, E.D.

    1996-06-01

    Methods for reconstruction chaotic dynamical system structure directly from experimental time series are described. Effectiveness of general methods is illustrated with the results of numerical simulation. It is of common interest that from the single time series it is possible to reconstruct a set of interconnected variables. Predictive power of dynamical models, provided by the nonlinear dynamics inverse problem solution, is limited firstly by the noise level in the system under study and is characterized by the horizon of predictability. New physical results are presented, concerning nonstationary and bifurcation nonlinear systems: (1) algorithms for revealing of nonstationarity in random-like chaotic time-series are suggested based on discriminant analysis with nonlinear discriminant function; (2) an opportunity is established to predict the final state in bifurcation system with quickly varying control parameters; (3) hysteresis is founded out in bifurcation system with quickly varying parameters; (4) delayed correlation {l_angle}noise-prediction error{r_angle} in chaotic systems is revealed. {copyright} {ital 1996 American Institute of Physics.}

  14. Fast prediction unit selection method for HEVC intra prediction based on salient regions

    NASA Astrophysics Data System (ADS)

    Feng, Lei; Dai, Ming; Zhao, Chun-lei; Xiong, Jing-ying

    2016-07-01

    In order to reduce the computational complexity of the high efficiency video coding (HEVC) standard, a new algorithm for HEVC intra prediction, namely, fast prediction unit (PU) size selection method for HEVC based on salient regions is proposed in this paper. We first build a saliency map for each largest coding unit (LCU) to reduce its texture complexity. Secondly, the optimal PU size is determined via a scheme that implements an information entropy comparison among sub-blocks of saliency maps. Finally, we apply the partitioning result of saliency map on the original LCUs, obtaining the optimal partitioning result. Our algorithm can determine the PU size in advance to the angular prediction in intra coding, reducing computational complexity of HEVC. The experimental results show that our algorithm achieves a 37.9% reduction in encoding time, while producing a negligible loss in Bjontegaard delta bit rate ( BDBR) of 0.62%.

  15. Predicting Production Costs for Advanced Aerospace Vehicles

    NASA Technical Reports Server (NTRS)

    Bao, Han P.; Samareh, J. A.; Weston, R. P.

    2002-01-01

    For early design concepts, the conventional approach to cost is normally some kind of parametric weight-based cost model. There is now ample evidence that this approach can be misleading and inaccurate. By the nature of its development, a parametric cost model requires historical data and is valid only if the new design is analogous to those for which the model was derived. Advanced aerospace vehicles have no historical production data and are nowhere near the vehicles of the past. Using an existing weight-based cost model would only lead to errors and distortions of the true production cost. This paper outlines the development of a process-based cost model in which the physical elements of the vehicle are soared according to a first-order dynamics model. This theoretical cost model, first advocated by early work at MIT, has been expanded to cover the basic structures of an advanced aerospace vehicle. Elemental costs based on the geometry of the design can be summed up to provide an overall estimation of the total production cost for a design configuration. This capability to directly link any design configuration to realistic cost estimation is a key requirement for high payoff MDO problems. Another important consideration in this paper is the handling of part or product complexity. Here the concept of cost modulus is introduced to take into account variability due to different materials, sizes, shapes, precision of fabrication, and equipment requirements. The most important implication of the development of the proposed process-based cost model is that different design configurations can now be quickly related to their cost estimates in a seamless calculation process easily implemented on any spreadsheet tool.

  16. Predicting Malignancy in Thyroid Nodules: Molecular Advances

    PubMed Central

    Melck, Adrienne L.; Yip, Linwah

    2016-01-01

    Over the last several years, a clearer understanding of the genetic alterations underlying thyroid carcinogenesis has developed. This knowledge can be utilized to tackle one of the greatest challenges facing thyroidologists: management of the indeterminate thyroid nodule. Despite the accuracy of fine needle aspiration cytology, many patients undergo invasive surgery in order to determine if a follicular or Hurthle cell neoplasm is malignant, and better diagnostic tools are required. A number of biomarkers have recently been studied and show promise in this setting. In particular, BRAF, RAS, PAX8-PPARγ, microRNAs and loss of heterozygosity have each been demonstrated as useful molecular tools for predicting malignancy and can thereby guide decisions regarding surgical management of nodular thyroid disease. This review summarizes the current literature surrounding each of these markers and highlights our institution’s prospective analysis of these markers and their subsequent incorporation into our management algorithms for thyroid nodules. PMID:21818817

  17. A Primer In Advanced Fatigue Life Prediction Methods

    NASA Technical Reports Server (NTRS)

    Halford, Gary R.

    2000-01-01

    Metal fatigue has plagued structural components for centuries, and it remains a critical durability issue in today's aerospace hardware. This is true despite vastly improved and advanced materials, increased mechanistic understanding, and development of accurate structural analysis and advanced fatigue life prediction tools. Each advance is quickly taken advantage of to produce safer, more reliable more cost effective, and better performing products. In other words, as the envelop is expanded, components are then designed to operate just as close to the newly expanded envelop as they were to the initial one. The problem is perennial. The economic importance of addressing structural durability issues early in the design process is emphasized. Tradeoffs with performance, cost, and legislated restrictions are pointed out. Several aspects of structural durability of advanced systems, advanced materials and advanced fatigue life prediction methods are presented. Specific items include the basic elements of durability analysis, conventional designs, barriers to be overcome for advanced systems, high-temperature life prediction for both creep-fatigue and thermomechanical fatigue, mean stress effects, multiaxial stress-strain states, and cumulative fatigue damage accumulation assessment.

  18. Regional characteristics relevant to advanced technology cogeneration development. [industrial energy

    NASA Technical Reports Server (NTRS)

    Manvi, R.

    1981-01-01

    To assist DOE in establishing research and development funding priorities in the area of advanced energy conversion technoloy, researchers at the Jet Propulsion Laboratory studied those specific factors within various regions of the country that may influence cogeneration with advanced energy conversion systems. Regional characteristics of advanced technology cogeneration possibilities are discussed, with primary emphasis given to coal derived fuels. Factors considered for the study were regional industry concentration, purchased fuel and electricity prices, environmental constraints, and other data of interest to industrial cogeneration.

  19. Sensitivity analysis of artificial neural network (ANN) brightness temperature predictions over snow-covered regions in North America using the Advanced Microwave Sounding Radiometer (AMSR-E) from 2002 to 2011

    NASA Astrophysics Data System (ADS)

    Xue, Y.; Forman, B. A.

    2013-12-01

    Snow is a significant contributor to the earth's hydrologic cycle, energy cycle, and climate system. Further, up to 80% of freshwater supply in the western United States originates as snow (and ice). Characterization of the mass of snow, or snow water equivalent (SWE), across regional and continental scales has commonly been conducted using satellite-based passive microwave (PMW) brightness temperatures (Tb) within a SWE retrieval algorithm. However, SWE retrievals often suffer from deficiencies related to deep snow, wet snow, snow evolution, snow aging, overlying vegetation, surface and internal ice lenses, depth hoar, and sub-grid scale lakes. As an alternative to SWE retrievals, this study explores the potential for using PMW Tb and machine learning within a data assimilation framework. An artificial neural network (ANN) is presented for eventual use as an observation operator to map the land surface model states into Tb space. This study explores the sensitivity of an ANN as a computationally efficient measurement model operator for the prediction of PMW Tb across North America. The analysis employs normalized sensitivity coefficients and a one-at-a-time approach such that each of the 11 different inputs could be examined separately in order to quantify the impact of perturbations to each input on the multi-frequency, multi-polarization Tb output from the ANN. Spatiotemporal variability in the Tb predictions across regional spatial scales and seasonal timescales is investigated from 2002 to 2011. Preliminary results suggest ANN-based Tb predictions are sensitive to certain snow states, such as SWE, snow density, and snow temperature in non-vegetated or sparsely vegetated regions. Further, sensitivity of ANN prediction of ΔTb=Tb, 18v*-Tb, 36v* to changes in SWE suggest the likelihood for success when the ANN is eventually implemented into a data assimilation framework. Despite the promise in these initial results, challenges remain at enhancing ANN sensitivity

  20. Advanced propeller noise prediction in the time domain

    NASA Technical Reports Server (NTRS)

    Farassat, F.; Dunn, M. H.; Spence, P. L.

    1992-01-01

    The time domain code ASSPIN gives acousticians a powerful technique of advanced propeller noise prediction. Except for nonlinear effects, the code uses exact solutions of the Ffowcs Williams-Hawkings equation with exact blade geometry and kinematics. By including nonaxial inflow, periodic loading noise, and adaptive time steps to accelerate computer execution, the development of this code becomes complete.

  1. Predictive Biomarkers to Chemoradiation in Locally Advanced Rectal Cancer.

    PubMed

    Conde-Muíño, Raquel; Cuadros, Marta; Zambudio, Natalia; Segura-Jiménez, Inmaculada; Cano, Carlos; Palma, Pablo

    2015-01-01

    There has been a high local recurrence rate in rectal cancer. Besides improvements in surgical techniques, both neoadjuvant short-course radiotherapy and long-course chemoradiation improve oncological results. Approximately 40-60% of rectal cancer patients treated with neoadjuvant chemoradiation achieve some degree of pathologic response. However, there is no effective method of predicting which patients will respond to neoadjuvant treatment. Recent studies have evaluated the potential of genetic biomarkers to predict outcome in locally advanced rectal adenocarcinoma treated with neoadjuvant chemoradiation. The articles produced by the PubMed search were reviewed for those specifically addressing a genetic profile's ability to predict response to neoadjuvant treatment in rectal cancer. Although tissue gene microarray profiling has led to promising data in cancer, to date, none of the identified signatures or molecular markers in locally advanced rectal cancer has been successfully validated as a diagnostic or prognostic tool applicable to routine clinical practice. PMID:26504848

  2. Predictive Biomarkers to Chemoradiation in Locally Advanced Rectal Cancer

    PubMed Central

    Conde-Muíño, Raquel; Cuadros, Marta; Zambudio, Natalia; Segura-Jiménez, Inmaculada; Cano, Carlos; Palma, Pablo

    2015-01-01

    There has been a high local recurrence rate in rectal cancer. Besides improvements in surgical techniques, both neoadjuvant short-course radiotherapy and long-course chemoradiation improve oncological results. Approximately 40–60% of rectal cancer patients treated with neoadjuvant chemoradiation achieve some degree of pathologic response. However, there is no effective method of predicting which patients will respond to neoadjuvant treatment. Recent studies have evaluated the potential of genetic biomarkers to predict outcome in locally advanced rectal adenocarcinoma treated with neoadjuvant chemoradiation. The articles produced by the PubMed search were reviewed for those specifically addressing a genetic profile's ability to predict response to neoadjuvant treatment in rectal cancer. Although tissue gene microarray profiling has led to promising data in cancer, to date, none of the identified signatures or molecular markers in locally advanced rectal cancer has been successfully validated as a diagnostic or prognostic tool applicable to routine clinical practice. PMID:26504848

  3. Regional price targets appropriate for advanced coal extraction. [Forecasting to 1985 and 2000; USA; Regional analysis

    SciTech Connect

    Terasawa, K.L.; Whipple, D.W.

    1980-12-01

    The object of the study is to provide a methodology for predicting coal prices in regional markets for the target time frames 1985 and 2000 that could subsequently be used to guide the development of an advanced coal extraction system. The model constructed for the study is a supply and demand model that focuses on underground mining, since the advanced technology is expected to be developed for these reserves by the target years. The supply side of the model is based on coal reserve data generated by Energy and Environmental Analysis, Inc. (EEA). Given this data and the cost of operating a mine (data from US Department of Energy and Bureau of Mines), the Minimum Acceptable Selling Price (MASP) is obtained. The MASP is defined as the smallest price that would induce the producer to bring the mine into production, and is sensitive to the current technology and to assumptions concerning miner productivity. Based on this information, market supply curves can then be generated. On the demand side of the model, demand by region is calculated based on an EEA methodology that emphasizes demand by electric utilities and demand by industry. The demand and supply curves are then used to obtain the price targets. This last step is accomplished by allocating the demands among the suppliers so that the combined cost of producing and transporting coal is minimized.

  4. Recent advances in predictability studies in China (1999-2002)

    NASA Astrophysics Data System (ADS)

    Mu, Mu; Wansuo, Duan; Jifan, Chou

    2004-06-01

    and the correlation coefficient are calculated to explore the distribution characteristics of the mean-square errors. Finally, the predictability of short-term climate prediction is investigated by using statistical methods or numerical simulation methods. It is demonstrated that the predictability of short-term climate in China depends not only on the region of China being investigated, but also on the time scale and the atmospheric internal dynamical process.

  5. Ground Motion Prediction Models for Caucasus Region

    NASA Astrophysics Data System (ADS)

    Jorjiashvili, Nato; Godoladze, Tea; Tvaradze, Nino; Tumanova, Nino

    2016-04-01

    Ground motion prediction models (GMPMs) relate ground motion intensity measures to variables describing earthquake source, path, and site effects. Estimation of expected ground motion is a fundamental earthquake hazard assessment. The most commonly used parameter for attenuation relation is peak ground acceleration or spectral acceleration because this parameter gives useful information for Seismic Hazard Assessment. Since 2003 development of Georgian Digital Seismic Network has started. In this study new GMP models are obtained based on new data from Georgian seismic network and also from neighboring countries. Estimation of models is obtained by classical, statistical way, regression analysis. In this study site ground conditions are additionally considered because the same earthquake recorded at the same distance may cause different damage according to ground conditions. Empirical ground-motion prediction models (GMPMs) require adjustment to make them appropriate for site-specific scenarios. However, the process of making such adjustments remains a challenge. This work presents a holistic framework for the development of a peak ground acceleration (PGA) or spectral acceleration (SA) GMPE that is easily adjustable to different seismological conditions and does not suffer from the practical problems associated with adjustments in the response spectral domain.

  6. Ensemble-based Regional Climate Prediction: Political Impacts

    NASA Astrophysics Data System (ADS)

    Miguel, E.; Dykema, J.; Satyanath, S.; Anderson, J. G.

    2008-12-01

    Accurate forecasts of regional climate, including temperature and precipitation, have significant implications for human activities, not just economically but socially. Sub Saharan Africa is a region that has displayed an exceptional propensity for devastating civil wars. Recent research in political economy has revealed a strong statistical relationship between year to year fluctuations in precipitation and civil conflict in this region in the 1980s and 1990s. To investigate how climate change may modify the regional risk of civil conflict in the future requires a probabilistic regional forecast that explicitly accounts for the community's uncertainty in the evolution of rainfall under anthropogenic forcing. We approach the regional climate prediction aspect of this question through the application of a recently demonstrated method called generalized scalar prediction (Leroy et al. 2009), which predicts arbitrary scalar quantities of the climate system. This prediction method can predict change in any variable or linear combination of variables of the climate system averaged over a wide range spatial scales, from regional to hemispheric to global. Generalized scalar prediction utilizes an ensemble of model predictions to represent the community's uncertainty range in climate modeling in combination with a timeseries of any type of observational data that exhibits sensitivity to the scalar of interest. It is not necessary to prioritize models in deriving with the final prediction. We present the results of the application of generalized scalar prediction for regional forecasts of temperature and precipitation and Sub Saharan Africa. We utilize the climate predictions along with the established statistical relationship between year-to-year rainfall variability in Sub Saharan Africa to investigate the potential impact of climate change on civil conflict within that region.

  7. Rain attenuation prediction during rain events in different climatic regions

    NASA Astrophysics Data System (ADS)

    Das, Dalia; Maitra, Animesh

    2015-06-01

    A rain attenuation prediction method has been applied to different climatic regions to test the validity of the model. The significant difference in rain rate and attenuation statistics for the tropical and temperate region needs to be considered in developing channel model to predict time series of rain attenuation for earth space communication links. Model parameters obtained for a tropical location has been successfully applied to predict time series of rain attenuation at other tropical locations. Separate model parameters are derived from the experimental data obtained at a temperate location and these are used to predict rain attenuation during rain events for other temperate locations showing the effectiveness of the technique.

  8. Design of the advanced regional aircraft, the DART-75

    NASA Technical Reports Server (NTRS)

    Elliot, Steve; Gislason, Jason; Huffstetler, Mark; Mann, Jon; Withers, Ashley; Zimmerman, Mark

    1992-01-01

    The need for regional aircraft stems from the problem of hub airport congestion. Regional travel will allow a passenger to commute from one spoke city to another spoke city without entering the congested hub airport. In addition, those people traveling longer routes may begin the flight at home instead of traveling to the hub airport. At this time, there is no American aerospace company that produces a regional transport for under 100 passengers. The intention of the Developmental Advanced Regional Transport (DART-75) is to fill this void with a modern, efficient regional aircraft. This design achieves the efficiency through a number of advanced features including three lifting surfaces, partial composite construction, and an advanced engine design. Efficiency is not the only consideration. Structural integrity, fatigue life, ease of maintenance, passenger comfort and convenience, and environmental aspects must all be considered. These factors force the design team to face many tradeoffs that are studied to find the best solution. The final consideration that cannot be overlooked is that of cost. The DART-75 is a 75-passenger medium-range regional transport intended for spoke-to-spoke, spoke-to-hub, and some hub-to-hub operations. Included are the general descriptions of the structures, weight and balance, stability and control, performance, and engine design.

  9. QUEST FOR AN ADVANCED REGIONAL AIR QUALITY MODEL

    EPA Science Inventory

    Organizations interested in advancing the science and technology of regional air quality modeling on the "grand challenge" scale have joined to form CAMRAQ. hey plan to leverage their research finds by collaborating on the development and evaluation of CMSs so ambitious in scope ...

  10. Estimating observing locations for advancing beyond the winter predictability barrier of Indian Ocean dipole event predictions

    NASA Astrophysics Data System (ADS)

    Feng, Rong; Duan, Wansuo; Mu, Mu

    2016-04-01

    In this paper, we explored potential observing locations (i.e., the sensitive areas) of positive Indian Ocean dipole (IOD) events to advance beyond the winter predictability barrier (WPB) using the geophysical fluid dynamics laboratory climate model version 2p1 (GFDL CM2p1). The sensitivity analysis is conducted through perfect model predictability experiments, in which the model is assumed to be perfect and so any prediction errors are caused by initial errors. The results show that the initial errors with an east-west dipole pattern are more likely to result in a significant WPB than spatially correlated noises; the areas where the large values of the dipole pattern initial errors are located have great effects on prediction uncertainties in winter and provide useful information regarding the sensitive areas. Further, the prediction uncertainties in winter are more sensitive to the initial errors in the subsurface large value areas than to those in the surface large value areas. The results indicate that the subsurface large value areas are sensitive areas for advancing beyond the WPB of IOD predictions and if we carry out intensive observations across these areas, the prediction errors in winter may be largely reduced. This will lead to large improvements in the skill of wintertime IOD event forecasts.

  11. Predicting binary merger event rates for advanced LIGO/Virgo

    NASA Astrophysics Data System (ADS)

    Holz, Daniel; Belczynski, Chris; O'Shaughnessy, Richard; Bulik, Tomek; LIGO Collaboration

    2016-03-01

    We discuss estimates of the rates of mergers of binary systems composed of neutron stars and/or stellar mass black holes. We use the StarTrack population synthesis code, and make predictions for the detection rate of compact binary coalescences with the advanced LIGO/Virgo gravitational wave detectors. Because these instruments are sensitive to massive (M > 20M⊙) stellar-mass binary black holes mergers out to high redshift (z > 1), we discuss the cosmological effects which must be taken into account when calculating LIGO detection rates, including a generalization of the calculation of the ``peanut factor'' and the sensitive time-volume.

  12. Random Forests for Global and Regional Crop Yield Predictions

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Traditional regression models have limitations when applied for predicting crop yield responses at multiple spatial scales. An alternative modeling method, Random Forest (RF) regression, was utilized to predict crop yield responses for wheat, maize, and potato at regional scales. This RF regressio...

  13. Predicting human age using regional morphometry and inter-regional morphological similarity

    NASA Astrophysics Data System (ADS)

    Wang, Xun-Heng; Li, Lihua

    2016-03-01

    The goal of this study is predicting human age using neuro-metrics derived from structural MRI, as well as investigating the relationships between age and predictive neuro-metrics. To this end, a cohort of healthy subjects were recruited from 1000 Functional Connectomes Project. The ages of the participations were ranging from 7 to 83 (36.17+/-20.46). The structural MRI for each subject was preprocessed using FreeSurfer, resulting in regional cortical thickness, mean curvature, regional volume and regional surface area for 148 anatomical parcellations. The individual age was predicted from the combination of regional and inter-regional neuro-metrics. The prediction accuracy is r = 0.835, p < 0.00001, evaluated by Pearson correlation coefficient between predicted ages and actual ages. Moreover, the LASSO linear regression also found certain predictive features, most of which were inter-regional features. The turning-point of the developmental trajectories in human brain was around 40 years old based on regional cortical thickness. In conclusion, structural MRI could be potential biomarkers for the aging in human brain. The human age could be successfully predicted from the combination of regional morphometry and inter-regional morphological similarity. The inter-regional measures could be beneficial to investigating human brain connectome.

  14. Comparison of two imaging programs in predicting the soft tissue changes with mandibular advancement surgery.

    PubMed

    Ravindranath, Sneha; Krishnaswamy, Nathamuni Rengarajan; Sundaram, Venkateswaran

    2011-01-01

    Establishing common objectives and expectations concerning the outcome of proposed surgical orthodontic therapy is a crucial part of the treatment planning process, which has been greatly simplified by imaging software. The purpose of this study was to investigate the reliability of two surgical imaging programs--Dolphin Imaging 10 and Vistadent OC--in simulating the actual outcome of mandibular advancement surgery by using a visual analog scale (VAS) judged by a panel of orthodontists, oral surgeons, and laypersons. The predictions were also analyzed with soft tissue cephalometric evaluation. The results of the study showed that in predicting the surgical outcome evaluated by the VAS, both programs received a mean rating of fair. One was marginally superior for the overall assessment among all three panelist groups. Region-wise, rating indicated the lower lip region to be the least accurate, and the submental region received the highest scores. The soft tissue cephalometric parameters showed minimal differences except for the lower lip parameters. Thus, Dolphin Imaging 10 and Vistadent OC are reliable in predicting mandibular advancement surgical outcomes with inaccuracies chiefly in the lower lip region. PMID:22299108

  15. Thermal Model Predictions of Advanced Stirling Radioisotope Generator Performance

    NASA Technical Reports Server (NTRS)

    Wang, Xiao-Yen J.; Fabanich, William Anthony; Schmitz, Paul C.

    2014-01-01

    This paper presents recent thermal model results of the Advanced Stirling Radioisotope Generator (ASRG). The three-dimensional (3D) ASRG thermal power model was built using the Thermal Desktop(trademark) thermal analyzer. The model was correlated with ASRG engineering unit test data and ASRG flight unit predictions from Lockheed Martin's (LM's) I-deas(trademark) TMG thermal model. The auxiliary cooling system (ACS) of the ASRG is also included in the ASRG thermal model. The ACS is designed to remove waste heat from the ASRG so that it can be used to heat spacecraft components. The performance of the ACS is reported under nominal conditions and during a Venus flyby scenario. The results for the nominal case are validated with data from Lockheed Martin. Transient thermal analysis results of ASRG for a Venus flyby with a representative trajectory are also presented. In addition, model results of an ASRG mounted on a Cassini-like spacecraft with a sunshade are presented to show a way to mitigate the high temperatures of a Venus flyby. It was predicted that the sunshade can lower the temperature of the ASRG alternator by 20 C for the representative Venus flyby trajectory. The 3D model also was modified to predict generator performance after a single Advanced Stirling Convertor failure. The geometry of the Microtherm HT insulation block on the outboard side was modified to match deformation and shrinkage observed during testing of a prototypic ASRG test fixture by LM. Test conditions and test data were used to correlate the model by adjusting the thermal conductivity of the deformed insulation to match the post-heat-dump steady state temperatures. Results for these conditions showed that the performance of the still-functioning inboard ACS was unaffected.

  16. Computational methods in the prediction of advanced subsonic and supersonic propeller induced noise: ASSPIN users' manual

    NASA Technical Reports Server (NTRS)

    Dunn, M. H.; Tarkenton, G. M.

    1992-01-01

    This document describes the computational aspects of propeller noise prediction in the time domain and the use of high speed propeller noise prediction program ASSPIN (Advanced Subsonic and Supersonic Propeller Induced Noise). These formulations are valid in both the near and far fields. Two formulations are utilized by ASSPIN: (1) one is used for subsonic portions of the propeller blade; and (2) the second is used for transonic and supersonic regions on the blade. Switching between the two formulations is done automatically. ASSPIN incorporates advanced blade geometry and surface pressure modelling, adaptive observer time grid strategies, and contains enhanced numerical algorithms that result in reduced computational time. In addition, the ability to treat the nonaxial inflow case has been included.

  17. Random Forests for Global and Regional Crop Yield Predictions

    PubMed Central

    Jeong, Jig Han; Resop, Jonathan P.; Mueller, Nathaniel D.; Fleisher, David H.; Yun, Kyungdahm; Butler, Ethan E.; Timlin, Dennis J.; Shim, Kyo-Moon; Gerber, James S.; Reddy, Vangimalla R.

    2016-01-01

    Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato in comparison with multiple linear regressions (MLR) serving as a benchmark. We used crop yield data from various sources and regions for model training and testing: 1) gridded global wheat grain yield, 2) maize grain yield from US counties over thirty years, and 3) potato tuber and maize silage yield from the northeastern seaboard region. RF was found highly capable of predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared. For example, the root mean square errors (RMSE) ranged between 6 and 14% of the average observed yield with RF models in all test cases whereas these values ranged from 14% to 49% for MLR models. Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in data analysis. RF may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data. PMID:27257967

  18. On the Encoding of Proteins for Disordered Regions Prediction

    PubMed Central

    Becker, Julien; Maes, Francis; Wehenkel, Louis

    2013-01-01

    Disordered regions, i.e., regions of proteins that do not adopt a stable three-dimensional structure, have been shown to play various and critical roles in many biological processes. Predicting and understanding their formation is therefore a key sub-problem of protein structure and function inference. A wide range of machine learning approaches have been developed to automatically predict disordered regions of proteins. One key factor of the success of these methods is the way in which protein information is encoded into features. Recently, we have proposed a systematic methodology to study the relevance of various feature encodings in the context of disulfide connectivity pattern prediction. In the present paper, we adapt this methodology to the problem of predicting disordered regions and assess it on proteins from the 10th CASP competition, as well as on a very large subset of proteins extracted from PDB. Our results, obtained with ensembles of extremely randomized trees, highlight a novel feature function encoding the proximity of residues according to their accessibility to the solvent, which is playing the second most important role in the prediction of disordered regions, just after evolutionary information. Furthermore, even though our approach treats each residue independently, our results are very competitive in terms of accuracy with respect to the state-of-the-art. A web-application is available at http://m24.giga.ulg.ac.be:81/x3Disorder. PMID:24358161

  19. On the encoding of proteins for disordered regions prediction.

    PubMed

    Becker, Julien; Maes, Francis; Wehenkel, Louis

    2013-01-01

    Disordered regions, i.e., regions of proteins that do not adopt a stable three-dimensional structure, have been shown to play various and critical roles in many biological processes. Predicting and understanding their formation is therefore a key sub-problem of protein structure and function inference. A wide range of machine learning approaches have been developed to automatically predict disordered regions of proteins. One key factor of the success of these methods is the way in which protein information is encoded into features. Recently, we have proposed a systematic methodology to study the relevance of various feature encodings in the context of disulfide connectivity pattern prediction. In the present paper, we adapt this methodology to the problem of predicting disordered regions and assess it on proteins from the 10th CASP competition, as well as on a very large subset of proteins extracted from PDB. Our results, obtained with ensembles of extremely randomized trees, highlight a novel feature function encoding the proximity of residues according to their accessibility to the solvent, which is playing the second most important role in the prediction of disordered regions, just after evolutionary information. Furthermore, even though our approach treats each residue independently, our results are very competitive in terms of accuracy with respect to the state-of-the-art. A web-application is available at http://m24.giga.ulg.ac.be:81/x3Disorder. PMID:24358161

  20. Regionalization and Prediction of Seasonal Precipitation in Ethiopia

    NASA Astrophysics Data System (ADS)

    Zhang, Y.; Block, P.

    2014-12-01

    Rainfed agriculture continues to be an important part of Ethiopia's livelihoods and economy. Highly variable inter-annual precipitation, however, presents a serious challenge to sustainable production and subsistence survival. An improved understanding of what drives hydroclimatic extremes and an effective prediction system may help to buffer resulting impacts through improved decision-making. Precipitation data from the National Meteorological Agency at 0.1 x 0.1 grids for 1983 - 2011 during the June-September rainy season over western Ethiopia is evaluated through a cluster analysis to investigate homogeneous regions with similar rainfall patterns for subsequent prediction of seasonal precipitation for each region. A k-means clustering method is applied with the optimal number of clusters (K) selected by the within cluster sum of square errors (WSS) metric. Homogenous regions are defined with relatively clear and smooth boundaries, low inter-cluster correlations, and high intra-cluster correlations. The precipitation prediction models are statistically based, with a seasonal total prediction for each cluster; grid-based predictions are subsequently conditioned on the cluster level prediction through regression. Prospective model predictors include large-scale ocean-land-atmospheric climate variables and local variables and conditions. These predictions will be used in economic and water management models.

  1. Prediction of Corrosion of Advanced Materials and Fabricated Components

    SciTech Connect

    A. Anderko; G. Engelhardt; M.M. Lencka; M.A. Jakab; G. Tormoen; N. Sridhar

    2007-09-29

    The goal of this project is to provide materials engineers, chemical engineers and plant operators with a software tool that will enable them to predict localized corrosion of process equipment including fabricated components as well as base alloys. For design and revamp purposes, the software predicts the occurrence of localized corrosion as a function of environment chemistry and assists the user in selecting the optimum alloy for a given environment. For the operation of existing plants, the software enables the users to predict the remaining life of equipment and help in scheduling maintenance activities. This project combined fundamental understanding of mechanisms of corrosion with focused experimental results to predict the corrosion of advanced, base or fabricated, alloys in real-world environments encountered in the chemical industry. At the heart of this approach is the development of models that predict the fundamental parameters that control the occurrence of localized corrosion as a function of environmental conditions and alloy composition. The fundamental parameters that dictate the occurrence of localized corrosion are the corrosion and repassivation potentials. The program team, OLI Systems and Southwest Research Institute, has developed theoretical models for these parameters. These theoretical models have been applied to predict the occurrence of localized corrosion of base materials and heat-treated components in a variety of environments containing aggressive and non-aggressive species. As a result of this project, a comprehensive model has been established and extensively verified for predicting the occurrence of localized corrosion as a function of environment chemistry and temperature by calculating the corrosion and repassivation potentials.To support and calibrate the model, an experimental database has been developed to elucidate (1) the effects of various inhibiting species as well as aggressive species on localized corrosion of nickel

  2. Initializing decadal climate predictions over the North Atlantic region

    NASA Astrophysics Data System (ADS)

    Matei, Daniela Mihaela; Pohlmann, Holger; Jungclaus, Johann; Müller, Wolfgang; Haak, Helmuth; Marotzke, Jochem

    2010-05-01

    Decadal climate prediction aims to predict the internally-generated decadal climate variability in addition to externally-forced climate change signal. In order to achieve this it is necessary to start the predictions from the current climate state. In this study we investigate the forecast skill of the North Atlantic decadal climate predictions using two different ocean initialization strategies. First we apply an assimilation of ocean synthesis data provided by the GECCO project (Köhl and Stammer, 2008) as initial conditions for the coupled model ECHAM5/MPI-OM. Hindcast experiments are then performed over the period 1952-2001. An alternative approach is one in which the subsurface ocean temperature and salinity are diagnosed from an ensemble of ocean model runs forced by the NCEP-NCAR atmospheric reanalyzes for the period 1948-2007, then nudge into the coupled model to produce initial conditions for the hindcast experiments. An anomaly coupling scheme is used in both approaches to avoid the hindcast drift and the associated initial shock. Differences between the two assimilation approaches are discussed by comparing them with the observational data in key regions and processes. We asses the skill of the initialized decadal hindcast experiments against the prediction skill of the non-initialized hindcasts simulation. We obtain an overview of the regions with the highest predictability from the regional distribution of the anomaly correlation coefficients and RMSE for the SAT. For the first year the hindcast skill is increased over almost all ocean regions in the NCEP-forced approach. This increase in the hindcast skill for the 1 year lead time is somewhat reduced in the GECCO approach. At lead time 5yr and 10yr, the skill enhancement is still found over the North Atlantic and North Pacific regions. We also consider the potential predictability of the Atlantic Meridional Overturning Circulation (AMOC) and Nordic Seas Overflow by comparing the predicted values to

  3. Numerical Weather Prediction Over Caucasus Region With Nested Grid Models

    NASA Astrophysics Data System (ADS)

    Davitashvili, Dr.; Kutaladze, Dr.; Kvatadze, Dr.

    2010-09-01

    Global atmosphere models, which describe the weather processes, give the general character of the weather but can't catch the smaller scale processes, especially local weather for the territories with compound topography. Small-scale processes such as convection often dominate the local weather, which cannot be explicitly represented in models with grid size more then 10 km. A much finer grid is required to properly simulate frontal structures and represent cumulus convection. Georgia lies to the south of the Major Caucasian Ridge and the Lesser Caucasus mountains occupy the southern part of Georgia. About 85 percent of the total land area occupies complex mountain ranges.Therefore for the territory of Georgia it is necessary to use atmosphere models with a very high resolution nested grid system taking into account main orographic features of the area. We have elaborated and configured Whether Research Forecast - Advanced Researcher Weather (WRF-ARW) model for Caucasus region considering geographical-landscape character, topography height, land use, soil type and temperature in deep layers, vegetation monthly distribution, albedo and others. Porting of WRF-ARW application to the grid was a good opportunity for running model on larger number of CPUs and storing large amount of data on the grid storage elements. On the grid WRF was compiled for both Open MP and MPI (Shared + Distributed memory) environment and WPS was compiled for serial environment using PGI (v7.1.6, MPI- version 1.2.7) on the platform Linux-x86. In searching of optimal execution time for time saving different model directory structures and storage schema was used. Simulations were performed using a set of 2 domains with horizontal grid-point resolutions of 15 and 5 km, both defined as those currently being used for operational forecasts The coarser domain is a grid of 94x102 points which covers the South Caucasus region, while the nested inner domain has a grid size of 70x70 points mainly

  4. Science and Technology to Advance Regional Security in Central Asia

    SciTech Connect

    Rosenberg, N

    2002-07-05

    This morning I will describe a program that we refer to as STARS, for Science and Technology to Advance Regional Security, in Central Asia. It is a program that is based on cooperative, bilateral and multilateral, science and technology projects. It is our premise that such cooperative projects provide an opportunity for engagement while addressing real problems that could otherwise lead to destabilizing tensions in the region. The STARS program directly supports USCENTCOM's activities and objectives in environmental security. In fact, we think that STARS is a great vehicle for implementing and amplifying USCENTCOM's environmental security objectives and activities. We are very grateful and very pleased to have General DeLong's support in this matter. I am going to briefly describe the program. I want to stress again that it is a cooperative program. We would like to get input, suggestions, and feedback from the Central Asians here today so we can move forward together.

  5. Advance Prediction of the March 11, 2011 Great East Japan Earthquake: A Missed Opportunity for Disaster Preparedness

    NASA Astrophysics Data System (ADS)

    Davis, C. A.; Keilis-Borok, V. I.; Kossobokov, V. G.; Soloviev, A.

    2012-12-01

    There was a missed opportunity for implementing important disaster preparedness measures following an earthquake prediction that was announced as an alarm in mid-2001. This intermediate-term middle-range prediction was the initiation of a chain of alarms that successfully detected the time, region, and magnitude range for the magnitude 9.0 March 11, 2011 Great East Japan Earthquake. The prediction chains were made using an algorithm called M8 and is the latest of many predictions tested worldwide for more than 25 years, the results of which show at least a 70% success rate. The earthquake detection could have been utilized to implement measures and improve earthquake preparedness in advance; unfortunately this was not done, in part due to the predictions' limited distribution and the lack of applying existing methods for using intermediate-term predictions to make decisions for taking action. The resulting earthquake and induced tsunami caused tremendous devastation to north-east Japan. Methods that were known in advance of the predication and further advanced during the prediction timeframe are presented in a scenario describing some possibilities on how the 2001 prediction may have been utilized to reduce significant damage, including damage to the Fukushima nuclear power plant, and to show prudent cost-effective actions can be taken if the prediction certainty is known, but not necessarily high. The purpose of this presentation is to show how the prediction information can be strategically used to enhance disaster preparedness and reduce future impacts from the world's largest earthquakes.

  6. Prediction of the becalmed region for LP turbine profile design

    SciTech Connect

    Schulte, V.; Hodson, H.P.

    1998-10-01

    Recent attention has focused on the so-called ``becalmed region`` that is observed inside the boundary layers of turbomachinery blading and is associated with the process of wake-induced transition. Significant reductions of profile loss have been shown for high lift LP turbine blades at low Reynolds numbers due to the effects of the becalmed region on the diffusing flow at the rear of the suction surface. In this paper the nature and the significance of the becalmed region are examined using experimental observations and computational studies. It is shown that the becalmed region may be modeled using the unsteady laminar boundary layer equations. Therefore, it is predictable independent of the transition or turbulence models employed. The effect of the becalmed region on the transition process is modeled using a spot-based intermittency transition model. An unsteady differential boundary layer code was used to simulate a deterministic experiment involving an isolated turbulent spot numerically. The predictability of the becalmed region means that the rate of entropy production can be calculated in that region. It is found to be of the order of that in a laminar boundary layer. It is for this reason and because the becalmed region may be encroached upon by pursuing turbulent flows that for attached boundary layers, wake-induced transition cannot significantly reduce the profile loss. However, the becalmed region is less prone to separation than a conventional laminar boundary layer. Therefore, the becalmed region may be exploited in order to prevent boundary layer separation and the increase in loss that this entails. It is shown that it should now be possible to design efficient high lift LP turbine blades.

  7. Prediction of concurrent chemoradiotherapy outcome in advanced oropharyngeal cancer

    PubMed Central

    HASEGAWA, MASAHIRO; MAEDA, HIROYUKI; DENG, ZEYI; KIYUNA, ASANORI; GANAHA, AKIRA; YAMASHITA, YUKASHI; MATAYOSHI, SEN; AGENA, SHINYA; TOITA, TAKAFUMI; UEHARA, TAKAYUKI; SUZUKI, MIKIO

    2014-01-01

    The aim of this study was to investigate human papillomavirus (HPV) infection as a predictor of concurrent chemoradiotherapy (CCRT) response and indicator of planned neck dissection (PND) for patients with advanced oropharyngeal squamous cell carcinoma (OPSCC; stage III/IV). Overall, 39 OPSCC patients (32 men, 7 women; median age 61 years, range 39–79 years) were enrolled. The primary lesion and whole neck were irradiated up to 50.4 Gy, and subsequently the primary site and metastatic lymph nodes were boosted with a further 16.2 Gy. Although several chemotherapy regimens were employed, 82.1% of OPSCC patients received the combination of nedaplatin and 5-fluorouracil. HPV-related OPSCC (16 cases) was defined as both HPV DNA-positive status by polymerase chain reaction and p16INK4a overexpression by immunohistochemistry. Patients with N2 and N3 disease received PND 2–3 months after CCRT completion. Compared to non-responders, CCRT responders showed significantly lower nodal stage (N0 to N2b) and HPV-positive status in univariate analysis. Patients with HPV-related OPSCC had longer time to treatment failure (TTF) than those with HPV-unrelated OPSCC (p=0.040). Three-year TTF was 81.3 and 47.8% in the HPV-related and HPV-unrelated groups, respectively. There were also significant differences in disease-free survival (DFS) between the two OPSCC patient groups (p=0.042). Three-year DFS was 93.8 and 66.7% in patients with HPV-related and HPV-unrelated OPSCC, respectively. Multivariate logistic analysis showed a lower risk of TTF event occurrence in HPV-related OPSCC (p=0.041) than in HPV-unrelated OPSCC. Thus, HPV testing in addition to nodal stage was useful for predicting CCRT response, especially in advanced OPSCC. Because patients who received PND showed moderate locoregional control, PND is an effective surgical procedure for controlling neck lesions in patients with advanced HPV-unrelated disease. PMID:24969413

  8. Wiggle—Predicting Functionally Flexible Regions from Primary Sequence

    PubMed Central

    Gu, Jenny; Gribskov, Michael; Bourne, Philip E

    2006-01-01

    The Wiggle series are support vector machine–based predictors that identify regions of functional flexibility using only protein sequence information. Functionally flexible regions are defined as regions that can adopt different conformational states and are assumed to be necessary for bioactivity. Many advances have been made in understanding the relationship between protein sequence and structure. This work contributes to those efforts by making strides to understand the relationship between protein sequence and flexibility. A coarse-grained protein dynamic modeling approach was used to generate the dataset required for support vector machine training. We define our regions of interest based on the participation of residues in correlated large-scale fluctuations. Even with this structure-based approach to computationally define regions of functional flexibility, predictors successfully extract sequence-flexibility relationships that have been experimentally confirmed to be functionally important. Thus, a sequence-based tool to identify flexible regions important for protein function has been created. The ability to identify functional flexibility using a sequence based approach complements structure-based definitions and will be especially useful for the large majority of proteins with unknown structures. The methodology offers promise to identify structural genomics targets amenable to crystallization and the possibility to engineer more flexible or rigid regions within proteins to modify their bioactivity. PMID:16839194

  9. Prediction of regional streamflow frequency using model tree ensembles

    NASA Astrophysics Data System (ADS)

    Schnier, Spencer; Cai, Ximing

    2014-09-01

    This study introduces a novel data-driven method called model tree ensembles (MTEs) to predict streamflow frequency statistics based on known drainage area characteristics, which yields insights into the dominant controls of regional streamflow. The database used to induce the models contains both natural and anthropogenic drainage area characteristics for 294 USGS stream gages (164 in Texas and 130 in Illinois). MTEs were used to predict complete flow duration curves (FDCs) of ungaged streams by developing 17 models corresponding to 17 points along the FDC. Model accuracy was evaluated using ten-fold cross-validation and the coefficient of determination (R2). During the validation, the gages withheld from the analysis represent ungaged watersheds. MTEs are shown to outperform global multiple-linear regression models for predictions in ungaged watersheds. The accuracy of models for low flow is enhanced by explicit consideration of variables that capture human interference in watershed hydrology (e.g., population). Human factors (e.g., population and groundwater use) appear in the regionalizations for low flows, while annual and seasonal precipitation and drainage area are important for regionalizations of all flows. The results of this study have important implications for predictions in ungaged watersheds as well as gaged watersheds subject to anthropogenically-driven hydrologic changes.

  10. Which motor cortical region best predicts imagined movement?

    PubMed

    Park, Chang-Hyun; Chang, Won Hyuk; Lee, Minji; Kwon, Gyu Hyun; Kim, Laehyun; Kim, Sung Tae; Kim, Yun-Hee

    2015-06-01

    In brain-computer interfacing (BCI), motor imagery is used to provide a gateway to an effector action or behavior. However, in contrast to the main functional role of the primary motor cortex (M1) in motor execution, the M1's involvement in motor imagery has been debated, while the roles of secondary motor areas such as the premotor cortex (PMC) and supplementary motor area (SMA) in motor imagery have been proposed. We examined which motor cortical region had the greatest predictive ability for imagined movement among the primary and secondary motor areas. For two modes of motor performance, executed movement and imagined movement, in 12 healthy subjects who performed two types of motor task, hand grasping and hand rotation, we used the multivariate Bayes method to compare predictive ability between the primary and secondary motor areas (M1, PMC, and SMA) contralateral to the moved hand. With the distributed representation of activation, executed movement was best predicted from the M1 while imagined movement from the SMA, among the three motor cortical regions, in both types of motor task. In addition, the most predictive information about the distinction between executed movement and imagined movement was contained in the M1. The greater predictive ability of the SMA for imagined movement suggests its functional role that could be applied to motor imagery-based BCI. PMID:25800212

  11. Cancer Pharmacogenomics: Integrating Discoveries in Basic, Clinical and Population Sciences to Advance Predictive Cancer Care

    Cancer.gov

    Cancer Pharmacogenomics: Integrating Discoveries in Basic, Clinical and Population Sciences to Advance Predictive Cancer Care, a 2010 workshop sponsored by the Epidemiology and Genomics Research Program.

  12. Quantitative Earthquake Prediction on Global and Regional Scales

    SciTech Connect

    Kossobokov, Vladimir G.

    2006-03-23

    for mega-earthquakes of M9.0+. The monitoring at regional scales may require application of a recently proposed scheme for the spatial stabilization of the intermediate-term middle-range predictions. The scheme guarantees a more objective and reliable diagnosis of times of increased probability and is less restrictive to input seismic data. It makes feasible reestablishment of seismic monitoring aimed at prediction of large magnitude earthquakes in Caucasus and Central Asia, which to our regret, has been discontinued in 1991. The first results of the monitoring (1986-1990) were encouraging, at least for M6.5+.

  13. Life prediction methodology for ceramic components of advanced heat engines. Phase 1: Volume 1, Final report

    SciTech Connect

    Cuccio, J.C.; Brehm, P.; Fang, H.T.

    1995-03-01

    Emphasis of this program is to develop and demonstrate ceramics life prediction methods, including fast fracture, stress rupture, creep, oxidation, and nondestructive evaluation. Significant advancements were made in these methods and their predictive capabilities successfully demonstrated.

  14. Defining and predicting structurally conserved regions in protein superfamilies

    PubMed Central

    Huang, Ivan K.; Grishin, Nick V.

    2013-01-01

    Motivation: The structures of homologous proteins are generally better conserved than their sequences. This phenomenon is demonstrated by the prevalence of structurally conserved regions (SCRs) even in highly divergent protein families. Defining SCRs requires the comparison of two or more homologous structures and is affected by their availability and divergence, and our ability to deduce structurally equivalent positions among them. In the absence of multiple homologous structures, it is necessary to predict SCRs of a protein using information from only a set of homologous sequences and (if available) a single structure. Accurate SCR predictions can benefit homology modelling and sequence alignment. Results: Using pairwise DaliLite alignments among a set of homologous structures, we devised a simple measure of structural conservation, termed structural conservation index (SCI). SCI was used to distinguish SCRs from non-SCRs. A database of SCRs was compiled from 386 SCOP superfamilies containing 6489 protein domains. Artificial neural networks were then trained to predict SCRs with various features deduced from a single structure and homologous sequences. Assessment of the predictions via a 5-fold cross-validation method revealed that predictions based on features derived from a single structure perform similarly to ones based on homologous sequences, while combining sequence and structural features was optimal in terms of accuracy (0.755) and Matthews correlation coefficient (0.476). These results suggest that even without information from multiple structures, it is still possible to effectively predict SCRs for a protein. Finally, inspection of the structures with the worst predictions pinpoints difficulties in SCR definitions. Availability: The SCR database and the prediction server can be found at http://prodata.swmed.edu/SCR. Contact: 91huangi@gmail.com or grishin@chop.swmed.edu Supplementary information: Supplementary data are available at Bioinformatics

  15. Regional prediction of basin-scale brown trout habitat suitability

    NASA Astrophysics Data System (ADS)

    Ceola, S.; Pugliese, A.

    2014-09-01

    In this study we propose a novel method for the estimation of ecological indices describing the habitat suitability of brown trout (Salmo trutta). Traditional hydrological tools are coupled with an innovative regional geostatistical technique, aiming at the prediction of the brown trout habitat suitability index where partial or totally ungauged conditions occur. Several methods for the assessment of ecological indices are already proposed in the scientific literature, but the possibility of exploiting a geostatistical prediction model, such as Topological Kriging, has never been investigated before. In order to develop a regional habitat suitability model we use the habitat suitability curve, obtained from measured data of brown trout adult individuals collected in several river basins across the USA. The Top-kriging prediction model is then employed to assess the spatial correlation between upstream and downstream habitat suitability indices. The study area is the Metauro River basin, located in the central part of Italy (Marche region), for which both water depth and streamflow data were collected. The present analysis focuses on discharge values corresponding to the 0.1-, 0.5-, 0.9-empirical quantiles derived from flow-duration curves available for seven gauging stations located within the study area, for which three different suitability indices (i.e. ψ10, ψ50 and ψ90) are evaluated. The results of this preliminary analysis are encouraging showing Nash-Sutcliffe efficiencies equal to 0.52, 0.65, and 0.69, respectively.

  16. Advanced Hydrologic Prediction Services (AHPS) Science Infusion Strategy

    NASA Astrophysics Data System (ADS)

    Schaake, J.; Smith, G.; Carter, G.

    2002-05-01

    NWS is implementing an Advanced Hydrologic Prediction Services (AHPS) Science initiative to meet NWS Vision 2005 goals and related hydrologic services requirements, including the goal of being a world leader using state of the art forecast science and technology. AHPS includes a science infusion strategy to meet the following objectives: extend forecast lead time, improve forecast accuracy, and provide better information for user decisions. AHPS will meet these goals by implementing hydrologic forecast models tuned to local conditions and operated to account for uncertainty in hydrologic forecasts. AHPS will use ensemble weather and climate forecasts of precipitation and other conditions, such as air temperature, that affect the forecasts. This ensemble approach to weather, climate and water forecasting will provide a probabilistic basis for AHPS forecast products. Meeting AHPS goals and objectives requires an infusion of new science into the existing forecast system. Three AHPS requirements for science infusion are: 1. Quantify the uncertainty of river forecasts and provide users with a clear view of future hydrologic conditions together with hard evidence that AHPS products are based on valid forecast probability information; 2. Reduce the space and time scale, improve the accuracy, and extend the lead time of hydrologic forecasts. Demonstrate that new improvements to hydrologic forecast procedures add value to the forecasts and meet user requirements; 3. Improve the ability of forecasters to use the tools provided by integrating these into an efficient operational forecast system that includes automatic techniques for data quality control, access to data, model calibration, data assimilation, processing of ensemble forecasts, verification of forecasts and monitoring of all stages of the forecast process.

  17. Prediction of Active-Region CME Productivity from Magnetograms

    NASA Technical Reports Server (NTRS)

    Falconer, D. A.; Moore, R. L.; Gary, G. A.

    2004-01-01

    We report results of an expanded evaluation of whole-active-region magnetic measures as predictors of active-region coronal mass ejection (CME) productivity. Previously, in a sample of 17 vector magnetograms of 12 bipolar active regions observed by the Marshall Space Flight Center (MSFC) vector magnetograph, from each magnetogram we extracted a measure of the size of the active region (the active region s total magnetic flux a) and four measures of the nonpotentiality of the active region: the strong-shear length L(sub SS), the strong-gradient length L(sub SG), the net vertical electric current I(sub N), and the net-current magnetic twist parameter alpha (sub IN). This sample size allowed us to show that each of the four nonpotentiality measures was statistically significantly correlated with active-region CME productivity in time windows of a few days centered on the day of the magnetogram. We have now added a fifth measure of active-region nonpotentiality (the best-constant-alpha magnetic twist parameter (alpha sub BC)), and have expanded the sample to 36 MSFC vector magnetograms of 31 bipolar active regions. This larger sample allows us to demonstrate statistically significant correlations of each of the five nonpotentiality measures with future CME productivity, in time windows of a few days starting from the day of the magnetogram. The two magnetic twist parameters (alpha (sub 1N) and alpha (sub BC)) are normalized measures of an active region s nonpotentially in that they do not depend directly on the size of the active region, while the other three nonpotentiality measures (L(sub SS), L(sub SG), and I(sub N)) are non-normalized measures in that they do depend directly on active-region size. We find (1) Each of the five nonpotentiality measures is statistically significantly correlated (correlation confidence level greater than 95%) with future CME productivity and has a CME prediction success rate of approximately 80%. (2) None of the nonpotentiality

  18. 76 FR 52954 - Workshop: Advancing Research on Mixtures; New Perspectives and Approaches for Predicting Adverse...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-08-24

    ... HUMAN SERVICES Workshop: Advancing Research on Mixtures; New Perspectives and Approaches for Predicting... ``Advancing Research on Mixtures: New Perspectives and Approaches for Predicting Adverse Human Health Effects... Research and Training, NIEHS, P.O. Box 12233, MD K3-04, Research Triangle Park, NC 27709, (telephone)...

  19. Body region dissatisfaction predicts attention to body regions on other women.

    PubMed

    Lykins, Amy D; Ferris, Tamara; Graham, Cynthia A

    2014-09-01

    The proliferation of "idealized" (i.e., very thin and attractive) women in the media has contributed to increasing rates of body dissatisfaction among women. However, it remains relatively unknown how women attend to these images: does dissatisfaction predict greater or lesser attention to these body regions on others? Fifty healthy women (mean age=21.8 years) viewed images of idealized and plus-size models; an eye-tracker recorded visual attention. Participants also completed measures of satisfaction for specific body regions, which were then used as predictors of visual attention to these regions on models. Consistent with an avoidance-type process, lower levels of satisfaction with the two regions of greatest reported concern (mid, lower torso) predicted less attention to these regions; greater satisfaction predicted more attention to these regions. While this visual attention bias may aid in preserving self-esteem when viewing idealized others, it may preclude the opportunity for comparisons that could improve self-esteem. PMID:25047004

  20. An Advanced Data Assimilation System for Estuary and Coastal Ocean Prediction

    NASA Astrophysics Data System (ADS)

    Hoffman, M. J.; Murtugudde, R.; Brown, C. W.

    2008-12-01

    We are developing an advanced data assimilation system for the Chesapeake Bay Forecast System, a regional Earth System Prediction model. To accomplish this, the Regional Ocean Modeling System (ROMS) implementation on the Chesapeake Bay (ChesROMS) has been interfaced with the Local Ensemble Transform Kalman Filter (LETKF) to create an efficient data assimilation system. The LETKF, an ensemble Kalman filtering scheme developed at the University of Maryland, is among the most advanced data assimilation methods and is very effective for large, non-linear dynamical systems in both sparse and dense data coverage situations. Crucial to the LETKF-ChesROMS assimilation system is having accurate open ocean boundary conditions from GODAE and other large scale products. Currently, the assimilation system is run with prescribed climatological boundary conditions in a relatively coarse resolution. In perfect model experiments using ChesROMS, the filter converges quickly and greatly reduces the analysis and subsequent forecast errors in the temperature, salinity, and velocity fields. This error reduction has proved fairly robust to sensitivity studies such as reduced data coverage. The LETKF also provides an efficient algorithm for error estimation and facilitates the investigation of the spatial distribution of the error. This information will be used to determine areas where more monitoring is needed and to address other issues of the observational impacts on the analyses and observational system simulation experiments, in addition to forecast initialization experiments and regional reanalyses for the past decade.

  1. Understanding and predicting the regional sun-hurricane count relationship

    NASA Astrophysics Data System (ADS)

    Hodges, Robert Edward

    North Atlantic hurricanes constitute a threat to both life and property. The warm seas found in tropical low-latitudes provide a breeding ground for hurricanes, with nearly continuous heat and moisture fluxes into near-surface air. Traditionally, the sun's role in hurricane climate studies is acknowledged as a time-marker for ocean heat content, with calendar date predicting hurricane frequency and intensity. However, a series of investigations into a different type of sun-hurricane relationship has uncovered a link between solar activity and hurricane intensity and frequency. High solar activity at a daily timescale is understood to weaken hurricanes in the southwest Atlantic yet correspond to increased hurricane intensity in the southeast Atlantic. At a seasonal timescale, high solar activity is shown to correspond with fewer U.S.-landfalling hurricanes. A gap in the knowledge exists on how and where solar activity influences seasonal hurricane frequency over and within the North Atlantic basin. This study is quantitative featuring exploratory analysis and inferential modeling, with diagnosis and prediction of the sun-hurricane count relationship over space being the primary contribution to science and society. It is carried out via exploratory data analysis and statistical modeling. Hurricane and climate data are binned in equal-area hexagon regions. Count differences for periods of high solar activity (i.e, high sunspot number) feature fewer hurricanes across the Caribbean, Gulf of Mexico, and along the eastern seaboard of the United States when sunspots are numerous. In contrast, fewer hurricanes are observed in the central North Atlantic when sunspots are few. The sun-hurricane connection is as important as the El Nino Southern Oscillation toward statistically explaining regional hurricane occurrences. Regression results indicate a 30% reduction in probability of annual hurricane occurrence for southeastern Cuba, the southern Bahama islands, Haiti, and

  2. [Research advance in the drug target prediction based on chemoinformatics].

    PubMed

    Fang, Jian-song; Liu, Ai-lin; Du, Guan-hua

    2014-10-01

    The emerging of network pharmacology and polypharmacology forces the scientists to recognize and explore new mechanisms of existing drugs. The drug target prediction can play a key significance on the elucidation of the molecular mechanism of drugs and drug reposition. In this paper, we systematically review the existing approaches to the prediction of biological targets of small molecule based on chemoinformatics, including ligand-based prediction, receptor-based prediction and data mining-based prediction. We also depict the strength of these methods as well as their applications, and put forward their developing direction. PMID:25577863

  3. Predicting regional lung deposition of environmental tobacco smoke particles

    SciTech Connect

    Nazaroff, W.W.; Hung, W.Y.; Sasse, A.G.B.M.; Gadgil, A.J.

    1993-10-01

    Inhalation exposure of environmental tobacco smoke (ETS) particles may increase health risks, but only to the extent that the particles deposit in the respiratory tract. We describe a technique to predict regional lung deposition of environmental tobacco smoke particles. Interpretation of particle size distribution measurements after cigarette combustion by a smoking machine in a test room yields an effective emissions profile. An aerosol dynamics model is used to predict indoor particle concentrations resulting from a specified combination of smoking frequency and building factors. By utilizing a lung deposition model, the rate of ETS mass accumulation in human lungs is then determined as a function of particle size and lung airway generation. Considering emissions of sidestream smoke only, residential exposures of nonsmokers to ETS are predicted to cause rates of total respiratory tract particle deposition in the range of 0.4-0.7 {mu}g/day per kg of body weight for light smoking in a well-ventilated residence and 8-13 {mu}g/day per kg for moderately heavy smoking in a poorly ventilated residence. Emissions of sidestream plus mainstream smoke lead to predicted deposition rates about a factor of 4 higher. This technique should be useful for evaluating health risks and control techniques associated with exposure to ETS particles. 36 refs., 6 figs., 3 tabs.

  4. Application of NASA's Advanced Life Support Technologies in Polar Regions

    NASA Technical Reports Server (NTRS)

    Bubenheim, David L.

    1997-01-01

    The problems of obtaining adequate pure drinking water and disposing of liquid and solid waste in the U.S Arctic, a region where virtually all water is frozen solid for much of the year, has led to unsanitary solutions. Sanitation and a safe water supply are particularly problems in rural villages. These villages are without running water and use plastic buckets for toilets. The outbreak of diseases is believed to be partially attributable to exposure to human waste and lack of sanitation. Villages with the most frequent outbreaks of disease are those in which running water is difficult to obtain. Waste is emptied into open lagoons, rivers, or onto the sea coast. It does not degrade rapidly and in addition to affecting human health, can be harmful to the fragile ecology of the Arctic and the indigenous wildlife and fish populations. Current practices for waste management and sanitation pose serious human hazards as well as threaten the environment. NASA's unique knowledge of water/wastewater treatment systems for extreme environments, identified in the Congressional Office of Technology Assessment report entitled An Alaskan Challenge: Native Villagt Sanitation, may offer practical solutions addressing the issues of safe drinking water and effective sanitation practices in rural villages. NASA's advanced life support technologies are being combined with Arctic science and engineering knowledge to address the unique needs of the remote communities of Alaska through the Advanced Life Systems for Extreme Environments (ALSEE) project. ALSEE is a collaborative effort involving the NASA, the State of Alaska, the University of Alaska, the North Slope Borough of Alaska, Ilisagvik College in Barrow and the National Science Foundation (NSF). The focus is a major issue in the State of Alaska and other areas of the Circumpolar North; the health and welfare of its people, their lives and the subsistence lifestyle in remote communities, economic opportunity, and care for the

  5. Thermal Model Predictions of Advanced Stirling Radioisotope Generator Performance

    NASA Technical Reports Server (NTRS)

    Wang, Xiao-Yen J.; Fabanich, William Anthony; Schmitz, Paul C.

    2014-01-01

    This presentation describes the capabilities of three-dimensional thermal power model of advanced stirling radioisotope generator (ASRG). The performance of the ASRG is presented for different scenario, such as Venus flyby with or without the auxiliary cooling system.

  6. Multivariate weather prediction with atmospheric analogs: predictors and probabilistic prediction skill for different European regions

    NASA Astrophysics Data System (ADS)

    Raynaud, Damien; Hingray, Benoit; Chardon, Jeremy; Anquetin, Sandrine; Favre, Anne-Catherine; François, Baptiste; Vautard, Robert; Tobin, Isabelle

    2015-04-01

    Among the usual methodologies of dynamical or statistical downscaling of climate model, the Analog method appears to be one of the simplest regarding its conceptual nature and its computational costs (Lorentz, 1969). It assumes strong relationships between large scale meteorological variables (predictors) and local weather variables (predictants) so that for two similar large scale situations, the regional consequences on local weather are supposed to be identical. Despite its simplicity, its skill for local scale and/or regional scale prediction is often reported to be very satisfactory. The Analog method has been widely used in Europe to produce precipitation and temperature predictions. For an increasing number of impact studies (e.g. hydrological ones), weather scenarios have to be multivariate and must include additional variables such as wind or radiation. The development of relevant multivariate weather series is however challenging. Weather scenarios have especially to be physically consistent between all weather variables. This issue, which may be critical when relevant hydrological scenarios have to be produced, was to our knowledge fairly not explored. The Analog method has the ability to easily tackle this problem selecting the same analog date for all the weather predictants and thus insuring automatically the physical consistency. However, the best analogs of a given simulation day are likely to depend on the predictant considered. Achieving physical consistency between variables, which implies optimizing the method in a multivariate approach, therefore a priori requires finding a compromise between the different predictors which would be the best for the different predictant taken separately. For the present study, we use a stepwise Analog method for the probabilistic prediction of regional precipitation, temperature, wind and solar radiation. We explore for 12 regions across Europe the variability and diversity of the most skillful parameterisation

  7. Change in avian abundance predicted from regional forest inventory data

    USGS Publications Warehouse

    Twedt, Daniel J.; Tirpak, John M.; Jones-Farrand, D. Todd; Thompson, Frank R., III; Uihlein, William B.; Fitzgerald, Jane A.

    2010-01-01

    An inability to predict population response to future habitat projections is a shortcoming in bird conservation planning. We sought to predict avian response to projections of future forest conditions that were developed from nationwide forest surveys within the Forest Inventory and Analysis (FIA) program. To accomplish this, we evaluated the historical relationship between silvicolous bird populations and FIA-derived forest conditions within 25 ecoregions that comprise the southeastern United States. We aggregated forest area by forest ownership, forest type, and tree size-class categories in county-based ecoregions for 5 time periods spanning 1963-2008. We assessed the relationship of forest data with contemporaneous indices of abundance for 24 silvicolous bird species that were obtained from Breeding Bird Surveys. Relationships between bird abundance and forest inventory data for 18 species were deemed sufficient as predictive models. We used these empirically derived relationships between regional forest conditions and bird populations to predict relative changes in abundance of these species within ecoregions that are anticipated to coincide with projected changes in forest variables through 2040. Predicted abundances of these 18 species are expected to remain relatively stable in over a quarter (27%) of the ecoregions. However, change in forest area and redistribution of forest types will likely result in changed abundance of some species within many ecosystems. For example, abundances of 11 species, including pine warbler (Dendroica pinus), brown-headed nuthatch (Sitta pusilla), and chuckwills- widow (Caprimulgus carolinensis), are projected to increase within more ecoregions than ecoregions where they will decrease. For 6 other species, such as blue-winged warbler (Vermivora pinus), Carolina wren (Thryothorus ludovicianus), and indigo bunting (Passerina cyanea), we projected abundances will decrease within more ecoregions than ecoregions where they will

  8. Prostate cancer region prediction using MALDI mass spectra

    NASA Astrophysics Data System (ADS)

    Vadlamudi, Ayyappa; Chuang, Shao-Hui; Sun, Xiaoyan; Cazares, Lisa; Nyalwidhe, Julius; Troyer, Dean; Semmes, O. John; Li, Jiang; McKenzie, Frederic D.

    2010-03-01

    For the early detection of prostate cancer, the analysis of the Prostate-specific antigen (PSA) in serum is currently the most popular approach. However, previous studies show that 15% of men have prostate cancer even their PSA concentrations are low. MALDI Mass Spectrometry (MS) proves to be a better technology to discover molecular tools for early cancer detection. The molecular tools or peptides are termed as biomarkers. Using MALDI MS data from prostate tissue samples, prostate cancer biomarkers can be identified by searching for molecular or molecular combination that can differentiate cancer tissue regions from normal ones. Cancer tissue regions are usually identified by pathologists after examining H&E stained histological microscopy images. Unfortunately, histopathological examination is currently done on an adjacent slice because the H&E staining process will change tissue's protein structure and it will derogate MALDI analysis if the same tissue is used, while the MALDI imaging process will destroy the tissue slice so that it is no longer available for histopathological exam. For this reason, only the most confident cancer region resulting from the histopathological examination on an adjacent slice will be used to guide the biomarker identification. It is obvious that a better cancer boundary delimitation on the MALDI imaging slice would be beneficial. In this paper, we proposed methods to predict the true cancer boundary, using the MALDI MS data, from the most confident cancer region given by pathologists on an adjacent slice.

  9. The role of radiation-dynamics interaction in regional numerical weather prediction

    NASA Technical Reports Server (NTRS)

    Chang, Chia-Bo

    1988-01-01

    The role of radiation-dynamics interaction in regional numerical weather prediction of severe storm environment and mesoscale convective systems over the United States is researched. Based upon the earlier numerical model simulation experiments, it is believed that such interaction can have a profound impact on the dynamics and thermodynamics of regional weather systems. The research will be carried out using real-data model forecast experiments performed on the Cray-X/MP computer. The forecasting system to be used is a comprehensive mesoscale prediction system which includes analysis and initialization, the dynamic model, and the post-forecast diagnosis codes. The model physics are currently undergoing many improvements in parameterizing radiation processes in the model atmosphere. The forecast experiments in conjunction with in-depth model verification and diagnosis are aimed at a quantitative understanding of the interaction between atmospheric radiation and regional dynamical processes in mesoscale models as well as in nature. Thus, significant advances in regional numerical weather prediction can be made. Results shall also provide valuable information for observational designs in the area of remote sensing techniques to study the characteristics of air-land thermal interaction and moist processes under various atmospheric conditions.

  10. Predicting Redox Conditions in Groundwater at a Regional Scale.

    PubMed

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

    2015-08-18

    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. PMID:26230618

  11. Advances and Computational Tools towards Predictable Design in Biological Engineering

    PubMed Central

    2014-01-01

    The design process of complex systems in all the fields of engineering requires a set of quantitatively characterized components and a method to predict the output of systems composed by such elements. This strategy relies on the modularity of the used components or the prediction of their context-dependent behaviour, when parts functioning depends on the specific context. Mathematical models usually support the whole process by guiding the selection of parts and by predicting the output of interconnected systems. Such bottom-up design process cannot be trivially adopted for biological systems engineering, since parts function is hard to predict when components are reused in different contexts. This issue and the intrinsic complexity of living systems limit the capability of synthetic biologists to predict the quantitative behaviour of biological systems. The high potential of synthetic biology strongly depends on the capability of mastering this issue. This review discusses the predictability issues of basic biological parts (promoters, ribosome binding sites, coding sequences, transcriptional terminators, and plasmids) when used to engineer simple and complex gene expression systems in Escherichia coli. A comparison between bottom-up and trial-and-error approaches is performed for all the discussed elements and mathematical models supporting the prediction of parts behaviour are illustrated. PMID:25161694

  12. Predicting regional episodic acidification of surface waters using empirical models

    NASA Astrophysics Data System (ADS)

    Eshleman, Keith N.

    1988-07-01

    Studies of individual lakes and streams have documented the occurrence of transient, short-term acidification of surface waters during hydrologic events, but a regional assessment of episodic chemical effects has not been made. Application of a two-box mixing model, together with regional chemistry and deposition data, indicates that acidic episodes (acid neutralizing capacity <0) are likely an important regional phenomenon. Population estimates of the total proportion of acidic stream reaches increased by 40-640% in six subrogions of the eastern United States when episodes were taken into account. Data from a small sample of lakes in the Adirondacks (which appear to be representative of the lake population) show that fall "index" acid neutralizing capacity is an excellent predictor of the minimum episodic ANC measured at the outlets of these lakes during spring snowmelt. While 11% of the Adirondack lakes were acidic at fall overturn, a linear regression model predicts that more than 35% were acidic at their outlets during the spring of 1986.

  13. Advancing Satellite-Based Flood Prediction in Complex Terrain Using High-Resolution Numerical Weather Prediction

    NASA Astrophysics Data System (ADS)

    Zhang, X.; Anagnostou, E. N.; Nikolopoulos, E. I.; Bartsotas, N. S.

