Advanced Hepatocellular Carcinoma: Which Staging Systems Best Predict Prognosis?

PubMed Central

Huitzil-Melendez, Fidel-David; Capanu, Marinela; O'Reilly, Eileen M.; Duffy, Austin; Gansukh, Bolorsukh; Saltz, Leonard L.; Abou-Alfa, Ghassan K.

2010-01-01

Purpose The purpose of cancer staging systems is to accurately predict patient prognosis. The outcome of advanced hepatocellular carcinoma (HCC) depends on both the cancer stage and the extent of liver dysfunction. Many staging systems that include both aspects have been developed. It remains unknown, however, which of these systems is optimal for predicting patient survival. Patients and Methods Patients with advanced HCC treated over a 5-year period at Memorial Sloan-Kettering Cancer Center were identified from an electronic medical record database. Patients with sufficient data for utilization in all staging systems were included. TNM sixth edition, Okuda, Barcelona Clinic Liver Cancer (BCLC), Cancer of the Liver Italian Program (CLIP), Chinese University Prognostic Index (CUPI), Japan Integrated Staging (JIS), and Groupe d'Etude et de Traitement du Carcinome Hepatocellulaire (GETCH) systems were ranked on the basis of their accuracy at predicting survival by using concordance index (c-index). Other independent prognostic variables were also identified. Results Overall, 187 eligible patients were identified and were staged by using the seven staging systems. CLIP, CUPI, and GETCH were the three top-ranking staging systems. BCLC and TNM sixth edition lacked any meaningful prognostic discrimination. Performance status, AST, abdominal pain, and esophageal varices improved the discriminatory ability of CLIP. Conclusion In our selected patient population, CLIP, CUPI, and GETCH were the most informative staging systems in predicting survival in patients with advanced HCC. Prospective validation is required to determine if they can be accurately used to stratify patients in clinical trials and to direct the appropriate need for systemic therapy versus best supportive care. BCLC and TNM sixth edition were not helpful in predicting survival outcome, and their use is not supported by our data. PMID:20458042

Skillful regional prediction of Arctic sea ice on seasonal timescales

NASA Astrophysics Data System (ADS)

Bushuk, Mitchell; Msadek, Rym; Winton, Michael; Vecchi, Gabriel A.; Gudgel, Rich; Rosati, Anthony; Yang, Xiaosong

2017-05-01

Recent Arctic sea ice seasonal prediction efforts and forecast skill assessments have primarily focused on pan-Arctic sea ice extent (SIE). In this work, we move toward stakeholder-relevant spatial scales, investigating the regional forecast skill of Arctic sea ice in a Geophysical Fluid Dynamics Laboratory (GFDL) seasonal prediction system. Using a suite of retrospective initialized forecasts spanning 1981-2015 made with a coupled atmosphere-ocean-sea ice-land model, we show that predictions of detrended regional SIE are skillful at lead times up to 11 months. Regional prediction skill is highly region and target month dependent and generically exceeds the skill of an anomaly persistence forecast. We show for the first time that initializing the ocean subsurface in a seasonal prediction system can yield significant regional skill for winter SIE. Similarly, as suggested by previous work, we find that sea ice thickness initial conditions provide a crucial source of skill for regional summer SIE.

Pretreatment tables predicting pathologic stage of locally advanced prostate cancer.

PubMed

Joniau, Steven; Spahn, Martin; Briganti, Alberto; Gandaglia, Giorgio; Tombal, Bertrand; Tosco, Lorenzo; Marchioro, Giansilvio; Hsu, Chao-Yu; Walz, Jochen; Kneitz, Burkhard; Bader, Pia; Frohneberg, Detlef; Tizzani, Alessandro; Graefen, Markus; van Cangh, Paul; Karnes, R Jeffrey; Montorsi, Francesco; van Poppel, Hein; Gontero, Paolo

2015-02-01

Pretreatment tables for the prediction of pathologic stage have been published and validated for localized prostate cancer (PCa). No such tables are available for locally advanced (cT3a) PCa. To construct tables predicting pathologic outcome after radical prostatectomy (RP) for patients with cT3a PCa with the aim to help guide treatment decisions in clinical practice. This was a multicenter retrospective cohort study including 759 consecutive patients with cT3a PCa treated with RP between 1987 and 2010. Retropubic RP and pelvic lymphadenectomy. Patients were divided into pretreatment prostate-specific antigen (PSA) and biopsy Gleason score (GS) subgroups. These parameters were used to construct tables predicting pathologic outcome and the presence of positive lymph nodes (LNs) after RP for cT3a PCa using ordinal logistic regression. In the model predicting pathologic outcome, the main effects of biopsy GS and pretreatment PSA were significant. A higher GS and/or higher PSA level was associated with a more unfavorable pathologic outcome. The validation procedure, using a repeated split-sample method, showed good predictive ability. Regression analysis also showed an increasing probability of positive LNs with increasing PSA levels and/or higher GS. Limitations of the study are the retrospective design and the long study period. These novel tables predict pathologic stage after RP for patients with cT3a PCa based on pretreatment PSA level and biopsy GS. They can be used to guide decision making in men with locally advanced PCa. Our study might provide physicians with a useful tool to predict pathologic stage in locally advanced prostate cancer that might help select patients who may need multimodal treatment. Copyright © 2014 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Predicting Post-Harvest Performance of Advance Red Oak Reproduction in the Southern Appalachians

Treesearch

David L. Loftis

1990-01-01

Models are presented for predicting: (1) height growth of red oak advance reproduction after clearcutting, and (2) the probability of stems becoming dominants or codominants in new stands as a function of preharvest size of advance reproduction andsitequafity. The second model permits silviculturists to predict, prior to harvest, the contribution to a new stand of an...

Human mobility prediction from region functions with taxi trajectories.

PubMed

Wang, Minjie; Yang, Su; Sun, Yi; Gao, Jun

2017-01-01

People in cities nowadays suffer from increasingly severe traffic jams due to less awareness of how collective human mobility is affected by urban planning. Besides, understanding how region functions shape human mobility is critical for business planning but remains unsolved so far. This study aims to discover the association between region functions and resulting human mobility. We establish a linear regression model to predict the traffic flows of Beijing based on the input referred to as bag of POIs. By solving the predictor in the sense of sparse representation, we find that the average prediction precision is over 74% and each type of POI contributes differently in the predictor, which accounts for what factors and how such region functions attract people visiting. Based on these findings, predictive human mobility could be taken into account when planning new regions and region functions.

Human mobility prediction from region functions with taxi trajectories

PubMed Central

Wang, Minjie; Sun, Yi; Gao, Jun

2017-01-01

People in cities nowadays suffer from increasingly severe traffic jams due to less awareness of how collective human mobility is affected by urban planning. Besides, understanding how region functions shape human mobility is critical for business planning but remains unsolved so far. This study aims to discover the association between region functions and resulting human mobility. We establish a linear regression model to predict the traffic flows of Beijing based on the input referred to as bag of POIs. By solving the predictor in the sense of sparse representation, we find that the average prediction precision is over 74% and each type of POI contributes differently in the predictor, which accounts for what factors and how such region functions attract people visiting. Based on these findings, predictive human mobility could be taken into account when planning new regions and region functions. PMID:29190708

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

Random Forests for Global and Regional Crop Yield Predictions.

PubMed

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; Kim, Soo-Hyung

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.

Advancing alternatives analysis: The role of predictive toxicology in selecting safer chemical products and processes.

PubMed

Malloy, Timothy; Zaunbrecher, Virginia; Beryt, Elizabeth; Judson, Richard; Tice, Raymond; Allard, Patrick; Blake, Ann; Cote, Ila; Godwin, Hilary; Heine, Lauren; Kerzic, Patrick; Kostal, Jakub; Marchant, Gary; McPartland, Jennifer; Moran, Kelly; Nel, Andre; Ogunseitan, Oladele; Rossi, Mark; Thayer, Kristina; Tickner, Joel; Whittaker, Margaret; Zarker, Ken

2017-09-01

Alternatives analysis (AA) is a method used in regulation and product design to identify, assess, and evaluate the safety and viability of potential substitutes for hazardous chemicals. It requires toxicological data for the existing chemical and potential alternatives. Predictive toxicology uses in silico and in vitro approaches, computational models, and other tools to expedite toxicological data generation in a more cost-effective manner than traditional approaches. The present article briefly reviews the challenges associated with using predictive toxicology in regulatory AA, then presents 4 recommendations for its advancement. It recommends using case studies to advance the integration of predictive toxicology into AA, adopting a stepwise process to employing predictive toxicology in AA beginning with prioritization of chemicals of concern, leveraging existing resources to advance the integration of predictive toxicology into the practice of AA, and supporting transdisciplinary efforts. The further incorporation of predictive toxicology into AA would advance the ability of companies and regulators to select alternatives to harmful ingredients, and potentially increase the use of predictive toxicology in regulation more broadly. Integr Environ Assess Manag 2017;13:915-925. © 2017 SETAC. © 2017 SETAC.

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.

Sequence fingerprints distinguish erroneous from correct predictions of intrinsically disordered protein regions.

PubMed

Saravanan, Konda Mani; Dunker, A Keith; Krishnaswamy, Sankaran

2017-12-27

More than 60 prediction methods for intrinsically disordered proteins (IDPs) have been developed over the years, many of which are accessible on the World Wide Web. Nearly, all of these predictors give balanced accuracies in the ~65%-~80% range. Since predictors are not perfect, further studies are required to uncover the role of amino acid residues in native IDP as compared to predicted IDP regions. In the present work, we make use of sequences of 100% predicted IDP regions, false positive disorder predictions, and experimentally determined IDP regions to distinguish the characteristics of native versus predicted IDP regions. A higher occurrence of asparagine is observed in sequences of native IDP regions but not in sequences of false positive predictions of IDP regions. The occurrences of certain combinations of amino acids at the pentapeptide level provide a distinguishing feature in the IDPs with respect to globular proteins. The distinguishing features presented in this paper provide insights into the sequence fingerprints of amino acid residues in experimentally determined as compared to predicted IDP regions. These observations and additional work along these lines should enable the development of improvements in the accuracy of disorder prediction algorithm.

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

Dynamics and Predictability of The Eta Regional Model: The Role of Domain Size

NASA Astrophysics Data System (ADS)

Vannitsem, S.; Chomé, F.; Nicolis, C.

This paper investigates the dynamical properties of the Eta model, a state-of-the- art nested limited-area model, following the approach previously developed by the present authors. It is first shown that the intrinsic dynamics of the model depends crucially on the size of the domain, with a non-chaotic behavior for small domains, supporting earlier findings on the absence of sensitivity to the initial conditions in these models. The quality of the predictions of several Eta model versions differing by their domain size is next evaluated and compared with the Avn analyses on a targeted region, centered on France. Contrary to what is usually taken for granted, a non-trivial relation between predictability and domain size is found, the best model versions be- ing the ones integrated on the smallest and the largest domain sizes. An explanation in connection with the intrinsic dynamics of the model is advanced.

Early Prediction of Lupus Nephritis Using Advanced Proteomics

DTIC Science & Technology

2012-06-01

urine samples for research were obtained, and information on the following laboratory measures was collected: BUN ( urea ), serum creatinine, serum... urine chemistry), medications and other clinical outcomes (overall disease activity, renal and overall damage). Specific Aim 2: Advanced proteomic...measured by the external standards. We concluded that serial measurements of plasma and urine NGAL may be valuable in predicting impending worsening of

Using Markov chains of nucleotide sequences as a possible precursor to predict functional roles of human genome: a case study on inactive chromatin regions.

PubMed

Lee, K-E; Lee, E-J; Park, H-S

2016-08-30

Recent advances in computational epigenetics have provided new opportunities to evaluate n-gram probabilistic language models. In this paper, we describe a systematic genome-wide approach for predicting functional roles in inactive chromatin regions by using a sequence-based Markovian chromatin map of the human genome. We demonstrate that Markov chains of sequences can be used as a precursor to predict functional roles in heterochromatin regions and provide an example comparing two publicly available chromatin annotations of large-scale epigenomics projects: ENCODE project consortium and Roadmap Epigenomics consortium.

Advanced Daily Prediction Model for National Suicide Numbers with Social Media Data.

PubMed

Lee, Kyung Sang; Lee, Hyewon; Myung, Woojae; Song, Gil-Young; Lee, Kihwang; Kim, Ho; Carroll, Bernard J; Kim, Doh Kwan

2018-04-01

Suicide is a significant public health concern worldwide. Social media data have a potential role in identifying high suicide risk individuals and also in predicting suicide rate at the population level. In this study, we report an advanced daily suicide prediction model using social media data combined with economic/meteorological variables along with observed suicide data lagged by 1 week. The social media data were drawn from weblog posts. We examined a total of 10,035 social media keywords for suicide prediction. We made predictions of national suicide numbers 7 days in advance daily for 2 years, based on a daily moving 5-year prediction modeling period. Our model predicted the likely range of daily national suicide numbers with 82.9% accuracy. Among the social media variables, words denoting economic issues and mood status showed high predictive strength. Observed number of suicides one week previously, recent celebrity suicide, and day of week followed by stock index, consumer price index, and sunlight duration 7 days before the target date were notable predictors along with the social media variables. These results strengthen the case for social media data to supplement classical social/economic/climatic data in forecasting national suicide events.

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.

Advancing coastal ocean modelling, analysis, and prediction for the US Integrated Ocean Observing System

USGS Publications Warehouse

Wilkin, John L.; Rosenfeld, Leslie; Allen, Arthur; Baltes, Rebecca; Baptista, Antonio; He, Ruoying; Hogan, Patrick; Kurapov, Alexander; Mehra, Avichal; Quintrell, Josie; Schwab, David; Signell, Richard; Smith, Jane

2017-01-01

This paper outlines strategies that would advance coastal ocean modelling, analysis and prediction as a complement to the observing and data management activities of the coastal components of the US Integrated Ocean Observing System (IOOS®) and the Global Ocean Observing System (GOOS). The views presented are the consensus of a group of US-based researchers with a cross-section of coastal oceanography and ocean modelling expertise and community representation drawn from Regional and US Federal partners in IOOS. Priorities for research and development are suggested that would enhance the value of IOOS observations through model-based synthesis, deliver better model-based information products, and assist the design, evaluation, and operation of the observing system itself. The proposed priorities are: model coupling, data assimilation, nearshore processes, cyberinfrastructure and model skill assessment, modelling for observing system design, evaluation and operation, ensemble prediction, and fast predictors. Approaches are suggested to accomplish substantial progress in a 3–8-year timeframe. In addition, the group proposes steps to promote collaboration between research and operations groups in Regional Associations, US Federal Agencies, and the international ocean research community in general that would foster coordination on scientific and technical issues, and strengthen federal–academic partnerships benefiting IOOS stakeholders and end users.

Echocardiography and risk prediction in advanced heart failure: incremental value over clinical markers.

PubMed

Agha, Syed A; Kalogeropoulos, Andreas P; Shih, Jeffrey; Georgiopoulou, Vasiliki V; Giamouzis, Grigorios; Anarado, Perry; Mangalat, Deepa; Hussain, Imad; Book, Wendy; Laskar, Sonjoy; Smith, Andrew L; Martin, Randolph; Butler, Javed

2009-09-01

Incremental value of echocardiography over clinical parameters for outcome prediction in advanced heart failure (HF) is not well established. We evaluated 223 patients with advanced HF receiving optimal therapy (91.9% angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, 92.8% beta-blockers, 71.8% biventricular pacemaker, and/or defibrillator use). The Seattle Heart Failure Model (SHFM) was used as the reference clinical risk prediction scheme. The incremental value of echocardiographic parameters for event prediction (death or urgent heart transplantation) was measured by the improvement in fit and discrimination achieved by addition of standard echocardiographic parameters to the SHFM. After a median follow-up of 2.4 years, there were 38 (17.0%) events (35 deaths; 3 urgent transplants). The SHFM had likelihood ratio (LR) chi(2) 32.0 and C statistic 0.756 for event prediction. Left ventricular end-systolic volume, stroke volume, and severe tricuspid regurgitation were independent echocardiographic predictors of events. The addition of these parameters to SHFM improved LR chi(2) to 72.0 and C statistic to 0.866 (P < .001 and P=.019, respectively). Reclassifying the SHFM-predicted risk with use of the echocardiography-added model resulted in improved prognostic separation. Addition of standard echocardiographic variables to the SHFM results in significant improvement in risk prediction for patients with advanced HF.

Impact of Cloud Analysis on Numerical Weather Prediction in the Galician Region of Spain.

NASA Astrophysics Data System (ADS)

Souto, M. J.; Balseiro, C. F.; Pérez-Muñuzuri, V.; Xue, M.; Brewster, K.

2003-01-01

The Advanced Regional Prediction System (ARPS) is applied to operational numerical weather prediction in Galicia, northwest Spain. The model is run daily for 72-h forecasts at a 10-km horizontal spacing. Located on the northwest coast of Spain and influenced by the Atlantic weather systems, Galicia has a high percentage (nearly 50%) of rainy days per year. For these reasons, the precipitation processes and the initialization of moisture and cloud fields are very important. Even though the ARPS model has a sophisticated data analysis system (`ADAS') that includes a 3D cloud analysis package, because of operational constraints, the current forecast starts from the 12-h forecast of the National Centers for Environmental Prediction Aviation Model (AVN). Still, procedures from the ADAS cloud analysis are being used to construct the cloud fields based on AVN data and then are applied to initialize the microphysical variables in ARPS. Comparisons of the ARPS predictions with local observations show that ARPS can predict very well both the daily total precipitation and its spatial distribution. ARPS also shows skill in predicting heavy rains and high winds, as observed during November 2000, and especially in the prediction of the 5 November 2000 storm that caused widespread wind and rain damage in Galicia. It is demonstrated that the cloud analysis contributes to the success of the precipitation forecasts.

Improving Air Quality (and Weather) Predictions using Advanced Data Assimilation Techniques Applied to Coupled Models during KORUS-AQ

NASA Astrophysics Data System (ADS)

Carmichael, G. R.; Saide, P. E.; Gao, M.; Streets, D. G.; Kim, J.; Woo, J. H.

2017-12-01

Ambient aerosols are important air pollutants with direct impacts on human health and on the Earth's weather and climate systems through their interactions with radiation and clouds. Their role is dependent on their distributions of size, number, phase and composition, which vary significantly in space and time. There remain large uncertainties in simulated aerosol distributions due to uncertainties in emission estimates and in chemical and physical processes associated with their formation and removal. These uncertainties lead to large uncertainties in weather and air quality predictions and in estimates of health and climate change impacts. Despite these uncertainties and challenges, regional-scale coupled chemistry-meteorological models such as WRF-Chem have significant capabilities in predicting aerosol distributions and explaining aerosol-weather interactions. We explore the hypothesis that new advances in on-line, coupled atmospheric chemistry/meteorological models, and new emission inversion and data assimilation techniques applicable to such coupled models, can be applied in innovative ways using current and evolving observation systems to improve predictions of aerosol distributions at regional scales. We investigate the impacts of assimilating AOD from geostationary satellite (GOCI) and surface PM2.5 measurements on predictions of AOD and PM in Korea during KORUS-AQ through a series of experiments. The results suggest assimilating datasets from multiple platforms can improve the predictions of aerosol temporal and spatial distributions.

Prediction of monthly regional groundwater levels through hybrid soft-computing techniques

NASA Astrophysics Data System (ADS)

Chang, Fi-John; Chang, Li-Chiu; Huang, Chien-Wei; Kao, I.-Feng

2016-10-01

Groundwater systems are intrinsically heterogeneous with dynamic temporal-spatial patterns, which cause great difficulty in quantifying their complex processes, while reliable predictions of regional groundwater levels are commonly needed for managing water resources to ensure proper service of water demands within a region. In this study, we proposed a novel and flexible soft-computing technique that could effectively extract the complex high-dimensional input-output patterns of basin-wide groundwater-aquifer systems in an adaptive manner. The soft-computing models combined the Self Organized Map (SOM) and the Nonlinear Autoregressive with Exogenous Inputs (NARX) network for predicting monthly regional groundwater levels based on hydrologic forcing data. The SOM could effectively classify the temporal-spatial patterns of regional groundwater levels, the NARX could accurately predict the mean of regional groundwater levels for adjusting the selected SOM, the Kriging was used to interpolate the predictions of the adjusted SOM into finer grids of locations, and consequently the prediction of a monthly regional groundwater level map could be obtained. The Zhuoshui River basin in Taiwan was the study case, and its monthly data sets collected from 203 groundwater stations, 32 rainfall stations and 6 flow stations during 2000 and 2013 were used for modelling purpose. The results demonstrated that the hybrid SOM-NARX model could reliably and suitably predict monthly basin-wide groundwater levels with high correlations (R2 > 0.9 in both training and testing cases). The proposed methodology presents a milestone in modelling regional environmental issues and offers an insightful and promising way to predict monthly basin-wide groundwater levels, which is beneficial to authorities for sustainable water resources management.

Deep learning for predicting the monsoon over the homogeneous regions of India

NASA Astrophysics Data System (ADS)

Saha, Moumita; Mitra, Pabitra; Nanjundiah, Ravi S.

2017-06-01

Indian monsoon varies in its nature over the geographical regions. Predicting the rainfall not just at the national level, but at the regional level is an important task. In this article, we used a deep neural network, namely, the stacked autoencoder to automatically identify climatic factors that are capable of predicting the rainfall over the homogeneous regions of India. An ensemble regression tree model is used for monsoon prediction using the identified climatic predictors. The proposed model provides forecast of the monsoon at a long lead time which supports the government to implement appropriate policies for the economic growth of the country. The monsoon of the central, north-east, north-west, and south-peninsular India regions are predicted with errors of 4.1%, 5.1%, 5.5%, and 6.4%, respectively. The identified predictors show high skill in predicting the regional monsoon having high variability. The proposed model is observed to be competitive with the state-of-the-art prediction models.

Advanced Daily Prediction Model for National Suicide Numbers with Social Media Data

PubMed Central

Lee, Kyung Sang; Lee, Hyewon; Myung, Woojae; Song, Gil-Young; Lee, Kihwang; Kim, Ho; Carroll, Bernard J.; Kim, Doh Kwan

2018-01-01

Objective Suicide is a significant public health concern worldwide. Social media data have a potential role in identifying high suicide risk individuals and also in predicting suicide rate at the population level. In this study, we report an advanced daily suicide prediction model using social media data combined with economic/meteorological variables along with observed suicide data lagged by 1 week. Methods The social media data were drawn from weblog posts. We examined a total of 10,035 social media keywords for suicide prediction. We made predictions of national suicide numbers 7 days in advance daily for 2 years, based on a daily moving 5-year prediction modeling period. Results Our model predicted the likely range of daily national suicide numbers with 82.9% accuracy. Among the social media variables, words denoting economic issues and mood status showed high predictive strength. Observed number of suicides one week previously, recent celebrity suicide, and day of week followed by stock index, consumer price index, and sunlight duration 7 days before the target date were notable predictors along with the social media variables. Conclusion These results strengthen the case for social media data to supplement classical social/economic/climatic data in forecasting national suicide events. PMID:29614852

Circulating CD147 predicts mortality in advanced hepatocellular carcinoma.

PubMed

Lee, Aimei; Rode, Anthony; Nicoll, Amanda; Maczurek, Annette E; Lim, Lucy; Lim, Seok; Angus, Peter; Kronborg, Ian; Arachchi, Niranjan; Gorelik, Alexandra; Liew, Danny; Warner, Fiona J; McCaughan, Geoffrey W; McLennan, Susan V; Shackel, Nicholas A

2016-02-01

The glycoprotein CD147 has a role in tumor progression, is readily detectable in the circulation, and is abundantly expressed in hepatocellular carcinoma (HCC). Advanced HCC patients are a heterogeneous group with some individuals having dismal survival. The aim of this study was to examine circulating soluble CD147 levels as a prognostic marker in HCC patients. CD147 was measured in 277 patients (110 HCC, 115 chronic liver disease, and 52 non-liver disease). Clinical data included etiology, tumor progression, Barcelona Clinic Liver Cancer (BCLC) stage, and treatment response. Patients with HCC were stratified into two groups based upon the 75th percentile of CD147 levels (24 ng/mL). CD147 in HCC correlated inversely with poor survival (P = 0.031). Increased CD147 predicted poor survival in BCLC stages C and D (P = 0.045), and CD147 levels >24 ng/mL predicted a significantly diminished 90-day and 180-day survival time (hazard ratio [HR] = 6.1; 95% confidence interval [CI]: 2.1-63.2; P = 0.0045 and HR = 2.8; 95% CI: 1.2-12.6; P = 0.028, respectively). In BCLC stage C, CD147 predicted prognosis; levels >24 ng/mL were associated with a median survival of 1.5 months compared with 6.5 months with CD147 levels ≤24 ng/mL (P = 0.03). CD147 also identified patients with a poor prognosis independent from treatment frequency, modality, and tumor size. Circulating CD147 is an independent marker of survival in advanced HCC. CD147 requires further evaluation as a potential new prognostic measure in HCC to identify patients with advanced disease who have a poor prognosis. © 2015 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Validation of High Frequency (HF) Propagation Prediction Models in the Arctic region

NASA Astrophysics Data System (ADS)

Athieno, R.; Jayachandran, P. T.

2014-12-01

Despite the emergence of modern techniques for long distance communication, Ionospheric communication in the high frequency (HF) band (3-30 MHz) remains significant to both civilian and military users. However, the efficient use of the ever-varying ionosphere as a propagation medium is dependent on the reliability of ionospheric and HF propagation prediction models. Most available models are empirical implying that data collection has to be sufficiently large to provide good intended results. The models we present were developed with little data from the high latitudes which necessitates their validation. This paper presents the validation of three long term High Frequency (HF) propagation prediction models over a path within the Arctic region. Measurements of the Maximum Usable Frequency for a 3000 km range (MUF (3000) F2) for Resolute, Canada (74.75° N, 265.00° E), are obtained from hand-scaled ionograms generated by the Canadian Advanced Digital Ionosonde (CADI). The observations have been compared with predictions obtained from the Ionospheric Communication Enhanced Profile Analysis Program (ICEPAC), Voice of America Coverage Analysis Program (VOACAP) and International Telecommunication Union Recommendation 533 (ITU-REC533) for 2009, 2011, 2012 and 2013. A statistical analysis shows that the monthly predictions seem to reproduce the general features of the observations throughout the year though it is more evident in the winter and equinox months. Both predictions and observations show a diurnal and seasonal variation. The analysed models did not show large differences in their performances. However, there are noticeable differences across seasons for the entire period analysed: REC533 gives a better performance in winter months while VOACAP has a better performance for both equinox and summer months. VOACAP gives a better performance in the daily predictions compared to ICEPAC though, in general, the monthly predictions seem to agree more with the

Collaborative Research: Improving Decadal Prediction of Arctic Climate Variability and Change Using a Regional Arctic

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

Gutowski, William J.

This project developed and applied a regional Arctic System model for enhanced decadal predictions. It built on successful research by four of the current PIs with support from the DOE Climate Change Prediction Program, which has resulted in the development of a fully coupled Regional Arctic Climate Model (RACM) consisting of atmosphere, land-hydrology, ocean and sea ice components. An expanded RACM, a Regional Arctic System Model (RASM), has been set up to include ice sheets, ice caps, mountain glaciers, and dynamic vegetation to allow investigation of coupled physical processes responsible for decadal-scale climate change and variability in the Arctic. RASMmore » can have high spatial resolution (~4-20 times higher than currently practical in global models) to advance modeling of critical processes and determine the need for their explicit representation in Global Earth System Models (GESMs). The pan-Arctic region is a key indicator of the state of global climate through polar amplification. However, a system-level understanding of critical arctic processes and feedbacks needs further development. Rapid climate change has occurred in a number of Arctic System components during the past few decades, including retreat of the perennial sea ice cover, increased surface melting of the Greenland ice sheet, acceleration and thinning of outlet glaciers, reduced snow cover, thawing permafrost, and shifts in vegetation. Such changes could have significant ramifications for global sea level, the ocean thermohaline circulation and heat budget, ecosystems, native communities, natural resource exploration, and commercial transportation. The overarching goal of the RASM project has been to advance understanding of past and present states of arctic climate and to improve seasonal to decadal predictions. To do this the project has focused on variability and long-term change of energy and freshwater flows through the arctic climate system. The three foci of this research are

Do plasma concentrations of apelin predict prognosis in patients with advanced heart failure?