    2015-12-01

    Floods constitute one of the most significant and frequent natural hazard in mountainous regions. Satellite-based precipitation products offer in many cases the only available source of QPE. However, satellite-based QPE over complex terrain suffer from significant bias that limits their applicability for hydrologic modeling. In this work we investigate the potential of a new correction procedure, which involves the use of high-resolution numerical weather prediction (NWP) model simulations to adjust satellite QPE. Adjustment is based on the pdf matching of satellite and NWP (used as reference) precipitation distribution. The impact of correction procedure on simulating the hydrologic response is examined for 15 storm events that generated floods over the mountainous Upper Adige region of Northern Italy. Atmospheric simulations were performed at 1-km resolution from a state-of-the-art atmospheric model (RAMS/ICLAMS). The proposed error correction procedure was then applied on the widely used TRMM 3B42 satellite precipitation product and the evaluation of the correction was based on independent in situ precipitation measurements from a dense rain gauge network (1 gauge / 70 km2) available in the study area. Satellite QPE, before and after correction, are used to simulate flood response using ARFFS (Adige River Flood Forecasting System), a semi-distributed hydrologic model, which is used for operational flood forecasting in the region. Results showed that bias in satellite QPE before correction was significant and had a tremendous impact on the simulation of flood peak, however the correction procedure was able to reduce bias in QPE and therefore improve considerably the simulated flood hydrograph.

  14. firestar--advances in the prediction of functionally important residues.

    PubMed

    Lopez, Gonzalo; Maietta, Paolo; Rodriguez, Jose Manuel; Valencia, Alfonso; Tress, Michael L

    2011-07-01

    firestar is a server for predicting catalytic and ligand-binding residues in protein sequences. Here, we present the important developments since the first release of firestar. Previous versions of the server required human interpretation of the results; the server is now fully automatized. firestar has been implemented as a web service and can now be run in high-throughput mode. Prediction coverage has been greatly improved with the extension of the FireDB database and the addition of alignments generated by HHsearch. Ligands in FireDB are now classified for biological relevance. Many of the changes have been motivated by the critical assessment of techniques for protein structure prediction (CASP) ligand-binding prediction experiment, which provided us with a framework to test the performance of firestar. URL: http://firedb.bioinfo.cnio.es/Php/FireStar.php. PMID:21672959

  15. Advanced GIS Exercise: Predicting Rainfall Erosivity Index Using Regression Analysis

    ERIC Educational Resources Information Center

    Post, Christopher J.; Goddard, Megan A.; Mikhailova, Elena A.; Hall, Steven T.

    2006-01-01

    Graduate students from a variety of agricultural and natural resource fields are incorporating geographic information systems (GIS) analysis into their graduate research, creating a need for teaching methodologies that help students understand advanced GIS topics for use in their own research. Graduate-level GIS exercises help students understand…

  16. Factors that Predict Who Takes Advanced Courses in Cognitive Therapy

    ERIC Educational Resources Information Center

    Pehlivanidis, Artemios

    2007-01-01

    Training in Cognitive Therapy (CT) includes theoretical and didactic components combined with clinical supervision. An introductory course in CT might satisfy training needs in psychotherapy and help in the selection of those trainees who wish to continue to an advanced training level. Predictors of success at such an introductory course have been…

  17. Evaluation of precipitation predictions in a regional climate simulation

    SciTech Connect

    Costigan, K.R.; Bossert, J.E.; Langely, D.L.

    1998-12-01

    The research reported here is part of a larger project that is coupling a suite of environmental models to simulate the hydrologic cycle within river basins (Bossert et al., 1999). These models include the Regional Atmospheric Modeling System (RAMS), which provides meteorological variables and precipitation to the Simulator for Processes of Landscapes, Surface/Subsurface Hydrology (SPLASH). SPLASH partitions precipitation into evaporation, transpiration, soil water storage, surface runoff, and subsurface recharge. The runoff is collected within a simple river channel model and the Finite element Heat and Mass (FEHM) subsurface model is linked to the land surface and river flow model components to simulate saturated and unsaturated flow and changes in aquifer levels. The goal is to produce a fully interactive system of atmospheric, surface hydrology, river and groundwater models to allow water and energy feedbacks throughout the system. This paper focuses on the evaluation of the precipitation fields predicted by the RAMS model at different times during the 1992--1993 water year in the Rio Grande basin. The evaluation includes comparing the model predictions to the observed precipitation as reported by Cooperative Summary of the Day and SNOTEL reporting stations.

  18. Advances in fatigue life prediction methodology for metallic materials

    NASA Technical Reports Server (NTRS)

    Newman, J. C., Jr.

    1992-01-01

    The capabilities of a plasticity-induced crack-closure model to predict small- and large-crack growth rates, and in some cases total fatigue life, for four aluminum alloys and three titanium alloys under constant-amplitude, variable-amplitude, and spectrum loading are described. Equations to calculate a cyclic-plastic-zone corrected effective stress-intensity factor range from a cyclic J-integral and crack-closure analysis of large cracks were reviewed. The effective stress-intensity factor range against crack growth rate relations were used in the closure model to predict small- and large-crack growth under variable-amplitude and spectrum loading. Using the closure model and microstructural features, a total fatigue life prediction method is demonstrated for three aluminum alloys under various load histories.

  19. New space technology advances knowledge of the remote polar regions. [Arctic and Antarctic regions

    NASA Technical Reports Server (NTRS)

    Macdonald, W. R.

    1974-01-01

    The application of ERTS-1 imagery is rapidly increasing man's knowledge of polar regions. Products compiled from this imagery at scales of 1:250,000, 1:500,000 and 1:1,000,000 are already providing valuable information to earth scientists working in Antarctica. Significant finds detected by these bench mark products were glaciological changes, advancement in ice fronts, discovery of new geographic features, and the repositioning of nunataks, islands, and ice tongues. Tests conducted in Antarctica have proven the feasibility of tracking Navy navigation satellites to establish ground control for positioning ERTS-1 imagery in remote areas. ERTS imagery coupled with satellite geodesy shows great promise and may prove to be the most practical and cost effective way to meet the small-scale cartographic requirements of the polar science community.

  20. Advances in the Assessment and Prediction of Interpersonal Violence

    ERIC Educational Resources Information Center

    Mills, Jeremy F.

    2005-01-01

    This article underscores the weakness of clinical judgment as a mechanism for prediction with examples from other areas in the psychological literature. Clinical judgment has as its Achilles' heel the reliance on a person to incorporate multiple pieces of information while overcoming human judgment errors - a feat insurmountable thus far. The…

  1. Predictions of the Impacts of Future Marcellus Shale Natural Gas Development on Regional Ozone

    NASA Astrophysics Data System (ADS)

    Roy, A.; Adams, P. J.; Robinson, A. L.

    2012-12-01

    Recent discovery of shale gas reserves, combined with advances in drilling and fracturing technology, are leading to extensive development of natural gas in the Marcellus Shale formation which underlies parts of Pennsylvania, West Virginia, Ohio and New York. To assess the impacts of this development on regional air quality, we have constructed a VOC, NOx and PM2.5 emissions inventory for the development and production of gas from the Marcellus formation. In 2020, we estimate that Marcellus activities will contribute about 12% to both regional NOx and VOC emissions. These numbers were obtained as a best estimate (mean) from a distribution obtained through several Monte Carlo runs. We speciated these emissions for use in a 3-D chemical transport model (PMCAMx) to simulate their effects on regional ozone. The projected Marcellus emissions for 2020 were added to a 2007 base inventory developed from the NEI. We have performed multiple simulations to investigate the effects of Marcellus development on regional air quality. The model predicts significant ozone changes in the Marcellus region with a uniform increase of few ppb across a wide region of the Northeast. Sensitivity studies are being performed to investigate the effects of emissions controls and sensitivity to VOC and NOx emissions.

  2. Climatic Instability and Regional Glacial Advances in the Late Ediacaran

    NASA Astrophysics Data System (ADS)

    Hannah, J. L.; Stein, H. J.; Marolf, N.; Bingen, B.

    2014-12-01

    "snowball Earth" event; rather, there may have been multiple Ediacaran glacial advances - perhaps only at high latitudes - marked by tillites of regional, but not global extent. [1] Bowring et al. 2002, Astrobiology 2: 457-458. [2] Shen et al. 2010, Prec. Res. 177: 241-252. [3] Hannah et al. 2007, Geochim. Cosmochim. Acta 71: A378.

  3. The Source Physics Experiments and Advances in Seismic Explosion Monitoring Predictive Capabilities

    NASA Astrophysics Data System (ADS)

    Walter, W. R.; Ford, S. R.; Antoun, T.; Pitarka, A.; Xu, H.; Vorobiev, O.; Rodgers, A.; Pyle, M. L.

    2012-12-01

    Despite many years of study, a number of seismic explosion phenomena remain incompletely understood. These include the generation of S-waves, the variation of absolute amplitudes with emplacement media differences, and the occasional generation of reversed Rayleigh waves. Advances in numerical methods and increased computational power have improved the physics contained in the modeling software and it is possible to couple non-linear source-region effects to far-field propagation codes to predict seismic observables, thereby allowing end-to-end modeling. However, despite the many sensor records from prior nuclear tests, the data available to develop and validate the simulation codes remain limited in important ways. This is particularly the case for the range of both scaled depths of burial and of source media, especially where full near-field to far-field records are available along with key quantitative parameter data such as depth, material properties and yield. For example, two of the most widely used seismic source models, both derived from the best empirical data, Mueller and Murphy (1971) and Denny and Johnson (1989), predict very different amplitudes for greatly overburied explosions. To provide new data to advance predictive explosion modeling capabilities, the National Nuclear Security Administration (NNSA) is carrying out a series of seven chemical explosions over a range of depths and sizes in the Source Physics Experiments (SPE). These shots are taking place in the Climax Stock granite at the Nevada National Security Site, the location where reversed Rayleigh waves from a nuclear test were first observed in the 1962 HARDHAT event (e.g. Brune and Pomeroy, 1963). Three of the SPE shots have successfully occurred so far, and were well-recorded by an extensive set of instrumentation including seismic, acoustic, EM, and remote sensing. In parallel, detailed site characterization has been conducted using geologic mapping and sampling, borehole geophysics

  4. Life prediction of advanced materials for gas turbine application

    SciTech Connect

    Zamrik, S.Y.; Ray, A.; Koss, D.A.

    1995-10-01

    Most of the studies on the low cycle fatigue life prediction have been reported under isothermal conditions where the deformation of the material is strain dependent. In the development of gas turbines, components such as blades and vanes are exposed to temperature variations in addition to strain cycling. As a result, the deformation process becomes temperature and strain dependent. Therefore, the life of the component becomes sensitive to temperature-strain cycling which produces a process known as {open_quotes}thermomechanical fatigue, or TMF{close_quotes}. The TMF fatigue failure phenomenon has been modeled using conventional fatigue life prediction methods, which are not sufficiently accurate to quantitatively establish an allowable design procedure. To add to the complexity of TMF life prediction, blade and vane substrates are normally coated with aluminide, overlay or thermal barrier type coatings (TBC) where the durability of the component is dominated by the coating/substrate constitutive response and by the fatigue behavior of the coating. A number of issues arise from TMF depending on the type of temperature/strain phase cycle: (1) time-dependent inelastic behavior can significantly affect the stress response. For example, creep relaxation during a tensile or compressive loading at elevated temperatures leads to a progressive increase in the mean stress level under cyclic loading. (2) the mismatch in elastic and thermal expansion properties between the coating and the substrate can lead to significant deviations in the coating stress levels due to changes in the elastic modulii. (3) the {open_quotes}dry{close_quotes} corrosion resistance coatings applied to the substrate may act as primary crack initiation sites. Crack initiation in the coating is a function of the coating composition, its mechanical properties, creep relaxation behavior, thermal strain range and the strain/temperature phase relationship.

  5. Short range prediction and monitoring of downbursts over Indian region

    NASA Astrophysics Data System (ADS)

    Johny, C. J.; Prasad, V. S.; Singh, S. K.; Basu, Swati

    2016-05-01

    Convective downdraft motions and related outflow wind considered as an eventual source of potential damage which can be more severe in the aviation sector. A great variety of atmospheric environments can produce these downdraft motions. These events are not easily detectable using conventional weather radar or wind shear alert systems, while Doppler radars are useful for identifying these Downbursts. In order to identify the situations that can cause these downdraft events different diagnostic tools are designed. Recently launched Indian satellite INSAT-3D, with atmospheric sounder and imager on board, is capable of identifying regions of downburst occurrence and can help in monitoring them in real time. Some Downburst events reported over different parts of India, during January-April period is investigated using Microburst Wind Speed Potential Index (MWPI) and thermodynamic characteristics derived from the NCMRWF GFS (NGFS) model. An attempt is made to make a short range prediction of these events using MWPI computed from NGFS model forecasts. The results are validated with in-situ observations and also by employing INSAT-3D data and it is shown that the method has a reasonable success. All the investigated downdraft events are associated with the hybrid Microburst environment.

  6. Climate fails to predict wood decomposition at regional scales

    NASA Astrophysics Data System (ADS)

    Bradford, Mark A.; Warren, Robert J., II; Baldrian, Petr; Crowther, Thomas W.; Maynard, Daniel S.; Oldfield, Emily E.; Wieder, William R.; Wood, Stephen A.; King, Joshua R.

    2014-07-01

    Decomposition of organic matter strongly influences ecosystem carbon storage. In Earth-system models, climate is a predominant control on the decomposition rates of organic matter. This assumption is based on the mean response of decomposition to climate, yet there is a growing appreciation in other areas of global change science that projections based on mean responses can be irrelevant and misleading. We test whether climate controls on the decomposition rate of dead wood--a carbon stock estimated to represent 73 +/- 6 Pg carbon globally--are sensitive to the spatial scale from which they are inferred. We show that the common assumption that climate is a predominant control on decomposition is supported only when local-scale variation is aggregated into mean values. Disaggregated data instead reveal that local-scale factors explain 73% of the variation in wood decomposition, and climate only 28%. Further, the temperature sensitivity of decomposition estimated from local versus mean analyses is 1.3-times greater. Fundamental issues with mean correlations were highlighted decades ago, yet mean climate-decomposition relationships are used to generate simulations that inform management and adaptation under environmental change. Our results suggest that to predict accurately how decomposition will respond to climate change, models must account for local-scale factors that control regional dynamics.

  7. Modelling Aerodynamically Generated Sound: Recent Advances in Rotor Noise Prediction

    NASA Technical Reports Server (NTRS)

    Brentner, Kenneth S.

    2000-01-01

    A great deal of progress has been made in the modeling of aerodynamically generated sound for rotors over the past decade. The Ffowcs Williams-Hawkings (FW-H ) equation has been the foundation for much of the development. Both subsonic and supersonic quadrupole noise formulations have been developed for the prediction of high-speed impulsive noise. In an effort to eliminate the need to compute the quadrupole contribution, the FW-H has also been utilized on permeable surfaces surrounding all physical noise sources. Comparison of the Kirchhoff formulation for moving surfaces with the FW-H equation have shown that the Kirchhoff formulation for moving surfaces can give erroneous results for aeroacoustic problems.

  8. Recent advances using rodent models for predicting human allergenicity

    SciTech Connect

    Knippels, Leon M.J. . E-mail: Knippels@voeding.tno.nl; Penninks, Andre H.

    2005-09-01

    The potential allergenicity of newly introduced proteins in genetically engineered foods has become an important safety evaluation issue. However, to evaluate the potential allergenicity and the potency of new proteins in our food, there are still no widely accepted and reliable test systems. The best-known allergy assessment proposal for foods derived from genetically engineered plants was the careful stepwise process presented in the so-called ILSI/IFBC decision tree. A revision of this decision tree strategy was proposed by a FAO/WHO expert consultation. As prediction of the sensitizing potential of the novel introduced protein based on animal testing was considered to be very important, animal models were introduced as one of the new test items, despite the fact that non of the currently studied models has been widely accepted and validated yet. In this paper, recent results are summarized of promising models developed in rat and mouse.

  9. Advanced electric field computation for RF sheaths prediction with TOPICA

    NASA Astrophysics Data System (ADS)

    Milanesio, Daniele; Maggiora, Riccardo

    2012-10-01

    The design of an Ion Cyclotron (IC) launcher is not only driven by its coupling properties, but also by its capability of maintaining low parallel electric fields in front of it, in order to provide good power transfer to plasma and to reduce the impurities production. However, due to the impossibility to verify the antenna performances before the starting of the operations, advanced numerical simulation tools are the only alternative to carry out a proper antenna design. With this in mind, it should be clear that the adoption of a code, such as TOPICA [1], able to precisely take into account a realistic antenna geometry and an accurate plasma description, is extremely important to achieve these goals. Because of the recently introduced features that allow to compute the electric field distribution everywhere inside the antenna enclosure and in the plasma column, the TOPICA code appears to be the only alternative to understand which elements may have a not negligible impact on the antenna design and then to suggest further optimizations in order to mitigate RF potentials. The present work documents the evaluation of the electric field map from actual antennas, like the Tore Supra Q5 and the JET A2 launchers, and the foreseen ITER IC antenna. [4pt] [1] D. Milanesio et al., Nucl. Fusion 49, 115019 (2009).

  10. Investigation of advanced UQ for CRUD prediction with VIPRE.

    SciTech Connect

    Eldred, Michael Scott

    2011-09-01

    This document summarizes the results from a level 3 milestone study within the CASL VUQ effort. It demonstrates the application of 'advanced UQ,' in particular dimension-adaptive p-refinement for polynomial chaos and stochastic collocation. The study calculates statistics for several quantities of interest that are indicators for the formation of CRUD (Chalk River unidentified deposit), which can lead to CIPS (CRUD induced power shift). Stochastic expansion methods are attractive methods for uncertainty quantification due to their fast convergence properties. For smooth functions (i.e., analytic, infinitely-differentiable) in L{sup 2} (i.e., possessing finite variance), exponential convergence rates can be obtained under order refinement for integrated statistical quantities of interest such as mean, variance, and probability. Two stochastic expansion methods are of interest: nonintrusive polynomial chaos expansion (PCE), which computes coefficients for a known basis of multivariate orthogonal polynomials, and stochastic collocation (SC), which forms multivariate interpolation polynomials for known coefficients. Within the DAKOTA project, recent research in stochastic expansion methods has focused on automated polynomial order refinement ('p-refinement') of expansions to support scalability to higher dimensional random input spaces [4, 3]. By preferentially refining only in the most important dimensions of the input space, the applicability of these methods can be extended from O(10{sup 0})-O(10{sup 1}) random variables to O(10{sup 2}) and beyond, depending on the degree of anisotropy (i.e., the extent to which randominput variables have differing degrees of influence on the statistical quantities of interest (QOIs)). Thus, the purpose of this study is to investigate the application of these adaptive stochastic expansion methods to the analysis of CRUD using the VIPRE simulation tools for two different plant models of differing random dimension, anisotropy, and

  11. Advance prediction of hypotension at cesarean delivery under spinal anesthesia.

    PubMed

    Kinsella, S M; Norris, M C

    1996-01-01

    Cardiovascular responses to supine inferior vena cava compression might predict hypotension risk during elective cesarean delivery using spinal anesthesia. In this pilot study we investigated 27 women before operation by taking blood pressure and heart rate measurements for 5 min in the left lateral position, 5 min supine, and then performed one further reading in the left lateral position and one sitting. Anesthesia with hyperbaric bupivacaine was rigorously standardised. A pre-operative 'supine stress test', combining an increase in maternal heart rate of greater than 10 beats/min or leg flexion movements while supine, was analysed. A positive supine stress test (SST) was 4.1 times more frequent in those with severe systolic hypotension below 70% of baseline (12 out of 16 women) than in those without (2 out of 11 women), with a sensitivity of 75% (95% C.I. 48% to 93%) and specificity of 82% (95% C.I. 48% to 98%). A positive test was associated with twice as much vasopressor use as a negative test (30.7 +/-/14.5 mg versus 13.5 +/-/ 9.9 mg; P = 0.0014). Unlike the SST, cardiovascular responses to the change from recumbent to sitting (tilt test) were not useful as a predictor of hypotension. PMID:15321375

  12. Hourly Wind Speed Interval Prediction in Arid Regions

    NASA Astrophysics Data System (ADS)

    Chaouch, M.; Ouarda, T.

    2013-12-01

    The long and extended warm and dry summers, the low rate of rain and humidity are the main factors that explain the increase of electricity consumption in hot arid regions. In such regions, the ventilating and air-conditioning installations, that are typically the most energy-intensive among energy consumption activities, are essential for securing healthy, safe and suitable indoor thermal conditions for building occupants and stored materials. The use of renewable energy resources such as solar and wind represents one of the most relevant solutions to overcome the increase of the electricity demand challenge. In the recent years, wind energy is gaining more importance among the researchers worldwide. Wind energy is intermittent in nature and hence the power system scheduling and dynamic control of wind turbine requires an estimate of wind energy. Accurate forecast of wind speed is a challenging task for the wind energy research field. In fact, due to the large variability of wind speed caused by the unpredictable and dynamic nature of the earth's atmosphere, there are many fluctuations in wind power production. This inherent variability of wind speed is the main cause of the uncertainty observed in wind power generation. Furthermore, producing wind power forecasts might be obtained indirectly by modeling the wind speed series and then transforming the forecasts through a power curve. Wind speed forecasting techniques have received substantial attention recently and several models have been developed. Basically two main approaches have been proposed in the literature: (1) physical models such as Numerical Weather Forecast and (2) statistical models such as Autoregressive integrated moving average (ARIMA) models, Neural Networks. While the initial focus in the literature has been on point forecasts, the need to quantify forecast uncertainty and communicate the risk of extreme ramp events has led to an interest in producing probabilistic forecasts. In short term

  13. Investigation of adolescent accident predictive variables in hilly regions.

    PubMed

    Mohanty, Malaya; Gupta, Ankit

    2016-09-01

    The study aims to determine the significant personal and environmental factors in predicting the adolescent accidents in the hilly regions taking into account two cities Hamirpur and Dharamshala, which lie at an average elevation of 700--1000 metres above the mean sea level (MSL). Detailed comparisons between the results of 2 cities are also studied. The results are analyzed to provide the list of most significant factors responsible for adolescent accidents. Data were collected from different schools and colleges of the city with the help of a questionnaire survey. Around 690 responses from Hamirpur and 460 responses from Dharamshala were taken for study and analysis. Standard deviations (SD) of various factors affecting accidents were calculated and factors with relatively very low SD were discarded and other variables were considered for correlations. Correlation was developed using Kendall's-tau and chi-square tests and factors those were found significant were used for modelling. They were - the victim's age, the character of road, the speed of vehicle, and the use of helmet for Hamirpur and for Dharamshala, the kind of vehicle involved was an added variable found responsible for adolescent accidents. A logistic regression was performed to know the effect of each category present in a variable on the occurrence of accidents. Though the age and the speed of vehicle were considered to be important factors for accident occurrence according to Indian accident data records, even the use of helmet comes out as a major concern. The age group of 15-18 and 18-21 years were found to be more susceptible to accidents than the higher age groups. Due to the presence of hilly area, the character of road becomes a major concern for cause of accidents and the topography of the area makes the kind of vehicle involved as a major variable for determining the severity of accidents. PMID:26077876

  14. Context predicts word order processing in Broca's region.

    PubMed

    Kristensen, Line Burholt; Engberg-Pedersen, Elisabeth; Wallentin, Mikkel

    2014-12-01

    The function of the left inferior frontal gyrus (L-IFG) is highly disputed. A number of language processing studies have linked the region to the processing of syntactical structure. Still, there is little agreement when it comes to defining why linguistic structures differ in their effects on the L-IFG. In a number of languages, the processing of object-initial sentences affects the L-IFG more than the processing of subject-initial ones, but frequency and distribution differences may act as confounding variables. Syntactically complex structures (like the object-initial construction in Danish) are often less frequent and only viable in certain contexts. With this confound in mind, the L-IFG activation may be sensitive to other variables than a syntax manipulation on its own. The present fMRI study investigates the effect of a pragmatically appropriate context on the processing of subject-initial and object-initial clauses with the IFG as our ROI. We find that Danish object-initial clauses yield a higher BOLD response in L-IFG, but we also find an interaction between appropriateness of context and word order. This interaction overlaps with traditional syntax areas in the IFG. For object-initial clauses, the effect of an appropriate context is bigger than for subject-initial clauses. This result is supported by an acceptability study that shows that, given appropriate contexts, object-initial clauses are considered more appropriate than subject-initial clauses. The increased L-IFG activation for processing object-initial clauses without a supportive context may be interpreted as reflecting either reinterpretation or the recipients' failure to correctly predict word order from contextual cues. PMID:25000525

  15. Massive global ozone loss predicted following regional nuclear conflict

    PubMed Central

    Mills, Michael J.; Toon, Owen B.; Turco, Richard P.; Kinnison, Douglas E.; Garcia, Rolando R.

    2008-01-01

    We use a chemistry-climate model and new estimates of smoke produced by fires in contemporary cities to calculate the impact on stratospheric ozone of a regional nuclear war between developing nuclear states involving 100 Hiroshima-size bombs exploded in cities in the northern subtropics. We find column ozone losses in excess of 20% globally, 25–45% at midlatitudes, and 50–70% at northern high latitudes persisting for 5 years, with substantial losses continuing for 5 additional years. Column ozone amounts remain near or <220 Dobson units at all latitudes even after three years, constituting an extratropical “ozone hole.” The resulting increases in UV radiation could impact the biota significantly, including serious consequences for human health. The primary cause for the dramatic and persistent ozone depletion is heating of the stratosphere by smoke, which strongly absorbs solar radiation. The smoke-laden air rises to the upper stratosphere, where removal mechanisms are slow, so that much of the stratosphere is ultimately heated by the localized smoke injections. Higher stratospheric temperatures accelerate catalytic reaction cycles, particularly those of odd-nitrogen, which destroy ozone. In addition, the strong convection created by rising smoke plumes alters the stratospheric circulation, redistributing ozone and the sources of ozone-depleting gases, including N2O and chlorofluorocarbons. The ozone losses predicted here are significantly greater than previous “nuclear winter/UV spring” calculations, which did not adequately represent stratospheric plume rise. Our results point to previously unrecognized mechanisms for stratospheric ozone depletion. PMID:18391218

  16. Application of NASA's advanced life support technologies in polar regions

    NASA Astrophysics Data System (ADS)

    Bubenheim, D. L.; Lewis, C.

    1997-01-01

    NASA's advanced life support technologies are being combined with Arctic science and engineering knowledge in the Advanced Life Systems for Extreme Environments (ALSEE) project. This project addresses treatment and reduction of waste, purification and recycling of water, and production of food in remote communities of Alaska. The project focus is a major issue in the state of Alaska and other areas of the Circumpolar North; the health and welfare of people, their lives and the subsistence lifestyle in remote communities, care for the environment, and economic opportunity through technology transfer. The challenge is to implement the technologies in a manner compatible with the social and economic structures of native communities, the state, and the commercial sector. NASA goals are technology selection, system design and methods development of regenerative life support systems for planetary and Lunar bases and other space exploration missions. The ALSEE project will provide similar advanced technologies to address the multiple problems facing the remote communities of Alaska and provide an extreme environment testbed for future space applications. These technologies have never been assembled for this purpose. They offer an integrated approach to solving pressing problems in remote communities.

  17. On Reliability of Regional Decadal Ensemble Prediction for Europe

    NASA Astrophysics Data System (ADS)

    Davary Adalatpanah, F.; Frueh, B.; Lenz, C. J.

    2014-12-01

    Within the MiKlip project the coupled model MPI-ESM is used to perform global decadal hindcast experiments. These experiments, baseline0, are performed in a low-resolution configuration (MPI-ESM-LR) with the latest version of the ocean model MPIOM and the atmospheric component ECHAM6, in a resolution GR15L40 and T063L47 respectively. The MPI-ESM-LR hindcasts are downscaled to the CORDEX-Europe domain with a horizontal grid resolution of 0.22° using the mesoscale non-hydrostatic regional climate model COSMO-CLM (CCLM) (Rockel et al. 2008) with the version of COSMO4.8-clm17 for the time period 1961-2010 realizing hindcasts from 1961 to 2001 each 10 years for one decade. The evaluation run (ERA40 extended by ERA-Interim and downscaled by CCLM) are used to initialize temperature and humidity in/at the soil/surface in the hindcasts. By using driving data with 1-day-lagged initialization, the "initial conditions" perturbation strategy is implemented. The gridded observational E-OBS data in version 8.0 (Haylock et al., 2008) and the CCLM evaluation run are used for evaluation. The focus of this study is on the 2-m temperature over Europe. To filter out the systematic error, anomalies are calculated by considering the time period 1981-2010 as reference period. Before the evaluation of reliability, the forecast quality is assessed by the anomaly correlation (Fig. 1) and the root mean square error (Fig. 2) (Wilks, 2006). The low-pass filtered 2-m temperature anomaly averaged over Europe from reference datasets and the ensemble mean reveals that the CCLM captures the climate change signal. An ensemble prediction system is perfectly reliable when the mean ensemble spread equals the mean RMSE of the ensemble-mean forecast (Palmer, 2006 and Doblas-Reyes, 2013). Therefore the ratio of the ensemble spread to ensemble error defined as ensemble spread score (ESS) (Keller, 2011), is assessed for reliability. The evaluation shows that there is added value for reliability in

  18. Regional Advanced Manufacturing Academy: An Agent of Change

    ERIC Educational Resources Information Center

    Schmeling, Daniel M.; Rose, Kevin

    2010-01-01

    Three Northeast Texas community colleges put aside service delivery areas and matters of "turf" to create Centers of Excellence that provided training throughout a nine county area. This consortium; along with 14 manufacturers, seven economic development corporations, and the regional workforce board, led the change in training a highly skilled…

  19. 76 FR 23543 - The Jobs and Innovation Accelerator Challenge; a Coordinated Initiative To Advance Regional...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-04-27

    ... Economic Development Administration The Jobs and Innovation Accelerator Challenge; a Coordinated Initiative To Advance Regional Competitiveness AGENCY: Economic Development Administration (EDA), Department of... platform for integrating and coordinating the wide range of Federal economic development resources....

  20. Assimilation of thermodynamic information from advanced infrared sounders under partially cloudy skies for regional NWP

    NASA Astrophysics Data System (ADS)

    Wang, Pei; Li, Jun; Goldberg, Mitchell D.; Schmit, Timothy J.; Lim, Agnes H. N.; Li, Zhenglong; Han, Hyojin; Li, Jinlong; Ackerman, Steve A.

    2015-06-01

    Generally, only clear-infrared spectral radiances (not affected by clouds) are assimilated in weather analysis systems. This is due to difficulties in modeling cloudy radiances as well as in observing their vertical structure from space. To take full advantage of the thermodynamic information in advanced infrared (IR) sounder observations requires assimilating radiances from cloud-contaminated regions. An optimal imager/sounder cloud-clearing technique has been developed by the Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin-Madison. This technique can be used to retrieve clear column radiances through combining collocated multiband imager IR clear radiances and the sounder cloudy radiances; no background information is needed in this method. The imager/sounder cloud-clearing technique is similar to that of the microwave/IR cloud clearing in the derivation of the clear-sky equivalent radiances. However, it retains the original IR sounder resolution, which is critical for regional numerical weather prediction applications. In this study, we have investigated the assimilation of cloud-cleared IR sounder radiances using Atmospheric Infrared Sounder (AIRS)/Moderate Resolution Imaging Spectroradiometer for three hurricanes, Sandy (2012), Irene (2011), and Ike (2008). Results show that assimilating additional cloud-cleared AIRS radiances reduces the 48 and 72 h temperature forecast root-mean-square error by 0.1-0.3 K between 300 and 850 hPa. Substantial improvement in reducing track forecasts errors in the range of 10 km to 50 km was achieved.

  1. Development and Application of Advanced Weather Prediction Technologies for the Wind Energy Industry (Invited)

    NASA Astrophysics Data System (ADS)

    Mahoney, W. P.; Wiener, G.; Liu, Y.; Myers, W.; Johnson, D.

    2010-12-01

    Wind energy decision makers are required to make critical judgments on a daily basis with regard to energy generation, distribution, demand, storage, and integration. Accurate knowledge of the present and future state of the atmosphere is vital in making these decisions. As wind energy portfolios expand, this forecast problem is taking on new urgency because wind forecast inaccuracies frequently lead to substantial economic losses and constrain the national expansion of renewable energy. Improved weather prediction and precise spatial analysis of small-scale weather events are crucial for renewable energy management. In early 2009, the National Center for Atmospheric Research (NCAR) began a collaborative project with Xcel Energy Services, Inc. to perform research and develop technologies to improve Xcel Energy's ability to increase the amount of wind energy in their generation portfolio. The agreement and scope of work was designed to provide highly detailed, localized wind energy forecasts to enable Xcel Energy to more efficiently integrate electricity generated from wind into the power grid. The wind prediction technologies are designed to help Xcel Energy operators make critical decisions about powering down traditional coal and natural gas-powered plants when sufficient wind energy is predicted. The wind prediction technologies have been designed to cover Xcel Energy wind resources spanning a region from Wisconsin to New Mexico. The goal of the project is not only to improve Xcel Energy’s wind energy prediction capabilities, but also to make technological advancements in wind and wind energy prediction, expand our knowledge of boundary layer meteorology, and share the results across the renewable energy industry. To generate wind energy forecasts, NCAR is incorporating observations of current atmospheric conditions from a variety of sources including satellites, aircraft, weather radars, ground-based weather stations, wind profilers, and even wind sensors on

  2. Prediction models for platinum-based chemotherapy response and toxicity in advanced NSCLC patients.

    PubMed

    Yin, Ji-Ye; Li, Xi; Li, Xiang-Ping; Xiao, Ling; Zheng, Wei; Chen, Juan; Mao, Chen-Xue; Fang, Chao; Cui, Jia-Jia; Guo, Cheng-Xian; Zhang, Wei; Gao, Yang; Zhang, Chun-Fang; Chen, Zi-Hua; Zhou, Hui; Zhou, Hong-Hao; Liu, Zhao-Qian

    2016-07-10

    In this study, we aimed to establish a platinum-based chemotherapy response and toxicity prediction model in advanced non-small cell lung cancer (NSCLC) patients. 416 single nucleotide polymorphisms (SNPs) in 185 genes were genotyped, and their association with drug response and toxicity were estimated using logistic regression. Nine data mining techniques were employed to establish the prediction model; the sensitivity, specificity, overall accuracy and receiver operating characteristic (ROC) curve were used to assess the models' performance. Finally, selected models were validated in an independent cohort. The models established by naïve Bayesian algorithm had the best performance. The response prediction model achieved a sensitivity of 0.90 and a specificity of 0.47 with the ROC area under curve (AUC) of 0.80. The overall toxicity prediction model achieved a sensitivity of 0.86 and a specificity of 0.46 with the ROC AUC of 0.73. The hematological toxicity prediction model achieved a sensitivity of 0.89 and a specificity of 0.39 with the ROC AUC of 0.76. The gastrointestinal toxicity prediction model achieved a sensitivity of 0.93 and a specificity of 0.35 with the ROC AUC of 0.80. In conclusion, we provided platinum-based chemotherapy response and toxicity prediction models for advanced NSCLC patients. PMID:27126360

  3. Interannual to decadal predictability in the North Atlantic Europe region

    NASA Astrophysics Data System (ADS)

    Jouzeau, A.; Terray, L.

    2003-04-01

    A 200-year control experiment is performed with the third version of the ARPEGE-Climat atmospheric model coupled to the ORCALIM2 (ORCA/Louvain Ice Model) sea-ice/ocean model. This study takes place in the framework of the PREDICATE project. The simulation shows low frequency fluctuations (period of 30-50 years) in the Thermohaline Circulation (THC) of about 15% of the mean transport. Two 25-year long ensemble experiments are then conducted, contrasting opposite phases of the THC: the first ensemble starts at a maximum of the intensity of the THC, the second one at a minimum. For each ensemble, the different members (6 members for each ensemble) only differ by infinitesimal perturbations of their initial atmospheric conditions. We use these ensembles to study the potential predictability at interannual to decadal time scales. The preliminary results suggest the existence of predictability up to several years in the THC and SST in the North Atlantic. On the other hand, there seems to be very little predictability (beyond one year) arising from atmospheric variables. These results are obtained using a simple predictability index introduced by Collins and Allen (2001) which measures the rate of spread of the ensembles of simulations against climatology. A cluster analysis will then be performed to investigate the modification of the frequency of occurrence of the main climatic regimes and their links with the THC states.

  4. Advancement into the Arctic Region for Bioactive Sponge Secondary Metabolites

    PubMed Central

    Abbas, Samuel; Kelly, Michelle; Bowling, John; Sims, James; Waters, Amanda; Hamann, Mark

    2011-01-01

    Porifera have long been a reservoir for the discovery of bioactive compounds and drug discovery. Most research in the area has focused on sponges from tropical and temperate waters, but more recently the focus has shifted to the less accessible colder waters of the Antarctic and, to a lesser extent, the Arctic. The Antarctic region in particular has been a more popular location for natural products discovery and has provided promising candidates for drug development. This article reviews groups of bioactive compounds that have been isolated and reported from the southern reaches of the Arctic Circle, surveys the known sponge diversity present in the Arctic waters, and details a recent sponge collection by our group in the Aleutian Islands, Alaska. The collection has yielded previously undescribed sponge species along with primary activity against opportunistic infectious diseases, malaria, and HCV. The discovery of new sponge species and bioactive crude extracts gives optimism for the isolation of new bioactive compounds from a relatively unexplored source. PMID:22163194

  5. Advanced implementations of the iterative multi region technique

    NASA Astrophysics Data System (ADS)

    Kaburcuk, Fatih

    The integration of the finite-difference time-domain (FDTD) method into the iterative multi-region (IMR) technique, an iterative approach used to solve large-scale electromagnetic scattering and radiation problems, is presented in this dissertation. The idea of the IMR technique is to divide a large problem domain into smaller subregions, solve each subregion separately, and combine the solutions of subregions after introducing the effect of interaction to obtain solutions at multiple frequencies for the large domain. Solution of the subregions using the frequency domain solvers has been the preferred approach as such solutions using time domain solvers require computationally expensive bookkeeping of time signals between subregions. In this contribution we present an algorithm that makes it feasible to use the FDTD method, a time domain numerical technique, in the IMR technique to obtain solutions at a pre-specified number of frequencies in a single simulation. As a result, a considerable reduction in memory storage requirements and computation time is achieved. A hybrid method integrated into the IMR technique is also presented in this work. This hybrid method combines the desirable features of the method of moments (MoM) and the FDTD method to solve large-scale radiation problems more efficiently. The idea of this hybrid method based on the IMR technique is to divide an original problem domain into unconnected subregions and use the more appropriate method in each domain. The most prominent feature of this proposed method is to obtain solutions at multiple frequencies in a single IMR simulation by constructing time-limited waveforms. The performance of the proposed method is investigated numerically using different configurations composed of two, three, and four objects.

  6. New Ground Motion Prediction Models for Caucasus Region

    NASA Astrophysics Data System (ADS)

    Jorjiashvili, N.

    2012-12-01

    The Caucasus is a region of numerous natural hazards and ensuing disasters. Analysis of the losses due to past disasters indicates the those most catastrophic in the region have historically been due to strong earthquakes. Estimation of expected ground motion is a fundamental earthquake hazard assessment. The most commonly used parameter for attenuation relation is peak ground acceleration because this parameter gives useful information for Seismic Hazard Assessment. Because of this, many peak ground acceleration attenuation relations have been developed by different authors. Besides, a few attenuation relations were developed for Caucasus region: Ambraseys et al. (1996,2005) which were based on entire European region and they were not focused locally on Caucasus Region; Smit et.al. (2000) that was based on a small amount of acceleration data that really is not enough. Since 2003 construction of Georgian Digital Seismic Network has started with the help of number of International organizations, Projects and Private companies. The works conducted involved scientific as well as organizational activities: Resolving technical problems concerning communication and data transmission. Thus, today we have a possibility to get real time data and make scientific research based on digital seismic data. Generally, ground motion and damage are influenced by the magnitude of the earthquake, the distance from the seismic source to site, the local ground conditions and the characteristics of buildings. Estimation of expected ground motion is a fundamental earthquake hazard assessment. This is the reason why this topic is emphasized in this study. In this study new GMP models are obtained based on new data from Georgian seismic network and also from neighboring countries. Estimation of models are obtained by classical, statistical way, regression analysis. Also site ground conditions are considered because the same earthquake recorded at the same distance may cause different damage

  7. Predicting Regional Self-identification from Spatial Network Models

    PubMed Central

    Almquist, Zack W.; Butts, Carter T.

    2014-01-01

    Social scientists characterize social life as a hierarchy of environments, from the micro level of an individual’s knowledge and perceptions to the macro level of large-scale social networks. In accordance with this typology, individuals are typically thought to reside in micro- and macro-level structures, composed of multifaceted relations (e.g., acquaintanceship, friendship, and kinship). This article analyzes the effects of social structure on micro outcomes through the case of regional identification. Self identification occurs in many different domains, one of which is regional; i.e., the identification of oneself with a locationally-associated group (e.g., a “New Yorker” or “Parisian”). Here, regional self-identification is posited to result from an influence process based on the location of an individual’s alters (e.g., friends, kin or coworkers), such that one tends to identify with regions in which many of his or her alters reside. The structure of this paper is laid out as follows: initially, we begin with a discussion of the relevant social science literature for both social networks and identification. This discussion is followed with one about competing mechanisms for regional identification that are motivated first from the social network literature, and second by the social psychological and cognitive literature of decision making and heuristics. Next, the paper covers the data and methods employed to test the proposed mechanisms. Finally, the paper concludes with a discussion of its findings and further implications for the larger social science literature. PMID:25684791

  8. Urinary π-glutathione S-transferase Predicts Advanced Acute Kidney Injury Following Cardiovascular Surgery.

    PubMed

    Shu, Kai-Hsiang; Wang, Chih-Hsien; Wu, Che-Hsiung; Huang, Tao-Min; Wu, Pei-Chen; Lai, Chien-Heng; Tseng, Li-Jung; Tsai, Pi-Ru; Connolly, Rory; Wu, Vin-Cent

    2016-01-01

    Urinary biomarkers augment the diagnosis of acute kidney injury (AKI), with AKI after cardiovascular surgeries being a prototype of prognosis scenario. Glutathione S-transferases (GST) were evaluated as biomarkers of AKI. Urine samples were collected in 141 cardiovascular surgical patients and analyzed for urinary alpha-(α-) and pi-(π-) GSTs. The outcomes of advanced AKI (KDIGO stage 2, 3) and all-cause in-patient mortality, as composite outcome, were recorded. Areas under the receiver operator characteristic (ROC) curves and multivariate generalized additive model (GAM) were applied to predict outcomes. Thirty-eight (26.9%) patients had AKI, while 12 (8.5%) were with advanced AKI. Urinary π-GST differentiated patients with/without advanced AKI or composite outcome after surgery (p < 0.05 by generalized estimating equation). Urinary π-GST predicted advanced AKI at 3 hrs post-surgery (p = 0.033) and composite outcome (p = 0.009), while the corresponding ROC curve had AUC of 0.784 and 0.783. Using GAM, the cutoff value of 14.7 μg/L for π-GST showed the best performance to predict composite outcome. The addition of π-GST to the SOFA score improved risk stratification (total net reclassification index = 0.47). Thus, urinary π-GST levels predict advanced AKI or hospital mortality after cardiovascular surgery and improve in SOFA outcome assessment specific to AKI. PMID:27527370

  9. Urinary π-glutathione S-transferase Predicts Advanced Acute Kidney Injury Following Cardiovascular Surgery

    PubMed Central

    Shu, Kai-Hsiang; Wang, Chih-Hsien; Wu, Che-Hsiung; Huang, Tao-Min; Wu, Pei-Chen; Lai, Chien-Heng; Tseng, Li-Jung; Tsai, Pi-Ru; Connolly, Rory; Wu, Vin-Cent

    2016-01-01

    Urinary biomarkers augment the diagnosis of acute kidney injury (AKI), with AKI after cardiovascular surgeries being a prototype of prognosis scenario. Glutathione S-transferases (GST) were evaluated as biomarkers of AKI. Urine samples were collected in 141 cardiovascular surgical patients and analyzed for urinary alpha-(α-) and pi-(π-) GSTs. The outcomes of advanced AKI (KDIGO stage 2, 3) and all-cause in-patient mortality, as composite outcome, were recorded. Areas under the receiver operator characteristic (ROC) curves and multivariate generalized additive model (GAM) were applied to predict outcomes. Thirty-eight (26.9%) patients had AKI, while 12 (8.5%) were with advanced AKI. Urinary π-GST differentiated patients with/without advanced AKI or composite outcome after surgery (p < 0.05 by generalized estimating equation). Urinary π-GST predicted advanced AKI at 3 hrs post-surgery (p = 0.033) and composite outcome (p = 0.009), while the corresponding ROC curve had AUC of 0.784 and 0.783. Using GAM, the cutoff value of 14.7 μg/L for π-GST showed the best performance to predict composite outcome. The addition of π-GST to the SOFA score improved risk stratification (total net reclassification index = 0.47). Thus, urinary π-GST levels predict advanced AKI or hospital mortality after cardiovascular surgery and improve in SOFA outcome assessment specific to AKI. PMID:27527370

  10. Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms

    NASA Astrophysics Data System (ADS)

    Erdal, Halil Ibrahim; Karakurt, Onur

    2013-01-01

    SummaryStreamflow forecasting is one of the most important steps in the water resources planning and management. Ensemble techniques such as bagging, boosting and stacking have gained popularity in hydrological forecasting in the recent years. The study investigates the potential usage of two ensemble learning paradigms (i.e., bagging; stochastic gradient boosting) in building classification and regression trees (CARTs) ensembles to advance the streamflow prediction accuracy. The study, initially, investigates the use of classification and regression trees for monthly streamflow forecasting and employs a support vector regression (SVR) model as the benchmark model. The analytic results indicate that CART outperforms SVR in both training and testing phases. Although the obtained results of CART model in training phase are considerable, it is not in testing phase. Thus, to optimize the prediction accuracy of CART for monthly streamflow forecasting, we incorporate bagging and stochastic gradient boosting which are rooted in same philosophy, advancing the prediction accuracy of weak learners. Comparing with the results of bagged regression trees (BRTs) and stochastic gradient boosted regression trees (GBRTs) models possess satisfactory monthly streamflow forecasting performance than CART and SVR models. Overall, it is found that ensemble learning paradigms can remarkably advance the prediction accuracy of CART models in monthly streamflow forecasting.

  11. Small Engine Technology (SET) - Task 4, Regional Turboprop/Turbofan Engine Advanced Combustor Study

    NASA Technical Reports Server (NTRS)

    Reynolds, Robert; Srinivasan, Ram; Myers, Geoffrey; Cardenas, Manuel; Penko, Paul F. (Technical Monitor)

    2003-01-01

    Under the SET Program Task 4 - Regional Turboprop/Turbofan Engine Advanced Combustor Study, a total of ten low-emissions combustion system concepts were evaluated analytically for three different gas turbine engine geometries and three different levels of oxides of nitrogen (NOx) reduction technology, using an existing AlliedSignal three-dimensional (3-D) Computational Fluid Dynamics (CFD) code to predict Landing and Takeoff (LTO) engine cycle emission values. A list of potential Barrier Technologies to the successful implementation of these low-NOx combustor designs was created and assessed. A trade study was performed that ranked each of the ten study configurations on the basis of a number of manufacturing and durability factors, in addition to emissions levels. The results of the trade study identified three basic NOx-emissions reduction concepts that could be incorporated in proposed follow-on combustor technology development programs aimed at demonstrating low-NOx combustor hardware. These concepts are: high-flow swirlers and primary orifices, fuel-preparation cans, and double-dome swirlers.

  12. Applications of tree-structured regression for regional precipitation prediction

    NASA Astrophysics Data System (ADS)

    Li, Xiangshang

    2000-11-01

    This thesis presents a Tree-Structured Regression (TSR) method to relate daily precipitation with a variety of free atmosphere variables. Historical data were used to identify distinct weather patterns associated with differing types of precipitation events. Models were developed using 67% of the data for training and the remaining data for model validation. Seasonal models were built for each of four U.S. sites; New Orleans Louisiana, San Antonio and Amarillo of Texas as well as San Francisco California. The average correlation by site between observed and simulated daily precipitation data series range from 0.69 to 0.79 for the training set, and 0.64 to 0.79 for the validation set. Relative humidity related variables were found to be the dominant variables in these TSR models. Output from an NCAR Climate System Model (CSM) transient simulation of climate change were then used to drive the TSR models for predicting precipitation characteristics under climate change. A preliminary screening of the GCM output variables for current climate, however, revealed significant problems for the New Orleans, San Antonio and Amarillo sites. Specifically, the CSM missed the annual trends in humidity for the grid cells containing these sites. CSM output for the San Francisco site was found to be much more reliable. Therefore, we present future precipitation estimates only for the San Francisco site. While both GCM and TSR predict very small change in overall annual precipitation, they differ significantly from season to season.

  13. Evaluation of the NMC regional ensemble prediction system during the Beijing 2008 Olympic Games

    NASA Astrophysics Data System (ADS)

    Li, Xiaoli; Tian, Hua; Deng, Guo

    2011-10-01

    Based on the B08RDP (Beijing 2008 Olympic Games Mesoscale Ensemble Prediction Research and Development Project) that was launched by the World Weather Research Programme (WWRP) in 2004, a regional ensemble prediction system (REPS) at a 15-km horizontal resolution was developed at the National Meteorological Center (NMC) of the China Meteorological Administration (CMA). Supplementing to the forecasters' subjective affirmation on the promising performance of the REPS during the 2008 Beijing Olympic Games (BOG), this paper focuses on the objective verification of the REPS for precipitation forecasts during the BOG period. By use of a set of advanced probabilistic verification scores, the value of the REPS compared to the quasi-operational global ensemble prediction system (GEPS) is assessed for a 36-day period (21 July-24 August 2008). The evaluation here involves different aspects of the REPS and GEPS, including their general forecast skills, specific attributes (reliability and resolution), and related economic values. The results indicate that the REPS generally performs significantly better for the short-range precipitation forecasts than the GEPS, and for light to heavy rainfall events, the REPS provides more skillful forecasts for accumulated 6- and 24-h precipitation. By further identifying the performance of the REPS through the attribute-focused measures, it is found that the advantages of the REPS over the GEPS come from better reliability (smaller biases and better dispersion) and increased resolution. Also, evaluation of a decision-making score reveals that a much larger group of users benefits from using the REPS forecasts than using the single model (the control run) forecasts, especially for the heavy rainfall events.

  14. Prognostication of Survival in Patients With Advanced Cancer: Predicting the Unpredictable?

    PubMed Central

    Hui, David

    2016-01-01

    Background Prognosis is a key driver of clinical decision-making. However, available prognostication tools have limited accuracy and variable levels of validation. Methods Principles of survival prediction and literature on clinician prediction of survival, prognostic factors, and prognostic models were reviewed, with a focus on patients with advanced cancer and a survival rate of a few months or less. Results The 4 principles of survival prediction are (a) prognostication is a process instead of an event, (b) prognostic factors may evolve over the course of the disease, (c) prognostic accuracy for a given prognostic factor/tool varies by the definition of accuracy, the patient population, and the time frame of prediction, and (d) the exact timing of death cannot be predicted with certainty. Clinician prediction of survival rate is the most commonly used approach to formulate prognosis. However, clinicians often overestimate survival rates with the temporal question. Other clinician prediction of survival approaches, such as surprise and probabilistic questions, have higher rates of accuracy. Established prognostic factors in the advanced cancer setting include decreased performance status, delirium, dysphagia, cancer anorexia–cachexia, dyspnea, inflammation, and malnutrition. Novel prognostic factors, such as phase angle, may improve rates of accuracy. Many prognostic models are available, including the Palliative Prognostic Score, the Palliative Prognostic Index, and the Glasgow Prognostic Score. Conclusions Despite the uncertainty in survival prediction, existing prognostic tools can facilitate clinical decision-making by providing approximated time frames (months, weeks, or days). Future research should focus on clarifying and comparing the rates of accuracy for existing prognostic tools, identifying and validating novel prognostic factors, and linking prognostication to decision-making. PMID:26678976

  15. On spatiotemporal series analysis and its application to predict the regional short term climate process

    NASA Astrophysics Data System (ADS)

    Wang, Geli; Yang, Peicai; Lü, Daren

    2004-04-01

    Based on the theory of reconstructing state space, a technique for spatiotemporal series prediction is presented. By means of this technique and NCEP/NCAR data of the monthly mean geopotential height anomaly of the 500-hPa isobaric surface in the Northern Hemisphere, a regional prediction experiment is also carried out. If using the correlation coefficient R between the observed field and the prediction field to measure the prediction accuracy, the averaged R given by 48 prediction samples reaches 21%, which corresponds to the current prediction level for the short range climate process.

  16. Empirical relation between carbonate porosity and thermal maturity: an approach to regional porosity prediction.

    USGS Publications Warehouse

    Schmoker, J.W.

    1984-01-01

    Carbonate porosity can be predicted approximately on a regional scale as a function of thermal maturity. Thus: theta = a (TTI) b, where theta = regional porosity, a = a constant for a given region and varies by an order of magnitude, TTI = Lopatin's time-T index of thermal maturity and b approx -0.372. -K.A.R.

  17. Development of advanced structural analysis methodologies for predicting widespread fatigue damage in aircraft structures

    NASA Technical Reports Server (NTRS)

    Harris, Charles E.; Starnes, James H., Jr.; Newman, James C., Jr.

    1995-01-01

    NASA is developing a 'tool box' that includes a number of advanced structural analysis computer codes which, taken together, represent the comprehensive fracture mechanics capability required to predict the onset of widespread fatigue damage. These structural analysis tools have complementary and specialized capabilities ranging from a finite-element-based stress-analysis code for two- and three-dimensional built-up structures with cracks to a fatigue and fracture analysis code that uses stress-intensity factors and material-property data found in 'look-up' tables or from equations. NASA is conducting critical experiments necessary to verify the predictive capabilities of the codes, and these tests represent a first step in the technology-validation and industry-acceptance processes. NASA has established cooperative programs with aircraft manufacturers to facilitate the comprehensive transfer of this technology by making these advanced structural analysis codes available to industry.

  18. An integrated theory for predicting the hydrothermomechanical response of advanced composite structural components

    NASA Technical Reports Server (NTRS)

    Chamis, C. C.; Lark, R. F.; Sinclair, J. H.

    1977-01-01

    A theory is developed for predicting the hydrothermomechanical response of advanced composite structural components. The combined hydrothermal effects on the mechanical properties of unidirectional composites loaded along the material axis and off-axis, and of angleplied laminates are also evaluated. The materials investigated consist of neat PR-288 epoxy matrix resin and an AS-type graphite fiber/PR-288 resin unidirectional composite.

  19. NOAA Drought Task Force: A Coordinated Research Initiative to Advance Drought Understanding, Monitoring and Prediction

    NASA Astrophysics Data System (ADS)

    Mariotti, A.; Barrie, D.

    2014-12-01

    The NOAA's Drought Task Force was first established in October 2011 and renewed in October 2014 with the goal of achieving significant new advances in the ability to understand, monitor and predict drought over North America. The Task Force is an initiative of NOAA's Climate Program Office Modeling, Analysis, Predictions, and Projections (MAPP) program in support of the National Integrated Drought Information System NIDIS. The Drought Task Force also represents an important research contribution to efforts to develop an international Global Drought Information System (GDIS). The Drought Task Force brings together leading drought scientists research laboratories and/or operational centers from NOAA, other U.S. agencies laboratories and academia. Their concerted research effort builds on individual MAPP research projects and related drought-research sector developments. The projects span the wide spectrum of drought research needed to make fundamental advances, from those aimed at the basic understanding of drought mechanisms to those evaluating new drought monitoring and prediction tools for operational and service purposes. This contribution will present an overview of Drought Task Force activities and plans to date, including highlights of research activities and how the group has been working in partnership with NIDIS and synergy with GDIS to advance the science underpinning the development, assessment and provision of drought information.

  20. Advancing the understanding, monitoring and prediction of North American drought in support of NIDIS

    NASA Astrophysics Data System (ADS)

    Mariotti, Annarita; Pulwarty, Roger

    2014-05-01

    The NOAA's Drought Task Force was established in October 2011 with the goal of achieving significant new advances in the ability to understand, monitor and predict drought over North America. The Task Force is an initiative of NOAA's Climate Program Office Modeling, Analysis, Predictions, and Projections (MAPP) program in support of the National Integrated Drought Information System NIDIS. It brings together over thirty-five leading drought scientists research laboratories and/or operational centers from NOAA, other U.S. agencies laboratories and academia. Their concerted research effort builds on individual MAPP research projects and related drought-research sector developments. The projects span the wide spectrum of drought research needed to make fundamental advances, from those aimed at the basic understanding of drought mechanisms to those evaluating new drought monitoring and prediction tools for operational and service purposes. In this presentation we will show how a coordinated, sustained multidisciplinary effort to assess understanding of both past droughts and emergent events contributes to the effectiveness of early warning systems. This contribution will present an overview of Drought Task Force activities to date, including highlights of research activities and how the group has been working in partnership with NIDIS to advance the science underpinning the development, assessment and provision of drought information.

  1. Measured and predicted rotor performance for the SERI advanced wind turbine blades

    SciTech Connect

    Tangler, J.; Smith, B.; Kelley, N.; Jager, D.

    1992-02-01

    Measured and predicted rotor performance for the SERI advanced wind turbine blades were compared to assess the accuracy of predictions and to identify the sources of error affecting both predictions and measurements. An awareness of these sources of error contributes to improved prediction and measurement methods that will ultimately benefit future rotor design efforts. Propeller/vane anemometers were found to underestimate the wind speed in turbulent environments such as the San Gorgonio Pass wind farm area. Using sonic or cup anemometers, good agreement was achieved between predicted and measured power output for wind speeds up to 8 m/sec. At higher wind speeds an optimistic predicted power output and the occurrence of peak power at wind speeds lower than measurements resulted from the omission of turbulence and yaw error. In addition, accurate two-dimensional (2-D) airfoil data prior to stall and a post stall airfoil data synthesization method that reflects three-dimensional (3-D) effects were found to be essential for accurate performance prediction. 11 refs.

  2. Improved NASA-ANOPP Noise Prediction Computer Code for Advanced Subsonic Propulsion Systems

    NASA Technical Reports Server (NTRS)

    Kontos, K. B.; Janardan, B. A.; Gliebe, P. R.

    1996-01-01

    Recent experience using ANOPP to predict turbofan engine flyover noise suggests that it over-predicts overall EPNL by a significant amount. An improvement in this prediction method is desired for system optimization and assessment studies of advanced UHB engines. An assessment of the ANOPP fan inlet, fan exhaust, jet, combustor, and turbine noise prediction methods is made using static engine component noise data from the CF6-8OC2, E(3), and QCSEE turbofan engines. It is shown that the ANOPP prediction results are generally higher than the measured GE data, and that the inlet noise prediction method (Heidmann method) is the most significant source of this overprediction. Fan noise spectral comparisons show that improvements to the fan tone, broadband, and combination tone noise models are required to yield results that more closely simulate the GE data. Suggested changes that yield improved fan noise predictions but preserve the Heidmann model structure are identified and described. These changes are based on the sets of engine data mentioned, as well as some CFM56 engine data that was used to expand the combination tone noise database. It should be noted that the recommended changes are based on an analysis of engines that are limited to single stage fans with design tip relative Mach numbers greater than one.

  3. Two-Dimensional Finite-Difference Modeling of Broadband Regional Wave Propagation Phenomena: Validation of Regional Three-Dimensional Earth Models and Prediction of Anomalous Regional Phases

    SciTech Connect

    Goldstein, P; Ryall, F D; Pasyanos, M E; Schultz, C A; Walter, W R

    2000-07-18

    An important challenge for seismic monitoring of nuclear explosions at low magnitude to verify a nuclear-test-ban treaty is the development of techniques that use regional phases for detection, location, and identification. In order to use such phases, region-specific earth models and tools are needed that accurately predict features such as travel times, amplitudes, and spectral characteristics. In this paper, we present our efforts to use two-dimensional finite-difference modeling to help develop and validate regional earth models for the Middle East and North Africa and to develop predictive algorithms for identifying anomalous regional phases. To help develop and validate a model for the Middle East and North Africa, we compare data and finite-difference simulations for selected regions. We show that the proposed three-dimensional regional model is a significant improvement over standard one-dimensional models by comparing features of broadband data and simulations and differences between observed and predicted features such as narrow-band group velocities. We show how a potential trade-off between source and structure can be avoided by constraining source parameters such as depth, mechanism, and moment/source-time function with independent data. We also present numerous observations of anomalous timing and amplitude of regional phases and show how incorporation of two-dimensional structure can explain many of these observations. Based on these observations, and the predictive capability of our simulations, we develop a simple model that can accurately predict the timing of such phases.

  4. Plastic Instability in Complex Strain Paths Predicted by Advanced Constitutive Equations

    NASA Astrophysics Data System (ADS)

    Butuc, Marilena C.; Barlat, Frédéric; Gracio, José J.; Vincze, Gabriela

    2011-08-01

    The present paper aims at predicting plastic instabilities under complex loading histories using an advanced sheet metal forming limit model. The onset of localized necking is computed using the Marciniak-Kuczinsky (MK) analysis [1] with a physically-based hardening model and the phenomenological anisotropic yield criterion Yld2000-2d [2]. The hardening model accounts for anisotropic work-hardening induced by the microstructural evolution at large strains, which was proposed by Teodosiu and Hu [3]. Simulations are carried out for linear and complex strain paths. Experimentally, two deep-drawing quality sheet metals are selected: a bake-hardening steel (BH) and a DC06 steel sheet. The validity of the model is assessed by comparing the predicted and experimental forming limits. The remarkable accuracy of the developed software to predict the forming limits under linear and non-linear strain path is obviously due to the performance of the advanced constitutive equations to describe with great detail the material behavior. The effect of strain-induced anisotropy on formability evolution under strain path changes, as predicted by the microstructural hardening model, is particularly well captured by the model.

  5. Unsteady blade surface pressures on a large-scale advanced propeller - Prediction and data

    NASA Technical Reports Server (NTRS)

    Nallasamy, M.; Groeneweg, J. F.

    1990-01-01

    An unsteady three dimensional Euler analysis technique is employed to compute the flowfield of an advanced propeller operating at an angle of attack. The predicted blade pressure waveforms are compared with wind tunnel data at two Mach numbers, 0.5 and 0.2. The inflow angle is three degrees. For an inflow Mach number of 0.5, the predicted pressure response is in fair agreement with data: the predicted phases of the waveforms are in close agreement with data while the magnitudes are underpredicted. At the low Mach number of 0.2 (take-off) the numerical solution shows the formation of a leading edge vortex which is in qualitative agreement with measurements. However, the highly nonlinear pressure response measured on the blade suction surface is not captured in the present inviscid analysis.

  6. Unsteady blade-surface pressures on a large-scale advanced propeller: Prediction and data

    NASA Technical Reports Server (NTRS)

    Nallasamy, M.; Groeneweg, J. F.

    1990-01-01

    An unsteady 3-D Euler analysis technique is employed to compute the flow field of an advanced propeller operating at an angle of attack. The predicted blade pressure waveforms are compared with wind tunnel data at two Mach numbers, 0.5 and 0.2. The inflow angle is three degrees. For an inflow Mach number of 0.5, the predicted pressure response is in fair agreement with data: the predicted phases of the waveforms are in close agreement with data while the magnitudes are underpredicted. At the low Mach number of 0.2 (takeoff), the numerical solution shows the formation of a leading edge vortex which is in qualitative agreement with measurements. However, the highly nonlinear pressure response measured on the blade suction surface is not captured in the present inviscid analysis.

  7. Correlating CCM upper atmosphere parameters to surface observations for regional climate change predictions

    SciTech Connect

    Li, Xiangshang; Sailor, D.J.

    1997-11-01

    This paper explores the use of statistical downscaling of General Circulation Model (GCM) results for the purpose of regional climate change analysis. The strong correlation between surface observations and GCM upper air predictions is used in an approach very similar to the Model Output Statistics approach used in numerical weather prediction. The primary assumption in this analysis is that the statistical relationships remain unchanged under conditions of climatic change. These relations are applied to GCM upper atmosphere predictions for future (2*CO{sub 2}) climate predictions. The result is a set of regional climate change predictions conceptually valid at the scale of cities. The downscaling for specific cities within a GCM grid cell reveals some of the anticipated variability within the grid cell. In addition, multiple linear regression analysis may indicate warming that is significantly higher or lower for a particular region than the raw data from the GCM runs. 3 refs., 3 figs., 2 tabs.

  8. Advanced Technology Tech Prep Partnership for Northern Kane Regional Delivery System. Final Report.

    ERIC Educational Resources Information Center

    Elgin Community Coll., IL.

    A 1-year project was undertaken to continue implementation, evaluation, and revision of a model advanced technology partnership between Elgin Community College (ECC) and the Northern Kane Regional Delivery System in Illinois. The model program, which originally included three high schools, was expanded to include five additional high schools in…

  9. ArcRegionalization: a GIS-based Toolkit to Predict Streamflow in Ungauged Basins

    NASA Astrophysics Data System (ADS)

    Yuan, Y.; Cheng, Q.

    2005-12-01

    Regionalization, which aims to estimate the model parameters for the ungauged basins from the model parameters for the gauged basins within the same region, provides a method to predict the streamflow in the ungauged basins. However, most of regionalization researches have to calibrate model parameters and derive basin descriptors using two isolated software tools: hydrological modelling software and GIS. In order to facilitate model regionalization research, an ArcGIS extension: ArcRegionalization is developed with C# language to integrate basin descriptor derivation and hydrological models. ArcRegionalization not only provides basin descriptors derivation methods, widely used hydrological models, and regionalization methods, but also provides the interfaces to incorporate new basin descriptors, new hydrological models, and new regionalization methods. Finally, a case study of model regionalization in Oak Ridges Moraine area, southern Ontario, Canada is used to demonstrate the functions of ArcRegionalization.

  10. Identification of a DNA methylation signature to predict disease-free survival in locally advanced rectal cancer

    PubMed Central

    Claus, Rainer; Weichenhan, Dieter; Jung, Klaus; Kitz, Julia; Grade, Marian; Wolff, Hendrik A.; Jo, Peter; Doyen, Jérôme; Gérard, Jean-Pierre; Johnsen, Steven A.; Plass, Christoph; Beißbarth, Tim; Ghadimi, Michael

    2014-01-01

    In locally advanced rectal cancer a preoperative predictive biomarker is necessary to adjust treatment specifically for those patients expected to suffer relapse. We applied whole genome methylation CpG island array analyses to an initial set of patients (n=11) to identify differentially methylated regions (DMRs) that separate a good from a bad prognosis group. Using a quantitative high-resolution approach, candidate DMRs were first validated in a set of 61 patients (test set) and then confirmed DMRs were further validated in additional independent patient cohorts (n=71, n=42). We identified twenty highly discriminative DMRs and validated them in the test set using the MassARRAY technique. Ten DMRs could be confirmed which allowed separation into prognosis groups (p=0.0207, HR=4.09). The classifier was validated in two additional cohorts (n=71, p=0.0345, HR=3.57 and n=42, p=0.0113, HR=3.78). Interestingly, six of the ten DMRs represented regions close to the transcriptional start sites of genes which are also marked by the Polycomb Repressor Complex component EZH2. In conclusion we present a classifier comprising 10 DMRs which predicts patient prognosis with a high degree of accuracy. These data may now help to discriminate between patients that may respond better to standard treatments from those that may require alternative modalities. PMID:25261372

  11. A Simple Tool to Predict ESRD Within 1 Year in Elderly Patients with Advanced CKD

    PubMed Central

    Drawz, Paul E.; Goswami, Puja; Azem, Reem; Babineau, Denise C.; Rahman, Mahboob

    2013-01-01

    BACKGROUND/OBJECTIVES Chronic kidney disease (CKD) is common in older patients; currently, no tools are available to predict the risk of end-stage renal disease (ESRD) within 1 year. The goal of this study was to develop and validate a model to predict the 1 year risk for ESRD in elderly subjects with advanced CKD. DESIGN Retrospective study SETTING Veterans Affairs Medical Center PARTICIPANTS Patients over 65 years of age with CKD with an estimated (eGFR) less than 30mL/min/1.73m2. MEASUREMENTS The outcome was ESRD within 1 year of the index eGFR. Cox regression was used to develop a predictive model (VA risk score) which was validated in a separate cohort. RESULTS Of the 1,866 patients in the developmental cohort, 77 developed ESRD. Risk factors for ESRD in the final model were age, congestive heart failure, systolic blood pressure, eGFR, potassium, and albumin. In the validation cohort, the C index for the VA risk score was 0.823. The risk for developing ESRD at 1 year from lowest to highest tertile was 0.08%, 2.7%, and 11.3% (P<0.001). The C-index for the recently published Tangri model in the validation cohort was 0.780. CONCLUSION A new model using commonly available clinical measures shows excellent ability to predict the onset of ESRD within the next year in elderly subjects. Additionally, the Tangri model had very good predictive ability. Patients and physicians can use these risk models to inform decisions regarding preparation for renal replacement therapy in patients with advanced CKD. PMID:23617782

  12. Overview of Heat Addition and Efficiency Predictions for an Advanced Stirling Convertor

    NASA Technical Reports Server (NTRS)

    Wilson, Scott D.; Reid, Terry V.; Schifer, Nicholas A.; Briggs, Maxwell H.