PubMed

Dalzell, Jonathan R; Jackson, Colette E; Chong, Kwok S; McDonagh, Theresa A; Gardner, Roy S

2014-01-01

Apelin is an endogenous vasodilator and inotrope, plasma concentrations of which are reduced in advanced heart failure (HF). We determined the prognostic significance of plasma concentrations of apelin in advanced HF. Plasma concentrations of apelin were measured in 182 patients with advanced HF secondary to left ventricular systolic dysfunction. The predictive value of apelin for the primary end point of all-cause mortality was assessed over a median follow-up period of 544 (IQR: 196-923) days. In total, 30 patients (17%) reached the primary end point. Of those patients with a plasma apelin concentration above the median, 14 (16%) reached the primary end point compared with 16 (17%) of those with plasma apelin levels below the median (p = NS). NT-proBNP was the most powerful prognostic marker in this population (log rank statistic: 10.37; p = 0.001). Plasma apelin concentrations do not predict medium to long-term prognosis in patients with advanced HF secondary to left ventricular systolic dysfunction.

MGMT expression levels predict disease stabilisation, progression-free and overall survival in patients with advanced melanomas treated with DTIC.

PubMed

Busch, Christian; Geisler, Jürgen; Lillehaug, Johan R; Lønning, Per Eystein

2010-07-01

Metastatic melanoma responds poorly to systemic treatment. We report the results of a prospective single institution study evaluating O(6)-methylguanine-DNA methyltransferase (MGMT) status as a potential predictive and/or prognostic marker among patients treated with dacarbazine (DTIC) 800-1000 mg/m(2) monotherapy administered as a 3-weekly schedule for advanced malignant melanomas. The study was approved by the Regional Ethical Committee. Surgical biopsies from metastatic or loco-regional deposits obtained prior to DTIC treatment were snap-frozen immediately upon removal and stored in liquid nitrogen up to processing. Median time from enrolment to end of follow-up was 67 months. MGMT expression levels evaluated by qRT-PCR correlated significantly to DTIC benefit (CR/PR/SD; p=0.005), time to progression (TTP) (p=0.005) and overall survival (OS) (p=0.003). MGMT expression also correlated to Breslow thickness in the primary tumour (p=0.014). While MGMT promoter hypermethylation correlated to MGMT expression, MGMT promoter hypermethylation did not correlate to treatment benefit, TTP or OS, suggesting that other factors may be critical in determining MGMT expression levels in melanomas. In a Cox proportional regression analysis, serum lactate dehydrogenase (LDH, p<0.001), MGMT expression (p=0.022) and p16(INK4a) expression (p=0.037) independently predicted OS, while TTP correlated to DTIC benefit after 6 weeks only (p=0.001). Our data reveal MGMT expression levels to be associated with disease stabilisation and prognosis in patients receiving DTIC monotherapy for advanced melanoma. The role of MGMT expression as a predictor to DTIC sensitivity versus a general prognostic factor in advanced melanomas warrants further evaluation. Copyright 2010 Elsevier Ltd. All rights reserved.

Predictive factors for overall quality of life in patients with advanced cancer.

PubMed

Cramarossa, Gemma; Chow, Edward; Zhang, Liying; Bedard, Gillian; Zeng, Liang; Sahgal, Arjun; Vassiliou, Vassilios; Satoh, Takefumi; Foro, Palmira; Ma, Brigette B Y; Chie, Wei-Chu; Chen, Emily; Lam, Henry; Bottomley, Andrew

2013-06-01

This study examined which domains/symptoms from the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Core 15 Palliative (QLQ-C15-PAL), an abbreviated version of the health-related EORTC QLQ-C30 questionnaire designed for palliative cancer patients, were predictive of overall quality of life (QOL) in advanced cancer patients. Patients with advanced cancer from six countries completed the QLQ-C15-PAL at consultation and at one follow-up point. Univariate and multivariate regression analyses were conducted to determine the predictive value of the EORTC QLQ-C15-PAL functional/symptom scores for global QOL (question 15). Three hundred forty-nine patients completed the EORTC QLQ-C15-PAL at baseline. In the total patient sample, worse emotional functioning, pain, and appetite loss were the most significant predictive factors for worse QOL. In the subgroup of patients with bone metastases (n = 240), the domains mentioned above were also the most significant predictors, whereas in patients with brain metastases (n = 109), worse physical and emotional functioning most significantly predicted worse QOL. One-month follow-up in 267 patients revealed that the significant predictors changed somewhat over time. For example, in the total patient sample, physical functioning, fatigue, and appetite loss were significant predictors at the follow-up point. A sub-analysis of predictive factors affecting QOL by primary cancer (lung, breast, and prostate) was also conducted for the total patient sample. Deterioration of certain EORTC QLQ-C15-PAL functional/symptom scores significantly contributes to worse overall QOL. Special attention should be directed to managing factors most influential on overall QOL to ensure optimal management of advanced cancer patients.

PREDICT: a diagnostic accuracy study of a tool for predicting mortality within one year: who should have an advance healthcare directive?

PubMed

Richardson, Philip; Greenslade, Jaimi; Shanmugathasan, Sulochana; Doucet, Katherine; Widdicombe, Neil; Chu, Kevin; Brown, Anthony

2015-01-01

CARING is a screening tool developed to identify patients who have a high likelihood of death in 1 year. This study sought to validate a modified CARING tool (termed PREDICT) using a population of patients presenting to the Emergency Department. In total, 1000 patients aged over 55 years who were admitted to hospital via the Emergency Department between January and June 2009 were eligible for inclusion in this study. Data on the six prognostic indicators comprising PREDICT were obtained retrospectively from patient records. One-year mortality data were obtained from the State Death Registry. Weights were applied to each PREDICT criterion, and its final score ranged from 0 to 44. Receiver operator characteristic analyses and diagnostic accuracy statistics were used to assess the accuracy of PREDICT in identifying 1-year mortality. The sample comprised 976 patients with a median (interquartile range) age of 71 years (62-81 years) and a 1-year mortality of 23.4%. In total, 50% had ≥1 PREDICT criteria with a 1-year mortality of 40.4%. Receiver operator characteristic analysis gave an area under the curve of 0.86 (95% confidence interval: 0.83-0.89). Using a cut-off of 13 points, PREDICT had a 95.3% (95% confidence interval: 93.6-96.6) specificity and 53.9% (95% confidence interval: 47.5-60.3) sensitivity for predicting 1-year mortality. PREDICT was simpler than the CARING criteria and identified 158 patients per 1000 admitted who could benefit from advance care planning. PREDICT was successfully applied to the Australian healthcare system with findings similar to the original CARING study conducted in the United States. This tool could improve end-of-life care by identifying who should have advance care planning or an advance healthcare directive. © The Author(s) 2014.

Collaborative Proposal: Improving Decadal Prediction of Arctic Climate Variability and Change Using a Regional Arctic System Model (RASM)

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

Maslowski, Wieslaw

This project aims to develop, apply and evaluate a regional Arctic System model (RASM) for enhanced decadal predictions. Its overarching goal is to advance understanding of the past and present states of arctic climate and to facilitate improvements in seasonal to decadal predictions. In particular, it will focus on variability and long-term change of energy and freshwater flows through the arctic climate system. The project will also address modes of natural climate variability as well as extreme and rapid climate change in a region of the Earth that is: (i) a key indicator of the state of global climate throughmore » polar amplification and (ii) which is undergoing environmental transitions not seen in instrumental records. RASM will readily allow the addition of other earth system components, such as ecosystem or biochemistry models, thus allowing it to facilitate studies of climate impacts (e.g., droughts and fires) and of ecosystem adaptations to these impacts. As such, RASM is expected to become a foundation for more complete Arctic System models and part of a model hierarchy important for improving climate modeling and predictions.« less

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.

Predictive factors of esophageal stenosis associated with tumor regression in radiation therapy for locally advanced esophageal cancer.

PubMed

Atsumi, Kazushige; Shioyama, Yoshiyuki; Nakamura, Katsumasa; Nomoto, Satoshi; Ohga, Saiji; Yoshitake, Tadamasa; Nonoshita, Takeshi; Ueda, Masanobu; Hirata, Hideki; Honda, Hiroshi

2010-01-01

The purpose of this retrospective study was to clarify the predictive factors correlated with esophageal stenosis within three months after radiation therapy for locally advanced esophageal cancer. We enrolled 47 patients with advanced esophageal cancer with T2-4 and stage II-III who were treated with definitive radiation therapy and achieving complete response of primary lesion at Kyushu University Hospital between January 1998 and December 2005. Esophagography was performed for all patients before treatment and within three months after completion of the radiation therapy, the esophageal stenotic ratio was evaluated. The stenotic ratio was used to define four levels of stenosis: stenosis level 1, stenotic ratio of 0-25%; 2, 25-50%; 3,50-75%; 4,75-100%. We then estimated the correlation between the esophageal stenosis level after radiation therapy and each of numerous factors. The numbers and total percentages of patients at each stenosis level were as follows: level 1: n = 14 (30%); level 2: 8 (17%); level 3: 14 (30%); and level 4: 11 (23%). Esophageal stenosis in the case of full circumference involvement tended to be more severe and more frequent. Increases in wall thickness tended to be associated with increases in esophageal stenosis severity and frequency. The extent of involved circumference and wall thickness of tumor region were significantly correlated with esophageal stenosis associated with tumor regression in radiation therapy (p = 0.0006, p = 0.005). For predicting the possibility of esophageal stenosis with tumor regression within three months in radiation therapy, the extent of involved circumference and esophageal wall thickness of the tumor region may be useful.

Variability, trends, and predictability of seasonal sea ice retreat and advance in the Chukchi Sea

NASA Astrophysics Data System (ADS)

Serreze, Mark C.; Crawford, Alex D.; Stroeve, Julienne C.; Barrett, Andrew P.; Woodgate, Rebecca A.

2016-10-01

As assessed over the period 1979-2014, the date that sea ice retreats to the shelf break (150 m contour) of the Chukchi Sea has a linear trend of -0.7 days per year. The date of seasonal ice advance back to the shelf break has a steeper trend of about +1.5 days per year, together yielding an increase in the open water period of 80 days. Based on detrended time series, we ask how interannual variability in advance and retreat dates relate to various forcing parameters including radiation fluxes, temperature and wind (from numerical reanalyses), and the oceanic heat inflow through the Bering Strait (from in situ moorings). Of all variables considered, the retreat date is most strongly correlated (r ˜ 0.8) with the April through June Bering Strait heat inflow. After testing a suite of statistical linear models using several potential predictors, the best model for predicting the date of retreat includes only the April through June Bering Strait heat inflow, which explains 68% of retreat date variance. The best model predicting the ice advance date includes the July through September inflow and the date of retreat, explaining 67% of advance date variance. We address these relationships by discussing heat balances within the Chukchi Sea, and the hypothesis of oceanic heat transport triggering ocean heat uptake and ice-albedo feedback. Developing an operational prediction scheme for seasonal retreat and advance would require timely acquisition of Bering Strait heat inflow data. Predictability will likely always be limited by the chaotic nature of atmospheric circulation patterns.

Analysis of the regional MiKlip decadal prediction system over Europe: skill, added value of regionalization, and ensemble size dependeny

NASA Astrophysics Data System (ADS)

Reyers, Mark; Moemken, Julia; Pinto, Joaquim; Feldmann, Hendrik; Kottmeier, Christoph; MiKlip Module-C Team

2017-04-01

Decadal climate predictions can provide a useful basis for decision making support systems for the public and private sectors. Several generations of decadal hindcasts and predictions have been generated throughout the German research program MiKlip. Together with the global climate predictions computed with MPI-ESM, the regional climate model (RCM) COSMO-CLM is used for regional downscaling by MiKlip Module-C. The RCMs provide climate information on spatial and temporal scales closer to the needs of potential users. In this study, two downscaled hindcast generations are analysed (named b0 and b1). The respective global generations are both initialized by nudging them towards different reanalysis anomaly fields. An ensemble of five starting years (1961, 1971, 1981, 1991, and 2001), each comprising ten ensemble members, is used for both generations in order to quantify the regional decadal prediction skill for precipitation and near-surface temperature and wind speed over Europe. All datasets (including hindcasts, observations, reanalysis, and historical MPI-ESM runs) are pre-processed in an analogue manner by (i) removing the long-term trend and (ii) re-gridding to a common grid. Our analysis shows that there is potential for skillful decadal predictions over Europe in the regional MiKlip ensemble, but the skill is not systematic and depends on the PRUDENCE region and the variable. Further, the differences between the two hindcast generations are mostly small. As we used detrended time series, the predictive skill found in our study can probably attributed to reasonable predictions of anomalies which are associated with the natural climate variability. In a sensitivity study, it is shown that the results may strongly change when the long-term trend is kept in the datasets, as here the skill of predicting the long-term trend (e.g. for temperature) also plays a major role. The regionalization of the global ensemble provides an added value for decadal predictions for

Desire for predictive testing for Alzheimer's disease and impact on advance care planning: a cross-sectional study.

PubMed

Sheffrin, Meera; Stijacic Cenzer, Irena; Steinman, Michael A

2016-12-13

It is unknown whether older adults in the United States would be willing to take a test predictive of future Alzheimer's disease, or whether testing would change behavior. Using a nationally representative sample, we explored who would take a free and definitive test predictive of Alzheimer's disease, and examined how using such a test may impact advance care planning. A cross-sectional study within the 2012 Health and Retirement Study of adults aged 65 years or older asked questions about a test predictive of Alzheimer's disease (N = 874). Subjects were asked whether they would want to take a hypothetical free and definitive test predictive of future Alzheimer's disease. Then, imagining they knew they would develop Alzheimer's disease, subjects rated the chance of completing advance care planning activities from 0 to 100. We classified a score > 50 as being likely to complete that activity. We evaluated characteristics associated with willingness to take a test for Alzheimer's disease, and how such a test would impact completing an advance directive and discussing health plans with loved ones. Overall, 75% (N = 648) of the sample would take a free and definitive test predictive of Alzheimer's disease. Older adults willing to take the test had similar race and educational levels to those who would not, but were more likely to be ≤75 years old (odds ratio 0.71 (95% CI 0.53-0.94)). Imagining they knew they would develop Alzheimer's, 81% would be likely to complete an advance directive, although only 15% had done so already. In this nationally representative sample, 75% of older adults would take a free and definitive test predictive of Alzheimer's disease. Many participants expressed intent to increase activities of advance care planning with this knowledge. This confirms high public interest in predictive testing for Alzheimer's disease and suggests this may be an opportunity to engage patients in advance care planning discussions.

Predicted changes in advanced turboprop noise with shaft angle of attack

NASA Technical Reports Server (NTRS)

Padula, S. L.; Block, P. J. W.

1984-01-01

Advanced turboprop blade designs and new propeller installation schemes motivated an effort to include unsteady loading effects in existing propeller noise prediction computer programs. The present work validates the prediction capability while studing the effects of shaft inclination on the radiated sound field. Classical methods of propeller performance analysis supply the time-dependent blade loading needed to calculate noise. Polar plots of the sound pressure level (SPL) of the first four harmonics and overall SPL are indicative of the change in directivity pattern as a function of propeller angle of attack. Noise predictions are compared with newly available wind tunnel data and the accuracy and applicability of the prediction method are discussed. It is concluded that unsteady blade loading caused by inclining the propeller with respect to the flow changes the directionality and the intensity of the radiated noise. These changes are well modeled by the present quasi-steady prediction method.

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.

Potential for western US seasonal snowpack prediction

USGS Publications Warehouse

Kapnick, Sarah B.; Yang, Xiaosong; Vecchi, Gabriel A.; Delworth, Thomas L.; Gudgel, Rich; Malyshev, Sergey; Milly, Paul C. D.; Shevliakova, Elena; Underwood, Seth; Margulis, Steven A.

2018-01-01

Western US snowpack—snow that accumulates on the ground in the mountains—plays a critical role in regional hydroclimate and water supply, with 80% of snowmelt runoff being used for agriculture. While climate projections provide estimates of snowpack loss by the end of th ecentury and weather forecasts provide predictions of weather conditions out to 2 weeks, less progress has been made for snow predictions at seasonal timescales (months to 2 years), crucial for regional agricultural decisions (e.g., plant choice and quantity). Seasonal predictions with climate models first took the form of El Niño predictions 3 decades ago, with hydroclimate predictions emerging more recently. While the field has been focused on single-season predictions (3 months or less), we are now poised to advance our predictions beyond this timeframe. Utilizing observations, climate indices, and a suite of global climate models, we demonstrate the feasibility of seasonal snowpack predictions and quantify the limits of predictive skill 8 month sin advance. This physically based dynamic system outperforms observation-based statistical predictions made on July 1 for March snowpack everywhere except the southern Sierra Nevada, a region where prediction skill is nonexistent for every predictor presently tested. Additionally, in the absence of externally forced negative trends in snowpack, narrow maritime mountain ranges with high hydroclimate variability pose a challenge for seasonal prediction in our present system; natural snowpack variability may inherently be unpredictable at this timescale. This work highlights present prediction system successes and gives cause for optimism for developing seasonal predictions for societal needs.

Predicting Regional Pattern of Longitudinal β-Amyloid Accumulation by Baseline PET.

PubMed

Guo, Tengfei; Brendel, Matthias; Grimmer, Timo; Rominger, Axel; Yakushev, Igor

2017-04-01

Knowledge about spatial and temporal patterns of β-amyloid (Aβ) accumulation is essential for understanding Alzheimer disease (AD) and for design of antiamyloid drug trials. Here, we tested whether the regional pattern of longitudinal Aβ accumulation can be predicted by baseline amyloid PET. Methods: Baseline and 2-y follow-up 18 F-florbetapir PET data from 58 patients with incipient and manifest dementia due to AD were analyzed. With the determination of how fast amyloid deposits in a given region relative to the whole-brain gray matter, a pseudotemporal accumulation rate for each region was calculated. The actual accumulation rate of 18 F-florbetapir was calculated from follow-up data. Results: Pseudotemporal measurements from baseline PET data explained 87% ( P < 0.001) of the variance in longitudinal accumulation rate across 62 regions. The method accurately predicted the top 10 fast and slow accumulating regions. Conclusion: Pseudotemporal analysis of baseline PET images is capable of predicting the regional pattern of longitudinal Aβ accumulation in AD at a group level. This approach may be useful in exploring spatial patterns of Aβ accumulation in other amyloid-associated disorders such as Lewy body disease and atypical forms of AD. In addition, the method allows identification of brain regions with a high accumulation rate of Aβ, which are of particular interest for antiamyloid clinical trials. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Toward Process-resolving Synthesis and Prediction of Arctic Climate Change Using the Regional Arctic System Model

NASA Astrophysics Data System (ADS)

Maslowski, W.

2017-12-01

The Regional Arctic System Model (RASM) has been developed to better understand the operation of Arctic System at process scale and to improve prediction of its change at a spectrum of time scales. RASM is a pan-Arctic, fully coupled ice-ocean-atmosphere-land model with marine biogeochemistry extension to the ocean and sea ice models. The main goal of our research is to advance a system-level understanding of critical processes and feedbacks in the Arctic and their links with the Earth System. The secondary, an equally important objective, is to identify model needs for new or additional observations to better understand such processes and to help constrain models. Finally, RASM has been used to produce sea ice forecasts for September 2016 and 2017, in contribution to the Sea Ice Outlook of the Sea Ice Prediction Network. Future RASM forecasts, are likely to include increased resolution for model components and ecosystem predictions. Such research is in direct support of the US environmental assessment and prediction needs, including those of the U.S. Navy, Department of Defense, and the recent IARPC Arctic Research Plan 2017-2021. In addition to an overview of RASM technical details, selected model results are presented from a hierarchy of climate models together with available observations in the region to better understand potential oceanic contributions to polar amplification. RASM simulations are analyzed to evaluate model skill in representing seasonal climatology as well as interannual and multi-decadal climate variability and predictions. Selected physical processes and resulting feedbacks are discussed to emphasize the need for fully coupled climate model simulations, high model resolution and sensitivity of simulated sea ice states to scale dependent model parameterizations controlling ice dynamics, thermodynamics and coupling with the atmosphere and ocean.

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.

Predicting protein-binding regions in RNA using nucleotide profiles and compositions.

PubMed

Choi, Daesik; Park, Byungkyu; Chae, Hanju; Lee, Wook; Han, Kyungsook

2017-03-14

Motivated by the increased amount of data on protein-RNA interactions and the availability of complete genome sequences of several organisms, many computational methods have been proposed to predict binding sites in protein-RNA interactions. However, most computational methods are limited to finding RNA-binding sites in proteins instead of protein-binding sites in RNAs. Predicting protein-binding sites in RNA is more challenging than predicting RNA-binding sites in proteins. Recent computational methods for finding protein-binding sites in RNAs have several drawbacks for practical use. We developed a new support vector machine (SVM) model for predicting protein-binding regions in mRNA sequences. The model uses sequence profiles constructed from log-odds scores of mono- and di-nucleotides and nucleotide compositions. The model was evaluated by standard 10-fold cross validation, leave-one-protein-out (LOPO) cross validation and independent testing. Since actual mRNA sequences have more non-binding regions than protein-binding regions, we tested the model on several datasets with different ratios of protein-binding regions to non-binding regions. The best performance of the model was obtained in a balanced dataset of positive and negative instances. 10-fold cross validation with a balanced dataset achieved a sensitivity of 91.6%, a specificity of 92.4%, an accuracy of 92.0%, a positive predictive value (PPV) of 91.7%, a negative predictive value (NPV) of 92.3% and a Matthews correlation coefficient (MCC) of 0.840. LOPO cross validation showed a lower performance than the 10-fold cross validation, but the performance remains high (87.6% accuracy and 0.752 MCC). In testing the model on independent datasets, it achieved an accuracy of 82.2% and an MCC of 0.656. Testing of our model and other state-of-the-art methods on a same dataset showed that our model is better than the others. Sequence profiles of log-odds scores of mono- and di-nucleotides were much more powerful

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. © 2014 John Wiley & Sons Ltd.

Predicting survival time in noncurative patients with advanced cancer: a prospective study in China.

PubMed

Cui, Jing; Zhou, Lingjun; Wee, B; Shen, Fengping; Ma, Xiuqiang; Zhao, Jijun

2014-05-01

Accurate prediction of prognosis for cancer patients is important for good clinical decision making in therapeutic and care strategies. The application of prognostic tools and indicators could improve prediction accuracy. This study aimed to develop a new prognostic scale to predict survival time of advanced cancer patients in China. We prospectively collected items that we anticipated might influence survival time of advanced cancer patients. Participants were recruited from 12 hospitals in Shanghai, China. We collected data including demographic information, clinical symptoms and signs, and biochemical test results. Log-rank tests, Cox regression, and linear regression were performed to develop a prognostic scale. Three hundred twenty patients with advanced cancer were recruited. Fourteen prognostic factors were included in the prognostic scale: Karnofsky Performance Scale (KPS) score, pain, ascites, hydrothorax, edema, delirium, cachexia, white blood cell (WBC) count, hemoglobin, sodium, total bilirubin, direct bilirubin, aspartate aminotransferase (AST), and alkaline phosphatase (ALP) values. The score was calculated by summing the partial scores, ranging from 0 to 30. When using the cutoff points of 7-day, 30-day, 90-day, and 180-day survival time, the scores were calculated as 12, 10, 8, and 6, respectively. We propose a new prognostic scale including KPS, pain, ascites, hydrothorax, edema, delirium, cachexia, WBC count, hemoglobin, sodium, total bilirubin, direct bilirubin, AST, and ALP values, which may help guide physicians in predicting the likely survival time of cancer patients more accurately. More studies are needed to validate this scale in the future.

A multi-objective framework to predict flows of ungauged rivers within regions of sparse hydrometeorologic observation

NASA Astrophysics Data System (ADS)

Alipour, M.; Kibler, K. M.

2017-12-01

Despite advances in flow prediction, managers of ungauged rivers located within broad regions of sparse hydrometeorologic observation still lack prescriptive methods robust to the data challenges of such regions. We propose a multi-objective streamflow prediction framework for regions of minimum observation to select models that balance runoff efficiency with choice of accurate parameter values. We supplement sparse observed data with uncertain or low-resolution information incorporated as `soft' a priori parameter estimates. The performance of the proposed framework is tested against traditional single-objective and constrained single-objective calibrations in two catchments in a remote area of southwestern China. We find that the multi-objective approach performs well with respect to runoff efficiency in both catchments (NSE = 0.74 and 0.72), within the range of efficiencies returned by other models (NSE = 0.67 - 0.78). However, soil moisture capacity estimated by the multi-objective model resonates with a priori estimates (parameter residuals of 61 cm versus 289 and 518 cm for maximum soil moisture capacity in one catchment, and 20 cm versus 246 and 475 cm in the other; parameter residuals of 0.48 versus 0.65 and 0.7 for soil moisture distribution shape factor in one catchment, and 0.91 versus 0.79 and 1.24 in the other). Thus, optimization to a multi-criteria objective function led to very different representations of soil moisture capacity as compared to models selected by single-objective calibration, without compromising runoff efficiency. These different soil moisture representations may translate into considerably different hydrological behaviors. The proposed approach thus offers a preliminary step towards greater process understanding in regions of severe data limitations. For instance, the multi-objective framework may be an adept tool to discern between models of similar efficiency to select models that provide the "right answers for the right reasons

The UKC2 regional coupled environmental prediction system

NASA Astrophysics Data System (ADS)

Lewis, Huw W.; Castillo Sanchez, Juan Manuel; Graham, Jennifer; Saulter, Andrew; Bornemann, Jorge; Arnold, Alex; Fallmann, Joachim; Harris, Chris; Pearson, David; Ramsdale, Steven; Martínez-de la Torre, Alberto; Bricheno, Lucy; Blyth, Eleanor; Bell, Victoria A.; Davies, Helen; Marthews, Toby R.; O'Neill, Clare; Rumbold, Heather; O'Dea, Enda; Brereton, Ashley; Guihou, Karen; Hines, Adrian; Butenschon, Momme; Dadson, Simon J.; Palmer, Tamzin; Holt, Jason; Reynard, Nick; Best, Martin; Edwards, John; Siddorn, John

2018-01-01

It is hypothesized that more accurate prediction and warning of natural hazards, such as of the impacts of severe weather mediated through various components of the environment, require a more integrated Earth System approach to forecasting. This hypothesis can be explored using regional coupled prediction systems, in which the known interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land can be simulated. Such systems are becoming increasingly common research tools. This paper describes the development of the UKC2 regional coupled research system, which has been delivered under the UK Environmental Prediction Prototype project. This provides the first implementation of an atmosphere-land-ocean-wave modelling system focussed on the United Kingdom and surrounding seas at km-scale resolution. The UKC2 coupled system incorporates models of the atmosphere (Met Office Unified Model), land surface with river routing (JULES), shelf-sea ocean (NEMO) and ocean waves (WAVEWATCH III). These components are coupled, via OASIS3-MCT libraries, at unprecedentedly high resolution across the UK within a north-western European regional domain. A research framework has been established to explore the representation of feedback processes in coupled and uncoupled modes, providing a new research tool for UK environmental science. This paper documents the technical design and implementation of UKC2, along with the associated evaluation framework. An analysis of new results comparing the output of the coupled UKC2 system with relevant forced control simulations for six contrasting case studies of 5-day duration is presented. Results demonstrate that performance can be achieved with the UKC2 system that is at least comparable to its component control simulations. For some cases, improvements in air temperature, sea surface temperature, wind speed, significant wave height and mean wave period highlight the potential

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

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

PubMed

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

2015-09-28

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.

The UKC2 regional coupled prediction system

NASA Astrophysics Data System (ADS)

Castillo, Juan; Lewis, Huw; Graham, Jennifer; Saulter, Andrew; Arnold, Alex; Fallmann, Joachim; Martinez de la Torre, Alberto; Blyth, Eleanor; Bricheno, Lucy

2017-04-01

It is hypothesized that more accurate prediction and warning of natural hazards, such as of the impacts of severe weather through the environment, requires a more integrated approach to forecasting. This approach also delivers research benefits through providing tools with which to explore the known interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land. This hypothesis is being tested in a UK regional context at km-scale through the UK Environmental Prediction Project. This presentation will provide an introduction to the UKC2 UK Environmental Prediction research system. This incorporates models of the atmosphere (Met Office Unified Model), land surface (JULES), shelf-sea ocean (NEMO) and ocean waves (WAVEWATCH III). These components are coupled (via OASIS3-MCT libraries) at unprecedentedly high resolution across the UK and the wider north-west European regional domain. A research framework has been established to explore the representation of feedback processes in coupled and uncoupled modes, providing a unique new research tool for UK environmental science. The presentation will highlight work undertaken to review and improve the computational cost of running these systems for efficient research application. Research will be presented highlighting case study evaluation on the sensitivity of the ocean and surface waves to the representation of feedbacks to the atmosphere, and on the sensitivity of weather systems and boundary layer cloud development to the exchange of heat and momentum at the ocean surface modified through sea surface temperature and wave-induced roughness. The presentation will discuss plans for future development through UKC3 and beyond.

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.

Prediction of Indian Summer-Monsoon Onset Variability: A Season in Advance.

PubMed

Pradhan, Maheswar; Rao, A Suryachandra; Srivastava, Ankur; Dakate, Ashish; Salunke, Kiran; Shameera, K S

2017-10-27

Monsoon onset is an inherent transient phenomenon of Indian Summer Monsoon and it was never envisaged that this transience can be predicted at long lead times. Though onset is precipitous, its variability exhibits strong teleconnections with large scale forcing such as ENSO and IOD and hence may be predictable. Despite of the tremendous skill achieved by the state-of-the-art models in predicting such large scale processes, the prediction of monsoon onset variability by the models is still limited to just 2-3 weeks in advance. Using an objective definition of onset in a global coupled ocean-atmosphere model, it is shown that the skillful prediction of onset variability is feasible under seasonal prediction framework. The better representations/simulations of not only the large scale processes but also the synoptic and intraseasonal features during the evolution of monsoon onset are the comprehensions behind skillful simulation of monsoon onset variability. The changes observed in convection, tropospheric circulation and moisture availability prior to and after the onset are evidenced in model simulations, which resulted in high hit rate of early/delay in monsoon onset in the high resolution model.

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

NASA Astrophysics Data System (ADS)

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

1992-02-01

Measured and predicted rotor performance for the Solar Energy Research Institute (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.

Modified CLIP with objective liver reserve assessment retains prognosis prediction for patients with advanced hepatocellular carcinoma.

PubMed

Shao, Yu-Yun; Liu, Tsung-Hao; Lee, Ying-Hui; Hsu, Chih-Hung; Cheng, Ann-Lii

2016-07-01

The Cancer of the Liver Italian Program (CLIP) score is a commonly used staging system for hepatocellular carcinoma (HCC) helpful with predicting prognosis of advanced HCC. CLIP uses the Child-Turcotte-Pugh (CTP) score to evaluate liver reserve. A new scoring system, the albumin-bilirubin (ALBI) grade, has been proposed as they objectively evaluate liver reserve. We examined whether the modification of CLIP with ALBI retained its prognosis prediction for patients with advanced HCC. We included patients who received first-line antiangiogenic therapy for advanced HCC. Liver reserve was assessed using CTP and ALBI scores, which were then incorporated into CLIP and ALBI-CLIP, respectively. To assess their efficacies of prognostic prediction, the Cox's proportional hazard model and concordance indexes were used. A total of 142 patients were included; 137 of them were classified CTP A and 5 patients CTP B. Patients could be divided into four or five groups with different prognosis according to CLIP and ALBI-CLIP, respectively. Higher R(2) (0.249 vs 0.216) and lower Akaike information criterion (995.0 vs 1001.1) were observed for ALBI-CLIP than for CLIP in the Cox's model predicting overall survival. ALBI-CLIP remained an independent predictor for overall survival when CLIP and ALBI-CLIP were simultaneously incorporated in Cox's models allowing variable selection with adjustment for hepatitis etiology, treatment, and performance status. The concordance index was also higher for ALBI-CLIP than for CLIP (0.724 vs 0.703). Modification of CLIP scoring with ALBI, which objectively assesses liver reserve, retains and might have improved prognosis prediction for advanced HCC. © 2016 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines.

PubMed

Nielsen, Nanna Hellum; Jahoor, Ahmed; Jensen, Jens Due; Orabi, Jihad; Cericola, Fabio; Edriss, Vahid; Jensen, Just

2016-01-01

Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines) are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families.

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines

PubMed Central

Nielsen, Nanna Hellum; Jahoor, Ahmed; Jensen, Jens Due; Orabi, Jihad; Cericola, Fabio; Edriss, Vahid; Jensen, Just

2016-01-01

Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines) are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families. PMID:27783639

Life Prediction of Fretting Fatigue with Advanced Surface Treatments (Preprint)

DTIC Science & Technology

2006-05-01

surfaces and not the fretting pads. The chosen coatings included DLC, Ni-B, Molybdenum, and Nitride. These 4 coatings, their application to the titanium ...Article Preprint 5a. CONTRACT NUMBER In-house 5b. GRANT NUMBER 4 . TITLE AND SUBTITLE LIFE PREDICTION OF FRETTING FATIGUE WITH ADVANCED SURFACE...TREATMENTS (PREPRINT) 5c. PROGRAM ELEMENT NUMBER N/A 5d. PROJECT NUMBER M02R 5e. TASK NUMBER 30 6 . AUTHOR(S) Patrick J. Golden and Michael

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.

HotRegion: a database of predicted hot spot clusters.

PubMed

Cukuroglu, Engin; Gursoy, Attila; Keskin, Ozlem

2012-01-01

Hot spots are energetically important residues at protein interfaces and they are not randomly distributed across the interface but rather clustered. These clustered hot spots form hot regions. Hot regions are important for the stability of protein complexes, as well as providing specificity to binding sites. We propose a database called HotRegion, which provides the hot region information of the interfaces by using predicted hot spot residues, and structural properties of these interface residues such as pair potentials of interface residues, accessible surface area (ASA) and relative ASA values of interface residues of both monomer and complex forms of proteins. Also, the 3D visualization of the interface and interactions among hot spot residues are provided. HotRegion is accessible at http://prism.ccbb.ku.edu.tr/hotregion.

Complex Burn Region Module (CBRM) update

NASA Technical Reports Server (NTRS)

Adams, Carl L.; Jenkins, Billy

1991-01-01

Presented here is a Complex Burn Region Module (CBRM) update for the Solid Rocket Internal Ballistics Module (SRIBM) Program for the Advanced Solid Rocket Motor (ASRM) design/performance assessments. The goal was to develop an improved version of the solid rocket internal ballistics module program that contains a diversified complex region model for motor grain design, performance prediction, and evaluation.

A Serum Protein Profile Predictive of the Resistance to Neoadjuvant Chemotherapy in Advanced Breast Cancers*

PubMed Central

Hyung, Seok-Won; Lee, Min Young; Yu, Jong-Han; Shin, Byunghee; Jung, Hee-Jung; Park, Jong-Moon; Han, Wonshik; Lee, Kyung-Min; Moon, Hyeong-Gon; Zhang, Hui; Aebersold, Ruedi; Hwang, Daehee; Lee, Sang-Won; Yu, Myeong-Hee; Noh, Dong-Young

2011-01-01

Prediction of the responses to neoadjuvant chemotherapy (NACT) can improve the treatment of patients with advanced breast cancer. Genes and proteins predictive of chemoresistance have been extensively studied in breast cancer tissues. However, noninvasive serum biomarkers capable of such prediction have been rarely exploited. Here, we performed profiling of N-glycosylated proteins in serum from fifteen advanced breast cancer patients (ten patients sensitive to and five patients resistant to NACT) to discover serum biomarkers of chemoresistance using a label-free liquid chromatography-tandem MS method. By performing a series of statistical analyses of the proteomic data, we selected thirteen biomarker candidates and tested their differential serum levels by Western blotting in 13 independent samples (eight patients sensitive to and five patients resistant to NACT). Among the candidates, we then selected the final set of six potential serum biomarkers (AHSG, APOB, C3, C9, CP, and ORM1) whose differential expression was confirmed in the independent samples. Finally, we demonstrated that a multivariate classification model using the six proteins could predict responses to NACT and further predict relapse-free survival of patients. In summary, global N-glycoproteome profile in serum revealed a protein pattern predictive of the responses to NACT, which can be further validated in large clinical studies. PMID:21799047

Advances in the assessment and prediction of interpersonal violence.

PubMed

Mills, Jeremy F

2005-02-01

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 actuarial approach to risk assessment has overcome many of the weaknesses of clinical judgment and has been shown to be a much superior method. Nonetheless, the static/historical nature of the risk factors associated with most actuarial approaches is limiting. Advances in risk prediction will be found in part in the development of dynamic actuarial instruments that will measure both static/historical and changeable risk factors. The dynamic risk factors can be reevaluated on an ongoing basis, and it is proposed that the level of change in dynamic factors necessary to represent a significant change in overall risk will be an interactive function with static risk factors.

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

Mammalian genomic regulatory regions predicted by utilizing human genomics, transcriptomics, and epigenetics data

PubMed Central

Nguyen, Quan H; Tellam, Ross L; Naval-Sanchez, Marina; Porto-Neto, Laercio R; Barendse, William; Reverter, Antonio; Hayes, Benjamin; Kijas, James; Dalrymple, Brian P

2018-01-01

Abstract Genome sequences for hundreds of mammalian species are available, but an understanding of their genomic regulatory regions, which control gene expression, is only beginning. A comprehensive prediction of potential active regulatory regions is necessary to functionally study the roles of the majority of genomic variants in evolution, domestication, and animal production. We developed a computational method to predict regulatory DNA sequences (promoters, enhancers, and transcription factor binding sites) in production animals (cows and pigs) and extended its broad applicability to other mammals. The method utilizes human regulatory features identified from thousands of tissues, cell lines, and experimental assays to find homologous regions that are conserved in sequences and genome organization and are enriched for regulatory elements in the genome sequences of other mammalian species. Importantly, we developed a filtering strategy, including a machine learning classification method, to utilize a very small number of species-specific experimental datasets available to select for the likely active regulatory regions. The method finds the optimal combination of sensitivity and accuracy to unbiasedly predict regulatory regions in mammalian species. Furthermore, we demonstrated the utility of the predicted regulatory datasets in cattle for prioritizing variants associated with multiple production and climate change adaptation traits and identifying potential genome editing targets. PMID:29618048

Mammalian genomic regulatory regions predicted by utilizing human genomics, transcriptomics, and epigenetics data.

PubMed

Nguyen, Quan H; Tellam, Ross L; Naval-Sanchez, Marina; Porto-Neto, Laercio R; Barendse, William; Reverter, Antonio; Hayes, Benjamin; Kijas, James; Dalrymple, Brian P

2018-03-01

Genome sequences for hundreds of mammalian species are available, but an understanding of their genomic regulatory regions, which control gene expression, is only beginning. A comprehensive prediction of potential active regulatory regions is necessary to functionally study the roles of the majority of genomic variants in evolution, domestication, and animal production. We developed a computational method to predict regulatory DNA sequences (promoters, enhancers, and transcription factor binding sites) in production animals (cows and pigs) and extended its broad applicability to other mammals. The method utilizes human regulatory features identified from thousands of tissues, cell lines, and experimental assays to find homologous regions that are conserved in sequences and genome organization and are enriched for regulatory elements in the genome sequences of other mammalian species. Importantly, we developed a filtering strategy, including a machine learning classification method, to utilize a very small number of species-specific experimental datasets available to select for the likely active regulatory regions. The method finds the optimal combination of sensitivity and accuracy to unbiasedly predict regulatory regions in mammalian species. Furthermore, we demonstrated the utility of the predicted regulatory datasets in cattle for prioritizing variants associated with multiple production and climate change adaptation traits and identifying potential genome editing targets.

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.

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.

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.

Validating a Predictive Model of Acute Advanced Imaging Biomarkers in Ischemic Stroke.

PubMed

Bivard, Andrew; Levi, Christopher; Lin, Longting; Cheng, Xin; Aviv, Richard; Spratt, Neil J; Lou, Min; Kleinig, Tim; O'Brien, Billy; Butcher, Kenneth; Zhang, Jingfen; Jannes, Jim; Dong, Qiang; Parsons, Mark

2017-03-01

Advanced imaging to identify tissue pathophysiology may provide more accurate prognostication than the clinical measures used currently in stroke. This study aimed to derive and validate a predictive model for functional outcome based on acute clinical and advanced imaging measures. A database of prospectively collected sub-4.5 hour patients with ischemic stroke being assessed for thrombolysis from 5 centers who had computed tomographic perfusion and computed tomographic angiography before a treatment decision was assessed. Individual variable cut points were derived from a classification and regression tree analysis. The optimal cut points for each assessment variable were then used in a backward logic regression to predict modified Rankin scale (mRS) score of 0 to 1 and 5 to 6. The variables remaining in the models were then assessed using a receiver operating characteristic curve analysis. Overall, 1519 patients were included in the study, 635 in the derivation cohort and 884 in the validation cohort. The model was highly accurate at predicting mRS score of 0 to 1 in all patients considered for thrombolysis therapy (area under the curve [AUC] 0.91), those who were treated (AUC 0.88) and those with recanalization (AUC 0.89). Next, the model was highly accurate at predicting mRS score of 5 to 6 in all patients considered for thrombolysis therapy (AUC 0.91), those who were treated (0.89) and those with recanalization (AUC 0.91). The odds ratio of thrombolysed patients who met the model criteria achieving mRS score of 0 to 1 was 17.89 (4.59-36.35, P <0.001) and for mRS score of 5 to 6 was 8.23 (2.57-26.97, P <0.001). This study has derived and validated a highly accurate model at predicting patient outcome after ischemic stroke. © 2017 American Heart Association, Inc.

Prediction of lake depth across a 17-state region in the United States

USGS Publications Warehouse

Oliver, Samantha K.; Soranno, Patricia A.; Fergus, C. Emi; Wagner, Tyler; Winslow, Luke A.; Scott, Caren E.; Webster, Katherine E.; Downing, John A.; Stanley, Emily H.

2016-01-01

Lake depth is an important characteristic for understanding many lake processes, yet it is unknown for the vast majority of lakes globally. Our objective was to develop a model that predicts lake depth using map-derived metrics of lake and terrestrial geomorphic features. Building on previous models that use local topography to predict lake depth, we hypothesized that regional differences in topography, lake shape, or sedimentation processes could lead to region-specific relationships between lake depth and the mapped features. We therefore used a mixed modeling approach that included region-specific model parameters. We built models using lake and map data from LAGOS, which includes 8164 lakes with maximum depth (Zmax) observations. The model was used to predict depth for all lakes ≥4 ha (n = 42 443) in the study extent. Lake surface area and maximum slope in a 100 m buffer were the best predictors of Zmax. Interactions between surface area and topography occurred at both the local and regional scale; surface area had a larger effect in steep terrain, so large lakes embedded in steep terrain were much deeper than those in flat terrain. Despite a large sample size and inclusion of regional variability, model performance (R2 = 0.29, RMSE = 7.1 m) was similar to other published models. The relative error varied by region, however, highlighting the importance of taking a regional approach to lake depth modeling. Additionally, we provide the largest known collection of observed and predicted lake depth values in the United States.

Evaluation of performance of seasonal precipitation prediction at regional scale over India

NASA Astrophysics Data System (ADS)

Mohanty, U. C.; Nageswararao, M. M.; Sinha, P.; Nair, A.; Singh, A.; Rai, R. K.; Kar, S. C.; Ramesh, K. J.; Singh, K. K.; Ghosh, K.; Rathore, L. S.; Sharma, R.; Kumar, A.; Dhekale, B. S.; Maurya, R. K. S.; Sahoo, R. K.; Dash, G. P.

2018-03-01

The seasonal scale precipitation amount is an important ingredient in planning most of the agricultural practices (such as a type of crops, and showing and harvesting schedules). India being an agroeconomic country, the seasonal scale prediction of precipitation is directly linked to the socioeconomic growth of the nation. At present, seasonal precipitation prediction at regional scale is a challenging task for the scientific community. In the present study, an attempt is made to develop multi-model dynamical-statistical approach for seasonal precipitation prediction at the regional scale (meteorological subdivisions) over India for four prominent seasons which are winter (from December to February; DJF), pre-monsoon (from March to May; MAM), summer monsoon (from June to September; JJAS), and post-monsoon (from October to December; OND). The present prediction approach is referred as extended range forecast system (ERFS). For this purpose, precipitation predictions from ten general circulation models (GCMs) are used along with the India Meteorological Department (IMD) rainfall analysis data from 1982 to 2008 for evaluation of the performance of the GCMs, bias correction of the model results, and development of the ERFS. An extensive evaluation of the performance of the ERFS is carried out with dependent data (1982-2008) as well as independent predictions for the period 2009-2014. In general, the skill of the ERFS is reasonably better and consistent for all the seasons and different regions over India as compared to the GCMs and their simple mean. The GCM products failed to explain the extreme precipitation years, whereas the bias-corrected GCM mean and the ERFS improved the prediction and well represented the extremes in the hindcast period. The peak intensity, as well as regions of maximum precipitation, is better represented by the ERFS than the individual GCMs. The study highlights the improvement of forecast skill of the ERFS over 34 meteorological subdivisions

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. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

Measurement and prediction of model-rotor flow fields

NASA Technical Reports Server (NTRS)

Owen, F. K.; Tauber, M. E.

1985-01-01

This paper shows that a laser velocimeter can be used to measure accurately the three-component velocities induced by a model rotor at transonic tip speeds. The measurements, which were made at Mach numbers from 0.85 to 0.95 and at zero advance ratio, yielded high-resolution, orthogonal velocity values. The measured velocities were used to check the ability of the ROT22 full-potential rotor code to predict accurately the transonic flow field in the crucial region around and beyond the tip of a high-speed rotor blade. The good agreement between the calculated and measured velocities established the code's ability to predict the off-blade flow field at transonic tip speeds. This supplements previous comparisons in which surface pressures were shown to be well predicted on two different tips at advance ratios to 0.45, especially at the critical 90 deg azimuthal blade position. These results demonstrate that the ROT22 code can be used with confidence to predict the important tip-region flow field, including the occurrence, strength, and location of shock waves causing high drag and noise.

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

Regional approaches to the management of patients with advanced, radioactive iodine-refractory differentiated thyroid carcinoma.

PubMed

Brose, Marcia S; Smit, Johannes; Capdevila, Jaume; Elisei, Rossella; Nutting, Christopher; Pitoia, Fabian; Robinson, Bruce; Schlumberger, Martin; Shong, Young Kee; Takami, Hiroshi

2012-09-01

For patients with advanced, radioactive iodine-refractory differentiated thyroid cancer, current treatment guidelines recommend clinical trial enrollment or small-molecule kinase inhibitor therapy. However, details of patient management vary between countries depending on trial availability and national regulatory policies. Insufficient clinical trial data and variable disease characteristics challenge the creation of universal guidelines, and treatment plans often reflect regional influences. A multidisciplinary, multiregional panel of experts met to discuss regional approaches to managing patients with advanced, radioactive iodine-refractory differentiated thyroid cancer and the potential impact of emerging therapies on current treatment strategies. Despite process-oriented regional differences, the decision-making strategies were similar. Multidisciplinary teams used to manage high-risk patients varied in composition across regions, particularly regarding the responsible physician's specialty. Cytotoxic chemotherapy was viewed as limited in clinical benefit, and targeted agents as attractive, based on promising data. Panel members support clinical trial enrollment as the preferred treatment strategy for managing these patients.

Predictive models for customizing chemotherapy in advanced non-small cell lung cancer (NSCLC).

PubMed

Bonanno, Laura

2013-06-01

The backbone of first-line treatment for Epidermal Growth Factor (EGFR) wild-type (wt) advanced Non-small cell lung cancer (NSCLC) patients is the use of a platinum-based chemotherapy combination. The treatment is characterized by great inter-individual variability in outcome. Molecular predictive markers are extremely needed in order to identify patients most likely to benefit from platinum-based treatment and resistant ones, thus optimizing chemotherapy approach in NSCLC. Several components of DNA repair response (DRR) have been investigated as potential predictive markers. Among them, high levels of expression of ERCC1, both at protein and mRNA levels, have been associated with resistance to cisplatin in NSCLC. In addition, low levels of expression of RRM1, a target for gemcitabine, have been associated with improved OS in advanced NSCLC patients treated with cisplatin and gemcitabine. Preclinical data and retrospective analyses showed that BRCA1 is able to induce resistance to cisplatin and sensitivity to antimicrotubule agents. In addition, the mRNA levels of expression of RAP80, encoding for a protein cooperating with BRCA1 in homologous recombination (HR), have demonstrated to further sub-classify low BRCA1 NSCLC tumors, improving the predictive model. On the basis of biological knowledge on DNA repair pathway and recent controversial results from clinical validation of potential molecular markers, integrated analysis of multiple DNA repair components could improve predictive information and pave the way to a new approach to customized chemotherapy clinical trials.

Prognostic factors in patients with advanced cancer: use of the patient-generated subjective global assessment in survival prediction.

PubMed

Martin, Lisa; Watanabe, Sharon; Fainsinger, Robin; Lau, Francis; Ghosh, Sunita; Quan, Hue; Atkins, Marlis; Fassbender, Konrad; Downing, G Michael; Baracos, Vickie

2010-10-01

To determine whether elements of a standard nutritional screening assessment are independently prognostic of survival in patients with advanced cancer. A prospective nested cohort of patients with metastatic cancer were accrued from different units of a Regional Palliative Care Program. Patients completed a nutritional screen on admission. Data included age, sex, cancer site, height, weight history, dietary intake, 13 nutrition impact symptoms, and patient- and physician-reported performance status (PS). Univariate and multivariate survival analyses were conducted. Concordance statistics (c-statistics) were used to test the predictive accuracy of models based on training and validation sets; a c-statistic of 0.5 indicates the model predicts the outcome as well as chance; perfect prediction has a c-statistic of 1.0. A training set of patients in palliative home care (n = 1,164) was used to identify prognostic variables. Primary disease site, PS, short-term weight change (either gain or loss), dietary intake, and dysphagia predicted survival in multivariate analysis (P < .05). A model including only patients separated by disease site and PS with high c-statistics between predicted and observed responses for survival in the training set (0.90) and validation set (0.88; n = 603). The addition of weight change, dietary intake, and dysphagia did not further improve the c-statistic of the model. The c-statistic was also not altered by substituting physician-rated palliative PS for patient-reported PS. We demonstrate a high probability of concordance between predicted and observed survival for patients in distinct palliative care settings (home care, tertiary inpatient, ambulatory outpatient) based on patient-reported information.

A mathematical model for predicting the adult height of girls with advanced puberty after spontaneous growth.

PubMed

Lemaire, Pierre; Pierre, Delphine; Bertrand, Jean-Baptiste; Brauner, Raja

2014-07-03

Advanced puberty in girls is defined as the onset of puberty between the ages of 8 yr and 10 yr. The objective was to predict adult height (AH) at initial evaluation and to characterize patients with an actual AH below -2 SD (152 cm) and/or lower than their target height (TH) by > one SD (5.6 cm). Data analysis using multiple linear regression models was performed in 50 girls with advanced puberty who reached their AH after spontaneous puberty. The actual AH (159.0 ± 6.1 cm) was similar to the TH (161.2 ± 4.6 cm) and to the AH predicted at the initial evaluation (160.8 ± 6.0 cm), and the actual AH correlated positively with both (R = 0.76, P = 0.0003; R = 0.71, P = 0.008, respectively).The AH was below 152 cm in 7 girls, of whom 3 were characterized by paternal transmission of the advanced puberty. The AH was lower than the TH by >5.6 cm in 8 girls.The AH (cm) could be calculated at the initial evaluation: 1.8822 age + 3.3510 height (SD) - 0.7465 bone age - 1.7993 pubic hair stage + 2.8409 TH (SD) + 150.32.The formula is available online at http://www.kamick.org/lemaire/med/girls-advpub.html.The calculated AH (159.0 ± 5.7 cm) and the actual AH were highly correlated (R = 0.93). The actual AH was lower than the calculated AH by > 0.5 SD in only one case (4.35 cm). We established a formula that can be used at an initial evaluation to predict the AH, and then to assess the risk of reduced AH as a result of advanced puberty. According to this formula, the actual AH was lower than the calculated AH by more than 2.8 cm (0.5 SD) in only one girl. The AHs of the untreated girls with advanced puberty did not differ from those predicted at the initial evaluation by the Bayley and Pinneau table or from the THs. However, this study provides a useful and ready-to-use formula that can be an additional assessment of girls with advanced puberty.

Combining turbulent kinetic energy and Haines Index predictions for fire-weather assessments

Treesearch

Warren E. Heilman; Xindi Bian

2007-01-01

The 24- to 72-hour fire-weather predictions for different regions of the United States are now readily available from the regional Fire Consortia for Advanced Modeling of Meteorology and Smoke (FCAMMS) that were established as part of the U.S. National Fire Plan. These predictions are based on daily real-time MM5 model simulations of atmospheric conditions and fire-...

Prelude and Fugue, predicting local protein structure, early folding regions and structural weaknesses.

PubMed

Kwasigroch, Jean Marc; Rooman, Marianne

2006-07-15

Prelude&Fugue are bioinformatics tools aiming at predicting the local 3D structure of a protein from its amino acid sequence in terms of seven backbone torsion angle domains, using database-derived potentials. Prelude(&Fugue) computes all lowest free energy conformations of a protein or protein region, ranked by increasing energy, and possibly satisfying some interresidue distance constraints specified by the user. (Prelude&)Fugue detects sequence regions whose predicted structure is significantly preferred relative to other conformations in the absence of tertiary interactions. These programs can be used for predicting secondary structure, tertiary structure of short peptides, flickering early folding sequences and peptides that adopt a preferred conformation in solution. They can also be used for detecting structural weaknesses, i.e. sequence regions that are not optimal with respect to the tertiary fold. http://babylone.ulb.ac.be/Prelude_and_Fugue.

Classification of TP53 Mutations and HPV Predict Survival in Advanced Larynx Cancer

PubMed Central

Scheel, Adam; Bellile, Emily; McHugh, Jonathan B.; Walline, Heather M.; Prince, Mark E.; Urba, Susan; Wolf, Gregory T.; Eisbruch, Avraham; Worden, Francis; Carey, Thomas E.; Bradford, Carol

2016-01-01

OBJECTIVE Assess TP53 functional mutations in the context of other biomarkers in advanced larynx cancer. STUDY DESIGN Prospective analysis of pretreatment tumor TP53, HPV, Bcl-xL and cyclin D1 status in stage III and IV larynx cancer patients in a clinical trial. METHODS TP53 exons 4-9 from 58 tumors were sequenced. Mutations were grouped using three classifications based on their expected function. Each functional group was analyzed for response to induction chemotherapy, time to surgery, survival, HPV status, p16INK4a, Bcl-xl and cyclin D1 expression. RESULTS TP53 Mutations were found in 22/58 (37.9%) patients with advanced larynx cancer, including missense mutations in 13/58 (22.4%) patients, nonsense mutations in 4/58 (6.9%), and deletions in 5/58 (8.6%). High risk HPV was found in 20/52 (38.5%) tumors. A classification based on crystal Evolutionary Action score of p53 (EAp53) distinguished missense mutations with high risk for decreased survival from low risk mutations (p=0.0315). A model including this TP53 classification, HPV status, cyclin D1 and Bcl-xL staining significantly predicts survival (p=0.0017). CONCLUSION EAp53 functional classification of TP53 mutants and biomarkers predict survival in advanced larynx cancer. PMID:27345657

Plasma D-dimer Can Effectively Predict the Prospective Occurrence of Ascites in Advanced Schistosomiasis Japonica Patients.