    2012-01-01

    The U.S. Department of Energy (DOE) and Lockheed Martin Space Systems Company (LMSSC) have been developing the Advanced Stirling Radioisotope Generator (ASRG) for use as a power system for space science missions. This generator would use two high-efficiency Advanced Stirling Convertors (ASCs), developed by Sunpower Inc. and NASA Glenn Research Center (GRC). The ASCs convert thermal energy from a radioisotope heat source into electricity. As part of ground testing of these ASCs, different operating conditions are used to simulate expected mission conditions. These conditions require achieving a particular operating frequency, hot end and cold end temperatures, and specified electrical power output for a given net heat input. Microporous bulk insulation is used in the ground support test hardware to minimize the loss of thermal energy from the electric heat source to the environment. The insulation package is characterized before operation to predict how much heat will be absorbed by the convertor and how much will be lost to the environment during operation. In an effort to validate these predictions, numerous tasks have been performed, which provided a more accurate value for net heat input into the ASCs. This test and modeling effort included: (a) making thermophysical property measurements of test setup materials to provide inputs to the numerical models, (b) acquiring additional test data that was collected during convertor tests to provide numerical models with temperature profiles of the test setup via thermocouple and infrared measurements, (c) using multidimensional numerical models (computational fluid dynamics code) to predict net heat input of an operating convertor, and (d) using validation test hardware to provide direct comparison of numerical results and validate the multidimensional numerical models used to predict convertor net heat input. This effort produced high fidelity ASC net heat input predictions, which were successfully validated using

  13. Regional Characterization of the Crust in Metropolitan Areas for Prediction of Strong Ground Motion

    NASA Astrophysics Data System (ADS)

    Hirata, N.; Sato, H.; Koketsu, K.; Umeda, Y.; Iwata, T.; Kasahara, K.

    2003-12-01

    Introduction: After the 1995 Kobe earthquake, the Japanese government increased its focus and funding of earthquake hazards evaluation, studies of man-made structures integrity, and emergency response planning in the major urban centers. A new agency, the Ministry of Education, Science, Sports and Culture (MEXT) has started a five-year program titled as Special Project for Earthquake Disaster Mitigation in Urban Areas (abbreviated to Dai-dai-toku in Japanese) since 2002. The project includes four programs: I. Regional characterization of the crust in metropolitan areas for prediction of strong ground motion. II. Significant improvement of seismic performance of structure. III. Advanced disaster management system. IV. Investigation of earthquake disaster mitigation research results. We will present the results from the first program conducted in 2002 and 2003. Regional Characterization of the Crust in Metropolitan Areas for Prediction of Strong Ground Motion: A long-term goal is to produce map of reliable estimations of strong ground motion. This requires accurate determination of ground motion response, which includes a source process, an effect of propagation path, and near surface response. The new five-year project was aimed to characterize the "source" and "propagation path" in the Kanto (Tokyo) region and Kinki (Osaka) region. The 1923 Kanto Earthquake is one of the important targets to be addressed in the project. The proximity of the Pacific and Philippine Sea subducting plates requires study of the relationship between earthquakes and regional tectonics. This project focuses on identification and geometry of: 1) Source faults, 2) Subducting plates and mega-thrust faults, 3) Crustal structure, 4) Seismogenic zone, 5) Sedimentary basins, 6) 3D velocity properties We have conducted a series of seismic reflection and refraction experiment in the Kanto region. In 2002 we have completed to deploy seismic profiling lines in the Boso peninsula (112 km) and the

  14. Predictive biomarkers of sorafenib efficacy in advanced hepatocellular carcinoma: Are we getting there?

    PubMed Central

    Shao, Yu-Yun; Hsu, Chih-Hung; Cheng, Ann-Lii

    2015-01-01

    Sorafenib is the current standard treatment for advanced hepatocellular carcinoma (HCC), but its efficacy is modest with low response rates and short response duration. Predictive biomarkers for sorafenib efficacy are necessary. However, efforts to determine biomarkers for sorafenib have led only to potential candidates rather than clinically useful predictors. Studies based on patient cohorts identified the potential of blood levels of angiopoietin-2, hepatocyte growth factor, insulin-like growth factor-1, and transforming growth factor-β1 for predicting sorafenib efficacy. Alpha-fetoprotein response, dynamic contrast-enhanced magnetic resonance imaging, and treatment-related side effects may serve as early surrogate markers. Novel approaches based on super-responders or experimental mouse models may provide new directions in biomarker research. These studies identified tumor amplification of FGF3/FGF4 or VEGFA and tumor expression of phospho-Mapk14 and phospho-Atf2 as possible predictive markers that await validation. A group effort that considers various prognostic factors and proper collection of tumor tissues before treatment is imperative for the success of future biomarker research in advanced HCC. PMID:26420960

  15. Interannual variability and predictability over the Arabian Penuinsula Winter monsoon region

    NASA Astrophysics Data System (ADS)

    Adnan Abid, Muhammad; Kucharski, Fred; Almazroui, Mansour; Kang, In-Sik

    2016-04-01

    Interannual winter rainfall variability and its predictability are analysed over the Arabian Peninsula region by using observed and hindcast datasets from the state-of-the-art European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal prediction System 4 for the period 1981-2010. An Arabian winter monsoon index (AWMI) is defined to highlight the Arabian Peninsula as the most representative region for the Northern Hemispheric winter dominating the summer rainfall. The observations show that the rainfall variability is relatively large over the northeast of the Arabian Peninsula. The correlation coefficient between the Nino3.4 index and rainfall in this region is 0.33, suggesting potentially some modest predictability, and indicating that El Nino increases and La Nina decreases the rainfall. Regression analysis shows that upper-level cyclonic circulation anomalies that are forced by El Nino Southern Oscillation (ENSO) are responsible for the winter rainfall anomalies over the Arabian region. The stronger (weaker) mean transient-eddy activity related to the upper-level trough induced by the warm (cold) sea-surface temperatures during El Nino (La Nina) tends to increase (decrease) the rainfall in the region. The model hindcast dataset reproduces the ENSO-rainfall connection. The seasonal mean predictability of the northeast Arabian rainfall index is 0.35. It is shown that the noise variance is larger than the signal over the Arabian Peninsula region, which tends to limit the prediction skill. The potential predictability is generally increased in ENSO years and is, in particular, larger during La Nina compared to El Nino years in the region. Furthermore, central Pacific ENSO events and ENSO events with weak signals in the Indian Ocean tend to increase predictability over the Arabian region.

  16. Prediction of Potential Cancer-Risk Regions Based on Transcriptome Data: Towards a Comprehensive View

    PubMed Central

    Alisoltani, Arghavan; Fallahi, Hossein; Ebrahimi, Mahdi; Ebrahimi, Mansour; Ebrahimie, Esmaeil

    2014-01-01

    A novel integrative pipeline is presented for discovery of potential cancer-susceptibility regions (PCSRs) by calculating the number of altered genes at each chromosomal region, using expression microarray datasets of different human cancers (HCs). Our novel approach comprises primarily predicting PCSRs followed by identification of key genes in these regions to obtain potential regions harboring new cancer-associated variants. In addition to finding new cancer causal variants, another advantage in prediction of such risk regions is simultaneous study of different types of genomic variants in line with focusing on specific chromosomal regions. Using this pipeline we extracted numbers of regions with highly altered expression levels in cancer condition. Regulatory networks were also constructed for different types of cancers following the identification of altered mRNA and microRNAs. Interestingly, results showed that GAPDH, LIFR, ZEB2, mir-21, mir-30a, mir-141 and mir-200c, all located at PCSRs, are common altered factors in constructed networks. We found a number of clusters of altered mRNAs and miRNAs on predicted PCSRs (e.g.12p13.31) and their common regulators including KLF4 and SOX10. Large scale prediction of risk regions based on transcriptome data can open a window in comprehensive study of cancer risk factors and the other human diseases. PMID:24796549

  17. Seasonal Predictability of the Regional Climate of the Mississippi River Basin

    NASA Technical Reports Server (NTRS)

    Tribbia, Joseph; Giorgi, Filippo

    2002-01-01

    This is a report on our accomplishments during the previous year and our wrap-up plans for the coming months for our work in studying the seasonal predictability of precipitation over the Mississippi River Basin. The work accomplished during the grant falls into two broad catagories: (1) diagnosis of regional skill of CCM3; and (2) regional and global model development.

  18. Recent advances in dynamical extra-seasonal to annual climate prediction at IAP/CAS

    NASA Astrophysics Data System (ADS)

    Lin, Zhaohui; Wang, Huijun; Zhou, Guangqing; Chen, Hong; Lang, Xianmei; Zhao, Yan; Zeng, Qingcun

    2004-06-01

    Recent advances in dynamical climate prediction at the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP/CAS) during the last five years have been briefly described in this paper. Firstly, the second generation of the IAP dynamical climate prediction system (IAP DCP-II) has been described, and two sets of hindcast experiments of the summer rainfall anomalies over China for the periods of 1980 1994 with different versions of the IAP AGCM have been conducted. The comparison results show that the predictive skill of summer rainfall anomalies over China is improved with the improved IAP AGCM in which the surface albedo parameterization is modified. Furthermore, IAP DCP-II has been applied to the real-time prediction of summer rainfall anomalies over China since 1998, and the verification results show that IAP DCP-II can quite well capture the large scale patterns of the summer flood/drought situations over China during the last five years (1998 2002). Meanwhile, an investigation has demonstrated the importance of the atmospheric initial conditions on the seasonal climate prediction, along with studies on the influences from surface boundary conditions (e.g., land surface characteristics, sea surface temperature). Certain conclusions have been reached, such as, the initial atmospheric anomalies in spring may play an important role in the summer climate anomalies, and soil moisture anomalies in spring can also have a significant impact on the summer climate anomalies over East Asia. Finally, several practical techniques (e.g., ensemble technique, correction method, etc.), which lead to the increase of the prediction skill for summer rainfall anomalies over China, have also been illustrated. The paper concludes with a list of critical requirements needed for the further improvement of dynamical seasonal climate prediction.

  19. Long-term predictability of regions and dates of strong earthquakes

    NASA Astrophysics Data System (ADS)

    Kubyshen, Alexander; Doda, Leonid; Shopin, Sergey

    2016-04-01

    Results on the long-term predictability of strong earthquakes are discussed. It is shown that dates of earthquakes with M>5.5 could be determined in advance of several months before the event. The magnitude and the region of approaching earthquake could be specified in the time-frame of a month before the event. Determination of number of M6+ earthquakes, which are expected to occur during the analyzed year, is performed using the special sequence diagram of seismic activity for the century time frame. Date analysis could be performed with advance of 15-20 years. Data is verified by a monthly sequence diagram of seismic activity. The number of strong earthquakes expected to occur in the analyzed month is determined by several methods having a different prediction horizon. Determination of days of potential earthquakes with M5.5+ is performed using astronomical data. Earthquakes occur on days of oppositions of Solar System planets (arranged in a single line). At that, the strongest earthquakes occur under the location of vector "Sun-Solar System barycenter" in the ecliptic plane. Details of this astronomical multivariate indicator still require further research, but it's practical significant is confirmed by practice. Another one empirical indicator of approaching earthquake M6+ is a synchronous variation of meteorological parameters: abrupt decreasing of minimal daily temperature, increasing of relative humidity, abrupt change of atmospheric pressure (RAMES method). Time difference of predicted and actual date is no more than one day. This indicator is registered 104 days before the earthquake, so it was called as Harmonic 104 or H-104. This fact looks paradoxical, but the works of A. Sytinskiy and V. Bokov on the correlation of global atmospheric circulation and seismic events give a physical basis for this empirical fact. Also, 104 days is a quarter of a Chandler period so this fact gives insight on the correlation between the anomalies of Earth orientation

  20. Simulation studies of the impact of advanced observing systems on numerical weather prediction

    NASA Technical Reports Server (NTRS)

    Atlas, R.; Kalnay, E.; Susskind, J.; Reuter, D.; Baker, W. E.; Halem, M.

    1984-01-01

    To study the potential impact of advanced passive sounders and lidar temperature, pressure, humidity, and wind observing systems on large-scale numerical weather prediction, a series of realistic simulation studies between the European Center for medium-range weather forecasts, the National Meteorological Center, and the Goddard Laboratory for Atmospheric Sciences is conducted. The project attempts to avoid the unrealistic character of earlier simulation studies. The previous simulation studies and real-data impact tests are reviewed and the design of the current simulation system is described. Consideration is given to the simulation of observations of space-based sounding systems.

  1. Development of a constitutive model for creep and life prediction of advanced silicon nitride ceramics

    SciTech Connect

    Ding, J.L.; Liu, K.C.; Brinkman, C.R.

    1992-12-31

    A constitutive model capable of describing deformation and predicting rupture life was developed for high temperature ceramic materials under general thermal-mechanical loading conditions. The model was developed based on the deformation and fracture behavior observed from a systematic experimental study on an advanced silicon nitride (Si{sub 3}N{sub 4}) ceramic material. Validity of the model was evaluated with reference to creep and creep rupture data obtained under constant and stepwise-varied loading conditions, including the effects of annealing on creep and creep rupture behavior.

  2. A proposed predictive model for advanced fibrosis in patients with chronic hepatitis B and its validation

    PubMed Central

    Nishikawa, Hiroki; Hasegawa, Kunihiro; Ishii, Akio; Takata, Ryo; Enomoto, Hirayuki; Yoh, Kazunori; Kishino, Kyohei; Shimono, Yoshihiro; Iwata, Yoshinori; Nakano, Chikage; Nishimura, Takashi; Aizawa, Nobuhiro; Sakai, Yoshiyuki; Ikeda, Naoto; Takashima, Tomoyuki; Iijima, Hiroko; Nishiguchi, Shuhei

    2016-01-01

    Abstract We created a predictive model using serum-based biomarkers for advanced fibrosis (F3 or more) in patients with chronic hepatitis B (CHB) and to confirm the accuracy in an independent cohort. A total of 249 CHB patients were analyzed. To achieve our study aim, a training group (n = 125) and a validation group (n = 124) were formed. In the training group, parameters related to the presence of advanced fibrosis in univariate and multivariate analyses were examined, and a formula for advanced fibrosis was created. Next, we verified the applicability of the predictive model in the validation group. Multivariate analysis identified that gamma-glutamyl transpeptidase (GGT, P = 0.0343) and platelet count (P = 0.0034) were significant predictors of the presence of advanced fibrosis, while Wisteria floribunda agglutinin-positive Mac-2-binding protein (WFA+-M2BP, P = 0.0741) and hyaluronic acid (P = 0.0916) tended to be significant factors. Using these 4 parameters, we created the following formula: GMPH score = −0.755 − (0.015 × GGT) − (0.268 × WFA+-M2BP) + (0.167 × platelet count) + (0.003 × hyaluronic acid). In 8 analyzed variables (WFA+-M2BP, aspartate aminotransferase-to-platelet ratio index, FIB-4 index, prothrombin time, platelet count, hyaluronic acid, Forns index, and GMPH score), GMPH score had the highest area under the receiver operating characteristic (AUROC) curve for advanced fibrosis with a value of 0.8064 in the training group and in the validation group, GMPH score also had the highest AUROC (0.7782). In all subgroup analyses of the hepatitis B virus (HBV) status (HB surface antigen quantification, HBV-DNA quantification, and HBe antigen seropositivity), GMPH score in F3 or F4 was significantly lower than that in F0 to F2. In the above mentioned 8 variables, differences between the liver fibrosis stages (F0 to F1 vs F2, F2 vs F3, F3 vs F4, F0 to F1 vs F3, F0 to F1 vs F4, and F2 vs

  3. A proposed predictive model for advanced fibrosis in patients with chronic hepatitis B and its validation.

    PubMed

    Nishikawa, Hiroki; Hasegawa, Kunihiro; Ishii, Akio; Takata, Ryo; Enomoto, Hirayuki; Yoh, Kazunori; Kishino, Kyohei; Shimono, Yoshihiro; Iwata, Yoshinori; Nakano, Chikage; Nishimura, Takashi; Aizawa, Nobuhiro; Sakai, Yoshiyuki; Ikeda, Naoto; Takashima, Tomoyuki; Iijima, Hiroko; Nishiguchi, Shuhei

    2016-08-01

    We created a predictive model using serum-based biomarkers for advanced fibrosis (F3 or more) in patients with chronic hepatitis B (CHB) and to confirm the accuracy in an independent cohort.A total of 249 CHB patients were analyzed. To achieve our study aim, a training group (n = 125) and a validation group (n = 124) were formed. In the training group, parameters related to the presence of advanced fibrosis in univariate and multivariate analyses were examined, and a formula for advanced fibrosis was created. Next, we verified the applicability of the predictive model in the validation group.Multivariate analysis identified that gamma-glutamyl transpeptidase (GGT, P = 0.0343) and platelet count (P = 0.0034) were significant predictors of the presence of advanced fibrosis, while Wisteria floribunda agglutinin-positive Mac-2-binding protein (WFA-M2BP, P = 0.0741) and hyaluronic acid (P = 0.0916) tended to be significant factors. Using these 4 parameters, we created the following formula: GMPH score = -0.755 - (0.015 × GGT) - (0.268 × WFA-M2BP) + (0.167 × platelet count) + (0.003 × hyaluronic acid). In 8 analyzed variables (WFA-M2BP, aspartate aminotransferase-to-platelet ratio index, FIB-4 index, prothrombin time, platelet count, hyaluronic acid, Forns index, and GMPH score), GMPH score had the highest area under the receiver operating characteristic (AUROC) curve for advanced fibrosis with a value of 0.8064 in the training group and in the validation group, GMPH score also had the highest AUROC (0.7782). In all subgroup analyses of the hepatitis B virus (HBV) status (HB surface antigen quantification, HBV-DNA quantification, and HBe antigen seropositivity), GMPH score in F3 or F4 was significantly lower than that in F0 to F2. In the above mentioned 8 variables, differences between the liver fibrosis stages (F0 to F1 vs F2, F2 vs F3, F3 vs F4, F0 to F1 vs F3, F0 to F1 vs F4, and F2 vs F4) for the entire

  4. OrfPredictor: predicting protein-coding regions in EST-derived sequences.

    PubMed

    Min, Xiang Jia; Butler, Gregory; Storms, Reginald; Tsang, Adrian

    2005-07-01

    OrfPredictor is a web server designed for identifying protein-coding regions in expressed sequence tag (EST)-derived sequences. For query sequences with a hit in BLASTX, the program predicts the coding regions based on the translation reading frames identified in BLASTX alignments, otherwise, it predicts the most probable coding region based on the intrinsic signals of the query sequences. The output is the predicted peptide sequences in the FASTA format, and a definition line that includes the query ID, the translation reading frame and the nucleotide positions where the coding region begins and ends. OrfPredictor facilitates the annotation of EST-derived sequences, particularly, for large-scale EST projects. OrfPredictor is available at https://fungalgenome.concordia.ca/tools/OrfPredictor.html. PMID:15980561

  5. Predictive and preventive strategies to advance the treatments of cardiovascular and cerebrovascular diseases: the Ukrainian context

    PubMed Central

    2012-01-01

    Despite great efforts in treatments of cardiovascular diseases, the field requires innovative strategies because of high rates of morbidity, mortality and disability, indicating evident deficits in predictive vascular diagnosis and individualized treatment approaches. Talking about the vascular system, currently, physicians are not provided with integrated medical approaches to diagnose and treat vascular diseases. Only an individual global approach to the analysis of all segments in the vascular system of a patient allows finding the optimal way for vascular disease treatment. As for the existing methodology, there is a dominance of static methods such as X-ray contrast angiography and magnetic resonance imaging in angiomode. Taking into account the world experience, this article deals with innovative strategies, aiming at predictive diagnosis in vascular system, personalization of the biomedical treatment approaches, and targeted prevention of individual patient cohorts. Clinical examples illustrate the advances in corresponding healthcare sectors. Recommendations are provided to promote the field. PMID:23083430

  6. Strong topographic sheltering effects lead to spatially complex treeline advance and increased forest density in a subtropical mountain region.

    PubMed

    Greenwood, Sarah; Chen, Jan-Chang; Chen, Chaur-Tzuhn; Jump, Alistair S

    2014-12-01

    Altitudinal treelines are typically temperature limited such that increasing temperatures linked to global climate change are causing upslope shifts of treelines worldwide. While such elevational increases are readily predicted based on shifting isotherms, at the regional level the realized response is often much more complex, with topography and local environmental conditions playing an important modifying role. Here, we used repeated aerial photographs in combination with forest inventory data to investigate changes in treeline position in the Central Mountain Range of Taiwan over the last 60 years. A highly spatially variable upslope advance of treeline was identified in which topography is a major driver of both treeline form and advance. The changes in treeline position that we observed occurred alongside substantial increases in forest density, and lead to a large increase in overall forest area. These changes will have a significant impact on carbon stocking in the high altitude zone, while the concomitant decrease in alpine grassland area is likely to have negative implications for alpine species. The complex and spatially variable changes that we report highlight the necessity for considering local factors such as topography when attempting to predict species distributional responses to warming climate. PMID:25141823

  7. Multiscale Modeling of Advanced Materials for Damage Prediction and Structural Health Monitoring

    NASA Astrophysics Data System (ADS)

    Borkowski, Luke

    Advanced aerospace materials, including fiber reinforced polymer and ceramic matrix composites, are increasingly being used in critical and demanding applications, challenging the current damage prediction, detection, and quantification methodologies. Multiscale computational models offer key advantages over traditional analysis techniques and can provide the necessary capabilities for the development of a comprehensive virtual structural health monitoring (SHM) framework. Virtual SHM has the potential to drastically improve the design and analysis of aerospace components through coupling the complementary capabilities of models able to predict the initiation and propagation of damage under a wide range of loading and environmental scenarios, simulate interrogation methods for damage detection and quantification, and assess the health of a structure. A major component of the virtual SHM framework involves having micromechanics-based multiscale composite models that can provide the elastic, inelastic, and damage behavior of composite material systems under mechanical and thermal loading conditions and in the presence of microstructural complexity and variability. Quantification of the role geometric and architectural variability in the composite microstructure plays in the local and global composite behavior is essential to the development of appropriate scale-dependent unit cells and boundary conditions for the multiscale model. Once the composite behavior is predicted and variability effects assessed, wave-based SHM simulation models serve to provide knowledge on the probability of detection and characterization accuracy of damage present in the composite. The research presented in this dissertation provides the foundation for a comprehensive SHM framework for advanced aerospace materials. The developed models enhance the prediction of damage formation as a result of ceramic matrix composite processing, improve the understanding of the effects of architectural and

  8. A hybrid numerical technique for predicting the aerodynamic and acoustic fields of advanced turboprops

    NASA Technical Reports Server (NTRS)

    Homicz, G. F.; Moselle, J. R.

    1985-01-01

    A hybrid numerical procedure is presented for the prediction of the aerodynamic and acoustic performance of advanced turboprops. A hybrid scheme is proposed which in principle leads to a consistent simultaneous prediction of both fields. In the inner flow a finite difference method, the Approximate-Factorization Alternating-Direction-Implicit (ADI) scheme, is used to solve the nonlinear Euler equations. In the outer flow the linearized acoustic equations are solved via a Boundary-Integral Equation (BIE) method. The two solutions are iteratively matched across a fictitious interface in the flow so as to maintain continuity. At convergence the resulting aerodynamic load prediction will automatically satisfy the appropriate free-field boundary conditions at the edge of the finite difference grid, while the acoustic predictions will reflect the back-reaction of the radiated field on the magnitude of the loading source terms, as well as refractive effects in the inner flow. The equations and logic needed to match the two solutions are developed and the computer program implementing the procedure is described. Unfortunately, no converged solutions were obtained, due to unexpectedly large running times. The reasons for this are discussed and several means to alleviate the situation are suggested.

  9. National and regional approaches for the prediction of rainfall-induced landslides in Italy: an overview

    NASA Astrophysics Data System (ADS)

    Peruccacci, Silvia; Brunetti, Maria Teresa; Capparelli, Giovanna; Di Pilla, Sergio; Guzzetti, Fausto; Molari, Maurizio; Niccoli, Raffaele; Pagliara, Paola; Pizziolo, Marco; Ponziani, Francesco; Ratto, Sara Maria; Segoni, Samuele; Speranza, Gabriella; Tiranti, Davide; Tonellato, Robero; Vischi, Matteo

    2014-05-01

    In Italy, rainfall-induced landslides with severe consequences in terms of economic damage and casualties occur every year. The Italian National Department for Civil Protection (DPC) has the responsibility, in agreement with regional and local governments, to protect individuals and communities from natural hazards, including landslides. In particular, the DPC has a guiding role in projects and activities for the prevention, forecast and monitoring of landslide risk. The alert system for the landslide risk is assured by the DPC and by the Italian Regions through the network of the regional functional centres, the regional structures and the competence centres. More specifically, each Region has to define procedures and methods to set up customized early warning system for the prediction of rainfall-induced landslides. In this work, we report an overview of approaches, methods and early warning systems adopted by the DPC and by the Italian Regions to forecast the occurrence of rainfall-induced slope failures. This study is a description of the state of the art in the prediction of landslides triggered by rainfall at national and regional scale. The collection and organization of this information is of considerable interest both for the DPC, and for the individual Regions. This overview can be a starting point for a constructive debate about the local expertise, in order to improve the landslide prediction capability and to contribute to reducing landslide risk.

  10. Plasma genetic and genomic abnormalities predict treatment response and clinical outcome in advanced prostate cancer.

    PubMed

    Xia, Shu; Kohli, Manish; Du, Meijun; Dittmar, Rachel L; Lee, Adam; Nandy, Debashis; Yuan, Tiezheng; Guo, Yongchen; Wang, Yuan; Tschannen, Michael R; Worthey, Elizabeth; Jacob, Howard; See, William; Kilari, Deepak; Wang, Xuexia; Hovey, Raymond L; Huang, Chiang-Ching; Wang, Liang

    2015-06-30

    Liquid biopsies, examinations of tumor components in body fluids, have shown promise for predicting clinical outcomes. To evaluate tumor-associated genomic and genetic variations in plasma cell-free DNA (cfDNA) and their associations with treatment response and overall survival, we applied whole genome and targeted sequencing to examine the plasma cfDNAs derived from 20 patients with advanced prostate cancer. Sequencing-based genomic abnormality analysis revealed locus-specific gains or losses that were common in prostate cancer, such as 8q gains, AR amplifications, PTEN losses and TMPRSS2-ERG fusions. To estimate tumor burden in cfDNA, we developed a Plasma Genomic Abnormality (PGA) score by summing the most significant copy number variations. Cox regression analysis showed that PGA scores were significantly associated with overall survival (p < 0.04). After androgen deprivation therapy or chemotherapy, targeted sequencing showed significant mutational profile changes in genes involved in androgen biosynthesis, AR activation, DNA repair, and chemotherapy resistance. These changes may reflect the dynamic evolution of heterozygous tumor populations in response to these treatments. These results strongly support the feasibility of using non-invasive liquid biopsies as potential tools to study biological mechanisms underlying therapy-specific resistance and to predict disease progression in advanced prostate cancer. PMID:25915538

  11. A Model to Predict Nitrogen Losses in Advanced Soil-Based Wastewater Treatment Systems

    NASA Astrophysics Data System (ADS)

    Morales, I.; Cooper, J.; Loomis, G.; Kalen, D.; Amador, J.; Boving, T. B.

    2014-12-01

    Most of the non-point source Nitrogen (N) load in rural areas is attributed to onsite wastewater treatment systems (OWTS). Nitrogen compounds are considered environmental pollutants because they deplete the oxygen availability in water bodies and produce eutrophication. The objective of this study was to simulate the fate and transport of Nitrogen in OWTS. The commercially-available 2D/3D HYDRUS software was used to develop a transport and fate model. Experimental data from a laboratory meso-cosm study included the soil moisture content, NH4 and NO3- data. That data set was used to calibrate the model. Three types of OWTS were simulated: (1) pipe-and-stone (P&S), (2) advanced soil drainfields, pressurized shallow narrow drainfield (SND) and (3) Geomat (GEO), a variation of SND. To better understand the nitrogen removal mechanism and the performance of OWTS technologies, replicate (n = 3) intact soil mesocosms were used with 15N-labelled nitrogen inputs. As a result, it was estimated that N removal by denitrification was predominant in P&S. However, it is suggested that N was removed by nitrification in SND and GEO. The calibrated model was used to estimate Nitrogen fluxes for both conventional and advanced OWTS. Also, the model predicted the N losses from nitrification and denitrification in all OWTS. These findings help to provide practitioners with guidelines to estimate N removal efficiencies for OWTS, and predict N loads and spatial distribution for identifying non-point sources.

  12. Plasma genetic and genomic abnormalities predict treatment response and clinical outcome in advanced prostate cancer

    PubMed Central

    Du, Meijun; Dittmar, Rachel L.; Lee, Adam; Nandy, Debashis; Yuan, Tiezheng; Guo, Yongchen; Wang, Yuan; Tschannen, Michael R.; Worthey, Elizabeth; Jacob, Howard; See, William; Kilari, Deepak; Wang, Xuexia; Hovey, Raymond L.; Huang, Chiang-Ching; Wang, Liang

    2015-01-01

    Liquid biopsies, examinations of tumor components in body fluids, have shown promise for predicting clinical outcomes. To evaluate tumor-associated genomic and genetic variations in plasma cell-free DNA (cfDNA) and their associations with treatment response and overall survival, we applied whole genome and targeted sequencing to examine the plasma cfDNAs derived from 20 patients with advanced prostate cancer. Sequencing-based genomic abnormality analysis revealed locus-specific gains or losses that were common in prostate cancer, such as 8q gains, AR amplifications, PTEN losses and TMPRSS2-ERG fusions. To estimate tumor burden in cfDNA, we developed a Plasma Genomic Abnormality (PGA) score by summing the most significant copy number variations. Cox regression analysis showed that PGA scores were significantly associated with overall survival (p < 0.04). After androgen deprivation therapy or chemotherapy, targeted sequencing showed significant mutational profile changes in genes involved in androgen biosynthesis, AR activation, DNA repair, and chemotherapy resistance. These changes may reflect the dynamic evolution of heterozygous tumor populations in response to these treatments. These results strongly support the feasibility of using non-invasive liquid biopsies as potential tools to study biological mechanisms underlying therapy-specific resistance and to predict disease progression in advanced prostate cancer. PMID:25915538

  13. A prospective study of the efficacy of magnetic resonance spectroscopy imaging for predicting locally advanced prostate cancer

    PubMed Central

    Razi, Ali; Parizi, Mehdi Kardoust; Kazemeini, Seid Mohammad; Abedi, Akbar

    2015-01-01

    Objective: To evaluate the efficacy of magnetic resonance spectroscopy imaging (MRSI) for predicting locally advanced prostate cancer (PC). Materials and methods: Between April 2009 and July 2012, 80 consecutive patients with clinically localized PC had undergone endorectal MRSI before radical retropubic prostatectomy. Clinicopathological parameters, including age, preoperative prostate-specific antigen (PSA), Gleason score (GS) at biopsy, perinural invasion at biopsy, prostate weight at surgery, GS of surgical specimen, and pathological staging were recorded. The MRSI findings were compared with the histopathological findings of the radical prostatectomy. The diagnostic accuracy measures consisting of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of MRSI, and other variables in the diagnosis of locally advanced PC (Pathology Stages pT3a, pT3b, or pT4) were evaluated. Results: Sensitivity, specificity, PPV, and NPV of MRSI in detecting locally advanced PC is 42.4%, 93.6%, 82.3%, and 69.8%, respectively [area under the receiver operating characteristic (ROC) curve=0.658, p value <0.0001]. MRSI, cancer-positive core percentage at biopsy, and GS at biopsy are more accurate factors among all the predictive variables in predicting locally advanced PC. Conclusion: MRSI may be considered as a complementary diagnostic modality with high specificity and moderate sensitivity in predicting locally advanced PC. Combination of this modality with other predictive factors helps the surgeon and patient to select an appropriate treatment strategy. PMID:26328204

  14. Advanced validation of CFD-FDTD combined method using highly applicable solver for reentry blackout prediction

    NASA Astrophysics Data System (ADS)

    Takahashi, Yusuke

    2016-01-01

    An analysis model of plasma flow and electromagnetic waves around a reentry vehicle for radio frequency blackout prediction during aerodynamic heating was developed in this study. The model was validated based on experimental results from the radio attenuation measurement program. The plasma flow properties, such as electron number density, in the shock layer and wake region were obtained using a newly developed unstructured grid solver that incorporated real gas effect models and could treat thermochemically non-equilibrium flow. To predict the electromagnetic waves in plasma, a frequency-dependent finite-difference time-domain method was used. Moreover, the complicated behaviour of electromagnetic waves in the plasma layer during atmospheric reentry was clarified at several altitudes. The prediction performance of the combined model was evaluated with profiles and peak values of the electron number density in the plasma layer. In addition, to validate the models, the signal losses measured during communication with the reentry vehicle were directly compared with the predicted results. Based on the study, it was suggested that the present analysis model accurately predicts the radio frequency blackout and plasma attenuation of electromagnetic waves in plasma in communication.

  15. Advancing decadal-scale climate prediction in the North Atlantic sector.

    PubMed

    Keenlyside, N S; Latif, M; Jungclaus, J; Kornblueh, L; Roeckner, E

    2008-05-01

    The climate of the North Atlantic region exhibits fluctuations on decadal timescales that have large societal consequences. Prominent examples include hurricane activity in the Atlantic, and surface-temperature and rainfall variations over North America, Europe and northern Africa. Although these multidecadal variations are potentially predictable if the current state of the ocean is known, the lack of subsurface ocean observations that constrain this state has been a limiting factor for realizing the full skill potential of such predictions. Here we apply a simple approach-that uses only sea surface temperature (SST) observations-to partly overcome this difficulty and perform retrospective decadal predictions with a climate model. Skill is improved significantly relative to predictions made with incomplete knowledge of the ocean state, particularly in the North Atlantic and tropical Pacific oceans. Thus these results point towards the possibility of routine decadal climate predictions. Using this method, and by considering both internal natural climate variations and projected future anthropogenic forcing, we make the following forecast: over the next decade, the current Atlantic meridional overturning circulation will weaken to its long-term mean; moreover, North Atlantic SST and European and North American surface temperatures will cool slightly, whereas tropical Pacific SST will remain almost unchanged. Our results suggest that global surface temperature may not increase over the next decade, as natural climate variations in the North Atlantic and tropical Pacific temporarily offset the projected anthropogenic warming. PMID:18451859

  16. Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data

    PubMed Central

    Ribay, Kathryn; Kim, Marlene T.; Wang, Wenyi; Pinolini, Daniel; Zhu, Hao

    2016-01-01

    Estrogen receptors (ERα) are a critical target for drug design as well as a potential source of toxicity when activated unintentionally. Thus, evaluating potential ERα binding agents is critical in both drug discovery and chemical toxicity areas. Using computational tools, e.g., Quantitative Structure-Activity Relationship (QSAR) models, can predict potential ERα binding agents before chemical synthesis. The purpose of this project was to develop enhanced predictive models of ERα binding agents by utilizing advanced cheminformatics tools that can integrate publicly available bioassay data. The initial ERα binding agent data set, consisting of 446 binders and 8307 non-binders, was obtained from the Tox21 Challenge project organized by the NIH Chemical Genomics Center (NCGC). After removing the duplicates and inorganic compounds, this data set was used to create a training set (259 binders and 259 non-binders). This training set was used to develop QSAR models using chemical descriptors. The resulting models were then used to predict the binding activity of 264 external compounds, which were available to us after the models were developed. The cross-validation results of training set [Correct Classification Rate (CCR) = 0.72] were much higher than the external predictivity of the unknown compounds (CCR = 0.59). To improve the conventional QSAR models, all compounds in the training set were used to search PubChem and generate a profile of their biological responses across thousands of bioassays. The most important bioassays were prioritized to generate a similarity index that was used to calculate the biosimilarity score between each two compounds. The nearest neighbors for each compound within the set were then identified and its ERα binding potential was predicted by its nearest neighbors in the training set. The hybrid model performance (CCR = 0.94 for cross validation; CCR = 0.68 for external prediction) showed significant improvement over the original QSAR

  17. DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields

    PubMed Central

    Wang, Sheng; Weng, Shunyan; Ma, Jianzhu; Tang, Qingming

    2015-01-01

    Intrinsically disordered proteins or protein regions are involved in key biological processes including regulation of transcription, signal transduction, and alternative splicing. Accurately predicting order/disorder regions ab initio from the protein sequence is a prerequisite step for further analysis of functions and mechanisms for these disordered regions. This work presents a learning method, weighted DeepCNF (Deep Convolutional Neural Fields), to improve the accuracy of order/disorder prediction by exploiting the long-range sequential information and the interdependency between adjacent order/disorder labels and by assigning different weights for each label during training and prediction to solve the label imbalance issue. Evaluated by the CASP9 and CASP10 targets, our method obtains 0.855 and 0.898 AUC values, which are higher than the state-of-the-art single ab initio predictors. PMID:26230689

  18. Early Identification of Students Predicted to Enroll in Advanced, Upper-Level High School Courses: An Examination of Validity

    ERIC Educational Resources Information Center

    DeRose, Diego S.; Clement, Russell W.

    2011-01-01

    Broward County Public Schools' Research Services department uses logistic regression analysis to compute an indicator to predict student enrollment in advanced high school courses, for students entering ninth grade for the first time. This prediction indicator, along with other student characteristics, supports high school guidance staffs in…

  19. A research program for improving heat transfer prediction for the laminar to turbulent transition region of turbine vanes/blades

    NASA Technical Reports Server (NTRS)

    Simon, Frederick F.

    1993-01-01

    A program sponsored by NASA for the investigation of the heat transfer in the transition region of turbine vanes and blades with the objective of improving the capability for predicting heat transfer is described. The accurate prediction of gas-side heat transfer is important to the determination of turbine longevity, engine performance, and developmental costs. The need for accurate predictions will become greater as the operating temperatures and stage loading levels of advanced turbine engines increase. The present methods for predicting transition shear stress and heat transfer on turbine blades are based on incomplete knowledge and are largely empirical. To meet the objective of the NASA program, a team approach consisting of researchers from government, universities, a research institute, and a small business is presented. The research is divided into the areas of experiments, direct numerical simulations (DNS), and turbulence modeling. A summary of the results to date is given for the above research areas in a high-disturbance environment (bypass transition) with a discussion of the model development necessary for use in numerical codes.

  20. Predicting groundwater redox status on a regional scale using linear discriminant analysis.

    PubMed

    Close, M E; Abraham, P; Humphries, B; Lilburne, L; Cuthill, T; Wilson, S

    2016-08-01

    Reducing conditions are necessary for denitrification, thus the groundwater redox status can be used to identify subsurface zones where potentially significant nitrate reduction can occur. Groundwater chemistry in two contrasting regions of New Zealand was classified with respect to redox status and related to mappable factors, such as geology, topography and soil characteristics using discriminant analysis. Redox assignment was carried out for water sampled from 568 and 2223 wells in the Waikato and Canterbury regions, respectively. For the Waikato region 64% of wells sampled indicated oxic conditions in the water; 18% indicated reduced conditions and 18% had attributes indicating both reducing and oxic conditions termed "mixed". In Canterbury 84% of wells indicated oxic conditions; 10% were mixed; and only 5% indicated reduced conditions. The analysis was performed over three different well depths, <25m, 25 to 100 and >100m. For both regions, the percentage of oxidised groundwater decreased with increasing well depth. Linear discriminant analysis was used to develop models to differentiate between the three redox states. Models were derived for each depth and region using 67% of the data, and then subsequently validated on the remaining 33%. The average agreement between predicted and measured redox status was 63% and 70% for the Waikato and Canterbury regions, respectively. The models were incorporated into GIS and the prediction of redox status was extended over the whole region, excluding mountainous land. This knowledge improves spatial prediction of reduced groundwater zones, and therefore, when combined with groundwater flow paths, improves estimates of denitrification. PMID:27182792

  1. Predicting groundwater redox status on a regional scale using linear discriminant analysis

    NASA Astrophysics Data System (ADS)

    Close, M. E.; Abraham, P.; Humphries, B.; Lilburne, L.; Cuthill, T.; Wilson, S.

    2016-08-01

    Reducing conditions are necessary for denitrification, thus the groundwater redox status can be used to identify subsurface zones where potentially significant nitrate reduction can occur. Groundwater chemistry in two contrasting regions of New Zealand was classified with respect to redox status and related to mappable factors, such as geology, topography and soil characteristics using discriminant analysis. Redox assignment was carried out for water sampled from 568 and 2223 wells in the Waikato and Canterbury regions, respectively. For the Waikato region 64% of wells sampled indicated oxic conditions in the water; 18% indicated reduced conditions and 18% had attributes indicating both reducing and oxic conditions termed "mixed". In Canterbury 84% of wells indicated oxic conditions; 10% were mixed; and only 5% indicated reduced conditions. The analysis was performed over three different well depths, < 25 m, 25 to 100 and > 100 m. For both regions, the percentage of oxidised groundwater decreased with increasing well depth. Linear discriminant analysis was used to develop models to differentiate between the three redox states. Models were derived for each depth and region using 67% of the data, and then subsequently validated on the remaining 33%. The average agreement between predicted and measured redox status was 63% and 70% for the Waikato and Canterbury regions, respectively. The models were incorporated into GIS and the prediction of redox status was extended over the whole region, excluding mountainous land. This knowledge improves spatial prediction of reduced groundwater zones, and therefore, when combined with groundwater flow paths, improves estimates of denitrification.

  2. Bias reduction in decadal predictions of West African monsoon rainfall using regional climate models

    NASA Astrophysics Data System (ADS)

    Paxian, A.; Sein, D.; Panitz, H.-J.; Warscher, M.; Breil, M.; Engel, T.; Tödter, J.; Krause, A.; Cabos Narvaez, W. D.; Fink, A. H.; Ahrens, B.; Kunstmann, H.; Jacob, D.; Paeth, H.

    2016-02-01

    The West African monsoon rainfall is essential for regional food production, and decadal predictions are necessary for policy makers and farmers. However, predictions with global climate models reveal precipitation biases. This study addresses the hypotheses that global prediction biases can be reduced by dynamical downscaling with a multimodel ensemble of three regional climate models (RCMs), a RCM coupled to a global ocean model and a RCM applying more realistic soil initialization and boundary conditions, i.e., aerosols, sea surface temperatures (SSTs), vegetation, and land cover. Numerous RCM predictions have been performed with REMO, COSMO-CLM (CCLM), and Weather Research and Forecasting (WRF) in various versions and for different decades. Global predictions reveal typical positive and negative biases over the Guinea Coast and the Sahel, respectively, related to a southward shifted Intertropical Convergence Zone (ITCZ) and a positive tropical Atlantic SST bias. These rainfall biases are reduced by some regional predictions in the Sahel but aggravated by all RCMs over the Guinea Coast, resulting from the inherited SST bias, increased westerlies and evaporation over the tropical Atlantic and shifted African easterly waves. The coupled regional predictions simulate high-resolution atmosphere-ocean interactions strongly improving the SST bias, the ITCZ shift and the Guinea Coast and Central Sahel precipitation biases. Some added values in rainfall bias are found for more realistic SST and land cover boundary conditions over the Guinea Coast and improved vegetation in the Central Sahel. Thus, the ability of RCMs and improved boundary conditions to reduce rainfall biases for climate impact research depends on the considered West African region.

  3. Assessing Decadal Predictability of the Regional MiKlip Forecast Ensemble for Europe (1961-2010)

    NASA Astrophysics Data System (ADS)

    Mieruch-Schnuelle, S.; Schädler, G.; Feldmann, H.; Lenz, C.; Kothe, S.; Kottmeier, C.

    2013-12-01

    The aim of the large German Ministry for Education and Research (BMBF) project MiKlip (Mittelfristige Klimaprognose, Decadal Climate Prediction) is to develop a decadal forecast system. To explore the feasability and prospects of global and regional predictions on decadal time scales, global as well as regional predictive ensemble hindcasts have been generated. The focus of this contribution lies on the description of the 10 member regional hindcast ensemble for Europe (1961-2010) at 25 km resolution. The regional model COSMO-CLM has been driven by CMIP5 MPI-ESM simulations, which have been generated by 1-day time-lagged initialization of the atmosphere and an anomaly initialization for the ocean. The study aims at the assessment of the decadal variability and predictability against observations. After removing the long term bias as well as the long term linear trend from the data, we applied low pass filters to the original data to separate the decadal climate signal from high frequency noise. The decadal variability and predictability assessment is applied to temperature and precipitation data for the summer and winter half-year averages/sums. The best results have been found for the prediction of decadal temperature anomalies, i.e.~we have detected a distinct predictive skill and reasonable reliability. Hence it is possible to predict temperature variability on decadal timescales, However, the situation is less satisfactory for precipitation. Here we have found regions showing good predictability, but also regions without any predictive skill. Summer half-year temperature anomalies of E-OBS observations (lilac) and CCLM (orange) at a single grid point near Lodz (Poland). The original data are shown as thin lines, whereas the 9 year moving average filters are plotted as thick lines. The gray shaded area depicts the CCLM ensemble spread, i.e. the standard deviation over the ensemble. Additionally, because of the decadal initialization, the decades have been

  4. The prediction of the building precision in the Laser Engineered Net Shaping process using advanced networks

    NASA Astrophysics Data System (ADS)

    Lu, Z. L.; Li, D. C.; Lu, B. H.; Zhang, A. F.; Zhu, G. X.; Pi, G.

    2010-05-01

    Laser Engineered Net Shaping (LENS) is an advanced manufacturing technology, but it is difficult to control the depositing height (DH) of the prototype because there are many technology parameters influencing the forming process. The effect of main parameters (laser power, scanning speed and powder feeding rate) on the DH of single track is firstly analyzed, and then it shows that there is the complex nonlinear intrinsic relationship between them. In order to predict the DH, the back propagation (BP) based network improved with Adaptive learning rate and Momentum coefficient (AM) algorithm, and the least square support vector machine (LS-SVM) network are both adopted. The mapping relationship between above parameters and the DH is constructed according to training samples collected by LENS experiments, and then their generalization ability, function-approximating ability and real-time are contrastively investigated. The results show that although the predicted result by the BP-AM approximates the experimental result, above performance index of the LS-SVM are better than those of the BP-AM. Finally, high-definition thin-walled parts of AISI316L are successfully fabricated. Hence, the LS-SVM network is more suitable for the prediction of the DH.

  5. Neutron flux measurements in the side-core region of Hunterston B advanced gas-cooled reactor

    SciTech Connect

    Allen, D.A.; Shaw, S.E.; Huggon, A.P.; Steadman, R.J.; Thornton, D.A.; Whiley, G.S.

    2011-07-01

    The core restraints of advanced gas-cooled reactors are important structural components that are required to maintain the geometric integrity of the cores. A review of neutron dosimetry for the sister stations Hunterston B and Hinkley Point B identified that earlier conservative assessments predicted high thermal neutron dose rates to key components of the restraint structure (the restraint rod welds), with the implication that some of them may be predicted to fail during a seismic event. A revised assessment was therefore undertaken [Thornton, D. A., Allen, D. A., Tyrrell, R. J., Meese, T. C., Huggon, A.P., Whiley, G. S., and Mossop, J. R., 'A Dosimetry Assessment for the Core Restraint of an Advanced Gas Cooled Reactor,' Proceedings of the 13. International Symposium on Reactor Dosimetry (ISRD-13, May 2008), World Scientific, River Edge, NJ, 2009, W. Voorbraak, L. Debarberis, and P. D'hondt, Eds., pp. 679-687] using a detailed 3D model and a Monte Carlo radiation transport program, MCBEND. This reassessment resulted in more realistic fast and thermal neutron dose recommendations, the latter in particular being much lower than had been thought previously. It is now desirable to improve confidence in these predictions by providing direct validation of the MCBEND model through the use of neutron flux measurements. This paper describes the programme of work being undertaken to deploy two neutron flux measurement 'stringers' within the side-core region of one of the Hunterston B reactors for the purpose of validating the MCBEND model. The design of the stringers and the determination of the preferred deployment locations have been informed by the use of detailed MCBEND flux calculations. These computational studies represent a rare opportunity to design a flux measurement beforehand, with the clear intention of minimising the anticipated uncertainties and obtaining measurements that are known to be representative of the neutron fields to which the vulnerable steel

  6. Skill and predictability in multimodel ensemble forecasts for Northern Hemisphere regions with dominant winter precipitation

    NASA Astrophysics Data System (ADS)

    Ehsan, Muhammad Azhar; Tippett, Michael K.; Almazroui, Mansour; Ismail, Muhammad; Yousef, Ahmed; Kucharski, Fred; Omar, Mohamed; Hussein, Mahmoud; Alkhalaf, Abdulrahman A.

    2016-07-01

    Northern Hemisphere winter precipitation reforecasts from the European Centre for Medium Range Weather Forecast System-4 and six of the models in the North American Multi-Model Ensemble are evaluated, focusing on two regions (Region-A: 20°N-45°N, 10°E-65°E and Region-B: 20°N-55°N, 205°E-255°E) where winter precipitation is a dominant fraction of the annual total and where precipitation from mid-latitude storms is important. Predictability and skill (deterministic and probabilistic) are assessed for 1983-2013 by the multimodel composite (MME) of seven prediction models. The MME climatological mean and variability over the two regions is comparable to observation with some regional differences. The statistically significant decreasing trend observed in Region-B precipitation is captured well by the MME and most of the individual models. El Niño Southern Oscillation is a source of forecast skill, and the correlation coefficient between the Niño3.4 index and precipitation over region A and B is 0.46 and 0.35, statistically significant at the 95 % level. The MME reforecasts weakly reproduce the observed teleconnection. Signal, noise and signal to noise ratio analysis show that the signal variance over two regions is very small as compared to noise variance which tends to reduce the prediction skill. The MME ranked probability skill score is higher than that of individual models, showing the advantage of a multimodel ensemble. Observed Region-A rainfall anomalies are strongly associated with the North Atlantic Oscillation, but none of the models reproduce this relation, which may explain the low skill over Region-A. The superior quality of multimodel ensemble compared with individual models is mainly due to larger ensemble size.

  7. Science and Technology to Advance Regional Security in the Middle East and Central Asia

    SciTech Connect

    Tompson, A F B; Richardson, J H; Ragaini, R C; Knapp, R B; Rosenberg, N D; Smith, D K; Ball, D Y

    2002-10-09

    This paper is concerned with the promotion and advancement of regional security in the Middle East and Central Asia through the development of bilateral and multilateral cooperation on targeted scientific and technical projects. It is widely recognized that increasing tensions and instability in many parts of the world emphasize--or reemphasize--a need to seek and promote regional security in these areas. At the Lawrence Livermore National Laboratory (LLNL), a national security research facility operated for the US Department of Energy, we are pursuing an effort to use science and technology as a ''low risk'' means of engagement in regions of strategic importance to the United States. In particular, we are developing collaborations and cooperative projects among (and between) national laboratory scientists in the US and our various counterparts in the countries of interest.

  8. RCWIM - an improved global water isotope pattern prediction model using fuzzy climatic clustering regionalization

    NASA Astrophysics Data System (ADS)

    Terzer, Stefan; Araguás-Araguás, Luis; Wassenaar, Leonard I.; Aggarwal, Pradeep K.

    2013-04-01

    Prediction of geospatial H and O isotopic patterns in precipitation has become increasingly important to diverse disciplines beyond hydrology, such as climatology, ecology, food authenticity, and criminal forensics, because these two isotopes of rainwater often control the terrestrial isotopic spatial patterns that facilitate the linkage of products (food, wildlife, water) to origin or movement (food, criminalistics). Currently, spatial water isotopic pattern prediction relies on combined regression and interpolation techniques to create gridded datasets by using data obtained from the Global Network of Isotopes In Precipitation (GNIP). However, current models suffer from two shortcomings: (a) models may have limited covariates and/or parameterization fitted to a global domain, which results in poor predictive outcomes at regional scales, or (b) the spatial domain is intentionally restricted to regional settings, and thereby of little use in providing information at global geospatial scales. Here we present a new global climatically regionalized isotope prediction model which overcomes these limitations through the use of fuzzy clustering of climatic data subsets, allowing us to better identify and customize appropriate covariates and their multiple regression coefficients instead of aiming for a one-size-fits-all global fit (RCWIM - Regionalized Climate Cluster Water Isotope Model). The new model significantly reduces the point-based regression residuals and results in much lower overall isotopic prediction uncertainty, since residuals are interpolated onto the regression surface. The new precipitation δ2H and δ18O isoscape model is available on a global scale at 10 arc-minutes spatial and at monthly, seasonal and annual temporal resolution, and will provide improved predicted stable isotope values used for a growing number of applications. The model further provides a flexible framework for future improvements using regional climatic clustering.

  9. DFLpred: High-throughput prediction of disordered flexible linker regions in protein sequences

    PubMed Central

    Meng, Fanchi; Kurgan, Lukasz

    2016-01-01

    Motivation: Disordered flexible linkers (DFLs) are disordered regions that serve as flexible linkers/spacers in multi-domain proteins or between structured constituents in domains. They are different from flexible linkers/residues because they are disordered and longer. Availability of experimentally annotated DFLs provides an opportunity to build high-throughput computational predictors of these regions from protein sequences. To date, there are no computational methods that directly predict DFLs and they can be found only indirectly by filtering predicted flexible residues with predictions of disorder. Results: We conceptualized, developed and empirically assessed a first-of-its-kind sequence-based predictor of DFLs, DFLpred. This method outputs propensity to form DFLs for each residue in the input sequence. DFLpred uses a small set of empirically selected features that quantify propensities to form certain secondary structures, disordered regions and structured regions, which are processed by a fast linear model. Our high-throughput predictor can be used on the whole-proteome scale; it needs <1 h to predict entire proteome on a single CPU. When assessed on an independent test dataset with low sequence-identity proteins, it secures area under the receiver operating characteristic curve equal 0.715 and outperforms existing alternatives that include methods for the prediction of flexible linkers, flexible residues, intrinsically disordered residues and various combinations of these methods. Prediction on the complete human proteome reveals that about 10% of proteins have a large content of over 30% DFL residues. We also estimate that about 6000 DFL regions are long with ≥30 consecutive residues. Availability and implementation: http://biomine.ece.ualberta.ca/DFLpred/. Contact: lkurgan@vcu.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307636

  10. Diesel engine emissions and combustion predictions using advanced mixing models applicable to fuel sprays

    NASA Astrophysics Data System (ADS)

    Abani, Neerav; Reitz, Rolf D.

    2010-09-01

    An advanced mixing model was applied to study engine emissions and combustion with different injection strategies ranging from multiple injections, early injection and grouped-hole nozzle injection in light and heavy duty diesel engines. The model was implemented in the KIVA-CHEMKIN engine combustion code and simulations were conducted at different mesh resolutions. The model was compared with the standard KIVA spray model that uses the Lagrangian-Drop and Eulerian-Fluid (LDEF) approach, and a Gas Jet spray model that improves predictions of liquid sprays. A Vapor Particle Method (VPM) is introduced that accounts for sub-grid scale mixing of fuel vapor and more accurately and predicts the mixing of fuel-vapor over a range of mesh resolutions. The fuel vapor is transported as particles until a certain distance from nozzle is reached where the local jet half-width is adequately resolved by the local mesh scale. Within this distance the vapor particle is transported while releasing fuel vapor locally, as determined by a weighting factor. The VPM model more accurately predicts fuel-vapor penetrations for early cycle injections and flame lift-off lengths for late cycle injections. Engine combustion computations show that as compared to the standard KIVA and Gas Jet spray models, the VPM spray model improves predictions of in-cylinder pressure, heat released rate and engine emissions of NOx, CO and soot with coarse mesh resolutions. The VPM spray model is thus a good tool for efficiently investigating diesel engine combustion with practical mesh resolutions, thereby saving computer time.

  11. Predictions for Swift Follow-up Observations of Advanced LIGO/Virgo Gravitational Wave Sources

    NASA Astrophysics Data System (ADS)

    Racusin, Judith; Evans, Phil; Connaughton, Valerie

    2015-04-01

    The likely detection of gravitational waves associated with the inspiral of neutron star binaries by the upcoming advanced LIGO/Virgo observatories will be complemented by searches for electromagnetic counterparts over large areas of the sky by Swift and other observatories. As short gamma-ray bursts (GRB) are the most likely electromagnetic counterpart candidates to these sources, we can make predictions based upon the last decade of GRB observations by Swift and Fermi. Swift is uniquely capable of accurately localizing new transients rapidly over large areas of the sky in single and tiled pointings, enabling ground-based follow-up. We describe simulations of the detectability of short GRB afterglows by Swift given existing and hypothetical tiling schemes with realistic observing conditions and delays, which guide the optimal observing strategy and improvements provided by coincident detection with observatories such as Fermi-GBM.

  12. Edge Fracture Prediction ofTraditional and Advanced Trimming Processes for AA6111-T4 Sheets

    SciTech Connect

    Hu, Xiaohua; Choi, Kyoo Sil; Sun, Xin; Golovashchenko, Segey F.

    2014-02-15

    This work examines the traditional and advanced trimming of AA6111-T4 aluminum sheets with finite element simulations. The Rice-Tracy damage model is used for the simulation with damage parameters estimated from experimental observation of grain aspect ratio near the fracture surface of trimmed parts. Fine meshes at the shearing zone, adaptive meshing, and adaptive contact techniques are used to accurately capture the contact interactions between the sharp corner of the trimming tools and the blank to be trimmed. To the knowledge of the authors, these are the first trimming simulations that can predict the effects of shearing clearance on burr heights with quantitative accuracy for AA6111-T4 aluminum sheets. In addition, the models have also accurately reproduced the crack initiation site as well as burr and sliver formation mechanisms observed experimentally.

  13. Surprise Questions for Survival Prediction in Patients With Advanced Cancer: A Multicenter Prospective Cohort Study

    PubMed Central

    Hamano, Jun; Morita, Tatsuya; Inoue, Satoshi; Ikenaga, Masayuki; Matsumoto, Yoshihisa; Sekine, Ryuichi; Yamaguchi, Takashi; Hirohashi, Takeshi; Tajima, Tsukasa; Tatara, Ryohei; Watanabe, Hiroaki; Otani, Hiroyuki; Takigawa, Chizuko; Matsuda, Yoshinobu; Nagaoka, Hiroka; Mori, Masanori; Yamamoto, Naoki; Shimizu, Mie; Sasara, Takeshi

    2015-01-01

    Background. Predicting the short-term survival in cancer patients is an important issue for patients, family, and oncologists. Although the prognostic accuracy of the surprise question has value in 1-year mortality for cancer patients, the prognostic value for short-term survival has not been formally assessed. The primary aim of the present study was to assess the prognostic value of the surprise question for 7-day and 30-day survival in patients with advanced cancer. Patients and Methods. The present multicenter prospective cohort study was conducted in Japan from September 2012 through April 2014, involving 16 palliative care units, 19 hospital-based palliative care teams, and 23 home-based palliative care services. Results. We recruited 2,425 patients and included 2,361 for analysis: 912 from hospital-based palliative care teams, 895 from hospital palliative care units, and 554 from home-based palliative care services. The sensitivity, specificity, positive predictive value, and negative predictive value of the 7-day survival surprise question were 84.7% (95% confidence interval [CI], 80.7%–88.0%), 68.0% (95% CI, 67.3%–68.5%), 30.3% (95% CI, 28.9%–31.5%), and 96.4% (95% CI, 95.5%–97.2%), respectively. The sensitivity, specificity, positive predictive value, and negative predictive value for the 30-day surprise question were 95.6% (95% CI, 94.4%–96.6%), 37.0% (95% CI, 35.9%–37.9%), 57.6% (95% CI, 56.8%–58.2%), and 90.4% (95% CI, 87.7%–92.6%), respectively. Conclusion. Surprise questions are useful for screening patients for short survival. However, the high false-positive rates do not allow clinicians to provide definitive prognosis prediction. Implications for Practice: The findings of this study indicate that clinicians can screen patients for 7- or 30-day survival using surprise questions with 90% or more sensitivity. Clinicians cannot provide accurate prognosis estimation, and all patients will not always die within the defined periods. The

  14. Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.

    PubMed

    Khan, Waqasuddin; Duffy, Fergal; Pollastri, Gianluca; Shields, Denis C; Mooney, Catherine

    2013-01-01

    Disordered regions of proteins often bind to structured domains, mediating interactions within and between proteins. However, it is difficult to identify a priori the short disordered regions involved in binding. We set out to determine if docking such peptide regions to peptide binding domains would assist in these predictions.We assembled a redundancy reduced dataset of SLiM (Short Linear Motif) containing proteins from the ELM database. We selected 84 sequences which had an associated PDB structures showing the SLiM bound to a protein receptor, where the SLiM was found within a 50 residue region of the protein sequence which was predicted to be disordered. First, we investigated the Vina docking scores of overlapping tripeptides from the 50 residue SLiM containing disordered regions of the protein sequence to the corresponding PDB domain. We found only weak discrimination of docking scores between peptides involved in binding and adjacent non-binding peptides in this context (AUC 0.58).Next, we trained a bidirectional recurrent neural network (BRNN) using as input the protein sequence, predicted secondary structure, Vina docking score and predicted disorder score. The results were very promising (AUC 0.72) showing that multiple sources of information can be combined to produce results which are clearly superior to any single source.We conclude that the Vina docking score alone has only modest power to define the location of a peptide within a larger protein region known to contain it. However, combining this information with other knowledge (using machine learning methods) clearly improves the identification of peptide binding regions within a protein sequence. This approach combining docking with machine learning is primarily a predictor of binding to peptide-binding sites, and is not intended as a predictor of specificity of binding to particular receptors. PMID:24019881

  15. Prediction of Daily Flow Duration Curves and Streamflow for Ungauged Catchments Using Regional Flow Duration Curves

    EPA Science Inventory

    This study presents a method to predict flow duration curves (FDCs) and streamflow for ungauged catchments in the Mid-Atlantic Region, USA. We selected 29 catchments from the Appalachian Plateau, Ridge and Valley, and Piedmont physiographic provinces to develop and test the propo...

  16. Numerical prediction of three-dimensional juncture region flow using the parabolic Navier-Stokes equations

    NASA Technical Reports Server (NTRS)

    Baker, A. J.; Manhardt, P. D.; Orzechowski, J. A.

    1979-01-01

    A numerical solution algorithm is established for prediction of subsonic turbulent three-dimensional flows in aerodynamic configuration juncture regions. A turbulence closure model is established using the complete Reynolds stress. Pressure coupling is accomplished using the concepts of complementary and particular solutions to a Poisson equation. Specifications for data input juncture geometry modification are presented.

  17. Short communication: Best prediction of 305-day lactation yields with regional and seasonal effects

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In the United States, lactation yields are calculated using best prediction (BP), a method in which test day (TD) data are compared to breed- and parity-specific herd lactation curves that do not account for differences among regions of the country or seasons of calving. This may result in biased es...

  18. Best prediction of lactation yields accounting for regional and seasonal differences

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In the United States, lactation yields are calculated using best prediction (BP), a method in which test day (TD) data are compared to breed- and parity-specific herd lactation curves that do not account for differences among regions of the country or seasons of calving. This may result in biased es...

  19. Warship detection in smoke screen interference based on region of interest for CMAC-prediction

    NASA Astrophysics Data System (ADS)

    Yan, Xiaoke; Shi, Caicheng

    2015-10-01

    Warship detection in smoke screen interference background belongs to the field of object extraction from image with low contrast and low signal/noise ratio. Aimed at the specialty of the complex background, a novel algorithm of warship detection in smoke screen interference based on region of interest for CMAC-prediction is proposed in the article. The regions-of-interest (ROI) must be predicted in target tracking of IR image for increasing capture probability. CMAC estimator can effectually resolve conflict between operational counts and predicting precision. The local fractal dimension is used to differentiate the warship from the ROI. The experimental results show that CMAC can accurately estimate the ROI and a similar performance in a low-noise environment and superiority of the fractal operators in a high noise, the algorithms are effectively for smoke screen interference and are easy to be implemented by parallel processing hardware.

  20. Perspective on Advances in Resonance-Region Nuclear Modeling and Opportunities for Future Research

    SciTech Connect

    Dunn, Michael E; Larson, Nancy M; Derrien, Herve; Leal, Luiz C

    2007-01-01

    The advent of high-fidelity radiation-transport modeling capabilities, coupled with the need to analyze complex nuclear systems, has served to emphasize the importance of high-precision cross section data, including the associated covariance information. Due to the complex nature of resonance-region interactions, cross section data cannot be calculated directly from theory; rather, high-precision resonance-region cross section measurements must be made at facilities such as the Oak Ridge Electron Linear Accelerator (ORELA) at Oak Ridge National Laboratory (ORNL), Geel Electron Linear Accelerator (GELINA), Rensselaer Polytechnic Institute (RPI). To extract accurate cross section data from these measurements, detailed nuclear modeling of the measured data is performed to parameterize the cross section behavior in the resonance range. The objective of this paper is to highlight recent advances in resonance-region nuclear modeling with particular emphasis on the covariance analysis capabilities. Opportunities for future research are identified in an effort to stimulate further advances in the state of the art nuclear modeling capabilities.

  1. Intra- and interseasonal autoregressive prediction of dengue outbreaks using local weather and regional climate for a tropical environment in Colombia.

    PubMed

    Eastin, Matthew D; Delmelle, Eric; Casas, Irene; Wexler, Joshua; Self, Cameron

    2014-09-01

    Dengue fever transmission results from complex interactions between the virus, human hosts, and mosquito vectors-all of which are influenced by environmental factors. Predictive models of dengue incidence rate, based on local weather and regional climate parameters, could benefit disease mitigation efforts. Time series of epidemiological and meteorological data for the urban environment of Cali, Colombia are analyzed from January of 2000 to December of 2011. Significant dengue outbreaks generally occur during warm-dry periods with extreme daily temperatures confined between 18°C and 32°C--the optimal range for mosquito survival and viral transmission. Two environment-based, multivariate, autoregressive forecast models are developed that allow dengue outbreaks to be anticipated from 2 weeks to 6 months in advance. These models have the potential to enhance existing dengue early warning systems, ultimately supporting public health decisions on the timing and scale of vector control efforts. PMID:24957546

  2. The effects of regional insolation differences upon advanced solar thermal electric power plant performance and energy costs

    NASA Technical Reports Server (NTRS)

    Latta, A. F.; Bowyer, J. M.; Fujita, T.; Richter, P. H.

    1979-01-01

    The performance and cost of the 10 MWe advanced solar thermal electric power plants sited in various regions of the continental United States were determined. The regional insolation data base is discussed. A range for the forecast cost of conventional electricity by region and nationally over the next several cades are presented.

  3. Regional white matter hyperintensity volume, not hippocampal atrophy, predicts incident Alzheimer disease in the community.