PubMed

Wu, Xiaoying; Ren, Jianwei; Gao, Zulu; Xu, Yun; Xie, Huiqun; Li, Tingfang; Cheng, Yanhua; Hu, Fei; Liu, Hongyun; Gong, Zhihong; Liang, Jinyi; Shen, Jia; Liu, Zhen; Wu, Feng; Sun, Xi; Niu, Zhongzheng; Ning, An

2017-04-01

China still has more than 30,000 patients of advanced schistosomiasis while new cases being reported consistently. D-dimer is a fibrin degradation product. As ascites being the dominating symptom in advanced schistosomiasis, the present study aimed to explore a prediction model of ascites with D-dimer and other clinical easy-achievable indicators. A case-control study nested in a prospective cohort was conducted in schistosomiasis-endemic area of southern China. A total of 291 patients of advanced schistosomiasis were first investigated in 2013 and further followed in 2014. Information on clinical history, physical examination, and abdominal ultrasonography, including the symptom of ascites was repeatedly collected. Result showed 44 patients having ascites. Most of the patients' ascites were confined in the kidney area with median area of 20 mm 2 . The level of plasma D-dimer and pertinent liver function indicators were measured at the initial investigation in 2013. Compared with those without ascites, cases with ascites had significantly higher levels of D-dimer (0.71±2.44 μg/L vs 0.48±2.12 μg/L, P =0.005), as well ALB (44.5 vs 46.2, g/L) and Type IV collagen (50.04 vs 44.50 μg/L). Receiver operating characteristic curve analyses indicated a moderate predictive value of D-dimer by its own area under curve (AUC) of 0.64 (95% CI: 0.54-0.73) and the cutoff value as 0.81 μg/L. Dichotomized by the cutoff level, D-dimer along with other categorical variables generated a prediction model with AUC of 0.76 (95% CI: 0.68-0.89). Risks of patients with specific characteristics in the prediction model were summarized. Our study suggests that the plasma D-dimer level is a reliable predictor for incident ascites in advanced schistosomiasis japonica patients.

Plasma D-dimer Can Effectively Predict the Prospective Occurrence of Ascites in Advanced Schistosomiasis Japonica Patients

PubMed Central

Wu, Xiaoying; Ren, Jianwei; Gao, Zulu; Xu, Yun; Xie, Huiqun; Li, Tingfang; Cheng, Yanhua; Hu, Fei; Liu, Hongyun; Gong, Zhihong; Liang, Jinyi; Shen, Jia; Liu, Zhen; Wu, Feng; Sun, Xi; Niu, Zhongzheng; Ning, An

2017-01-01

China still has more than 30,000 patients of advanced schistosomiasis while new cases being reported consistently. D-dimer is a fibrin degradation product. As ascites being the dominating symptom in advanced schistosomiasis, the present study aimed to explore a prediction model of ascites with D-dimer and other clinical easy-achievable indicators. A case-control study nested in a prospective cohort was conducted in schistosomiasis-endemic area of southern China. A total of 291 patients of advanced schistosomiasis were first investigated in 2013 and further followed in 2014. Information on clinical history, physical examination, and abdominal ultrasonography, including the symptom of ascites was repeatedly collected. Result showed 44 patients having ascites. Most of the patients’ ascites were confined in the kidney area with median area of 20 mm2. The level of plasma D-dimer and pertinent liver function indicators were measured at the initial investigation in 2013. Compared with those without ascites, cases with ascites had significantly higher levels of D-dimer (0.71±2.44 μg/L vs 0.48±2.12 μg/L, P=0.005), as well ALB (44.5 vs 46.2, g/L) and Type IV collagen (50.04 vs 44.50 μg/L). Receiver operating characteristic curve analyses indicated a moderate predictive value of D-dimer by its own area under curve (AUC) of 0.64 (95% CI: 0.54–0.73) and the cutoff value as 0.81 μg/L. Dichotomized by the cutoff level, D-dimer along with other categorical variables generated a prediction model with AUC of 0.76 (95% CI: 0.68–0.89). Risks of patients with specific characteristics in the prediction model were summarized. Our study suggests that the plasma D-dimer level is a reliable predictor for incident ascites in advanced schistosomiasis japonica patients. PMID:28506039

Acoustic prediction methods for the NASA generalized advanced propeller analysis system (GAPAS)

NASA Technical Reports Server (NTRS)

Padula, S. L.; Block, P. J. W.

1984-01-01

Classical methods of propeller performance analysis are coupled with state-of-the-art Aircraft Noise Prediction Program (ANOPP:) techniques to yield a versatile design tool, the NASA Generalized Advanced Propeller Analysis System (GAPAS) for the novel quiet and efficient propellers. ANOPP is a collection of modular specialized programs. GAPAS as a whole addresses blade geometry and aerodynamics, rotor performance and loading, and subsonic propeller noise.

Improving hot region prediction by parameter optimization of density clustering in PPI.

PubMed

Hu, Jing; Zhang, Xiaolong

2016-11-01

This paper proposed an optimized algorithm which combines density clustering of parameter selection with feature-based classification for hot region prediction. First, all the residues are classified by SVM to remove non-hot spot residues, then density clustering of parameter selection is used to find hot regions. In the density clustering, this paper studies how to select input parameters. There are two parameters radius and density in density-based incremental clustering. We firstly fix density and enumerate radius to find a pair of parameters which leads to maximum number of clusters, and then we fix radius and enumerate density to find another pair of parameters which leads to maximum number of clusters. Experiment results show that the proposed method using both two pairs of parameters provides better prediction performance than the other method, and compare these two predictive results, the result by fixing radius and enumerating density have slightly higher prediction accuracy than that by fixing density and enumerating radius. Copyright © 2016. Published by Elsevier Inc.

Predicting pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review.

PubMed

Ryan, J E; Warrier, S K; Lynch, A C; Ramsay, R G; Phillips, W A; Heriot, A G

2016-03-01

Approximately 20% of patients treated with neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer achieve a pathological complete response (pCR) while the remainder derive the benefit of improved local control and downstaging and a small proportion show a minimal response. The ability to predict which patients will benefit would allow for improved patient stratification directing therapy to those who are likely to achieve a good response, thereby avoiding ineffective treatment in those unlikely to benefit. A systematic review of the English language literature was conducted to identify pathological factors, imaging modalities and molecular factors that predict pCR following chemoradiotherapy. PubMed, MEDLINE and Cochrane Database searches were conducted with the following keywords and MeSH search terms: 'rectal neoplasm', 'response', 'neoadjuvant', 'preoperative chemoradiation', 'tumor response'. After review of title and abstracts, 85 articles addressing the prediction of pCR were selected. Clear methods to predict pCR before chemoradiotherapy have not been defined. Clinical and radiological features of the primary cancer have limited ability to predict response. Molecular profiling holds the greatest potential to predict pCR but adoption of this technology will require greater concordance between cohorts for the biomarkers currently under investigation. At present no robust markers of the prediction of pCR have been identified and the topic remains an area for future research. This review critically evaluates existing literature providing an overview of the methods currently available to predict pCR to nCRT for locally advanced rectal cancer. The review also provides a comprehensive comparison of the accuracy of each modality. Colorectal Disease © 2015 The Association of Coloproctology of Great Britain and Ireland.

Neutrophil-lymphocyte ratio predicts survival in patients with advanced cholangiocarcinoma on chemotherapy.

PubMed

Lee, Ban Seok; Lee, Sang Hyub; Son, Jun Hyuk; Jang, Dong Kee; Chung, Kwang Hyun; Lee, Yoon Suk; Paik, Woo Hyun; Ryu, Ji Kon; Kim, Yong-Tae

2016-02-01

The blood neutrophil-to-lymphocyte ratio (NLR) is reported to be a prognostic marker in several cancers. However, the prognostic role of NLR in patients with advanced cholangiocarcinoma on chemotherapy is unknown. A total of 221 patients with pathologically confirmed locally advanced or metastatic cholangiocarcinoma receiving first-line palliative chemotherapy were enrolled. Associations between baseline clinical and laboratory variables including NLR and survival were investigated. Patients were classified into two groups according to the NLR level (≤ 5 vs. >5). Median overall survival (OS) and time to progression (TTP) in patients with NLR ≤ 5 were 10.9 and 6.7 months, respectively, and 6.8 and 4.1 months in patients with NLR > 5 (P < 0.001, P = 0.002, respectively). In multivariate analysis, number of cycles of chemotherapy was a significant predictor of longer OS (HR 0.86, P < 0.001), whereas adverse prognostic factors for OS were CA 19-9 > 300 (HR 1.43, P = 0.025), CEA > 5 (HR 1.44, P = 0.029), higher stage (HR 1.69, P = 0.004), and NLR > 5 (HR 1.87, P < 0.001). NLR > 5 was also associated with reduced TTP (HR 1.66, P = 0.007). Among 50 patients with initial NLR > 5, 33 patients had NLR ≤ 5 after two cycles of chemotherapy and they had significantly better survival than the others (HR 0.48, P = 0.015). NLR independently predicts survival in patients with advanced cholangiocarcinoma undergoing chemotherapy. Considering cost-effectiveness and easy availability, NLR may be a useful biomarker for prognosis prediction.

Prognostic Value of Plasma Epstein-Barr Virus DNA for Local and Regionally Advanced Nasopharyngeal Carcinoma Treated With Cisplatin-Based Concurrent Chemoradiotherapy in Intensity-Modulated Radiotherapy Era.

PubMed

Chen, Wen-Hui; Tang, Lin-Quan; Guo, Shan-Shan; Chen, Qiu-Yan; Zhang, Lu; Liu, Li-Ting; Qian, Chao-Nan; Guo, Xiang; Xie, Dan; Zeng, Mu-Sheng; Mai, Hai-Qiang

2016-02-01

This study aimed to evaluate the prognostic value of plasma Epstein-Barr Virus DNA (EBV DNA) for local and regionally advanced nasopharyngeal carcinoma (NPC) patients treated with concurrent chemoradiotherapy in intensity-modulated radiotherapy (IMRT) era.In this observational study, 404 nonmetastatic local and regionally advanced NPC patients treated with IMRT and cisplatin-based concurrent chemotherapy were recruited. Blood samples were collected before treatment for examination of plasma EBV DNA levels. We evaluated the association of pretreatment plasma EBV DNA levels with progression-free survival rate (PFS), distant metastasis-free survival rate (DMFS), and overall survival rate (OS).Compared to patients with an EBV DNA level < 4000  copies/mL, patients with an EBV DNA ≥ 4000  copies/mL had a lower rate of 3-year PFS (76%, 95% CI [68-84]) versus (93%, 95% CI [90-96], P < 0.001), DMFS (83%, 95% CI [76-89]) versus (97%, 95% CI [94-99], P < 0.001), and OS (85%, 95% CI [78-92]) versus (98%, 95% CI [95-100], P < 0.001). Multivariate analysis showed that pretreatment EBV DNA levels (HR = 3.324, 95% CI, 1.80-6.138, P < 0.001) and clinical stage (HR = 1.878, 95% CI, 1.036-3.404, P = 0.038) were the only independent factor associated with PFS, pretreatment EBV DNA level was the only significant factor to predict DMFS (HR = 6.292, 95% CI, 2.647-14.956, P < 0.001), and pretreatment EBV DNA levels (HR = 3.753, 95% CI, 1.701-8.284, P < 0.001) and clinical stage (HR = 2.577, 95% CI, 1.252-5.050, P = 0.010) were significantly associated with OS. In subgroup analysis, higher plasma EBV DNA levels still predicted a worse PFS, DMFS, and OS for the patients stage III or stage IVa-b, compared with those with low EBV DNA levels.Elevated plasma EBV DNA was still effective prognostic biomarker for local and regionally advanced NPC patients treated with IMRT and cisplatin-based concurrent chemotherapy. Future ramdomized

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

Prediction of hot regions in protein-protein interaction by combining density-based incremental clustering with feature-based classification.

PubMed

Hu, Jing; Zhang, Xiaolong; Liu, Xiaoming; Tang, Jinshan

2015-06-01

Discovering hot regions in protein-protein interaction is important for drug and protein design, while experimental identification of hot regions is a time-consuming and labor-intensive effort; thus, the development of predictive models can be very helpful. In hot region prediction research, some models are based on structure information, and others are based on a protein interaction network. However, the prediction accuracy of these methods can still be improved. In this paper, a new method is proposed for hot region prediction, which combines density-based incremental clustering with feature-based classification. The method uses density-based incremental clustering to obtain rough hot regions, and uses feature-based classification to remove the non-hot spot residues from the rough hot regions. Experimental results show that the proposed method significantly improves the prediction performance of hot regions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Advances in regional anaesthesia: A review of current practice, newer techniques and outcomes

PubMed Central

Wahal, Christopher; Kumar, Amanda; Pyati, Srinivas

2018-01-01

Advances in ultrasound guided regional anaesthesia and introduction of newer long acting local anaesthetics have given clinicians an opportunity to apply novel approaches to block peripheral nerves with ease. Consequently, improvements in outcomes such as quality of analgesia, early rehabilitation and patient satisfaction have been observed. In this article we will review some of the newer regional anaesthetic techniques, long acting local anaesthetics and adjuvants, and discuss evidence for key outcomes such as cancer recurrence and safety with ultrasound guidance. PMID:29491513

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.

Robust face alignment under occlusion via regional predictive power estimation.

PubMed

Heng Yang; Xuming He; Xuhui Jia; Patras, Ioannis

2015-08-01

Face alignment has been well studied in recent years, however, when a face alignment model is applied on facial images with heavy partial occlusion, the performance deteriorates significantly. In this paper, instead of training an occlusion-aware model with visibility annotation, we address this issue via a model adaptation scheme that uses the result of a local regression forest (RF) voting method. In the proposed scheme, the consistency of the votes of the local RF in each of several oversegmented regions is used to determine the reliability of predicting the location of the facial landmarks. The latter is what we call regional predictive power (RPP). Subsequently, we adapt a holistic voting method (cascaded pose regression based on random ferns) by putting weights on the votes of each fern according to the RPP of the regions used in the fern tests. The proposed method shows superior performance over existing face alignment models in the most challenging data sets (COFW and 300-W). Moreover, it can also estimate with high accuracy (72.4% overlap ratio) which image areas belong to the face or nonface objects, on the heavily occluded images of the COFW data set, without explicit occlusion modeling.

Regional Ocean Data Assimilation

NASA Astrophysics Data System (ADS)

Edwards, Christopher A.; Moore, Andrew M.; Hoteit, Ibrahim; Cornuelle, Bruce D.

2015-01-01

This article reviews the past 15 years of developments in regional ocean data assimilation. A variety of scientific, management, and safety-related objectives motivate marine scientists to characterize many ocean environments, including coastal regions. As in weather prediction, the accurate representation of physical, chemical, and/or biological properties in the ocean is challenging. Models and observations alone provide imperfect representations of the ocean state, but together they can offer improved estimates. Variational and sequential methods are among the most widely used in regional ocean systems, and there have been exciting recent advances in ensemble and four-dimensional variational approaches. These techniques are increasingly being tested and adapted for biogeochemical applications.

Regional ocean data assimilation.

PubMed

Edwards, Christopher A; Moore, Andrew M; Hoteit, Ibrahim; Cornuelle, Bruce D

2015-01-01

This article reviews the past 15 years of developments in regional ocean data assimilation. A variety of scientific, management, and safety-related objectives motivate marine scientists to characterize many ocean environments, including coastal regions. As in weather prediction, the accurate representation of physical, chemical, and/or biological properties in the ocean is challenging. Models and observations alone provide imperfect representations of the ocean state, but together they can offer improved estimates. Variational and sequential methods are among the most widely used in regional ocean systems, and there have been exciting recent advances in ensemble and four-dimensional variational approaches. These techniques are increasingly being tested and adapted for biogeochemical applications.

Advances in Predictive Toxicology for Discovery Safety through High Content Screening.

PubMed

Persson, Mikael; Hornberg, Jorrit J

2016-12-19

High content screening enables parallel acquisition of multiple molecular and cellular readouts. In particular the predictive toxicology field has progressed from the advances in high content screening, as more refined end points that report on cellular health can be studied in combination, at the single cell level, and in relatively high throughput. Here, we discuss how high content screening has become an essential tool for Discovery Safety, the discipline that integrates safety and toxicology in the drug discovery process to identify and mitigate safety concerns with the aim to design drug candidates with a superior safety profile. In addition to customized mechanistic assays to evaluate target safety, routine screening assays can be applied to identify risk factors for frequently occurring organ toxicities. We discuss the current state of high content screening assays for hepatotoxicity, cardiotoxicity, neurotoxicity, nephrotoxicity, and genotoxicity, including recent developments and current advances.

Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer.

PubMed

Petersen, Japke F; Stuiver, Martijn M; Timmermans, Adriana J; Chen, Amy; Zhang, Hongzhen; O'Neill, James P; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T; Koch, Wayne; van den Brekel, Michiel W M

2018-05-01

TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P < .001). The model was able to distinguish well among three risk groups based on tertiles of the risk score. Adding treatment modality to the model did not decrease the predictive power. As a post hoc analysis, we tested the added value of comorbidity as scored by American Society of Anesthesiologists score in a subsample, which increased the C statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

Decadal predictions of the North Atlantic CO2 uptake.

PubMed

Li, Hongmei; Ilyina, Tatiana; Müller, Wolfgang A; Sienz, Frank

2016-03-30

As a major CO2 sink, the North Atlantic, especially its subpolar gyre region, is essential for the global carbon cycle. Decadal fluctuations of CO2 uptake in the North Atlantic subpolar gyre region are associated with the evolution of the North Atlantic Oscillation, the Atlantic meridional overturning circulation, ocean mixing and sea surface temperature anomalies. While variations in the physical state of the ocean can be predicted several years in advance by initialization of Earth system models, predictability of CO2 uptake has remained unexplored. Here we investigate the predictability of CO2 uptake variations by initialization of the MPI-ESM decadal prediction system. We find large multi-year variability in oceanic CO2 uptake and demonstrate that its potential predictive skill in the western subpolar gyre region is up to 4-7 years. The predictive skill is mainly maintained in winter and is attributed to the improved physical state of the ocean.

Polymorphism of TS 3'-UTR predicts survival of Chinese advanced gastric cancer patients receiving first-line capecitabine plus paclitaxel.

PubMed

Gao, J; He, Q; Hua, D; Mao, Y; Li, Y; Shen, L

2013-08-01

Capecitabine-containing chemotherapy was widely used in clinic medication. We investigated the association of the thymidylate synthase (TS), methylenetetrahydrofolate reductase (MTHFR), and dihydropyrimidine dehydrogenase (DPD) polymorphisms with the clinical outcome of Chinese advanced gastric cancer patients receiving first-line capecitabine plus paclitaxel. Blood samples were collected prior to treatment from 125 patients with advanced gastric cancer and the TS (two or three repeats of a 28 bp sequence in 5'-untranslated region and 6 bp insertion or deletion in 3'-untranslated region), MTHFR (C677T) and DPD (IVS14+1G > A) polymorphisms were determined using PCR amplification and Sanger sequencing. The median age of 125 patients was 58 years (range, 23-76) with female 42 and male 83, and the response rate, median progression-free survival and overall survival (OS) were 43.2 %, 5.2 and 11.0 months. The median OS in patients with TS ins6/ins6 genotype (6.8 months) was significantly shorter than those in patients with ins6/del6 (11.0 months, P = 0.016) and del6/del6 (11.5 months, P = 0.039) genotypes. Cox multivariate analysis also showed that TS ins6/ins6 genotype was the independent poor OS predictor (P = 0.001, HR = 3.182). No significant associations were found between the polymorphisms of TS 5'-UTR/MTHFR and clinical outcome, and no IVS14+1G > A polymorphism of DPD was found in this study. We first reported that TS 3'-UTR ins6/ins6 genotype could predict the poor survival of advanced gastric cancer patients treated with capecitabine plus paclitaxel, which would be further verified in a large multicenter study.

Quantitative Earthquake Prediction on Global and Regional Scales

NASA Astrophysics Data System (ADS)

Kossobokov, Vladimir G.

2006-03-01

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+.

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

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.

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.

Classification of TP53 mutations and HPV predict survival in advanced larynx cancer.

PubMed

Scheel, Adam; Bellile, Emily; McHugh, Jonathan B; Walline, Heather M; Prince, Mark E; Urba, Susan; Wolf, Gregory T; Eisbruch, Avraham; Worden, Francis; Carey, Thomas E; Bradford, Carol

2016-09-01

Assess tumor suppressor p53 (TP53) functional mutations in the context of other biomarkers in advanced larynx cancer. Prospective analysis of pretreatment tumor TP53, human papillomavirus (HPV), Bcl-xL, and cyclin D1 status in stage III and IV larynx cancer patients in a clinical trial. TP53 exons 4 through 9 from 58 tumors were sequenced. Mutations were grouped using three classifications based on their expected function. Each functional group was analyzed for response to induction chemotherapy, time to surgery, survival, HPV status, p16INK4a, Bcl-xl, and cyclin D1 expression. TP53 mutations were found in 22 of 58 (37.9%) patients with advanced larynx cancer, including missense mutations in 13 of 58 (22.4%) patients, nonsense mutations in four of 58 (6.9%), and deletions in five of 58 (8.6%). High-risk HPV was found in 20 of 52 (38.5%) tumors. A classification based on Evolutionary Action score of p53 (EAp53) distinguished missense mutations with high risk for decreased survival from low-risk mutations (P = 0.0315). A model including this TP53 classification, HPV status, cyclin D1, and Bcl-xL staining significantly predicts survival (P = 0.0017). EAp53 functional classification of TP53 mutants and biomarkers predict survival in advanced larynx cancer. NA. Laryngoscope, 126:E292-E299, 2016. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.

Sexual selection predicts advancement of avian spring migration in response to climate change.

PubMed

Spottiswoode, Claire N; Tøttrup, Anders P; Coppack, Timothy

2006-12-22

Global warming has led to earlier spring arrival of migratory birds, but the extent of this advancement varies greatly among species, and it remains uncertain to what degree these changes are phenotypically plastic responses or microevolutionary adaptations to changing environmental conditions. We suggest that sexual selection could help to understand this variation, since early spring arrival of males is favoured by female choice. Climate change could weaken the strength of natural selection opposing sexual selection for early migration, which would predict greatest advancement in species with stronger female choice. We test this hypothesis comparatively by investigating the degree of long-term change in spring passage at two ringing stations in northern Europe in relation to a synthetic estimate of the strength of female choice, composed of degree of extra-pair paternity, relative testes size and degree of sexually dichromatic plumage colouration. We found that species with a stronger index of sexual selection have indeed advanced their date of spring passage to a greater extent. This relationship was stronger for the changes in the median passage date of the whole population than for changes in the timing of first-arriving individuals, suggesting that selection has not only acted on protandrous males. These results suggest that sexual selection may have an impact on the responses of organisms to climate change, and knowledge of a species' mating system might help to inform attempts at predicting these.

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.

Prediction of 30-day morbidity after primary cytoreductive surgery for advanced stage ovarian cancer.

PubMed

Gerestein, C G; Nieuwenhuyzen-de Boer, G M; Eijkemans, M J; Kooi, G S; Burger, C W

2010-01-01

Treatment in advanced stage epithelial ovarian cancer (EOC) is based on primary cytoreductive surgery followed by platinum-based chemotherapy. Successful cytoreduction to minimal residual tumour burden is the most important determinant of prognosis. However, extensive surgical procedures to achieve maximal debulking are inevitably associated with postoperative morbidity and mortality. The objective of this study is to determine predictors of 30-day morbidity after primary cytoreductive surgery for advanced stage EOC. All patients in the South Western part of the Netherlands who underwent primary cytoreductive surgery for advanced stage EOC between January 2004 and December 2007 were identified from the Rotterdam Cancer Registry database. All peri- and postoperative complications within 30 days after surgery were registered and classified according to the definitions of the National Surgical Quality Improvement Programme (NSQIP). To investigate independent predictors of 30-day morbidity, a Cox proportional hazards model with backward stepwise elimination was utilised. The identified predictors were entered into a nomogram. Two hundred and ninety-three patients entered the study protocol. Optimal cytoreduction was achieved in 136 (46%) patients. 30-day morbidity was seen in 99 (34%) patients. Postoperative morbidity could be predicted by age (P=0.007; odds ratio [OR] 1.034), WHO performance status (P=0.046; OR 1.757), extent of surgery (P=0.1308; OR=2.101), and operative time (P=0.017; OR 1.007) with an optimism corrected c-statistic of 0.68. 30-day morbidity could be predicted by age, WHO performance status, operative time and extent of surgery. The generated nomogram could be valuable for predicting operative risk in the individual patient.

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.

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…

[Prediction of regional soil quality based on mutual information theory integrated with decision tree algorithm].

PubMed

Lin, Fen-Fang; Wang, Ke; Yang, Ning; Yan, Shi-Guang; Zheng, Xin-Yu

2012-02-01

In this paper, some main factors such as soil type, land use pattern, lithology type, topography, road, and industry type that affect soil quality were used to precisely obtain the spatial distribution characteristics of regional soil quality, mutual information theory was adopted to select the main environmental factors, and decision tree algorithm See 5.0 was applied to predict the grade of regional soil quality. The main factors affecting regional soil quality were soil type, land use, lithology type, distance to town, distance to water area, altitude, distance to road, and distance to industrial land. The prediction accuracy of the decision tree model with the variables selected by mutual information was obviously higher than that of the model with all variables, and, for the former model, whether of decision tree or of decision rule, its prediction accuracy was all higher than 80%. Based on the continuous and categorical data, the method of mutual information theory integrated with decision tree could not only reduce the number of input parameters for decision tree algorithm, but also predict and assess regional soil quality effectively.

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).

Rainfall prediction of Cimanuk watershed regions with canonical correlation analysis (CCA)

NASA Astrophysics Data System (ADS)

Rustiana, Shailla; Nurani Ruchjana, Budi; Setiawan Abdullah, Atje; Hermawan, Eddy; Berliana Sipayung, Sinta; Gede Nyoman Mindra Jaya, I.; Krismianto

2017-10-01

Rainfall prediction in Indonesia is very influential on various development sectors, such as agriculture, fisheries, water resources, industry, and other sectors. The inaccurate predictions can lead to negative effects. Cimanuk watershed is one of the main pillar of water resources in West Java. This watersheds divided into three parts, which is a headwater of Cimanuk sub-watershed, Middle of Cimanuk sub-watershed and downstream of Cimanuk sub- watershed. The flow of this watershed will flow through the Jatigede reservoir and will supply water to the north-coast area in the next few years. So, the reliable model of rainfall prediction is very needed in this watershed. Rainfall prediction conducted with Canonical Correlation Analysis (CCA) method using Climate Predictability Tool (CPT) software. The prediction is every 3months on 2016 (after January) based on Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data over West Java. Predictors used in CPT were the monthly data index of Nino3.4, Dipole Mode (DMI), and Monsoon Index (AUSMI-ISMI-WNPMI-WYMI) with initial condition January. The initial condition is chosen by the last data update. While, the predictant were monthly rainfall data CHIRPS region of West Java. The results of prediction rainfall showed by skill map from Pearson Correlation. High correlation of skill map are on MAM (Mar-Apr-May), AMJ (Apr-May-Jun), and JJA (Jun-Jul-Aug) which means the model is reliable to forecast rainfall distribution over Cimanuk watersheds region (over West Java) on those seasons. CCA score over those season prediction mostly over 0.7. The accuracy of the model CPT also indicated by the Relative Operating Characteristic (ROC) curve of the results of Pearson correlation 3 representative point of sub-watershed (Sumedang, Majalengka, and Cirebon), were mostly located in the top line of non-skill, and evidenced by the same of rainfall patterns between observation and forecast. So, the model of CPT with CCA method

The predictive value of MRI in detecting thyroid gland invasion in patients with advanced laryngeal or hypopharyngeal carcinoma.

PubMed

Lin, Peiliang; Huang, Xiaoming; Zheng, Chushan; Cai, Qian; Guan, Zhong; Liang, Faya; Zheng, Yiqing

2017-01-01

The aim of this study was to evaluate the predictive value of magnetic resonance imaging (MRI) in detecting thyroid gland invasion (TGI) in patients with advanced laryngeal or hypopharyngeal carcinoma. In a retrospective chart review, 41 patients with advanced laryngeal or hypopharyngeal carcinoma underwent MRI scan before total laryngectomy and ipsilateral or bilateral thyroidectomy during the past 5 years. The MRI findings were compared with the postoperative pathological results. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Among the 41 patients, 3 had thyroid gland invasion in postoperative pathological results. MRI correctly predicted the absence of TGI in 37 of 38 patients and TGI in all 3 patients. The sensitivity, specificity, PPV, and NPV of MRI were 100.0, 97.4, 75.0, and 100 %, respectively, with the diagnostic accuracy of 97.6 %. In consideration of the high negative predictive value of MRI, it may help surgeons selectively preserve thyroid gland in total laryngectomy and reduce the incidence of hypothyroidism and hypoparathyroidism postoperatively.