    PubMed

    Brickman, Adam M; Provenzano, Frank A; Muraskin, Jordan; Manly, Jennifer J; Blum, Sonja; Apa, Zoltan; Stern, Yaakov; Brown, Truman R; Luchsinger, José A; Mayeux, Richard

    2012-12-01

    BACKGROUND New-onset Alzheimer disease (AD) is often attributed to degenerative changes in the hippocampus. However, the contribution of regionally distributed small vessel cerebrovascular disease, visualized as white matter hyperintensities (WMHs) on magnetic resonance imaging, remains unclear. OBJECTIVE To determine whether regional WMHs and hippocampal volume predict incident AD in an epidemiological study. DESIGN A longitudinal community-based epidemiological study of older adults from northern Manhattan, New York. SETTING The Washington Heights/Inwood Columbia Aging Project. PARTICIPANTS Between 2005 and 2007, 717 participants without dementia received magnetic resonance imaging scans. A mean (SD) of 40.28 (9.77) months later, 503 returned for follow-up clinical examination and 46 met criteria for incident dementia (45 with AD). Regional WMHs and relative hippocampal volumes were derived. Three Cox proportional hazards models were run to predict incident dementia, controlling for relevant variables. The first included all WMH measurements; the second included relative hippocampal volume; and the third combined the 2 measurements. MAIN OUTCOME MEASURE Incident AD. RESULTS White matter hyperintensity volume in the parietal lobe predicted time to incident dementia (hazard ratio [HR] = 1.194; P = .03). Relative hippocampal volume did not predict incident dementia when considered alone (HR = 0.419; P = .77) or with the WMH measures included in the model (HR = 0.302; P = .70). Including hippocampal volume in the model did not notably alter the predictive utility of parietal lobe WMHs (HR = 1.197; P = .049). CONCLUSIONS The findings highlight the regional specificity of the association of WMHs with AD. It is not clear whether parietal WMHs solely represent a marker for cerebrovascular burden or point to distinct injury compared with other regions. Future work should elucidate pathogenic mechanisms linking WMHs and AD pathology. PMID:22945686

  4. Broca's region and Visual Word Form Area activation differ during a predictive Stroop task.

    PubMed

    Wallentin, Mikkel; Gravholt, Claus Højbjerg; Skakkebæk, Anne

    2015-12-01

    Competing theories attempt to explain the function of Broca's area in single word processing. Studies have found the region to be more active during processing of pseudo words than real words and during infrequent words relative to frequent words and during Stroop (incongruent) color words compared to Non-Stroop (congruent) words. Two related theories explain these findings as reflecting either "cognitive control" processing in the face of conflicting input or a linguistic prediction error signal, based on a predictive coding approach. The latter implies that processing cost refers to violations of expectations based on the statistical distributions of input. In this fMRI experiment we attempted to disentangle single word processing cost originating from cognitive conflict and that stemming from predictive expectation violation. Participants (N = 49) responded to whether the words "GREEN" or "RED" were displayed in green or red (incongruent vs congruent colors). One of the colors, however, was presented three times as often as the other, making it possible to study both congruency and frequency effects independently. Auditory stimuli saying "GREEN" or "RED" had the same distribution, making it possible to study frequency effects across modalities. We found significant behavioral effects of both incongruency and frequency. A significant effect (p < .05 FWE) of incongruency was found in Broca's region, but no effect of frequency was observed and no interaction. Conjoined effects of incongruency and frequency were found in parietal regions as well as in the Visual Word Form Area (VWFA). No interaction between perceptual modality and frequency was found in VWFA suggesting that the region is not strictly visual. These findings speak against a strong version of the prediction error processing hypothesis in Broca's region. They support the idea that prediction error processes in the intermediate timeframe are allocated to more posterior parts of the brain. PMID:26478962

  5. Interleukin 10 promoter region polymorphisms and susceptibility to advanced alcoholic liver disease

    PubMed Central

    Grove, J; Daly, A; Bassendine, M; Gilvarry, E; Day, C

    2000-01-01

    BACKGROUND—The factors determining why less than 10% of heavy drinkers develop advanced alcoholic liver disease (ALD) remain elusive, although genetic factors may be important. Interleukin 10 (IL-10) is an important cytokine with anti-inflammatory, anti-immune, and antifibrotic functions. Several polymorphisms have been identified in the IL-10 promoter and recent evidence suggests that some of these may have functional effects on IL-10 secretion.
AIMS—To test the hypothesis that IL-10 promoter region polymorphisms are associated with susceptibility to ALD.
METHODS—The allele frequencies for the two single base pair substitutions at positions −627 (C→A) and −1117 (A→G) in the IL-10 promoter were determined in 287 heavy drinkers with biopsy proved advanced ALD, 107 heavy drinkers with no evidence of liver disease or steatosis only on biopsy, and 227 local healthy volunteers.
RESULTS—At position −627, 50% of patients with advanced ALD had a least one A allele compared with 33% of controls (p<0.0001) and 34% of drinkers with no or mild disease (p=0.017). At position −1117, the slight excess of the A allele in drinkers with advanced disease was because of linkage disequilibrium between the A alleles at the two sites.
CONCLUSIONS—Among heavy drinkers, possession of the A allele at position −627 in the IL-10 promoter is associated with an increased risk of advanced liver disease. This is consistent with recent functional data that the −627*A allele is associated with low IL-10 expression which will favour inflammatory, immune mediated, and profibrotic mechanisms of alcohol related liver injury.


Keywords: ethyl alcohol; cirrhosis; interleukin 10; genetic polymorphism PMID:10716685

  6. Investigation to advance prediction techniques of the low-speed aerodynamics of V/STOL aircraft

    NASA Technical Reports Server (NTRS)

    Maskew, B.; Strash, D.; Nathman, J.; Dvorak, F. A.

    1985-01-01

    A computer program, VSAERO, has been applied to a number of V/STOL configurations with a view to advancing prediction techniques for the low-speed aerodynamic characteristics. The program couples a low-order panel method with surface streamline calculation and integral boundary layer procedures. The panel method--which uses piecewise constant source and doublet panels-includes an iterative procedure for wake shape and models boundary layer displacement effect using the source transpiration technique. Certain improvements to a basic vortex tube jet model were installed in the code prior to evaluation. Very promising results were obtained for surface pressures near a jet issuing at 90 deg from a flat plate. A solid core model was used in the initial part of the jet with a simple entrainment model. Preliminary representation of the downstream separation zone significantly improve the correlation. The program accurately predicted the pressure distribution inside the inlet on the Grumman 698-411 design at a range of flight conditions. Furthermore, coupled viscous/potential flow calculations gave very close correlation with experimentally determined operational boundaries dictated by the onset of separation inside the inlet. Experimentally observed degradation of these operational boundaries between nacelle-alone tests and tests on the full configuration were also indicated by the calculation. Application of the program to the General Dynamics STOL fighter design were equally encouraging. Very close agreement was observed between experiment and calculation for the effects of power on pressure distribution, lift and lift curve slope.

  7. [Plasma Biomarkers as Predictive Factors for Advanced Hepatocellular Carcinoma with Sorafenib].

    PubMed

    Shiozawa, Kazue; Watanabe, Manabu; Ikehara, Takashi; Matsukiyo, Yasushi; Kogame, Michio; Shinohara, Mie; Kikuchi, Yoshinori; Igarashi, Yoshinori; Sumino, Yasukiyo

    2016-07-01

    We examined plasma biomarkers as predictive factors for advanced hepatocellular carcinoma(ad-HCC)patients treated with sorafenib. We analyzed a-fetoprotein(AFP), AFP-L3, des-g-carboxy prothrombin(DCP), neutrophil-to-lymphocyte ratio(NLR), platelet-to-lymphocyte ratio(PLR), and vascular endothelial growth factor(VEGF)before sorafenib therapy, and changes in AFP-L3, NLR, PLR, and VEGF 1 month after sorafenib therapy in 16 patients. High AFP-L3(hazard ratio: 1.058, 95%CI: 1.019-1.098, p=0.003)and high NLR(hazard ratio: 1.475, 95%CI: 1.045-2.082, p=0.027)were significantly associated with poor prognosis in ad-HCC patients treated with sorafenib. There were no significant differences in changes in AFP-L3, NLR, PLR, and VEGF 1 month after sorafenib therapy. We suggest that AFP-L3 and NLR levels before sorafenib therapy in patients with ad-HCC are an important predictive factor for the therapeutic effect of sorafenib and patient survival. PMID:27431630

  8. Advanced error-prediction LDPC with temperature compensation for highly reliable SSDs

    NASA Astrophysics Data System (ADS)

    Tokutomi, Tsukasa; Tanakamaru, Shuhei; Iwasaki, Tomoko Ogura; Takeuchi, Ken

    2015-09-01

    To improve the reliability of NAND Flash memory based solid-state drives (SSDs), error-prediction LDPC (EP-LDPC) has been proposed for multi-level-cell (MLC) NAND Flash memory (Tanakamaru et al., 2012, 2013), which is effective for long retention times. However, EP-LDPC is not as effective for triple-level cell (TLC) NAND Flash memory, because TLC NAND Flash has higher error rates and is more sensitive to program-disturb error. Therefore, advanced error-prediction LDPC (AEP-LDPC) has been proposed for TLC NAND Flash memory (Tokutomi et al., 2014). AEP-LDPC can correct errors more accurately by precisely describing the error phenomena. In this paper, the effects of AEP-LDPC are investigated in a 2×nm TLC NAND Flash memory with temperature characterization. Compared with LDPC-with-BER-only, the SSD's data-retention time is increased by 3.4× and 9.5× at room-temperature (RT) and 85 °C, respectively. Similarly, the acceptable BER is increased by 1.8× and 2.3×, respectively. Moreover, AEP-LDPC can correct errors with pre-determined tables made at higher temperatures to shorten the measurement time before shipping. Furthermore, it is found that one table can cover behavior over a range of temperatures in AEP-LDPC. As a result, the total table size can be reduced to 777 kBytes, which makes this approach more practical.

  9. Disparities in the Use of Radiation Therapy in Patients With Local-Regionally Advanced Breast Cancer

    SciTech Connect

    Martinez, Steve R.; Beal, Shannon H.; Chen, Steven L.; Canter, Robert J.; Khatri, Vijay P.; Chen, Allen; Bold, Richard J.

    2010-11-01

    Background: Radiation therapy (RT) is indicated for the treatment of local-regionally advanced breast cancer (BCa). Hypothesis: We hypothesized that black and Hispanic patients with local-regionally advanced BCa would receive lower rates of RT than their white counterparts. Methods: The Surveillance Epidemiology and End Results database was used to identify white, black, Hispanic, and Asian patients with invasive BCa and {>=}10 metastatic lymph nodes diagnosed between 1988 and 2005. Univariate and multivariate logistic regression evaluated the relationship of race/ethnicity with use of RT. Multivariate models stratified for those undergoing mastectomy or lumpectomy. Results: Entry criteria were met by 12,653 patients. Approximately half of the patients did not receive RT. Most patients were white (72%); the remainder were Hispanic (10.4%), black (10.3%), and Asian (7.3%). On univariate analysis, Hispanics (odd ratio [OR] 0.89; 95% confidence interval [CI], 0.79-1.00) and blacks (OR 0.79; 95% CI, 0.70-0.89) were less likely to receive RT than whites. On multivariate analysis, blacks (OR 0.76; 95% CI, 0.67-0.86) and Hispanics (OR 0.80; 95% CI, 0.70-0.90) were less likely than whites to receive RT. Disparities persisted for blacks (OR 0.74; 95% CI, 0.64-0.85) and Hispanics (OR 0.77; 95% CI, 0.67-0.89) who received mastectomy, but not for those who received lumpectomy. Conclusions: Many patients with local-regionally advanced BCa do not receive RT. Blacks and Hispanics were less likely than whites to receive RT. This disparity was noted predominately in patients who received mastectomy. Future efforts at improving rates of RT are warranted. Efforts at eliminating racial/ethnic disparities should focus on black and Hispanic candidates for postmastectomy RT.

  10. Predicting Regional Drought on Sub-Seasonal to Decadal Time Scales

    NASA Technical Reports Server (NTRS)

    Schubert, Siegfried; Wang, Hailan; Suarez, Max; Koster, Randal

    2011-01-01

    Drought occurs on a wide range of time scales, and within a variety of different types of regional climates. It is driven foremost by an extended period of reduced precipitation, but it is the impacts on such quantities as soil moisture, streamflow and crop yields that are often most important from a users perspective. While recognizing that different users have different needs for drought information, it is nevertheless important to understand that progress in predicting drought and satisfying such user needs, largely hinges on our ability to improve predictions of precipitation. This talk reviews our current understanding of the physical mechanisms that drive precipitation variations on subseasonal to decadal time scales, and the implications for predictability and prediction skill. Examples are given highlighting the phenomena and mechanisms controlling precipitation on monthly (e.g., stationary Rossby waves, soil moisture), seasonal (ENSO) and decadal time scales (PD and AMO).

  11. Regional boreal summer intraseasonal oscillation over Indian Ocean and Western Pacific: comparison and predictability study

    NASA Astrophysics Data System (ADS)

    Lee, Sun-Seon; Wang, Bin

    2016-04-01

    The boreal summer intraseasonal oscillation (BSISO) has two major activity centers, the northern Indian Ocean and tropical Western North Pacific, which dominate the monsoon intraseasonal variability over South Asia and East Asia, respectively. The spatial-temporal structures of BSISO over the Indian Ocean (10°S-30°N, 60°-105°E) (IOISO) and Western Pacific (10°S-30°N, 105°-150°E) (WPISO) are examined by corresponding the leading modes of daily OLR and 850-hPa zonal wind (U850). The IOISO features a northeastward propagation with a 30-45 days energy peak and the first principal component (PC1) has maximum variance in May, while the WPISO propagates northward with a broad spectral peak on 10-60 days and the PC1 has maximum variance in August. Because of the large regional differences, two regional indices, the IOISO index and WPISO index, are defined by their corresponding first two leading PCs. The combined IOISO-WPISO index captures about 30 % (10 %) of U850 (OLR) daily variance over the entire IO-WP region (10°S-30°N, 60°-150°E), which doubles that captured by the Madden-Julian Oscillation (MJO) index (Wheeler and Hendon 2004) and is 50 % higher than that captured by the BSISO index (Lee et al. 2013). The combined index also shows superior performance in representing biweekly and pentad-mean variations in the Asian-Pacific summer monsoon region (north of 10°N). The predictability/prediction skill and simulated principal modes of two regional BSISO indices are explored by using data derived from the Intraseasonal Variability Hindcast Experiment project. The major regional modes are reasonably well captured, but the forecasted fractional variances of the leading modes and variability center's locations exhibit significant deficiencies. The multi-model mean estimate of the predictability is 40-45 days for the IOISO index, whereas 33-37 days for the WPISO index. The less predictable WPISO is likely due to the existence of its significant biweekly component

  12. Parameter estimation method and updating of regional prediction equations for ungaged sites in the desert region of California

    USGS Publications Warehouse

    Barth, Nancy A.; Veilleux, Andrea G.

    2012-01-01

    The U.S. Geological Survey (USGS) is currently updating at-site flood frequency estimates for USGS streamflow-gaging stations in the desert region of California. The at-site flood-frequency analysis is complicated by short record lengths (less than 20 years is common) and numerous zero flows/low outliers at many sites. Estimates of the three parameters (mean, standard deviation, and skew) required for fitting the log Pearson Type 3 (LP3) distribution are likely to be highly unreliable based on the limited and heavily censored at-site data. In a generalization of the recommendations in Bulletin 17B, a regional analysis was used to develop regional estimates of all three parameters (mean, standard deviation, and skew) of the LP3 distribution. A regional skew value of zero from a previously published report was used with a new estimated mean squared error (MSE) of 0.20. A weighted least squares (WLS) regression method was used to develop both a regional standard deviation and a mean model based on annual peak-discharge data for 33 USGS stations throughout California’s desert region. At-site standard deviation and mean values were determined by using an expected moments algorithm (EMA) method for fitting the LP3 distribution to the logarithms of annual peak-discharge data. Additionally, a multiple Grubbs-Beck (MGB) test, a generalization of the test recommended in Bulletin 17B, was used for detecting multiple potentially influential low outliers in a flood series. The WLS regression found that no basin characteristics could explain the variability of standard deviation. Consequently, a constant regional standard deviation model was selected, resulting in a log-space value of 0.91 with a MSE of 0.03 log units. Yet drainage area was found to be statistically significant at explaining the site-to-site variability in mean. The linear WLS regional mean model based on drainage area had a Pseudo- 2 R of 51 percent and a MSE of 0.32 log units. The regional parameter

  13. The Climate Variability & Predictability (CVP) Program at NOAA - DYNAMO Recent Project Advancements

    NASA Astrophysics Data System (ADS)

    Lucas, S. E.; Todd, J. F.; Higgins, W.

    2013-12-01

    The Climate Variability & Predictability (CVP) Program supports research aimed at providing process-level understanding of the climate system through observation, modeling, analysis, and field studies. This vital knowledge is needed to improve climate models and predictions so that scientists can better anticipate the impacts of future climate variability and change. To achieve its mission, the CVP Program supports research carried out at NOAA and other federal laboratories, NOAA Cooperative Institutes, and academic institutions. The Program also coordinates its sponsored projects with major national and international scientific bodies including the World Climate Research Programme (WCRP), the International Geosphere-Biosphere Programme (IGBP), and the U.S. Global Change Research Program (USGCRP). The CVP program sits within the Earth System Science (ESS) Division at NOAA's Climate Program Office. Dynamics of the Madden-Julian Oscillation (DYNAMO): The Indian Ocean is one of Earth's most sensitive regions because the interactions between ocean and atmosphere there have a discernable effect on global climate patterns. The tropical weather that brews in that region can move eastward along the equator and reverberate around the globe, shaping weather and climate in far-off places. The vehicle for this variability is a phenomenon called the Madden-Julian Oscillation, or MJO. The MJO, which originates over the Indian Ocean roughly every 30 to 90 days, is known to influence the Asian and Australian monsoons. It can also enhance hurricane activity in the northeast Pacific and Gulf of Mexico, trigger torrential rainfall along the west coast of North America, and affect the onset of El Niño. CVP-funded scientists participated in the DYNAMO field campaign in 2011-12. Results from this international campaign are expected to improve researcher's insights into this influential phenomenon. A better understanding of the processes governing MJO is an essential step toward

  14. Advances in Landslide Nowcasting: Evaluation of a Global and Regional Modeling Approach

    NASA Technical Reports Server (NTRS)

    Kirschbaum, Dalia Bach; Peters-Lidard, Christa; Adler, Robert; Hong, Yang; Kumar, Sujay; Lerner-Lam, Arthur

    2011-01-01

    represents an important step forward in advancing regional and global-scale landslide hazard assessment.

  15. Improved sequence-based prediction of disordered regions with multilayer fusion of multiple information sources

    PubMed Central

    Mizianty, Marcin J.; Stach, Wojciech; Chen, Ke; Kedarisetti, Kanaka Durga; Disfani, Fatemeh Miri; Kurgan, Lukasz

    2010-01-01

    Motivation: Intrinsically disordered proteins play a crucial role in numerous regulatory processes. Their abundance and ubiquity combined with a relatively low quantity of their annotations motivate research toward the development of computational models that predict disordered regions from protein sequences. Although the prediction quality of these methods continues to rise, novel and improved predictors are urgently needed. Results: We propose a novel method, named MFDp (Multilayered Fusion-based Disorder predictor), that aims to improve over the current disorder predictors. MFDp is as an ensemble of 3 Support Vector Machines specialized for the prediction of short, long and generic disordered regions. It combines three complementary disorder predictors, sequence, sequence profiles, predicted secondary structure, solvent accessibility, backbone dihedral torsion angles, residue flexibility and B-factors. Our method utilizes a custom-designed set of features that are based on raw predictions and aggregated raw values and recognizes various types of disorder. The MFDp is compared at the residue level on two datasets against eight recent disorder predictors and top-performing methods from the most recent CASP8 experiment. In spite of using training chains with ≤25% similarity to the test sequences, our method consistently and significantly outperforms the other methods based on the MCC index. The MFDp outperforms modern disorder predictors for the binary disorder assignment and provides competitive real-valued predictions. The MFDp's outputs are also shown to outperform the other methods in the identification of proteins with long disordered regions. Availability: http://biomine.ece.ualberta.ca/MFDp.html Supplementary information: Supplementary data are available at Bioinformatics online. Contact: lkurgan@ece.ualberta.ca PMID:20823312

  16. Carbon emissions from deforestation in the Brazilian Amazon region predicted from satellite data and ecosystem modeling

    NASA Astrophysics Data System (ADS)

    Potter, C.; Klooster, S.; Genovese, V.

    2009-03-01

    A simulation model based on satellite observations of monthly vegetation cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate monthly carbon fluxes in terrestrial ecosystems of Brazilian Amazon and Cerrado regions over the period 2000-2002. The NASA-CASA (Carnegie Ames Stanford Approach) model estimates of annual forest production were used as the basis to generate a prediction for the standing pool of carbon in above-ground biomass (AGB; g C m-2) for forested areas of the Brazilian Amazon region. Plot-level measurements of the residence time of carbon in wood in Amazon forest from Malhi et al. (2006) were interpolated by inverse distance weighting algorithms and used with CASA to generate a new regional map of AGB. Data from the Brazilian PRODES (Estimativa do Desflorestamento da Amazônia) project were used to map deforested areas. Results show that net primary production (NPP) sinks for carbon are highest across the eastern and northern Amazon areas, whereas deforestation sources of CO2 flux from decomposition of residual woody debris are more rapid and less seasonal in the central Amazon than in the eastern and southern areas. Increased woody debris from past deforestation events was predicted to alter the net ecosystem carbon balance of the Amazon region to generate annual CO2 source fluxes at least two times higher than previously predicted by CASA modeling studies. Variations in climate, land cover, and forest burning were predicted to release carbon at rates of 0.5 to 1 Pg C yr-1 from the Brazilian Amazon. When direct carbon emissions from forest burning of between 0.2 and 0.6-1 in the Legal Amazon are overlooked in regional budgets, the year-to-year variations in this net biome flux may appear to be large, whereas our model results implies net biome fluxes had actually been relatively consistent from year to year during the period 2000-2002.

  17. The effects of regional insolation differences upon advanced solar thermal electric power plant performance and energy costs

    NASA Technical Reports Server (NTRS)

    Latta, A. F.; Bowyer, J. M.; Fujita, T.

    1979-01-01

    This paper presents the performance and cost of four 10-MWe advanced solar thermal electric power plants sited in various regions of the continental United States. Each region has different insolation characteristics which result in varying collector field areas, plant performance, capital costs, and energy costs. The paraboloidal dish, central receiver, cylindrical parabolic trough, and compound parabolic concentrator (CPC) comprise the advanced concepts studied. This paper contains a discussion of the regional insolation data base, a description of the solar systems' performances and costs, and a presentation of a range for the forecast cost of conventional electricity by region and nationally over the next several decades.

  18. Toward a unified system for understanding, predicting and projecting regional hurricane activity

    NASA Astrophysics Data System (ADS)

    Vecchi, G. A.; Delworth, T. L.; Yang, X.; Murakami, H.; Zhang, W.; Underwood, S.; Zeng, F. J.; Jia, L.; Kapnick, S. B.; Paffendorf, K.; Krishnamurthy, L.; Wittenberg, A. T.; Msadek, R.; Villarini, G.; Chen, J. H.; Lin, S. J.; Harris, L.; Gudgel, R.; Stern, B.; Zhang, S.

    2015-12-01

    A family of high-resolution (50km and 25km atmospheric/land resolution) global coupled climate models provide a unified framework towards the understanding, intraseasonal-to-decadal prediction and decadal to multi-decadal projection of regional and extreme climate, including tropical cyclones. Initialized predictions of global hurricane activity show skill on regional scales, comparable to the skill on basin-wide scales, suggesting that regional seasonal TC predictions may be a feasible forecast target. The 25km version of the model shows skill at seasonal predictions of the frequency of the most intense hurricanes (Cat. 3-4-5 and Cat. 4-5). It is shown that large-scale systematic errors in the mean-state are a key constraint on the simulation and prediction of variations of regional climate and extremes, and methodologies for overcoming model biases are explored. Improvements in predictions of regional climate are due both to improved representation of local processes, and to improvements in the large-scale climate and variability from improved process representation. These models are used to explore the the response of tropical cyclones, both globally and regionally, to increasing greenhouse gases and to internal climate variations. The 25km model in generally shows a more faithful representation of the impact of climate variability on hurricane activity than the 50km model. The response of the total number and the total power dissipation index of tropical cyclones to increasing greenhouse gases can differ substantially between models of two atmospheric resolutions, 50km and 25km - with the 25km version of the model showing a larger increase in power dissipation from increasing greenhouse gases, principally because - in contrast to that of the 50km model - its global hurricane frequency does not decrease with increasing CO2. Some thoughts on the reasons behind those differences will be offered. The 25km model shows an increase in the frequency of intense tropical

  19. Reward Region Responsivity Predicts Future Weight Gain and Moderating Effects of the TaqIA Allele

    PubMed Central

    Burger, Kyle S.; Yokum, Sonja

    2015-01-01

    Because no large prospective study has investigated neural vulnerability factors that predict future weight gain, we tested whether neural response to receipt and anticipated receipt of palatable food and monetary reward predicted body fat gain over a 3-year follow-up in healthy-weight adolescent humans and whether the TaqIA polymorphism moderates these relations. A total of 153 adolescents completed fMRI paradigms assessing response to these events; body fat was assessed annually over follow-up. Elevated orbitofrontal cortex response to cues signaling impending milkshake receipt predicted future body fat gain (r = 0.32), which is a novel finding that provides support for the incentive sensitization theory of obesity. Neural response to receipt and anticipated receipt of monetary reward did not predict body fat gain, which has not been tested previously. Replicating an earlier finding (Stice et al., 2008a), elevated caudate response to milkshake receipt predicted body fat gain for adolescents with a genetic propensity for greater dopamine signaling by virtue of possessing the TaqIA A2/A2 allele, but lower caudate response predicted body fat gain for adolescents with a genetic propensity for less dopamine signaling by virtue of possessing a TaqIA A1 allele, though this interaction was only marginal [p-value <0.05 corrected using voxel-level familywise error rate (pFWE) = 0.06]. Parental obesity, which correlated with TaqIA allele status (odds ratio = 2.7), similarly moderated the relation of caudate response to milkshake receipt to future body fat gain, which is another novel finding. The former interaction implies that too much or too little dopamine signaling and reward region responsivity increases risk for overeating, suggesting qualitatively distinct reward surfeit and reward deficit pathways to obesity. SIGNIFICANCE STATEMENT Because no large prospective study has investigated neural vulnerability factors that predict future weight gain we tested whether

  20. Proposed parameters of specific rain attenuation prediction for Free Space Optics link operating in tropical region

    NASA Astrophysics Data System (ADS)

    Suriza, A. Z.; Md Rafiqul, Islam; Wajdi, A. K.; Naji, A. W.

    2013-03-01

    As the demand for higher and unlimited bandwidth for communication channel is increased, Free Space Optics (FSO) is a good alternative solution. As it is protocol transparent, easy to install, cost effective and have capabilities like fiber optics, its demand rises very fast. Weather condition, however is the limiting factor for FSO link. In the temperate region the major blockage for FSO link feasibility is fog. In the tropical region high rainfall rate is expected to be the major drawback of FSO link availability. Rain attenuation is the most significant to influence FSO link availability in tropical region. As for now the available k and α values are developed using data from temperate regions. Therefore, the objective of this paper is to propose new parameters for specific rain attenuation prediction model that represents tropical weather condition. The proposed values are derived from data measured in Malaysia and using methods recommended by ITU-R.

  1. Development of Crop Yield Estimation Method by Applying Seasonal Climate Prediction in Asia-Pacific Region

    NASA Astrophysics Data System (ADS)

    Shin, Y.; Lee, E.

    2015-12-01

    Under the influence of recent climate change, abnormal weather condition such as floods and droughts has issued frequently all over the world. The occurrence of abnormal weather in major crop production areas leads to soaring world grain prices because it influence the reduction of crop yield. Development of crop yield estimation method is important means to accommodate the global food crisis caused by abnormal weather. However, due to problems with the reliability of the seasonal climate prediction, application research on agricultural productivity has not been much progress yet. In this study, it is an object to develop long-term crop yield estimation method in major crop production countries worldwide using multi seasonal climate prediction data collected by APEC Climate Center. There are 6-month lead seasonal predictions produced by six state-of-the-art global coupled ocean-atmosphere models(MSC_CANCM3, MSC_CANCM4, NASA, NCEP, PNU, POAMA). First of all, we produce a customized climate data through temporal and spatial downscaling methods for use as a climatic input data to the global scale crop model. Next, we evaluate the uncertainty of climate prediction by applying multi seasonal climate prediction in the crop model. Because rice is the most important staple food crop in the Asia-Pacific region, we assess the reliability of the rice yields using seasonal climate prediction for main rice production countries. RMSE(Root Mean Squire Error) and TCC(Temporal Correlation Coefficient) analysis is performed in Asia-Pacific countries, major 14 rice production countries, to evaluate the reliability of the rice yield according to the climate prediction models. We compare the rice yield data obtained from FAOSTAT and estimated using the seasonal climate prediction data in Asia-Pacific countries. In addition, we show that the reliability of seasonal climate prediction according to the climate models in Asia-Pacific countries where rice cultivation is being carried out.

  2. Compression of regions in the global advanced very high resolution radiometer 1-km data set

    NASA Technical Reports Server (NTRS)

    Kess, Barbara L.; Steinwand, Daniel R.; Reichenbach, Stephen E.

    1994-01-01

    The global advanced very high resolution radiometer (AVHRR) 1-km data set is a 10-band image produced at USGS' EROS Data Center for the study of the world's land surfaces. The image contains masked regions for non-land areas which are identical in each band but vary between data sets. They comprise over 75 percent of this 9.7 gigabyte image. The mask is compressed once and stored separately from the land data which is compressed for each of the 10 bands. The mask is stored in a hierarchical format for multi-resolution decompression of geographic subwindows of the image. The land for each band is compressed by modifying a method that ignores fill values. This multi-spectral region compression efficiently compresses the region data and precludes fill values from interfering with land compression statistics. Results show that the masked regions in a one-byte test image (6.5 Gigabytes) compress to 0.2 percent of the 557,756,146 bytes they occupy in the original image, resulting in a compression ratio of 89.9 percent for the entire image.

  3. 15-PGDH expression as a predictive factor response to neoadjuvant chemotherapy in advanced gastric cancer

    PubMed Central

    Hu, Min; Li, Kai; Maskey, Ninu; Xu, Zhigao; Peng, ChunWei; Tian, Sufang; Li, Yan; Yang, Guifang

    2015-01-01

    Given the various clinical and pathologic responses to neoadjuvant chemotherapy (NACT) in gastric cancer (GC), potential biomarkers that reflecting the efficacy of NACT on GC should be investigated. The aim of this study was to investigate the 15-PGDH expression response to NACT in GC patients and its relationship with prognosis of GC. Immunohistochemical method was used to assess the level of 15-PGDH expression in 56 GC patients who received NACT before surgery and 46 patients who underwent surgical treatment without NACT as well as their corresponding adjacent non-neoplastic tissues. We found that there was no correlation of 15-PGDH expression between non-cancerous gastric tissues and GC tissues (P=0.519), while 15-PGDH expression level in NACT group was higher than that in nNACT group (P=0.015). In patients with NACT, the higher level of 15-PGDH expression was significantly associated with well-moderately differentiated grade (P=0.023), I/II stage (P=0.014) and with no lymph node metastasis (P=0.016). Moreover, statistically significant differences in overall survival (OS) were found among 15-PGDH expression (log-rank test, P<0.001) and TNM stage (log-rank test, P=0.032). Most importantly, expression of 15-PGDH was found to be an independent predictive factor by multivariate analysis (Hazard ratio (HR) 0.315 [0.120-0.827], P=0.019). These findings indicated that NACT could increase 15-PGDH expression in advanced GC patients, and 15-PGDH may serve as a candidate prognostic biomarker of advanced GC response to NACT. PMID:26261578

  4. The role of advanced reactive surface area characterization in improving predictions of mineral reaction rates

    NASA Astrophysics Data System (ADS)

    Beckingham, L. E.; Zhang, S.; Mitnick, E.; Cole, D. R.; Yang, L.; Anovitz, L. M.; Sheets, J.; Swift, A.; Kneafsey, T. J.; Landrot, G.; Mito, S.; Xue, Z.; Steefel, C. I.; DePaolo, D. J.; Ajo Franklin, J. B.

    2014-12-01

    Geologic sequestration of CO2 in deep sedimentary formations is a promising means of mitigating carbon emissions from coal-fired power plants but the long-term fate of injected CO2 is challenging to predict. Reactive transport models are used to gain insight over long times but rely on laboratory determined mineral reaction rates that have been difficult to extrapolate to field systems. This, in part, is due to a lack of understanding of mineral reactive surface area. Many models use an arbitrary approximation of reactive surface area, applying orders of magnitude scaling factors to measured BET or geometric surface areas. Recently, a few more sophisticated approaches have used 2D and 3D image analyses to determine mineral-specific reactive surface areas that account for the accessibility of minerals. However, the ability of these advanced surface area estimates to improve predictions of mineral reaction rates has yet to be determined. In this study, we fuse X-ray microCT, SEM QEMSCAN, XRD, SANS, and SEM-FIB analysis to determine mineral-specific accessible reactive surface areas for a core sample from the Nagaoka pilot CO2 injection site (Japan). This sample is primarily quartz, plagioclase, smectite, K-feldspar, and pyroxene. SEM imaging shows abundant smectite cement and grain coatings that decrease the fluid accessibility of other minerals. However, analysis of FIB-SEM images reveals that smectite nano-pores are well connected such that access to underlying minerals is not occluded by smectite coatings. Mineral-specific accessible surfaces are determined, accounting for the connectivity of the pore space with and without connected smectite nano-pores. The large-scale impact of variations in accessibility and dissolution rates are then determined through continuum scale modeling using grid-cell specific information on accessible surface areas. This approach will be compared with a traditional continuum scale model using mineral abundances and common surface area

  5. Global warming and climate change - predictive models for temperate and tropical regions

    SciTech Connect

    Malini, B.H.

    1997-12-31

    Based on the assumption of 4{degree}C increase of global temperature by the turn of 21st century due to the accumulation of greenhouse gases an attempt is made to study the possible variations in different climatic regimes. The predictive climatic water balance model for Hokkaido island of Japan (a temperate zone) indicates the possible occurrence of water deficit for two to three months, which is a unknown phenomenon in this region at present. Similarly, India which represents tropical region also will experience much drier climates with increased water deficit conditions. As a consequence, the thermal region of Hokkaido which at present is mostly Tundra and Micro thermal will change into a Meso thermal category. Similarly, the moisture regime which at present supports per humid (A2, A3 and A4) and Humid (B4) climates can support A1, B4, B3, B2 and B1 climates indicating a shift towards drier side of the climatic spectrum. Further, the predictive modes of both the regions have indicated increased evapotranspiration rates. Although there is not much of change in the overall thermal characteristics of the Indian region the moisture regime indicates a clear shift towards the aridity in the country.

  6. Regional Scale High Resolution δ18O Prediction in Precipitation Using MODIS EVI

    PubMed Central

    Huang, Cho-Ying; Wang, Chung-Ho; Lin, Shou-De; Lo, Yi-Chen; Huang, Bo-Wen; Hatch, Kent A.; Shiu, Hau-Jie; You, Cheng-Feng; Chang, Yuan-Mou; Shen, Sheng-Feng

    2012-01-01

    The natural variation in stable water isotope ratio data, also known as water isoscape, is a spatiotemporal fingerprint and a powerful natural tracer that has been widely applied in disciplines as diverse as hydrology, paleoclimatology, ecology and forensic investigation. Although much effort has been devoted to developing a predictive water isoscape model, it remains a central challenge for scientists to generate high accuracy, fine scale spatiotemporal water isoscape prediction. Here we develop a novel approach of using the MODIS-EVI (the Moderate Resolution Imagining Spectroradiometer-Enhanced Vegetation Index), to predict δ18O in precipitation at the regional scale. Using a structural equation model, we show that the EVI and precipitated δ18O are highly correlated and thus the EVI is a good predictor of precipitated δ18O. We then test the predictability of our EVI-δ18O model and demonstrate that our approach can provide high accuracy with fine spatial (250×250 m) and temporal (16 days) scale δ18O predictions (annual and monthly predictabilities [r] are 0.96 and 0.80, respectively). We conclude the merging of the EVI and δ18O in precipitation can greatly extend the spatial and temporal data availability and thus enhance the applicability for both the EVI and water isoscape. PMID:23029053

  7. Regional scale high resolution δ18O prediction in precipitation using MODIS EVI.

    PubMed

    Chan, Wei-Ping; Yuan, Hsiao-Wei; Huang, Cho-Ying; Wang, Chung-Ho; Lin, Shou-De; Lo, Yi-Chen; Huang, Bo-Wen; Hatch, Kent A; Shiu, Hau-Jie; You, Cheng-Feng; Chang, Yuan-Mou; Shen, Sheng-Feng

    2012-01-01

    The natural variation in stable water isotope ratio data, also known as water isoscape, is a spatiotemporal fingerprint and a powerful natural tracer that has been widely applied in disciplines as diverse as hydrology, paleoclimatology, ecology and forensic investigation. Although much effort has been devoted to developing a predictive water isoscape model, it remains a central challenge for scientists to generate high accuracy, fine scale spatiotemporal water isoscape prediction. Here we develop a novel approach of using the MODIS-EVI (the Moderate Resolution Imagining Spectroradiometer-Enhanced Vegetation Index), to predict δ(18)O in precipitation at the regional scale. Using a structural equation model, we show that the EVI and precipitated δ(18)O are highly correlated and thus the EVI is a good predictor of precipitated δ(18)O. We then test the predictability of our EVI-δ(18)O model and demonstrate that our approach can provide high accuracy with fine spatial (250×250 m) and temporal (16 days) scale δ(18)O predictions (annual and monthly predictabilities [r] are 0.96 and 0.80, respectively). We conclude the merging of the EVI and δ(18)O in precipitation can greatly extend the spatial and temporal data availability and thus enhance the applicability for both the EVI and water isoscape. PMID:23029053

  8. In-Service Design & Performance Prediction of Advanced Fusion Material Systems by Computational Modeling and Simulation

    SciTech Connect

    G. R. Odette; G. E. Lucas

    2005-11-15

    This final report on "In-Service Design & Performance Prediction of Advanced Fusion Material Systems by Computational Modeling and Simulation" (DE-FG03-01ER54632) consists of a series of summaries of work that has been published, or presented at meetings, or both. It briefly describes results on the following topics: 1) A Transport and Fate Model for Helium and Helium Management; 2) Atomistic Studies of Point Defect Energetics, Dynamics and Interactions; 3) Multiscale Modeling of Fracture consisting of: 3a) A Micromechanical Model of the Master Curve (MC) Universal Fracture Toughness-Temperature Curve Relation, KJc(T - To), 3b) An Embrittlement DTo Prediction Model for the Irradiation Hardening Dominated Regime, 3c) Non-hardening Irradiation Assisted Thermal and Helium Embrittlement of 8Cr Tempered Martensitic Steels: Compilation and Analysis of Existing Data, 3d) A Model for the KJc(T) of a High Strength NFA MA957, 3e) Cracked Body Size and Geometry Effects of Measured and Effective Fracture Toughness-Model Based MC and To Evaluations of F82H and Eurofer 97, 3-f) Size and Geometry Effects on the Effective Toughness of Cracked Fusion Structures; 4) Modeling the Multiscale Mechanics of Flow Localization-Ductility Loss in Irradiation Damaged BCC Alloys; and 5) A Universal Relation Between Indentation Hardness and True Stress-Strain Constitutive Behavior. Further details can be found in the cited references or presentations that generally can be accessed on the internet, or provided upon request to the authors. Finally, it is noted that this effort was integrated with our base program in fusion materials, also funded by the DOE OFES.

  9. Advances in Landslide Hazard Forecasting: Evaluation of Global and Regional Modeling Approach

    NASA Technical Reports Server (NTRS)

    Kirschbaum, Dalia B.; Adler, Robert; Hone, Yang; Kumar, Sujay; Peters-Lidard, Christa; Lerner-Lam, Arthur

    2010-01-01

    algorithm performance accuracy include incorporating additional triggering factors such as tectonic activity, anthropogenic impacts and soil moisture into the algorithm calculation. Despite these limitations, the methodology presented in this regional evaluation is both straightforward to calculate and easy to interpret, making results transferable between regions and allowing findings to be placed within an inter-comparison framework. The regional algorithm scenario represents an important step in advancing regional and global-scale landslide hazard assessment and forecasting.

  10. A finite Reynolds number approach for the prediction of boundary layer receptivity in localized regions

    NASA Technical Reports Server (NTRS)

    Choudhari, Meelan; Street, Craig L.

    1991-01-01

    Previous theoretical work on the boundary layer receptivity problem has utilized large Reynolds number asymptotic theories, thus being limited to a narrow part of the frequency - Reynolds number domain. An alternative approach is presented for the prediction of localized instability generation which has a general applicability, and also accounts for finite Reynolds number effects. This approach is illustrated for the case of Tollmien-Schlichting wave generation in a Blasius boundary layer due to the interaction of a free stream acoustic wave with a region of short scale variation in the surface boundary condition. The specific types of wall inhomogeneities studied are: regions of short scale variations in wall suction, wall admittance, and wall geometry (roughness). Extensive comparison is made between the results of the finite Reynolds number approach and previous asymptotic predictions, which also suggests an alternative way of using the latter at Reynolds numbers of interest in practice.

  11. Machine learning and hurdle models for improving regional predictions of stream water acid neutralizing capacity

    NASA Astrophysics Data System (ADS)

    Povak, Nicholas A.; Hessburg, Paul F.; Reynolds, Keith M.; Sullivan, Timothy J.; McDonnell, Todd C.; Salter, R. Brion

    2013-06-01

    In many industrialized regions of the world, atmospherically deposited sulfur derived from industrial, nonpoint air pollution sources reduces stream water quality and results in acidic conditions that threaten aquatic resources. Accurate maps of predicted stream water acidity are an essential aid to managers who must identify acid-sensitive streams, potentially affected biota, and create resource protection strategies. In this study, we developed correlative models to predict the acid neutralizing capacity (ANC) of streams across the southern Appalachian Mountain region, USA. Models were developed using stream water chemistry data from 933 sampled locations and continuous maps of pertinent environmental and climatic predictors. Environmental predictors were averaged across the upslope contributing area for each sampled stream location and submitted to both statistical and machine-learning regression models. Predictor variables represented key aspects of the contributing geology, soils, climate, topography, and acidic deposition. To reduce model error rates, we employed hurdle modeling to screen out well-buffered sites and predict continuous ANC for the remainder of the stream network. Models predicted acid-sensitive streams in forested watersheds with small contributing areas, siliceous lithologies, cool and moist environments, low clay content soils, and moderate or higher dry sulfur deposition. Our results confirmed findings from other studies and further identified several influential climatic variables and variable interactions. Model predictions indicated that one quarter of the total stream network was sensitive to additional sulfur inputs (i.e., ANC < 100 µeq L-1), while <10% displayed much lower ANC (<50 µeq L-1). These methods may be readily adapted in other regions to assess stream water quality and potential biotic sensitivity to acidic inputs.

  12. The predicted circulation response to global warming and implications for regional hydroclimate.

    NASA Astrophysics Data System (ADS)

    Simpson, Isla; Seager, Richard; Ting, Mingfang; Shaw, Tiffany

    2016-04-01

    A critical aspect of human-induced climate change is how it will affect regional hydroclimate around the world. To leading order, the increased ability of the atmosphere to hold moisture as it warms, intensifies moisture transports, making sub-tropical dry regions drier and mid- to high latitude wet regions wetter. But regional changes in hydroclimate will also depend on how the atmospheric circulation responds to warming. Here, the predictions of the future of the mid-latitude circulation by the current generation of global climate models will be discussed, with a particular focus on circulation changes that impact on regional hydroclimate. In the Northern Hemisphere winter, stationary wave changes are a leading order effect and impact on both North American and European hydroclimate. However, in certain regions, models exhibit considerable diversity in this response, motivating the need for improved understanding of the mechanisms involved and the reasons behind such a model spread. This is particularly true in the Pacific-North American sector during winter and so the mechanisms involved in circulation changes in this region and the reason for the inter-model spread will be discussed in detail.

  13. Evaluating observations in the context of predictions for the death valley regional groundwater system

    USGS Publications Warehouse

    Ely, D.M.; Hill, M.C.; Tiedeman, C.R.; O'Brien, G. M.

    2004-01-01

    When a model is calibrated by nonlinear regression, calculated diagnostic and inferential statistics provide a wealth of information about many aspects of the system. This work uses linear inferential statistics that are measures of prediction uncertainty to investigate the likely importance of continued monitoring of hydraulic head to the accuracy of model predictions. The measurements evaluated are hydraulic heads; the predictions of interest are subsurface transport from 15 locations. The advective component of transport is considered because it is the component most affected by the system dynamics represented by the regional-scale model being used. The problem is addressed using the capabilities of the U.S. Geological Survey computer program MODFLOW-2000, with its Advective Travel Observation (ADV) Package. Copyright ASCE 2004.

  14. Models to Predict Flowering Time in the Main Saffron Production Regions of Khorasan Province

    NASA Astrophysics Data System (ADS)

    Behdani, M. A.; Koocheki, A.; Nassiri, M.; Rezvani, P.

    The objective of this study was to develop a thermal model that can be used for prediction of saffron flowering time. For this purpose, existing data on saffron flower emergence time were collected in a wide range of temperature regimes over the saffron production regions of Khorasan province, Iran. Linear second-order polynomial and 5-parameter beta models were used and statistically compared for their ability in predicting saffron flowering time as a function of temperature. The results showed a significant delay in flowering date across the temperature gradient. While beta model had a better statistical performance but the simple linear model also showed a good predicting ability and therefore, can be used as a reliable model.

  15. Advances in the regionalization approach: geostatistical techniques for estimating flood quantiles

    NASA Astrophysics Data System (ADS)

    Chiarello, Valentina; Caporali, Enrica; Matthies, Hermann G.

    2015-04-01

    The knowledge of peak flow discharges and associated floods is of primary importance in engineering practice for planning of water resources and risk assessment. Streamflow characteristics are usually estimated starting from measurements of river discharges at stream gauging stations. However, the lack of observations at site of interest as well as the measurement inaccuracies, bring inevitably to the necessity of developing predictive models. Regional analysis is a classical approach to estimate river flow characteristics at sites where little or no data exists. Specific techniques are needed to regionalize the hydrological variables over the considered area. Top-kriging or topological kriging, is a kriging interpolation procedure that takes into account the geometric organization and structure of hydrographic network, the catchment area and the nested nature of catchments. The continuous processes in space defined for the point variables are represented by a variogram. In Top-kriging, the measurements are not point values but are defined over a non-zero catchment area. Top-kriging is applied here over the geographical space of Tuscany Region, in Central Italy. The analysis is carried out on the discharge data of 57 consistent runoff gauges, recorded from 1923 to 2014. Top-kriging give also an estimation of the prediction uncertainty in addition to the prediction itself. The results are validated using a cross-validation procedure implemented in the package rtop of the open source statistical environment R The results are compared through different error measurement methods. Top-kriging seems to perform better in nested catchments and larger scale catchments but no for headwater or where there is a high variability for neighbouring catchments.

  16. Elevated Reward Region Responsivity Predicts Future Substance Use Onset But Not Overweight/Obesity Onset

    PubMed Central

    Stice, Eric; Yokum, Sonja; Burger, Kyle S.

    2013-01-01

    Background We tested the hypotheses that adolescents who show elevated reward region responsivity are at increased risk for initial onset of overweight/obesity and substance use, which is important because there have been no such prospective tests of the reward surfeit model of these motivated behaviors. Methods One hundred sixty-two adolescents (mean age = 15.3 ± 1.06 years) with healthy weights (mean body mass index = 20.8 ± 1.90) completed functional magnetic resonance imaging paradigms that assessed neural activation in response to receipt and anticipated receipt of palatable food and monetary reward; body fat and substance use were assessed at baseline and 1-year follow-up. Results Elevated caudate (r = .31, p < .001) and putamen (r = .28, p < .001) response to monetary reward predicted substance use onset over 1-year follow-up, but reward circuitry responsivity did not predict future overweight/obesity onset. Adolescents who reported substance use versus abstinence at baseline also showed less caudate (r = –.31, p < .001) response to monetary reward. Discussion Results show that hyper-responsivity of reward circuitry increases risk for future substance use onset, providing novel support for the reward surfeit model. Results also imply that even a limited substance use history was associated with reduced reward region responsivity, extending results from studies that compared substance-dependent individuals with healthy control subjects and suggesting that substance use downregulates reward circuitry. However, aberrant reward region responsivity did not predict initial unhealthy weight gain. PMID:23312561

  17. Regional climate change predictions from the Goddard Institute for Space Studies high resolution GCM

    NASA Technical Reports Server (NTRS)

    Crane, Robert G.; Hewitson, Bruce

    1990-01-01

    Model simulations of global climate change are seen as an essential component of any program aimed at understanding human impact on the global environment. A major weakness of current general circulation models (GCMs), however, is their inability to predict reliably the regional consequences of a global scale change, and it is these regional scale predictions that are necessary for studies of human/environmental response. This research is directed toward the development of a methodology for the validation of the synoptic scale climatology of GCMs. This is developed with regard to the Goddard Institute for Space Studies (GISS) GCM Model 2, with the specific objective of using the synoptic circulation form a doubles CO2 simulation to estimate regional climate change over North America, south of Hudson Bay. This progress report is specifically concerned with validating the synoptic climatology of the GISS GCM, and developing the transfer function to derive grid-point temperatures from the synoptic circulation. Principal Components Analysis is used to characterize the primary modes of the spatial and temporal variability in the observed and simulated climate, and the model validation is based on correlations between component loadings, and power spectral analysis of the component scores. The results show that the high resolution GISS model does an excellent job of simulating the synoptic circulation over the U.S., and that grid-point temperatures can be predicted with reasonable accuracy from the circulation patterns.

  18. A Regional Model for Seasonal Sea Ice Prediction in the Pacific Sector of the Arctic

    NASA Astrophysics Data System (ADS)

    Yuan, X.; Li, Y.; Chen, D.; Zhang, Q.; Li, C.; Niu, F.; Sun, Y.

    2015-12-01

    The recent results from a linear Markov model for seasonal prediction of pan-Arctic sea ice concentration (SIC) show that sea ice in the Pacific sector has the lowest predictability compared to other regions. One reason could be that the climate variability in the Atlantic sector is so dominant that other signals in the Arctic climate system do not appear in the leading modes used for model construction. This study develops a regional Markov model to improve seasonal forecasting of SIC in the Pacific sector. The model climate system consists of various combinations of the monthly mean series of SIC, sea surface temperature (SST), surface air temperature (SAT), pressure/geopotential height fields and winds at pressure levels. Multivariate empirical orthogonal functions (MEOF) and rotated MEOF are applied to each set of data to reduce the model dimensions. After a series of experiments, the final model configuration selects 23 rotated MEOF modes from a data matrix of three variables (SIC, SST and SAT). This regional model shows considerable improvement in the prediction skill in the Pacific sector in all seasons. The anomaly correlation skill increases by 0.2 at 1- to 4-month leads in the Bering Sea, and by 0.1 at 1- to 10-month leads in the Sea of Okhotsk. In general, the model performs better in summer and fall than in winter and spring. On average, the correlation skill can reach 0.6 at a 2-month (4-month) lead in the Bering Sea (the Sea of Okhotsk).

  19. Prediction of Low Community Sanitation Coverage Using Environmental and Sociodemographic Factors in Amhara Region, Ethiopia.

    PubMed

    Oswald, William E; Stewart, Aisha E P; Flanders, W Dana; Kramer, Michael R; Endeshaw, Tekola; Zerihun, Mulat; Melaku, Birhanu; Sata, Eshetu; Gessesse, Demelash; Teferi, Tesfaye; Tadesse, Zerihun; Guadie, Birhan; King, Jonathan D; Emerson, Paul M; Callahan, Elizabeth K; Moe, Christine L; Clasen, Thomas F

    2016-09-01

    This study developed and validated a model for predicting the probability that communities in Amhara Region, Ethiopia, have low sanitation coverage, based on environmental and sociodemographic conditions. Community sanitation coverage was measured between 2011 and 2014 through trachoma control program evaluation surveys. Information on environmental and sociodemographic conditions was obtained from available data sources and linked with community data using a geographic information system. Logistic regression was used to identify predictors of low community sanitation coverage (< 20% versus ≥ 20%). The selected model was geographically and temporally validated. Model-predicted probabilities of low community sanitation coverage were mapped. Among 1,502 communities, 344 (22.90%) had coverage below 20%. The selected model included measures for high topsoil gravel content, an indicator for low-lying land, population density, altitude, and rainfall and had reasonable predictive discrimination (area under the curve = 0.75, 95% confidence interval = 0.72, 0.78). Measures of soil stability were strongly associated with low community sanitation coverage, controlling for community wealth, and other factors. A model using available environmental and sociodemographic data predicted low community sanitation coverage for areas across Amhara Region with fair discrimination. This approach could assist sanitation programs and trachoma control programs, scaling up or in hyperendemic areas, to target vulnerable areas with additional activities or alternate technologies. PMID:27430547

  20. Development of Regional Models that Use Meteorological Variables for Predicting Stripe Rust Disease on Winter Wheat.

    NASA Astrophysics Data System (ADS)

    Melugin Coakley, Stella; Boyd, William S.; Line, Roland F.

    1984-08-01

    Meteorological variables can be used to predict stripe rust, a disease of wheat caused by Puccinia striiformis West., at Lind, Pullman, and Walla Walla, Washington and Pendleton, Oregon in the Pacific Northwest of the United States. Regional models developed using different methodologies are described and evaluated for accuracy. Disease intensity data, collected from 1968 to 1981, were converted to a 0-9 disease index (DI) and were used as the dependent variable in regression analysis. Meteorological data were expressed as standardized negative degree days (NDDZ) accumulated during December and January, the Julian date of spring (JDS) [defined as the date when 40 or more positive degree days (PDD) accumulated during the subsequent 14 days] and PDD for the 80-day period after the JDS. In one of the regional models, NDDZ was accumulated for adjusted time periods at sites other than Pullman. Mallow's Cp criterion was used to evaluate the regression equations with different numbers of independent variables. The most accurate model uses NDDZ and JDS as the independent variables. The models were cross-validated by randomly removing 2 years' data and reformulating the model based on the remaining data; the new model was then used to compare actual and predicted DI. Predicted DI was within one standard error of the actual DI 60% of the time. Incorrect predictions occurred during years when spring was unusually favorable or unfavorable for disease development. The methodology described is applicable to developing statistical models relating other pest occurrences to meteorological conditions.

  1. Sensitivity of soil moisture initialization for decadal predictions under different regional climatic conditions in Europe

    NASA Astrophysics Data System (ADS)

    Khodayar, S.; Sehlinger, A.; Feldmann, H.; Kottmeier, C.

    2015-12-01

    The impact of soil initialization is investigated through perturbation simulations with the regional climate model COSMO-CLM. The focus of the investigation is to assess the sensitivity of simulated extreme periods, dry and wet, to soil moisture initialization in different climatic regions over Europe and to establish the necessary spin up time within the framework of decadal predictions for these regions. Sensitivity experiments consisted of a reference simulation from 1968 to 1999 and 5 simulations from 1972 to 1983. The Effective Drought Index (EDI) is used to select and quantify drought status in the reference run to establish the simulation time period for the sensitivity experiments. Different soil initialization procedures are investigated. The sensitivity of the decadal predictions to soil moisture initial conditions is investigated through the analysis of water cycle components' (WCC) variability. In an episodic time scale the local effects of soil moisture on the boundary-layer and the propagated effects on the large-scale dynamics are analysed. The results show: (a) COSMO-CLM reproduces the observed features of the drought index. (b) Soil moisture initialization exerts a relevant impact on WCC, e.g., precipitation distribution and intensity. (c) Regional characteristics strongly impact the response of the WCC. Precipitation and evapotranspiration deviations are larger for humid regions. (d) The initial soil conditions (wet/dry), the regional characteristics (humid/dry) and the annual period (wet/dry) play a key role in the time that soil needs to restore quasi-equilibrium and the impact on the atmospheric conditions. Humid areas, and for all regions, a humid initialization, exhibit shorter spin up times, also soil reacts more sensitive when initialised during dry periods. (e) The initial soil perturbation may markedly modify atmospheric pressure field, wind circulation systems and atmospheric water vapour distribution affecting atmospheric stability

  2. Predictive value of serum medroxyprogesterone acetate concentration for response in advanced or recurrent breast cancer.

    PubMed

    Nishimura, R; Nagao, K; Matsuda, M; Baba, K; Matsuoka, Y; Yamashita, H; Fukuda, M; Higuchi, A; Ikeda, K

    1997-08-01

    Medroxyprogesterone acetate (MPA) is one of the most commonly prescribed drugs for endocrine therapy of metastatic breast cancer. In this study, the serum MPA concentration was measured by high-performance liquid chromatography (HPLC) and evaluated for its usefulness in predicting the response in 79 cases of advanced or recurrent breast cancers. Overall, 29 patients (37%) achieved an objective response. The response rate correlated significantly with the oestrogen receptor (ER) status (P = 0.03), proliferative activity determined by DNA polymerase alpha (P = 0.04), the disease-free interval (DFI) (P = 0.05) and the serum MPA concentration (P < 0.001). Patients with ER-positive tumours, lower proliferative activity, a longer (DFI) or a higher serum MPA concentration responded more frequently. The mean serum MPA concentration in the responders with ER-positive tumours (P = 0.01) or tumours with a lower proliferative activity (P = 0.008) were significantly lower than in cases with ER-negative tumours or tumours with a higher proliferative activity, respectively. Cases with soft tissue metastases showed responses at significantly lower MPA concentrations (P = 0.003) than those with bone or visceral metastases. Furthermore, there was a dramatic decrease in the MPA concentration when a responder with a high concentration became unresponsive to the therapy. Thus, the serum MPA concentration is a determining factor for the response to treatment. PMID:9337682

  3. Methodological advances in predicting flow-induced dynamics of plants using mechanical-engineering theory.

    PubMed

    de Langre, Emmanuel

    2012-03-15

    The modeling of fluid-structure interactions, such as flow-induced vibrations, is a well-developed field of mechanical engineering. Many methods exist, and it seems natural to apply them to model the behavior of plants, and potentially other cantilever-like biological structures, under flow. Overcoming this disciplinary divide, and the application of such models to biological systems, will significantly advance our understanding of ecological patterns and processes and improve our predictive capabilities. Nonetheless, several methodological issues must first be addressed, which I describe here using two practical examples that have strong similarities: one from agricultural sciences and the other from nuclear engineering. Very similar issues arise in both: individual and collective behavior, small and large space and time scales, porous modeling, standard and extreme events, trade-off between the surface of exchange and individual or collective risk of damage, variability, hostile environments and, in some aspects, evolution. The conclusion is that, although similar issues do exist, which need to be exploited in some detail, there is a significant gap that requires new developments. It is obvious that living plants grow in and adapt to their environment, which certainly makes plant biomechanics fundamentally distinct from classical mechanical engineering. Moreover, the selection processes in biology and in human engineering are truly different, making the issue of safety different as well. A thorough understanding of these similarities and differences is needed to work efficiently in the application of a mechanistic approach to ecology. PMID:22357585

  4. A Priori Attitudes Predict Amniocentesis Uptake in Women of Advanced Maternal Age: A Pilot Study.

    PubMed

    Grinshpun-Cohen, Julia; Miron-Shatz, Talya; Rhee-Morris, Laila; Briscoe, Barbara; Pras, Elon; Towner, Dena

    2015-01-01

    Amniocentesis is an invasive procedure performed during pregnancy to determine, among other things, whether the fetus has Down syndrome. It is often preceded by screening, which gives a probabilistic risk assessment. Thus, ample information is conveyed to women with the goal to inform their decisions. This study examined the factors that predict amniocentesis uptake among pregnant women of advanced maternal age (older than 35 years old at the time of childbirth). Participants filled out a questionnaire regarding risk estimates, demographics, and attitudes on screening and pregnancy termination before their first genetic counseling appointment and were followed up to 24 weeks of gestation. Findings show that women's decisions are not always informed by screening results or having a medical indication. Psychological factors measured at the beginning of pregnancy: amniocentesis risk tolerance, pregnancy termination tolerance, and age risk perception affected amniocentesis uptake. Although most women thought that screening for Down syndrome risk would inform their decision, they later stated other reasons for screening, such as preparing for the possibility of a child with special needs. Findings suggest that women's decisions regarding amniocentesis are driven not only by medical factors, but also by a priori attitudes. The authors believe that these should be addressed in the dialogue on women's informed use of prenatal tests. PMID:26065331

  5. APPLICATION OF ADVANCED IN VITRO TECHNIQUES TO MEASURE, UNDERSTAND AND PREDICT THE KINETICS AND MECHANISMS OF XENOBIOTIC METABOLISM

    EPA Science Inventory

    We have developed a research program in metabolism that involves numerous collaborators across EPA as well as other federal and academic labs. A primary goal is to develop and apply advanced in vitro techniques to measure, understand and predict the kinetics and mechanisms of xen...

  6. A risk score for the prediction of advanced age-related macular degeneration: Development and validation in 2 prospective cohorts

    Technology Transfer Automated Retrieval System (TEKTRAN)

    We aimed to develop an eye specific model which used readily available information to predict risk for advanced age-related macular degeneration (AMD). We used the Age-Related Eye Disease Study (AREDS) as our training dataset, which consisted of the 4,507 participants (contributing 1,185 affected v...

  7. A regional neural network model for predicting mean daily river water temperature

    USGS Publications Warehouse

    Wagner, Tyler; DeWeber, Jefferson Tyrell

    2014-01-01

    Water temperature is a fundamental property of river habitat and often a key aspect of river resource management, but measurements to characterize thermal regimes are not available for most streams and rivers. As such, we developed an artificial neural network (ANN) ensemble model to predict mean daily water temperature in 197,402 individual stream reaches during the warm season (May–October) throughout the native range of brook trout Salvelinus fontinalis in the eastern U.S. We compared four models with different groups of predictors to determine how well water temperature could be predicted by climatic, landform, and land cover attributes, and used the median prediction from an ensemble of 100 ANNs as our final prediction for each model. The final model included air temperature, landform attributes and forested land cover and predicted mean daily water temperatures with moderate accuracy as determined by root mean squared error (RMSE) at 886 training sites with data from 1980 to 2009 (RMSE = 1.91 °C). Based on validation at 96 sites (RMSE = 1.82) and separately for data from 2010 (RMSE = 1.93), a year with relatively warmer conditions, the model was able to generalize to new stream reaches and years. The most important predictors were mean daily air temperature, prior 7 day mean air temperature, and network catchment area according to sensitivity analyses. Forest land cover at both riparian and catchment extents had relatively weak but clear negative effects. Predicted daily water temperature averaged for the month of July matched expected spatial trends with cooler temperatures in headwaters and at higher elevations and latitudes. Our ANN ensemble is unique in predicting daily temperatures throughout a large region, while other regional efforts have predicted at relatively coarse time steps. The model may prove a useful tool for predicting water temperatures in sampled and unsampled rivers under current conditions and future projections of climate

  8. A regional neural network ensemble for predicting mean daily river water temperature

    NASA Astrophysics Data System (ADS)

    DeWeber, Jefferson Tyrell; Wagner, Tyler

    2014-09-01

    Water temperature is a fundamental property of river habitat and often a key aspect of river resource management, but measurements to characterize thermal regimes are not available for most streams and rivers. As such, we developed an artificial neural network (ANN) ensemble model to predict mean daily water temperature in 197,402 individual stream reaches during the warm season (May-October) throughout the native range of brook trout Salvelinus fontinalis in the eastern U.S. We compared four models with different groups of predictors to determine how well water temperature could be predicted by climatic, landform, and land cover attributes, and used the median prediction from an ensemble of 100 ANNs as our final prediction for each model. The final model included air temperature, landform attributes and forested land cover and predicted mean daily water temperatures with moderate accuracy as determined by root mean squared error (RMSE) at 886 training sites with data from 1980 to 2009 (RMSE = 1.91 °C). Based on validation at 96 sites (RMSE = 1.82) and separately for data from 2010 (RMSE = 1.93), a year with relatively warmer conditions, the model was able to generalize to new stream reaches and years. The most important predictors were mean daily air temperature, prior 7 day mean air temperature, and network catchment area according to sensitivity analyses. Forest land cover at both riparian and catchment extents had relatively weak but clear negative effects. Predicted daily water temperature averaged for the month of July matched expected spatial trends with cooler temperatures in headwaters and at higher elevations and latitudes. Our ANN ensemble is unique in predicting daily temperatures throughout a large region, while other regional efforts have predicted at relatively coarse time steps. The model may prove a useful tool for predicting water temperatures in sampled and unsampled rivers under current conditions and future projections of climate and land use

  9. Development of Computational Capabilities to Predict the Corrosion Wastage of Boiler Tubes in Advanced Combustion Systems

    SciTech Connect

    Kung, Steven; Rapp, Robert

    2014-08-31

    coal-fired boilers resulting from the coexistence of sulfur and chlorine in the fuel. A new corrosion mechanism, i.e., “Active Sulfidation Corrosion Mechanism,” has been proposed to account for the accelerated corrosion wastage observed on the furnace walls of utility boilers burning coals containing sulfur and chlorine. In addition, a second corrosion mechanism, i.e., “Active Sulfide-to-Oxide Corrosion Mechanism,” has been identified to account for the rapid corrosion attack on superheaters and reheaters. Both of the newly discovered corrosion mechanisms involve the formation of iron chloride (FeCl2) vapor from iron sulfide (FeS) and HCl, followed by the decomposition of FeCl2 via self-sustaining cycling reactions. For higher alloys containing sufficient chromium, the attack on superheaters and reheaters is dominated by Hot Corrosion in the presence of a fused salt. Furthermore, two stages of the hot corrosion mechanism have been identified and characterized in detail. The initiation of hot corrosion attack induced by molten sulfate leads to Stage 1 “acidic” fluxing and re-precipitation of the protective scale formed initially on the deposit-covered alloy surfaces. Once the protective scale is penetrated, Stage 2 Hot Corrosion is initiated, which is dominated by “basic” fluxing and re-precipitation of the scale in the fused salt. Based on the extensive corrosion information generated from this project, corrosion modeling was performed using non-linear regression analysis. As a result of the modeling efforts, two predictive equations have been formulated, one for furnace walls and the other for superheaters and reheaters. These first-of-the-kind equations can be used to estimate the corrosion rates of boiler tubes based on coal chemistry, alloy compositions, and boiler operating conditions for advanced boiler systems.

  10. Predicting Earthquake Occurrence at Subduction-Zone Plate Boundaries Through Advanced Computer Simulation

    NASA Astrophysics Data System (ADS)

    Matsu'Ura, M.; Hashimoto, C.; Fukuyama, E.

    2004-12-01

    equation governing the entire process of earthquake generation. Third, combining all these elements, we developed a simulation model for quasi-static stress accumulation driven by relative plate motion. Fourth, we also developed a simulation model for dynamic rupture propagation on a 3D curved plate interface by applying BIEM. Finally, to simulate the complete earthquake generation cycle, we couple these quasi-static and the dynamic models on the Earth Simulator, which is a high-performance, massively parallel processing computer system with 10 TB Memory and 40 TF peak speed. With this system, given the past slip history and the present stress state, we can predict the next step fault slip and stress changes through computer simulation. As an example of predictive simulation, we show the quasi-static process of stress accumulation at the source region of the 1968 Tokachi-oki earthquake, northeast Japan, and the subsequent dynamic process of rupture initiation, propagation and stop. In this simulation we forced dynamic rupture to start by giving an artificial stress drop, which corresponds to some external disturbance. The dynamic rupture is accelerated, if the stress state is in critical. Otherwise the started rupture is not accelerated. This indicates that the stepwise predictive simulation with the real-time data of stress states at plate interfaces is crucial for the prediction of large interplate earthquakes.

  11. Decadal predictability of regional scale wind speed and wind energy potentials over Central Europe

    NASA Astrophysics Data System (ADS)

    Moemken, Julia; Reyers, Mark; Buldmann, Benjamin; Pinto, Joaquim G.

    2016-04-01

    Regional climate predictions on timescales from one year to one decade are gaining importance since this time frame falls within the planning horizon of politics, economy, and society. In this context, decadal predictions are of particular interest for the development of renewable energies such as wind energy. The present study examines the decadal predictability of regional scale wind speed and wind energy potentials in the framework of the MiKlip consortium ("Mittelfristige Klimaprognosen"; www.fona-miklip.de). This consortium aims to develop a model system based on the Max-Planck-Institute Earth System Model (MPI-ESM) that can provide skilful decadal predictions on regional and global scales. Three generations of the decadal prediction system, which differ primarily in their ocean initialisation, are analysed here. Ensembles of uninitialised historical and yearly initialised hindcast experiments are used to assess different skill scores for 10m wind speeds and wind energy output (Eout) over Central Europe, with special focus given to Germany. With this aim, a statistical-dynamical downscaling (SDD) approach is used for the regionalisation of the global datasets. Its added value is evaluated by comparison of skill scores for MPI-ESM large-scale wind speeds and SDD simulated regional wind speeds. All three MPI-ESM ensemble generations show some forecast skill for annual mean wind speed and Eout over Central Europe on yearly and multi-yearly time scales. The forecast skill is mostly limited to the first years after initialisation. Differences between the three ensemble generations are generally small. The regionalisation preserves and sometimes increases the forecast skill of the global runs but results depend on lead time and ensemble generation. Moreover, regionalisation often improves the ensemble spread. Seasonal Eout skills are generally lower than for annual means. Skill scores are lowest during summer, and persist longest in autumn. A large-scale westerly

  12. Mathematical model to predict regions of chromatin attachment to the nuclear matrix.

    PubMed Central

    Singh, G B; Kramer, J A; Krawetz, S A

    1997-01-01

    The potentiation and subsequent initiation of transcription are complex biological phenomena. The region of attachment of the chromatin fiber to the nuclear matrix, known as the matrix attachment region or scaffold attachment region (MAR or SAR), are thought to be requisite for the transcriptional regulation of the eukaryotic genome. As expressed sequences should be contained in these regions, it becomes significant to answer the following question: can these regions be identified from the primary sequence data alone and subsequently used as markers for expressed sequences? This paper represents an effort toward achieving this goal and describes a mathematical model for the detection of MARs. The location of matrix associated regions has been linked to a variety of sequence patterns. Consequently, a list of these patterns is compiled and represented as a set of decision rules using an AND-OR formulation. The DNA sequence was then searched for the presence of these patterns and a statistical significance was associated with the frequency of occurrence of the various patterns. Subsequently, a mathematical potential value,MAR-Potential, was assigned to a sequence region as the inverse proportion to the probability that the observed pattern population occurred at random. Such a MAR detection process was applied to the analysis of a variety of known MAR containing sequences. Regions of matrix association predicted by the software essentially correspond to those determined experimentally. The human T-cell receptor and the DNA sequence from the Drosophila bithorax region were also analyzed. This demonstrates the usefulness of the approach described as a means to direct experimental resources. PMID:9060438

  13. ADAPTATION OF THE ADVANCED STATISTICAL TRAJECTORY REGIONAL AIR POLLUTION (ASTRAP) MODEL TO THE EPA VAX COMPUTER - MODIFICATIONS AND TESTING

    EPA Science Inventory

    The Advanced Statistical Trajectory Regional Air Pollution (ASTRAP) model simulates long-term transport and deposition of oxides of and nitrogen. t is a potential screening tool for assessing long-term effects on regional visibility from sulfur emission sources. owever, a rigorou...