Hypothesis testing and earthquake prediction.

PubMed

Jackson, D D

1996-04-30

Requirements for testing include advance specification of the conditional rate density (probability per unit time, area, and magnitude) or, alternatively, probabilities for specified intervals of time, space, and magnitude. Here I consider testing fully specified hypotheses, with no parameter adjustments or arbitrary decisions allowed during the test period. Because it may take decades to validate prediction methods, it is worthwhile to formulate testable hypotheses carefully in advance. Earthquake prediction generally implies that the probability will be temporarily higher than normal. Such a statement requires knowledge of "normal behavior"--that is, it requires a null hypothesis. Hypotheses can be tested in three ways: (i) by comparing the number of actual earth-quakes to the number predicted, (ii) by comparing the likelihood score of actual earthquakes to the predicted distribution, and (iii) by comparing the likelihood ratio to that of a null hypothesis. The first two tests are purely self-consistency tests, while the third is a direct comparison of two hypotheses. Predictions made without a statement of probability are very difficult to test, and any test must be based on the ratio of earthquakes in and out of the forecast regions.

Hypothesis testing and earthquake prediction.

PubMed Central

Jackson, D D

1996-01-01

Requirements for testing include advance specification of the conditional rate density (probability per unit time, area, and magnitude) or, alternatively, probabilities for specified intervals of time, space, and magnitude. Here I consider testing fully specified hypotheses, with no parameter adjustments or arbitrary decisions allowed during the test period. Because it may take decades to validate prediction methods, it is worthwhile to formulate testable hypotheses carefully in advance. Earthquake prediction generally implies that the probability will be temporarily higher than normal. Such a statement requires knowledge of "normal behavior"--that is, it requires a null hypothesis. Hypotheses can be tested in three ways: (i) by comparing the number of actual earth-quakes to the number predicted, (ii) by comparing the likelihood score of actual earthquakes to the predicted distribution, and (iii) by comparing the likelihood ratio to that of a null hypothesis. The first two tests are purely self-consistency tests, while the third is a direct comparison of two hypotheses. Predictions made without a statement of probability are very difficult to test, and any test must be based on the ratio of earthquakes in and out of the forecast regions. PMID:11607663

Predicting redox conditions in groundwater at a regional scale

USGS Publications Warehouse

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

2015-01-01

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

Predicting exotic earthworm distribution in the northern Great Lakes region

Treesearch

Lindsey M. Shartell; Erik A. Lilleskov; Andrew J. Storer

2013-01-01

Identifying influences of earthworm invasion and distribution in the northern Great Lakes is an important step in predicting the potential extent and impact of earthworms across the region. The occurrence of earthworm signs, indicating presence in general, and middens, indicating presence of Lumbricus terrestris exclusively, in the Huron Mountains...

Summer precipitation prediction in the source region of the Yellow River using climate indices

NASA Astrophysics Data System (ADS)

Yuan, F.

2016-12-01

The source region of the Yellow River contributes about 35% of the total water yield in the Yellow River basin playing an important role in meeting downstream water resources requirements. The summer precipitation from June to September in the source region of the Yellow River accounts for about 70% of the annual total, and its decrease would cause further water shortage problems. Consequently, the objectives of this study are to improve the understanding of the linkages between the precipitation in the source region of the Yellow River and global teleconnection patterns, and to predict the summer precipitation based on revealed teleconnections. Spatial variability of precipitation was investigated based on three homogeneous sub-regions. Principal component analysis and singular value decomposition were used to find significant relations between the precipitation in the source region of the Yellow River and global teleconnection patterns using climate indices. A back-propagation neural network was developed to predict the summer precipitation using significantly correlated climate indices. It was found that precipitation in the study area is positively related to North Atlantic Oscillation, West Pacific Pattern and El Nino Southern Oscillation, and inversely related to Polar Eurasian pattern. Summer precipitation was overall well predicted using these significantly correlated climate indices, and the Pearson correlation coefficient between predicted and observed summer precipitation was in general larger than 0.6. The results are useful for integrated water resources management in the Yellow River basin.

The inhabited environment, infrastructure development and advanced urbanization in China’s Yangtze River Delta Region

NASA Astrophysics Data System (ADS)

Zhu, Xiaoqing; Gao, Weijun; Zhou, Nan; Kammen, Daniel M.; Wu, Yiqun; Zhang, Yao; Chen, Wei

2016-12-01

This paper analyzes the relationship among the inhabited environment, infrastructure development and environmental impacts in China’s heavily urbanized Yangtze River Delta region. Using primary human environment data for the period 2006-2014, we examine factors affecting the inhabited environment and infrastructure development: urban population, GDP, built-up area, energy consumption, waste emission, transportation, real estate and urban greenery. Then we empirically investigate the impact of advanced urbanization with consideration of cities’ differences. Results from this study show that the growth rate of the inhabited environment and infrastructure development is strongly influenced by regional development structure, functional orientations, traffic network and urban size and form. The effect of advanced urbanization is more significant in large and mid-size cities than huge and mega cities. Energy consumption, waste emission and real estate in large and mid-size cities developed at an unprecedented rate with the rapid increase of economy. However, urban development of huge and mega cities gradually tended to be saturated. The transition development in these cities improved the inhabited environment and ecological protection instead of the urban construction simply. To maintain a sustainable advanced urbanization process, policy implications included urban sprawl control polices, ecological development mechanisms and reforming the economic structure for huge and mega cities, and construct major cross-regional infrastructure, enhance the carrying capacity and improvement of energy efficiency and structure for large and mid-size cities.

Genome-scale prediction of proteins with long intrinsically disordered regions.

PubMed

Peng, Zhenling; Mizianty, Marcin J; Kurgan, Lukasz

2014-01-01

Proteins with long disordered regions (LDRs), defined as having 30 or more consecutive disordered residues, are abundant in eukaryotes, and these regions are recognized as a distinct class of biologically functional domains. LDRs facilitate various cellular functions and are important for target selection in structural genomics. Motivated by the lack of methods that directly predict proteins with LDRs, we designed Super-fast predictor of proteins with Long Intrinsically DisordERed regions (SLIDER). SLIDER utilizes logistic regression that takes an empirically chosen set of numerical features, which consider selected physicochemical properties of amino acids, sequence complexity, and amino acid composition, as its inputs. Empirical tests show that SLIDER offers competitive predictive performance combined with low computational cost. It outperforms, by at least a modest margin, a comprehensive set of modern disorder predictors (that can indirectly predict LDRs) and is 16 times faster compared to the best currently available disorder predictor. Utilizing our time-efficient predictor, we characterized abundance and functional roles of proteins with LDRs over 110 eukaryotic proteomes. Similar to related studies, we found that eukaryotes have many (on average 30.3%) proteins with LDRs with majority of proteomes having between 25 and 40%, where higher abundance is characteristic to proteomes that have larger proteins. Our first-of-its-kind large-scale functional analysis shows that these proteins are enriched in a number of cellular functions and processes including certain binding events, regulation of catalytic activities, cellular component organization, biogenesis, biological regulation, and some metabolic and developmental processes. A webserver that implements SLIDER is available at http://biomine.ece.ualberta.ca/SLIDER/. Copyright © 2013 Wiley Periodicals, Inc.

The Potential of Tropospheric Gradients for Regional Precipitation Prediction

NASA Astrophysics Data System (ADS)

Boisits, Janina; Möller, Gregor; Wittmann, Christoph; Weber, Robert

2017-04-01

Changes of temperature and humidity in the neutral atmosphere cause variations in tropospheric path delays and tropospheric gradients. By estimating zenith wet delays (ZWD) and gradients using a GNSS reference station network the obtained time series provide information about spatial and temporal variations of water vapour in the atmosphere. Thus, GNSS-based tropospheric parameters can contribute to the forecast of regional precipitation events. In a recently finalized master thesis at TU Wien the potential of tropospheric gradients for weather prediction was investigated. Therefore, ZWD and gradient time series at selected GNSS reference stations were compared to precipitation data over a period of six months (April to September 2014). The selected GNSS stations form two test areas within Austria. All required meteorological data was provided by the Central Institution for Meteorology and Geodynamics (ZAMG). Two characteristics in ZWD and gradient time series can be anticipated in case of an approaching weather front. First, an induced asymmetry in tropospheric delays results in both, an increased magnitude of the gradient and in gradients pointing towards the weather front. Second, an increase in ZWD reflects the increased water vapour concentration right before a precipitation event. To investigate these characteristics exemplary test events were processed. On the one hand, the sequence of the anticipated increase in ZWD at each GNSS station obtained by cross correlation of the time series indicates the direction of the approaching weather front. On the other hand, the corresponding peak in gradient time series allows the deduction of the direction of movement as well. To verify the results precipitation data from ZAMG was used. It can be deduced, that tropospheric gradients show high potential for predicting precipitation events. While ZWD time series rather indicate the orientation of the air mass boundary, gradients rather indicate the direction of movement

Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens.

PubMed

Abdollahi-Arpanahi, Rostam; Morota, Gota; Valente, Bruno D; Kranis, Andreas; Rosa, Guilherme J M; Gianola, Daniel

2016-02-03

Genome-wide association studies in humans have found enrichment of trait-associated single nucleotide polymorphisms (SNPs) in coding regions of the genome and depletion of these in intergenic regions. However, a recent release of the ENCyclopedia of DNA elements showed that ~80 % of the human genome has a biochemical function. Similar studies on the chicken genome are lacking, thus assessing the relative contribution of its genic and non-genic regions to variation is relevant for biological studies and genetic improvement of chicken populations. A dataset including 1351 birds that were genotyped with the 600K Affymetrix platform was used. We partitioned SNPs according to genome annotation data into six classes to characterize the relative contribution of genic and non-genic regions to genetic variation as well as their predictive power using all available quality-filtered SNPs. Target traits were body weight, ultrasound measurement of breast muscle and hen house egg production in broiler chickens. Six genomic regions were considered: intergenic regions, introns, missense, synonymous, 5' and 3' untranslated regions, and regions that are located 5 kb upstream and downstream of coding genes. Genomic relationship matrices were constructed for each genomic region and fitted in the models, separately or simultaneously. Kernel-based ridge regression was used to estimate variance components and assess predictive ability. Contribution of each class of genomic regions to dominance variance was also considered. Variance component estimates indicated that all genomic regions contributed to marked additive genetic variation and that the class of synonymous regions tended to have the greatest contribution. The marked dominance genetic variation explained by each class of genomic regions was similar and negligible (~0.05). In terms of prediction mean-square error, the whole-genome approach showed the best predictive ability. All genic and non-genic regions contributed to

Immunogenetic mechanisms leading to thyroid autoimmunity: recent advances in identifying susceptibility genes and regions.

PubMed

Brand, Oliver J; Gough, Stephen C L

2011-12-01

The autoimmune thyroid diseases (AITD) include Graves' disease (GD) and Hashimoto's thyroiditis (HT), which are characterised by a breakdown in immune tolerance to thyroid antigens. Unravelling the genetic architecture of AITD is vital to better understanding of AITD pathogenesis, required to advance therapeutic options in both disease management and prevention. The early whole-genome linkage and candidate gene association studies provided the first evidence that the HLA region and CTLA-4 represented AITD risk loci. Recent improvements in; high throughput genotyping technologies, collection of larger disease cohorts and cataloguing of genome-scale variation have facilitated genome-wide association studies and more thorough screening of candidate gene regions. This has allowed identification of many novel AITD risk genes and more detailed association mapping. The growing number of confirmed AITD susceptibility loci, implicates a number of putative disease mechanisms most of which are tightly linked with aspects of immune system function. The unprecedented advances in genetic study will allow future studies to identify further novel disease risk genes and to identify aetiological variants within specific gene regions, which will undoubtedly lead to a better understanding of AITD patho-physiology.

Immunogenetic Mechanisms Leading to Thyroid Autoimmunity: Recent Advances in Identifying Susceptibility Genes and Regions

PubMed Central

Brand, Oliver J; Gough, Stephen C.L

2011-01-01

The autoimmune thyroid diseases (AITD) include Graves’ disease (GD) and Hashimoto’s thyroiditis (HT), which are characterised by a breakdown in immune tolerance to thyroid antigens. Unravelling the genetic architecture of AITD is vital to better understanding of AITD pathogenesis, required to advance therapeutic options in both disease management and prevention. The early whole-genome linkage and candidate gene association studies provided the first evidence that the HLA region and CTLA-4 represented AITD risk loci. Recent improvements in; high throughput genotyping technologies, collection of larger disease cohorts and cataloguing of genome-scale variation have facilitated genome-wide association studies and more thorough screening of candidate gene regions. This has allowed identification of many novel AITD risk genes and more detailed association mapping. The growing number of confirmed AITD susceptibility loci, implicates a number of putative disease mechanisms most of which are tightly linked with aspects of immune system function. The unprecedented advances in genetic study will allow future studies to identify further novel disease risk genes and to identify aetiological variants within specific gene regions, which will undoubtedly lead to a better understanding of AITD patho-physiology. PMID:22654554

Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer.

PubMed

Cheng, Nai-Ming; Fang, Yu-Hua Dean; Lee, Li-yu; Chang, Joseph Tung-Chieh; Tsan, Din-Li; Ng, Shu-Hang; Wang, Hung-Ming; Liao, Chun-Ta; Yang, Lan-Yan; Hsu, Ching-Han; Yen, Tzu-Chen

2015-03-01

The question as to whether the regional textural features extracted from PET images predict prognosis in oropharyngeal squamous cell carcinoma (OPSCC) remains open. In this study, we investigated the prognostic impact of regional heterogeneity in patients with T3/T4 OPSCC. We retrospectively reviewed the records of 88 patients with T3 or T4 OPSCC who had completed primary therapy. Progression-free survival (PFS) and disease-specific survival (DSS) were the main outcome measures. In an exploratory analysis, a standardized uptake value of 2.5 (SUV 2.5) was taken as the cut-off value for the detection of tumour boundaries. A fixed threshold at 42 % of the maximum SUV (SUVmax 42 %) and an adaptive threshold method were then used for validation. Regional textural features were extracted from pretreatment (18)F-FDG PET/CT images using the grey-level run length encoding method and grey-level size zone matrix. The prognostic significance of PET textural features was examined using receiver operating characteristic (ROC) curves and Cox regression analysis. Zone-size nonuniformity (ZSNU) was identified as an independent predictor of PFS and DSS. Its prognostic impact was confirmed using both the SUVmax 42 % and the adaptive threshold segmentation methods. Based on (1) total lesion glycolysis, (2) uniformity (a local scale texture parameter), and (3) ZSNU, we devised a prognostic stratification system that allowed the identification of four distinct risk groups. The model combining the three prognostic parameters showed a higher predictive value than each variable alone. ZSNU is an independent predictor of outcome in patients with advanced T-stage OPSCC, and may improve their prognostic stratification.

Lake nutrient stoichiometry is less predictable than nutrient concentrations at regional and sub-continental scales

USGS Publications Warehouse

Collins, Sarah M.; Oliver, Samantha K.; Lapierre, Jean-Francois; Stanley, Emily H.; Jones, John R.; Wagner, Tyler; Soranno, Patricia A.

2017-01-01

Production in many ecosystems is co-limited by multiple elements. While a known suite of drivers associated with nutrient sources, nutrient transport, and internal processing controls concentrations of phosphorus (P) and nitrogen (N) in lakes, much less is known about whether the drivers of single nutrient concentrations can also explain spatial or temporal variation in lake N:P stoichiometry. Predicting stoichiometry might be more complex than predicting concentrations of individual elements because some drivers have similar relationships with N and P, leading to a weak relationship with their ratio. Further, the dominant controls on elemental concentrations likely vary across regions, resulting in context dependent relationships between drivers, lake nutrients and their ratios. Here, we examine whether known drivers of N and P concentrations can explain variation in N:P stoichiometry, and whether explaining variation in stoichiometry differs across regions. We examined drivers of N:P in ~2,700 lakes at a sub-continental scale and two large regions nested within the sub-continental study area that have contrasting ecological context, including differences in the dominant type of land cover (agriculture vs. forest). At the sub-continental scale, lake nutrient concentrations were correlated with nutrient loading and lake internal processing, but stoichiometry was only weakly correlated to drivers of lake nutrients. At the regional scale, drivers that explained variation in nutrients and stoichiometry differed between regions. In the Midwestern U.S. region, dominated by agricultural land use, lake depth and the percentage of row crop agriculture were strong predictors of stoichiometry because only phosphorus was related to lake depth and only nitrogen was related to the percentage of row crop agriculture. In contrast, all drivers were related to N and P in similar ways in the Northeastern U.S. region, leading to weak relationships between drivers and stoichiometry

A novel 2-step approach combining the NAFLD fibrosis score and liver stiffness measurement for predicting advanced fibrosis.

PubMed

Chan, Wah-Kheong; Nik Mustapha, Nik Raihan; Mahadeva, Sanjiv

2015-10-01

The non-alcoholic fatty liver disease (NAFLD) fibrosis score (NFS) is indeterminate in a proportion of NAFLD patients. Combining the NFS with liver stiffness measurement (LSM) may improve prediction of advanced fibrosis. We aim to evaluate the NFS and LSM in predicting advanced fibrosis in NAFLD patients. The NFS was calculated and LSM obtained for consecutive adult NAFLD patients scheduled for liver biopsy. The accuracy of predicting advanced fibrosis using either modality and in combination were assessed. An algorithm combining the NFS and LSM was developed from a training cohort and subsequently tested in a validation cohort. There were 101 and 46 patients in the training and validation cohort, respectively. In the training cohort, the percentages of misclassifications using the NFS alone, LSM alone, LSM alone (with grey zone), both tests for all patients and a 2-step approach using LSM only for patients with indeterminate and high NFS were 5.0, 28.7, 2.0, 2.0 and 4.0 %, respectively. The percentages of patients requiring liver biopsy were 30.7, 0, 36.6, 36.6 and 18.8 %, respectively. In the validation cohort, the percentages of misclassifications were 8.7, 28.3, 2.2, 2.2 and 8.7 %, respectively. The percentages of patients requiring liver biopsy were 28.3, 0, 41.3, 43.5 and 19.6 %, respectively. The novel 2-step approach further reduced the number of patients requiring a liver biopsy whilst maintaining the accuracy to predict advanced fibrosis. The combination of NFS and LSM for all patients provided no apparent advantage over using either of the tests alone.

Regional Lymph Node Uptake of [{sup 18}F]Fluorodeoxyglucose After Definitive Chemoradiation Therapy Predicts Local-Regional Failure of Locally Advanced Non-Small Cell Lung Cancer: Results of ACRIN 6668/RTOG 0235

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

Markovina, Stephanie; Duan, Fenghai; Snyder, Bradley S.

2015-11-01

Purpose: The American College of Radiology Imaging Network (ACRIN) 6668/Radiation Therapy Oncology Group (RTOG) 0235 study demonstrated that standardized uptake values (SUV) on post-treatment [{sup 18}F]fluorodeoxyglucose-positron emission tomography (FDG-PET) correlated with survival in locally advanced non-small cell lung cancer (NSCLC). This secondary analysis determined whether SUV of regional lymph nodes (RLNs) on post-treatment FDG-PET correlated with patient outcomes. Methods and Materials: Included for analysis were patients treated with concurrent chemoradiation therapy, using radiation doses ≥60 Gy, with identifiable FDG-avid RLNs (distinct from primary tumor) on pretreatment FDG-PET, and post-treatment FDG-PET data. ACRIN core laboratory SUV measurements were used. Event time was calculatedmore » from the date of post-treatment FDG-PET. Local-regional failure was defined as failure within the treated RT volume and reported by the treating institution. Statistical analyses included Wilcoxon signed rank test, Kaplan-Meier curves (log rank test), and Cox proportional hazards regression modeling. Results: Of 234 trial-eligible patients, 139 (59%) had uptake in both primary tumor and RLNs on pretreatment FDG-PET and had SUV data from post-treatment FDG-PET. Maximum SUV was greater for primary tumor than for RLNs before treatment (P<.001) but not different post-treatment (P=.320). Post-treatment SUV of RLNs was not associated with overall survival. However, elevated post-treatment SUV of RLNs, both the absolute value and the percentage of residual activity compared to the pretreatment SUV were associated with inferior local-regional control (P<.001). Conclusions: High residual metabolic activity in RLNs on post-treatment FDG-PET is associated with worse local-regional control. Based on these data, future trials evaluating a radiation therapy boost should consider inclusion of both primary tumor and FDG-avid RLNs in the boost volume to maximize local-regional

Predicting domestic and community violence by soldiers living in a conflict region.

PubMed

Nandi, Corina; Elbert, Thomas; Bambonye, Manassé; Weierstall, Roland; Reichert, Manfred; Zeller, Anja; Crombach, Anselm

2017-11-01

Past research revealed war trauma and posttraumatic stress disorder (PTSD) symptoms as potential predictors for domestic and community violence in crisis regions and among soldiers in different armed conflicts. The impact of family violence and other adversities experienced in childhood as well as of a combat-enhanced appeal for aggressive behavior (appetitive aggression) remains to be specified. In the present study, the authors separately predicted violence against children, intimate partner violence and community violence in 381 Burundian soldiers returning from foreign deployment and living in a post- conflict region. Using path analysis, they aimed to disentangle the independent contributions and pathways of the following variables: Exposure to war trauma and childhood familial violence, PTSD and depression symptom severity, and appetitive aggression. Childhood familial violence had an independent effect on all contexts of violence and was the only significant predictor for violence against the soldiers' own children. Intimate partner violence was additionally predicted by depression symptom severity, while community violence was additionally predicted by PTSD symptom severity and appetitive aggression. Besides war-related mental ill-health and appetitive aggression, violent experiences during childhood development must not be overlooked as a factor fueling the cycle of violence in conflict regions. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Using timing of ice retreat to predict timing of fall freeze-up in the Arctic

NASA Astrophysics Data System (ADS)

Stroeve, Julienne C.; Crawford, Alex D.; Stammerjohn, Sharon

2016-06-01

Reliable forecasts of the timing of sea ice advance are needed in order to reduce risks associated with operating in the Arctic as well as planning of human and environmental emergencies. This study investigates the use of a simple statistical model relating the timing of ice retreat to the timing of ice advance, taking advantage of the inherent predictive power supplied by the seasonal ice-albedo feedback and ocean heat uptake. Results show that using the last retreat date to predict the first advance date is applicable in some regions, such as Baffin Bay and the Laptev and East Siberian seas, where a predictive skill is found even after accounting for the long-term trend in both variables. Elsewhere, in the Arctic, there is some predictive skills depending on the year (e.g., Kara and Beaufort seas), but none in regions such as the Barents and Bering seas or the Sea of Okhotsk. While there is some suggestion that the relationship is strengthening over time, this may reflect that higher correlations are expected during periods when the underlying trend is strong.

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

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

Parameter identifiability and regional calibration for reservoir inflow prediction

NASA Astrophysics Data System (ADS)

Kolberg, Sjur; Engeland, Kolbjørn; Tøfte, Lena S.; Bruland, Oddbjørn

2013-04-01

The large hydropower producer Statkraft is currently testing regional, distributed models for operational reservoir inflow prediction. The need for simultaneous forecasts and consistent updating in a large number of catchments supports the shift from catchment-oriented to regional models. Low-quality naturalized inflow series in the reservoir catchments further encourages the use of donor catchments and regional simulation for calibration purposes. MCMC based parameter estimation (the Dream algorithm; Vrugt et al, 2009) is adapted to regional parameter estimation, and implemented within the open source ENKI framework. The likelihood is based on the concept of effectively independent number of observations, spatially as well as in time. Marginal and conditional (around an optimum) parameter distributions for each catchment may be extracted, even though the MCMC algorithm itself is guided only by the regional likelihood surface. Early results indicate that the average performance loss associated with regional calibration (difference in Nash-Sutcliffe R2 between regionally and locally optimal parameters) is in the range of 0.06. The importance of the seasonal snow storage and melt in Norwegian mountain catchments probably contributes to the high degree of similarity among catchments. The evaluation continues for several regions, focusing on posterior parameter uncertainty and identifiability. Vrugt, J. A., C. J. F. ter Braak, C. G. H. Diks, B. A. Robinson, J. M. Hyman and D. Higdon: Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling. Int. J. of nonlinear sciences and numerical simulation 10, 3, 273-290, 2009.

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.

Implementing an advance care planning program in German nursing homes: results of an inter-regionally controlled intervention trial.

PubMed

In der Schmitten, Jürgen; Lex, Katharina; Mellert, Christine; Rothärmel, Sonja; Wegscheider, Karl; Marckmann, Georg

2014-01-24

Advance Care Planning (ACP) is a systematic approach to ensure that effective advance directives (ADs) are developed and respected. We studied the effects of implementing a regional ACP program in Germany. In a prospective, inter-regionally controlled trial focusing on nursing homes (n/hs), we compared the number, relevance and validity of new ADs completed in the intervention region versus the control region. Intervention n/h residents and their families were offered professional facilitation including standardized documentation. Data from 136 residents of three intervention n/hs were compared with data from 439 residents of 10 control n/hs over a study period of 16.5 months. In the intervention region, 49 (36.0%) participating residents completed a new AD over the period of the study, compared to 18 (4.1%) in the control region; these ADs included 30 ADs by proxy in the intervention region versus 10 in the control region. Proxies were designated in 94.7% versus 50.0% of cases, the AD was signed by a physician in 93.9% versus 16.7%, and an emergency order was included in 98.0% versus 44.4%. Resuscitation status was addressed in 95.9% versus 38.9% of cases (p<0.01 for all of the differences mentioned above). In the intervention region, new ADs were preceded by an average of 2.5 facilitated conversations (range, 2–5) with a mean total duration of 100 minutes (range, 60–240 minutes). The implementation of an ACP program in German nursing homes led, much more frequently than previously reported, to the creation of advance directives with potential relevance to medical decision-making. Future research should assess the effect of such programs on clinical and structural outcomes.

ADVANCIS Score Predicts Acute Kidney Injury After Percutaneous Coronary Intervention for Acute Coronary Syndrome.

PubMed

Fan, Pei-Chun; Chen, Tien-Hsing; Lee, Cheng-Chia; Tsai, Tsung-Yu; Chen, Yung-Chang; Chang, Chih-Hsiang

2018-01-01

Acute kidney injury (AKI), a common and crucial complication of acute coronary syndrome (ACS) after receiving percutaneous coronary intervention (PCI), is associated with increased mortality and adverse outcomes. This study aimed to develop and validate a risk prediction model for incident AKI after PCI for ACS. We included 82,186 patients admitted for ACS and receiving PCI between 1997 and 2011 from the Taiwan National Health Insurance Research Database and randomly divided them into a training cohort (n = 57,630) and validation cohort (n = 24,656) for risk model development and validation, respectively. Risk factor analysis revealed that age, diabetes mellitus, ventilator use, prior AKI, number of intervened vessels, chronic kidney disease (CKD), intra-aortic balloon pump (IABP) use, cardiogenic shock, female sex, prior stroke, peripheral arterial disease, hypertension, and heart failure were significant risk factors for incident AKI after PCI for ACS. The reduced model, ADVANCIS, comprised 8 clinical parameters (age, diabetes mellitus, ventilator use, prior AKI, number of intervened vessels, CKD, IABP use, cardiogenic shock), with a score scale ranging from 0 to 22, and performed comparably with the full model (area under the receiver operating characteristic curve, 87.4% vs 87.9%). An ADVANCIS score of ≥6 was associated with higher in-hospital mortality risk. In conclusion, the ADVANCIS score is a novel, simple, robust tool for predicting the risk of incident AKI after PCI for ACS, and it can aid in risk stratification to monitor patient care.

The prediction of progression-free and overall survival in women with an advanced stage of epithelial ovarian carcinoma.