  14. Decadal prediction of European soil moisture from 1961 to 2010 using a regional climate model

    NASA Astrophysics Data System (ADS)

    Mieruch-Schnuelle, S.; Schädler, G.; Feldmann, H.

    2014-12-01

    The German national research program on decadal climate prediction(MiKlip) aims at the development of an operational decadal predictionsystem. To explore the potential of decadal predictions a hindcastensemble from 1961 to 2010 has been generated by the MPI-ESM, the newEarth system model of the Max Planck Institute for Meteorology. Toimprove the decadal predictions on higher spatial resolutions wedownscaled the MPI-ESM simulations by the regional model COSMO-CLM(CCLM) for Europe. In this study we will characterize and validatethe predictability of extreme states of soil moisture in Europesimulated by the MPI-ESM and the value added by the CCLM. The wateramount stored in the soil is a crucial component of the climate systemand especially important for agriculture, and has an influence onevaporation, groundwater and runoff. Thus, skillful prediction of soilmoisture in the order of years up to a decade could be used tomitigate risk and benefit society. Since soil moisture observationsare rare and validation of model output is difficult, we will ratherinvestigate the effective drought index (EDI), which can be retrievedsolely from precipitation data. Therefore we show that the EDI is agood estimator of the soil water content.

  15. Predicting the magnetic structure of interplanetary magnetic clouds and their sheath regions: Space weather perspective

    NASA Astrophysics Data System (ADS)

    Kilpua, Emilia

    2016-04-01

    Magnetic clouds and their turbulent sheath regions drive the majority of intense space weather storms. The magnitude and the details of the magnetic storm (timing, affected current systems, response of the high energy radiation belt electron fluxes, etc.) depend strongly on the magnetic topology of the CME flux rope and whether the sheath region makes a significant contribution. Sheath regions are particularly geoeffective due to their large-amplitude magnetic field fluctuations and high Alfven Mach numbers, which may enhance solar wind - magnetospheric coupling efficiency. In this presentation I will present examples of space weather responses driven by different CME structures to demonstrate the necessity to develop detailed prediction models/scenarios for different magnetic field configurations and characteristics. The constraints for solar observations and models will be also discussed.

  16. Testing earthquake prediction algorithms: Statistically significant advance prediction of the largest earthquakes in the Circum-Pacific, 1992-1997

    USGS Publications Warehouse

    Kossobokov, V.G.; Romashkova, L.L.; Keilis-Borok, V. I.; Healy, J.H.

    1999-01-01

    Algorithms M8 and MSc (i.e., the Mendocino Scenario) were used in a real-time intermediate-term research prediction of the strongest earthquakes in the Circum-Pacific seismic belt. Predictions are made by M8 first. Then, the areas of alarm are reduced by MSc at the cost that some earthquakes are missed in the second approximation of prediction. In 1992-1997, five earthquakes of magnitude 8 and above occurred in the test area: all of them were predicted by M8 and MSc identified correctly the locations of four of them. The space-time volume of the alarms is 36% and 18%, correspondingly, when estimated with a normalized product measure of empirical distribution of epicenters and uniform time. The statistical significance of the achieved results is beyond 99% both for M8 and MSc. For magnitude 7.5 + , 10 out of 19 earthquakes were predicted by M8 in 40% and five were predicted by M8-MSc in 13% of the total volume considered. This implies a significance level of 81% for M8 and 92% for M8-MSc. The lower significance levels might result from a global change in seismic regime in 1993-1996, when the rate of the largest events has doubled and all of them become exclusively normal or reversed faults. The predictions are fully reproducible; the algorithms M8 and MSc in complete formal definitions were published before we started our experiment [Keilis-Borok, V.I., Kossobokov, V.G., 1990. Premonitory activation of seismic flow: Algorithm M8, Phys. Earth and Planet. Inter. 61, 73-83; Kossobokov, V.G., Keilis-Borok, V.I., Smith, S.W., 1990. Localization of intermediate-term earthquake prediction, J. Geophys. Res., 95, 19763-19772; Healy, J.H., Kossobokov, V.G., Dewey, J.W., 1992. A test to evaluate the earthquake prediction algorithm, M8. U.S. Geol. Surv. OFR 92-401]. M8 is available from the IASPEI Software Library [Healy, J.H., Keilis-Borok, V.I., Lee, W.H.K. (Eds.), 1997. Algorithms for Earthquake Statistics and Prediction, Vol. 6. IASPEI Software Library]. ?? 1999 Elsevier

  17. Advancing Ensemble Streamflow Prediction with Stochastic Meteorological Forcings for Hydrologic Modeling

    NASA Astrophysics Data System (ADS)

    Caraway, N.; Wood, A. W.; Rajagopalan, B.; Zagona, E. A.; Daugherty, L.

    2012-12-01

    River Forecast Centers of National Weather Service (NWS) produce seasonal streamflow forecasts via a method called Ensemble Streamflow Prediction (ESP). NWS ESP forces the temperature index Snow17 and Sacramento Soil Moisture Accounting model (SAC-SMA) models with historical weather sequences for the forecasting period, starting from models' current watershed initial conditions, to produce ensemble streamflow forecasts. There are two major drawbacks of this method: (i) the ensembles are limited to the length of historical, limiting ensemble variability and (ii) incorporating seasonal climate forecasts (e.g., El Nino Southern Oscillation) relies on adjustment or weighting of ESP streamflow sequences. These drawbacks motivate the research presented here, which has two components: (i) a multi-site stochastic weather generator and (ii) generation of ensemble weather forecast inputs to the NWS model to produce ensemble streamflow forecasts. We enhanced the K-nearest neighbor bootstrap based stochastic generator include: (i) clustering the forecast locations into climatologically homogeneous regions to better capture the spatial heterogeneity and, (ii) conditioning the weather forecasts on a probabilistic seasonal climate forecast. This multi-site stochastic weather generator runs in R and the NWS models run within the new Community Hydrologic Prediction System, a forecasting sequence we label WG-ESP. The WG-ESP framework was applied to generate ensemble forecasts of spring season (April-July) streamflow in the San Juan River Basin, one of the major tributaries of the Colorado River, for the period 1981-2010. The hydrologic model requires daily weather sequences at 66 locations in the basin. The enhanced daily weather generator sequences captured the distributional properties and spatial dependence of the climatological ESP, and also generated weather sequences consistent with conditioning on seasonal climate forecasts. Spring season ensemble forecast lead times from

  18. Regional Scale Meteorological Analysis and Prediction Using GPS Occultation and EOS Data

    NASA Technical Reports Server (NTRS)

    Bromwich, David H.; Shum, C. K.; Zhao, Changyin; Kuo, Bill; Rocken, Chris

    2004-01-01

    The main objective of the research under this award is to improve regional meteorological analysis and prediction for traditionally data limited regions, particularly over the Southern Ocean and Antarctica, using the remote sensing observations from current and upcoming GPS radio occultation missions and the EOS instrument suite. The major components of this project are: 1.Develop and improve the methods for retrieving temperature, moisture, and pressure profiles from GPS radio occultation data and EOS radiometer data. 2. Develop and improve a regional scale data assimilation system (MM5 4DVAR). 3. Perform case studies involving data analysis and numerical modeling to investigate the impact of different data for regional meteorological analysis and the importance of data assimilation for regional meteorological simulation over the Antarctic region. 4. Apply the findings and improvements from the above studies to weather forecasting experiments. 5. In the third year of the award we made significant progress toward the remaining goals of the project. The work included carefully evaluating the performance of an atmospheric mesoscale model, the Polar MM5 in Antarctic applications and improving the upper boundary condition.

  19. Numerical modeling of the flow in intracranial aneurysms: prediction of regions prone to thrombus formation

    PubMed Central

    Rayz, V.L.; Boussel, L.; Lawton, M.T.; Acevedo-Bolton, G.; Ge, L.; Young, W.L.; Higashida, R.T.; Saloner, D.

    2009-01-01

    The deposition of intralumenal thrombus in intracranial aneurysms adds a risk of thrombo-embolism over and above that posed by mass-effect and rupture. In addition to biochemical factors, hemodynamic factors that are governed by lumenal geometry and blood flow rates likely play an important role in the thrombus formation and deposition process. In this study, patient-specific computational fluid dynamics (CFD) models of blood flow were constructed from MRA data for three patients who had fusiform basilar aneurysms that were thrombus-free and then proceeded to develop intra-lumenal thrombus. In order to determine whether features of the flow fields could suggest which regions had an elevated potential for thrombus deposition, the flow was modeled in the baseline, thrombus-free geometries. Pulsatile flow simulations were carried out using patient-specific inlet flow conditions measured with MR velocimetry. Newtonian and non-Newtonian blood behavior was considered. A strong similarity was found between the intra-aneurysmal regions with CFD-predicted slow, recirculating flows and the regions of thrombus deposition observed in vivo in the follow-up MR studies. In two cases with larger aneurysms, the agreement between the low velocity zones and clotted off regions improved when non-Newtonian blood behavior was taken into account. A similarity was also found between the calculated low shear stress regions and the regions that were later observed to clot. PMID:18787954

  20. Quantification of the uncertainties in soil and vegetation parameterizations for regional climate predictions

    NASA Astrophysics Data System (ADS)

    Breil, Marcus; Schädler, Gerd

    2016-04-01

    The aim of the german research program MiKlip II is the development of an operational climate prediction system that can provide reliable forecasts on a decadal time scale. Thereby, one goal of MiKlip II is to investigate the feasibility of regional climate predictions. Results of recent studies indicate that the regional climate is significantly affected by the interactions between the soil, the vegetation and the atmosphere. Thus, within the framework of MiKlip II a workpackage was established to assess the impact of these interactions on the regional decadal climate predictability. In a Regional Climate Model (RCM) the soil-vegetation-atmosphere interactions are represented in a Land Surface Model (LSM). Thereby, the LSM describes the current state of the land surface by calculating the soil temperature, the soil water content and the turbulent heat fluxes, serving the RCM as lower boundary condition. To be able to solve the corresponding equations, soil and vegetation processes are parameterized within the LSM. Such parameterizations are mainly derived from observations. But in most cases observations are temporally and spatially limited and consequently not able to represent the diversity of nature completely. Thus, soil and vegetation parameterizations always exhibit a certain degree of uncertainty. In the presented study, the uncertainties within a LSM are assessed by stochastic variations of the relevant parameterizations in VEG3D, a LSM developed at the Karlsruhe Institute of Technology (KIT). In a first step, stand-alone simulations of VEG3D are realized with varying soil and vegetation parameters, to identify sensitive model parameters. In a second step, VEG3D is coupled to the RCM COSMO-CLM. With this new model system regional decadal hindcast simulations, driven by global simulations of the Max-Planck-Institute for Meteorology Earth System Model (MPI-ESM), are performed for the CORDEX-EU domain in a resolution of 0.22°. The identified sensitive model

  1. Linkage disequilibrium predicts physical distance in the adenomatous polyposis coli region

    SciTech Connect

    Jorde, L.B.; Watkins, W.S.; Carlson, M.; Albertsen, H.; Thliveris, A.; Leppert, M. )

    1994-05-01

    To test the reliability of linkage-disequilibrium analysis for gene mapping, the authors compared physical distance and linkage disequilibrium among seven polymorphisms in the adenomatous polyposis coli (APC) region on chromosome 5. Three of them lie within the APC gene, and two lie within the nearby MCC (mutated in colon cancer) gene. One polymorphism lies between the two genes, and one is likely to be 5' of MCC. Five of these polymorphisms are newly reported. All polymorphisms were typed in the CEPH kindreds, yielding 179-205 unrelated two-locus haplotypes. Linkage disequilibrium between each pair of polymorphisms is highly correlated with physical distance in this 550-kb region (correlation coefficient [minus].80, P < .006). This result is replicated in both the Utah and non-Utah CEPH kindreds. There is a tendency for greater disequilibrium among pairs of polymorphisms located within the same gene than among other pairs of polymorphisms. Trigenic, quadrigenic, three-locus, and four-locus disequilibrium measures were also estimated, but these measures revealed much less disequilibrium than did the two-locus disequilibrium measures. A review of 19 published disequilibrium studies, including this one, shows that linkage disequilibrium nearly always correlates significantly with physical distance in genomic regions >50-60 kb but that it does not do so in smaller genomic regions. The authors show that this agrees with theoretical predictions. This finding helps to resolve controversies regarding the use of disequilibrium for inferring gene order. Disequilibrium mapping is unlikely to predict gene order correctly in regions <50-60 kb in size but can often be applied successfully in regions of 50-500 kb or so in size. It is convenient that this is the range in which other mapping techniques, including chromosome walking and linkage mapping, become difficult. 81 refs., 3 figs., 5 tabs.

  2. Saudi Arabia: A future regional hub for advanced education, research, science and technology.

    PubMed

    Meo, Sultan Ayoub

    2015-10-01

    Saudi Arabia is the largest country of the Arabian Peninsula, blessed with significant natural resources, including oil, gas and minerals. Saudi Arabia has recognised the importance of education in social and economic transformation, and has established a large number of universities, research and advanced technical institutes which have broken the metropolitan boundaries and have been extended to the far-flung areas of the country. There are 68 universities and degree-awarding institutes. The educational budget reached its highest-ever level of $56.56 billion for the year 2014. About 124,000 Saudi students are pursuing higher education in about 500 universities around the world. Saudi Arabia produced 177826 research papers in Institute for Scientific Information (ISI) database and in the year 2014 alone, 26168 research papers were published in indexed science journals with a rising h-index of 144. The country is turning into a regional hub for advanced education, research, science and technology while swiftly shifting from an oil-based to a knowledge-based economy. PMID:26440844

  3. Advancement of Satellite-based Rainfall Applications for Hydrologic Modeling in Topographically Complex Regions

    NASA Astrophysics Data System (ADS)

    Yilmaz, Koray; Derin, Yagmur

    2014-05-01

    Accuracy and reliability of hydrological modeling studies heavily depends on quality and availability of precipitation estimates. However hydrological studies in developing countries, especially over complex topography, are limited due to unavailability and scarcity of ground-based networks. In this study we evaluate three different satellite-based rainfall retrieval algorithms namely, Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA), NOAA/Climate Prediction Center Morphing Method (CMORPH) and EUMETSAT's Multi-Sensor Precipitation Estimate (MPE) over orographically complex Western Black Sea Basin in Turkey, using a relatively dense rain gauge network. Our results indicated that satellite-based products significantly underestimated the rainfall in regions characterized by orographic rainfall and overestimated the rainfall in the drier regions with seasonal dependency. Further, we devised a new bias adjustment algorithm for the satellite-based rainfall products based on the "physiographic similarity" concept. Our results showed that proposed bias adjustment algorithm is better suited to regions with complex topography and provided improved results compared to the baseline "inverse distance weighting" method. To evaluate the utility of satellite-based products in hydrologic modeling studies, we implemented the MIKE SHE-MIKE 11 integrated fully distributed physically based hydrological model in the study region driven by ground-based and satellite-based precipitation estimates. Model parameter estimation was performed using a constrained calibration approach guided by multiple "signature measures" to estimate model parameters in a hydrologically meaningful way rather than using the traditional "statistical" objective functions that largely mask valuable hydrologic information during calibration process. In this presentation we will provide a discussion of evaluation and bias correction of the satellite-based precipitation products and

  4. Gemcitabine-Based Regional Intra-Arterial Infusion Chemotherapy in Patients With Advanced Pancreatic Adenocarcinoma

    PubMed Central

    Liu, Xiaoyu; Yang, Xuerong; Zhou, Guofeng; Chen, Yi; Li, Changyu; Wang, Xiaolin

    2016-01-01

    Abstract The present study was carried out to investigate the prognostic factors in patients who received intra-arterial infusion for advanced pancreatic cancer. In addition, the detailed procedure of intra-arterial infusion chemotherapy was described. A total of 354 patients with advanced unresectable pancreatic adenocarcinoma were recruited from January 2012, to April 2015, at Zhongshan Hospital Fudan University, Shanghai, China. Demographic and clinic characteristics of the patients were extracted from electronic medical records. Restricted cubic spline was used to assess the nonliner regression between baseline CA19-9 value and overall survival. Kaplan–Meier analysis and Cox proportional hazard models were used to estimate the association between overall survival and clinical characteristics. Of all 354 included patients, 230 (65%) were male (male/female ratio = 1.8), and 72 (20%) patients were diagnosed with detectable distant metastases. Pretreatment CA19-9 value of patients with metastases was significantly higher as compared to those with locally advanced cancer (median: 922.30 vs 357.00 U/mL, P = 0.0090). Totally 274 patients completed 1 cycle of intra-arterial infusion, whereas 80 patients received 2 or more cycles of the chemotherapy. For all the 354 patients, median OS was 7.0 months (95% CI: 6.0, 8.0 months) with a 6-, 12-, and 18-month survival rate of 0.48, 0.28, and 0.18, respectively. The median OS of patients, who received 1 cycle of intra-arterial infusion therapy, was 6.0 months (95% CI: 5.0, 8.0 months), which was similar to 7.0 months (95% CI: 6.0, 9.0 months) in patients who received 2 or more cycles. Restricted cubic spline revealed the nonline association between baseline CA19-9 and prognosis. The Cox proportional hazard model showed that age, CA19-9 baseline, CA19-9 value, and tumor location were significantly associated with the OS. In conclusion, the gemcitabine-based RIAC presented a potential treatment method for advanced

  5. Regional Seismic Travel-Time Prediction, Uncertainty, and Location Improvement in Western Eurasia

    NASA Astrophysics Data System (ADS)

    Flanagan, M. P.; Myers, S. C.

    2004-12-01

    We investigate our ability to improve regional travel-time prediction and seismic event location using an a priori, three-dimensional velocity model of Western Eurasia and North Africa: WENA1.0 [Pasyanos et al., 2004]. Our objective is to improve the accuracy of seismic location estimates and calculate representative location uncertainty estimates. As we focus on the geographic region of Western Eurasia, the Middle East, and North Africa, we develop, test, and validate 3D model-based travel-time prediction models for 30 stations in the study region. Three principal results are presented. First, the 3D WENA1.0 velocity model improves travel-time prediction over the iasp91 model, as measured by variance reduction, for regional Pg, Pn, and P phases recorded at the 30 stations. Second, a distance-dependent uncertainty model is developed and tested for the WENA1.0 model. Third, an end-to-end validation test based on 500 event relocations demonstrates improved location performance over the 1-dimensional iasp91 model. Validation of the 3D model is based on a comparison of approximately 11,000 Pg, Pn, and P travel-time predictions and empirical observations from ground truth (GT) events. Ray coverage for the validation dataset is chosen to provide representative, regional-distance sampling across Eurasia and North Africa. The WENA1.0 model markedly improves travel-time predictions for most stations with an average variance reduction of 25% for all ray paths. We find that improvement is station dependent, with some stations benefiting greatly from WENA1.0 predictions (52% at APA, 33% at BKR, and 32% at NIL), some stations showing moderate improvement (12% at KEV, 14% at BOM, and 12% at TAM), some benefiting only slightly (6% at MOX, and 4% at SVE), and some are degraded (-6% at MLR and -18% at QUE). We further test WENA1.0 by comparing location accuracy with results obtained using the iasp91 model. Again, relocation of these events is dependent on ray paths that evenly

  6. Evaluating the ability of regional models to predict local avian abundance

    USGS Publications Warehouse

    LeBrun, Jaymi J.; Thogmartin, Wayne E.; Miller, James R.

    2012-01-01

    Spatial modeling over broad scales can potentially direct conservation efforts to areas with high species-specific abundances. We examined the performance of regional models for predicting bird abundance at spatial scales typically addressed in conservation planning. Specifically, we used point count data on wood thrush (Hylocichla mustelina) and blue-winged warbler (Vermivora cyanoptera) from 2 time periods (1995-1998 and 2006-2007) to evaluate the ability of regional models derived via Bayesian hierarchical techniques to predict bird abundance. We developed models for each species within Bird Conservation Region (BCR) 23 in the upper midwestern United States at 800-ha, 8,000-ha, and approximately 80,000-ha scales. We obtained count data from the Breeding Bird Survey and land cover data from the National Land Cover Dataset (1992). We evaluated predictions from the best models, as defined by an information-theoretic criterion, using point count data collected within an ecological subregion of BCR 23 at 131 count stations in the 1990s and again in 2006-2007. Competing (Deviance Information Criteria rs = 0.57; P = 0.14), the survey period that most closely aligned with the time period of data used for regional model construction. Wood thrush models exhibited positive correlations with point count data for all survey areas and years combined (rs = 0.58, P ≤ 0.001). In comparison, blue-winged warbler models performed worse as time increased between the point count surveys and vintage of the model building data (rs = 0.03, P = 0.92 for Iowa and rs = 0.13, P = 0.51 for all areas, 2006-2007), likely related to the ephemeral nature of their preferred early successional habitat. Species abundance and sensitivity to changing habitat conditions seems to be an important factor in determining the predictive ability of regional models. Hierarchical models can be a useful tool for concentrating efforts at the scale of management units and should be one of many tools used by

  7. Advanced MRI may complement histological diagnosis of lower grade gliomas and help in predicting survival.

    PubMed

    Cuccarini, Valeria; Erbetta, A; Farinotti, M; Cuppini, L; Ghielmetti, F; Pollo, B; Di Meco, F; Grisoli, M; Filippini, G; Finocchiaro, G; Bruzzone, M G; Eoli, M

    2016-01-01

    MRI grading of grade II and III gliomas may have an important impact on treatment decisions. Occasionally,both conventional MRI (cMRI) and histology fail to clearly establish the tumour grade. Three cMRI features(no necrosis; no relevant oedema; absent or faint contrast enhancement) previously validated in 196 patients with supratentorial gliomas directed our selection of 68 suspected low-grade gliomas (LGG) that were also investigated by advanced MRI (aMRI), including perfusion weighted imaging (PWI), diffusion weighted imaging(DWI) and spectroscopy. All the gliomas had histopathological diagnoses. Sensitivity and specificity of cMRI preoperative diagnosis were 78.5 and 38.5 %, respectively, and 85.7 and 53.8 % when a MRI was included, respectively. ROC analysis showed that cut-off values of 1.29 for maximum rCBV, 1.69 for minimum rADC, 2.1 for rCho/Cr ratio could differentiate between LGG and HGG with a sensitivity of 61.5, 53.8, and 53.8 % and a specificity of 54.7, 43 and 64.3 %, respectively. A significantly longer OS was observed in patients with a maximum rCBV<1.46 and minimum rADC>1.69 (80 vs 55 months, p = 0.01; 80 vs 51 months, p = 0.002, respectively). This result was also confirmed when cases were stratified according to pathology (LGG vs HGG). The ability of a MRI to differentiate between LGG and HGG and to predict survival improved as the number of a MRI techniques considered increased. In a selected population of suspected LGG,classification by cMRI underestimated the actual fraction of HGG. aMRI slightly increased the diagnostic accuracy compared to histopathology. However, DWI and PWI were prognostic markers independent of histological grade. PMID:26468137

  8. Comparison between IRI-2007 model predictions and ionospheric observations at European region during extended solar minimum

    NASA Astrophysics Data System (ADS)

    Zakharenkova, Irina; Krankowski, Andrzej; Bilitza, Dieter; Cherniak, Iurii; Shagimuratov, Irk; Krypiak-Gregorczyk, Anna

    The solar minimum began around March 2006 and many predictions of the start and size of Solar Cycle 24 were given since then. In 2007 the Solar Cycle 24 Prediction Panel anticipates the solar minimum marking the onset of Cycle 24 will occur in March 2008 (6 months). Then this date was shifted to the August 2008, after that -to the December 2008. At least solar minimum is extended to the end of 2009. This unusually deep and extended solar minimum makes corrections to the predicted values of solar cycle progression. With every update the predicted values of sunspot number is decreased. It leads to the significant discrepancies in IRI model results in depend on the predicted indices. To calculate the ionospheric parameters the IRI model uses indices file with ionospheric index IG12 and solar sunspot number (12-months running median) Rz12. This IRI file is regularly updated with the newest available indices and predictions. It was considered the IRI model results obtained with use of different predicted and observed IG and Rz indices during 2007-2009 years. For the given study it was done the comparison of the IRI-2007 predicted values of F2 layer critical frequency (foF2) with those observed at several mid-latitude ionospheric stations in European region. Values of foF2 have been scaled manually from ionograms to avoid the evident risks related with using of the autoscaled data that have ionosonde-related errors and uncertainties. It was the ionograms and foF2 values provided by European Digital Upper Atmosphere Server (DIAS). The DIAS bases on real-time and historical data provided by most operating ionospheric stations in Europe. This server collects information from stations located in Rome, Pruhonice, Juliusruh, Athens, Chilton, Ebre and El Arenosillo. For each station it was calculated monthly median of foF2 variation on the base of full month data analysis. We have considered observations taken in the months of January, April, July and October for 2007

  9. Predictability of Regional Climate: A Bayesian Approach to Analysing a WRF Model Ensemble

    NASA Astrophysics Data System (ADS)

    Bruyere, C. L.; Mesquita, M. D. S.; Paimazumder, D.

    2013-12-01

    This study investigates aspects of climate predictability with a focus on climatic variables and different characteristics of extremes over nine North American climatic regions and two selected Atlantic sectors. An ensemble of state-of-the-art Weather Research and Forecasting Model (WRF) simulations is used for the analysis. The ensemble is comprised of a combination of various physics schemes, initial conditions, domain sizes, boundary conditions and breeding techniques. The main objectives of this research are: 1) to increase our understanding of the ability of WRF to capture regional climate information - both at the individual and collective ensemble members, 2) to investigate the role of different members and their synergy in reproducing regional climate 3) to estimate the associated uncertainty. In this study, we propose a Bayesian framework to study the predictability of extremes and associated uncertainties in order to provide a wealth of knowledge about WRF reliability and provide further clarity and understanding of the sensitivities and optimal combinations. The choice of the Bayesian model, as opposed to standard methods, is made because: a) this method has a mean square error that is less than standard statistics, which makes it a more robust method; b) it allows for the use of small sample sizes, which are typical in high-resolution modeling; c) it provides a probabilistic view of uncertainty, which is useful when making decisions concerning ensemble members.

  10. The Niño1+2 region and the Niño4 region predictability.

    NASA Astrophysics Data System (ADS)

    Miguel, Tasambay-Salazar; Jose, Ortizbevia Maria; Francisco Jose, Alvarez-Garcia; Antonio, Ruizdeelvira

    2016-04-01

    The El Niño-Southern Oscillation variability is monitored basically by the the Niño3.4 Index. In addition, the Niño1+2 and the Niño4 Indexes are also used to characterise ENSO variability, by reason of their relationships with some of the variability of the neighboring regions, like the air temperature in South America or Australia. However, with the increased length of the available instrumental ENSO records, the need of considering the two different ENSO types identified, Eastern Pacific (EP) or Central Pacific (CP), has become more evident. (Yu and Kim 2013). While the Nino3.4 Index is used to monitor the EP events, the CP events are currently identified by removing from the Niño4 Index the variability associated to the Niño1+2 Index (Kao and Yu 2009). Therefore there is a renewed interest on the predictability of both Indexes. In this study we focus on the predictability of the Niño1+2 region variability and those of the Niño4 region, in the recent post-satellital period. We develop a methodology to identify potential predictors among climate modes, represented by their respective indexes. Among the tropical predictors tested we include the most commonly used,like the Southern Oscillation Index or the Warm Water Volume in the equatorial Pacific (WWV) Index, but also some whose part in the ENSO generation and evolution has been pointed only recently, like the Pacific Meridional Mode (PMM) Index or the North Tropical Zonal Gradient and South Tropical Zonal Gradient Indexes.We also include in our study some other tropical Indexes outside the Pacific basin, like the Tropical North Atlantic, the Tropical South Atlantic and the Indian Ocean Dipole Indexes. We use a seasonal approach, based in a linear statistical relationship and focus on leads going from one season to one year. In the case of the Niño1+2 Index, the number of potential predictors is much higher in spring, followed by winter and summer and last of all autumn. The potential predictor most

  11. Bleomycin, cyclophosphamide and radiotherapy in regionally advanced epidermoid carcinoma of the head and neck

    SciTech Connect

    Seagren, S.L.; Byfield, J.E.; Davidson, T.M.; Sharp, T.R.

    1982-01-01

    Twenty-four patients with squamous carcinoma of the head and neck and advanced regional (N/sub 2-3) disease were treated. The regimen consisted of 3 cycles, each of 28 days. Cyclophosphamide (1 gm/ M/sup 2/ I.V.) was given on day 1, bleomycin (15 u I.M.) on days 2, 4, 9 and 11, and ionizing radiation (/sup 60/Co, 180 rad/fraction) days 1-5, and 8-12. No therapy was given on days 13-28. After three cycles of therapy, 13 patients had a complete response; following further therapy (surgery, interstitial or external beam radiation), 16 patients were free of disease. However, remissions were not durable and 11/16 patients recurred loco-regionally with a median time to recurrence of 5 months; most (7/11) also developed distant metatases. These patients have biologically aggressive disease and may have a worse prognosis than patients who are Stage IV based on a T/sub 4/ primary lesion only.

  12. Bleomycin, cyclophosphamide and radiotherapy in regionally advanced epidermoid carcinoma of the head and neck

    SciTech Connect

    Seagren, S.L.; Byfield, J.E.; Davidson, T.M.; Sharp, T.R.

    1982-01-01

    Twenty four patients with squamous carcinoma of the head and neck and advanced regional (N/sub 2//sub -//sub 3/) disease were treated. The regimen consisted of 3 cycles, each of 28 days. Cyclophosphamide (I gm/m/sup 2/ I.V.) were given on day 1, bleomycin (15 ..mu.. I.M.) on days 2, 4, 9 and 11, and ionizing radiation (/sup 60/Co, 180 rad/fraction) days 1-5, and 8-12. No therapy was given on days 13-28. After three cycles of therapy, 13 patients had a complete response; following further therapy (surgery, interstitial or extenal beam radiation), 16 patients were free of disease. However, remissions were not durable and 11/16 patients recurred loco-regionally with a median time to recurrence of 5 months; most (7/11) also developed distant metastases. These patients have biologically aggressive disease and may have a worse prognosis than patients who are Stage IV based on a T/sub 4/ primary lesion only.

  13. Evaluation of GCMs in the context of regional predictive climate impact studies.

    NASA Astrophysics Data System (ADS)

    Kokorev, Vasily; Anisimov, Oleg

    2016-04-01

    Significant improvements in the structure, complexity, and general performance of earth system models (ESMs) have been made in the recent decade. Despite these efforts, the range of uncertainty in predicting regional climate impacts remains large. The problem is two-fold. Firstly, there is an intrinsic conflict between the local and regional scales of climate impacts and adaptation strategies, on one hand, and larger scales, at which ESMs demonstrate better performance, on the other. Secondly, there is a growing understanding that majority of the impacts involve thresholds, and are thus driven by extreme climate events, whereas accent in climate projections is conventionally made on gradual changes in means. In this study we assess the uncertainty in projecting extreme climatic events within a region-specific and process-oriented context by examining the skills and ranking of ESMs. We developed a synthetic regionalization of Northern Eurasia that accounts for the spatial features of modern climatic changes and major environmental and socio-economical impacts. Elements of such fragmentation could be considered as natural focus regions that bridge the gap between the spatial scales adopted in climate-impacts studies and patterns of climate change simulated by ESMs. In each focus region we selected several target meteorological variables that govern the key regional impacts, and examined the ability of the models to replicate their seasonal and annual means and trends by testing them against observations. We performed a similar evaluation with regard to extremes and statistics of the target variables. And lastly, we used the results of these analyses to select sets of models that demonstrate the best performance at selected focus regions with regard to selected sets of target meteorological parameters. Ultimately, we ranked the models according to their skills, identified top-end models that "better than average" reproduce the behavior of climatic parameters, and

  14. Seasonal forecasts in the Sahel region: the use of rainfall-based predictive variables

    NASA Astrophysics Data System (ADS)

    Lodoun, Tiganadaba; Sanon, Moussa; Giannini, Alessandra; Traoré, Pierre Sibiry; Somé, Léopold; Rasolodimby, Jeanne Millogo

    2014-08-01

    In the Sahel region, seasonal predictions are crucial to alleviate the impacts of climate variability on populations' livelihoods. Agricultural planning (e.g., decisions about sowing date, fertilizer application date, and choice of crop or cultivar) is based on empirical predictive indices whose accuracy to date has not been scientifically proven. This paper attempts to statistically test whether the pattern of rainfall distribution over the May-July period contributes to predicting the real onset date and the nature (wet or dry) of the rainy season, as farmers believe. To that end, we considered historical records of daily rainfall from 51 stations spanning the period 1920-2008 and the different agro-climatic zones in Burkina Faso. We performed (1) principal component analysis to identify climatic zones, based on the patterns of intra-seasonal rainfall, (2) and linear discriminant analysis to find the best rainfall-based variables to distinguish between real and false onset dates of the rainy season, and between wet and dry seasons in each climatic zone. A total of nine climatic zones were identified in each of which, based on rainfall records from May to July, we derived linear discriminant functions to correctly predict the nature of a potential onset date of the rainy season (real or false) and that of the rainy season (dry or wet) in at least three cases out of five. These functions should contribute to alleviating the negative impacts of climate variability in the different climatic zones of Burkina Faso.

  15. Motion estimation and compensation based on region-constrained warping prediction

    NASA Astrophysics Data System (ADS)

    Chang, Dong-Il; Sung, Joon H.; Kim, Jeong K.; Lee, ChoongWoong

    1998-01-01

    The visually annoying artifacts resulting form block matching algorithm (BMA), blocky artifacts, become noticeable in applications for low bit rates. Warping prediction (WP) based schemes can remove the blocky artifacts of BMA successfully, but they also produce severe prediction errors around the boundaries of moving objects. Since the errors around the boundaries of objects are visually sensitive, they may sometimes look more annoying than blocky artifacts. The lack of ability of modeling motion discontinuities is the major reason of the errors from WP. Motion discontinuities usually exist in practical video sequences, so that it is required to develop a more reliable motion estimation and usually exist in practical video sequences, so that it is required to develop a more reliable motion estimation and compensation scheme for low bit rate applications. In this paper, we propose a new WP scheme, named region constrained warping prediction (RCWP), which places motion discontinuities according to the segmentation results. In RCWP, there is mutual dependency between estimated motion field and segmentation mask. Because of the mutual dependency, an iterative refinement process is also introduced. Experimental results have shown that the proposed algorithm can provide much better subjective and objective performance than the BMA and the conventional warping prediction.

  16. Prediction of multipactor in the iris region of rf deflecting mode cavities

    NASA Astrophysics Data System (ADS)

    Burt, G.; Dexter, A. C.

    2011-12-01

    Multipactor is a major cause of field limitation in many superconducting rf cavities. Multipacting is a particular issue for deflecting mode cavities as the typical behavior is not well studied, understood, or parametrized. In this paper an approximate analytical model for the prediction of multipactor in the iris region of deflecting mode cavities is developed. This new but simple model yields a clear explanation on the broad range of rf field levels over which the multipactor can occur. The principle multipactors under investigation here are two-point multipactors associated with cyclotron motion in the cavity’s rf magnetic field. The predictions from the model are compared to numerical simulations and good agreement is obtained. The results are also compared to experimental results previously reported by KEK and are also found in good agreement.

  17. A PBL-radiation model for application to regional numerical weather prediction

    NASA Technical Reports Server (NTRS)

    Chang, Chia-Bo

    1989-01-01

    Often in the short-range limited-area numerical weather prediction (NWP) of extratropical weather systems the effects of planetary boundary layer (PBL) processes are considered secondarily important. However, it may not be the case for the regional NWP of mesoscale convective systems over the arid and semi-arid highlands of the southwestern and south-central United States in late spring and summer. Over these dry regions, the PBL can grow quite high up into the lower middle troposphere (600 mb) due to very effective solar heating and hence a vigorous air-land thermal interaction can occur. The interaction representing a major heat source for regional dynamical systems can not be ignored. A one-dimensional PBL-radiation model was developed. The model PBL consists of a constant-flux surface layer superposed with a well-mixed (Ekman) layer. The vertical eddy mixing coefficients for heat and momentum in the surface layer are determined according to the surface similarity theory, while their vertical profiles in the Ekman layer are specified with a cubic polynomial. Prognostic equations are used for predicting the height of the nonneutral PBL. The atmospheric radiation is parameterized to define the surface heat source/sink for the growth and decay of the PBL. A series of real-data numerical experiments has been carried out to obtain a physical understanding how the model performs under various atmospheric and surface conditions. This one-dimensional model will eventually be incorporated into a mesoscale prediction system. The ultimate goal of this research is to improve the NWP of mesoscale convective storms over land.

  18. A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles.

    PubMed

    Murphy, J M; Booth, B B B; Collins, M; Harris, G R; Sexton, D M H; Webb, M J

    2007-08-15

    A methodology is described for probabilistic predictions of future climate. This is based on a set of ensemble simulations of equilibrium and time-dependent changes, carried out by perturbing poorly constrained parameters controlling key physical and biogeochemical processes in the HadCM3 coupled ocean-atmosphere global climate model. These (ongoing) experiments allow quantification of the effects of earth system modelling uncertainties and internal climate variability on feedbacks likely to exert a significant influence on twenty-first century climate at large regional scales. A further ensemble of regional climate simulations at 25km resolution is being produced for Europe, allowing the specification of probabilistic predictions at spatial scales required for studies of climate impacts. The ensemble simulations are processed using a set of statistical procedures, the centrepiece of which is a Bayesian statistical framework designed for use with complex but imperfect models. This supports the generation of probabilities constrained by a wide range of observational metrics, and also by expert-specified prior distributions defining the model parameter space. The Bayesian framework also accounts for additional uncertainty introduced by structural modelling errors, which are estimated using our ensembles to predict the results of alternative climate models containing different structural assumptions. This facilitates the generation of probabilistic predictions combining information from perturbed physics and multi-model ensemble simulations. The methodology makes extensive use of emulation and scaling techniques trained on climate model results. These are used to sample the equilibrium response to doubled carbon dioxide at any required point in the parameter space of surface and atmospheric processes, to sample time-dependent changes by combining this information with ensembles sampling uncertainties in the transient response of a wider set of earth system processes

  19. Uncertainties in freshwater and MOC predictions in the North Atlantic region

    NASA Astrophysics Data System (ADS)

    Martin, T.; Latif, M.; Reintges, A.

    2012-04-01

    Future changes in the Atlantic meridional overturning circulation (MOC) will result from processes both internal and external to the climate system. Global warming leads to an amplified hydrological cycle, which affects the vertical salinity and temperature profiles. The meridional changes in the ocean-atmosphere interaction diminish the meridional oceanic density contrast. In the North Atlantic sinking regions, these changes are strongly related to salinity anomalies at the surface. Most climate models predict a weakening of the North Atlantic meridional overturning circulation (MOC) during the twenty-first century when forced by increasing levels of greenhouse gas concentrations. However, large uncertainty exists in comparing different climate model predictions, even under identical forcing. Individual studies suggest that multidecadal changes in the MOC are strongly related to large-scale salinity anomalies and therefore probably to changes in the surface freshwater fluxes and freshwater transport. We derived the general relationship between the MOC and freshwater budget of the Northern Hemisphere analyzing the CMIP3 20th century simulations and the A1B scenario prediction. A quantification of the different sources of uncertainty (external, internal and model uncertainties) indicates the model error as the largest component. The internal variability is significant during the first decades, while scenario uncertainty is almost negligible. The different contributions to model uncertainty like surface wind and density, salinity versus temperature has been analyzed additionally. Overall, the strongest MOC changes have been predicted in the models around 40°N, whereas the strongest signal-to-noise ratio is located south of 40°N. Uncertainties in meridional ocean density profiles are dominated by model uncertainties in the salinity distribution. The local signal-to-noise ratio of the ocean freshwater flux is low in the arctic and subpolar region. First analyses of

  20. Risk prediction of Critical Infrastructures against extreme natural hazards: local and regional scale analysis

    NASA Astrophysics Data System (ADS)

    Rosato, Vittorio; Hounjet, Micheline; Burzel, Andreas; Di Pietro, Antonio; Tofani, Alberto; Pollino, Maurizio; Giovinazzi, Sonia

    2016-04-01

    Natural hazard events can induce severe impacts on the built environment; they can hit wide and densely populated areas, where there is a large number of (inter)dependent technological systems whose damages could cause the failure or malfunctioning of further different services, spreading the impacts on wider geographical areas. The EU project CIPRNet (Critical Infrastructures Preparedness and Resilience Research Network) is realizing an unprecedented Decision Support System (DSS) which enables to operationally perform risk prediction on Critical Infrastructures (CI) by predicting the occurrence of natural events (from long term weather to short nowcast predictions, correlating intrinsic vulnerabilities of CI elements with the different events' manifestation strengths, and analysing the resulting Damage Scenario. The Damage Scenario is then transformed into an Impact Scenario, where punctual CI element damages are transformed into micro (local area) or meso (regional) scale Services Outages. At the smaller scale, the DSS simulates detailed city models (where CI dependencies are explicitly accounted for) that are of important input for crisis management organizations whereas, at the regional scale by using approximate System-of-Systems model describing systemic interactions, the focus is on raising awareness. The DSS has allowed to develop a novel simulation framework for predicting earthquakes shake maps originating from a given seismic event, considering the shock wave propagation in inhomogeneous media and the subsequent produced damages by estimating building vulnerabilities on the basis of a phenomenological model [1, 2]. Moreover, in presence of areas containing river basins, when abundant precipitations are expected, the DSS solves the hydrodynamic 1D/2D models of the river basins for predicting the flux runoff and the corresponding flood dynamics. This calculation allows the estimation of the Damage Scenario and triggers the evaluation of the Impact Scenario

  1. Prediction of Bulk Density of Soils in the Loess Plateau Region of China

    NASA Astrophysics Data System (ADS)

    Wang, Yunqiang; Shao, Ming'an; Liu, Zhipeng; Zhang, Chencheng

    2013-08-01

    Soil bulk density (BD) is a key soil physical property that may affect the transport of water and solutes and is essential to estimate soil carbon/nutrients reserves. However, BD data are often lacking in soil databases due to the challenge of directly measuring BD, which is considered to be labor intensive, time consuming, and expensive especially for the lower layers of deep soils such as those of the Chinese Loess Plateau region. We determined the factors that were closely correlated with BD at the regional scale and developed a robust pedotransfer function (PTF) for BD by measuring BD and potentially related soil and environmental factors at 748 selected sites across the Loess Plateau of China (620,000 km2) at which we collected undisturbed and disturbed soil samples from two soil layers (0-5 and 20-25 cm). Regional BD values were normally distributed and demonstrated weak spatial variation (CV = 12 %). Pearson's correlation and stepwise multiple linear regression analyses identified silt content, slope gradient (SG), soil organic carbon content (SOC), clay content, slope aspect (SA), and altitude as the factors that were closely correlated with BD and that explained 25.8, 6.3, 5.8, 1.4, 0.3, and 0.3 % of the BD variation, respectively. Based on these closely correlated variables, a reasonably robust PTF was developed for BD using multiple linear regression, which performed equally with the artificial neural network method in the current study. The inclusion of topographic factors significantly improved the predictive capability of the BD PTF and in which SG was an important input variable that could be used in place of SA and altitude without compromising its capability for predicting BD. Thus, the developed PTF with only four input variables (clay, silt, SOC, SG), including their common transformations and interactive terms, predicted BD with reasonable accuracy and is thus useful for most applications on the Loess Plateau of China. More attention should be

  2. Scale-dependent regional climate predictability over North America inferred from CMIP3 and CMIP5 ensemble simulations

    NASA Astrophysics Data System (ADS)

    Zhang, Fuqing; Li, Wei; Mann, Michael E.

    2016-08-01

    Through the analysis of ensembles of coupled model simulations and projections collected from CMIP3 and CMIP5, we demonstrate that a fundamental spatial scale limit might exist below which useful additional refinement of climate model predictions and projections may not be possible. That limit varies among climate variables and from region to region. We show that the uncertainty (noise) in surface temperature predictions (represented by the spread among an ensemble of global climate model simulations) generally exceeds the ensemble mean (signal) at horizontal scales below 1000 km throughout North America, implying poor predictability at those scales. More limited skill is shown for the predictability of regional precipitation. The ensemble spread in this case tends to exceed or equal the ensemble mean for scales below 2000 km. These findings highlight the challenges in predicting regionally specific future climate anomalies, especially for hydroclimatic impacts such as drought and wetness.

  3. HOTAIR is a predictive and prognostic biomarker for patients with advanced gastric adenocarcinoma receiving fluorouracil and platinum combination chemotherapy

    PubMed Central

    Zhao, Wei; Dong, Shuang; Duan, Bensong; Chen, Ping; Shi, Lei; Gao, Hengjun; Qi, Haizhi

    2015-01-01

    Accumulating evidence suggests that long non-coding RNA (lncRNA) HOTAIR participates in many types of cancer such as gastric cancer and may confer malignant phenotype to tumor cells. Fluorouracil and platinum combination chemotherapy is the first line therapy for gastric cancer. However, it is still unknown whether HOTAIR influences the outcome of cancer patients treated with chemotherapy. This study aimed to evaluate the association of HOTAIR expression with the prognosis of patients with advanced gastric adenocarcinoma (GA) receiving fluorouracil and platinum based chemotherapy. We examined the levels of HOTAIR in 168 GA samples using quantitative real-time PCR and analyzed its relationship with clinical features and prognosis of patients with advanced GA treated with fluorouracil and platinum based chemotherapy. Compared with paracancerous tissues, HOTAIR was significantly upregulated in GA tissues, especially in more advanced cases. High HOTAIR expression was an independent poor prognostic factor for patients with advanced GA. Further stratification analyses revealed that the association between HOTAIR expression and survival in patients with advanced GA remained significant in the subgroup of patients with TNM stages IIIA and IIIB, poorly differentiated, and smaller tumors. In conclusion, our results provide first evidence that HOTAIR may be served as a biomarker that predicts which patient with advanced GA will benefit from fluorouracil and platinum combination chemotherapy. PMID:26328013

  4. Predicting Advanced Placement Examination Success from FCAT Scores. Research Brief. Volume 0709

    ERIC Educational Resources Information Center

    Froman, Terry; Brown, Shelly; Tirado, Arleti

    2008-01-01

    Advanced Placement courses are offered at M-DCPS for students to acquire college credit or advanced college academic standing. A system has been developed in the past by the College Board to use the PSAT for 10th grade students to estimate their potential for AP Examination success. The same test has recently been applied in this district to 9th…

  5. Data-driven methods to improve baseflow prediction of a regional groundwater model

    NASA Astrophysics Data System (ADS)

    Xu, Tianfang; Valocchi, Albert J.

    2015-12-01

    Physically-based models of groundwater flow are powerful tools for water resources assessment under varying hydrologic, climate and human development conditions. One of the most important topics of investigation is how these conditions will affect the discharge of groundwater to rivers and streams (i.e. baseflow). Groundwater flow models are based upon discretized solution of mass balance equations, and contain important hydrogeological parameters that vary in space and cannot be measured. Common practice is to use least squares regression to estimate parameters and to infer prediction and associated uncertainty. Nevertheless, the unavoidable uncertainty associated with physically-based groundwater models often results in both aleatoric and epistemic model calibration errors, thus violating a key assumption for regression-based parameter estimation and uncertainty quantification. We present a complementary data-driven modeling and uncertainty quantification (DDM-UQ) framework to improve predictive accuracy of physically-based groundwater models and to provide more robust prediction intervals. First, we develop data-driven models (DDMs) based on statistical learning techniques to correct the bias of the calibrated groundwater model. Second, we characterize the aleatoric component of groundwater model residual using both parametric and non-parametric distribution estimation methods. We test the complementary data-driven framework on a real-world case study of the Republican River Basin, where a regional groundwater flow model was developed to assess the impact of groundwater pumping for irrigation. Compared to using only the flow model, DDM-UQ provides more accurate monthly baseflow predictions. In addition, DDM-UQ yields prediction intervals with coverage probability consistent with validation data. The DDM-UQ framework is computationally efficient and is expected to be applicable to many geoscience models for which model structural error is not negligible.

  6. Application of Genetic Algorithm to Predict Optimal Sowing Region and Timing for Kentucky Bluegrass in China

    PubMed Central

    Peng, Tingting; Jiang, Bo; Guo, Jiangfeng; Lu, Hongfei; Du, Liqun

    2015-01-01

    Temperature is a predominant environmental factor affecting grass germination and distribution. Various thermal-germination models for prediction of grass seed germination have been reported, in which the relationship between temperature and germination were defined with kernel functions, such as quadratic or quintic function. However, their prediction accuracies warrant further improvements. The purpose of this study is to evaluate the relative prediction accuracies of genetic algorithm (GA) models, which are automatically parameterized with observed germination data. The seeds of five P. pratensis (Kentucky bluegrass, KB) cultivars were germinated under 36 day/night temperature regimes ranging from 5/5 to 40/40°C with 5°C increments. Results showed that optimal germination percentages of all five tested KB cultivars were observed under a fluctuating temperature regime of 20/25°C. Meanwhile, the constant temperature regimes (e.g., 5/5, 10/10, 15/15°C, etc.) suppressed the germination of all five cultivars. Furthermore, the back propagation artificial neural network (BP-ANN) algorithm was integrated to optimize temperature-germination response models from these observed germination data. It was found that integrations of GA-BP-ANN (back propagation aided genetic algorithm artificial neural network) significantly reduced the Root Mean Square Error (RMSE) values from 0.21~0.23 to 0.02~0.09. In an effort to provide a more reliable prediction of optimum sowing time for the tested KB cultivars in various regions in the country, the optimized GA-BP-ANN models were applied to map spatial and temporal germination percentages of blue grass cultivars in China. Our results demonstrate that the GA-BP-ANN model is a convenient and reliable option for constructing thermal-germination response models since it automates model parameterization and has excellent prediction accuracy. PMID:26154163

  7. Predicting Violence Among Forensic-Correctional Populations: The Past 2 Decades of Advancements and Future Endeavors

    ERIC Educational Resources Information Center

    Loza, Wagdy; Dhaliwal, Gurmeet K.

    2005-01-01

    Research on violence prediction during the past 2 decades has evolved appreciably in terms of depicting determinants of violence and developing psychometrically sound actuarial measures to predict the probability of future violent behavior. This article provides a brief synopsis of information on predicting violence gained in the past 2 decades,…

  8. Effects of lateral boundary condition resolution and update frequency on regional climate model predictions

    NASA Astrophysics Data System (ADS)

    Pankatz, Klaus; Kerkweg, Astrid

    2015-04-01

    The work presented is part of the joint project "DecReg" ("Regional decadal predictability") which is in turn part of the project "MiKlip" ("Decadal predictions"), an effort funded by the German Federal Ministry of Education and Research to improve decadal predictions on a global and regional scale. In MiKlip, one big question is if regional climate modeling shows "added value", i.e. to evaluate, if regional climate models (RCM) produce better results than the driving models. However, the scope of this study is to look more closely at the setup specific details of regional climate modeling. As regional models only simulate a small domain, they have to inherit information about the state of the atmosphere at their lateral boundaries from external data sets. There are many unresolved questions concerning the setup of lateral boundary conditions (LBC). External data sets come from global models or from global reanalysis data-sets. A temporal resolution of six hours is common for this kind of data. This is mainly due to the fact, that storage space is a limiting factor, especially for climate simulations. However, theoretically, the coupling frequency could be as high as the time step of the driving model. Meanwhile, it is unclear if a more frequent update of the LBCs has a significant effect on the climate in the domain of the RCM. The first study examines how the RCM reacts to a higher update frequency. The study is based on a 30 year time slice experiment for three update frequencies of the LBC, namely six hours, one hour and six minutes. The evaluation of means, standard deviations and statistics of the climate in the regional domain shows only small deviations, some statistically significant though, of 2m temperature, sea level pressure and precipitation. The second part of the first study assesses parameters linked to cyclone activity, which is affected by the LBC update frequency. Differences in track density and strength are found when comparing the simulations

  9. Predicted reliability of aerospace electronics: Application of two advanced probabilistic concepts

    NASA Astrophysics Data System (ADS)

    Suhir, E.

    Two advanced probabilistic design-for-reliability (PDfR) concepts are addressed and discussed in application to the prediction, quantification and assurance of the aerospace electronics reliability: 1) Boltzmann-Arrhenius-Zhurkov (BAZ) model, which is an extension of the currently widely used Arrhenius model and, in combination with the exponential law of reliability, enables one to obtain a simple, easy-to-use and physically meaningful formula for the evaluation of the probability of failure (PoF) of a material or a device after the given time in operation at the given temperature and under the given stress (not necessarily mechanical), and 2) Extreme Value Distribution (EVD) technique that can be used to assess the number of repetitive loadings that result in the material/device degradation and eventually lead to its failure by closing, in a step-wise fashion, the gap between the bearing capacity (stress-free activation energy) of the material or the device and the demand (loading). It is shown that the material degradation (aging, damage accumulation, flaw propagation, etc.) can be viewed, when BAZ model is considered, as a Markovian process, and that the BAZ model can be obtained as the ultimate steady-state solution to the well-known Fokker-Planck equation in the theory of Markovian processes. It is shown also that the BAZ model addresses the worst, but a reasonably conservative, situation. It is suggested therefore that the transient period preceding the condition addressed by the steady-state BAZ model need not be accounted for in engineering evaluations. However, when there is an interest in understanding the transient degradation process, the obtained solution to the Fokker-Planck equation can be used for this purpose. As to the EVD concept, it attributes the degradation process to the accumulation of damages caused by a train of repetitive high-level loadings, while loadings of levels that are considerably lower than their extreme values do not contribute

  10. Prediction of helicopter rotor discrete frequency noise: A computer program incorporating realistic blade motions and advanced acoustic formulation

    NASA Technical Reports Server (NTRS)

    Brentner, K. S.

    1986-01-01

    A computer program has been developed at the Langley Research Center to predict the discrete frequency noise of conventional and advanced helicopter rotors. The program, called WOPWOP, uses the most advanced subsonic formulation of Farassat that is less sensitive to errors and is valid for nearly all helicopter rotor geometries and flight conditions. A brief derivation of the acoustic formulation is presented along with a discussion of the numerical implementation of the formulation. The computer program uses realistic helicopter blade motion and aerodynamic loadings, input by the user, for noise calculation in the time domain. A detailed definition of all the input variables, default values, and output data is included. A comparison with experimental data shows good agreement between prediction and experiment; however, accurate aerodynamic loading is needed.

  11. Test-sites for earthquake prediction experiments within the Colli Albani region

    NASA Astrophysics Data System (ADS)

    Quattrocchi, F.; Calcara, M.

    In this paper we discuss some geochemical data gathered by discrete and continuous monitoring during the 1995-1996 period, carried out for earthquake prediction test-experiments throughout the Colli Albani quiescent volcano, seat of seismicity, selecting some gas discharge sites with peri-volcanic composition. In particular we stressed the results obtained at the continuous geochemical monitoring station (GMS I, BAR site), designed by ING for geochemical surveillance of seismic events. The 12/6/1995 (M=3.6-3.8) Roma earthquake together with the 3/11/1995 (M=3.1) Tivoli earthquake was the most energetic events within the Colli Albani - Roma area, after the beginning of the continuous monitoring (1991) up today: strict correlation between these seismic events and fluid geochemical anomalies in groundwater has been discovered (temperature, Eh, 222Rn, CO 2, NH 3). Separation at depth of a vapour phase, rich in reducing-acidic gases (CO 2, H 2S, etc...), from a hyper-saline brine, within the deep geothermal reservoir is hypothesised to explain the geochemical anomalies: probably the transtensional episodes accompanying the seismic sequences caused an increasing and/or triggering of the phase-separation process and fluid migration, on the regional scale of the Western sector of the Colli Albani, beyond the seismogenic depth (2-4 Km) up to surface. We draw the state of art of GMS II monitoring prototype and the selection criteria of test-sites for earthquake prediction experiments in the Colli Albani region.

  12. Fog Prediction for Road Traffic Safety in a Coastal Desert Region

    NASA Astrophysics Data System (ADS)

    Bartok, Juraj; Bott, Andreas; Gera, Martin

    2012-12-01

    Modern weather prediction models use relatively high grid resolutions as well as sophisticated parametrization schemes for microphysical and other subgrid-scale atmospheric processes. Nonetheless, with these models it remains a difficult task to perform successful numerical fog forecasts since many factors controlling a particular fog event are not yet sufficiently simulated. Here we describe our efforts to create a mechanism that produces successful predictions of fog in the territory located on the north coast of the Arabian Peninsula. Our approach consists in the coupling of the one-dimensional PAFOG fog model with the three-dimensional WRF 3.0 (Weather Research and Forecast) modelling system. The proposed method allows us to construct an efficient operative road traffic warning system for the occurrence of fog in the investigated region. In total 84 historical situations were studied during the period 2008-2009. Moreover, results of operative day-by-day fog forecasting during January and February 2010 are presented. For the investigated arid and hot climate region the land-sea breeze circulation seems to be the major factor affecting the diurnal variations of the meteorological conditions, frequently resulting in the formation of fog.

  13. Heavy metal contents, distribution, and prediction in a regional soil-wheat system.

    PubMed

    Ran, Jing; Wang, Dejian; Wang, Can; Zhang, Gang; Zhang, Hailin

    2016-02-15

    The entry of heavy metals into the food chain is of concern for potential health risks. To investigate the spatial relationships of heavy metals in a regional soil-wheat system, 99 pairs of surface soil (0-15 cm) and wheat grain samples were collected from Changshu, China, a typical county in the Yangtze Delta region. Both soil and wheat grain samples were analyzed for total Cd, Cu, Ni, Pb, and Zn. DTPA-extractable metals and major physico-chemical properties were also determined for soil samples. Moderate accumulation of heavy metals was found in soils and wheat grains, especially Cd. However, the levels were within the target hazard quotients (THQ) safe values with respect to non-carcinogenic risks, but more attention should be paid to Cd. Spatially, Cd, Cu, Ni, and Zn in wheat grains and soils had similar geographical patterns, whereas Pb showed opposite trends. Cross-correlograms further quantitatively confirmed the spatial relationships of heavy metals in wheat grains and soils. In addition, heavy metals in wheat grains were significantly spatially correlated with most soil physio-chemical properties. Particularly, a set of regression models for Cd in wheat grains were established with a maximum predictive success of 65%. These models can be used to predict Cd in wheat grains, and thus allows farmers to decrease the threat by certain framing practices such as ameliorating soil pH or growing a less metal-accumulating cultivar. PMID:26657387

  14. Engagement of temporal lobe regions predicts response to educational interventions in adolescent struggling readers.

    PubMed

    Rezaie, Roozbeh; Simos, Panagiotis G; Fletcher, Jack M; Cirino, Paul T; Vaughn, Sharon; Papanicolaou, Andrew C

    2011-01-01

    Brain activation profiles obtained using magnetoencephalography were compared between middle-school students experiencing reading difficulties and non-reading-impaired students during performance of a continuous printed word recognition task. Struggling readers underwent small-group remedial instruction, and students who showed significant gains in word reading efficiency at a one-year follow-up assessment were classified as Adequate Responders whereas those not demonstrating such gains as Inadequate Responders. At baseline, compared to Inadequate Responders, the activation profiles of Adequate Responders featured increased activity in the left middle, superior temporal, and ventral occipitotemporal regions, as well as in the right mesial temporal cortex. The degree of activity in these regions was a significant predictor of improvement in word reading efficiency beyond the prediction afforded by baseline reading accuracy or fluency measures. The engagement of brain areas that typically serve as key components of the brain circuit for reading may be an important factor in predicting response to intervention in older students who experience reading difficulties. PMID:21978010

  15. Engagement of Temporal Lobe Regions Predicts Response to Educational Interventions in Adolescent Struggling Readers

    PubMed Central

    Rezaie, Roozbeh; Simos, Panagiotis G.; Fletcher, Jack M.; Cirino, Paul T.; Vaughn, Sharon; Papanicolaou, Andrew C.

    2012-01-01

    Brain activation profiles obtained using magnetoencephalography were compared between middle-school students experiencing reading difficulties and non-reading-impaired students during performance of a continuous printed word recognition task. Struggling readers underwent small–group remedial instruction, and students who showed significant gains in word reading efficiency at a one-year follow-up assessment were classified as Adequate Responders whereas those not demonstrating such gains as Inadequate Responders. At baseline, compared to Inadequate Responders, the activation profiles of Adequate Responders featured increased activity in the left middle, superior temporal, and ventral occipitotemporal regions, as well as in the right mesial temporal cortex. The degree of activity in these regions was a significant predictor of improvement in word reading efficiency beyond the prediction afforded by baseline reading accuracy or fluency measures. The engagement of brain areas that typically serve as key components of the brain circuit for reading may be an important factor in predicting response to intervention in older students who experience reading difficulties. PMID:21978010

  16. Gene Expression Profile for Predicting Survival in Advanced-Stage Serous Ovarian Cancer Across Two Independent Datasets

    PubMed Central

    Yoshihara, Kosuke; Tajima, Atsushi; Yahata, Tetsuro; Kodama, Shoji; Fujiwara, Hiroyuki; Suzuki, Mitsuaki; Onishi, Yoshitaka; Hatae, Masayuki; Sueyoshi, Kazunobu; Fujiwara, Hisaya; Kudo, Yoshiki; Kotera, Kohei; Masuzaki, Hideaki; Tashiro, Hironori; Katabuchi, Hidetaka; Inoue, Ituro; Tanaka, Kenichi

    2010-01-01

    Background Advanced-stage ovarian cancer patients are generally treated with platinum/taxane-based chemotherapy after primary debulking surgery. However, there is a wide range of outcomes for individual patients. Therefore, the clinicopathological factors alone are insufficient for predicting prognosis. Our aim is to identify a progression-free survival (PFS)-related molecular profile for predicting survival of patients with advanced-stage serous ovarian cancer. Methodology/Principal Findings Advanced-stage serous ovarian cancer tissues from 110 Japanese patients who underwent primary surgery and platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays. We selected 88 PFS-related genes by a univariate Cox model (p<0.01) and generated the prognostic index based on 88 PFS-related genes after adjustment of regression coefficients of the respective genes by ridge regression Cox model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66–5.43; p<0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20–1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008). Conclusions/Significance The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer. PMID:20300634

  17. The Clinical Significance of MiR-148a as a Predictive Biomarker in Patients with Advanced Colorectal Cancer

    PubMed Central

    Takahashi, Masanobu; Cuatrecasas, Miriam; Balaguer, Francesc; Hur, Keun; Toiyama, Yuji; Castells, Antoni; Boland, C. Richard; Goel, Ajay

    2012-01-01

    Aim Development of robust prognostic and/or predictive biomarkers in patients with colorectal cancer (CRC) is imperative for advancing treatment strategies for this disease. We aimed to determine whether expression status of certain miRNAs might have prognostic/predictive value in CRC patients treated with conventional cytotoxic chemotherapies. Methods We studied a cohort of 273 CRC specimens from stage II/III patients treated with 5-fluorouracil-based adjuvant chemotherapy and stage IV patients subjected to 5-fluorouracil and oxaliplatin-based chemotherapy. In a screening set (n = 44), 13 of 21 candidate miRNAs were successfully quantified by multiplex quantitative RT-PCR. In the validation set comprising of the entire patient cohort, miR-148a expression status was assessed by quantitative RT-PCR, and its promoter methylation was quantified by bisulfite pyrosequencing. Lastly, we analyzed the associations between miR-148a expression and patient survival. Results Among the candidate miRNAs studied, miR-148a expression was most significantly down-regulated in advanced CRC tissues. In stage III and IV CRC, low miR-148a expression was associated with significantly shorter disease free-survival (DFS), a worse therapeutic response, and poor overall survival (OS). Furthermore, miR-148a methylation status correlated inversely with its expression, and was associated with worse survival in stage IV CRC. In multivariate analysis, miR-148a expression was an independent prognostic/predictive biomarker for advanced CRC patients (DFS in stage III, low vs. high expression, HR 2.11; OS in stage IV, HR 1.93). Discussion MiR-148a status has a prognostic/predictive value in advanced CRC patients treated with conventional chemotherapy, which has important clinical implications in improving therapeutic strategies and personalized management of this malignancy. PMID:23056401

  18. From field to region yield predictions in response to pedo-climatic variations in Eastern Canada

    NASA Astrophysics Data System (ADS)

    JÉGO, G.; Pattey, E.; Liu, J.

    2013-12-01

    The increase in global population coupled with new pressures to produce energy and bioproducts from agricultural land requires an increase in crop productivity. However, the influence of climate and soil variations on crop production and environmental performance is not fully understood and accounted for to define more sustainable and economical management strategies. Regional crop modeling can be a great tool for understanding the impact of climate variations on crop production, for planning grain handling and for assessing the impact of agriculture on the environment, but it is often limited by the availability of input data. The STICS ("Simulateur mulTIdisciplinaire pour les Cultures Standard") crop model, developed by INRA (France) is a functional crop model which has a built-in module to optimize several input parameters by minimizing the difference between calculated and measured output variables, such as Leaf Area Index (LAI). STICS crop model was adapted to the short growing season of the Mixedwood Plains Ecozone using field experiments results, to predict biomass and yield of soybean, spring wheat and corn. To minimize the numbers of inference required for regional applications, 'generic' cultivars rather than specific ones have been calibrated in STICS. After the calibration of several model parameters, the root mean square error (RMSE) of yield and biomass predictions ranged from 10% to 30% for the three crops. A bit more scattering was obtained for LAI (20%prediction to climate variations. Using RS data to re-initialize input parameters that are not readily available (e.g. seeding date) is considered an effective way

  19. Initial Staging of Locally Advanced Rectal Cancer and Regional Lymph Nodes

    PubMed Central

    Cerny, Milena; Dunet, Vincent; Prior, John Olivier; Hahnloser, Dieter; Wagner, Anna Dorothea; Meuli, Reto Antoine; Schmidt, Sabine

    2016-01-01

    Purpose The aim of the study was to compare diffusion-weighted MRI (DW-MRI) parameters with 18F-FDG PET/CT in primary locally advanced rectal cancer (LARC). Methods From October 2012 to September 2014, 24 patients with histologically confirmed and untreated LARC (T3–T4) prospectively underwent a pelvic 1.5-T DW-MRI (b = 0 s/mm2, b = 600 s/mm2) and a whole-body 18F-FDG PET/CT, before neoadjuvant therapy. The 2 examinations were performed on the same day. Two readers measured 18F-FDG SUVmax and SUVmean of the rectal tumor and of the pathological regional lymph nodes on PET/CT and compared these with minimum and mean values of the ADC (ADCmin and ADCmean) on maps generated from DW-MRI. The diagnostic performance of ADC values in identifying pathological lymph nodes was also assessed. Results Regarding tumors (n = 24), we found a significant negative correlation between SUVmean and corresponding ADCmean values (ρ = −0.61, P = 0.0017) and between ADCmin and SUVmax (ρ = −0.66, P = 0.0005). Regarding the lymph nodes (n = 63), there was a significant negative correlation between ADCmean and SUVmean values (ρ = −0.38, P = 0.0021), but not between ADCmin and SUVmax values (ρ = −0.11, P = 0.41). Neither ADCmean nor ADCmin values helped distinguish pathological from benign lymph nodes (AUC of 0.24 [confidence interval, 0.10–0.38] and 0.41 [confidence interval, 0.22–0.60], respectively). Conclusions The correlations between ADCmean and SUVmean suggest an association between tumor cellularity and metabolic activity in untreated LARC and in regional lymph nodes. However, compared with 18F-FDG PET/CT, ADC values are not reliable for identifying pathological lymph nodes. PMID:26828149

  20. Evaluation of Regional Extended-Range Prediction for Tropical Waves Using COAMPS®

    NASA Astrophysics Data System (ADS)

    Hong, X.; Reynolds, C. A.; Doyle, J. D.; May, P. W.; Chen, S.; Flatau, M. K.; O'Neill, L. W.

    2014-12-01

    The Navy's Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS1) in a two-way coupled mode is used for two-month regional extended-range prediction for the Madden-Julian Oscillation (MJO) and Tropical Cyclone 05 (TC05) that occurred during the DYNAMO period from November to December 2011. Verification and statistics from two experiments with 45-km and 27-km horizontal resolutions indicate that 27-km run provides a better representation of the three MJO events that occurred during this 2-month period, including the two convectively-coupled Kelvin waves associated with the second MJO event as observed. The 27-km run also significantly reduces forecast error after 15-days, reaching a maximum bias reduction of 89% in the third 15-day period due to the well represented MJO propagation over the Maritime Continent. Correlations between the model forecasts and observations or ECMWF analyses show that the MJO suppressed period is more difficult to predict than the active period. In addition, correlation coefficients for cloud liquid water path (CLWP) and precipitation are relatively low for both cases compared to other variables. The study suggests that a good simulation of TC05 and a good simulation of the Kelvin waves and westerly wind bursts are linked. Further research is needed to investigate the capability in regional extended-range forecasts when the lateral boundary conditions are provided from a long-term global forecast to allow for an assessment of potential operational forecast skill. _____________________________________________________ 1COAMPS is a registered trademark of U.S. Naval Research Laboratory

  1. Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients

    PubMed Central

    Hashem, Somaya; Esmat, Gamal; Elakel, Wafaa; Habashy, Shahira; Abdel Raouf, Safaa; Darweesh, Samar; Soliman, Mohamad; Elhefnawi, Mohamed; El-Adawy, Mohamed; ElHefnawi, Mahmoud

    2016-01-01

    Background/Aim. Respectively with the prevalence of chronic hepatitis C in the world, using noninvasive methods as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy is significantly increasing. The aim of this study is to combine the serum biomarkers and clinical information to develop a classification model that can predict advanced liver fibrosis. Methods. 39,567 patients with chronic hepatitis C were included and randomly divided into two separate sets. Liver fibrosis was assessed via METAVIR score; patients were categorized as mild to moderate (F0–F2) or advanced (F3-F4) fibrosis stages. Two models were developed using alternating decision tree algorithm. Model 1 uses six parameters, while model 2 uses four, which are similar to FIB-4 features except alpha-fetoprotein instead of alanine aminotransferase. Sensitivity and receiver operating characteristic curve were performed to evaluate the performance of the proposed models. Results. The best model achieved 86.2% negative predictive value and 0.78 ROC with 84.8% accuracy which is better than FIB-4. Conclusions. The risk of advanced liver fibrosis, due to chronic hepatitis C, could be predicted with high accuracy using decision tree learning algorithm that could be used to reduce the need to assess the liver biopsy. PMID:26880886

  2. Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients.

    PubMed

    Hashem, Somaya; Esmat, Gamal; Elakel, Wafaa; Habashy, Shahira; Abdel Raouf, Safaa; Darweesh, Samar; Soliman, Mohamad; Elhefnawi, Mohamed; El-Adawy, Mohamed; ElHefnawi, Mahmoud

    2016-01-01

    Background/Aim. Respectively with the prevalence of chronic hepatitis C in the world, using noninvasive methods as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy is significantly increasing. The aim of this study is to combine the serum biomarkers and clinical information to develop a classification model that can predict advanced liver fibrosis. Methods. 39,567 patients with chronic hepatitis C were included and randomly divided into two separate sets. Liver fibrosis was assessed via METAVIR score; patients were categorized as mild to moderate (F0-F2) or advanced (F3-F4) fibrosis stages. Two models were developed using alternating decision tree algorithm. Model 1 uses six parameters, while model 2 uses four, which are similar to FIB-4 features except alpha-fetoprotein instead of alanine aminotransferase. Sensitivity and receiver operating characteristic curve were performed to evaluate the performance of the proposed models. Results. The best model achieved 86.2% negative predictive value and 0.78 ROC with 84.8% accuracy which is better than FIB-4. Conclusions. The risk of advanced liver fibrosis, due to chronic hepatitis C, could be predicted with high accuracy using decision tree learning algorithm that could be used to reduce the need to assess the liver biopsy. PMID:26880886

  3. The importance of climatic factors and outliers in predicting regional monthly campylobacteriosis risk in Georgia, USA

    NASA Astrophysics Data System (ADS)

    Weisent, J.; Seaver, W.; Odoi, A.; Rohrbach, B.

    2014-01-01

    Incidence of Campylobacter infection exhibits a strong seasonal component and regional variations in temperate climate zones. Forecasting the risk of infection regionally may provide clues to identify sources of transmission affected by temperature and precipitation. The objectives of this study were to (1) assess temporal patterns and differences in campylobacteriosis risk among nine climatic divisions of Georgia, USA, (2) compare univariate forecasting models that analyze campylobacteriosis risk over time with those that incorporate temperature and/or precipitation, and (3) investigate alternatives to supposedly random walk series and non-random occurrences that could be outliers. Temporal patterns of campylobacteriosis risk in Georgia were visually and statistically assessed. Univariate and multivariable forecasting models were used to predict the risk of campylobacteriosis and the coefficient of determination (R 2) was used for evaluating training (1999-2007) and holdout (2008) samples. Statistical control charting and rolling holdout periods were investigated to better understand the effect of outliers and improve forecasts. State and division level campylobacteriosis risk exhibited seasonal patterns with peaks occurring between June and August, and there were significant associations between campylobacteriosis risk, precipitation, and temperature. State and combined division forecasts were better than divisions alone, and models that included climate variables were comparable to univariate models. While rolling holdout techniques did not improve predictive ability, control charting identified high-risk time periods that require further investigation. These findings are important in (1) determining how climatic factors affect environmental sources and reservoirs of Campylobacter spp. and (2) identifying regional spikes in the risk of human Campylobacter infection and their underlying causes.

  4. Absence of Q waves after thrombolysis predicts more rapid improvement of regional left ventricular dysfunction.

    PubMed

    Isselbacher, E M; Siu, S C; Weyman, A E; Picard, M H

    1996-04-01

    Although the natural history of regional left ventricular (LV) dysfunction after Q-wave and non-Q-wave myocardial infarction (MI) was well defined in the prethrombolytic era, the functional and structural implications of the absence of Q waves after thrombolysis are less clear. Echocardiography was performed within 48 hours of admission (entry) in 86 patients treated with thrombolysis for their first MI. The extent of abnormal wall motion (AWM; square centimeters) and LV endocardial surface area index (ESA; square centimeters per square meters) were quantified by using a previously validated echocardiographic endocardial surface-mapping technique. Electrocardiography (ECG) performed at 48 hours after thrombolysis was used to classify patients into groups with (Q; n=70) and without (non-Q; n=16) Q waves. All patients in the Q group had regional LV dysfunction on initial echocardiogram compared with 69 percent of those in the non-Q group (p<0.001). When the patients in the non-Q group without AWM were excluded from analysis, there was no significant difference in the extent of AWM between the Q and non-Q groups. Among those patients with AWM on entry, follow-up echocardiography at 6 to 12 weeks demonstrated a significant reduction in extent of AWM for both the Q and non-Q groups. However, the fractional change in AWM was significantly greater in the non-Q than in the Q group (-0.74 +/- 0.28 vs -0.29 +/- 0.44; p<0.02), with a trend toward less AWM at follow-up in the non-Q than in the Q group. The mean ESAi was not significantly different between the two groups at entry or at follow-up. In conclusion, failure to develop Q waves after thrombolysis predicts a lower likelihood of developing regional LV dysfunction and, when such dysfunction is present, predicts a greater degree of recovery. PMID:8721634

  5. Spatial prediction of soil texture in region Centre (France) from summary data

    NASA Astrophysics Data System (ADS)

    Dobarco, Mercedes Roman; Saby, Nicolas; Paroissien, Jean-Baptiste; Orton, Tom G.

    2015-04-01

    Soil texture is a key controlling factor of important soil functions like water and nutrient holding capacity, retention of pollutants, drainage, soil biodiversity, and C cycling. High resolution soil texture maps enhance our understanding of the spatial distribution of soil properties and provide valuable information for decision making and crop management, environmental protection, and hydrological planning. We predicted the soil texture of agricultural topsoils in the Region Centre (France) combining regression and area-to-point kriging. Soil texture data was collected from the French soil-test database (BDAT), which is populated with soil analysis performed by farmers' demand. To protect the anonymity of the farms the data was treated by commune. In a first step, summary statistics of environmental covariates by commune were used to develop prediction models with Cubist, boosted regression trees, and random forests. In a second step the residuals of each individual observation were summarized by commune and kriged following the method developed by Orton et al. (2012). This approach allowed to include non-linear relationships among covariates and soil texture while accounting for the uncertainty on areal means in the area-to-point kriging step. Independent validation of the models was done using data from the systematic soil monitoring network of French soils. Future work will compare the performance of these models with a non-stationary variance geostatistical model using the most important covariates and summary statistics of texture data. The results will inform on whether the later and statistically more-challenging approach improves significantly texture predictions or whether the more simple area-to-point regression kriging can offer satisfactory results. The application of area-to-point regression kriging at national level using BDAT data has the potential to improve soil texture predictions for agricultural topsoils, especially when combined with

  6. Kinematic earthquake source inversion and tsunami runup prediction with regional geophysical data

    NASA Astrophysics Data System (ADS)

    Melgar, D.; Bock, Y.

    2015-05-01

    Rapid near-source earthquake source modeling relying only on strong motion data is limited by instrumental offsets and magnitude saturation, adversely affecting subsequent tsunami prediction. Seismogeodetic displacement and velocity waveforms estimated from an optimal combination of high-rate GPS and strong motion data overcome these limitations. Supplementing land-based data with offshore wave measurements by seafloor pressure sensors and GPS-equipped buoys can further improve the image of the earthquake source and prediction of tsunami extent, inundation, and runup. We present a kinematic source model obtained from a retrospective real-time analysis of a heterogeneous data set for the 2011 Mw9.0 Tohoku-Oki, Japan, earthquake. Our model is consistent with conceptual models of subduction zones, exhibiting depth dependent behavior that is quantified through frequency domain analysis of slip rate functions. The stress drop distribution is found to be significantly more correlated with aftershock locations and mechanism types when off-shore data are included. The kinematic model parameters are then used as initial conditions in a fully nonlinear tsunami propagation analysis. Notably, we include the horizontal advection of steeply sloping bathymetric features. Comparison with post-event on-land survey measurements demonstrates that the tsunami's inundation and runup are predicted with considerable accuracy, only limited in scale by the resolution of available topography and bathymetry. We conclude that it is possible to produce credible and rapid, kinematic source models and tsunami predictions within minutes of earthquake onset time for near-source coastal regions most susceptible to loss of life and damage to critical infrastructure, regardless of earthquake magnitude.

  7. Effects of regional differences in the long term carbon balance on predicted net CO2 fluxes

    NASA Astrophysics Data System (ADS)

    Ziehn, Tilo; Scholze, Marko; Knorr, Wolfgang

    2010-05-01

    The Carbon Cycle Data Assimilation System (CCDAS) allows the current fluxes of CO2 to the atmosphere to be mapped and the evolution of these fluxes into the future to be predicted. In this work we concentrate on the calibration mode of CCDAS where an optimal parameter set is derived from 10 years of atmospheric CO2 concentration observations using an adjoint approach. Global and regional process parameters are considered via a mapping routine. The parameters are then optimised by calculating the mismatch of the observations and prior knowledge of the parameters via a defined cost function. Further, parameter uncertainty estimates, which are obtained during the parameter optimisation step, can be propagated in order to estimate uncertainties of any given output such as of the predicted net CO2 fluxes. The process based terrestrial biosphere model BETHY is the core of CCDAS. It simulates carbon assimilation and soil respiration within a full energy and water balance and phenology scheme. Produced fluxes are then mapped onto atmospheric concentrations using the atmospheric transport model TM2. BETHY has 20 parameters for each plant functional type (PFT). There is a choice from a single global description up to independent parameter sets for every grid point. In the base case, all parameters are applied globally. Additionally, the key photosynthetic parameters (maximum electron transport and maximum carboxylation rate) and the key carbon storage parameter β vary with each of the 13 PFTs which gives a total of 56 control parameters. The β parameter is a scaling parameter for a constraint that exists for the long term carbon balance. This constraint is implemented in BETHY in order to consider unknown processes such as climate forcing and disturbance. On the contrary to the other process parameters, β is not necessarily a global parameter. In fact, there might be a strong regional dependency, because β represents information about the history of the site and the

  8. Accounting for dependencies in regionalized signatures for predictions in ungauged catchments

    NASA Astrophysics Data System (ADS)

    Almeida, Susana; Le Vine, Nataliya; McIntyre, Neil; Wagener, Thorsten; Buytaert, Wouter

    2016-02-01

    A recurrent problem in hydrology is the absence of streamflow data to calibrate rainfall-runoff models. A commonly used approach in such circumstances conditions model parameters on regionalized response signatures. While several different signatures are often available to be included in this process, an outstanding challenge is the selection of signatures that provide useful and complementary information. Different signatures do not necessarily provide independent information and this has led to signatures being omitted or included on a subjective basis. This paper presents a method that accounts for the inter-signature error correlation structure so that regional information is neither neglected nor double-counted when multiple signatures are included. Using 84 catchments from the MOPEX database, observed signatures are regressed against physical and climatic catchment attributes. The derived relationships are then utilized to assess the joint probability distribution of the signature regionalization errors that is subsequently used in a Bayesian procedure to condition a rainfall-runoff model. The results show that the consideration of the inter-signature error structure may improve predictions when the error correlations are strong. However, other uncertainties such as model structure and observational error may outweigh the importance of these correlations. Further, these other uncertainties cause some signatures to appear repeatedly to be misinformative.

  9. Predictability and Ensemble Modeling of the Space-Atmosphere Interaction Region

    NASA Astrophysics Data System (ADS)

    Matsuo, T.; Fuller-Rowell, T. J.; Akmaev, R. A.; Wang, H.; Fang, T. W.; Ide, K.; Kleist, D. T.; Whitaker, J. S.; Yue, X.; Codrescu, M.; Richmond, A. D.; Immel, T. J.; Anderson, B. J.; Paxton, L. J.; Liu, J. Y.

    2014-12-01

    The Space-Atmosphere Interaction Region (SAIR), encompassing the mesosphere, thermosphere and ionosphere, is an intersection between geospace and the Earth's atmosphere, and is exposed to vacillating conditions of both space and terrestrial weather. Recent observational and modeling studies have revealed clear reaches of terrestrial weather far beyond the mesosphere lower-thermosphere region into the topside ionosphere. At the same time, the region lends itself to forcing originating from the Sun and solar-wind magnetosphere interactions. The predictability of the SAIR is a fundamental question in Heliophysics, and calls for a paradigm shift from a deterministic to a probabilistic modeling framework. To meet with this contemporary modeling and simulation challenge, we will systematically compare and combine ensemble simulations of a comprehensive whole atmosphere model, coupled with an ionosphere and plasmasphere model called the Integrated Dynamics in Earth's Atmosphere (IDEA) with global Earth and geospace observations. Building on the National Weather Service's operational ensemble forecasting and data assimilation systems as well as our earlier efforts, we will construct an ensemble forecasting and data assimilation system that will ultimately be capable of assimilating observations from the ground to SAIR. We will present the project overview along with some initial results from our new interdisciplinary initiatives.

  10. Comparison of Season-ahead Prediction Techniques on Regionalized Grid-level Precipitation: Application to Western Ethiopia

    NASA Astrophysics Data System (ADS)

    Zhang, Y.; Moges, S. A.; Block, P.

    2015-12-01

    Season-ahead precipitation predictions offer utility in decision-making relative to water resource utilization and management, including agricultural planning and reservoir operation, particularly for regions with highly variable spatial-temporal precipitation patterns. Preprocessing precipitation by objective regionalization has the potential to improve prediction by defining appropriate scales of homogenous clusters. Statistical prediction techniques and downscaling approaches are evaluated over western Ethiopia, including principal component and hierarchical Bayesian approaches, at the cluster and grid scales. Predictors are drawn from large scale climate indices and variables and local drivers (e.g. soil moisture, elevation, spring rains, etc.). Preliminary results indicate substantial improvements in prediction skill when applying regionalization and, for locations/grids with more complex geographic characteristics, through the addition of local variables. Grid-scale screening of prediction techniques and suitable predictors is undertaken to identify optimal model combinations.

  11. Prediction and monitoring of monsoon intraseasonal oscillations over Indian monsoon region in an ensemble prediction system using CFSv2

    NASA Astrophysics Data System (ADS)

    Abhilash, S.; Sahai, A. K.; Borah, N.; Chattopadhyay, R.; Joseph, S.; Sharmila, S.; De, S.; Goswami, B. N.; Kumar, Arun

    2014-05-01

    An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISO) of Indian summer monsoon (ISM) using National Centers for Environmental Prediction Climate Forecast System model version 2 at T126 horizontal resolution. The EPS is formulated by generating 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001-2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio of the forecasted rainfall becomes unity by about 18 days. The potential predictability error of the forecasted rainfall saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are found even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of large-scale MISO amplitude as well as the initial conditions related to the different phases of MISO. An analysis of categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead.

  12. Source regional contributions to PM2.5 in a megacity in China using an advanced source regional apportionment method.

    PubMed

    Tian, Ying-Ze; Chen, Gang; Wang, Hai-Ting; Huang-Fu, Yan-Qi; Shi, Guo-Liang; Han, Bo; Feng, Yin-Chang

    2016-03-01

    To quantify contributions of individual source categories from diverse regions to PM2.5, PM2.5 samples were collected in a megacity in China and analyzed through a newly developed source regional apportionment (SRA) method. Levels, compositions and seasonal variations of speciated PM2.5 dataset were investigated. Sources were determined by Multilinear Engine 2 (ME2) model, and results showed that the PM2.5 in Tianjin was mainly influenced by secondary sulphate & secondary organic carbon SOC (percent contribution of 26.2%), coal combustion (24.6%), crustal dust & cement dust (20.3%), secondary nitrate (14.9%) and traffic emissions (14.0%). The SRA method showed that northwest region R2 was the highest regional contributor to secondary sources, with percent contributions to PM2.5 being 9.7% for secondary sulphate & SOC and 6.0% for secondary nitrates; the highest coal combustion was from local region R1 (6.2%) and northwest R2 (8.0%); the maximum contributing region to crustal & cement dust was southeast region R4 (5.0%); and contributions of traffic emissions were relatively spatial homogeneous. The seasonal variation of regional source contributions was observed: in spring, the crustal and cement dust contributed a higher percentage and the R4 was an important contributor; the secondary process attributed an increase fraction in summer; the mixed coal combustion from southwest R5 enhanced in autumn. PMID:26766363

  13. A Novel Risk Stratification to Predict Local-Regional Failures in Urothelial Carcinoma of the Bladder After Radical Cystectomy

    SciTech Connect

    Baumann, Brian C.; Guzzo, Thomas J.; He Jiwei; Keefe, Stephen M.; Tucker, Kai; Bekelman, Justin E.; Hwang, Wei-Ting; Vaughn, David J.; Malkowicz, S. Bruce; Christodouleas, John P.

    2013-01-01

    Purpose: Local-regional failures (LF) following radical cystectomy (RC) plus pelvic lymph node dissection (PLND) with or without chemotherapy for invasive urothelial bladder carcinoma are more common than previously reported. Adjuvant radiation therapy (RT) could reduce LF but currently has no defined role because of previously reported morbidity. Modern techniques with improved normal tissue sparing have rekindled interest in RT. We assessed the risk of LF and determined those factors that predict recurrence to facilitate patient selection for future adjuvant RT trials. Methods and Materials: From 1990-2008, 442 patients with urothelial bladder carcinoma at University of Pennsylvania were prospectively followed after RC plus PLND with or without chemotherapy with routine pelvic computed tomography (CT) or magnetic resonance imaging (MRI). One hundred thirty (29%) patients received chemotherapy. LF was any pelvic failure detected before or within 3 months of distant failure. Competing risk analyses identified factors predicting increased LF risk. Results: On univariate analysis, pathologic stage {>=}pT3, <10 nodes removed, positive margins, positive nodes, hydronephrosis, lymphovascular invasion, and mixed histology significantly predicted LF; node density was marginally predictive, but use of chemotherapy, number of positive nodes, type of surgical diversion, age, gender, race, smoking history, and body mass index were not. On multivariate analysis, only stage {>=}pT3 and <10 nodes removed were significant independent LF predictors with hazard ratios of 3.17 and 2.37, respectively (P<.01). Analysis identified 3 patient subgroups with significantly different LF risks: low-risk ({<=}pT2), intermediate-risk ({>=}pT3 and {>=}10 nodes removed), and high-risk ({>=}pT3 and <10 nodes) with 5-year LF rates of 8%, 23%, and 42%, respectively (P<.01). Conclusions: This series using routine CT and MRI surveillance to detect LF confirms that such failures are relatively common

  14. Curriculum development for an advanced regional anesthesia education program: one institution's experience from apprenticeship to comprehensive teaching.

    PubMed

    Ouanes, Jean-Pierre P; Schwengel, Deborah; Mathur, Vineesh; Ahmed, Omar I; Hanna, Marie N

    2014-02-01

    Results of recent attitude survey studies suggest that most practicing physicians are inadequately treating postoperative pain. Residents in anesthesia are confident in performing lumbar epidural and spinal anesthesia, but many are not confident in performing the blocks with which they have the least exposure. Changes need to be made in the training processes to a comprehensive model that prepares residents to perform a wider array of blocks in postgraduate practice. Here, we describe one institution's approach to creating a standardized, advanced regional anesthesia curriculum for residents that follows the six core competencies of the ACGME. Residents received training in anatomy dissection, ultrasound-guided regional anesthesia, traditional nerve stimulation techniques, problem-based learning and simulation sessions, oral board presentation sessions, and journal club sessions. Residents kept a detailed log for their use of peripheral nerve block procedures. We have now redesigned and implemented an advanced regional anesthesia program within our institution to provide residents with experience in regional anesthesia at a competent level. Resident's knowledge in regional anesthesia did improve after the first year of implementation as reflected in improvements between the pre- and post-tests. As the advanced regional anesthesia education program continues to improve, we hope to demonstrate levels of validity, reliability, and usability by other programs. PMID:25007696

  15. Overview of Heat Addition and Efficiency Predictions for an Advanced Stirling Convertor

    NASA Technical Reports Server (NTRS)

    Wilson, Scott D.; Reid, Terry; Schifer, Nicholas; Briggs, Maxwell

    2011-01-01

    Past methods of predicting net heat input needed to be validated. Validation effort pursued with several paths including improving model inputs, using test hardware to provide validation data, and validating high fidelity models. Validation test hardware provided direct measurement of net heat input for comparison to predicted values. Predicted value of net heat input was 1.7 percent less than measured value and initial calculations of measurement uncertainty were 2.1 percent (under review). Lessons learned during validation effort were incorporated into convertor modeling approach which improved predictions of convertor efficiency.

  16. Prediction Model for Prevalence and Incidence of Advanced Age-Related Macular Degeneration Based on Genetic, Demographic, and Environmental Variables

    PubMed Central

    Seddon, Johanna M.; Reynolds, Robyn; Maller, Julian; Fagerness, Jesen A.; Daly, Mark J.; Rosner, Bernard

    2013-01-01

    Purpose The joint effects of genetic, ocular, and environmental variables were evaluated and predictive models for prevalence and incidence of AMD were assessed. Methods Participants in the multicenter Age-Related Eye Disease Study (AREDS) were included in a prospective evaluation of 1446 individuals, of which 279 progressed to advanced AMD (geographic atrophy or neovascular disease) and 1167 did not progress during 6.3 years of follow-up. For prevalent AMD, 509 advanced cases were compared with 222 controls. Covariates for the incidence analysis included age, sex, education, smoking, body mass index (BMI), baseline AMD grade, and the AREDS vitamin–mineral treatment assignment. DNA specimens were evaluated for six variants in five genes related to AMD. Unconditional logistic regression analyses were performed for prevalent and incident advanced AMD. An algorithm was developed and receiver operating characteristic curves and C statistics were calculated to assess the predictive ability of risk scores to discriminate progressors from nonprogressors. Results All genetic polymorphisms were independently related to prevalence of advanced AMD, controlling for genetic factors, smoking, BMI, and AREDS treatment. Multivariate odds ratios (ORs) were 3.5 (95% confidence interval [CI], 1.7–7.1) for CFH Y402H; 3.7 (95% CI, 1.6 – 8.4) for CFH rs1410996; 25.4 (95% CI, 8.6 –75.1) for LOC387715 A69S (ARMS2); 0.3 (95% CI, 0.1– 0.7) for C2 E318D; 0.3 (95% CI, 0.1– 0.5) for CFB; and 3.6 (95% CI, 1.4 –9.4) for C3 R102G, comparing the homozygous risk/protective genotypes to the referent genotypes. For incident AMD, all these variants except CFB were significantly related to progression to advanced AMD, after controlling for baseline AMD grade and other factors, with ORs from 1.8 to 4.0 for presence of two risk alleles and 0.4 for the protective allele. An interaction was seen between CFH402H and treatment, after controlling for all genotypes. Smoking was independently

  17. Statistical Method Based on Confidence and Prediction Regions for Analysis of Volatile Organic Compounds in Human Breath Gas

    NASA Astrophysics Data System (ADS)

    Wimmer, G.

    2008-01-01

    In this paper we introduce two confidence and two prediction regions for statistical characterization of concentration measurements of product ions in order to discriminate various groups of persons for prospective better detection of primary lung cancer. Two MATLAB algorithms have been created for more adequate description of concentration measurements of volatile organic compounds in human breath gas for potential detection of primary lung cancer and for evaluation of the appropriate confidence and prediction regions.

  18. African Regional Seminar for Advanced Training In Systematic Curriculum Development and Evaluation. (Achimota, Ghana, 14 July--15 August 1975). Report.

    ERIC Educational Resources Information Center

    Swedish International Development Authority (SIDA).

    This report summarizes the African Regional Seminar for Advanced Training in Systematic Curriculum Development and Evaluation that was held at Achimota, Ghana, July 14-August 15 1975. Attending the seminar were 67 participants from 12 African countries, including Cameroon, Gambia, Ghana, Kenya, Liberia, Malawi, Nigeria, Sierra Leone, Swaziland,…

  19. Prediction and Monitoring of Monsoon Intraseasonal Oscillations over Indian Monsoon Region in an Ensemble Prediction System using CFSv2

    NASA Astrophysics Data System (ADS)

    Borah, N.; Abhilash, S.; Sahai, A. K.; Chattopadhyay, R.; Joseph, S.; Sharmila, S.; de, S.; Goswami, B.; Kumar, A.

    2013-12-01

    An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISOs) of Indian summer monsoon (ISM) using NCEP Climate Forecast System model version2 at T126 horizontal resolution. The EPS is formulated by producing 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001-2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio becomes unity by about18 days and the predictability error saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are observed even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of amplitude of large scale MISO as well as the initial conditions related to the different phases of MISO. Categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead.

  20. Prediction and Monitoring of Monsoon Intraseasonal Oscillations over Indian Monsoon Region in an Ensemble Prediction System using CFSv2

    NASA Astrophysics Data System (ADS)

    Borah, Nabanita; Sukumarpillai, Abhilash; Sahai, Atul Kumar; Chattopadhyay, Rajib; Joseph, Susmitha; De, Soumyendu; Nath Goswami, Bhupendra; Kumar, Arun

    2014-05-01

    An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISO) of Indian summer monsoon (ISM) using NCEP Climate Forecast System model version2 at T126 horizontal resolution. The EPS is formulated by producing 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001-2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio becomes unity by about18 days and the predictability error saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are observed even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of amplitude of large scale MISO as well as the initial conditions related to the different phases of MISO. Categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead.

  1. The effects of regional insolation differences upon advanced solar thermal electric power plant performance and energy costs

    NASA Technical Reports Server (NTRS)

    Latta, A. F.; Bowyer, J. M.; Fujita, T.; Richter, P. H.

    1980-01-01

    The performance and cost of four 10 MWe advanced solar thermal electric power plants sited in various regions of the continental United States was studied. Each region has different insolation characteristics which result in varying collector field areas, plant performance, capital costs and energy costs. The regional variation in solar plant performance was assessed in relation to the expected rise in the future cost of residential and commercial electricity supplied by conventional utility power systems in the same regions. A discussion of the regional insolation data base is presented along with a description of the solar systems performance and costs. A range for the forecast cost of conventional electricity by region and nationally over the next several decades is given.

  2. A radar-based hydrological model for flash flood prediction in the dry regions of Israel

    NASA Astrophysics Data System (ADS)

    Ronen, Alon; Peleg, Nadav; Morin, Efrat

    2014-05-01

    Flash floods are floods which follow shortly after rainfall events, and are among the most destructive natural disasters that strike people and infrastructures in humid and arid regions alike. Using a hydrological model for the prediction of flash floods in gauged and ungauged basins can help mitigate the risk and damage they cause. The sparsity of rain gauges in arid regions requires the use of radar measurements in order to get reliable quantitative precipitation estimations (QPE). While many hydrological models use radar data, only a handful do so in dry climate. This research presents a robust radar-based hydro-meteorological model built specifically for dry climate. Using this model we examine the governing factors of flash floods in the arid and semi-arid regions of Israel in particular and in dry regions in general. The hydrological model built is a semi-distributed, physically-based model, which represents the main hydrological processes in the area, namely infiltration, flow routing and transmission losses. Three infiltration functions were examined - Initial & Constant, SCS-CN and Green&Ampt. The parameters for each function were found by calibration based on 53 flood events in three catchments, and validation was performed using 55 flood events in six catchments. QPE were obtained from a C-band weather radar and adjusted using a weighted multiple regression method based on a rain gauge network. Antecedent moisture conditions were calculated using a daily recharge assessment model (DREAM). We found that the SCS-CN infiltration function performed better than the other two, with reasonable agreement between calculated and measured peak discharge. Effects of storm characteristics were studied using synthetic storms from a high resolution weather generator (HiReS-WG), and showed a strong correlation between storm speed, storm direction and rain depth over desert soils to flood volume and peak discharge.

  3. Analysis and Prediction of the Critical Regions of Antimicrobial Peptides Based on Conditional Random Fields

    PubMed Central

    Chang, Kuan Y.; Lin, Tung-pei; Shih, Ling-Yi; Wang, Chien-Kuo

    2015-01-01

    Antimicrobial peptides (AMPs) are potent drug candidates against microbes such as bacteria, fungi, parasites, and viruses. The size of AMPs ranges from less than ten to hundreds of amino acids. Often only a few amino acids or the critical regions of antimicrobial proteins matter the functionality. Accurately predicting the AMP critical regions could benefit the experimental designs. However, no extensive analyses have been done specifically on the AMP critical regions and computational modeling on them is either non-existent or settled to other problems. With a focus on the AMP critical regions, we thus develop a computational model AMPcore by introducing a state-of-the-art machine learning method, conditional random fields. We generate a comprehensive dataset of 798 AMPs cores and a low similarity dataset of 510 representative AMP cores. AMPcore could reach a maximal accuracy of 90% and 0.79 Matthew’s correlation coefficient on the comprehensive dataset and a maximal accuracy of 83% and 0.66 MCC on the low similarity dataset. Our analyses of AMP cores follow what we know about AMPs: High in glycine and lysine, but low in aspartic acid, glutamic acid, and methionine; the abundance of α-helical structures; the dominance of positive net charges; the peculiarity of amphipathicity. Two amphipathic sequence motifs within the AMP cores, an amphipathic α-helix and an amphipathic π-helix, are revealed. In addition, a short sequence motif at the N-terminal boundary of AMP cores is reported for the first time: arginine at the P(-1) coupling with glycine at the P1 of AMP cores occurs the most, which might link to microbial cell adhesion. PMID:25803302

  4. Prediction of Frost Risks and Plagues using WRF model: a Port Wine region case study

    NASA Astrophysics Data System (ADS)

    Rodrigues, M. A.; Rocha, A.; Monteiro, A.; Quénol, H.; de Freitas, J. R.

    2012-04-01

    In viticulture where the quality of the wine, the selection of the grapevines or even the characteristics of the farming soil, also depending from local soil features like topography, proximity of a river or water body, will act locally on the weather. Frosts are of significant concern to growers of many cultures crops such as winegrapes. Because of their high latitude and some altitude, the vineyards of the Demarcated Douro Region (DDR) are subjected to the frost, which cause serious damages. But the hazards of vineyard don't confine to the incidents of the fortuitous and meteorological character. The illnesses and plagues affect frequently the vineyards of Demarcated Douro Region due, namely to the weather, to the high power of the regional stocks, to the dense vegetation badly drained and favourable to the setting of numberless fungi, viruses and/or poisonous insects. In the case of DDR it is worth noticing the meteorological conditions due to the weather characteristics. Although there are several illnesses and plagues the most important enemies for the vine in the DDR are the mildew, oidium, grey rottenness, grape moth,. . . , if the climatic conditions favour their appearance and development. For this study, we selected some months for different periods, at the 16 weather stations of the Region of Douro. We use the Weather Research and Forecast Model (WRF) to study and possibly predict the occurrence of risk and plagues (mildew) episodes. The model is first validated with the meteorological data obtained at the weather stations. The knowledge of frost and plagues occurrence allows one to decrease its risks not only by selecting the cultural species and varieties but also the places of growth and the planting and sowing dates.

  5. Modelling and predicting urban atmospheric pollutants in the Aosta Valley region of Italy using a site-optimised model

    NASA Astrophysics Data System (ADS)

    Dirks, Kim N.; Nanni, Alessandro; Dirks, Vincent I.

    2006-01-01

    An effective simple site-optimised urban air pollution model for predicting CO, PM10, NO and NO2 concentrations is developed for the topographically complex region of the Aosta Valley in Italy. The good results suggest that such a model could be used to downscale mesoscale-forecasted surface conditions to give real-time site-specific pollution predictions.

  6. Soil erosion predictions from upland areas – a discussion of selected RUSLE2 advances and needs

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Obtaining more accurate soil loss estimates from upland areas is important for improving management practices on agricultural fields. Much of the soil erosion prediction research of the last 25 years has been concerned with this goal. The most widely used predictive relationships have been the Unive...

  7. Prediction of plasma-induced damage distribution during silicon nitride etching using advanced three-dimensional voxel model

    SciTech Connect

    Kuboi, Nobuyuki Tatsumi, Tetsuya; Kinoshita, Takashi; Shigetoshi, Takushi; Fukasawa, Masanaga; Komachi, Jun; Ansai, Hisahiro

    2015-11-15

    The authors modeled SiN film etching with hydrofluorocarbon (CH{sub x}F{sub y}/Ar/O{sub 2}) plasma considering physical (ion bombardment) and chemical reactions in detail, including the reactivity of radicals (C, F, O, N, and H), the area ratio of Si dangling bonds, the outflux of N and H, the dependence of the H/N ratio on the polymer layer, and generation of by-products (HCN, C{sub 2}N{sub 2}, NH, HF, OH, and CH, in addition to CO, CF{sub 2}, SiF{sub 2}, and SiF{sub 4}) as ion assistance process parameters for the first time. The model was consistent with the measured C-F polymer layer thickness, etch rate, and selectivity dependence on process variation for SiN, SiO{sub 2}, and Si film etching. To analyze the three-dimensional (3D) damage distribution affected by the etched profile, the authors developed an advanced 3D voxel model that can predict the time-evolution of the etched profile and damage distribution. The model includes some new concepts for gas transportation in the pattern using a fluid model and the property of voxels called “smart voxels,” which contain details of the history of the etching situation. Using this 3D model, the authors demonstrated metal–oxide–semiconductor field-effect transistor SiN side-wall etching that consisted of the main-etch step with CF{sub 4}/Ar/O{sub 2} plasma and an over-etch step with CH{sub 3}F/Ar/O{sub 2} plasma under the assumption of a realistic process and pattern size. A large amount of Si damage induced by irradiated hydrogen occurred in the source/drain region, a Si recess depth of 5 nm was generated, and the dislocated Si was distributed in a 10 nm deeper region than the Si recess, which was consistent with experimental data for a capacitively coupled plasma. An especially large amount of Si damage was also found at the bottom edge region of the metal–oxide–semiconductor field-effect transistors. Furthermore, our simulation results for bulk fin-type field-effect transistor side-wall etching

  8. Advanced Methods for Determining Prediction Uncertainty in Model-Based Prognostics with Application to Planetary Rovers

    NASA Technical Reports Server (NTRS)

    Daigle, Matthew J.; Sankararaman, Shankar

    2013-01-01

    Prognostics is centered on predicting the time of and time until adverse events in components, subsystems, and systems. It typically involves both a state estimation phase, in which the current health state of a system is identified, and a prediction phase, in which the state is projected forward in time. Since prognostics is mainly a prediction problem, prognostic approaches cannot avoid uncertainty, which arises due to several sources. Prognostics algorithms must both characterize this uncertainty and incorporate it into the predictions so that informed decisions can be made about the system. In this paper, we describe three methods to solve these problems, including Monte Carlo-, unscented transform-, and first-order reliability-based methods. Using a planetary rover as a case study, we demonstrate and compare the different methods in simulation for battery end-of-discharge prediction.

  9. Expression, fermentation and purification of a predicted intrinsically disordered region of the transcription factor, NFAT5.

    PubMed

    DuMond, Jenna F; He, Yi; Burg, Maurice B; Ferraris, Joan D

    2015-11-01

    Hypertonicity stimulates Nuclear Factor of Activated T-cells 5 (NFAT5) nuclear localization and transactivating activity. Many transcription factors are known to contain intrinsically disordered regions (IDRs) which become more structured with local environmental changes such as osmolality, temperature and tonicity. The transactivating domain of NFAT5 is predicted to be intrinsically disordered under normal tonicity, and under high NaCl, the activity of this domain is increased. To study the binding of co-regulatory proteins at IDRs a cDNA construct expressing the NFAT5 TAD was created and transformed into Escherichia coli cells. Transformed E. coli cells were mass produced by fermentation and extracted by cell lysis to release the NFAT5 TAD. The NFAT5 TAD was subsequently purified using a His-tag column, cation exchange chromatography as well as hydrophobic interaction chromatography and then characterized by mass spectrometry (MS). PMID:26256058

  10. Monitoring, mapping and prediction of ionospheric scintillation over the Brazilian equatorial and low latitude regions

    NASA Astrophysics Data System (ADS)

    Becker-Guedes, Fabio; de Paula, E. R.; de Rezende, L. F. C.; Stephany, S.; Kantor, I. J.; Muella, M. T. A. H.; Siqueira, P. M.; Correa, K. S.; Dutra, A. P.; Guedes, C.; Takahashi, H.; Silva, J. D. S.

    It is well known, today, that equatorial ionospheric scintillations affect performance of GPS receivers. Scintillation occurs when a radio wave crosses the ionosphere and suffers distortion in phase and amplitude. It also contributes to loss of lock of GPS receivers, resulting decrease of the number of available satellites and consequently yielding poor satellite geometry. Therefore, the required accuracy and positioning precision for aerial navigation are affected. Among other activities, EMBRACE, the space weather program of INPE, is monitoring and mapping the ionospheric scintillation over the South American equatorial and low latitude region in real time. This mapping is available in the internet by means of computer programs that retrieve data from a network of GPS receivers distributed in Brazil. These data are also being used to survey and predict the occurrence of ionospheric scintillation through data mining techniques.

  11. Prediction of water content at different potentials from soil property data in Jazan region

    NASA Astrophysics Data System (ADS)

    Alturki, Ali; Ibrahim, Hesham

    2016-04-01

    In dry regions effective irrigation management is crucial to maintain crop production and sustain limited water resources. Effective irrigation requires good knowledge of soil water content in the root zone. However, measurement of soil water in the root zone over time is extremely expensive and time consuming. On the other hand, weather and basic soil property data are more available, either from existing databases or by direct measurement in the field. Simulation models can be used to efficiently and accurately estimate soil water content and subsequent irrigation requirements based on the available weather and soil data. In this study we investigated three hierarchical approaches to predict water content at variable potentials (0, 10, 33, 60, 100, 300, 500, 800, 1000, and 1500 kPa) using the Rosetta model: soil texture class (STC); percent of sand, silt, and clay (SSC); bulk density, percent of sand, silt, and clay, and water content measurements at 33 and 1500 kPa (SSC+WC). Estimation of soil water content at 43 locations in Jazan region using the three hierarchical approaches was compared with gravimetric water content. Results showed that the three approaches failed to describe water content accurately at saturation conditions (<10kPa). At water potentials lower than 10 kPa, good agreement was obtained, in general, between measured and simulated soil water content indicating that soil property data can be used to provide adequate estimates of the average soil water content in the root zone. The third approach gave the best results as indicated by an average NSCE value of 0.75 as compared to 0.16 and 0.18 for the first and second approaches, respectively. The ability to predict the amount of available water in the soil profile will facilitate the accurate estimate of irrigation requirements and achieve effective irrigation scheduling especially in locations where only limited weather and soil date are available.

  12. Ozone phytotoxicity evaluation and prediction of crops production in tropical regions

    NASA Astrophysics Data System (ADS)

    Mohammed, Nurul Izma; Ramli, Nor Azam; Yahya, Ahmad Shukri

    2013-04-01

    Increasing ozone concentration in the atmosphere can threaten food security due to its effects on crop production. Since the 1980s, ozone has been believed to be the most damaging air pollutant to crops. In Malaysia, there is no index to indicate the reduction of crops due to the exposure of ozone. Therefore, this study aimed to identify the accumulated exposure over a threshold of X ppb (AOTX) indexes in assessing crop reduction in Malaysia. In European countries, crop response to ozone exposure is mostly expressed as AOT40. This study was designed to evaluate and predict crop reduction in tropical regions and in particular, the Malaysian climate, by adopting the AOT40 index method and modifying it based on Malaysian air quality and crop data. Nine AOTX indexes (AOT0, AOT5, AOT10, AOT15, AOT20, AOT25, AOT30, AOT40, and AOT50) were analyzed, crop responses tested and reduction in crops predicted. The results showed that the AOT50 resulted in the highest reduction in crops and the highest R2 value between the AOT50 and the crops reduction from the linear regression analysis. Hence, this study suggests that the AOT50 index is the most suitable index to estimate the potential ozone impact on crops in tropical regions. The result showed that the critical level for AOT50 index if the estimated crop reduction is 5% was 1336 ppb h. Additionally, the results indicated that the AOT40 index in Malaysia gave a minimum percentage of 6% crop reduction; as contrasted with the European guideline of 5% (due to differences in the climate e.g., average amount of sunshine).

  13. Advances in modelling the coevolving soils, landforms and vegetation in semiarid regions: a multidisciplinary approach.

    NASA Astrophysics Data System (ADS)

    Saco, Patricia M.; Moreno-de las Heras, Mariano; Willgoose, Garry R.

    2014-05-01

    Semiarid landscapes exhibit highly nonlinear interactions between coevolving physical and biological processes. Coevolution in these systems leads to the emergence of remarkable soil, landform and vegetation patterns. Growing concern over ecosystem resilience to climate and land use perturbations that could result in irreversible degradation imposes a pressing need for research, aiming at elucidating the processes, feedbacks, and dynamics leading to these coevolving patterns. This is particularly important since degradation in drylands has been frequently linked to feedback effects between soils, biota and erosion processes. In many dryland regions, feedbacks are responsible for the emergence of areas with low infiltration in unvegetated soil patches (due to surface crusting) and high infiltration rates in the vegetated soil patches (due to improved soil aggregation and macroporosity). This variable infiltration field gives rise to runoff-runon redistribution which determines areas of soil erosion and deposition. We have combined a coupled landform-soil-vegetation model with remote sensing and field data to capture these feedbacks and improve our knowledge of these coevolving biotic-abiotic processes. We discuss and present results showing that the dynamics of the individual processes and their response to climatic and anthropic disturbances cannot be fully understood or predicted if nonlinear feedbacks and coevolution are not considered. Implications for management and restoration efforts are illustrated using data and observations from agricultural sites in central Australia and reclaimed mining sites in Spain.

  14. Predicting the effects of nanoscale cerium additives in diesel fuel on regional-scale air quality.

    PubMed

    Erdakos, Garnet B; Bhave, Prakash V; Pouliot, George A; Simon, Heather; Mathur, Rohit

    2014-11-01

    Diesel vehicles are a major source of air pollutant emissions. Fuel additives containing nanoparticulate cerium (nCe) are currently being used in some diesel vehicles to improve fuel efficiency. These fuel additives also reduce fine particulate matter (PM2.5) emissions and alter the emissions of carbon monoxide (CO), nitrogen oxides (NOx), and hydrocarbon (HC) species, including several hazardous air pollutants (HAPs). To predict their net effect on regional air quality, we review the emissions literature and develop a multipollutant inventory for a hypothetical scenario in which nCe additives are used in all on-road and nonroad diesel vehicles. We apply the Community Multiscale Air Quality (CMAQ) model to a domain covering the eastern U.S. for a summer and a winter period. Model calculations suggest modest decreases of average PM2.5 concentrations and relatively larger decreases in particulate elemental carbon. The nCe additives also have an effect on 8 h maximum ozone in summer. Variable effects on HAPs are predicted. The total U.S. emissions of fine-particulate cerium are estimated to increase 25-fold and result in elevated levels of airborne cerium (up to 22 ng/m3), which might adversely impact human health and the environment. PMID:25271762

  15. The New 4D-En-Var Regional Deterministic Prediction System at the Canadian Meteorological Center

    NASA Astrophysics Data System (ADS)

    Caron, Jean-François; Milewski, Thomas; Reszka, Mateusz; Fillion, Luc; Buehner, Mark; St-James, Judy; Pellerin, Simon

    2014-05-01

    The regional deterministic prediction system (RDPS) at the Canadian Meteorological Center will be replaced in the near future by the 4D-En-Var scheme, in which the background error covariances are a combination of climatological covariances and flow-dependent covariances derived from an ensemble-Kalman-filter global prediction system. The new approach is computationally less expensive than 4D-Var (currently operational) and has shown to be a promising technique in the context of global data assimilation. The RDPS uses a limited-area domain covering all of North America and a horizontal grid spacing of 10 km. Here we discuss the final stages of the development of this scheme, shortly before introduction into operations. Specifically, we show a comparison of the forecasting skill between the operational system and the new 4D-En-Var scheme, as well as the impact of several improvements related to the treatment of observations and the addition of new observation sources such as ground-based GPS.

  16. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty

    NASA Astrophysics Data System (ADS)

    Ling, J.; Templeton, J.

    2015-08-01

    Reynolds Averaged Navier Stokes (RANS) models are widely used in industry to predict fluid flows, despite their acknowledged deficiencies. Not only do RANS models often produce inaccurate flow predictions, but there are very limited diagnostics available to assess RANS accuracy for a given flow configuration. If experimental or higher fidelity simulation results are not available for RANS validation, there is no reliable method to evaluate RANS accuracy. This paper explores the potential of utilizing machine learning algorithms to identify regions of high RANS uncertainty. Three different machine learning algorithms were evaluated: support vector machines, Adaboost decision trees, and random forests. The algorithms were trained on a database of canonical flow configurations for which validated direct numerical simulation or large eddy simulation results were available, and were used to classify RANS results on a point-by-point basis as having either high or low uncertainty, based on the breakdown of specific RANS modeling assumptions. Classifiers were developed for three different basic RANS eddy viscosity model assumptions: the isotropy of the eddy viscosity, the linearity of the Boussinesq hypothesis, and the non-negativity of the eddy viscosity. It is shown that these classifiers are able to generalize to flows substantially different from those on which they were trained. Feature selection techniques, model evaluation, and extrapolation detection are discussed in the context of turbulence modeling applications.

  17. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty

    SciTech Connect

    Ling, Julia; Templeton, Jeremy Alan

    2015-08-04

    Reynolds Averaged Navier Stokes (RANS) models are widely used in industry to predict fluid flows, despite their acknowledged deficiencies. Not only do RANS models often produce inaccurate flow predictions, but there are very limited diagnostics available to assess RANS accuracy for a given flow configuration. If experimental or higher fidelity simulation results are not available for RANS validation, there is no reliable method to evaluate RANS accuracy. This paper explores the potential of utilizing machine learning algorithms to identify regions of high RANS uncertainty. Three different machine learning algorithms were evaluated: support vector machines, Adaboost decision trees, and random forests. The algorithms were trained on a database of canonical flow configurations for which validated direct numerical simulation or large eddy simulation results were available, and were used to classify RANS results on a point-by-point basis as having either high or low uncertainty, based on the breakdown of specific RANS modeling assumptions. Classifiers were developed for three different basic RANS eddy viscosity model assumptions: the isotropy of the eddy viscosity, the linearity of the Boussinesq hypothesis, and the non-negativity of the eddy viscosity. It is shown that these classifiers are able to generalize to flows substantially different from those on which they were trained. As a result, feature selection techniques, model evaluation, and extrapolation detection are discussed in the context of turbulence modeling applications.

  18. Activities of the Climate Forecast Unit (CFU) on regional decadal prediction

    NASA Astrophysics Data System (ADS)

    Guemas, V.; Prodhomme, C.; Doblas-Reyes, F.; Volpi, D.; Caron, L. P.; Davis, M.; Menegoz, M.; Saurral, R. I.; Bellprat, O.

    2014-12-01

    The Climate Forecasting Unit (CFU) is a research unit devoted to develop climate forecast systems to contribute to the creation of climate services that aims to 1) develop climate forecast systems and prediction methodologies, 2) investigate the potential sources of skill and understand the limitation of state-of-the-art forecast systems, 3) formulate reliable climate forecasts that meet specific user needs and 4) contribute to the development of climate services. This presentation will provide an overview of the latest results of this research unit in the field of regional decadal prediction focusing on 1) an assessment of the relative merits of the full-field and the anomaly initialisation techniques, 2) a description of the forecast quality of North Atlantic tropical cyclone activity and South Pacific climate, 3) an evaluation of the impact of volcanic aerosol prescription during decadal forecasts, and 4) the strategy for the development of a climate service to ensure that forecasts are both useful and action-oriented. Results from several European projects, SPECS, PREFACE and EUPORIAS, will be used to illustrate these findings.

  19. Evaluation of machine learning algorithms for prediction of regions of high RANS uncertainty

    DOE PAGESBeta

    Ling, Julia; Templeton, Jeremy Alan

    2015-08-04

    Reynolds Averaged Navier Stokes (RANS) models are widely used in industry to predict fluid flows, despite their acknowledged deficiencies. Not only do RANS models often produce inaccurate flow predictions, but there are very limited diagnostics available to assess RANS accuracy for a given flow configuration. If experimental or higher fidelity simulation results are not available for RANS validation, there is no reliable method to evaluate RANS accuracy. This paper explores the potential of utilizing machine learning algorithms to identify regions of high RANS uncertainty. Three different machine learning algorithms were evaluated: support vector machines, Adaboost decision trees, and random forests.more » The algorithms were trained on a database of canonical flow configurations for which validated direct numerical simulation or large eddy simulation results were available, and were used to classify RANS results on a point-by-point basis as having either high or low uncertainty, based on the breakdown of specific RANS modeling assumptions. Classifiers were developed for three different basic RANS eddy viscosity model assumptions: the isotropy of the eddy viscosity, the linearity of the Boussinesq hypothesis, and the non-negativity of the eddy viscosity. It is shown that these classifiers are able to generalize to flows substantially different from those on which they were trained. As a result, feature selection techniques, model evaluation, and extrapolation detection are discussed in the context of turbulence modeling applications.« less

  20. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty

    DOE PAGESBeta

    Ling, Julia; Templeton, Jeremy Alan

    2015-08-04

    Reynolds Averaged Navier Stokes (RANS) models are widely used in industry to predict fluid flows, despite their acknowledged deficiencies. Not only do RANS models often produce inaccurate flow predictions, but there are very limited diagnostics available to assess RANS accuracy for a given flow configuration. If experimental or higher fidelity simulation results are not available for RANS validation, there is no reliable method to evaluate RANS accuracy. This paper explores the potential of utilizing machine learning algorithms to identify regions of high RANS uncertainty. Three different machine learning algorithms were evaluated: support vector machines, Adaboost decision trees, and random forests.more » The algorithms were trained on a database of canonical flow configurations for which validated direct numerical simulation or large eddy simulation results were available, and were used to classify RANS results on a point-by-point basis as having either high or low uncertainty, based on the breakdown of specific RANS modeling assumptions. Classifiers were developed for three different basic RANS eddy viscosity model assumptions: the isotropy of the eddy viscosity, the linearity of the Boussinesq hypothesis, and the non-negativity of the eddy viscosity. It is shown that these classifiers are able to generalize to flows substantially different from those on which they were trained. As a result, feature selection techniques, model evaluation, and extrapolation detection are discussed in the context of turbulence modeling applications.« less

  1. Evaluation of machine learning algorithms for prediction of regions of high RANS uncertainty

    SciTech Connect

    Ling, Julia; Templeton, Jeremy Alan

    2015-08-04

    Reynolds Averaged Navier Stokes (RANS) models are widely used in industry to predict fluid flows, despite their acknowledged deficiencies. Not only do RANS models often produce inaccurate flow predictions, but there are very limited diagnostics available to assess RANS accuracy for a given flow configuration. If experimental or higher fidelity simulation results are not available for RANS validation, there is no reliable method to evaluate RANS accuracy. This paper explores the potential of utilizing machine learning algorithms to identify regions of high RANS uncertainty. Three different machine learning algorithms were evaluated: support vector machines, Adaboost decision trees, and random forests. The algorithms were trained on a database of canonical flow configurations for which validated direct numerical simulation or large eddy simulation results were available, and were used to classify RANS results on a point-by-point basis as having either high or low uncertainty, based on the breakdown of specific RANS modeling assumptions. Classifiers were developed for three different basic RANS eddy viscosity model assumptions: the isotropy of the eddy viscosity, the linearity of the Boussinesq hypothesis, and the non-negativity of the eddy viscosity. It is shown that these classifiers are able to generalize to flows substantially different from those on which they were trained. As a result, feature selection techniques, model evaluation, and extrapolation detection are discussed in the context of turbulence modeling applications.

  2. A split explicit reformulation of the regional numerical weather prediction model of the Japan Meteorological Agency

    NASA Technical Reports Server (NTRS)

    Duffy, D. G.

    1981-01-01

    The split explicit integration scheme for numerical weather prediction models is employed in a version of the regional numerical weather prediction model of the Japan Meteorological Agency. The finite-difference scheme of the model is designed in the manner proposed by Okamura (1975). The horizontal advection terms in the governing equations are integrated with a time step limited by the wind speed while the terms which describe inertial-gravity oscillations are integrated in a succession of shorter time steps. The physical processes included within the model are precipitation, small-scale convection, surface exchanges of sensible and latent heat, and radiative heating and cooling. An example of a surface pressure forecast over Europe is shown for initial data observed at 0000 GMT 29 December 1979. Quantitative precipitation forecasts over Europe and North America for the 24 h period beginning at 0000 GMT 30 December 1979 are also shown. It is concluded that the model is capable of realistically depicting the evolution of synoptic-scale systems.

  3. Advances in regional crop yield estimation over the United States using satellite remote sensing data

    NASA Astrophysics Data System (ADS)

    Johnson, D. M.; Dorn, M. F.; Crawford, C.

    2015-12-01

    Since the dawn of earth observation imagery, particularly from systems like Landsat and the Advanced Very High Resolution Radiometer, there has been an overarching desire to regionally estimate crop production remotely. Research efforts integrating space-based imagery into yield models to achieve this need have indeed paralleled these systems through the years, yet development of a truly useful crop production monitoring system has been arguably mediocre in coming. As a result, relatively few organizations have yet to operationalize the concept, and this is most acute in regions of the globe where there are not even alternative sources of crop production data being collected. However, the National Agricultural Statistics Service (NASS) has continued to push for this type of data source as a means to complement its long-standing, traditional crop production survey efforts which are financially costly to the government and create undue respondent burden on farmers. Corn and soybeans, the two largest field crops in the United States, have been the focus of satellite-based production monitoring by NASS for the past decade. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been seen as the most pragmatic input source for modeling yields primarily based on its daily revisit capabilities and reasonable ground sample resolution. The research methods presented here will be broad but provides a summary of what is useful and adoptable with satellite imagery in terms of crop yield estimation. Corn and soybeans will be of particular focus but other major staple crops like wheat and rice will also be presented. NASS will demonstrate that while MODIS provides a slew of vegetation related products, the traditional normalized difference vegetation index (NDVI) is still ideal. Results using land surface temperature products, also generated from MODIS, will also be shown. Beyond the MODIS data itself, NASS research has also focused efforts on understanding a

  4. NASA Earth Science Research Results for Improved Regional Crop Yield Prediction

    NASA Astrophysics Data System (ADS)

    Mali, P.; O'Hara, C. G.; Shrestha, B.; Sinclair, T. R.; G de Goncalves, L. G.; Salado Navarro, L. R.

    2007-12-01

    spatial and temporal resolution remote sensing datasets; improved time-series meteorological inputs required for crop growth models; and regional prediction capability through geo-processing-based yield modeling.

  5. Can regional porosity occurrence in carbonate slope and basin facies systems be predicted

    SciTech Connect

    Mazzullo, S.J. )

    1994-03-01

    Subsurface carbonate depositional sequences (i.e., coeval platform-to-basin facies mosaics), which can most readily be identified by application of seismic sequence analysis, include porous units of complex geometry and occurrence. A highly sought-after occurrence of reservoir-grade porosity commonly, but not always, is associated with sequence boundaries. Therefore, porosity can be predicted to possible occur in already-lithified platform strata beneath significant unconformities of regional extent (e.g., cavernous porosity). By this method, however, the possible existence of porosity in carbonate slope and basin deposits may be overlooked because they do not lie beneath sequence-bounding unconformities insofar as they are not usually exposed subaerially during platform emergence. Yet, such facies compose locally significant hydrocarbon reservoirs in the Permian basin by virtue of high porosities and permeabilities and water-free production histories. Short of drilling or seismic wavelet analysis, can porosity in such facies be predicted a priori Reservoirs in carbonate slope and basin deposits in the Midland basin include both porous limestones and dolomites. Petrographic and geochemical studies indicate that syndepositional and shallow-burial diagenesis of such deposits mainly involved porosity reduction via calcite cementation, dolomitization, and compaction. Most of the porosity present in reservoirs in these facies was created by later dissolution in the deep-burial environment as a consequence of rock interaction with connate fluids enriched in carbonic and sulfuric acid and/or organic acids generated during hydrocarbon maturation. These fluids migrated up and out from deeper parts of the basin through slope and basin deposits and into platform carbonate strata. Insofar as fluid migration pathways can be mapped or at least inferred by various means, it follows that porosity created (or destroyed) by these fluids also can be mapped predictively.

  6. An Integrated Theory for Predicting the Hydrothermomechanical Response of Advanced Composite Structural Components

    NASA Technical Reports Server (NTRS)

    Chamis, C. C.; Lark, R. F.; Sinclair, J. H.

    1977-01-01

    An integrated theory is developed for predicting the hydrothermomechanical (HDTM) response of fiber composite components. The integrated theory is based on a combined theoretical and experimental investigation. In addition to predicting the HDTM response of components, the theory is structured to assess the combined hydrothermal effects on the mechanical properties of unidirectional composites loaded along the material axis and off-axis, and those of angleplied laminates. The theory developed predicts values which are in good agreement with measured data at the micromechanics, macromechanics, laminate analysis and structural analysis levels.

  7. The Predictive Validity of Selected Benchmark Assessments Used in the Mid-Atlantic Region. Issues & Answers. REL 2007-No. 017

    ERIC Educational Resources Information Center

    Brown, Richard S.; Coughlin, Ed

    2007-01-01

    This report examines the availability and quality of predictive validity data for a selection of benchmark assessments identified by state and district personnel as in use within Mid-Atlantic Region jurisdictions. Based on a review of practices within the school districts in the region, this report details the benchmark assessments being used, in…

  8. Structure-Based Prediction of Unstable Regions in Proteins: Applications to Protein Misfolding Diseases

    NASA Astrophysics Data System (ADS)

    Guest, Will; Cashman, Neil; Plotkin, Steven

    2009-03-01

    Protein misfolding is a necessary step in the pathogenesis of many diseases, including Creutzfeldt-Jakob disease (CJD) and familial amyotrophic lateral sclerosis (fALS). Identifying unstable structural elements in their causative proteins elucidates the early events of misfolding and presents targets for inhibition of the disease process. An algorithm was developed to calculate the Gibbs free energy of unfolding for all sequence-contiguous regions of a protein using three methods to parameterize energy changes: a modified G=o model, changes in solvent-accessible surface area, and solution of the Poisson-Boltzmann equation. The entropic effects of disulfide bonds and post-translational modifications are treated analytically. It incorporates a novel method for finding local dielectric constants inside a protein to accurately handle charge effects. We have predicted the unstable parts of prion protein and superoxide dismutase 1, the proteins involved in CJD and fALS respectively, and have used these regions as epitopes to prepare antibodies that are specific to the misfolded conformation and show promise as therapeutic agents.

  9. Forest production predicted from satellite image analysis for the Southeast Asia region

    PubMed Central

    2013-01-01

    Background The objective of this study was to demonstrate a new, cost-effective method to define the sustainable amounts of harvested wood products in Southeast Asian countries case studies, while avoiding degradation (net loss) of total wood carbon stocks. Satellite remote sensing from the MODIS sensor was used in the CASA (Carnegie Ames Stanford Approach) carbon cycle model to map forest production for the Southeast Asia region from 2000 to 2010. These CASA model results have been designed to be spatially detailed enough to support carbon cycle assessments in different wooded land cover classes, e.g., open woodlands, wetlands, and forest areas. Results The country with the highest average forest net primary production (NPP greater than 950 g C m-2 yr-1) over the period was the Philippines, followed by Malaysia and Indonesia. Myanmar and Vietnam had the lowest average forest NPP among the region’s countries at less than 815 g C m-2 yr-1. Case studies from throughout the Southeast Asia region for the maximum harvested wood products amount that could be sustainably extracted per year were generated using the CASA model NPP predictions. Conclusions The method of using CASA model’s estimated annual change in forest carbon on a yearly basis can conservatively define the upper limit for the amount of harvested wood products that can be removed and still avoid degradation (net loss) of the total wood carbon stock over that same time period. PMID:24016254

  10. Predicting wetland contamination from atmospheric deposition measurements of pesticides in the Canadian Prairie Pothole region

    NASA Astrophysics Data System (ADS)

    Messing, Paul G.; Farenhorst, Annemieke; Waite, Don T.; McQueen, D. A. Ross; Sproull, James F.; Humphries, David A.; Thompson, Laura L.

    2011-12-01

    Although it has been suggested that atmospheric deposition alone can result in detectable levels of pesticides in wetlands of the Pairie Pothole Region of Canada, this is the first field study to compare the masses of pesticides entering wetlands by atmospheric deposition with those concentrations of pesticides detected in the water-column of prairie wetlands. Weekly air and bulk deposition samples were collected from May 26th to Sept. 15th, 2008 at the Manitoba Zero Tillage Research Association (MZTRA) Farm, Brandon, Manitoba, with four on-site wetlands (approximate sizes 0.15-0.45 ha) monitored every second week. Twelve pesticides were detected in the air, with MCPA (one of the three pesticides applied on the farm in 2008 in addition to clopyralid and glyphosate), triallate, and γ-HCH being detected every week. Calculations were performed to predict wetland pesticide concentrations based on bulk deposits alone for those pesticides that had detectable concentrations in the bulk deposition samples (in order of the highest total seasonal deposition mass to the lowest): MCPA, glyphosate, 2,4-D, clopyralid, bromoxynil, atrazine, dicamba, metolachlor, and mecoprop. The estimated concentrations were closest to actual concentrations for MCPA (Pearson correlation coefficient's = 0.91 to 0.98; p-values < 0.001) and predictions were also reasonable for a range of other herbicides, but a source other than atmospheric deposition was clearly relevant to detections of clopyralid in the wetland water-column. Although the types and levels of pesticides detected in the wetlands of the current study suggest that regional pesticide applications can contribute to pesticide surface water contamination following atmospheric transport and deposition, the greater frequency and concentrations of clopyralid, MCPA, and glyphosate detections in wetlands confirm that on-farm pesticide applications have a greater impact on on-site water quality. Beneficial management practices that reduce

  11. Isoscapes of tree-ring carbon-13 perform like meteorological networks in predicting regional precipitation patterns

    NASA Astrophysics Data System (ADS)

    del Castillo, Jorge; Aguilera, Mònica; Voltas, Jordi; Ferrio, Juan Pedro

    2013-04-01

    Stable isotopes in tree rings provide climatic information with annual resolution dating back for centuries or even millennia. However, deriving spatially explicit climate models from isotope networks remains challenging. Here we propose a methodology to model regional precipitation from carbon isotope discrimination (Δ13C) in tree rings by (1) building regional spatial models of Δ13C (isoscapes), and (2) deriving precipitation maps from 13C-isoscapes, taking advantage of the response of Δ13C to precipitation in seasonally-dry climates. As a case study, we modeled the spatial distribution of mean annual precipitation (MAP) in the northeastern Iberian Peninsula, a region with complex orography and climate (MAP=303-1086 mm). We compiled wood Δ13C data for two Mediterranean species that exhibit complementary responses to seasonal precipitation (Pinus halepensis Mill., N=38; Quercus ilex L.; N=44; pooling period: 1975-2008). By combining multiple regression and geostatistical interpolation, we generated one 13C-isoscape for each species. A spatial model of MAP was then built as the sum of two complementary maps of seasonal precipitation, each one derived from the corresponding 13C-isoscape (September-November from Q. ilex; December-August from P. halepensis). Our approach showed a predictive power for MAP (RMSE=84 mm) nearly identical to that obtained by interpolating data directly from a similarly dense network of meteorological stations (RMSE=80-83 mm, N=65), being only outperformed when using a much denser meteorological network (RMSE=56-57 mm, N=340). This method offers new avenues for modeling spatial variability of past precipitation, exploiting the large amount of information currently available from tree-ring networks. Acknowledgements: This work was funded by MC-ERG-246725 (FP7, EU) and AGL 2012-40039-C02-02 (MINECO, Spain). JdC and JPF are supported by FPI fellowship (MCINN) and Ramón y Cajal programme (RYC-2008-02050, MINECO), respectively.

  12. Isoscapes of tree-ring carbon-13 perform like meteorological networks in predicting regional precipitation patterns

    NASA Astrophysics Data System (ADS)

    Castillo, Jorge; Aguilera, Mònica; Voltas, Jordi; Ferrio, Juan Pedro

    2013-03-01

    isotopes in tree rings provide climatic information with annual resolution dating back for centuries or even millennia. However, deriving spatially explicit climate models from isotope networks remains challenging. Here we propose a methodology to model regional precipitation from carbon isotope discrimination (Δ13C) in tree rings by (1) building regional spatial models of Δ13C (isoscapes) and (2) deriving precipitation maps from Δ13C-isoscapes, taking advantage of the response of Δ13C to precipitation in seasonally dry climates. As a case study, we modeled the spatial distribution of mean annual precipitation (MAP) in the northeastern Iberian Peninsula, a region with complex topography and climate (MAP = 303-1086 mm). We compiled wood Δ13C data for two Mediterranean species that exhibit complementary responses to seasonal precipitation (Pinus halepensis Mill., N = 38; Quercus ilex L.; N = 44; pooling period: 1975-2008). By combining multiple regression and geostatistical interpolation, we generated one Δ13 C-isoscape for each species. A spatial model of MAP was then built as the sum of two complementary maps of seasonal precipitation, each one derived from the corresponding Δ13C-isoscape (September-November from Q. ilex; December-August from P. halepensis). Our approach showed a predictive power for MAP (RMSE = 84 mm) nearly identical to that obtained by interpolating data directly from a similarly dense network of meteorological stations (RMSE = 80-83 mm, N = 65), being only outperformed when using a much denser meteorological network (RMSE = 56-57 mm, N = 340). This method offers new avenues for modeling spatial variability of past precipitation, exploiting the large amount of information currently available from tree-ring networks.

  13. Early prediction of pathological response in locally advanced rectal cancer based on sequential 18F-FDG PET

    PubMed Central

    HATT, MATHIEU; VAN STIPHOUT, RUUD; LE POGAM, ADRIEN; LAMMERING, GUIDO; VISVIKIS, DIMITRIS; LAMBIN, PHILIPPE

    2016-01-01

    Background The objectives of this study were to investigate the predictive value of sequential 18F-FDG PET scans for pathological tumor response grade (TRG) after preoperative chemoradiotherapy (PCRT) in locally advanced rectal cancer (LARC) and the impact of partial volume effects correction (PVC). Methods Twenty-eight LARC patients were included. Responders and non-responders status were determined in histopathology. PET indices [SUV max and mean, volume and total lesion glycolysis (TLG)] at baseline and their evolution after one and two weeks of PCRT were extracted by delineation of the PET images, with or without PVC. Their predictive value was investigated using Mann-Whitney-U tests and ROC analysis. Results Within baseline parameters, only SUVmean was correlated with response. No evolution after one week was predictive of the response, whereas after two weeks all the parameters except volume were, the best prediction being obtained with TLG (AUC 0.79, sensitivity 63%, specificity 92%). PVC had no significant impact on these results. Conclusion Several PET indices at baseline and their evolution after two weeks of PCRT are good predictors of response in LARC, with or without PVC, whereas results after one week are suboptimal. Best predictor was TLG reduction after two weeks, although baseline SUVmean had smaller but similar predictive power. PMID:22873767

  14. Predictive and prognostic significance of circulating endothelial cells in advanced non-small cell lung cancer patients.