PubMed

Gerestein, C G; Eijkemans, M J C; de Jong, D; van der Burg, M E L; Dykgraaf, R H M; Kooi, G S; Baalbergen, A; Burger, C W; Ansink, A C

2009-02-01

Prognosis in women with ovarian cancer mainly depends on International Federation of Gynecology and Obstetrics stage and the ability to perform optimal cytoreductive surgery. Since ovarian cancer has a heterogeneous presentation and clinical course, predicting progression-free survival (PFS) and overall survival (OS) in the individual patient is difficult. The objective of this study was to determine predictors of PFS and OS in women with advanced stage epithelial ovarian cancer (EOC) after primary cytoreductive surgery and first-line platinum-based chemotherapy. Retrospective observational study. Two teaching hospitals and one university hospital in the south-western part of the Netherlands. Women with advanced stage EOC. All women who underwent primary cytoreductive surgery for advanced stage EOC followed by first-line platinum-based chemotherapy between January 1998 and October 2004 were identified. To investigate independent predictors of PFS and OS, a Cox' proportional hazard model was used. Nomograms were generated with the identified predictive parameters. The primary outcome measure was OS and the secondary outcome measures were response and PFS. A total of 118 women entered the study protocol. Median PFS and OS were 15 and 44 months, respectively. Preoperative platelet count (P = 0.007), and residual disease <1 cm (P = 0.004) predicted PFS with a optimism corrected c-statistic of 0.63. Predictive parameters for OS were preoperative haemoglobin serum concentration (P = 0.012), preoperative platelet counts (P = 0.031) and residual disease <1 cm (P = 0.028) with a optimism corrected c-statistic of 0.67. PFS could be predicted by postoperative residual disease and preoperative platelet counts, whereas residual disease, preoperative platelet counts and preoperative haemoglobin serum concentration were predictive for OS. The proposed nomograms need to be externally validated.

Subduction and Slab Advance at Orogen Syntaxes: Predicting Exhumation Rates and Thermochronometric Ages with Numerical Modeling

NASA Astrophysics Data System (ADS)

Nettesheim, Matthias; Ehlers, Todd A.; Whipp, David M.

2017-04-01

The change in plate boundary orientation and subducting plate geometry along orogen syntaxes may have major control on the subduction and exhumation dynamics at these locations. Previous work documents that the curvature of subducting plates in 3D at orogen syntaxes forces a buckling and flexural stiffening of the downgoing plate. The geometry of this stiffened plate region, also called indenter, can be observed in various subduction zones around the world (e.g. St. Elias Range, Alaska; Cascadia, USA; Andean syntaxis, South America). The development of a subducting, flexurally stiffened indenter beneath orogen syntaxes influences deformation in the overriding plate and can lead to accelerated and focused rock uplift above its apex. Moreover, the style of deformation in the overriding plate is influenced by the amount of trench or slab advance, which is the amount of overall shortening not accommodated by underthrusting. While many subduction zones exhibit little to no slab advance, the Nazca-South America subduction and especially the early stages of the India-Eurasia collision provide end-member examples. Here, we use a transient, lithospheric-scale, thermomechanical 3D model of an orogen syntaxis to investigate the effects of subducting a flexurally stiffened plate geometry and slab advance on upper plate deformation. A visco-plastic upper-plate rheology is used, along with a buckled, rigid subducting plate. The free surface of the thermomechanical model is coupled to a landscape evolution model that accounts for erosion by fluvial and hillslope processes. The cooling histories of exhumed rocks are used to predict the evolution of low-temperature thermochronometer ages on the surface. With a constant overall shortening for all simulations, the magnitude of slab advance is varied stepwise from no advance, with all shortening accommodated by underthrusting, to full slab advance, i.e. no motion on the megathrust. We show that in models where most shortening is

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

... Jobs and Innovation Accelerator Challenge; a Coordinated Initiative To Advance Regional Competitiveness... Obama Administration announces the Jobs and Innovation Accelerator Challenge (Accelerator Challenge), an initiative of 16 Federal agencies and bureaus to accelerate innovation-fueled job creation and economic...

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.

Data Mining and Predictive Modeling in Institutional Advancement: How Ten Schools Found Success. Technical Report

ERIC Educational Resources Information Center

Luperchio, Dan

2009-01-01

This technical report, produced in partnership by the Council for Advancement and Support of Education (CASE) and SPSS Inc., explores the promise of data mining alumni records at educational institutions. Working with individual alumni records from The Johns Hopkins Zanvyl Krieger School of Arts and Sciences, a predictive regression model is…

[The third dimension tomography versus cranial X-ray cephalometry to predict maxilla advance by distraction in hypoplastic maxilla].

PubMed

Rosas-Muñoz, Arturo; Soriano-Padilla, Fernando; Rendón-Macías, Mario Enrique

2010-01-01

the osteogenic distraction is the treatment for the correction of the hypoplastic maxilla secondary to the repair of a cleft lip-palate. Its planning is based on articulated models. Our objective was to describe the accuracy of three-dimensional Cephalometry (CT3D) for projecting jaw displacement. three patients with hypoplastic maxilla. Interventions estimation of the advance required of lateral maxilla through Cephalometry of skull (CLC), CT3D and an articulated model (gold standard). Two months after distraction finalized the advance predicted was compared. the error of the advance projection in each patient was smaller with the CT3D versus CLC (+1, +1 and +1 mm versus -10, -14 and -9mm). Corrections post-distraction were of +25 %, +26 % and +38.4 % on the programmed one. CT3D predicted better the correction (+19 %, +10.8 %, +33.4 % versus CLC: -50 %; -60.8 % and -34.6 %). Chewing alterations were not seen in any patient. the planning of the necessary advance for distraction in patients with hypoplastic maxilla by CT3D can shorten the time of studies and should be consider as next to the projection of articulated model.

Lake nutrient stoichiometry is less predictable than nutrient concentrations at regional and sub-continental scales.

PubMed

Collins, Sarah M; Oliver, Samantha K; Lapierre, Jean-Francois; Stanley, Emily H; Jones, John R; Wagner, Tyler; Soranno, Patricia A

2017-07-01

Production in many ecosystems is co-limited by multiple elements. While a known suite of drivers associated with nutrient sources, nutrient transport, and internal processing controls concentrations of phosphorus (P) and nitrogen (N) in lakes, much less is known about whether the drivers of single nutrient concentrations can also explain spatial or temporal variation in lake N:P stoichiometry. Predicting stoichiometry might be more complex than predicting concentrations of individual elements because some drivers have similar relationships with N and P, leading to a weak relationship with their ratio. Further, the dominant controls on elemental concentrations likely vary across regions, resulting in context dependent relationships between drivers, lake nutrients and their ratios. Here, we examine whether known drivers of N and P concentrations can explain variation in N:P stoichiometry, and whether explaining variation in stoichiometry differs across regions. We examined drivers of N:P in ~2,700 lakes at a sub-continental scale and two large regions nested within the sub-continental study area that have contrasting ecological context, including differences in the dominant type of land cover (agriculture vs. forest). At the sub-continental scale, lake nutrient concentrations were correlated with nutrient loading and lake internal processing, but stoichiometry was only weakly correlated to drivers of lake nutrients. At the regional scale, drivers that explained variation in nutrients and stoichiometry differed between regions. In the Midwestern U.S. region, dominated by agricultural land use, lake depth and the percentage of row crop agriculture were strong predictors of stoichiometry because only phosphorus was related to lake depth and only nitrogen was related to the percentage of row crop agriculture. In contrast, all drivers were related to N and P in similar ways in the Northeastern U.S. region, leading to weak relationships between drivers and stoichiometry

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.

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.

Environmental Analysis and Prediction of Transmission Loss in the Region of the New England Shelfbreak

DTIC Science & Technology

2009-09-01

Environmental Analysis and Prediction of Transmission Loss in the Region of the New England Shelfbreak By Heather Rend Hornick B.S., University of... Analysis and Prediction of Transmission Loss in the Region of the New England Shelfbreak 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER... analysis of the ocean sound speed field defined a set of perturbations to the background sound speed field for each of the NEST Scanfish surveys

Adjusting the Stems Regional Forest Growth Model to Improve Local Predictions

Treesearch

W. Brad Smith

1983-01-01

A simple procedure using double sampling is described for adjusting growth in the STEMS regional forest growth model to compensate for subregional variations. Predictive accuracy of the STEMS model (a distance-independent, individual tree growth model for Lake States forests) was improved by using this procedure

Multi-scale landslide hazard assessment: Advances in global and regional methodologies

NASA Astrophysics Data System (ADS)

Kirschbaum, Dalia; Peters-Lidard, Christa; Adler, Robert; Hong, Yang

2010-05-01

The increasing availability of remotely sensed surface data and precipitation provides a unique opportunity to explore how smaller-scale landslide susceptibility and hazard assessment methodologies may be applicable at larger spatial scales. This research first considers an emerging satellite-based global algorithm framework, which evaluates how the landslide susceptibility and satellite derived rainfall estimates can forecast potential landslide conditions. An analysis of this algorithm using a newly developed global landslide inventory catalog suggests that forecasting errors are geographically variable due to improper weighting of surface observables, resolution of the current susceptibility map, and limitations in the availability of landslide inventory data. These methodological and data limitation issues can be more thoroughly assessed at the regional level, where available higher resolution landslide inventories can be applied to empirically derive relationships between surface variables and landslide occurrence. The regional empirical model shows improvement over the global framework in advancing near real-time landslide forecasting efforts; however, there are many uncertainties and assumptions surrounding such a methodology that decreases the functionality and utility of this system. This research seeks to improve upon this initial concept by exploring the potential opportunities and methodological structure needed to advance larger-scale landslide hazard forecasting and make it more of an operational reality. Sensitivity analysis of the surface and rainfall parameters in the preliminary algorithm indicates that surface data resolution and the interdependency of variables must be more appropriately quantified at local and regional scales. Additionally, integrating available surface parameters must be approached in a more theoretical, physically-based manner to better represent the physical processes underlying slope instability and landslide initiation

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

Regional cooling caused recent New Zealand glacier advances in a period of global warming.

PubMed

Mackintosh, Andrew N; Anderson, Brian M; Lorrey, Andrew M; Renwick, James A; Frei, Prisco; Dean, Sam M

2017-02-14

Glaciers experienced worldwide retreat during the twentieth and early twenty first centuries, and the negative trend in global glacier mass balance since the early 1990s is predominantly a response to anthropogenic climate warming. The exceptional terminus advance of some glaciers during recent global warming is thought to relate to locally specific climate conditions, such as increased precipitation. In New Zealand, at least 58 glaciers advanced between 1983 and 2008, and Franz Josef and Fox glaciers advanced nearly continuously during this time. Here we show that the glacier advance phase resulted predominantly from discrete periods of reduced air temperature, rather than increased precipitation. The lower temperatures were associated with anomalous southerly winds and low sea surface temperature in the Tasman Sea region. These conditions result from variability in the structure of the extratropical atmospheric circulation over the South Pacific. While this sequence of climate variability and its effect on New Zealand glaciers is unusual on a global scale, it remains consistent with a climate system that is being modified by humans.

Regional cooling caused recent New Zealand glacier advances in a period of global warming

NASA Astrophysics Data System (ADS)

Mackintosh, Andrew N.; Anderson, Brian M.; Lorrey, Andrew M.; Renwick, James A.; Frei, Prisco; Dean, Sam M.

2017-02-01

Glaciers experienced worldwide retreat during the twentieth and early twenty first centuries, and the negative trend in global glacier mass balance since the early 1990s is predominantly a response to anthropogenic climate warming. The exceptional terminus advance of some glaciers during recent global warming is thought to relate to locally specific climate conditions, such as increased precipitation. In New Zealand, at least 58 glaciers advanced between 1983 and 2008, and Franz Josef and Fox glaciers advanced nearly continuously during this time. Here we show that the glacier advance phase resulted predominantly from discrete periods of reduced air temperature, rather than increased precipitation. The lower temperatures were associated with anomalous southerly winds and low sea surface temperature in the Tasman Sea region. These conditions result from variability in the structure of the extratropical atmospheric circulation over the South Pacific. While this sequence of climate variability and its effect on New Zealand glaciers is unusual on a global scale, it remains consistent with a climate system that is being modified by humans.

Regional cooling caused recent New Zealand glacier advances in a period of global warming

PubMed Central

Mackintosh, Andrew N.; Anderson, Brian M.; Lorrey, Andrew M.; Renwick, James A.; Frei, Prisco; Dean, Sam M.

2017-01-01

Glaciers experienced worldwide retreat during the twentieth and early twenty first centuries, and the negative trend in global glacier mass balance since the early 1990s is predominantly a response to anthropogenic climate warming. The exceptional terminus advance of some glaciers during recent global warming is thought to relate to locally specific climate conditions, such as increased precipitation. In New Zealand, at least 58 glaciers advanced between 1983 and 2008, and Franz Josef and Fox glaciers advanced nearly continuously during this time. Here we show that the glacier advance phase resulted predominantly from discrete periods of reduced air temperature, rather than increased precipitation. The lower temperatures were associated with anomalous southerly winds and low sea surface temperature in the Tasman Sea region. These conditions result from variability in the structure of the extratropical atmospheric circulation over the South Pacific. While this sequence of climate variability and its effect on New Zealand glaciers is unusual on a global scale, it remains consistent with a climate system that is being modified by humans. PMID:28195582

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.

Acute Hospital Care Is The Chief Driver of Regional Spending Variation in Medicare Patients with Advanced Cancer

PubMed Central

Brooks, Gabriel A.; Li, Ling; Uno, Hajime; Hassett, Michael J.; Landon, Bruce E.; Schrag, Deborah

2014-01-01

The root causes of regional variation in medical spending are poorly understood and vary by clinical condition. To identify drivers of regional spending variation for Medicare patients with advanced cancer, we used linked Surveillance, Epidemiology, and End Results (SEER) program–Medicare data from 2004–10. We broke down Medicare spending into thirteen cancer-relevant service categories. We then calculated the contribution of each category to spending and regional spending variation. Acute hospital care was the largest component of spending and the chief driver of regional spending variation, accounting for 48 percent of spending and 67 percent of variation. In contrast, chemotherapy accounted for 16 percent of spending and 10 percent of variation. Hospice care comprised 5 percent of spending; however variation in hospice spending was fully offset by opposing variation in other categories. Our analysis suggests that the strategy with the greatest potential to improve the value of care for patients with advanced cancer is to reduce reliance on acute hospital care for this patient population. PMID:25288424

Post-fire debris flow prediction in Western United States: Advancements based on a nonparametric statistical technique

NASA Astrophysics Data System (ADS)

Nikolopoulos, E. I.; Destro, E.; Bhuiyan, M. A. E.; Borga, M., Sr.; Anagnostou, E. N.

2017-12-01

Fire disasters affect modern societies at global scale inducing significant economic losses and human casualties. In addition to their direct impacts they have various adverse effects on hydrologic and geomorphologic processes of a region due to the tremendous alteration of the landscape characteristics (vegetation, soil properties etc). As a consequence, wildfires often initiate a cascade of hazards such as flash floods and debris flows that usually follow the occurrence of a wildfire thus magnifying the overall impact in a region. Post-fire debris flows (PFDF) is one such type of hazards frequently occurring in Western United States where wildfires are a common natural disaster. Prediction of PDFD is therefore of high importance in this region and over the last years a number of efforts from United States Geological Survey (USGS) and National Weather Service (NWS) have been focused on the development of early warning systems that will help mitigate PFDF risk. This work proposes a prediction framework that is based on a nonparametric statistical technique (random forests) that allows predicting the occurrence of PFDF at regional scale with a higher degree of accuracy than the commonly used approaches that are based on power-law thresholds and logistic regression procedures. The work presented is based on a recently released database from USGS that reports a total of 1500 storms that triggered and did not trigger PFDF in a number of fire affected catchments in Western United States. The database includes information on storm characteristics (duration, accumulation, max intensity etc) and other auxiliary information of land surface properties (soil erodibility index, local slope etc). Results show that the proposed model is able to achieve a satisfactory prediction accuracy (threat score > 0.6) superior of previously published prediction frameworks highlighting the potential of nonparametric statistical techniques for development of PFDF prediction systems.

Research Area 3 - Mathematical Sciences: Multiscale Modeling of the Mechanics of Advanced Energetic Materials Relevant to Detonation Prediction

DTIC Science & Technology

2015-08-24

new energetic materials with enhanced energy release rates and reduced sensitivity to unintentional detonation . The following results have been...Mechanics of Advanced Energetic Materials Relevant to Detonation Prediction The views, opinions and/or findings contained in this report are those of the...modeling, molecular simulations, detonation prediction REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 10. SPONSOR/MONITOR’S

Quantitative predictions of streamflow variability in the Susquehanna River Basin

NASA Astrophysics Data System (ADS)

Alexander, R.; Boyer, E. W.; Leonard, L. N.; Duffy, C.; Schwarz, G. E.; Smith, R. A.

2012-12-01

Hydrologic researchers and water managers have increasingly sought an improved understanding of the major processes that control fluxes of water and solutes across diverse environmental settings and large spatial scales. Regional analyses of observed streamflow data have led to advances in our knowledge of relations among land use, climate, and streamflow, with methodologies ranging from statistical assessments of multiple monitoring sites to the regionalization of the parameters of catchment-scale mechanistic simulation models. However, gaps remain in our understanding of the best ways to transfer the knowledge of hydrologic response and governing processes among locations, including methods for regionalizing streamflow measurements and model predictions. We developed an approach to predict variations in streamflow using the SPARROW (SPAtially Referenced Regression On Watershed attributes) modeling infrastructure, with mechanistic functions, mass conservation constraints, and statistical estimation of regional and sub-regional parameters. We used the model to predict discharge in the Susquehanna River Basin (SRB) under varying hydrological regimes that are representative of contemporary flow conditions. The resulting basin-scale water balance describes mean monthly flows in stream reaches throughout the entire SRB (represented at a 1:100,000 scale using the National Hydrologic Data network), with water supply and demand components that are inclusive of a range of hydrologic, climatic, and cultural properties (e.g., precipitation, evapotranspiration, soil and groundwater storage, runoff, baseflow, water use). We compare alternative models of varying complexity that reflect differences in the number and types of explanatory variables and functional expressions as well as spatial and temporal variability in the model parameters. Statistical estimation of the models reveals the levels of complexity that can be uniquely identified, subject to the information content

Climatological Observations for Maritime Prediction and Analysis Support Service (COMPASS)

NASA Astrophysics Data System (ADS)

OConnor, A.; Kirtman, B. P.; Harrison, S.; Gorman, J.

2016-02-01

Current US Navy forecasting systems cannot easily incorporate extended-range forecasts that can improve mission readiness and effectiveness; ensure safety; and reduce cost, labor, and resource requirements. If Navy operational planners had systems that incorporated these forecasts, they could plan missions using more reliable and longer-term weather and climate predictions. Further, using multi-model forecast ensembles instead of single forecasts would produce higher predictive performance. Extended-range multi-model forecast ensembles, such as those available in the North American Multi-Model Ensemble (NMME), are ideal for system integration because of their high skill predictions; however, even higher skill predictions can be produced if forecast model ensembles are combined correctly. While many methods for weighting models exist, the best method in a given environment requires expert knowledge of the models and combination methods.We present an innovative approach that uses machine learning to combine extended-range predictions from multi-model forecast ensembles and generate a probabilistic forecast for any region of the globe up to 12 months in advance. Our machine-learning approach uses 30 years of hindcast predictions to learn patterns of forecast model successes and failures. Each model is assigned a weight for each environmental condition, 100 km2 region, and day given any expected environmental information. These weights are then applied to the respective predictions for the region and time of interest to effectively stitch together a single, coherent probabilistic forecast. Our experimental results demonstrate the benefits of our approach to produce extended-range probabilistic forecasts for regions and time periods of interest that are superior, in terms of skill, to individual NMME forecast models and commonly weighted models. The probabilistic forecast leverages the strengths of three NMME forecast models to predict environmental conditions for an

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.

2017-05-01

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.

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...

Development of a noise prediction model based on advanced fuzzy approaches in typical industrial workrooms.

PubMed

Aliabadi, Mohsen; Golmohammadi, Rostam; Khotanlou, Hassan; Mansoorizadeh, Muharram; Salarpour, Amir

2014-01-01

Noise prediction is considered to be the best method for evaluating cost-preventative noise controls in industrial workrooms. One of the most important issues is the development of accurate models for analysis of the complex relationships among acoustic features affecting noise level in workrooms. In this study, advanced fuzzy approaches were employed to develop relatively accurate models for predicting noise in noisy industrial workrooms. The data were collected from 60 industrial embroidery workrooms in the Khorasan Province, East of Iran. The main acoustic and embroidery process features that influence the noise were used to develop prediction models using MATLAB software. Multiple regression technique was also employed and its results were compared with those of fuzzy approaches. Prediction errors of all prediction models based on fuzzy approaches were within the acceptable level (lower than one dB). However, Neuro-fuzzy model (RMSE=0.53dB and R2=0.88) could slightly improve the accuracy of noise prediction compared with generate fuzzy model. Moreover, fuzzy approaches provided more accurate predictions than did regression technique. The developed models based on fuzzy approaches as useful prediction tools give professionals the opportunity to have an optimum decision about the effectiveness of acoustic treatment scenarios in embroidery workrooms.

A scoring model for predicting advanced colorectal neoplasia in a screened population of asymptomatic Japanese individuals.

PubMed

Sekiguchi, Masau; Kakugawa, Yasuo; Matsumoto, Minori; Matsuda, Takahisa

2018-01-22

Risk stratification of screened populations could help improve colorectal cancer (CRC) screening. Use of the modified Asia-Pacific Colorectal Screening (APCS) score has been proposed in the Asia-Pacific region. This study was performed to build a new useful scoring model for CRC screening. Data were reviewed from 5218 asymptomatic Japanese individuals who underwent their first screening colonoscopy. Multivariate logistic regression was used to investigate risk factors for advanced colorectal neoplasia (ACN), and a new scoring model for the prediction of ACN was developed based on the results. The discriminatory capability of the new model and the modified APCS score were assessed and compared. Internal validation was also performed. ACN was detected in 225 participants. An 8-point scoring model for the prediction of ACN was developed using five independent risk factors for ACN (male sex, higher age, presence of two or more first-degree relatives with CRC, body mass index of > 22.5 kg/m 2 , and smoking history of > 18.5 pack-years). The prevalence of ACN was 1.6% (34/2172), 5.3% (127/2419), and 10.2% (64/627) in participants with scores of < 3, ≥ 3 to < 5, and ≥ 5, respectively. The c-statistic of the scoring model was 0.70 (95% confidence interval, 0.67-0.73) in both the development and internal validation sets, and this value was higher than that of the modified APCS score [0.68 (95% confidence interval, 0.65-0.71), P = 0.03]. We built a new simple scoring model for prediction of ACN in a Japanese population that could stratify the screened population into low-, moderate-, and high-risk groups.

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

The Ordered Network Structure and Prediction Summary for M≥7 Earthquakes in Xinjiang Region of China

NASA Astrophysics Data System (ADS)

Men, Ke-Pei; Zhao, Kai

2014-12-01

M ≥7 earthquakes have showed an obvious commensurability and orderliness in Xinjiang of China and its adjacent region since 1800. The main orderly values are 30 a × k (k = 1,2,3), 11 12 a, 41 43 a, 18 19 a, and 5 6 a. In the guidance of the information forecasting theory of Wen-Bo Weng, based on previous research results, combining ordered network structure analysis with complex network technology, we focus on the prediction summary of M ≥ 7 earthquakes by using the ordered network structure, and add new information to further optimize network, hence construct the 2D- and 3D-ordered network structure of M ≥ 7 earthquakes. In this paper, the network structure revealed fully the regularity of seismic activity of M ≥ 7 earthquakes in the study region during the past 210 years. Based on this, the Karakorum M7.1 earthquake in 1996, the M7.9 earthquake on the frontier of Russia, Mongol, and China in 2003, and two Yutian M7.3 earthquakes in 2008 and 2014 were predicted successfully. At the same time, a new prediction opinion is presented that the future two M ≥ 7 earthquakes will probably occur around 2019 - 2020 and 2025 - 2026 in this region. The results show that large earthquake occurred in defined region can be predicted. The method of ordered network structure analysis produces satisfactory results for the mid-and-long term prediction of M ≥ 7 earthquakes.

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.

The discriminatory capability of existing scores to predict advanced colorectal neoplasia: a prospective colonoscopy study of 5,899 screening participants.

PubMed

Wong, Martin C S; Ching, Jessica Y L; Ng, Simpson; Lam, Thomas Y T; Luk, Arthur K C; Wong, Sunny H; Ng, Siew C; Ng, Simon S M; Wu, Justin C Y; Chan, Francis K L; Sung, Joseph J Y

2016-02-03

We evaluated the performance of seven existing risk scoring systems in predicting advanced colorectal neoplasia in an asymptomatic Chinese cohort. We prospectively recruited 5,899 Chinese subjects aged 50-70 years in a colonoscopy screening programme(2008-2014). Scoring systems under evaluation included two scoring tools from the US; one each from Spain, Germany, and Poland; the Korean Colorectal Screening(KCS) scores; and the modified Asia Pacific Colorectal Screening(APCS) scores. The c-statistics, sensitivity, specificity, positive predictive values(PPVs), and negative predictive values(NPVs) of these systems were evaluated. The resources required were estimated based on the Number Needed to Screen(NNS) and the Number Needed to Refer for colonoscopy(NNR). Advanced neoplasia was detected in 364 (6.2%) subjects. The German system referred the least proportion of subjects (11.2%) for colonoscopy, whilst the KCS scoring system referred the highest (27.4%). The c-statistics of all systems ranged from 0.56-0.65, with sensitivities ranging from 0.04-0.44 and specificities from 0.74-0.99. The modified APCS scoring system had the highest c-statistics (0.65, 95% C.I. 0.58-0.72). The NNS (12-19) and NNR (5-10) were similar among the scoring systems. The existing scoring systems have variable capability to predict advanced neoplasia among asymptomatic Chinese subjects, and further external validation should be performed.

BH3-only protein Bim predicts advanced stage of cutaneous melanoma.

PubMed

Gambichler, T; Rooms, I; Scholl, L; Stockfleth, E; Stücker, M; Sand, M

2016-11-01

Bim having strong pro-apoptotic effects belongs to the BH3-only proteins of the Bcl-2 protein family and contributes to survival pathways in cancer cells. We aimed to investigate Bim protein expression in cutaneous melanoma (CM). Bim protein expression was assessed by immunohistochemistry in primary and metastatic melanomas and correlated with clinical and histopathological features. The Bim immunoreactivity score of the primary melanomas investigated (4.6 ± 1.5) was significantly (P < 0.0001) higher than that observed in metastases (2.8 ± 1.1). Low Bim expression was significantly associated with primary nodular melanoma type (P = 0.005). Moreover, Bim expression was significantly inversely correlated with tumour thickness (r = -0.36; P = 0.0035), advanced stage of disease (stage III and IV; r = -0.60; P < 0.0001), disease relapse (r = -0.18; P = 0.034) and disease-related death (r = -0.19; P = 0.026). Advanced stage of disease was independently predicted by low Bim expression (P = 0.0010, odds ratio: 0.22, 95% CI: 0.10-0.56) on multivariate analysis; however, Bim was not shown to be an independent predictor for disease relapse (P = 0.40) and disease-related death (P = 0.77). Our data demonstrate that Bim protein expression is significantly inversely correlated with melanoma features that are associated with worse prognosis. We have shown that Bim protein expression in CM is an independent predictor for advanced disease confirming that this pro-apoptotic BH3-only protein might be a potent biomarker and promising therapeutic target. © 2016 European Academy of Dermatology and Venereology.

Global predictability of temperature extremes

NASA Astrophysics Data System (ADS)

Coughlan de Perez, Erin; van Aalst, Maarten; Bischiniotis, Konstantinos; Mason, Simon; Nissan, Hannah; Pappenberger, Florian; Stephens, Elisabeth; Zsoter, Ervin; van den Hurk, Bart

2018-05-01

Extreme temperatures are one of the leading causes of death and disease in both developed and developing countries, and heat extremes are projected to rise in many regions. To reduce risk, heatwave plans and cold weather plans have been effectively implemented around the world. However, much of the world’s population is not yet protected by such systems, including many data-scarce but also highly vulnerable regions. In this study, we assess at a global level where such systems have the potential to be effective at reducing risk from temperature extremes, characterizing (1) long-term average occurrence of heatwaves and coldwaves, (2) seasonality of these extremes, and (3) short-term predictability of these extreme events three to ten days in advance. Using both the NOAA and ECMWF weather forecast models, we develop global maps indicating a first approximation of the locations that are likely to benefit from the development of seasonal preparedness plans and/or short-term early warning systems for extreme temperature. The extratropics generally show both short-term skill as well as strong seasonality; in the tropics, most locations do also demonstrate one or both. In fact, almost 5 billion people live in regions that have seasonality and predictability of heatwaves and/or coldwaves. Climate adaptation investments in these regions can take advantage of seasonality and predictability to reduce risks to vulnerable populations.

Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia

NASA Astrophysics Data System (ADS)

Zhang, Ying; Moges, Semu; Block, Paul

2018-01-01

Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal precipitation, and a modeling structure spanning statistical and dynamical approaches makes clear advances in prediction skill and resolution, as compared with previous studies. The statistical model improves versus the non-clustered case or dynamical models for a number of specific clusters in northwestern Ethiopia, with clusters having regional average correlation and ranked probability skill score (RPSS) values of up to 0.5 and 33 %, respectively. The general skill (after bias correction) of the two best-performing dynamical models over the entire study region is superior to that of the statistical models, although the dynamical models issue predictions at a lower resolution and the raw predictions require bias correction to guarantee comparable skills.

Prediction of Peaks of Seasonal Influenza in Military Health-Care Data

PubMed Central

Buczak, Anna L.; Baugher, Benjamin; Guven, Erhan; Moniz, Linda; Babin, Steven M.; Chretien, Jean-Paul

2016-01-01

Influenza is a highly contagious disease that causes seasonal epidemics with significant morbidity and mortality. The ability to predict influenza peak several weeks in advance would allow for timely preventive public health planning and interventions to be used to mitigate these outbreaks. Because influenza may also impact the operational readiness of active duty personnel, the US military places a high priority on surveillance and preparedness for seasonal outbreaks. A method for creating models for predicting peak influenza visits per total health-care visits (ie, activity) weeks in advance has been developed using advanced data mining techniques on disparate epidemiological and environmental data. The model results are presented and compared with those of other popular data mining classifiers. By rigorously testing the model on data not used in its development, it is shown that this technique can predict the week of highest influenza activity for a specific region with overall better accuracy than other methods examined in this article. PMID:27127415

Substrate Deformation Predicts Neuronal Growth Cone Advance

PubMed Central

Athamneh, Ahmad I.M.; Cartagena-Rivera, Alexander X.; Raman, Arvind; Suter, Daniel M.

2015-01-01

Although pulling forces have been observed in axonal growth for several decades, their underlying mechanisms, absolute magnitudes, and exact roles are not well understood. In this study, using two different experimental approaches, we quantified retrograde traction force in Aplysia californica neuronal growth cones as they develop over time in response to a new adhesion substrate. In the first approach, we developed a novel method, to our knowledge, for measuring traction forces using an atomic force microscope (AFM) with a cantilever that was modified with an Aplysia cell adhesion molecule (apCAM)-coated microbead. In the second approach, we used force-calibrated glass microneedles coated with apCAM ligands to guide growth cone advance. The traction force exerted by the growth cone was measured by monitoring the microneedle deflection using an optical microscope. Both approaches showed that Aplysia growth cones can develop traction forces in the 100–102 nN range during adhesion-mediated advance. Moreover, our results suggest that the level of traction force is directly correlated to the stiffness of the microneedle, which is consistent with a reinforcement mechanism previously observed in other cell types. Interestingly, the absolute level of traction force did not correlate with growth cone advance toward the adhesion site, but the amount of microneedle deflection did. In cases of adhesion-mediated growth cone advance, the mean needle deflection was 1.05 ± 0.07 μm. By contrast, the mean deflection was significantly lower (0.48 ± 0.06 μm) when the growth cones did not advance. Our data support a hypothesis that adhesion complexes, which can undergo micron-scale elastic deformation, regulate the coupling between the retrogradely flowing actin cytoskeleton and apCAM substrates, stimulating growth cone advance if sufficiently abundant. PMID:26445437

Region-Based Prediction for Image Compression in the Cloud.

PubMed

Begaint, Jean; Thoreau, Dominique; Guillotel, Philippe; Guillemot, Christine

2018-04-01

Thanks to the increasing number of images stored in the cloud, external image similarities can be leveraged to efficiently compress images by exploiting inter-images correlations. In this paper, we propose a novel image prediction scheme for cloud storage. Unlike current state-of-the-art methods, we use a semi-local approach to exploit inter-image correlation. The reference image is first segmented into multiple planar regions determined from matched local features and super-pixels. The geometric and photometric disparities between the matched regions of the reference image and the current image are then compensated. Finally, multiple references are generated from the estimated compensation models and organized in a pseudo-sequence to differentially encode the input image using classical video coding tools. Experimental results demonstrate that the proposed approach yields significant rate-distortion performance improvements compared with the current image inter-coding solutions such as high efficiency video coding.

The status of perineural invasion predicts the outcomes of postoperative radiotherapy in locally advanced esophageal squamous cell carcinoma.

PubMed

Ning, Zhong-Hua; Zhao, Wei; Li, Xiao-Dong; Chen, Lu-Jun; Xu, Bin; Gu, Wen-Dong; Shao, Ying-Jie; Xu, Yun; Huang, Jin; Pei, Hong-Lei; Jiang, Jing-Ting

2015-01-01

Prognosis of locally advanced esophageal squamous cell carcinoma (ESCC) remains dismal even after curative resection and adjuvant radiotherapy. New biomarkers for predicting prognosis and treatment outcomes are needed for improved treatment stratification of patients with locally advanced ESCC. The prognostic and treatment predictive significance of perineural invasion (PNI) in the locally advanced ESCC remains unclear. This study aimed to examine the effect of PNI on the outcomes of locally advanced ESCC patients after curative resection with or without postoperative radiotherapy (PORT). We retrospectively reviewed 262 consecutive locally advanced ESCC patients who underwent curative resection. Tumors sections were re-evaluated for PNI by an independent pathologist blinded to the patients' outcomes. Overall survival (OS) and disease-free survival (DFS) were determined using the Kaplan-Meier method; univariate log-rank test and multivariate Cox proportional hazard model were used to evaluate the prognostic value of PNI. Finally, 243 patients were analyzed and enrolled into this study, of which 132 received PORT. PNI was identified in 22.2% (54/243) of the pathologic sections. The 5-year DFS was favorable for PNI-negative patients versus PNI-positive patients (21.3% vs. 36.7%, respectively; P = 0.005). The 5-year OS was 40.3% for PNI-negative patients versus 21.7% for PNI-positive patients (P < 0.001). On multivariate analysis, PNI was an independent prognostic factor. In a subset analysis for patients received PORT, PNI was evaluated as a prognostic predictor as well (P < 0.05). In contrast to patients without PORT, PORT couldn't improve the disease recurrence and survival in locally advanced ESCC patients with PNI-positive (P > 0.05). PNI could serve as an independent prognostic factor and prognosticate treatment outcomes in locally advanced ESCC patients. The PNI status should be considered when stratifying high-risk locally advanced ESCC patients for adjuvant

Advance in prediction of soil slope instabilities

NASA Astrophysics Data System (ADS)

Sigarán-Loría, C.; Hack, R.; Nieuwenhuis, J. D.

2012-04-01

Six generic soils (clays and sands) were systematically modeled with plane-strain finite elements (FE) at varying heights and inclinations. A dataset was generated in order to develop predictive relations of soil slope instabilities, in terms of co-seismic displacements (u), under strong motions with a linear multiple regression. For simplicity, the seismic loads are monochromatic artificial sinusoidal functions at four frequencies: 1, 2, 4, and 6 Hz, and the slope failure criterion used corresponds to near 10% Cartesian shear strains along a continuous region comparable to a slip surface. The generated dataset comprises variables from the slope geometry and site conditions: height, H, inclination, i, shear wave velocity from the upper 30 m, vs30, site period, Ts; as well as the input strong motion: yield acceleration, ay (equal to peak ground acceleration, PGA in this research), frequency, f; and in some cases moment magnitude, M, and Arias intensity, Ia, assumed from empirical correlations. Different datasets or scenarios were created: "Magnitude-independent", "Magnitude-dependent", and "Soil-dependent", and the data was statistically explored and analyzed with varying mathematical forms. Qualitative relations show that the permanent deformations are highly related to the soil class for the clay slopes, but not for the sand slopes. Furthermore, the slope height does not constrain the variability in the co-seismic displacements. The input frequency decreases the variability of the co-seismic displacements for the "Magnitude-dependent" and "Soil-dependent" datasets. The empirical models were developed with two and three predictors. For the sands it was not possible because they could not satisfy the constrains from the statistical method. For the clays, the best models with the smallest errors coincided with the simple general form of multiple regression with three predictors (e.g. near 0.16 and 0.21 standard error, S.E. and 0.75 and 0.55 R2 for the "M

Advanced Rural Transportation Information and Coordination (ARTIC) operational test evaluation report : location : Arrowhead region of northeastern Minnesota

DOT National Transportation Integrated Search

2000-07-01

This report presents the results of a one-year evaluation test of an Intelligent Transportation Systems (ITS) project known as Advanced Rural Transportation Information and Coordination (ARTIC), located in the Arrowhead Region of Northeastern Minneso...

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

PubMed Central

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

2013-01-01

Background New onset Alzheimer’s 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 (WMH) on MRI, remains unclear. Objective To determine whether regional WMH and hippocampal volume predict incident AD in an epidemiological study. Design A longitudinal community-based epidemiological study of older adults from northern Manhattan. Setting The Washington Heights/Inwood Columbia Aging Project Participants Between 2005 and 2007, 717 non-demented participants received MRI scans. An average of 40.28 (SD=9.77) months later, 503 returned for follow-up clinical examination and 46 met criteria for incident dementia (45 with AD). Regional WMH 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 two measurements. Main outcome measures Incident Alzheimer’s disease. Results White matter hyperintensity volume in the parietal lobe predicted time to incident dementia (HR=1.194, p=0.031). Relative hippocampal volume did not predict incident dementia when considered alone (HR=0.419, p=0.768) or with the WMH measures included in the model (HR=0.302, p=0.701). Including hippocampal volume in the model did not notably alter the predictive utility of parietal lobe WMH (HR=1.197, p=0.049). Conclusion The findings highlight the regional specificity of the association of WMH with AD. It is not clear whether parietal WMH solely represent a marker for cerebrovascular burden or point to distinct injury compared to other regions. Future work should elucidate pathogenic mechanisms linking WMH and AD pathology. PMID:22945686

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.

Parameter transferability within homogeneous regions and comparisons with predictions from a priori parameters in the eastern United States

NASA Astrophysics Data System (ADS)

Chouaib, Wafa; Alila, Younes; Caldwell, Peter V.

2018-05-01

The need for predictions of flow time-series persists at ungauged catchments, motivating the research goals of our study. By means of the Sacramento model, this paper explores the use of parameter transfer within homogeneous regions of similar climate and flow characteristics and makes comparisons with predictions from a priori parameters. We assessed the performance using the Nash-Sutcliffe (NS), bias, mean monthly hydrograph and flow duration curve (FDC). The study was conducted on a large dataset of 73 catchments within the eastern US. Two approaches to the parameter transferability were developed and evaluated; (i) the within homogeneous region parameter transfer using one donor catchment specific to each region, (ii) the parameter transfer disregarding the geographical limits of homogeneous regions, where one donor catchment was common to all regions. Comparisons between both parameter transfers enabled to assess the gain in performance from the parameter regionalization and its respective constraints and limitations. The parameter transfer within homogeneous regions outperformed the a priori parameters and led to a decrease in bias and increase in efficiency reaching a median NS of 0.77 and a NS of 0.85 at individual catchments. The use of FDC revealed the effect of bias on the inaccuracy of prediction from parameter transfer. In one specific region, of mountainous and forested catchments, the prediction accuracy of the parameter transfer was less satisfactory and equivalent to a priori parameters. In this region, the parameter transfer from the outsider catchment provided the best performance; less-biased with smaller uncertainty in medium flow percentiles (40%-60%). The large disparity of energy conditions explained the lack of performance from parameter transfer in this region. Besides, the subsurface stormflow is predominant and there is a likelihood of lateral preferential flow, which according to its specific properties further explained the reduced

Pharmacogenetics-based area-under-curve model can predict efficacy and adverse events from axitinib in individual patients with advanced renal cell carcinoma.

PubMed

Yamamoto, Yoshiaki; Tsunedomi, Ryouichi; Fujita, Yusuke; Otori, Toru; Ohba, Mitsuyoshi; Kawai, Yoshihisa; Hirata, Hiroshi; Matsumoto, Hiroaki; Haginaka, Jun; Suzuki, Shigeo; Dahiya, Rajvir; Hamamoto, Yoshihiko; Matsuyama, Kenji; Hazama, Shoichi; Nagano, Hiroaki; Matsuyama, Hideyasu

2018-03-30

We investigated the relationship between axitinib pharmacogenetics and clinical efficacy/adverse events in advanced renal cell carcinoma (RCC) and established a model to predict clinical efficacy and adverse events using pharmacokinetic and gene polymorphisms related to drug metabolism and efflux in a phase II trial. We prospectively evaluated the area under the plasma concentration-time curve (AUC) of axitinib, objective response rate, and adverse events in 44 consecutive advanced RCC patients treated with axitinib. To establish a model for predicting clinical efficacy and adverse events, polymorphisms in genes including ABC transporters ( ABCB1 and ABCG2 ), UGT1A , and OR2B11 were analyzed by whole-exome sequencing, Sanger sequencing, and DNA microarray. To validate this prediction model, calculated AUC by 6 gene polymorphisms was compared with actual AUC in 16 additional consecutive patients prospectively. Actual AUC significantly correlated with the objective response rate ( P = 0.0002) and adverse events (hand-foot syndrome, P = 0.0055; and hypothyroidism, P = 0.0381). Calculated AUC significantly correlated with actual AUC ( P < 0.0001), and correctly predicted objective response rate ( P = 0.0044) as well as adverse events ( P = 0.0191 and 0.0082, respectively). In the validation study, calculated AUC prior to axitinib treatment precisely predicted actual AUC after axitinib treatment ( P = 0.0066). Our pharmacogenetics-based AUC prediction model may determine the optimal initial dose of axitinib, and thus facilitate better treatment of patients with advanced RCC.

Pharmacogenetics-based area-under-curve model can predict efficacy and adverse events from axitinib in individual patients with advanced renal cell carcinoma

PubMed Central

Yamamoto, Yoshiaki; Tsunedomi, Ryouichi; Fujita, Yusuke; Otori, Toru; Ohba, Mitsuyoshi; Kawai, Yoshihisa; Hirata, Hiroshi; Matsumoto, Hiroaki; Haginaka, Jun; Suzuki, Shigeo; Dahiya, Rajvir; Hamamoto, Yoshihiko; Matsuyama, Kenji; Hazama, Shoichi; Nagano, Hiroaki; Matsuyama, Hideyasu

2018-01-01

We investigated the relationship between axitinib pharmacogenetics and clinical efficacy/adverse events in advanced renal cell carcinoma (RCC) and established a model to predict clinical efficacy and adverse events using pharmacokinetic and gene polymorphisms related to drug metabolism and efflux in a phase II trial. We prospectively evaluated the area under the plasma concentration–time curve (AUC) of axitinib, objective response rate, and adverse events in 44 consecutive advanced RCC patients treated with axitinib. To establish a model for predicting clinical efficacy and adverse events, polymorphisms in genes including ABC transporters (ABCB1 and ABCG2), UGT1A, and OR2B11 were analyzed by whole-exome sequencing, Sanger sequencing, and DNA microarray. To validate this prediction model, calculated AUC by 6 gene polymorphisms was compared with actual AUC in 16 additional consecutive patients prospectively. Actual AUC significantly correlated with the objective response rate (P = 0.0002) and adverse events (hand-foot syndrome, P = 0.0055; and hypothyroidism, P = 0.0381). Calculated AUC significantly correlated with actual AUC (P < 0.0001), and correctly predicted objective response rate (P = 0.0044) as well as adverse events (P = 0.0191 and 0.0082, respectively). In the validation study, calculated AUC prior to axitinib treatment precisely predicted actual AUC after axitinib treatment (P = 0.0066). Our pharmacogenetics-based AUC prediction model may determine the optimal initial dose of axitinib, and thus facilitate better treatment of patients with advanced RCC. PMID:29682213

Advances in air quality prediction with the use of integrated systems

NASA Astrophysics Data System (ADS)

Dragani, R.; Benedetti, A.; Engelen, R. J.; Peuch, V. H.

2017-12-01

Recent years have seen the rise of global operational atmospheric composition forecasting systems for several applications including climate monitoring, provision of boundary conditions for regional air quality forecasting, energy sector applications, to mention a few. Typically, global forecasts are provided in the medium-range up to five days ahead and are initialized with an analysis based on satellite data. In this work we present the latest advances in data assimilation using the ECMWF's 4D-Var system extended to atmospheric composition which is currently operational under the Copernicus Atmosphere Monitoring Service of the European Commission. The service is based on acquisition of all relevant data available in near-real-time, the processing of these datasets in the assimilation and the subsequent dissemination of global forecasts at ECMWF. The global forecasts are used by the CAMS regional models as boundary conditions for the European forecasts based on a multi-model ensemble. The global forecasts are also used to provide boundary conditions for other parts of the world (e.g., China) and are freely available to all interested entities. Some of the regional models also perform assimilation of satellite and ground-based observations. All products are assessed, validated and made publicly available on https://atmosphere.copernicus.eu/.

Importance of Foliar Nitrogen Concentration to Predict Forest Productivity in the Mid-Atlantic Region

Treesearch

Yude Pan; John Hom; Jennifer Jenkins; Richard Birdsey

2004-01-01

To assess what difference it might make to include spatially defined estimates of foliar nitrogen in the regional application of a forest ecosystem model (PnET-II), we composed model predictions of wood production from extensive ground-based forest inventory analysis data across the Mid-Atlantic region. Spatial variation in foliar N concentration was assigned based on...

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

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

Martinez, Steve R., E-mail: steve.martinez@ucdmc.ucdavis.ed; Beal, Shannon H.; Chen, Steven L.

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. Approximatelymore » 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.« less

EVALUATING THE PERFORMANCE OF REGIONAL-SCALE PHOTOCHEMICAL MODELING SYSTEMS: PART II--OZONE PREDICTIONS. (R825260)

EPA Science Inventory

In this paper, the concept of scale analysis is applied to evaluate ozone predictions from two regional-scale air quality models. To this end, seasonal time series of observations and predictions from the RAMS3b/UAM-V and MM5/MAQSIP (SMRAQ) modeling systems for ozone were spectra...

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.

Long Range River Discharge Forecasting Using the Gravity Recovery and Climate Experiment (GRACE) Satellite to Predict Conditions for Endemic Cholera

NASA Astrophysics Data System (ADS)

Jutla, A.; Akanda, A. S.; Colwell, R. R.

2014-12-01

Prediction of conditions of an impending disease outbreak remains a challenge but is achievable if the associated and appropriate large scale hydroclimatic process can be estimated in advance. Outbreaks of diarrheal diseases such as cholera, are related to episodic seasonal variability in river discharge in the regions where water and sanitation infrastructure are inadequate and insufficient. However, forecasting river discharge, few months in advance, remains elusive where cholera outbreaks are frequent, probably due to non-availability of geophysical data as well as transboundary water stresses. Here, we show that satellite derived water storage from Gravity Recovery and Climate Experiment Forecasting (GRACE) sensors can provide reliable estimates on river discharge atleast two months in advance over regional scales. Bayesian regression models predicted flooding and drought conditions, a prerequisite for cholera outbreaks, in Bengal Delta with an overall accuracy of 70% for upto 60 days in advance without using any other ancillary ground based data. Forecasting of river discharge will have significant impacts on planning and designing intervention strategies for potential cholera outbreaks in the coastal regions where the disease remain endemic and often fatal.

Dynamical systems proxies of atmospheric predictability and mid-latitude extremes

NASA Astrophysics Data System (ADS)

Messori, Gabriele; Faranda, Davide; Caballero, Rodrigo; Yiou, Pascal

2017-04-01

Extreme weather ocurrences carry enormous social and economic costs and routinely garner widespread scientific and media coverage. Many extremes (for e.g. storms, heatwaves, cold spells, heavy precipitation) are tied to specific patterns of midlatitude atmospheric circulation. The ability to identify these patterns and use them to enhance the predictability of the extremes is therefore a topic of crucial societal and economic value. We propose a novel predictability pathway for extreme events, by building upon recent advances in dynamical systems theory. We use two simple dynamical systems metrics - local dimension and persistence - to identify sets of similar large-scale atmospheric flow patterns which present a coherent temporal evolution. When these patterns correspond to weather extremes, they therefore afford a particularly good forward predictability. We specifically test this technique on European winter temperatures, whose variability largely depends on the atmospheric circulation in the North Atlantic region. We find that our dynamical systems approach provides predictability of large-scale temperature extremes up to one week in advance.

Genotype-driven identification of a molecular network predictive of advanced coronary calcium in ClinSeq® and Framingham Heart Study cohorts.

PubMed

Oguz, Cihan; Sen, Shurjo K; Davis, Adam R; Fu, Yi-Ping; O'Donnell, Christopher J; Gibbons, Gary H

2017-10-26

One goal of personalized medicine is leveraging the emerging tools of data science to guide medical decision-making. Achieving this using disparate data sources is most daunting for polygenic traits. To this end, we employed random forests (RFs) and neural networks (NNs) for predictive modeling of coronary artery calcium (CAC), which is an intermediate endo-phenotype of coronary artery disease (CAD). Model inputs were derived from advanced cases in the ClinSeq®; discovery cohort (n=16) and the FHS replication cohort (n=36) from 89 th -99 th CAC score percentile range, and age-matched controls (ClinSeq®; n=16, FHS n=36) with no detectable CAC (all subjects were Caucasian males). These inputs included clinical variables and genotypes of 56 single nucleotide polymorphisms (SNPs) ranked highest in terms of their nominal correlation with the advanced CAC state in the discovery cohort. Predictive performance was assessed by computing the areas under receiver operating characteristic curves (ROC-AUC). RF models trained and tested with clinical variables generated ROC-AUC values of 0.69 and 0.61 in the discovery and replication cohorts, respectively. In contrast, in both cohorts, the set of SNPs derived from the discovery cohort were highly predictive (ROC-AUC ≥0.85) with no significant change in predictive performance upon integration of clinical and genotype variables. Using the 21 SNPs that produced optimal predictive performance in both cohorts, we developed NN models trained with ClinSeq®; data and tested with FHS data and obtained high predictive accuracy (ROC-AUC=0.80-0.85) with several topologies. Several CAD and "vascular aging" related biological processes were enriched in the network of genes constructed from the predictive SNPs. We identified a molecular network predictive of advanced coronary calcium using genotype data from ClinSeq®; and FHS cohorts. Our results illustrate that machine learning tools, which utilize complex interactions between disease

What predicts the quality of advanced cancer care in Latin America? A look at five countries: Argentina, Brazil, Cuba, Mexico, and Peru.

PubMed

Torres Vigil, Isabel; Aday, Lu Ann; De Lima, Liliana; Cleeland, Charles S

2007-09-01

Cancer is now a leading cause of death among adults in most Latin American nations. Yet, until recently, there has been limited research on the quality of, and access to, advanced cancer care in developing regions such as Latin America. This landmark, cross-national study assessed the quality of advanced cancer care in five Latin American countries by surveying a convenience sample of 777 physicians and nurses, and identifying the most salient influences on their quality-of-care assessments based on multiple linear regression analyses. Strategies for disseminating this survey included mass mailings, distribution at professional meetings/conferences, collaboration with Latin American institutions, professional organizations, and the Pan American Health Organization, and online posting. Results indicate that the respondents' assessments of the quality of, access to, and affordability of advanced cancer care varied significantly across nations (P<0.001). The strongest predictor of providers' national-level assessments of the quality of care was their ratings of access to advanced cancer care (Beta=0.647). Other predictors included affordability of care, country (Cuba vs. the other four countries), income-gap quintile, and institutional availability of opioid analgesics. Low prioritization of palliative care in both health care policy formulation and provider education also predicted the quality-of-care ratings. Findings from this study suggest that providers from five different nations hold similar equitable notions of quality care that are dependent on the provision of accessible and affordable care. Measures of social equity, such as the income-gap quintile of nations, and measures of policy barriers, such as the scale developed in this study, should be replicated in future studies to enable policy makers to assess and improve advanced cancer care in their countries.

Improvement of Advanced Storm-scale Analysis and Prediction System (ASAPS) on Seoul Metropolitan Area, Korea

NASA Astrophysics Data System (ADS)

Park, Jeong-Gyun; Jee, Joon-Bum

2017-04-01

Dangerous weather such as severe rain, heavy snow, drought and heat wave caused by climate change make more damage in the urban area that dense populated and industry areas. Urban areas, unlike the rural area, have big population and transportation, dense the buildings and fuel consumption. Anthropogenic factors such as road energy balance, the flow of air in the urban is unique meteorological phenomena. However several researches are in process about prediction of urban meteorology. ASAPS (Advanced Storm-scale Analysis and Prediction System) predicts a severe weather with very short range (prediction with 6 hour) and high resolution (every hour with time and 1 km with space) on Seoul metropolitan area based on KLAPS (Korea Local Analysis and Prediction System) from KMA (Korea Meteorological Administration). This system configured three parts that make a background field (SUF5), analysis field (SU01) with observation and forecast field with high resolution (SUF1). In this study, we improve a high-resolution ASAPS model and perform a sensitivity test for the rainfall case. The improvement of ASAPS include model domain configuration, high resolution topographic data and data assimilation with WISE observation data.

Prediction skill of rainstorm events over India in the TIGGE weather prediction models

NASA Astrophysics Data System (ADS)

Karuna Sagar, S.; Rajeevan, M.; Vijaya Bhaskara Rao, S.; Mitra, A. K.

2017-12-01

Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007-2015. The comparison reveals that rainstorm events are predictable up to 5 days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24 hr forecast to 48 hr forecast in all the three models.

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.

[The survival prediction model of advanced gallbladder cancer based on Bayesian network: a multi-institutional study].

PubMed

Tang, Z H; Geng, Z M; Chen, C; Si, S B; Cai, Z Q; Song, T Q; Gong, P; Jiang, L; Qiu, Y H; He, Y; Zhai, W L; Li, S P; Zhang, Y C; Yang, Y

2018-05-01

Objective: To investigate the clinical value of Bayesian network in predicting survival of patients with advanced gallbladder cancer(GBC)who underwent curative intent surgery. Methods: The clinical data of patients with advanced GBC who underwent curative intent surgery in 9 institutions from January 2010 to December 2015 were analyzed retrospectively.A median survival time model based on a tree augmented naïve Bayes algorithm was established by Bayesia Lab software.The survival time, number of metastatic lymph nodes(NMLN), T stage, pathological grade, margin, jaundice, liver invasion, age, sex and tumor morphology were included in this model.Confusion matrix, the receiver operating characteristic curve and area under the curve were used to evaluate the accuracy of the model.A priori statistical analysis of these 10 variables and a posterior analysis(survival time as the target variable, the remaining factors as the attribute variables)was performed.The importance rankings of each variable was calculated with the polymorphic Birnbaum importance calculation based on the posterior analysis results.The survival probability forecast table was constructed based on the top 4 prognosis factors. The survival curve was drawn by the Kaplan-Meier method, and differences in survival curves were compared using the Log-rank test. Results: A total of 316 patients were enrolled, including 109 males and 207 females.The ratio of male to female was 1.0∶1.9, the age was (62.0±10.8)years.There was 298 cases(94.3%) R0 resection and 18 cases(5.7%) R1 resection.T staging: 287 cases(90.8%) T3 and 29 cases(9.2%) T4.The median survival time(MST) was 23.77 months, and the 1, 3, 5-year survival rates were 67.4%, 40.8%, 32.0%, respectively.For the Bayesian model, the number of correctly predicted cases was 121(≤23.77 months) and 115(>23.77 months) respectively, leading to a 74.86% accuracy of this model.The prior probability of survival time was 0.503 2(≤23.77 months) and 0.496 8

LMethyR-SVM: Predict Human Enhancers Using Low Methylated Regions based on Weighted Support Vector Machines.