    PubMed

    Yuan, Dong-mei; Zhang, Qin; Lv, Yan-ling; Ma, Xing-qun; Zhang, Yan; Liu, Hong-bing; Song, Yong

    2015-11-01

    The aim of this study was to evaluate the predictive and prognostic values of circulating endothelial cells (CECs) in patients with advanced non-small cell lung cancer (NSCLC). A total of 102 newly diagnosed advanced NSCLC patients were enrolled in this study. The amount of CECs was enumerated by flow cytometry (CD45- CD31+ CD146+) at baseline. CEC counts of 56 patients were detected before and after two cycles of chemotherapy. We correlated the baseline and reduction of CECs after therapy with objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). The CEC level was significantly higher in advanced NSCLC patients, ranging from 57 to 1300 cells/10(5) cells (mean ± SD = 299 ± 221 cells/10(5) cells), than in patients with benign lesions (205 ± 97 cells/10(5) cells) and healthy volunteers (117 ± 33 cells/10(5) cells). When the cutoff value of CEC counts was 210 cells/10(5) cells, there was no significant association between CEC counts and OR/PFS/OS of the enrolled patients. However, patients with CEC response after chemotherapy have more chances to achieve OR (P < 0.001), and such patients showed longer PFS (P = 0.048) and OS (P = 0.018) than those without CEC response. In the multivariate analysis, the independent prognostic roles of brain metastasis (HR 6.165, P = 0.001), and CEC response (HR 0.442, P = 0.044) were found. The CEC counts could be considered as diagnostic biomarker for advanced NSCLC patients. And the reduction of CECs after treatment might be more ideal than the baseline CEC counts as a predictive or prognostic factor in patients treated with chemotherapy or anti-angiogenic therapy. PMID:26084612

  15. One-Way Coupling of an Advanced CFD Multi-Physics Model to FEA for Predicting Stress-Strain in the Solidifying Shell during Continuous Casting of Steel

    NASA Astrophysics Data System (ADS)

    Svensson, Johan; Ramírez López, Pavel E.; Jalali, Pooria N.; Cervantes, Michel

    2015-06-01

    One of the main targets for Continuous Casting (CC) modelling is the actual prediction of defects during transient events. However, the majority of CC models are based on a statistical approach towards flow and powder performance, which is unable to capture the subtleties of small variations in casting conditions during real industrial operation or the combined effects of such changes leading eventually to defects. An advanced Computational Fluid Dynamics (CFD) model; which accounts for transient changes on lubrication during casting due to turbulent flow dynamics and mould oscillation has been presented on MCWASP XIV (Austria) to address these issues. The model has been successfully applied to the industrial environment to tackle typical problems such as lack of lubrication or unstable flows. However, a direct application to cracking had proven elusive. The present paper describes how results from this advanced CFD-CC model have been successfully coupled to structural Finite Element Analysis (FEA) for prediction of stress-strains as a function of irregular lubrication conditions in the mould. The main challenge for coupling was the extraction of the solidified shell from CFD calculations (carried out with a hybrid structured mesh) and creating a geometry by using iso-surfaces, re-meshing and mapping loads (e.g. temperature, pressure and external body forces), which served as input to mechanical stress-strain calculations. Preliminary results for CC of slabs show that the temperature distribution within the shell causes shrinkage and thermal deformation; which are in turn, the main source of stress. Results also show reasonable stress levels of 10-20 MPa in regions, where the shell is thin and exposed to large temperature gradients. Finally, predictions are in good agreement with prior works where stresses indicate compression at the slab surface, while tension is observed at the interior; generating a characteristic stress-strain state during solidification in CC.

  16. DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach

    PubMed Central

    Wang, Zhiheng; Yang, Qianqian; Li, Tonghua; Cong, Peisheng

    2015-01-01

    The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS) obtained by sequence-order/disorder alignment. Initially, near-disorder regions are defined on fragments located at both the terminus of an ordered region connecting a disordered region. Then the multi-class conservative score is generated by sequence alignment against a known structure database and represented as order, near-disorder and disorder conservative scores. The MCS of each amino acid has three elements: order, near-disorder and disorder profiles. Finally, the MCS is exploited as features to identify disordered regions in sequences. DisoMCS utilizes a non-redundant data set as the training set, MCS and predicted secondary structure as features, and a conditional random field as the classification algorithm. In predicted near-disorder regions a residue is determined as an order or a disorder according to the optimized decision threshold. DisoMCS was evaluated by cross-validation, large-scale prediction, independent tests and CASP (Critical Assessment of Techniques for Protein Structure Prediction) tests. All results confirmed that DisoMCS was very competitive in terms of accuracy of prediction when compared with well-established publicly available disordered region predictors. It also indicated our approach was more accurate when a query has higher homologous with the knowledge database. Availability The DisoMCS is available at http://cal.tongji.edu.cn/disorder/. PMID:26090958

  17. Life prediction methodology for ceramic components of advanced vehicular heat engines: Volume 1. Final report

    SciTech Connect

    Khandelwal, P.K.; Provenzano, N.J.; Schneider, W.E.

    1996-02-01

    One of the major challenges involved in the use of ceramic materials is ensuring adequate strength and durability. This activity has developed methodology which can be used during the design phase to predict the structural behavior of ceramic components. The effort involved the characterization of injection molded and hot isostatic pressed (HIPed) PY-6 silicon nitride, the development of nondestructive evaluation (NDE) technology, and the development of analytical life prediction methodology. Four failure modes are addressed: fast fracture, slow crack growth, creep, and oxidation. The techniques deal with failures initiating at the surface as well as internal to the component. The life prediction methodology for fast fracture and slow crack growth have been verified using a variety of confirmatory tests. The verification tests were conducted at room and elevated temperatures up to a maximum of 1371 {degrees}C. The tests involved (1) flat circular disks subjected to bending stresses and (2) high speed rotating spin disks. Reasonable correlation was achieved for a variety of test conditions and failure mechanisms. The predictions associated with surface failures proved to be optimistic, requiring re-evaluation of the components` initial fast fracture strengths. Correlation was achieved for the spin disks which failed in fast fracture from internal flaws. Time dependent elevated temperature slow crack growth spin disk failures were also successfully predicted.

  18. Ideal MHD Stability Prediction and Required Power for EAST Advanced Scenario

    NASA Astrophysics Data System (ADS)

    Chen, Junjie; Li, Guoqiang; Qian, Jinping; Liu, Zixi

    2012-11-01

    The Experimental Advanced Superconducting Tokamak (EAST) is the first fully superconducting tokamak with a D-shaped cross-sectional plasma presently in operation. The ideal magnetohydrodynamic (MHD) stability and required power for the EAST advanced tokamak (AT) scenario with negative central shear and double transport barrier (DTB) are investigated. With the equilibrium code TOQ and stability code GATO, the ideal MHD stability is analyzed. It is shown that a moderate ratio of edge transport barriers' (ETB) height to internal transport barriers' (ITBs) height is beneficial to ideal MHD stability. The normalized beta βN limit is about 2.20 (without wall) and 3.70 (with ideal wall). With the scaling law of energy confinement time, the required heating power for EAST AT scenario is calculated. The total heating power Pt increases as the toroidal magnetic field BT or the normalized beta βN is increased.

  19. Life prediction methodology for ceramic components of advanced heat engines. Phase 1: Volume 2, Appendices

    SciTech Connect

    1995-03-01

    This volume presents the following appendices: ceramic test specimen drawings and schematics, mixed-mode and biaxial stress fracture of structural ceramics for advanced vehicular heat engines (U. Utah), mode I/mode II fracture toughness and tension/torsion fracture strength of NT154 Si nitride (Brown U.), summary of strength test results and fractography, fractography photographs, derivations of statistical models, Weibull strength plots for fast fracture test specimens, and size functions.

  20. Development of Predictive Models of Advanced Propulsion Concepts for Low Cost Space Transportation

    NASA Technical Reports Server (NTRS)

    Morrell, Michael Randy

    2002-01-01

    This final report presents the Graduate Student Research Program (GSRP) work Mr. Morrell was able to complete as a summer intern at NASA MSFS during the summer of 2001, and represents work completed from inception through project termination. The topics include: 1) NASA TD40 Organization; 2) Combustion Physics Lab; 3) Advanced Hydrocarbon Fuels; 4) GSRP Summer Tasks; 5) High Pressure Facility Installation; 6) High Pressure Combustion Issues; 7) High Energy Density Matter (HEDM) Hydrocarbons; and 8) GSRP Summer Intern Summary.

  1. OSMOSE an experimental program for improving neutronic predictions of advanced nuclear fuels.

    SciTech Connect

    Klann, R. T.; Aliberti, G.; Zhong, Z.; Graczyk, D.; Loussi, A.; Nuclear Engineering Division; Commissariat a l Energie Atomique

    2007-10-18

    This report describes the technical results of tasks and activities conducted in FY07 to support the DOE-CEA collaboration on the OSMOSE program. The activities are divided into five high-level tasks: reactor modeling and pre-experiment analysis, sample fabrication and analysis, reactor experiments, data treatment and analysis, and assessment for relevance to high priority advanced reactor programs (such as GNEP and Gen-IV).

  2. Advances and Challenges In Uncertainty Quantification with Application to Climate Prediction, ICF design and Science Stockpile Stewardship

    NASA Astrophysics Data System (ADS)

    Klein, R.; Woodward, C. S.; Johannesson, G.; Domyancic, D.; Covey, C. C.; Lucas, D. D.

    2012-12-01

    Uncertainty Quantification (UQ) is a critical field within 21st century simulation science that resides at the very center of the web of emerging predictive capabilities. The science of UQ holds the promise of giving much greater meaning to the results of complex large-scale simulations, allowing for quantifying and bounding uncertainties. This powerful capability will yield new insights into scientific predictions (e.g. Climate) of great impact on both national and international arenas, allow informed decisions on the design of critical experiments (e.g. ICF capsule design, MFE, NE) in many scientific fields, and assign confidence bounds to scientifically predictable outcomes (e.g. nuclear weapons design). In this talk I will discuss a major new strategic initiative (SI) we have developed at Lawrence Livermore National Laboratory to advance the science of Uncertainty Quantification at LLNL focusing in particular on (a) the research and development of new algorithms and methodologies of UQ as applied to multi-physics multi-scale codes, (b) incorporation of these advancements into a global UQ Pipeline (i.e. a computational superstructure) that will simplify user access to sophisticated tools for UQ studies as well as act as a self-guided, self-adapting UQ engine for UQ studies on extreme computing platforms and (c) use laboratory applications as a test bed for new algorithms and methodologies. The initial SI focus has been on applications for the quantification of uncertainty associated with Climate prediction, but the validated UQ methodologies we have developed are now being fed back into Science Based Stockpile Stewardship (SSS) and ICF UQ efforts. To make advancements in several of these UQ grand challenges, I will focus in talk on the following three research areas in our Strategic Initiative: Error Estimation in multi-physics and multi-scale codes ; Tackling the "Curse of High Dimensionality"; and development of an advanced UQ Computational Pipeline to enable

  3. Predicted and measured boundary layer refraction for advanced turboprop propeller noise

    NASA Technical Reports Server (NTRS)

    Dittmar, James H.; Krejsa, Eugene A.

    1990-01-01

    Currently, boundary layer refraction presents a limitation to the measurement of forward arc propeller noise measured on an acoustic plate in the NASA Lewis 8- by 6-Foot Supersonic Wind Tunnel. The use of a validated boundary layer refraction model to adjust the data could remove this limitation. An existing boundary layer refraction model is used to predict the refraction for cases where boundary layer refraction was measured. In general, the model exhibits the same qualitative behavior as the measured refraction. However, the prediction method does not show quantitative agreement with the data. In general, it overpredicts the amount of refraction for the far forward angles at axial Mach number of 0.85 and 0.80 and underpredicts the refraction at axial Mach numbers of 0.75 and 0.70. A more complete propeller source description is suggested as a way to improve the prediction method.

  4. Prediction of future climate change for the Blue Nile, using a nested Regional Climate Model

    NASA Astrophysics Data System (ADS)

    Soliman, E.; Jeuland, M.

    2009-04-01

    Although the Nile River Basin is rich in natural resources, it faces many challenges. Rainfall is highly variable across the region, on both seasonal and inter-annual scales. This variability makes the region vulnerable to droughts and floods. Many development projects involving Nile waters are currently underway, or being studied. These projects will lead to land-use patterns changes and water distribution and availability. It is thus important to assess the effects of a) these projects and b) evolving water resource management and policies, on regional hydrological processes. This paper seeks to establish a basis for evaluation of such impacts within the Blue Nile River sub-basin, using the RegCM3 Regional Climate Model to simulate interactions between the land surface and climatic processes. We first present results from application of this RCM model nested with downscaled outputs obtained from the ECHAM5/MPI-OM1 transient simulations for the 20th Century. We then investigate changes associated with mid-21st century emissions forcing of the SRES A1B scenario. The results obtained from the climate model are then fed as inputs to the Nile Forecast System (NFS), a hydrologic distributed rainfall runoff model of the Nile Basin, The interaction between climatic and hydrological processes on the land surface has been fully coupled. Rainfall patterns and evaporation rates have been generated using RegCM3, and the resulting runoff and Blue Nile streamflow patterns have been simulated using the NFS. This paper compares the results obtained from the RegCM3 climate model with observational datasets for precipitation and temperature from the Climate Research Unit (UK) and the NASA Goddard Space Flight Center GPCP (USA) for 1985-2000. The validity of the streamflow predictions from the NFS is assessed using historical gauge records. Finally, we present results from modeling of the A1B emissions scenario of the IPCC for the years 2034-2055. Our results indicate that future

  5. An accelerated non-Gaussianity based multichannel predictive deconvolution method with the limited supporting region of filters

    NASA Astrophysics Data System (ADS)

    Li, Zhong-xiao; Li, Zhen-chun

    2016-09-01

    The multichannel predictive deconvolution can be conducted in overlapping temporal and spatial data windows to solve the 2D predictive filter for multiple removal. Generally, the 2D predictive filter can better remove multiples at the cost of more computation time compared with the 1D predictive filter. In this paper we first use the cross-correlation strategy to determine the limited supporting region of filters where the coefficients play a major role for multiple removal in the filter coefficient space. To solve the 2D predictive filter the traditional multichannel predictive deconvolution uses the least squares (LS) algorithm, which requires primaries and multiples are orthogonal. To relax the orthogonality assumption the iterative reweighted least squares (IRLS) algorithm and the fast iterative shrinkage thresholding (FIST) algorithm have been used to solve the 2D predictive filter in the multichannel predictive deconvolution with the non-Gaussian maximization (L1 norm minimization) constraint of primaries. The FIST algorithm has been demonstrated as a faster alternative to the IRLS algorithm. In this paper we introduce the FIST algorithm to solve the filter coefficients in the limited supporting region of filters. Compared with the FIST based multichannel predictive deconvolution without the limited supporting region of filters the proposed method can reduce the computation burden effectively while achieving a similar accuracy. Additionally, the proposed method can better balance multiple removal and primary preservation than the traditional LS based multichannel predictive deconvolution and FIST based single channel predictive deconvolution. Synthetic and field data sets demonstrate the effectiveness of the proposed method.

  6. Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances.

    PubMed

    Abut, Fatih; Akay, Mehmet Fatih

    2015-01-01

    Maximal oxygen uptake (VO2max) indicates how many milliliters of oxygen the body can consume in a state of intense exercise per minute. VO2max plays an important role in both sport and medical sciences for different purposes, such as indicating the endurance capacity of athletes or serving as a metric in estimating the disease risk of a person. In general, the direct measurement of VO2max provides the most accurate assessment of aerobic power. However, despite a high level of accuracy, practical limitations associated with the direct measurement of VO2max, such as the requirement of expensive and sophisticated laboratory equipment or trained staff, have led to the development of various regression models for predicting VO2max. Consequently, a lot of studies have been conducted in the last years to predict VO2max of various target audiences, ranging from soccer athletes, nonexpert swimmers, cross-country skiers to healthy-fit adults, teenagers, and children. Numerous prediction models have been developed using different sets of predictor variables and a variety of machine learning and statistical methods, including support vector machine, multilayer perceptron, general regression neural network, and multiple linear regression. The purpose of this study is to give a detailed overview about the data-driven modeling studies for the prediction of VO2max conducted in recent years and to compare the performance of various VO2max prediction models reported in related literature in terms of two well-known metrics, namely, multiple correlation coefficient (R) and standard error of estimate. The survey results reveal that with respect to regression methods used to develop prediction models, support vector machine, in general, shows better performance than other methods, whereas multiple linear regression exhibits the worst performance. PMID:26346869

  7. A hybrid approach to advancing quantitative prediction of tissue distribution of basic drugs in human

    SciTech Connect

    Poulin, Patrick; Ekins, Sean; Theil, Frank-Peter

    2011-01-15

    A general toxicity of basic drugs is related to phospholipidosis in tissues. Therefore, it is essential to predict the tissue distribution of basic drugs to facilitate an initial estimate of that toxicity. The objective of the present study was to further assess the original prediction method that consisted of using the binding to red blood cells measured in vitro for the unbound drug (RBCu) as a surrogate for tissue distribution, by correlating it to unbound tissue:plasma partition coefficients (Kpu) of several tissues, and finally to predict volume of distribution at steady-state (V{sub ss}) in humans under in vivo conditions. This correlation method demonstrated inaccurate predictions of V{sub ss} for particular basic drugs that did not follow the original correlation principle. Therefore, the novelty of this study is to provide clarity on the actual hypotheses to identify i) the impact of pharmacological mode of action on the generic correlation of RBCu-Kpu, ii) additional mechanisms of tissue distribution for the outlier drugs, iii) molecular features and properties that differentiate compounds as outliers in the original correlation analysis in order to facilitate its applicability domain alongside the properties already used so far, and finally iv) to present a novel and refined correlation method that is superior to what has been previously published for the prediction of human V{sub ss} of basic drugs. Applying a refined correlation method after identifying outliers would facilitate the prediction of more accurate distribution parameters as key inputs used in physiologically based pharmacokinetic (PBPK) and phospholipidosis models.

  8. Predicting Cost/Reliability/Maintainability of Advanced General Aviation Avionics Equipment

    NASA Technical Reports Server (NTRS)

    Davis, M. R.; Kamins, M.; Mooz, W. E.

    1978-01-01

    A methodology is provided for assisting NASA in estimating the cost, reliability, and maintenance (CRM) requirements for general avionics equipment operating in the 1980's. Practical problems of predicting these factors are examined. The usefulness and short comings of different approaches for modeling coast and reliability estimates are discussed together with special problems caused by the lack of historical data on the cost of maintaining general aviation avionics. Suggestions are offered on how NASA might proceed in assessing cost reliability CRM implications in the absence of reliable generalized predictive models.

  9. LiverTox: Advanced QSAR and Toxicogeomic Software for Hepatotoxicity Prediction

    SciTech Connect

    Lu, P-Y.; Yuracko, K.

    2011-02-25

    YAHSGS LLC and Oak Ridge National Laboratory (ORNL) established a CRADA in an attempt to develop a predictive system using a pre-existing ORNL computational neural network and wavelets format. This was in the interest of addressing national needs for toxicity prediction system to help overcome the significant drain of resources (money and time) being directed toward developing chemical agents for commerce. The research project has been supported through an STTR mechanism and funded by the National Institute of Environmental Health Sciences beginning Phase I in 2004 (CRADA No. ORNL-04-0688) and extending Phase II through 2007 (ORNL NFE-06-00020). To attempt the research objectives and aims outlined under this CRADA, state-of-the-art computational neural network and wavelet methods were used in an effort to design a predictive toxicity system that used two independent areas on which to base the system’s predictions. These two areas were quantitative structure-activity relationships and gene-expression data obtained from microarrays. A third area, using the new Massively Parallel Signature Sequencing (MPSS) technology to assess gene expression, also was attempted but had to be dropped because the company holding the rights to this promising MPSS technology went out of business. A research-scale predictive toxicity database system called Multi-Intelligent System for Toxicogenomic Applications (MISTA) was developed and its feasibility for use as a predictor of toxicological activity was tested. The fundamental focus of the CRADA was an attempt and effort to operate the MISTA database using the ORNL neural network. This effort indicated the potential that such a fully developed system might be used to assist in predicting such biological endpoints as hepatotoxcity and neurotoxicity. The MISTA/LiverTox approach if eventually fully developed might also be useful for automatic processing of microarray data to predict modes of action. A technical paper describing the

  10. Advanced turboprop noise prediction: Development of a code at NASA Langley based on recent theoretical results

    NASA Technical Reports Server (NTRS)

    Farassat, F.; Dunn, M. H.; Padula, S. L.

    1986-01-01

    The development of a high speed propeller noise prediction code at Langley Research Center is described. The code utilizes two recent acoustic formulations in the time domain for subsonic and supersonic sources. The structure and capabilities of the code are discussed. Grid size study for accuracy and speed of execution on a computer is also presented. The code is tested against an earlier Langley code. Considerable increase in accuracy and speed of execution are observed. Some examples of noise prediction of a high speed propeller for which acoustic test data are available are given. A brisk derivation of formulations used is given in an appendix.

  11. Advanced Models and Controls for Prediction and Extension of Battery Lifetime (Presentation)

    SciTech Connect

    Smith, K.; Wood, E.; Santhanagopalan, S.; Kim, G.; Pesaran, A.

    2014-02-01

    Predictive models of capacity and power fade must consider a multiplicity of degradation modes experienced by Li-ion batteries in the automotive environment. Lacking accurate models and tests, lifetime uncertainty must presently be absorbed by overdesign and excess warranty costs. To reduce these costs and extend life, degradation models are under development that predict lifetime more accurately and with less test data. The lifetime models provide engineering feedback for cell, pack and system designs and are being incorporated into real-time control strategies.

  12. Sensor-model prediction, monitoring and in-situ control of liquid RTM advanced fiber architecture composite processing

    NASA Astrophysics Data System (ADS)

    Kranbuehl, D.; Kingsley, P.; Hart, S.; Loos, A.; Hasko, G.; Dexter, B.

    In-situ frequency dependent electromagnetic sensors (FDEMS) and the Loos resin transfer model have been used to select and control the processing properties of an epoxy resin during liquid pressure RTM impregnation and cure. Once correlated with viscosity and degree of cure the FDEMS sensor monitors and the RTM processing model predicts the reaction advancement of the resin, viscosity and the impregnation of the fabric. This provides a direct means for predicting, monitoring, and controlling the liquid RTM process in-situ in the mold throughout the fabrication process and the effects of time, temperature, vacuum and pressure. Most importantly, the FDEMS-sensor model system has been developed to make intelligent decisions, thereby automating the liquid RTM process and removing the need for operator direction.

  13. Sensor-model prediction, monitoring and in-situ control of liquid RTM advanced fiber architecture composite processing

    NASA Technical Reports Server (NTRS)

    Kranbuehl, D.; Kingsley, P.; Hart, S.; Loos, A.; Hasko, G.; Dexter, B.

    1992-01-01

    In-situ frequency dependent electromagnetic sensors (FDEMS) and the Loos resin transfer model have been used to select and control the processing properties of an epoxy resin during liquid pressure RTM impregnation and cure. Once correlated with viscosity and degree of cure the FDEMS sensor monitors and the RTM processing model predicts the reaction advancement of the resin, viscosity and the impregnation of the fabric. This provides a direct means for predicting, monitoring, and controlling the liquid RTM process in-situ in the mold throughout the fabrication process and the effects of time, temperature, vacuum and pressure. Most importantly, the FDEMS-sensor model system has been developed to make intelligent decisions, thereby automating the liquid RTM process and removing the need for operator direction.

  14. Predicting plant vulnerability to drought in biodiverse regions using functional traits.

    PubMed

    Skelton, Robert Paul; West, Adam G; Dawson, Todd E

    2015-05-01

    Attempts to understand mechanisms underlying plant mortality during drought have led to the emergence of a hydraulic framework describing distinct hydraulic strategies among coexisting species. This framework distinguishes species that rapidly decrease stomatal conductance (gs), thereby maintaining high water potential (Px; isohydric), from those species that maintain relatively high gs at low Px, thereby maintaining carbon assimilation, albeit at the cost of loss of hydraulic conductivity (anisohydric). This framework is yet to be tested in biodiverse communities, potentially due to a lack of standardized reference values upon which hydraulic strategies can be defined. We developed a system of quantifying hydraulic strategy using indices from vulnerability curves and stomatal dehydration response curves and tested it in a speciose community from South Africa's Cape Floristic Region. Degree of stomatal regulation over cavitation was defined as the margin between Px at stomatal closure (Pg12) and Px at 50% loss of conductivity. To assess relationships between hydraulic strategy and mortality mechanisms, we developed proxies for carbon limitation and hydraulic failure using time since Pg12 and loss of conductivity at minimum seasonal Px, respectively. Our approach captured continuous variation along an isohydry/anisohydry axis and showed that this variation was linearly related to xylem safety margin. Degree of isohydry/anisohydry was associated with contrasting predictions for mortality during drought. Merging stomatal regulation strategies that represent an index of water use behavior with xylem vulnerability facilitates a more comprehensive framework with which to characterize plant response to drought, thus opening up an avenue for predicting the response of diverse communities to future droughts. PMID:25902534

  15. Predictive risk factors for chronic regional and multisite musculoskeletal pain: a 5-year prospective study in a working population.

    PubMed

    Herin, Fabrice; Vézina, Michel; Thaon, Isabelle; Soulat, Jean-Marc; Paris, Christophe

    2014-05-01

    The role of psychosocial and physical factors in the development of musculoskeletal pain (MSP) has now been clearly demonstrated. However, it is unclear whether these factors contribute to specific regional MSP or to multisite pain. The main goal of this study was to assess the impact of work-related factors according to gender on the development of regional and multisite MSP. A total of 12,591 subjects (65% men and 35% women) who were born in 1938, 1943, 1948, and 1953 and were participating in a French longitudinal prospective epidemiological survey (ESTEV) in 1990 to 1995 were eligible. Personal factors and work exposure were assessed by self-administered questionnaires. Statistical associations between chronic MSP (regional body site or multisite), personal factors, and occupational factors were analyzed using logistic regression modeling. The incidence of regional MSP and multisite pain in 1995 were, respectively, 17% and 25.6%. For women, highly repetitive movements predicted neck/shoulder pain; posture and vibrations predicted arm and low back pain; and effort with tools predicted arm pain. For men, forceful effort and vibrations predicted neck/shoulder pain; posture and forceful effort predicted lower limb and low back pain; and forceful effort and effort with tools predicted arm pain. Physical constraints (ie, forceful effort or vibrations) were associated with multisite pain in both genders. Only for women, psychological factors were risk factors predictive of upper limb pain and in 3 or 4 painful anatomical sites. These results support the hypothesis that some physical and psychological work-related factors are predictive of regional or multisite MSP but differ according to gender. Gender differences and risk factors for work-related musculoskeletal pain should be also taken into account to more effectively target preventive measures. PMID:24561229

  16. Advance of the Monitor of Drought for the Northern Region of Mexico

    NASA Astrophysics Data System (ADS)

    Reyes Gomez, V. M.; Nunez Lopez, D.

    2007-05-01

    In the last 13 years, the State of Chihuahua suffered a lingering drought that caused social, economical and environmental impacts hardly quantifiable. Since 2002, a monitoring system was implemented to watch the evolution of the meteorological drought in Chihuahua, recently being broadened for the states in the North of Mexico. Evaluation of the Meteorological Drought The Monitoring System on the Drought in Chihuahua includes the following steps: missing data gaps were completed basing on the statistical procedures described by Young (1992); the source code, was compiled to create a computer program, with which it can be derived a level of climatic station, historical series of values for the SPI in time scales of 1 to 48 months; under this classification scheme, it is considered that a drought event begins when the values of the SPI are inferior to -0.7 (McKee et al. 1995). The spatial distribution of the SPI was determined through spatial interpolation techniques using a reverse method of the distance between stations included in Arc/Info©. This same procedure was applied for the States of Sonora, Sinaloa, Durango and Zacatecas with the purpose of implementing this tool for the north of Mexico. Advances on the Monitoring System The monitoring system allows an analysis of the frequency, duration and intensity of the drought events that took place in several climatic regions (Núñez-López et al., 2005); un map of spatial distribution of the SPI for the northern region of Mexico, in the States of Sonora, Sinaloa, Durango and Chihuahua. The generated map will be published in a section on the CEISS web page (www.sequia.edu.mx), together with the monthly bulletin available to the public in general; it is monitoring to an annual scale, the tendencies of the deficits or surplus of the runoff volumes on three of the main dams in the State of Chihuahua Conclusions The Drought Monitoring System in Chihuahua complies with the following international rules for the

  17. Development of advanced stability theory suction prediction techniques for laminar flow control. [on swept wings

    NASA Technical Reports Server (NTRS)

    Srokowski, A. J.

    1978-01-01

    The problem of obtaining accurate estimates of suction requirements on swept laminar flow control wings was discussed. A fast accurate computer code developed to predict suction requirements by integrating disturbance amplification rates was described. Assumptions and approximations used in the present computer code are examined in light of flow conditions on the swept wing which may limit their validity.

  18. Advances in Toxico-Cheminformatics: Supporting a New Paradigm for Predictive Toxicology

    EPA Science Inventory

    EPA’s National Center for Computational Toxicology is building capabilities to support a new paradigm for toxicity screening and prediction through the harnessing of legacy toxicity data, creation of data linkages, and generation of new high-throughput screening (HTS) data. The D...

  19. Ground motion prediction and earthquake scenarios in the volcanic region of Mt. Etna (Southern Italy

    NASA Astrophysics Data System (ADS)

    Langer, Horst; Tusa, Giuseppina; Luciano, Scarfi; Azzaro, Raffaela

    2013-04-01

    One of the principal issues in the assessment of seismic hazard is the prediction of relevant ground motion parameters, e. g., peak ground acceleration, radiated seismic energy, response spectra, at some distance from the source. Here we first present ground motion prediction equations (GMPE) for horizontal components for the area of Mt. Etna and adjacent zones. Our analysis is based on 4878 three component seismograms related to 129 seismic events with local magnitudes ranging from 3.0 to 4.8, hypocentral distances up to 200 km, and focal depth shallower than 30 km. Accounting for the specific seismotectonic and geological conditions of the considered area we have divided our data set into three sub-groups: (i) Shallow Mt. Etna Events (SEE), i.e., typically volcano-tectonic events in the area of Mt. Etna having a focal depth less than 5 km; (ii) Deep Mt. Etna Events (DEE), i.e., events in the volcanic region, but with a depth greater than 5 km; (iii) Extra Mt. Etna Events (EEE), i.e., purely tectonic events falling outside the area of Mt. Etna. The predicted PGAs for the SEE are lower than those predicted for the DEE and the EEE, reflecting their lower high-frequency energy content. We explain this observation as due to the lower stress drops. The attenuation relationships are compared to the ones most commonly used, such as by Sabetta and Pugliese (1987)for Italy, or Ambraseys et al. (1996) for Europe. Whereas our GMPEs are based on small earthquakes, the magnitudes covered by the two above mentioned attenuation relationships regard moderate to large magnitudes (up to 6.8 and 7.9, respectively). We show that the extrapolation of our GMPEs to magnitues beyond the range covered by the data is misleading; at the same time also the afore mentioned relationships fail to predict ground motion parameters for our data set. Despite of these discrepancies, we can exploit our data for setting up scenarios for strong earthquakes for which no instrumental recordings are

  20. The Use of Fluid Mechanics to Predict Regions of Microscopic Thrombus Formation in Pulsatile VADs.

    PubMed

    Topper, Stephen R; Navitsky, Michael A; Medvitz, Richard B; Paterson, Eric G; Siedlecki, Christopher A; Slattery, Margaret J; Deutsch, Steven; Rosenberg, Gerson; Manning, Keefe B

    2014-03-01

    We compare the velocity and shear obtained from particle image velocimetry (PIV) and computational fluid dynamics (CFD) in a pulsatile ventricular assist device (VAD) to further test our thrombus predictive methodology using microscopy data from an explanted VAD. To mimic physiological conditions in vitro, a mock circulatory loop is used with a blood analog that matched blood's viscoelastic behavior at 40% hematocrit. Under normal physiologic pressures and for a heart rate of 75 bpm, PIV data is acquired and wall shear maps are produced. The resolution of the PIV shear rate calculations are tested using the CFD and found to be in the same range. A bovine study, using a model of the 50 cc Penn State V-2 VAD, for 30 days at a constant beat rate of 75 beats per minute (bpm) provides the microscopic data whereby after the 30 days, the device is explanted and the sac surface analyzed using scanning electron microscopy (SEM) and, after immunofluorescent labeling for platelets and fibrin, confocal microscopy. Areas are examined based on PIV measurements and CFD, with special attention to low shear regions where platelet and fibrin deposition are most likely to occur. Data collected within the outlet port in a direction normal to the front wall of the VAD shows that some regions experience wall shear rates less than 500 s(-1), which increases the likelihood of platelet and fibrin deposition. Despite only one animal study, correlations between PIV, CFD, and in vivo data show promise. Deposition probability is quantified by the thrombus susceptibility potential, a calculation to correlate low shear and time of shear with deposition. PMID:24634700

  1. The Use of Fluid Mechanics to Predict Regions of Microscopic Thrombus Formation in Pulsatile VADs

    PubMed Central

    Topper, Stephen R.; Navitsky, Michael A.; Medvitz, Richard B.; Paterson, Eric G.; Siedlecki, Christopher A.; Slattery, Margaret J.; Deutsch, Steven; Rosenberg, Gerson; Manning, Keefe B.

    2014-01-01

    We compare the velocity and shear obtained from particle image velocimetry (PIV) and computational fluid dynamics (CFD) in a pulsatile ventricular assist device (VAD) to further test our thrombus predictive methodology using microscopy data from an explanted VAD. To mimic physiological conditions in vitro, a mock circulatory loop is used with a blood analog that matched blood’s viscoelastic behavior at 40% hematocrit. Under normal physiologic pressures and for a heart rate of 75 bpm, PIV data is acquired and wall shear maps are produced. The resolution of the PIV shear rate calculations are tested using the CFD and found to be in the same range. A bovine study, using a model of the 50 cc Penn State V-2 VAD, for 30 days at a constant beat rate of 75 beats per minute (bpm) provides the microscopic data whereby after the 30 days, the device is explanted and the sac surface analyzed using scanning electron microscopy (SEM) and, after immunofluorescent labeling for platelets and fibrin, confocal microscopy. Areas are examined based on PIV measurements and CFD, with special attention to low shear regions where platelet and fibrin deposition are most likely to occur. Data collected within the outlet port in a direction normal to the front wall of the VAD shows that some regions experience wall shear rates less than 500 s−1, which increases the likelihood of platelet and fibrin deposition. Despite only one animal study, correlations between PIV, CFD, and in vivo data show promise. Deposition probability is quantified by the thrombus susceptibility potential, a calculation to correlate low shear and time of shear with deposition. PMID:24634700

  2. In silico assessment of the acute toxicity of chemicals: recent advances and new model for multitasking prediction of toxic effect.

    PubMed

    Kleandrova, Valeria V; Luan, Feng; Speck-Planche, Alejandro; Cordeiro, M Natália D S

    2015-01-01

    The assessment of acute toxicity is one of the most important stages to ensure the safety of chemicals with potential applications in pharmaceutical sciences, biomedical research, or any other industrial branch. A huge and indiscriminate number of toxicity assays have been carried out on laboratory animals. In this sense, computational approaches involving models based on quantitative-structure activity/toxicity relationships (QSAR/QSTR) can help to rationalize time and financial costs. Here, we discuss the most significant advances in the last 6 years focused on the use of QSAR/QSTR models to predict acute toxicity of drugs/chemicals in laboratory animals, employing large and heterogeneous datasets. The advantages and drawbacks of the different QSAR/QSTR models are analyzed. As a contribution to the field, we introduce the first multitasking (mtk) QSTR model for simultaneous prediction of acute toxicity of compounds by considering different routes of administration, diverse breeds of laboratory animals, and the reliability of the experimental conditions. The mtk-QSTR model was based on artificial neural networks (ANN), allowing the classification of compounds as toxic or non-toxic. This model correctly classified more than 94% of the 1646 cases present in the whole dataset, and its applicability was demonstrated by performing predictions of different chemicals such as drugs, dietary supplements, and molecules which could serve as nanocarriers for drug delivery. The predictions given by the mtk-QSTR model are in very good agreement with the experimental results. PMID:25694074

  3. The problem of what to expect when you are expecting regional change -- Different evaluation strategies of regional prediction and projection performance in the NCPP framework

    NASA Astrophysics Data System (ADS)

    Ammann, C. M.

    2012-12-01

    Ensembles of climate model experiments together with means and trends in instrumental records generally build the basis for evaluation of predictions and projections of regional climate change. Most are drawn from mean climatological changes and trends, and some describe how changes in modes of variability are simulated. But how good are these regional and/or mode changes from models? It is clear that at the regional scale also internal variability needs to be taken into account. This is the case for identifying the forced changes in the real world, but it is also critical when evaluating a model-based prediction or projection. The key question is about what part of the observed variability and change we can, and should, expect models to reproduce. This problem is not trivial and requires a host of conditional considerations covering different time scales. Next to the instrumental reference observations, even paleoclimatic information from well-dated and verified reconstructions can be of use for important elements of this evaluation, including a more complete representation of the full range of variability as well as potential information of systematic structural response in the climate system to radiative perturbations. This presentation provides an overview of how the National Climate Predictions and Projections platform is currently developing a catalog of strategies to evaluate performance in regional climate outlooks across seasonal to decadal and centennial time scales, and how new research can enrich and extend the tools for a scientifically sound evaluation of what to expect when one is expecting regional climate change.

  4. Low acute hematological toxicity during chemotherapy predicts reduced disease control in advanced Hodgkin's disease.

    PubMed

    Brosteanu, O; Hasenclever, D; Loeffler, M; Diehl, V

    2004-03-01

    Chemotherapy-treated patients with advanced Hodgkin's disease (HD) differ considerably in acute hematotoxicity. Hematotoxicity may be indicative of pharmacological and metabolic heterogeneity. We hypothesized that low hematotoxicity might correlate with reduced systemic dose and thus reduced disease control. A total of 266 patients with advanced HD treated with cyclophosphamide, vincristine, procarbazine, prednisone, doxorubicin, bleomycin, vinblastine, and dacarbazine (COPP-ABVD) were analyzed (HD6 trial of the German Hodgkin's Lymphoma Study Group). The reported WHO grade of leukocytopenia was averaged over chemotherapy cycles given and weighted with the reciprocal dose intensity of the corresponding cycle. The low and high toxicity groups were defined in retrospect as having had an averaged WHO grade of leukocytopenia 2.1, respectively. The independent impact of low hematological toxicity on freedom from treatment failure (FFTF) was assessed multivariately adjusting for the international prognostic score for advanced HD. The results were validated in two independent cohorts [181 patients treated with COPP-ABVD (HD9-trial) and 250 patients treated with COPP-ABV-ifosfamide, methotrexate, etoposide, and prednisone (IMEP) (HD6 trial)]. The 5-year FFTF rates were 68% for patients with high toxicity vs 47% for patients with low toxicity [multivariate relative risk (RR) 2.0, 95% confidence interval (CI) 1.4-3.0, p=0.0002]. Patients with low toxicity received significantly higher nominal dose ( p=0.02) and dose intensity ( p<0.0001). This finding was confirmed in both validation cohorts (multivariate RR 2.1, 95% CI 1.2-3.8, p=0.01 and RR 1.5, 95% CI 1.01-2.26, p=0.04, respectively). Patients with low hematotoxicity have significantly higher failure rates despite higher doses and dose intensity. Hematotoxicity is an independent prognostic factor for treatment outcome. This observation suggests a strategy of individualized dosing adapted to hematotoxicity

  5. Regional climate model downscaling may improve the prediction of alien plant species distributions

    NASA Astrophysics Data System (ADS)

    Liu, Shuyan; Liang, Xin-Zhong; Gao, Wei; Stohlgren, Thomas J.

    2014-12-01

    Distributions of invasive species are commonly predicted with species distribution models that build upon the statistical relationships between observed species presence data and climate data. We used field observations, climate station data, and Maximum Entropy species distribution models for 13 invasive plant species in the United States, and then compared the models with inputs from a General Circulation Model (hereafter GCM-based models) and a downscaled Regional Climate Model (hereafter, RCM-based models).We also compared species distributions based on either GCM-based or RCM-based models for the present (1990-1999) to the future (2046-2055). RCM-based species distribution models replicated observed distributions remarkably better than GCM-based models for all invasive species under the current climate. This was shown for the presence locations of the species, and by using four common statistical metrics to compare modeled distributions. For two widespread invasive taxa ( Bromus tectorum or cheatgrass, and Tamarix spp. or tamarisk), GCM-based models failed miserably to reproduce observed species distributions. In contrast, RCM-based species distribution models closely matched observations. Future species distributions may be significantly affected by using GCM-based inputs. Because invasive plants species often show high resilience and low rates of local extinction, RCM-based species distribution models may perform better than GCM-based species distribution models for planning containment programs for invasive species.

  6. Brain activity in valuation regions while thinking about the future predicts individual discount rates.

    PubMed

    Cooper, Nicole; Kable, Joseph W; Kim, B Kyu; Zauberman, Gal

    2013-08-01

    People vary widely in how much they discount delayed rewards, yet little is known about the sources of these differences. Here we demonstrate that neural activity in ventromedial prefrontal cortex (VMPFC) and ventral striatum (VS) when human subjects are asked to merely think about the future--specifically, to judge the subjective length of future time intervals--predicts delay discounting. High discounters showed lower activity for longer time delays, while low discounters showed the opposite pattern. Our results demonstrate that the correlation between VMPFC and VS activity and discounting occurs even in the absence of choices about future rewards, and does not depend on a person explicitly evaluating future outcomes or judging their self-relevance. This suggests a link between discounting and basic processes involved in thinking about the future, such as temporal perception. Our results also suggest that reducing impatience requires not suppression of VMPFC and VS activity altogether, but rather modulation of how these regions respond to the present versus the future. PMID:23926268

  7. Brain Activity in Valuation Regions while Thinking about the Future Predicts Individual Discount Rates

    PubMed Central

    Cooper, Nicole; Kim, B. Kyu; Zauberman, Gal

    2013-01-01

    People vary widely in how much they discount delayed rewards, yet little is known about the sources of these differences. Here we demonstrate that neural activity in ventromedial prefrontal cortex (VMPFC) and ventral striatum (VS) when human subjects are asked to merely think about the future—specifically, to judge the subjective length of future time intervals—predicts delay discounting. High discounters showed lower activity for longer time delays, while low discounters showed the opposite pattern. Our results demonstrate that the correlation between VMPFC and VS activity and discounting occurs even in the absence of choices about future rewards, and does not depend on a person explicitly evaluating future outcomes or judging their self-relevance. This suggests a link between discounting and basic processes involved in thinking about the future, such as temporal perception. Our results also suggest that reducing impatience requires not suppression of VMPFC and VS activity altogether, but rather modulation of how these regions respond to the present versus the future. PMID:23926268

  8. Stacked Predictive Sparse Coding for Classification of Distinct Regions of Tumor Histopathology.

    PubMed

    Chang, Hang; Zhou, Yin; Spellman, Paul; Parvin, Bahram

    2013-01-01

    Image-based classification of tissue histology, in terms of distinct histopathology (e.g., tumor or necrosis regions), provides a series of indices for tumor composition. Furthermore, aggregation of these indices from each whole slide image (WSI) in a large cohort can provide predictive models of clinical outcome. However, the performance of the existing techniques is hindered as a result of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. We suggest that, compared with human engineered features widely adopted in existing systems, unsupervised feature learning is more tolerant to batch effect (e.g., technical variations associated with sample preparation) and pertinent features can be learned without user intervention. This leads to a novel approach for classification of tissue histology based on unsupervised feature learning and spatial pyramid matching (SPM), which utilize sparse tissue morphometric signatures at various locations and scales. This approach has been evaluated on two distinct datasets consisting of different tumor types collected from The Cancer Genome Atlas (TCGA), and the experimental results indicate that the proposed approach is (i) extensible to different tumor types; (ii) robust in the presence of wide technical variations and biological heterogeneities; and (iii) scalable with varying training sample sizes. PMID:24770492

  9. A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data.

    PubMed

    Lu, Qiongshi; Hu, Yiming; Sun, Jiehuan; Cheng, Yuwei; Cheung, Kei-Hoi; Zhao, Hongyu

    2015-01-01

    Identifying functional regions in the human genome is a major goal in human genetics. Great efforts have been made to functionally annotate the human genome either through computational predictions, such as genomic conservation, or high-throughput experiments, such as the ENCODE project. These efforts have resulted in a rich collection of functional annotation data of diverse types that need to be jointly analyzed for integrated interpretation and annotation. Here we present GenoCanyon, a whole-genome annotation method that performs unsupervised statistical learning using 22 computational and experimental annotations thereby inferring the functional potential of each position in the human genome. With GenoCanyon, we are able to predict many of the known functional regions. The ability of predicting functional regions as well as its generalizable statistical framework makes GenoCanyon a unique and powerful tool for whole-genome annotation. The GenoCanyon web server is available at http://genocanyon.med.yale.edu. PMID:26015273

  10. Total Binding Affinity Profiles of Regulatory Regions Predict Transcription Factor Binding and Gene Expression in Human Cells

    PubMed Central

    Molineris, Ivan; Provero, Paolo

    2015-01-01

    Transcription factors regulate gene expression by binding regulatory DNA. Understanding the rules governing such binding is an essential step in describing the network of regulatory interactions, and its pathological alterations. We show that describing regulatory regions in terms of their profile of total binding affinities for transcription factors leads to increased predictive power compared to methods based on the identification of discrete binding sites. This applies both to the prediction of transcription factor binding as revealed by ChIP-seq experiments and to the prediction of gene expression through RNA-seq. Further significant improvements in predictive power are obtained when regulatory regions are defined based on chromatin states inferred from histone modification data. PMID:26599758

  11. A Research Program for Improving Heat Transfer Prediction Capability for the Laminar to Turbulent Transition Region of Turbine Vanes/Blades

    NASA Technical Reports Server (NTRS)

    Simon, Frederick F.

    2007-01-01

    A program sponsored by the National Aeronautics and Space Administration (NASA) for the investigation of the heat transfer in the transition region of turbine vanes and blades with the object of improving the capability for predicting heat transfer is described,. The accurate prediction of gas-side heat transfer is important to the determination of turbine longevity, engine performance and developmental costs. The need for accurate predictions will become greater as the operating temperatures and stage loading levels of advanced turbine engines increase. The present methods for predicting transition shear stress and heat transfer on turbine blades are based on incomplete knowledge and are largely empirical. To meet the objectives of the NASA program, a team approach consisting of researchers from government, universities, a research institute, and a small business is presented. The research is divided into areas of experimentation, direct numerical simulation (DNS) and turbulence modeling. A summary of the results to date is given for the above research areas in a high-disturbance environment (bypass transition) with a discussion of the model development necessary for use in numerical codes.

  12. Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science.

    PubMed

    Montes-Torres, Julio; Subirats, José Luis; Ribelles, Nuria; Urda, Daniel; Franco, Leonardo; Alba, Emilio; Jerez, José Manuel

    2016-01-01

    One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets. PMID:27532883

  13. Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science

    PubMed Central

    Montes-Torres, Julio; Subirats, José Luis; Ribelles, Nuria; Urda, Daniel; Franco, Leonardo; Alba, Emilio; Jerez, José Manuel

    2016-01-01

    One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets. PMID:27532883

  14. Predictive and prognostic biomarkers for neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

    PubMed

    Lim, S H; Chua, W; Henderson, C; Ng, W; Shin, J-S; Chantrill, L; Asghari, R; Lee, C S; Spring, K J; de Souza, P

    2015-10-01

    Locally advanced rectal cancer is regularly treated with trimodality therapy consisting of neoadjuvant chemoradiation, surgery and adjuvant chemotherapy. There is a need for biomarkers to assess treatment response, and aid in stratification of patient risk to adapt and personalise components of the therapy. Currently, pathological stage and tumour regression grade are used to assess response. Experimental markers include proteins involved in cell proliferation, apoptosis, angiogenesis, the epithelial to mesenchymal transition and microsatellite instability. As yet, no single marker is sufficiently robust to have clinical utility. Microarrays that screen a tumour for multiple promising candidate markers, gene expression and microRNA profiling will likely have higher yield and it is expected that a combination or panel of markers would prove most useful. Moving forward, utilising serial samples of circulating tumour cells or circulating nucleic acids can potentially allow us to demonstrate tumour heterogeneity, document mutational changes and subsequently measure treatment response. PMID:26032919

  15. Prediction of Unsteady Blade Surface Pressures on an Advanced Propeller at an Angle of Attack

    NASA Technical Reports Server (NTRS)

    Nallasamy, M.; Groeneweg, J. F.

    1989-01-01

    The numerical solution of the unsteady, three-dimensional, Euler equations is considered in order to obtain the blade surface pressures of an advanced propeller at an angle of attack. The specific configuration considered is the SR7L propeller at cruise conditions with a 4.6 deg inflow angle corresponding to the plus 2 deg nacelle tilt of the Propeller Test Assessment (PTA) flight test condition. The results indicate nearly sinusoidal response of the blade loading, with angle of attack. For the first time, detailed variations of the chordwise loading as a function of azimuthal angle are presented. It is observed that the blade is lightly loaded for part of the revolution and shocks appear from hub to about 80 percent radial station for the highly loaded portion of the revolution.

  16. Prediction of unsteady blade surface pressures on an advanced propeller at an angle of attack

    NASA Technical Reports Server (NTRS)

    Nallasamy, M.; Groeneweg, J. F.

    1989-01-01

    The paper considers the numerical solution of the unsteady, three-dimensional, Euler equations to obtain the blade surface pressures of an advanced propeller at an angle of attack. The specific configuration considered is the SR7L propeller at cruise conditions with a 4.6 deg inflow angle corresponding to the +2 deg nacelle tilt of the Propeller Test Assessment (PTA) flight test condition. The results indicate nearly sinusoidal response of the blade loading, with angle of attack. For the first time, detailed variations of the chordwise loading as a function of azimuthal angle are presented. It is observed that the blade is lightly loaded for part of the revolution and shocks appear from hub to about 80 percent radial station for the highly loaded portion of the revolution.

  17. Performance Prediction for a Hockey-Puck Silicon Crystal Monochromator at the Advanced Photon Source

    NASA Astrophysics Data System (ADS)

    Liu, Zunping; Rosenbaum, Gerd; Navrotski, Gary

    2014-03-01

    One of the Key Performance Parameters of the upgrade of the Advanced Photon Source (APS) is the increase of the storage ring current from 100 to 150 mA. In order to anticipate the impact of this increased heat load on the X-ray optics of the beamlines, the APS has implemented a systematic review, by means of finite element analysis and computational fluid dynamics, of the thermal performance of the different types of monochromators installed at the highest-heat-load insertion device beamlines. We present here simulations of the performance of a directly liquid nitrogen-cooled silicon crystal, the hockey-puck design. Calculations of the temperature and slope error at multiple ring currents under multiple operational conditions, including the influence of power, cooling, and diffraction surface thickness are included.

  18. Thermodynamic calculation and interatomic potential to predict the favored composition region for the Cu-Zr-Al metallic glass formation.

    PubMed

    Cui, Y Y; Wang, T L; Li, J H; Dai, Y; Liu, B X

    2011-03-01

    For the Cu-Zr-Al system, the glass forming compositions were firstly calculated based on the extended Miedema's model, suggesting that the amorphous phase could be thermodynamically favored over a large composition region. An n-body potential was then constructed under the smoothed and long-range second-moment-approximation of tight-binding formulism. Applying the constructed Cu-Zr-Al potential, molecular dynamics simulations were conducted using solid solution models to compare relative stability of crystalline solid solution versus its disordered counterpart. Simulations reveal that the physical origin of metallic glass formation is crystalline lattice collapsing while solute concentration exceeding the critical value, thus predicting a hexagonal composition region, within which the Cu-Zr-Al ternary metallic glass formation is energetically favored. The molecular dynamics simulations predicted composition region is defined as the quantitative glass-forming-ability or glass-forming-region of the Cu-Zr-Al system. PMID:21229150

  19. Prognostic and Predictive Blood-Based Biomarkers in Patients with Advanced Pancreatic Cancer: Results from CALGB80303 (Alliance)

    PubMed Central

    Nixon, Andrew B.; Pang, Herbert; Starr, Mark D.; Friedman, Paula N.; Bertagnolli, Monica M.; Kindler, Hedy L.; Goldberg, Richard M.; Venook, Alan P.; Hurwitz, Herbert I.

    2014-01-01

    Purpose CALGB80303 was a phase III trial of 602 patients with locally advanced or metastatic pancreatic cancer comparing gemcitabine/bevacizumab versus gemcitabine/placebo. The study found no benefit in any outcome from the addition of bevacizumab to gemcitabine. Blood samples were collected and multiple angiogenic factors were evaluated and then correlated with clinical outcome in general (prognostic markers) and with benefit specifically from bevacizumab treatment (predictive markers). Experimental Design Plasma samples were analyzed via a novel multiplex ELISA platform for 31 factors related to tumor growth, angiogenesis, and inflammation. Baseline values for these factors were correlated with overall survival (OS) using univariate Cox proportional hazard regression models and multivariable Cox regression models with leave-one-out cross validation. Predictive markers were identified using a treatment by marker interaction term in the Cox model. Results Baseline plasma was available from 328 patients. Univariate prognostic markers for OS were identified including: Ang2, CRP, ICAM-1, IGFBP-1, TSP-2 (all P < 0.001). These prognostic factors were found to be highly significant, even after adjustment for known clinical factors. Additional modeling approaches yielded prognostic signatures from multivariable Cox regression. The gemcitabine/bevacizumab signature consisted of IGFBP-1, interleukin-6, PDGF-AA, PDGF-BB, TSP-2; whereas the gemcitabine/ placebo signature consisted of CRP, IGFBP-1, PAI-1, PDGF-AA, P-selectin (both P < 0.0001). Finally, three potential predictive markers of bevacizumab efficacy were identified: VEGF-D (P <0.01), SDF1 (P <0.05), and Ang2 (P < 0.05). Conclusion This study identified strong prognostic markers for pancreatic cancer patients. Predictive marker analysis indicated that plasma levels of VEGF-D, Ang2, and SDF1 significantly predicted for benefit or lack of benefit from bevacizumab in this population. PMID:24097873

  20. Improving the Gap between Model Predictions and Observations of Formaldehyde over the Remote Marine Regions

    NASA Astrophysics Data System (ADS)

    Trueblood, J.; Meskhidze, N.

    2013-05-01

    Formaldehyde (HCHO) is a ubiquitous oxidation product that exists in polluted rural and urban areas, as well as remote background sites where it is an important photochemical intermediate. HCHO levels of up to six times above what is typically predicted by photochemical models have been reported over the Marine Boundary Layer (MBL). As proposed mechanisms for HCHO production remain to be insufficient to explain such large discrepancies between model predictions and measured values, the role of marine regions in the creation of HCHO continues to be one of the largest sources of uncertainty in current global chemistry-transport models. Here we examine the viability of a proposed mechanism for the photochemical production of formaldehyde involving aerosols enriched with biologically produced organic matter. In this study, the phytoplankton Emiliania Huxleyi was incubated in autoclaved seawater contained within a 9 liter Pyrex glass bottle. Quantitative analysis of the enrichment of transparent exopolymer particles (TEP) and other biologically produced organic matter (dissolved and particulate) in the surface microlayer was carried out by employing Alldredge's alcian blue staining technique. To produce organic aerosols, enriched seawater was bubbled with hydrocarbon free air using a sintered glass filter placed 5 cm below the surface. Utilizing a mixed flow reaction scheme, produced aerosols were then pushed through stainless steel flow tubes into a separate 9-liter Pyrex glass container acting as a residence chamber. The container was surrounded with six Ushio 9W Midrange UVB lights to allow for the irradiation of aerosols at 306 nm. A flow rate of approximately 0.1 l/min allowed for an average aerosol residence time of 90 minutes inside the residence chamber. All air from the chamber was then passed through a 5" long Pyrex desorber tube packed with 60/80 Tenax that had been soaked in the derivatizing agent pentafluorophenyl hydrazine (PFPH). Subsequent thermal

  1. Regional prediction of soil organic carbon content over croplands using airborne hyperspectral data

    NASA Astrophysics Data System (ADS)

    Vaudour, Emmanuelle; Gilliot, Jean-Marc; Bel, Liliane; Lefebvre, Josias; Chehdi, Kacem

    2015-04-01

    This study was carried out in the framework of the Prostock-Gessol3 and the BASC-SOCSENSIT projects, dedicated to the spatial monitoring of the effects of exogenous organic matter land application on soil organic carbon storage. It aims at identifying the potential of airborne hyperspectral AISA-Eagle data for predicting the topsoil organic carbon (SOC) content of bare cultivated soils over a large peri-urban area (221 km2) with both contrasted soils and SOC contents, located in the western region of Paris, France. Soils comprise hortic or glossic luvisols, calcaric, rendzic cambisols and colluvic cambisols. Airborne AISA-Eagle data (400-1000 nm, 126 bands) with 1 m-resolution were acquired on 17 April 2013 over 13 tracks which were georeferenced. Tracks were atmospherically corrected using a set of 22 synchronous field spectra of both bare soils, black and white targets and impervious surfaces. Atmospherically corrected track tiles were mosaicked at a 2 m-resolution resulting in a 66 Gb image. A SPOT4 satellite image was acquired the same day in the framework of the SPOT4-Take Five program of the French Space Agency (CNES) which provided it with atmospheric correction. The land use identification system layer (RPG) of 2012 was used to mask non-agricultural areas, then NDVI calculation and thresholding enabled to map agricultural fields with bare soil. All 18 sampled sites known to be bare at this very date were correctly included in this map. A total of 85 sites sampled in 2013 or in the 3 previous years were identified as bare by means of this map. Predictions were made from the mosaic spectra which were related to topsoil SOC contents by means of partial least squares regression (PLSR). Regression robustness was evaluated through a series of 1000 bootstrap data sets of calibration-validation samples. The use of the total sample including 27 sites under cloud shadows led to non-significant results. Considering 43 sites outside cloud shadows only, median

  2. Advanced Information Processing. Volume II. Instructor's Materials. Curriculum Improvement Project. Region II.

    ERIC Educational Resources Information Center

    Stanford, Linda

    This course curriculum is intended for use by community college insructors and administrators in implementing an advanced information processing course. It builds on the skills developed in the previous information processing course but goes one step further by requiring students to perform in a simulated office environment and improve their…

  3. Usefulness of human epididymis protein 4 in predicting cytoreductive surgical outcomes for advanced ovarian tubal and peritoneal carcinoma

    PubMed Central

    Tang, Zhijian; Chang, Xiaohong; Ye, Xue; Li, Yi; Cheng, Hongyan

    2015-01-01

    Objective Human epididymis protein 4 (HE4) is a promising biomarker of epithelial ovarian cancer (EOC). But its role in assessing the primary optimal debulking (OD) of EOC remains unknown. The purpose of this study is to elucidate the ability of preoperative HE4 in predicting the primary cytoreductive outcomes in advanced EOC, tubal or peritoneal carcinoma. Methods We reviewed the records of 90 patients with advanced ovarian, tubal or peritoneal carcinoma who underwent primary cytoreduction at the Department of Obstetrics and Gynecology of Peking University People’s Hospital between November 2005 and October 2010. Preoperative serum HE4 and CA125 levels were detected with EIA kit. A receiver operating characteristic (ROC) curve was used to determine the most useful HE4 cut-off value. Logistic regression analysis was performed to identify significant preoperative clinical characteristics to predict optimal primary cytoreduction. Results OD was achieved in 47.7% (43/48) of patients. The median preoperative HE4 level for patients with OD vs. suboptimal debulking was 423 and 820 pmol/L, respectively (P<0.001). The areas under the ROC curve for HE4 and CA125 were 0.716 and 0.599, respectively (P=0.080). The most useful HE4 cut-off value was 473 pmol/L. Suboptimal cytoreduction was obtained in 66.7% (38/57) of cases with HE4 ≥473 pmol/L compared with only 27.3% (9/33) of cases with HE4 <473 pmol/L. At this threshold, the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for diagnosing suboptimal debulking were 81%, 56%, 67%, and 73%, respectively. Logistic regression analysis showed that the patients with HE4 ≥473 pmol/L were less likely to achieve OD (odds ratio =5.044, P=0.002). Conclusions Preoperative serum HE4 may be helpful to predict whether optimal cytoreductive surgery could be obtained or whether extended cytoreduction would be needed by an interdisciplinary team. PMID:26157328

  4. Application of Suomi-NPP Green Vegetation Fraction and NUCAPS for Improving Regional Numerical Weather Prediction

    NASA Technical Reports Server (NTRS)

    Case, Jonathan L.; Berndt, Emily B.; Srikishen, Jayanthi; Zavodsky, Bradley T.

    2014-01-01

    The NASA SPoRT Center is working to incorporate Suomi-NPP products into its research and transition activities to improve regional numerical weather prediction (NWP). Specifically, SPoRT seeks to utilize two data products from NOAA/NESDIS: (1) daily global VIIRS green vegetation fraction (GVF), and (2) NOAA Unique CrIS and ATMS Processing System (NUCAPS) temperature and moisture retrieved profiles. The goal of (1) is to improve the representation of vegetation in the Noah land surface model (LSM) over existing climatological GVF datasets in order to improve the land-atmosphere energy exchanges in NWP models and produce better temperature, moisture, and precipitation forecasts. The goal of (2) is to assimilate NUCAPS retrieved profiles into the Gridpoint Statistical Interpolation (GSI) data assimilation system to assess the impact on a summer pre-frontal convection case. Most regional NWP applications make use of a monthly GVF climatology for use in the Noah LSM within the Weather Research and Forecasting (WRF) model. The GVF partitions incoming energy into direct surface heating/evaporation over bare soil versus evapotranspiration processes over vegetated surfaces. Misrepresentations of the fractional coverage of vegetation during anomalous weather/climate regimes (e.g., early/late bloom or freeze; drought) can lead to poor NWP model results when land-atmosphere feedback is important. SPoRT has been producing a daily MODIS GVF product based on the University of Wisconsin Direct Broadcast swaths of Normalized Difference Vegetation Index (NDVI). While positive impacts have been demonstrated in the WRF model for some cases, the reflectances composing these NDVI do not correct for atmospheric aerosols nor satellite view angle, resulting in temporal noisiness at certain locations (especially heavy vegetation). The method behind the NESDIS VIIRS GVF is expected to alleviate the issues seen in the MODIS GVF real-time product, thereby offering a higher-quality dataset for

  5. A model to predict deflection of bevel-tipped active needle advancing in soft tissue.

    PubMed

    Datla, Naresh V; Konh, Bardia; Honarvar, Mohammad; Podder, Tarun K; Dicker, Adam P; Yu, Yan; Hutapea, Parsaoran

    2014-03-01

    Active needles are recently being developed to improve steerability and placement accuracy for various medical applications. These active needles can bend during insertion by actuators attached to their bodies. The bending of active needles enables them to be steered away from the critical organs on the way to target and accurately reach target locations previously unachievable with conventional rigid needles. These active needles combined with an asymmetric bevel-tip can further improve their steerability. To optimize the design and to develop accurate path planning and control algorithms, there is a need to develop a tissue-needle interaction model. This work presents an energy-based model that predicts needle deflection of active bevel-tipped needles when inserted into the tissue. This current model was based on an existing energy-based model for bevel-tipped needles, to which work of actuation was included in calculating the system energy. The developed model was validated with needle insertion experiments with a phantom material. The model predicts needle deflection reasonably for higher diameter needles (11.6% error), whereas largest error was observed for the smallest needle diameter (24.7% error). PMID:24296105

  6. MicroRNA-31 Emerges as a Predictive Biomarker of Pathological Response and Outcome in Locally Advanced Rectal Cancer.

    PubMed

    Caramés, Cristina; Cristobal, Ion; Moreno, Víctor; Marín, Juan P; González-Alonso, Paula; Torrejón, Blanca; Minguez, Pablo; Leon, Ana; Martín, José I; Hernández, Roberto; Pedregal, Manuel; Martín, María J; Cortés, Delia; García-Olmo, Damian; Fernández, María J; Rojo, Federico; García-Foncillas, Jesús

    2016-01-01

    Neoadjuvant chemoradiotherapy (CRT) followed by total mesorectal excision has emerged as the standard treatment for locally advanced rectal cancer (LARC) patients. However, many cases do not respond to neoadjuvant CRT, suffering unnecessary toxicities and surgery delays. Thus, identification of predictive biomarkers for neoadjuvant CRT is a current clinical need. In the present study, microRNA-31 expression was measured in formalin-fixed paraffin-embedded (FFPE) biopsies from 78 patients diagnosed with LARC who were treated with neoadjuvant CRT. Then, the obtained results were correlated with clinical and pathological characteristics and outcome. High microRNA-31 (miR-31) levels were found overexpressed in 34.2% of cases. Its overexpression significantly predicted poor pathological response (p = 0.018) and worse overall survival (OS) (p = 0.008). The odds ratio for no pathological response among patients with miR-31 overexpression was 0.18 (Confidence Interval = 0.06 to 0.57; p = 0.003). Multivariate analysis corroborated the clinical impact of miR-31 in determining pathological response to neoadjuvant CRT as well as OS. Altogether, miR-31 quantification emerges as a novel valuable clinical tool to predict both pathological response and outcome in LARC patients. PMID:27271609

  7. MicroRNA-31 Emerges as a Predictive Biomarker of Pathological Response and Outcome in Locally Advanced Rectal Cancer

    PubMed Central

    Caramés, Cristina; Cristobal, Ion; Moreno, Víctor; Marín, Juan P.; González-Alonso, Paula; Torrejón, Blanca; Minguez, Pablo; Leon, Ana; Martín, José I.; Hernández, Roberto; Pedregal, Manuel; Martín, María J.; Cortés, Delia; García-Olmo, Damian; Fernández, María J.; Rojo, Federico; García-Foncillas, Jesús

    2016-01-01

    Neoadjuvant chemoradiotherapy (CRT) followed by total mesorectal excision has emerged as the standard treatment for locally advanced rectal cancer (LARC) patients. However, many cases do not respond to neoadjuvant CRT, suffering unnecessary toxicities and surgery delays. Thus, identification of predictive biomarkers for neoadjuvant CRT is a current clinical need. In the present study, microRNA-31 expression was measured in formalin-fixed paraffin-embedded (FFPE) biopsies from 78 patients diagnosed with LARC who were treated with neoadjuvant CRT. Then, the obtained results were correlated with clinical and pathological characteristics and outcome. High microRNA-31 (miR-31) levels were found overexpressed in 34.2% of cases. Its overexpression significantly predicted poor pathological response (p = 0.018) and worse overall survival (OS) (p = 0.008). The odds ratio for no pathological response among patients with miR-31 overexpression was 0.18 (Confidence Interval = 0.06 to 0.57; p = 0.003). Multivariate analysis corroborated the clinical impact of miR-31 in determining pathological response to neoadjuvant CRT as well as OS. Altogether, miR-31 quantification emerges as a novel valuable clinical tool to predict both pathological response and outcome in LARC patients. PMID:27271609

  8. Detailed regional predictions of N2O and NO emissions from a tropical highland rainforest

    NASA Astrophysics Data System (ADS)

    Gharahi Ghehi, N.; Werner, C.; Hufkens, K.; Kiese, R.; Van Ranst, E.; Nsabimana, D.; Wallin, G.; Klemedtsson, L.; Butterbach-Bahl, K.; Boeckx, P.

    2013-01-01

    Tropical forest soils are a significant source for the greenhouse gas N2O as well as for NO, a precursor of tropospheric ozone. However, current estimates are uncertain due to the limited number of field measurements. Furthermore, there is considerable spatial and temporal variability of N2O and NO emissions due to the variation of environmental conditions such as soil properties, vegetation characteristics and meteorology. In this study we used a process-based model (ForestDNDC-tropica) to estimate N2O and NO emissions from tropical highland forest (Nyungwe) soils in southwestern Rwanda. To extend the model inputs to regional scale, ForestDNDC-tropica was linked to an exceptionally large legacy soil dataset. There was agreement between N2O and NO measurements and the model predictions though the ForestDNDC-tropica resulted in considerable lower emissions for few sites. Low similarity was specifically found for acidic soil with high clay content and reduced metals, indicating that chemo-denitrification processes on acidic soils might be under-represented in the current ForestDNDC-tropica model. The results showed that soil bulk density and pH are the most influential factors driving spatial variations in soil N2O and NO emissions for tropical forest soils. The area investigated (1113 km2) was estimated to emit ca. 439 ± 50 t N2O-N yr-1 (2.8-5.5 kg N2O-N ha-1 yr-1) and 244 ± 16 t NO-N yr-1 (0.8-5.1 kg N ha-1 yr-1). Consistent with less detailed studies, we confirm that tropical highland rainforest soils are a major source of atmospheric N2O and NO.

  9. Chesapeake Inundation Prediction System (CIPS): A regional prototype for a national problem

    USGS Publications Warehouse

    Stamey, B.; Smith, W.; Carey, K.; Garbin, D.; Klein, F.; Wang, Hongfang; Shen, J.; Gong, W.; Cho, J.; Forrest, D.; Friedrichs, C.; Boicourt, W.; Li, M.; Koterba, M.; King, D.; Titlow, J.; Smith, E.; Siebers, A.; Billet, J.; Lee, J.; Manning, Douglas R.; Szatkowski, G.; Wilson, D.; Ahnert, P.; Ostrowski, J.

    2007-01-01

    Recent Hurricanes Katrina and Isabel, among others, not only demonstrated their immense destructive power, but also revealed the obvious, crucial need for improved storm surge forecasting and information delivery to save lives and property in future storms. Current operational methods and the storm surge and inundation products do not adequately meet requirements needed by Emergency Managers (EMs) at local, state, and federal levels to protect and inform our citizens. The Chesapeake Bay Inundation Prediction System (CIPS) is being developed to improve the accuracy, reliability, and capability of flooding forecasts for tropical cyclones and non-tropical wind systems such as nor'easters by modeling and visualizing expected on-land storm-surge inundation along the Chesapeake Bay and its tributaries. An initial prototype has been developed by a team of government, academic and industry partners through the Chesapeake Bay Observing System (CBOS) of the Mid-Atlantic Coastal Ocean Observing Regional Association (MACOORA) within the Integrated Ocean Observing System (IOOS). For demonstration purposes, this initial prototype was developed for the tidal Potomac River in the Washington, DC metropolitan area. The preliminary information from this prototype shows great potential as a mechanism by which NOAA National Weather Service (NWS) Forecast Offices (WFOs) can provide more specific and timely forecasts of likely inundation in individual localities from significant storm surge events. This prototype system has shown the potential to indicate flooding at the street level, at time intervals of an hour or less, and with vertical resolution of one foot or less. This information will significantly improve the ability of EMs and first responders to mitigate life and property loss and improve evacuation capabilities in individual communities. This paper provides an update and expansion of the initial prototype that was presented at the Oceans 2006 MTS/IEEE Conference in Boston, MA

  10. Voluntary Ethanol Intake Predicts κ-Opioid Receptor Supersensitivity and Regionally Distinct Dopaminergic Adaptations in Macaques

    PubMed Central

    Siciliano, Cody A.; Calipari, Erin S.; Cuzon Carlson, Verginia C.; Helms, Christa M.; Lovinger, David M.; Grant, Kathleen A.

    2015-01-01

    The dopaminergic projections from the ventral midbrain to the striatum have long been implicated in mediating motivated behaviors and addiction. Previously it was demonstrated that κ-opioid receptor (KOR) signaling in the striatum plays a critical role in the increased reinforcing efficacy of ethanol following ethanol vapor exposure in rodent models. Although rodents have been used extensively to determine the neurochemical consequences of chronic ethanol exposure, establishing high levels of voluntary drinking in these models has proven difficult. Conversely, nonhuman primates exhibit similar intake and pattern to humans in regard to drinking. Here we examine the effects of chronic voluntary ethanol self-administration on dopamine neurotransmission and the ability of KORs to regulate dopamine release in the dorsolateral caudate (DLC) and nucleus accumbens (NAc) core. Using voltammetry in brain slices from cynomolgus macaques after 6 months of ad libitum ethanol drinking, we found increased KOR sensitivity in both the DLC and NAc. The magnitude of ethanol intake predicted increases in KOR sensitivity in the NAc core, but not the DLC. Additionally, ethanol drinking increased dopamine release and uptake in the NAc, but decreased both of these measures in the DLC. These data suggest that chronic daily drinking may result in regionally distinct disruptions of striatal outputs. In concert with previous reports showing increased KOR regulation of drinking behaviors induced by ethanol exposure, the strong relationship between KOR activity and voluntary ethanol intake observed here gives further support to the hypothesis that KORs may provide a promising pharmacotherapeutic target in the treatment of alcoholism. PMID:25878269

  11. Towards a unified modeling system of predicting the transport of radionuclides in coastal sea regions

    NASA Astrophysics Data System (ADS)

    Jung, Kyung Tae; Brovchenko, Igor; Maderich, Vladimir; Kim, Kyeong Ok; Qiao, Fangli

    2016-04-01

    We present in this talk a recent progress in developing a unified modeling system of predicting three-dimensional transport of radionuclides coupled with multiple-scale circulation, wave and suspended sediment modules, keeping in mind the application to coastal sea regions with non-uniform distribution of suspended and bed sediments of both cohesive and non-cohesive types. The model calculates the concentration fields of dissolved and particulate radionuclides in bottom sediment as well as in water column. The transfer of radioactivity between the water column and the pore water in the upper layer of the bottom sediment is governed by diffusion processes. The phase change between dissolved and particulate radionuclides is written in terms of absorption/desorption rates and distribution coefficients. The dependence of distribution coefficients is inversely proportional to the sediment particle size. The hydrodynamic numerical model SELFE that solves equations for the multiple-scale circulation, the wave action and sand transport on the unstructured grids has been used as a base model. We have extended the non-cohesive sediment module of SELFE to the form applicable to mixture of cohesive and non-cohesive sedimentary regimes by implementing an extended form of erosional rate and a flocculation model for the determination of settling velocity of cohesive flocs. Issues related to the calibration of the sediment transport model in the Yellow Sea are described. The radionuclide transport model with one-step transfer kinetics and single bed layer has been initially developed and then applied to Fukushima Daiichi nuclear accident. The model has been in this study verified through the comparison with measurements of 137Cs concentration in bed sediments. Preliminary application to the Yellow and East China Seas with a hypothetical release scenario are described. On-going development of the radionuclide transport model using two-step transfer kinetics and multiple bed layers

  12. Comparison of Predicted and Measured Soil Retention Curve in Lombardy Region Northern of Italy

    NASA Astrophysics Data System (ADS)

    Wassar, Fatma; Rienzner, Michele; Chiaradia, Enrico Antonio; Gandolfi, Claudio

    2013-04-01

    Water retention characteristics are crucial input parameters in any modeling study on water flow and solute transport. These properties are difficult to measure and therefore the use of both direct and indirect methods is required in order to adequately describe them with sufficient accuracy. Several field methods, laboratory methods and theoretical models for such determinations exist, each having their own limitations and advantages (Stephens, 1994). Therefore, extensive comparisons between estimated, field and laboratory results to determine it still requires their validity for a range of different soils and specific cases. This study attempts to make a contribution specifically in this connection. The soil water retention characteristics were determined in two representative sites (PMI-1 and PMI-5) located in Landriano field, in Lombardy region, northern Italy. In the laboratory, values of both volumetric water content (θ) and soil water matric potential (h) are measured in the same sample using the tensiometric box and pressure plate apparatus. Field determination of soil water retention involved measurements of soil water content with SENTEK probes, and matric potential with tensiometers. The retention curve characteristics were also determined using some of the most commonly cited and some recently developed PTFs that use soil properties such as particle-size distribution (sand, silt, and clay content), organic matter or organic Carbon content, and dry bulk density. Field methods are considered to be more representative than laboratory and estimation methods for determining water retention characteristics (Marion et al., 1996). Therefore, field retention curves were compared against retention curves obtained from laboratory measurements and PTFs estimations. The performances of laboratory and PTFs in predicting field measured data were evaluated using root mean square error (RMSE) and bias. The comparison showed that laboratory measurements were the most

  13. A complex regional intervention to implement advance care planning in one town's nursing homes: Protocol of a controlled inter-regional study

    PubMed Central

    2011-01-01

    Background Advance Care Planning (ACP) is an emerging strategy to ensure that well-reflected, meaningful and clearly documented treatment preferences are available and respected when critical decisions about life-sustaining treatment need to be made for patients unable to consent. In Germany, recent legislation confirms that advance directives (AD) have to be followed if they apply to the medical situation, but implementation of ACP has not yet been described. Methods/Design In a longitudinal controlled study, we compare 1 intervention region (4 nursing homes [n/hs], altogether 421 residents) with 2 control regions (10 n/hs, altogether 985 residents). Inclusion went from 01.02.09 to 30.06.09, observation lasted until 30.06.10. Primary endpoint is the prevalence of ADs at follow-up, 17 (12) months after the first (last) possible inclusion. Secondary endpoints compare relevance and validity of ADs, process quality, the rate of life-sustaining interventions and, in deceased residents, location of death and intensity of treatment before death. The regional multifaceted intervention on the basis of the US program Respecting Choices® comprises training of n/h staff as facilitators, training of General Practitioners, education of hospital and ambulance staff, and development of eligible tools, including Physician Orders for Life-Sustaining Treatment in case of Emergency (POLST-E). Participation data: Of 1406 residents reported to live in the 14 n/hs plus an estimated turnover of 176 residents until the last possible inclusion date, 645 (41%) were willing to participate. Response rates were 38% in the intervention region and 42% in the control region. Non-responder analysis shows an equal distribution of sex and age but a bias towards dependency on nursing care in the responder group. Outcome analysis of this study will become available in the course of 2011. Discussion Implementing an ACP program for the n/hs and related health care providers of a region requires a

  14. Next generation aerosol-cloud microphysics for advanced high-resolution climate predictions

    SciTech Connect

    Bennartz, Ralf; Hamilton, Kevin P; Phillips, Vaughan T.J.; Wang, Yuqing; Brenguier, Jean-Louis

    2013-01-14

    The three top-level project goals are: -We proposed to develop, test, and run a new, physically based, scale-independent microphysical scheme for those cloud processes that most strongly affect greenhouse gas scenarios, i.e. warm cloud microphysics. In particular, we propsed to address cloud droplet activation, autoconversion, and accretion. -The new, unified scheme was proposed to be derived and tested using the University of Hawaii's IPRC Regional Atmospheric Model (iRAM). -The impact of the new parameterizations on climate change scenarios will be studied. In particular, the sensitivity of cloud response to climate forcing from increased greenhouse gas concentrations will be assessed.

  15. Correlation of predicted and measured thermal stresses on an advanced aircraft structure with similar materials

    NASA Technical Reports Server (NTRS)

    Jenkins, J. M.

    1979-01-01

    A laboratory heating test simulating hypersonic heating was conducted on a heat-sink type structure to provide basic thermal stress measurements. Six NASTRAN models utilizing various combinations of bar, shear panel, membrane, and plate elements were used to develop calculated thermal stresses. Thermal stresses were also calculated using a beam model. For a given temperature distribution there was very little variation in NASTRAN calculated thermal stresses when element types were interchanged for a given grid system. Thermal stresses calculated for the beam model compared similarly to the values obtained for the NASTRAN models. Calculated thermal stresses compared generally well to laboratory measured thermal stresses. A discrepancy of signifiance occurred between the measured and predicted thermal stresses in the skin areas. A minor anomaly in the laboratory skin heating uniformity resulted in inadequate temperature input data for the structural models.

  16. Recent advances in squamous non-small cell lung cancer: evidence beyond predictive biomarkers.

    PubMed

    Genova, Carlo; Rijavec, Erika; Grossi, Francesco

    2016-01-01

    Squamous non-small cell lung cancer (NSCLC) has always been characterized by a limited number of therapeutic options and by the lack of actionable biomarkers compared to its non-squamous counterpart. Recent clinical trials have led to the approval of new anti-neoplastic drugs available to both non-squamous and squamous NSCLC, consisting in a vascular-disrupting agent and two immune check-point inhibitors; additionally, a monoclonal antibody targeting the epidermal growth factor receptor (EGFR) is currently under evaluation by the Food and Drug Administration (FDA). While predictive molecular biomarkers have not been identified with consistency and are still highly demanded, these agents proved themselves noteworthy and can be considered a powerful addition to the available treatments for squamous NSCLC. PMID:26567561

  17. Advances in CFD prediction of shock wave turbulent boundary layer interactions

    NASA Astrophysics Data System (ADS)

    Knight, Doyle; Yan, Hong; Panaras, Argyris G.; Zheltovodov, Alexander

    2003-04-01

    The paper presents a summary of recent computational fluid dynamics (CFD) simulations of shock wave turbulent boundary layer interactions. This survey was prepared as part of the activity of NATO RTO Working Group 10 which was established in December 1998, and considers results obtained subsequent to the previous survey paper on the same topic by Knight and Degrez (“Shock Wave Boundary Layer Interactions in High Mach Number Flows-A Critical Survey of Current CFD Prediction Capabilities”, AGARD Advisory Report AR-319, Volume II, December 1998). Five configurations are considered: 2-D compression corner, 2-D shock impingement, 2-D expansion-compression corner, 3-D single fin and 3-D double fin. Recent direct numerical simulations (DNS), large eddy simulations (LES) and Reynolds-averaged Navier-Stokes (RANS) simulations are compared with experiment. The capabilities and limitations are described, and future research needs identified.

  18. Recovery Act. Development and Validation of an Advanced Stimulation Prediction Model for Enhanced Geothermal Systems

    SciTech Connect

    Gutierrez, Marte

    2013-12-31

    This research project aims to develop and validate an advanced computer model that can be used in the planning and design of stimulation techniques to create engineered reservoirs for Enhanced Geothermal Systems. The specific objectives of the proposal are to; Develop a true three-dimensional hydro-thermal fracturing simulator that is particularly suited for EGS reservoir creation; Perform laboratory scale model tests of hydraulic fracturing and proppant flow/transport using a polyaxial loading device, and use the laboratory results to test and validate the 3D simulator; Perform discrete element/particulate modeling of proppant transport in hydraulic fractures, and use the results to improve understand of proppant flow and transport; Test and validate the 3D hydro-thermal fracturing simulator against case histories of EGS energy production; and Develop a plan to commercialize the 3D fracturing and proppant flow/transport simulator. The project is expected to yield several specific results and benefits. Major technical products from the proposal include; A true-3D hydro-thermal fracturing computer code that is particularly suited to EGS; Documented results of scale model tests on hydro-thermal fracturing and fracture propping in an analogue crystalline rock; Documented procedures and results of discrete element/particulate modeling of flow and transport of proppants for EGS applications; and Database of monitoring data, with focus of Acoustic Emissions (AE) from lab scale modeling and field case histories of EGS reservoir creation.

  19. Forecasting phenological responses to climate change: Using hierarchical models to bridge local processes and regional predictions (Invited)

    NASA Astrophysics Data System (ADS)

    Diez, J.; Ibanez, I.

    2010-12-01

    Species’ phenological responses to climate change have large implications for future species distributions, trophic interactions, and ecosystem processes. Analyses of historical databases have shown that these responses are often species-specific and spatially variable. This variability makes predicting future responses more challenging. At the root of this challenge is the fundamental problem in ecology of how locally variable processes scale up to yield regional patterns. In this study, we show how hierarchical models of species phenological responses to climate may help address this challenge. Using long-term datasets (1953-2005) from the Japanese Meteorological Service for Morus bombysis (mulberry) at 100 sites distributed across Japan, we developed models using both monthly and daily climate data to predict bud burst dates. In both cases, hierarchical models were used to translate the different local responses among sites into more realistic predictions across the region and at unmeasured locations. The daily models represent a new approach to predicting phenology that is flexible enough to incorporate different mechanisms that may be important for some species, including forcing, chilling, photoperiod, and extreme events such as frosts. We use the daily models to show how spatial variability in bud burst dates results in part from different mechanisms being more important in different parts of the country. We compare these results to the monthly models to contrast the predictive value of the more detailed models. Our results emphasize the general utility of hierarchical models for understanding and forecasting regional changes in phenology, regardless of the specific model employed.

  20. Regional Differences in Brain Volume Predict the Acquisition of Skill in a Complex Real-Time Strategy Videogame

    ERIC Educational Resources Information Center

    Basak, Chandramallika; Voss, Michelle W.; Erickson, Kirk I.; Boot, Walter R.; Kramer, Arthur F.

    2011-01-01

    Previous studies have found that differences in brain volume among older adults predict performance in laboratory tasks of executive control, memory, and motor learning. In the present study we asked whether regional differences in brain volume as assessed by the application of a voxel-based morphometry technique on high resolution MRI would also…

  1. EVALUATING THE PERFORMANCE OF REGIONAL-SCALE PHOTOCHEMICAL MODELING SYSTEMS: PART I--METEOROLOGICAL PREDICTIONS. (R825260)

    EPA Science Inventory

    In this study, the concept of scale analysis is applied to evaluate two state-of-science meteorological models, namely MM5 and RAMS3b, currently being used to drive regional-scale air quality models. To this end, seasonal time series of observations and predictions for temperatur...

  2. The effect of binary evolution on the theoretically predicted distribution of WR and O-type stars in starburst regions and in abruptly-terminated star formation regions.

    NASA Astrophysics Data System (ADS)

    Vanbeveren, D.; van Bever, J.; De Donder, E.

    1997-01-01

    We first discuss in detail the massive close binary evolutionary model and how it has to be used in a population number synthesis study. We account for the evolution of case A, case B and case C systems, the effect of stellar wind during core hydrogen burning, hydrogen shell burning, the red supergiant phase and the WR phase, the effect of common envelope evolution in binaries with large periods, the consequences of spiral-in in binaries with small mass ratio, the effect of an asymmetric supernova explosion on binary system parameters using recent studies of pulsar velocities, the evolution of binaries with a compact companion. The parameters entering the population model where close binaries are included, are constrained by comparing predictions and observations of the massive star content in regions of continuous star formation. We then critically investigate the influence of massive close binary evolution on the variation of the massive star content in starburst regions. We separately consider regions where, after a long period of continuous star formation, the star formation rate decreases sharply (we propose to call this an abruptly-terminated star formation region) and we show that also in these regions WR/O number ratios are reached which are significantly larger than in regions of continuous star formation. The most important conclusion of the study is that within our present knowledge of observations of massive stars, massive close binary evolution plays an ESSENTIAL role in the evolution of starbursts and abruptly-terminated star formation regions.

  3. Ionospheric Variations in the Region of the Equatorial Ionization Anomaly Crest: Comparison Between Observations and IRI-2012 Model Predictions.

    NASA Astrophysics Data System (ADS)

    Oyeyemi, E. O.; Bolaji, S.; Adewale, A. O.; Akala, A. O.; Oladipo, O. A.; Olugbon, B.; Olawepo, O. A.; Adeniyi, J. O.; Adimula, I.

    2015-12-01

    The objective of this work is to study the variations of the F2-layer critical frequency (foF2) in the region of equatorial ionization anomaly crest and check the accuracy of International Reference Ionosphere (IRI-2012) model predictions using ionosonde measurements from a number of stations in this region. We have used data, based on availability, corresponding to different seasonal and solar activity periods from each station considered to carry out our investigations. Details of the statistical analysis using percentage deviation (PD), upper and lower inter-quartile range (IQR) and relative deviation module mean (RDMM) for the evaluation of the IRI model performance are presented. The results show that, generally, the IRI model predictions have agreement with the observed values in terms of the pattern of variations but there are number of cases where IRI model overestimates and underestimates the observed values. Results from this study will be of help to improving prediction ability of the IRI models.

  4. Advancements in near real time mapping of earthquake and rainfall induced landslides in the Avcilar Peninsula, Marmara Region

    NASA Astrophysics Data System (ADS)

    Coccia, Stella

    2014-05-01

    Stella COCCIA (1), Fiona THEOLEYRE (1), Pascal BIGARRE(1) , Semih ERGINTAV(2), Oguz OZEL(3) and Serdar ÖZALAYBEY(4) (1) National Institute of Industrial Environment and Risks (INERIS) Nancy, France, (2) Kandilli Observatory and Earthquake Research Institute (KOERI), Istanbul, Turkey, (3) Istanbul University (IU), Istanbul, Turkey, (4) TUBITAK MAM, Istanbul, Turkey The European Project MARsite (http://marsite.eu/), started in 2012 and leaded by the KOERI, aims to improve seismic risk evaluation and preparedness to face the next dreadful large event expected for the next three decades. MARsite is thus expected to move a "step forward" the most advanced monitoring technologies, and offering promising open databases to the worldwide scientific community in the frame of other European environmental large-scale infrastructures, such as EPOS (http://www.epos-eu.org/ ). Among the 11 work packages (WP), the main aim of the WP6 is to study seismically-induced landslide hazard, by using and improving observing and monitoring systems in geological, hydrogeotechnical and seismic onshore and offshore areas. One of the WP6 specific study area is the Avcilar Peninsula, situated between Kucukcekmece and Buyukcekmece Lakes in the north-west of the region of Marmara. There, more than 400 landslides are located. According to geological and geotechnical investigations and studies, soil movements of this area are related to underground water and pore pressure changes, seismic forces arising after earthquakes and decreasing sliding strength in fissured and heavily consolidated clays. The WP6 includes various tasks and one of these works on a methodology to develop a dynamic system to create combined earthquake and rainfall induced landslides hazard maps at near real time and automatically. This innovative system could be used to improve the prevention strategy as well as in disaster management and relief operations. Base on literature review a dynamic GIS platform is used to combine

  5. When Local Extinction and Colonization of River Fishes Can Be Predicted by Regional Occupancy: the Role of Spatial Scales

    PubMed Central

    Bergerot, Benjamin; Hugueny, Bernard; Belliard, Jérôme

    2013-01-01

    Background Predicting which species are likely to go extinct is perhaps one of the most fundamental yet challenging tasks for conservation biologists. This is particularly relevant for freshwater ecosystems which tend to have the highest proportion of species threatened with extinction. According to metapopulation theories, local extinction and colonization rates of freshwater subpopulations can depend on the degree of regional occupancy, notably due to rescue effects. However, relationships between extinction, colonization, regional occupancy and the spatial scales at which they operate are currently poorly known. Methods And Findings: We used a large dataset of freshwater fish annual censuses in 325 stream reaches to analyse how annual extinction/colonization rates of subpopulations depend on the regional occupancy of species. For this purpose, we modelled the regional occupancy of 34 fish species over the whole French river network and we tested how extinction/colonization rates could be predicted by regional occupancy described at five nested spatial scales. Results show that extinction and colonization rates depend on regional occupancy, revealing existence a rescue effect. We also find that these effects are scale dependent and their absolute contribution to colonization and extinction tends to decrease from river section to larger basin scales. Conclusions In terms of management, we show that regional occupancy quantification allows the evaluation of local species extinction/colonization dynamics and reduction of local extinction risks for freshwater fish species implies the preservation of suitable habitats at both local and drainage basin scales. PMID:24367636

  6. Fresnel-region fields and antenna noise-temperature calculations for advanced microwave sounding units

    NASA Technical Reports Server (NTRS)

    Schmidt, R. F.

    1982-01-01

    A transition from the antenna noise temperature formulation for extended noise sources in the far-field or Fraunhofer-region of an antenna to one of the intermediate near field or Fresnel-region is discussed. The effort is directed toward microwave antenna simulations and high-speed digital computer analysis of radiometric sounding units used to obtain water vapor and temperature profiles of the atmosphere. Fresnel-region fields are compared at various distances from the aperture. The antenna noise temperature contribution of an annular noise source is computed in the Fresnel-region (D squared/16 lambda) for a 13.2 cm diameter offset-paraboloid aperture at 60 GHz. The time-average Poynting vector is used to effect the computation.

  7. Global isoscapes for δ18O and δ2H in precipitation: improved prediction using regionalized climatic regression models

    NASA Astrophysics Data System (ADS)

    Terzer, S.; Wassenaar, L. I.; Araguás-Araguás, L. J.; Aggarwal, P. K.

    2013-11-01

    A regionalized cluster-based water isotope prediction (RCWIP) approach, based on the Global Network of Isotopes in Precipitation (GNIP), was demonstrated for the purposes of predicting point- and large-scale spatio-temporal patterns of the stable isotope composition (δ2H, δ18O) of precipitation around the world. Unlike earlier global domain and fixed regressor models, RCWIP predefined 36 climatic cluster domains and tested all model combinations from an array of climatic and spatial regressor variables to obtain the best predictive approach to each cluster domain, as indicated by root-mean-squared error (RMSE) and variogram analysis. Fuzzy membership fractions were thereafter used as the weights to seamlessly amalgamate results of the optimized climatic zone prediction models into a single predictive mapping product, such as global or regional amount-weighted mean annual, mean monthly, or growing-season δ18O/δ2H in precipitation. Comparative tests revealed the RCWIP approach outperformed classical global-fixed regression-interpolation-based models more than 67% of the time, and clearly improved upon predictive accuracy and precision. All RCWIP isotope mapping products are available as gridded GeoTIFF files from the IAEA website (www.iaea.org/water) and are for use in hydrology, climatology, food authenticity, ecology, and forensics.

  8. Global isoscapes for δ18O and δ2H in precipitation: improved prediction using regionalized climatic regression models

    NASA Astrophysics Data System (ADS)

    Terzer, S.; Wassenaar, L. I.; Araguás-Araguás, L. J.; Aggarwal, P. K.

    2013-06-01

    A Regionalized Climatic Water Isotope Prediction (RCWIP) approach, based on the Global Network for Isotopes in Precipitation (GNIP), was demonstrated for the purposes of predicting point- and large-scale spatiotemporal patterns of the stable isotope compositions of water (δ2H, δ18O) in precipitation around the world. Unlike earlier global domain and fixed regressor models, RCWIP pre-defined thirty-six climatic cluster domains, and tested all model combinations from an array of climatic and spatial regressor variables to obtain the best predictive approach to each cluster domain, as indicated by RMSE and variogram analysis. Fuzzy membership fractions were thereafter used as the weights to seamlessly amalgamate results of the optimized climatic zone prediction models into a single predictive mapping product, such as global or regional amount-weighted mean annual, mean monthly or growing-season δ18O/δ2H in precipitation. Comparative tests revealed the RCWIP approach outperformed classical global-fixed regression-interpolation based models more than 67% of the time, and significantly improved upon predictive accuracy and precision. All RCWIP isotope mapping products are available as gridded GeoTIFF files from the IAEA website (www.iaea.org/water) and are for use in hydrology, climatology, food authenticity, ecology, and forensics.

  9. Downscaling and predictability of historical monthly mean surface winds over a region of complex terrain and marine influence: Western Canada

    NASA Astrophysics Data System (ADS)

    Curry, C.; van der Kamp, D.; Monahan, A. H.

    2010-12-01

    Surface wind is a vector quantity exhibiting high spatial and temporal variability. Consequently, it presents a challenge for methods of statistical downscaling, which are used to establish a relationship between the large-scale atmospheric flow (predictors) and local climate variables (predictands). Simple regression-based techniques, for example, used with success for smoother predictands such as temperature, may not be as effective when applied to wind. In this work, the predictability of surface wind magnitude and direction at 28 stations in Western Canada over the period 1979-2006 was assessed using NCEP-2 reanalysis fields to derive large-scale predictors. Specifically, a combined principal components (PC) analysis was employed with the wind components at 500 hPa and mean sea level pressure as input fields, and the first 5 PCs used as predictors. The predictands were either wind speed or vector wind components oriented along directions ranging from 0 to 170 degrees at 10 degree intervals. Multiple linear regression was used for the downscaling, and its robustness assessed via cross-validation with an associated Pearson R2 value. This approach might be expected to display relatively high predictability, since it is comprised almost entirely of observations. However, our findings show that often this is not the case. Overall, wind speed was poorly predicted (R2<0.5), with the exception of a handful of stations in autumn and winter. By contrast, wind components were predicted with better skill than wind speeds at nearly all stations year-round, with the highest R2 values in autumn (SON) and lowest values in summer (JJA). The predictability of wind components was found to depend upon the topographic character of the region surrounding a given station. In mountainous regions, e.g., predictive skill was strongly related to the orientation of the components, with the best predicted components oriented along topographically significant directions such as constricted

  10. Godunov-Based Model of Swash Zone Dynamics to Advance Coastal Flood Prediction

    NASA Astrophysics Data System (ADS)

    Shakeri Majd, M.; Sanders, B. F.

    2012-12-01

    Urbanized lowlands in southern California are defended against coastal flooding by sandy beaches that dynamically adjust to changes in water level and wave conditions, particularly during storm events. Recent research has shown that coastal flood impacts are scaled by the volume of beach overtopping flows, and an improved characterization of dynamic overtopping rates is needed to improve coastal flood forecasting (Gallien et al. 2012). However, uncertainty in the beach slope and height makes it difficult to predict the onset of overtopping and the magnitude of resulting flooding. That is, beaches may evolve significantly over a storm event. Sallenger (Sallenger, 2000) describes Impact Levels to distinguish different impact regimes (swash, collision, overwash and inundation) on dunes and barrier islands. Our goal is to model processes in different regimes as was described by him. Godunov-based models adopt a depth-integrated, two-phase approach and the shallow-water hypothesis to resolve flow and sediment transport in a tightly coupled manner that resolves shocks in the air/fluid and fluid/sediment interface. These models are best known in the context of debris flow modeling where the ability to predict the flow of highly concentrated sediment/fluid mixtures is required. Here, the approach is directed at the swash zone. Existing Godunov-based models are reviewed and shown to have drawbacks relative to wetting and drying and "avalanching"—important processes in the swash zone. This nonphysical erosion can be described as the natural tendency of the schemes to smear out steep bed slopes. To denote and reduce these numerical errors, new numerical methods are presented to address these limitations and the resulting model is applied to a set of laboratory-scale test problems. The shallow-water hypothesis limits the applicability of the model to the swash zone, so it is forced by a time series of water level and cross-shore velocity that accounts for surf zone wave

  11. Advanced Procedures for Long-Term Creep Data Prediction for 2.25 Chromium Steels

    NASA Astrophysics Data System (ADS)

    Whittaker, Mark T.; Wilshire, Brian

    2013-01-01

    A critical review of recent creep studies concluded that traditional approaches such as steady-state behavior, power law equations, and the view that diffusional creep mechanisms are dominant at low stresses should be seriously reconsidered. Specifically, creep strain rate against time curves show that a decaying primary rate leads into an accelerating tertiary stage, giving a minimum rather than a secondary period. Conventional steady-state mechanisms should therefore be abandoned in favor of an understanding of the processes governing strain accumulation and the damage phenomena causing tertiary creep and fracture. Similarly, creep always takes place by dislocation processes, with no change to diffusional creep mechanisms with decreasing stress, negating the concept of deformation mechanism maps. Alternative descriptions are then provided by normalizing the applied stress through the ultimate tensile stress and yield stress at the creep temperature. In this way, the resulting Wilshire equations allow accurate prediction of 100,00 hours of creep data using only property values from tests lasting 5000 hours for a series of 2.25 chromium steels, namely grades 22, 23, and 24.

  12. The impact of transition training on adapting to Technically Advanced Aircraft at regional airlines: Perceptions of pilots and instructor pilots

    NASA Astrophysics Data System (ADS)

    di Renzo, John Carl, Jr.

    Scope and method of study. The purpose of this study was to test a hypothesis about pilot and instructor pilot perceptions of how effectively pilots learn and use new technology, found in Technically Advanced Aircraft (TAA), given initial type of instrumentation training. New aviation technologies such as Glass Cockpits in technically advanced aircraft are complex and can be difficult to learn and use. The research questions focused on the type of initial instrumentation training to determine the differences among pilots trained using various types of instrumentation ranging from aircraft equipped with traditional analog instrumentation to aircraft equipped with glass cockpits. A convenience sample of Pilots in Training (PT) and Instructor Pilots (IP) was selected from a regional airline. The research design used a mixed methodology. Pilots in training completed a thirty-two question quantitative questionnaire and instructor pilots completed a five question qualitative questionnaire. Findings and conclusions. This investigation failed to disprove the null hypothesis. The type of instrumentation training has no significant effect on newly trained regional airline pilot perceived ability to adapt to advanced technology cockpits. Therefore, no evidence exists from this investigation to support the early introduction and training of TAA. While the results of this investigation were surprising, they are nonetheless, instructive. Even though it would seem that there would be a relationship between exposure to and use of technically advanced instrumentation, apparently there was no perceived relationship for this group of airline transport pilots. However, a point of interest is that these pilots were almost evenly divided in their opinion of whether or not their previous training had prepared them for transition to TAA. The majority also believed that the type of initial instrumentation training received does make a difference when transitioning to TAA. Pilots believed

  13. The Fitness Landscape of HIV-1 Gag: Advanced Modeling Approaches and Validation of Model Predictions by In Vitro Testing

    PubMed Central

    Omarjee, Saleha; Walker, Bruce D.; Chakraborty, Arup; Ndung'u, Thumbi

    2014-01-01

    Viral immune evasion by sequence variation is a major hindrance to HIV-1 vaccine design. To address this challenge, our group has developed a computational model, rooted in physics, that aims to predict the fitness landscape of HIV-1 proteins in order to design vaccine immunogens that lead to impaired viral fitness, thus blocking viable escape routes. Here, we advance the computational models to address previous limitations, and directly test model predictions against in vitro fitness measurements of HIV-1 strains containing multiple Gag mutations. We incorporated regularization into the model fitting procedure to address finite sampling. Further, we developed a model that accounts for the specific identity of mutant amino acids (Potts model), generalizing our previous approach (Ising model) that is unable to distinguish between different mutant amino acids. Gag mutation combinations (17 pairs, 1 triple and 25 single mutations within these) predicted to be either harmful to HIV-1 viability or fitness-neutral were introduced into HIV-1 NL4-3 by site-directed mutagenesis and replication capacities of these mutants were assayed in vitro. The predicted and measured fitness of the corresponding mutants for the original Ising model (r = −0.74, p = 3.6×10−6) are strongly correlated, and this was further strengthened in the regularized Ising model (r = −0.83, p = 3.7×10−12). Performance of the Potts model (r = −0.73, p = 9.7×10−9) was similar to that of the Ising model, indicating that the binary approximation is sufficient for capturing fitness effects of common mutants at sites of low amino acid diversity. However, we show that the Potts model is expected to improve predictive power for more variable proteins. Overall, our results support the ability of the computational models to robustly predict the relative fitness of mutant viral strains, and indicate the potential value of this approach for understanding viral immune evasion

  14. Advanced prediction technique for the low speed aerodynamics of V/STOL aircraft. Volume 2: User's manual

    NASA Technical Reports Server (NTRS)

    Beatty, T. D.; Worthey, M. K.

    1984-01-01

    A computerized prediction method known as the Vought V/STOL Aircraft Propulsive Effects computer program (VAPE) for propulsive induced forces and moments in transition and Short TakeOff and Landing (STOL) flight is improved and evaluated. The VAPE program is capable of evaluating: (1) effects of relative wind about an aircraft, (2) effects of propulsive lift jet entrainment, vorticity and flow blockage, (3) effects of engine inlet flow on the aircraft flow field, (4) engine inlet forces and moments including inlet separation, (5) ground effects in the STOL region of flight, and (6) viscous effects on lifting surfaces.

  15. Executive Summary of the 2015 ISCD Position Development Conference on Advanced Measures From DXA and QCT: Fracture Prediction Beyond BMD.

    PubMed

    Shepherd, John A; Schousboe, John T; Broy, Susan B; Engelke, Klaus; Leslie, William D

    2015-01-01

    There have been many scientific advances in fracture risk prediction beyond bone density. The International Society for Clinical Densitometry (ISCD) convened a Position Development Conference (PDC) on the use of dual-energy X-ray absorptiometry beyond measurement of bone mineral density for fracture risk assessment, including trabecular bone score and hip geometry measures. Previously, no guidelines for nonbone mineral density DXA measures existed. Furthermore, there have been advances in the analysis of quantitative computed tomography (QCT) including finite element analysis, QCT of the hip, DXA-equivalent hip measurements, and opportunistic screening that were not included in the previous ISCD positions. The topics and questions for consideration were developed by the ISCD Board of Directors and the Scientific Advisory Committee and were designed to address the needs of clinical practitioners. Three task forces were created and asked to conduct comprehensive literature reviews to address specific questions. The task forces included participants from many countries and a variety of interests including academic institutions and private health care delivery organizations. Representatives from industry participated as consultants to the task forces. Task force reports with proposed position statements were then presented to an international panel of experts with backgrounds in bone densitometry. The PDC was held in Chicago, Illinois, USA, contemporaneously with the Annual Meeting of the ISCD, February 26 through February 28, 2015. This Executive Summary describes the methodology of the 2015 PDC on advanced measures from DXA and QCT and summarizes the approved official positions. Six separate articles in this issue will detail the rationale, discussion, and additional research topics for each question the task forces addressed. PMID:26277847

  16. HPV Genotypes Predict Survival Benefits From Concurrent Chemotherapy and Radiation Therapy in Advanced Squamous Cell Carcinoma of the Cervix

    SciTech Connect

    Wang, Chun-Chieh; Lai, Chyong-Huey; Huang, Yi-Ting; Chao, Angel; Chou, Hung-Hsueh; Hong, Ji-Hong

    2012-11-15

    Purpose: To study the prognostic value of human papillomavirus (HPV) genotypes in patients with advanced cervical cancer treated with radiation therapy (RT) alone or concurrent chemoradiation therapy (CCRT). Methods and Materials: Between August 1993 and May 2000, 327 patients with advanced squamous cell carcinoma of the cervix (International Federation of Gynecology and Obstetrics stage III/IVA or stage IIB with positive lymph nodes) were eligible for this study. HPV genotypes were determined using the Easychip Registered-Sign HPV genechip. Outcomes were analyzed using Kaplan-Meier survival analysis and the Cox proportional hazards model. Results: We detected 22 HPV genotypes in 323 (98.8%) patients. The leading 4 types were HPV16, 58, 18, and 33. The 5-year overall and disease-specific survival estimates for the entire cohort were 41.9% and 51.4%, respectively. CCRT improved the 5-year disease-specific survival by an absolute 9.8%, but this was not statistically significant (P=.089). There was a significant improvement in disease-specific survival in the CCRT group for HPV18-positive (60.9% vs 30.4%, P=.019) and HPV58-positive (69.3% vs 48.9%, P=.026) patients compared with the RT alone group. In contrast, the differences in survival with CCRT compared with RT alone in the HPV16-positive and HPV-33 positive subgroups were not statistically significant (P=.86 and P=.53, respectively). An improved disease-specific survival was observed for CCRT treated patients infected with both HPV16 and HPV18, but these differenced also were not statistically significant. Conclusions: The HPV genotype may be a useful predictive factor for the effect of CCRT in patients with advanced squamous cell carcinoma of the cervix. Verifying these results in prospective trials could have an impact on tailoring future treatment based on HPV genotype.

  17. The value of lactate dehydrogenase serum levels as a prognostic and predictive factor for advanced pancreatic cancer patients receiving sorafenib

    PubMed Central

    Faloppi, Luca; Bianconi, Maristella; Giampieri, Riccardo; Sobrero, Alberto; Labianca, Roberto; Ferrari, Daris; Barni, Sandro; Aitini, Enrico; Zaniboni, Alberto; Boni, Corrado; Caprioni, Francesco; Mosconi, Stefania; Fanello, Silvia; Berardi, Rossana; Bittoni, Alessandro; Andrikou, Kalliopi; Cinquini, Michela; Torri, Valter; Scartozzi, Mario; Cascinu, Stefano

    2015-01-01

    Although lactate dehydrogenase (LDH) serum levels, indirect markers of angiogenesis, are associated with a worse outcome in several tumours, their prognostic value is not defined in pancreatic cancer. Moreover, high levels are associated even with a lack of efficacy of tyrosine kinase inhibitors, contributing to explain negative results in clinical trials. We assessed the role of LDH in advanced pancreatic cancer receiving sorafenib. Seventy-one of 114 patients included in the randomised phase II trial MAPS (chemotherapy plus or not sorafenib) and with available serum LDH levels, were included in this analysis. Patients were categorized according to serum LDH levels (LDH ≤vs.> upper normal rate). A significant difference was found in progression free survival (PFS) and in overall survival (OS) between patients with LDH values under or above the cut-off (PFS: 5.2 vs. 2.7 months, p = 0.0287; OS: 10.7 vs. 5.9 months, p = 0.0021). After stratification according to LDH serum levels and sorafenib treatment, patients with low LDH serum levels treated with sorafenib showed an advantage in PFS (p = 0.05) and OS (p = 0.0012). LDH appears to be a reliable parameter to assess the prognosis of advanced pancreatic cancer patients, and it may be a predictive parameter to select patients candidate to receive sorafenib. PMID:26397228

  18. The value of lactate dehydrogenase serum levels as a prognostic and predictive factor for advanced pancreatic cancer patients receiving sorafenib.

    PubMed

    Faloppi, Luca; Bianconi, Maristella; Giampieri, Riccardo; Sobrero, Alberto; Labianca, Roberto; Ferrari, Daris; Barni, Sandro; Aitini, Enrico; Zaniboni, Alberto; Boni, Corrado; Caprioni, Francesco; Mosconi, Stefania; Fanello, Silvia; Berardi, Rossana; Bittoni, Alessandro; Andrikou, Kalliopi; Cinquini, Michela; Torri, Valter; Scartozzi, Mario; Cascinu, Stefano

    2015-10-27

    Although lactate dehydrogenase (LDH) serum levels, indirect markers of angiogenesis, are associated with a worse outcome in several tumours, their prognostic value is not defined in pancreatic cancer. Moreover, high levels are associated even with a lack of efficacy of tyrosine kinase inhibitors, contributing to explain negative results in clinical trials. We assessed the role of LDH in advanced pancreatic cancer receiving sorafenib. Seventy-one of 114 patients included in the randomised phase II trial MAPS (chemotherapy plus or not sorafenib) and with available serum LDH levels, were included in this analysis. Patients were categorized according to serum LDH levels (LDH ≤ vs.> upper normal rate). A significant difference was found in progression free survival (PFS) and in overall survival (OS) between patients with LDH values under or above the cut-off (PFS: 5.2 vs. 2.7 months, p = 0.0287; OS: 10.7 vs. 5.9 months, p = 0.0021). After stratification according to LDH serum levels and sorafenib treatment, patients with low LDH serum levels treated with sorafenib showed an advantage in PFS (p = 0.05) and OS (p = 0.0012). LDH appears to be a reliable parameter to assess the prognosis of advanced pancreatic cancer patients, and it may be a predictive parameter to select patients candidate to receive sorafenib. PMID:26397228

  19. Immunohistochemical prediction of lapatinib efficacy in advanced HER2-positive breast cancer patients

    PubMed Central

    Duchnowska, Renata; Wysocki, Piotr J.; Korski, Konstanty; Czartoryska-Arłukowicz, Bogumiła; Niwińska, Anna; Orlikowska, Marlena; Radecka, Barbara; Studziński, Maciej; Demlova, Regina; Ziółkowska, Barbara; Merdalska, Monika; Hajac, Łukasz; Myśliwiec, Paulina; Zuziak, Dorota; Dębska-Szmich, Sylwia; Lang, Istvan; Foszczyńska-Kłoda, Małgorzata; Karczmarek-Borowska, Bożenna; Żawrocki, Anton; Kowalczyk, Anna; Biernat, Wojciech; Jassem, Jacek

    2016-01-01

    Molecular mechanisms of lapatinib resistance in breast cancer are not well understood. The aim of this study was to correlate expression of selected proteins involved in ErbB family signaling pathways with clinical efficacy of lapatinib. Study group included 270 HER2-positive advanced breast cancer patients treated with lapatinib and capecitabine. Immunohistochemical expression of phosphorylated adenosine monophosphate-activated protein (p-AMPK), mitogen-activated protein kinase (p-MAPK), phospho (p)-p70S6K, cyclin E, phosphatase and tensin homolog were analyzed in primary breast cancer samples. The best discriminative value for progression-free survival (PFS) was established for each biomarker and subjected to multivariate analysis. At least one biomarker was determined in 199 patients. Expression of p-p70S6K was independently associated with longer (HR 0.45; 95% CI: 0.25–0.81; p = 0.009), and cyclin E with shorter PFS (HR 1.83; 95% CI: 1.06–3.14; p = 0.029). Expression of p-MAPK (HR 1.61; 95% CI 1.13–2.29; p = 0.009) and cyclin E (HR 2.99; 95% CI: 1.29–6.94; p = 0.011) was correlated with shorter, and expression of estrogen receptor (HR 0.65; 95% CI 0.43–0.98; p = 0.041) with longer overall survival. Expression of p-AMPK negatively impacted response to treatment (HR 3.31; 95% CI 1.48–7.44; p = 0.004) and disease control (HR 3.07; 95% CI 1.25–7.58; p = 0.015). In conclusion: the efficacy of lapatinib seems to be associated with the activity of downstream signaling pathways – AMPK/mTOR and Ras/Raf/MAPK. Further research is warranted to assess the clinical utility of these data and to determine a potential role of combining lapatinib with MAPK pathway inhibitors. PMID:26623720

  20. Predictability of Indian summer monsoon weather during active and break phases using a high resolution regional model

    NASA Astrophysics Data System (ADS)

    Taraphdar, S.; Mukhopadhyay, P.; Goswami, B. N.

    2010-11-01

    As the active and break phases of Indian monsoon are associated with different large scale background regimes, the predictability of monsoon weather is expected to be different during these phases. In the present study, an ensemble of ‘identical twin’ perturbation experiments are carried out using Weather Research Forecast model at 15 km resolution to demonstrate the predictability of weather during these phases. The initial conditions are taken from the 9 years (2001-2009) control simulations during periods of strong intra-seasonal oscillations events. The study revealed that the background estimates are different in these two contrasting regimes with more errors in the active phases confined mostly along the monsoon trough region. As a consequence, the predictability of active (break) period is found to be around 4 (10) days. Thus, the rapid (sluggish) error growth indicates that the monsoon weather such as lows are less (more) predictable during active (break) phases.

  1. Regionalized Hydrologic Parameters Estimates for a Seamless Prediction of Continental scale Water Fluxes and States

    NASA Astrophysics Data System (ADS)

    Kumar, R.; Mai, J.; Rakovec, O.; Cuntz, M.; Thober, S.; Zink, M.; Attinger, S.; Schaefer, D.; Schrön, M.; Samaniego, L. E.

    2015-12-01

    Accurate representation of water fluxes and states is crucial for hydrological assessments of societally relevant events such as floods and droughts. Hydrologic and/or land surface models are now commonly used for this purpose. The seamless prediction of continental scale water fluxes from these models requires among other things (i) a robust parameterization technique that allows the model to operate across a range of spatial resolutions and (ii) an efficient parameter estimation technique to derive a representative set of spatially consistent hydrologic parameters to avoid discontinuities of simulated hydrologic fields. In this study, we demonstrate the applicability of a mesoscale hydrologic modeling framework that incorporates a novel multiscale parameter regionalization technique (mHM-MPR) to derive the long-term gridded estimates of water fluxes and states over the Pan-EU domain. The MPR technique allows establishing linkages between hydrologic parameter fields and basin geophysical attributes (e.g., terrain, soil, vegetation properties) through a set of transfer functions and quasi-scale invariant global parameters. We devise a multi-basin parameter estimation strategy that utilizes observed streamflows from a reduced set of hydrologically diverse basins to infer a representative set of global parameters. The selection of diverse basins is guided through a stepwise clustering algorithm based on the basins geophysical and hydro-climatic attributes. Results of this strategy are contrasted against the single-basin calibration strategy across 400 European basins varying from approximately 100 km2 to 500000 km2. The single-basin parameter estimates although produced the site-specific best results, but their transferability to other basins resulted in poor performance. Initial results indicate that the multi-basin calibration strategy is at least as good as the best single-basin cross-validated results. Furthermore, the gridded fields of hydrologic parameters and

  2. Predicting gross primary production with high spatio-temporal resolution remote sensing datasets at regional scale

    NASA Astrophysics Data System (ADS)

    Fu, D.; Chen, B.

    2012-04-01

    -temporal resolution; (ii) the estimated GPP is produced by GR model using available reflectance data with high spatial resolution; (iii) the GR model's calibration process is done combined with SAFE model's pure footprint result and the observations of flux sites; (iv) the spatio-temporal distribution of GPP values at regional scale are predicted with specific parameters correspond to different ecosystem.

  3. Coupling FLEXPART to the regional scale numerical weather prediction model COSMO: Implementation, evaluation and first results

    NASA Astrophysics Data System (ADS)

    Henne, Stephan; Kaufmann, Pirmin; Schraner, Martin; Brunner, Dominik

    2013-04-01

    The Lagrangian particle dispersion model FLEXPART is a well-known and robust research tool used by many atmospheric scientists worldwide. In its standard version FLEXPART was developed for the use with global or limited area input files from the European Centre for Medium Range Weather Forecast (ECMWF). Further versions exist for input from the NCEP (National Centers for Environmental Prediction) GFS (Global Forecasting System) model and for regional scale input from the MM5 model and its successor WRF. In Europe several national weather services and research groups develop and operate the non-hydrostatic limited-area atmospheric model COSMO (Consortium for Small-scale Modeling). At MeteoSwiss COSMO is operationally run with data assimilation on two grids with approximately 7 km x 7 km and 2 km x 2 km horizontal resolution centered over Switzerland This offers the exceptional opportunity of studying atmospheric transport over complex terrain on an long-term basis. To this end, we have developed a new version of FLEXPART that is offline coupled to COSMO output (FLEXPART-COSMO hereafter) and supports output from multiple COSMO nests. The version features several new developments as compared to the standard version. Most importantly, particles are internally referenced against the native vertical coordinate system used in COSMO and not, as in standard FLEXPART, in a terrain following z-system. This eliminates the need for an additional interpolation step. A new flux deaccumulation scheme was introduced that removes the need for additional preprocessing of the input files. In addition to the existing Emmanuel based convection parameterisation, a convection parameterisation based on the Tiedtke scheme, which is identical to the one implemented in COSMO itself, was introduced. A possibility for offline nesting of a FLEXPART-COSMO run into a FLEXPART-ECMWF run for backward simulations was developed that only requires minor modifications on the FLEXPART-ECMWF version and

  4. Selecting statistical or machine learning techniques for regional landslide susceptibility modelling by evaluating spatial prediction

    NASA Astrophysics Data System (ADS)

    Goetz, Jason; Brenning, Alexander; Petschko, Helene; Leopold, Philip

    2015-04-01

    With so many techniques now available for landslide susceptibility modelling, it can be challenging to decide on which technique to apply. Generally speaking, the criteria for model selection should be tied closely to end users' purpose, which could be spatial prediction, spatial analysis or both. In our research, we focus on comparing the spatial predictive abilities of landslide susceptibility models. We illustrate how spatial cross-validation, a statistical approach for assessing spatial prediction performance, can be applied with the area under the receiver operating characteristic curve (AUROC) as a prediction measure for model comparison. Several machine learning and statistical techniques are evaluated for prediction in Lower Austria: support vector machine, random forest, bundling with penalized linear discriminant analysis, logistic regression, weights of evidence, and the generalized additive model. In addition to predictive performance, the importance of predictor variables in each model was estimated using spatial cross-validation by calculating the change in AUROC performance when variables are randomly permuted. The susceptibility modelling techniques were tested in three areas of interest in Lower Austria, which have unique geologic conditions associated with landslide occurrence. Overall, we found for the majority of comparisons that there were little practical or even statistically significant differences in AUROCs. That is the models' prediction performances were very similar. Therefore, in addition to prediction, the ability to interpret models for spatial analysis and the qualitative qualities of the prediction surface (map) are considered and discussed. The measure of variable importance provided some insight into the model behaviour for prediction, in particular for "black-box" models. However, there were no clear patterns in all areas of interest to why certain variables were given more importance over others.

  5. The role of prophylactic cranial irradiation in regionally advanced non-small cell lung cancer. A Southwest Oncology Group Study

    SciTech Connect

    Rusch, V.W.; Griffin, B.R.; Livingston, R.B. )

    1989-10-01

    Lung cancer is the most common malignant disease in the United States. Only the few tumors detected very early are curable, but there has been some progress in the management of more advanced non-small cell lung cancer, particularly in regionally inoperable disease. Prevention of central nervous system relapse is an important issue in this group of patients because brain metastases ultimately develop in 20% to 25% of them. Seventy-three patients with regionally advanced non-small cell lung cancer were entered into a Phase II trial of neutron chest radiotherapy sandwiched between four cycles of chemotherapy including cisplatin, vinblastine, and mitomycin C. Prophylactic cranial irradiation was administered concurrently with chest radiotherapy (3000 cGy in 10 fractions in 15 patients; 3600 cGy in 18 fractions in the remaining 50 patients). Patients underwent computed tomographic scan of the brain before treatment and every 3 months after treatment. The initial overall response rate was 79%, but 65 of the 73 patients have subsequently died of recurrent disease. Median follow-up is 9 months for all 73 patients and 26 months for eight long-term survivors. No patient who completed the prophylactic cranial irradiation program had clinical or radiologic brain metastases. Toxic reactions to prophylactic cranial irradiation included reversible alopecia in all patients, progressive dementia in one patient, and possible optic neuritis in one patient. Both of these patients received 300 cGy per fraction of irradiation. The use of prophylactic cranial irradiation has been controversial, but its safety and efficacy in this trial supports its application in a group of patients at high risk for central nervous system relapse. Further evaluation of prophylactic cranial irradiation in clinical trials for regionally advanced non-small cell lung cancer is warranted.

  6. GFDL's unified regional-global weather-climate modeling system with variable resolution capability for severe weather predictions and regional climate simulations

    NASA Astrophysics Data System (ADS)

    Lin, S. J.

    2015-12-01

    The NOAA/Geophysical Fluid Dynamics Laboratory has been developing a unified regional-global modeling system with variable resolution capabilities that can be used for severe weather predictions (e.g., tornado outbreak events and cat-5 hurricanes) and ultra-high-resolution (1-km) regional climate simulations within a consistent global modeling framework. The fundation of this flexible regional-global modeling system is the non-hydrostatic extension of the vertically Lagrangian dynamical core (Lin 2004, Monthly Weather Review) known in the community as FV3 (finite-volume on the cubed-sphere). Because of its flexability and computational efficiency, the FV3 is one of the final candidates of NOAA's Next Generation Global Prediction System (NGGPS). We have built into the modeling system a stretched (single) grid capability, a two-way (regional-global) multiple nested grid capability, and the combination of the stretched and two-way nests, so as to make convection-resolving regional climate simulation within a consistent global modeling system feasible using today's High Performance Computing System. One of our main scientific goals is to enable simulations of high impact weather phenomena (such as tornadoes, thunderstorms, category-5 hurricanes) within an IPCC-class climate modeling system previously regarded as impossible. In this presentation I will demonstrate that it is computationally feasible to simulate not only super-cell thunderstorms, but also the subsequent genesis of tornadoes using a global model that was originally designed for century long climate simulations. As a unified weather-climate modeling system, we evaluated the performance of the model with horizontal resolution ranging from 1 km to as low as 200 km. In particular, for downscaling studies, we have developed various tests to ensure that the large-scale circulation within the global varaible resolution system is well simulated while at the same time the small-scale can be accurately captured

  7. Continuous streamflow prediction in ungauged basins: The effects of equifinality and parameter set selection on uncertainty in regionalization approaches

    NASA Astrophysics Data System (ADS)

    Arsenault, Richard; Brissette, François P.

    2014-07-01

    This paper focuses on evaluating the uncertainty of three common regionalization methods for predicting continuous streamflow in ungauged basins. A set of 268 basins covering 1.6 million km2 in the province of Quebec was used to test the regionalization strategies. The multiple linear regression, spatial proximity, and physical similarity approaches were evaluated on the catchments using a leave-one-out cross-validation scheme. The lumped conceptual HSAMI hydrological model was used throughout the study. A bootstrapping method was chosen to further estimate uncertainty due to parameter set selection for each of the parameter set/regionalization method pairs. Results show that parameter set selection can play an important role in regionalization method performance depending on the regionalization methods (and their variants) used and that equifinality does not contribute significantly to the overall uncertainty witnessed throughout the regionalization methods applications. Regression methods fail to consistently assign behavioral parameter sets to the pseudoungauged basins (i.e., the ones left out). Spatial proximity and physical similarity score better, the latter being the best. It is also shown that combining either physical similarity or spatial proximity with the multiple linear regression method can lead to an even more successful prediction rate. However, even the best methods were shown to be unreliable to an extent, as successful prediction rates never surpass 75%. Finally, this paper shows that the selection of catchment descriptors is crucial to the regionalization strategies' performance and that for the HSAMI model, the optimal number of donor catchments for transferred parameter sets lies between four and seven.

  8. Recent Advances in Regional Climate System Modeling and ClimateChange Analyses of Extreme Heat

    SciTech Connect

    Miller, Norman L.

    2004-09-24

    During the period May 2003 to May 2004, there were two CEC/PIER funded primary research activities by the Atmosphere and Ocean Sciences Group/Earth Science Division at LBNL. These activities are the implementation and testing of the National Center for Atmospheric Research Community Land Model (CLM) into MM5, and the analysis of extreme heat days under a new set of climate simulations. The new version of MM5,MM5-CLM, has been tested for a 90 day snowmelt period in the northwestern U.S. Results show that this new code upgrade, as compared to the MM5-NOAH, has improved snowmelt, temperature, and precipitation when compared to observations. These are due in part to a subgrid scheme,advanced snow processes, and advanced vegetation. The climate change analysis is the upper and lower IPCC Special Report on Emission Scenarios, representing fossil fuel intensive and energy conserving future emission scenarios, and medium and low sensitivity Global Climate Models. Results indicate that California cities will see increases in the number of heat wave and temperature threshold days from two to six times.These results may be viewed as potential outcomes based on today's decisions on emissions.

  9. Regional Differences in Brain Volume Predict the Acquisition of Skill in a Complex Real-Time Strategy Videogame

    PubMed Central

    Basak, Chandramallika; Voss, Michelle W.; Erickson, Kirk I.; Boot, Walter R.; Kramer, Arthur F.

    2015-01-01

    Previous studies have found that differences in brain volume among older adults predict performance in laboratory tasks of executive control, memory, and motor learning. In the present study we asked whether regional differences in brain volume as assessed by the application of a voxel-based morphometry technique on high resolution MRI would also be useful in predicting the acquisition of skill in complex tasks, such as strategy-based video games. Twenty older adults were trained for over 20 hours to play Rise of Nations, a complex real-time strategy game. These adults showed substantial improvements over the training period in game performance. MRI scans obtained prior to training revealed that the volume of a number of brain regions, which have been previously associated with subsets of the trained skills, predicted a substantial amount of variance in learning on the complex game. Thus, regional differences in brain volume can predict learning in complex tasks that entail the use of a variety of perceptual, cognitive and motor processes. PMID:21546146

  10. Advances in dynamic modeling of colorectal cancer signaling-network regions, a path toward targeted therapies

    PubMed Central

    Kolch, Walter; Kholodenko, Boris N.; Ambrosi, Cristina De; Barla, Annalisa; Biganzoli, Elia M.; Nencioni, Alessio; Patrone, Franco; Ballestrero, Alberto; Zoppoli, Gabriele; Verri, Alessandro; Parodi, Silvio

    2015-01-01

    The interconnected network of pathways downstream of the TGFβ, WNT and EGF-families of receptor ligands play an important role in colorectal cancer pathogenesis. We studied and implemented dynamic simulations of multiple downstream pathways and described the section of the signaling network considered as a Molecular Interaction Map (MIM). Our simulations used Ordinary Differential Equations (ODEs), which involved 447 reactants and their interactions. Starting from an initial “physiologic condition”, the model can be adapted to simulate individual pathologic cancer conditions implementing alterations/mutations in relevant onco-proteins. We verified some salient model predictions using the mutated colorectal cancer lines HCT116 and HT29. We measured the amount of MYC and CCND1 mRNAs and AKT and ERK phosphorylated proteins, in response to individual or combination onco-protein inhibitor treatments. Experimental and simulation results were well correlated. Recent independently published results were also predicted by our model. Even in the presence of an approximate and incomplete signaling network information, a predictive dynamic modeling seems already possible. An important long term road seems to be open and can be pursued further, by incremental steps, toward even larger and better parameterized MIMs. Personalized treatment strategies with rational associations of signaling-proteins inhibitors, could become a realistic goal. PMID:25671297

  11. Using regional broccoli trial data to select experimental hybrids for input into advanced yield trials

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A large amount of phenotypic trait data are being generated in regional trials that are implemented as part of the Specialty Crop Research Initiative (SCRI) project entitled “Establishing an Eastern Broccoli Industry”. These data are used to identify the best entries in the trials for inclusion in ...

  12. Prediction models of CO, SPM and SO(2) concentrations in the Campo de Gibraltar Region, Spain: a multiple comparison strategy.

    PubMed

    Turias, Ignacio J; González, Francisco J; Martin, Ma Luz; Galindo, Pedro L

    2008-08-01

    The 'Campo de Gibraltar' region is a very industrialized area where very few air pollution studies have been carried out. Up to date, no model has been developed in order to predict air pollutant levels in the different towns spread in the region. Carbon monoxide (CO), Sulphur dioxide (SO(2)) and suspended particulate matter (SPM) series have been investigated (years 1999-2000-2001). Multilayer perceptron models (MLPs) with backpropagation learning rule have been used. A resampling strategy with two-fold crossvalidation allowed the statistical comparison of the different models considered in this study. Artificial neural networks (ANN) models were compared with Persistence and ARIMA models and also with models based on standard Multiple Linear Regression (MLR) over test sets with data that had not been used in the training stage. The models based on ANNs showed better capability of generalization than those based on MLR. The designed procedure of random resampling permits an adequate and robust multiple comparison of the tested models. Principal component analysis (PCA) is used to reduce the dimensionality of data and to transform exogenous variables into significant and independent components. Short-term predictions were better than medium-term predictions in the case of CO and SO(2) series. Conversely, medium-term predictions were better in the case of SPM concentrations. The predictions are significantly promising (e.g., d (SPM 24-ahead) = 0.906, d (CO 1-ahead) = 0.891, d (SO2 1-ahead) = 0.851). PMID:17929183

  13. Predicting ecosystem dynamics at regional scales: an evaluation of a terrestrial biosphere model for the forests of northeastern North America

    PubMed Central

    Medvigy, David; Moorcroft, Paul R.

    2012-01-01

    Terrestrial biosphere models are important tools for diagnosing both the current state of the terrestrial carbon cycle and forecasting terrestrial ecosystem responses to global change. While there are a number of ongoing assessments of the short-term predictive capabilities of terrestrial biosphere models using flux-tower measurements, to date there have been relatively few assessments of their ability to predict longer term, decadal-scale biomass dynamics. Here, we present the results of a regional-scale evaluation of the Ecosystem Demography version 2 (ED2)-structured terrestrial biosphere model, evaluating the model's predictions against forest inventory measurements for the northeast USA and Quebec from 1985 to 1995. Simulations were conducted using a default parametrization, which used parameter values from the literature, and a constrained model parametrization, which had been developed by constraining the model's predictions against 2 years of measurements from a single site, Harvard Forest (42.5° N, 72.1° W). The analysis shows that the constrained model parametrization offered marked improvements over the default model formulation, capturing large-scale variation in patterns of biomass dynamics despite marked differences in climate forcing, land-use history and species-composition across the region. These results imply that data-constrained parametrizations of structured biosphere models such as ED2 can be successfully used for regional-scale ecosystem prediction and forecasting. We also assess the model's ability to capture sub-grid scale heterogeneity in the dynamics of biomass growth and mortality of different sizes and types of trees, and then discuss the implications of these analyses for further reducing the remaining biases in the model's predictions. PMID:22144385

  14. Numerical Simulations of Optical Turbulence Using an Advanced Atmospheric Prediction Model: Implications for Adaptive Optics Design

    NASA Astrophysics Data System (ADS)

    Alliss, R.

    2014-09-01

    Optical turbulence (OT) acts to distort light in the atmosphere, degrading imagery from astronomical telescopes and reducing the data quality of optical imaging and communication links. Some of the degradation due to turbulence can be corrected by adaptive optics. However, the severity of optical turbulence, and thus the amount of correction required, is largely dependent upon the turbulence at the location of interest. Therefore, it is vital to understand the climatology of optical turbulence at such locations. In many cases, it is impractical and expensive to setup instrumentation to characterize the climatology of OT, so numerical simulations become a less expensive and convenient alternative. The strength of OT is characterized by the refractive index structure function Cn2, which in turn is used to calculate atmospheric seeing parameters. While attempts have been made to characterize Cn2 using empirical models, Cn2 can be calculated more directly from Numerical Weather Prediction (NWP) simulations using pressure, temperature, thermal stability, vertical wind shear, turbulent Prandtl number, and turbulence kinetic energy (TKE). In this work we use the Weather Research and Forecast (WRF) NWP model to generate Cn2 climatologies in the planetary boundary layer and free atmosphere, allowing for both point-to-point and ground-to-space seeing estimates of the Fried Coherence length (ro) and other seeing parameters. Simulations are performed using a multi-node linux cluster using the Intel chip architecture. The WRF model is configured to run at 1km horizontal resolution and centered on the Mauna Loa Observatory (MLO) of the Big Island. The vertical resolution varies from 25 meters in the boundary layer to 500 meters in the stratosphere. The model top is 20 km. The Mellor-Yamada-Janjic (MYJ) TKE scheme has been modified to diagnose the turbulent Prandtl number as a function of the Richardson number, following observations by Kondo and others. This modification

  15. An empirical model for probabilistic decadal prediction: global attribution and regional hindcasts

    NASA Astrophysics Data System (ADS)

    Suckling, Emma B.; van Oldenborgh, Geert Jan; Eden, Jonathan M.; Hawkins, Ed

    2016-07-01

    Empirical models, designed to predict surface variables over seasons to decades ahead, provide useful benchmarks for comparison against the performance of dynamical forecast systems; they may also be employable as predictive tools for use by climate services in their own right. A new global empirical decadal prediction system is presented, based on a multiple linear regression approach designed to produce probabilistic output for comparison against dynamical models. A global attribution is performed initially to identify the important forcing and predictor components of the model . Ensemble hindcasts of surface air temperature anomaly fields are then generated, based on the forcings and predictors identified as important, under a series of different prediction `modes' and their performance is evaluated. The modes include a real-time setting, a scenario in which future volcanic forcings are prescribed during the hindcasts, and an approach which exploits knowledge of the forced trend. A two-tier prediction system, which uses knowledge of future sea surface temperatures in the Pacific and Atlantic Oceans, is also tested, but within a perfect knowledge framework. Each mode is designed to identify sources of predictability and uncertainty, as well as investigate different approaches to the design of decadal prediction systems for operational use. It is found that the empirical model shows skill above that of persistence hindcasts for annual means at lead times of up to 10 years ahead in all of the prediction modes investigated. It is suggested that hindcasts which exploit full knowledge of the forced trend due to increasing greenhouse gases throughout the hindcast period can provide more robust estimates of model bias for the calibration of the empirical model in an operational setting. The two-tier system shows potential for improved real-time prediction, given the assumption that skilful predictions of large-scale modes of variability are available. The empirical

  16. [The role of regional intra-arterial chemotherapy in the combined treatment of locally advanced laryngeal cancer].

    PubMed

    Mashkova, T A; Ol'shanskiĭ, M S; Panchenko, I G; Ovsiannikov, Iu M; Mal'tsev, A B

    2013-01-01

    The objective of the present study was to estimate the possibilities and prospects for the use of regional intra-arterial chemotherapy in the combined treatment of locally advanced laryngeal cancer. The results of the chemoradiotherapeutic treatment of 26 patients presenting with locally advanced laryngeal cancer were analysed. The chemical agents were selectively administered intra-arterially three times from the right-hand femoral access by the standard procedure at a total focal dose (TFD) of 26 and 50 gram prior to the onset of and during of radiotherapy. The very first administration of the chemical agent resulted in the 30% decrease the tumour size. It further decreased by 70% on the average after the TFD of 50 gram was achieved. It made possible the continuation of gamma-therapy up to the total therapeutic dose. As a result, complete regression of the tumour was documented. The dynamic endoscopic control study and CT of the larynx revealed recurrent laryngeal cancer in 1 of the 26 patients (3.8%). The remaining patients did not develop metastases during the 18 month follow-up period. It is concluded that the results of the present study confirm high (96.2%) effectiveness of the method employed in this study which allows it to be recommended for the organ-preserving treatment of locally advanced laryngeal cancer. PMID:24300761

  17. Advances in high-order interaction region nonlinear optics correction at RHIC

    SciTech Connect

    Zimmer, C.; Binello, S.; Minty, M.; Pilat, F.

    2011-03-28

    A method to indirectly measure and deterministically correct the higher order magnetic errors of the final focusing magnets in the Relativistic Heavy Ion Collider has been in place for several years at BNL. This method yields control over the effects of multi-pole errors through application of closed orbit bumps followed by analysis and correction of the resulting betatron tune shifts using multi-pole correctors. The process has recently been automated in order to provide more efficient and effective corrections. The tune resolution along with the reliability of measurements has also been improved significantly due to advances/upgrades in the betatron tune measurement system employed at RHIC (BBQ). Here we describe the foundation of the IR bump method, followed by recent improvements along with experimental data.

  18. Up-front neck dissection followed by concurrent chemoradiation in patients with regionally advanced head and neck cancer

    PubMed Central

    Paximadis, Peter A.; Christensen, Michael E.; Dyson, Greg; Kamdar, Dev P.; Sukari, Ammar; Lin, Ho-Sheng; Yoo, George H.; Kim, Harold E.

    2013-01-01

    Background The appropriate management of the neck in patients with regionally advanced head and neck cancer remains controversial. The purpose of this study was to retrospectively analyze our institutional experience with up-front neck dissection followed by definitive chemoradiotherapy. Methods Fifty-five patients with radiographic evidence of large or necrotic lymph nodes underwent up-front neck dissection followed by definitive chemoradiation. Results The 5-year overall survival (OS) and progression-free survival (PFS) rates were estimated at 71.3% and 64.7%, respectively. There were 2 failures in the dissected neck, for a control rate of 96.7%. There were 7 locoregional failures and 12 distant failures, for locoregional and distant control rates of 87.3% and 78.2%, respectively. Conclusion Up-front neck dissection followed by chemoradiotherapy resulted in excellent locoregional control, OS, and PFS. Utilization of this strategy should be considered in carefully selected patients with regionally advanced head and neck cancer. PMID:22307819

  19. Using