PubMed

Xu, Jingting; Hu, Hong; Dai, Yang

The identification of enhancers is a challenging task. Various types of epigenetic information including histone modification have been utilized in the construction of enhancer prediction models based on a diverse panel of machine learning schemes. However, DNA methylation profiles generated from the whole genome bisulfite sequencing (WGBS) have not been fully explored for their potential in enhancer prediction despite the fact that low methylated regions (LMRs) have been implied to be distal active regulatory regions. In this work, we propose a prediction framework, LMethyR-SVM, using LMRs identified from cell-type-specific WGBS DNA methylation profiles and a weighted support vector machine learning framework. In LMethyR-SVM, the set of cell-type-specific LMRs is further divided into three sets: reliable positive, like positive and likely negative, according to their resemblance to a small set of experimentally validated enhancers in the VISTA database based on an estimated non-parametric density distribution. Then, the prediction model is obtained by solving a weighted support vector machine. We demonstrate the performance of LMethyR-SVM by using the WGBS DNA methylation profiles derived from the human embryonic stem cell type (H1) and the fetal lung fibroblast cell type (IMR90). The predicted enhancers are highly conserved with a reasonable validation rate based on a set of commonly used positive markers including transcription factors, p300 binding and DNase-I hypersensitive sites. In addition, we show evidence that the large fraction of the LMethyR-SVM predicted enhancers are not predicted by ChromHMM in H1 cell type and they are more enriched for the FANTOM5 enhancers. Our work suggests that low methylated regions detected from the WGBS data are useful as complementary resources to histone modification marks in developing models for the prediction of cell-type-specific enhancers.

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

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.

Advances in Applications of Hierarchical Bayesian Methods with Hydrological Models

NASA Astrophysics Data System (ADS)

Alexander, R. B.; Schwarz, G. E.; Boyer, E. W.

2017-12-01

Mechanistic and empirical watershed models are increasingly used to inform water resource decisions. Growing access to historical stream measurements and data from in-situ sensor technologies has increased the need for improved techniques for coupling models with hydrological measurements. Techniques that account for the intrinsic uncertainties of both models and measurements are especially needed. Hierarchical Bayesian methods provide an efficient modeling tool for quantifying model and prediction uncertainties, including those associated with measurements. Hierarchical methods can also be used to explore spatial and temporal variations in model parameters and uncertainties that are informed by hydrological measurements. We used hierarchical Bayesian methods to develop a hybrid (statistical-mechanistic) SPARROW (SPAtially Referenced Regression On Watershed attributes) model of long-term mean annual streamflow across diverse environmental and climatic drainages in 18 U.S. hydrological regions. Our application illustrates the use of a new generation of Bayesian methods that offer more advanced computational efficiencies than the prior generation. Evaluations of the effects of hierarchical (regional) variations in model coefficients and uncertainties on model accuracy indicates improved prediction accuracies (median of 10-50%) but primarily in humid eastern regions, where model uncertainties are one-third of those in arid western regions. Generally moderate regional variability is observed for most hierarchical coefficients. Accounting for measurement and structural uncertainties, using hierarchical state-space techniques, revealed the effects of spatially-heterogeneous, latent hydrological processes in the "localized" drainages between calibration sites; this improved model precision, with only minor changes in regional coefficients. Our study can inform advances in the use of hierarchical methods with hydrological models to improve their integration with stream

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

Regional differences in brain volume predict the acquisition of skill in a complex real-time strategy videogame.

PubMed

Basak, Chandramallika; Voss, Michelle W; Erickson, Kirk I; Boot, Walter R; Kramer, Arthur F

2011-08-01

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 h 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. Copyright © 2011 Elsevier Inc. All rights reserved.

Prediction model for prevalence and incidence of advanced age-related macular degeneration based on genetic, demographic, and environmental variables.

PubMed

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

2009-05-01

The joint effects of genetic, ocular, and environmental variables were evaluated and predictive models for prevalence and incidence of AMD were assessed. 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. 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 related to AMD, with a multiplicative joint

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

The Accuracy of Physicians' Clinical Predictions of Survival in Patients With Advanced Cancer.

PubMed

Amano, Koji; Maeda, Isseki; Shimoyama, Satofumi; Shinjo, Takuya; Shirayama, Hiroto; Yamada, Takeshi; Ono, Shigeki; Yamamoto, Ryo; Yamamoto, Naoki; Shishido, Hideki; Shimizu, Mie; Kawahara, Masanori; Aoki, Shigeru; Demizu, Akira; Goshima, Masahiro; Goto, Keiji; Gyoda, Yasuaki; Hashimoto, Kotaro; Otomo, Sen; Sekimoto, Masako; Shibata, Takemi; Sugimoto, Yuka; Morita, Tatsuya

2015-08-01

Accurate prognoses are needed for patients with advanced cancer. To evaluate the accuracy of physicians' clinical predictions of survival (CPS) and assess the relationship between CPS and actual survival (AS) in patients with advanced cancer in palliative care units, hospital palliative care teams, and home palliative care services, as well as those receiving chemotherapy. This was a multicenter prospective cohort study conducted in 58 palliative care service centers in Japan. The palliative care physicians evaluated patients on the first day of admission and followed up all patients to their death or six months after enrollment. We evaluated the accuracy of CPS and assessed the relationship between CPS and AS in the four groups. We obtained a total of 2036 patients: 470, 764, 404, and 398 in hospital palliative care teams, palliative care units, home palliative care services, and chemotherapy, respectively. The proportion of accurate CPS (0.67-1.33 times AS) was 35% (95% CI 33-37%) in the total sample and ranged from 32% to 39% in each setting. While the proportion of patients living longer than CPS (pessimistic CPS) was 20% (95% CI 18-22%) in the total sample, ranging from 15% to 23% in each setting, the proportion of patients living shorter than CPS (optimistic CPS) was 45% (95% CI 43-47%) in the total sample, ranging from 43% to 49% in each setting. Physicians tend to overestimate when predicting survival in all palliative care patients, including those receiving chemotherapy. Copyright © 2015 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

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.

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...

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

Predicted stem-loop structures and variation in nucleotide sequence of 3' noncoding regions among animal calicivirus genomes.

PubMed

Seal, B S; Neill, J D; Ridpath, J F

1994-07-01

Caliciviruses are nonenveloped with a polyadenylated genome of approximately 7.6 kb and a single capsid protein. The "RNA Fold" computer program was used to analyze 3'-terminal noncoding sequences of five feline calicivirus (FCV), rabbit hemorrhagic disease virus (RHDV), and two San Miguel sea lion virus (SMSV) isolates. The FCV 3'-terminal sequences are 40-46 nucleotides in length and 72-91% similar. The FCV sequences were predicted to contain two possible duplex structures and one stem-loop structure with free energies of -2.1 to -18.2 kcal/mole. The RHDV genomic 3'-terminal RNA sequences are 54 nucleotides in length and share 49% sequence similarity to homologous regions of the FCV genome. The RHDV sequence was predicted to form two duplex structures in the 3'-terminal noncoding region with a single stem-loop structure, resembling that of FCV. In contrast, the SMSV 1 and 4 genomic 3'-terminal noncoding sequences were 185 and 182 nucleotides in length, respectively. Ten possible duplex structures were predicted with an average structural free energy of -35 kcal/mole. Sequence similarity between the two SMSV isolates was 75%. Furthermore, extensive cloverleaflike structures are predicted in the 3' noncoding region of the SMSV genome, in contrast to the predicted single stem-loop structures of FCV or RHDV.

Novel approaches to reducing uncertainty in regional climate predictions (Invited)

NASA Astrophysics Data System (ADS)

Ammann, C. M.

2009-12-01

Regional planning in preparation for future climate changes is rapidly gaining importance. However, compared to the global mean projections, correctly anticipating regional climate is often much more difficult, particularly with regard to hydrologic changes. The reason for the high, inherent uncertainty in location specific forecasts arises on one hand from the superposition of large internal variability in the atmosphere-ocean system on the more gradual changes. On the other hand, this problem is confounded by the fact that regional climate records are often short and therefore offer little guidance as to how an underlying trend can be identified within the noise. The use of indirect climate information (proxy records) from a host of natural archives has made significant progress recently. Based on an extended record, process studies can help reveal the regional response to changes in large scale climate that likely have to be expected. But in order to come up with robust, season and parameter specific (temperature versus moisture) climate reconstructions, comprehensive data compilations are needed that integrate proxy records of different characteristics, temporal representations, and different systematic and sampling uncertainties. Based on understanding of physical processes, and making explicit use of that knowledge, new dynamical and statistical techniques in paleoclimatology are being developed and explored. In addition to improved estimates of the past climate, the cascade of uncertainties is directly taken into account so that errors can more comprehensively be assessed. A brief overview of the problems and its potential implications for regional planning is followed by an application that demonstrates how collaboration between paleoclimatologists, climate modelers and statisticians can advance our understanding of the climate system and how agencies, businesses and individuals might be able to make better informed decisions in preparation for future

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.

Skillful prediction of hot temperature extremes over the source region of ancient Silk Road.

PubMed

Zhang, Jingyong; Yang, Zhanmei; Wu, Lingyun

2018-04-27

The source region of ancient Silk Road (SRASR) in China, a region of around 150 million people, faces a rapidly increased risk of extreme heat in summer. In this study, we develop statistical models to predict summer hot temperature extremes over the SRASR based on a timescale decomposition approach. Results show that after removing the linear trends, the inter-annual components of summer hot days and heatwaves over the SRASR are significantly related with those of spring soil temperature over Central Asia and sea surface temperature over Northwest Atlantic while their inter-decadal components are closely linked to those of spring East Pacific/North Pacific pattern and Atlantic Multidecadal Oscillation for 1979-2016. The physical processes involved are also discussed. Leave-one-out cross-validation for detrended 1979-2016 time series indicates that the statistical models based on identified spring predictors can predict 47% and 57% of the total variances of summer hot days and heatwaves averaged over the SRASR, respectively. When the linear trends are put back, the prediction skills increase substantially to 64% and 70%. Hindcast experiments for 2012-2016 show high skills in predicting spatial patterns of hot temperature extremes over the SRASR. The statistical models proposed herein can be easily applied to operational seasonal forecasting.

Recent Advances in Resonance Region Nuclear Data Measurements and Analyses for Supporting Nuclear Energy Applications

NASA Astrophysics Data System (ADS)

Dunn, Michael

2008-10-01

For over 30 years, the Oak Ridge National Laboratory (ORNL) has performed research and development to provide more accurate nuclear cross-section data in the resonance region. The ORNL Nuclear Data (ND) Program consists of four complementary areas of research: (1) cross-section measurements at the Oak Ridge Electron Linear Accelerator; (2) resonance analysis methods development with the SAMMY R-matrix analysis software; (3) cross-section evaluation development; and (4) cross-section processing methods development with the AMPX software system. The ND Program is tightly coupled with nuclear fuel cycle analyses and radiation transport methods development efforts at ORNL. Thus, nuclear data work is performed in concert with nuclear science and technology needs and requirements. Recent advances in each component of the ORNL ND Program have led to improvements in resonance region measurements, R-matrix analyses, cross-section evaluations, and processing capabilities that directly support radiation transport research and development. Of particular importance are the improvements in cross-section covariance data evaluation and processing capabilities. The benefit of these advances to nuclear science and technology research and development will be discussed during the symposium on Nuclear Physics Research Connections to Nuclear Energy.

Predictive value of diminutive colonic adenoma trial: the PREDICT trial.

PubMed

Schoenfeld, Philip; Shad, Javaid; Ormseth, Eric; Coyle, Walter; Cash, Brooks; Butler, James; Schindler, William; Kikendall, Walter J; Furlong, Christopher; Sobin, Leslie H; Hobbs, Christine M; Cruess, David; Rex, Douglas

2003-05-01

Diminutive adenomas (1-9 mm in diameter) are frequently found during colon cancer screening with flexible sigmoidoscopy (FS). This trial assessed the predictive value of these diminutive adenomas for advanced adenomas in the proximal colon. In a multicenter, prospective cohort trial, we matched 200 patients with normal FS and 200 patients with diminutive adenomas on FS for age and gender. All patients underwent colonoscopy. The presence of advanced adenomas (adenoma >or= 10 mm in diameter, villous adenoma, adenoma with high grade dysplasia, and colon cancer) and adenomas (any size) was recorded. Before colonoscopy, patients completed questionnaires about risk factors for adenomas. The prevalence of advanced adenomas in the proximal colon was similar in patients with diminutive adenomas and patients with normal FS (6% vs. 5.5%, respectively) (relative risk, 1.1; 95% confidence interval [CI], 0.5-2.6). Diminutive adenomas on FS did not accurately predict advanced adenomas in the proximal colon: sensitivity, 52% (95% CI, 32%-72%); specificity, 50% (95% CI, 49%-51%); positive predictive value, 6% (95% CI, 4%-8%); and negative predictive value, 95% (95% CI, 92%-97%). Male gender (odds ratio, 1.63; 95% CI, 1.01-2.61) was associated with an increased risk of proximal colon adenomas. Diminutive adenomas on sigmoidoscopy may not accurately predict advanced adenomas in the proximal colon.

Advancement in Watershed Modelling Using Dynamic Lateral and Longitudinal Sediment (Dis)connectivity Prediction

NASA Astrophysics Data System (ADS)

Mahoney, D. T.; al Aamery, N. M. H.; Fox, J.

2017-12-01

The authors find that sediment (dis)connectivity has seldom taken precedence within watershed models, and the present study advances this modeling framework and applies the modeling within a bedrock-controlled system. Sediment (dis)connectivity, defined as the detachment and transport of sediment from source to sink between geomorphic zones, is a major control on sediment transport. Given the availability of high resolution geospatial data, coupling sediment connectivity concepts within sediment prediction models offers an approach to simulate sediment sources and pathways within a watershed's sediment cascade. Bedrock controlled catchments are potentially unique due to the presence of rock outcrops causing longitudinal impedance to sediment transport pathways in turn impacting the longitudinal distribution of the energy gradient responsible for conveying sediment. Therefore, the authors were motivated by the need to formulate a sediment transport model that couples sediment (dis)connectivity knowledge to predict sediment flux for bedrock controlled catchments. A watershed-scale sediment transport model was formulated that incorporates sediment (dis)connectivity knowledge collected via field reconnaissance and predicts sediment flux through coupling with the Partheniades equation and sediment continuity model. Sediment (dis)connectivity was formulated by coupling probabilistic upland lateral connectivity prediction with instream longitudinal connectivity assessments via discretization of fluid and sediment pathways. Flux predictions from the upland lateral connectivity model served as an input to the instream longitudinal connectivity model. Disconnectivity in the instream model was simulated via the discretization of stream reaches due to barriers such as bedrock outcroppings and man-made check dams. The model was tested for a bedrock controlled catchment in Kentucky, USA for which extensive historic water and sediment flux data was available. Predicted sediment

Biomechanics of injury prediction for anthropomorphic manikins - preliminary design considerations

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

Engin, A.E.

1996-12-31

The anthropomorphic manikins are used in automobile safety research as well as in aerospace related applications. There is now a strong need to advance the biomechanics knowledge to determine appropriate criteria for injury likelihood prediction as functions of manikin-measured responses. In this paper, three regions of a manikin, namely, the head, knee joint, and lumbar spine are taken as examples to introduce preliminary design considerations for injury prediction by means of responses of theoretical models and strategically placed sensing devices.

A Data Assimilation System For Operational Weather Forecast In Galicia Region (nw Spain)

NASA Astrophysics Data System (ADS)

Balseiro, C. F.; Souto, M. J.; Pérez-Muñuzuri, V.; Brewster, K.; Xue, M.

Regional weather forecast models, such as the Advanced Regional Prediction System (ARPS), over complex environments with varying local influences require an accurate meteorological analysis that should include all local meteorological measurements available. In this work, the ARPS Data Analysis System (ADAS) (Xue et al. 2001) is applied as a three-dimensional weather analysis tool to include surface station and rawinsonde data with the NCEP AVN forecasts as the analysis background. Currently in ADAS, a set of five meteorological variables are considered during the analysis: horizontal grid-relative wind components, pressure, potential temperature and spe- cific humidity. The analysis is used for high resolution numerical weather prediction for the Galicia region. The analysis method used in ADAS is based on the successive corrective scheme of Bratseth (1986), which asymptotically approaches the result of a statistical (optimal) interpolation, but at lower computational cost. As in the optimal interpolation scheme, the Bratseth interpolation method can take into account the rel- ative error between background and observational data, therefore they are relatively insensitive to large variations in data density and can integrate data of mixed accuracy. This method can be applied economically in an operational setting, providing signifi- cant improvement over the background model forecast as well as any analysis without high-resolution local observations. A one-way nesting is applied for weather forecast in Galicia region, and the use of this assimilation system in both domains shows better results not only in initial conditions but also in all forecast periods. Bratseth, A.M. (1986): "Statistical interpolation by means of successive corrections." Tellus, 38A, 439-447. Souto, M. J., Balseiro, C. F., Pérez-Muñuzuri, V., Xue, M. Brewster, K., (2001): "Im- pact of cloud analysis on numerical weather prediction in the galician region of Spain". Submitted to Journal of

Predicting Persuasion-Induced Behavior Change from the Brain

PubMed Central

Falk, Emily B.; Berkman, Elliot T.; Mann, Traci; Harrison, Brittany; Lieberman, Matthew D.

2011-01-01

Although persuasive messages often alter people’s self-reported attitudes and intentions to perform behaviors, these self-reports do not necessarily predict behavior change. We demonstrate that neural responses to persuasive messages can predict variability in behavior change in the subsequent week. Specifically, an a priori region of interest (ROI) in medial prefrontal cortex (MPFC) was reliably associated with behavior change (r = 0.49, p < 0.05). Additionally, an iterative cross-validation approach using activity in this MPFC ROI predicted an average 23% of the variance in behavior change beyond the variance predicted by self-reported attitudes and intentions. Thus, neural signals can predict behavioral changes that are not predicted from self-reported attitudes and intentions alone. Additionally, this is the first functional magnetic resonance imaging study to demonstrate that a neural signal can predict complex real world behavior days in advance. PMID:20573889

Collaboratory for the Study of Earthquake Predictability

NASA Astrophysics Data System (ADS)

Schorlemmer, D.; Jordan, T. H.; Zechar, J. D.; Gerstenberger, M. C.; Wiemer, S.; Maechling, P. J.

2006-12-01

Earthquake prediction is one of the most difficult problems in physical science and, owing to its societal implications, one of the most controversial. The study of earthquake predictability has been impeded by the lack of an adequate experimental infrastructure---the capability to conduct scientific prediction experiments under rigorous, controlled conditions and evaluate them using accepted criteria specified in advance. To remedy this deficiency, the Southern California Earthquake Center (SCEC) is working with its international partners, which include the European Union (through the Swiss Seismological Service) and New Zealand (through GNS Science), to develop a virtual, distributed laboratory with a cyberinfrastructure adequate to support a global program of research on earthquake predictability. This Collaboratory for the Study of Earthquake Predictability (CSEP) will extend the testing activities of SCEC's Working Group on Regional Earthquake Likelihood Models, from which we will present first results. CSEP will support rigorous procedures for registering prediction experiments on regional and global scales, community-endorsed standards for assessing probability-based and alarm-based predictions, access to authorized data sets and monitoring products from designated natural laboratories, and software to allow researchers to participate in prediction experiments. CSEP will encourage research on earthquake predictability by supporting an environment for scientific prediction experiments that allows the predictive skill of proposed algorithms to be rigorously compared with standardized reference methods and data sets. It will thereby reduce the controversies surrounding earthquake prediction, and it will allow the results of prediction experiments to be communicated to the scientific community, governmental agencies, and the general public in an appropriate research context.

Regional Sediment Management (RSM) Modeling Tools: Integration of Advanced Sediment Transport Tools into HEC-RAS

DTIC Science & Technology

2014-06-01

Integration of Advanced Sediment Transport Tools into HEC-RAS by Paul M. Boyd and Stanford A. Gibson PURPOSE: This Coastal and Hydraulics Engineering...Technical Note (CHETN) summarizes the development and initial testing of new sediment transport and modeling tools developed by the U.S. Army Corps...sediment transport within the USACE HEC River Analysis System (HEC-RAS) software package and to determine its applicability to Regional Sediment

Intra- and Interseasonal Autoregressive Prediction of Dengue Outbreaks Using Local Weather and Regional Climate for a Tropical Environment in Colombia

PubMed Central

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

2014-01-01

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

Derivation and External Validation of Prediction Models for Advanced Chronic Kidney Disease Following Acute Kidney Injury.

PubMed

James, Matthew T; Pannu, Neesh; Hemmelgarn, Brenda R; Austin, Peter C; Tan, Zhi; McArthur, Eric; Manns, Braden J; Tonelli, Marcello; Wald, Ron; Quinn, Robert R; Ravani, Pietro; Garg, Amit X

2017-11-14

Some patients will develop chronic kidney disease after a hospitalization with acute kidney injury; however, no risk-prediction tools have been developed to identify high-risk patients requiring follow-up. To derive and validate predictive models for progression of acute kidney injury to advanced chronic kidney disease. Data from 2 population-based cohorts of patients with a prehospitalization estimated glomerular filtration rate (eGFR) of more than 45 mL/min/1.73 m2 and who had survived hospitalization with acute kidney injury (defined by a serum creatinine increase during hospitalization > 0.3 mg/dL or > 50% of their prehospitalization baseline), were used to derive and validate multivariable prediction models. The risk models were derived from 9973 patients hospitalized in Alberta, Canada (April 2004-March 2014, with follow-up to March 2015). The risk models were externally validated with data from a cohort of 2761 patients hospitalized in Ontario, Canada (June 2004-March 2012, with follow-up to March 2013). Demographic, laboratory, and comorbidity variables measured prior to discharge. Advanced chronic kidney disease was defined by a sustained reduction in eGFR less than 30 mL/min/1.73 m2 for at least 3 months during the year after discharge. All participants were followed up for up to 1 year. The participants (mean [SD] age, 66 [15] years in the derivation and internal validation cohorts and 69 [11] years in the external validation cohort; 40%-43% women per cohort) had a mean (SD) baseline serum creatinine level of 1.0 (0.2) mg/dL and more than 20% had stage 2 or 3 acute kidney injury. Advanced chronic kidney disease developed in 408 (2.7%) of 9973 patients in the derivation cohort and 62 (2.2%) of 2761 patients in the external validation cohort. In the derivation cohort, 6 variables were independently associated with the outcome: older age, female sex, higher baseline serum creatinine value, albuminuria, greater severity of acute kidney injury, and higher

Building a collaborative network to understand regional forest dynamics and advance forestry initiatives in the Caribbean

Treesearch

Grizelle Gonzalez; Tamara Heartsill Scalley

2016-01-01

Herein we provide concluding remarks drawn from and inspired by the discussions of the 5 working groups of the 16th Caribbean Foresters Meeting (CFM) about the needs, challenges, and recommendations to advance forestry in the Caribbean region. We also list key considerations and potential future research directions as presented in the various manuscripts contained in...

From catchment scale hydrologic processes to numerical models and robust predictions of climate change impacts at regional scales

NASA Astrophysics Data System (ADS)

Wagener, T.

2017-12-01

Current societal problems and questions demand that we increasingly build hydrologic models for regional or even continental scale assessment of global change impacts. Such models offer new opportunities for scientific advancement, for example by enabling comparative hydrology or connectivity studies, and for improved support of water management decision, since we might better understand regional impacts on water resources from large scale phenomena such as droughts. On the other hand, we are faced with epistemic uncertainties when we move up in scale. The term epistemic uncertainty describes those uncertainties that are not well determined by historical observations. This lack of determination can be because the future is not like the past (e.g. due to climate change), because the historical data is unreliable (e.g. because it is imperfectly recorded from proxies or missing), or because it is scarce (either because measurements are not available at the right scale or there is no observation network available at all). In this talk I will explore: (1) how we might build a bridge between what we have learned about catchment scale processes and hydrologic model development and evaluation at larger scales. (2) How we can understand the impact of epistemic uncertainty in large scale hydrologic models. And (3) how we might utilize large scale hydrologic predictions to understand climate change impacts, e.g. on infectious disease risk.

AFRPL Graphite Performance Prediction Program. Improved Capability for the Design and Ablation Performance Prediction of Advanced Air Force Solid Propellant Rocket Nozzles

DTIC Science & Technology

1976-12-01

corrosive attack by both acids and alkali and, in addition, is provided with a special Dynel veil for protection against fluoride attack. 3.1.4...throat region, namely , the entrance, center, and exit. In addition, at each station, the diameters were determined at two angular positions 90° apart. The...characterization test matrix. 3.2.1.1 Rocket Motor Environments Rocket motor environments were based on three advanced MX propellants, namely , * XLDB * HTPB * PEG

The Nested Regional Climate Model: An Approach Toward Prediction Across Scales

NASA Astrophysics Data System (ADS)

Hurrell, J. W.; Holland, G. J.; Large, W. G.

2008-12-01

The reality of global climate change has become accepted and society is rapidly moving to questions of consequences on space and time scales that are relevant to proper planning and development of adaptation strategies. There are a number of urgent challenges for the scientific community related to improved and more detailed predictions of regional climate change on decadal time scales. Two important examples are potential impacts of climate change on North Atlantic hurricane activity and on water resources over the intermountain West. The latter is dominated by complex topography, so that accurate simulations of regional climate variability and change require much finer spatial resolution than is provided with state-of-the-art climate models. Climate models also do not explicitly resolve tropical cyclones, even though these storms have dramatic societal impacts and play an important role in regulating climate. Moreover, the debate over the impact of global warming on tropical cyclones has at times been acrimonious, and the lack of hard evidence has left open opportunities for misinterpretation and justification of pre-existing beliefs. These and similar topics are being assessed at NCAR, in partnership with university colleagues, through the development of a Nested Regional Climate Model (NRCM). This is an ambitious effort to combine a state of the science mesoscale weather model (WRF), a high resolution regional ocean modeling system (ROMS), and a climate model (CCSM) to better simulate the complex, multi-scale interactions intrinsic to atmospheric and oceanic fluid motions that are limiting our ability to predict likely future changes in regional weather statistics and climate. The NRCM effort is attracting a large base of earth system scientists together with societal groups as diverse as the Western Governor's Association and the offshore oil industry. All of these groups require climate data on scales of a few kilometers (or less), so that the NRCM program is

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.

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

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.

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.

{sup 18}F-Fluorodeoxyglucose/Positron Emission Tomography Predicts Patterns of Failure After Definitive Chemoradiation Therapy for Locally Advanced Non-Small Cell Lung Cancer

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

Ohri, Nitin, E-mail: ohri.nitin@gmail.com; Bodner, William R.; Halmos, Balazs

Background: We previously reported that pretreatment positron emission tomography (PET) identifies lesions at high risk for progression after concurrent chemoradiation therapy (CRT) for locally advanced non-small cell lung cancer (NSCLC). Here we validate those findings and generate tumor control probability (TCP) models. Methods: We identified patients treated with definitive, concurrent CRT for locally advanced NSCLC who underwent staging {sup 18}F-fluorodeoxyglucose/PET/computed tomography. Visible hypermetabolic lesions (primary tumors and lymph nodes) were delineated on each patient's pretreatment PET scan. Posttreatment imaging was reviewed to identify locations of disease progression. Competing risks analyses were performed to examine metabolic tumor volume (MTV) and radiation therapymore » dose as predictors of local disease progression. TCP modeling was performed to describe the likelihood of local disease control as a function of lesion size. Results: Eighty-nine patients with 259 hypermetabolic lesions (83 primary tumors and 176 regional lymph nodes) met the inclusion criteria. Twenty-eight patients were included in our previous report, and the remaining 61 constituted our validation cohort. The median follow-up time was 22.7 months for living patients. In 20 patients, the first site of progression was a primary tumor or lymph node treated with radiation therapy. The median time to progression for those patients was 11.5 months. Data from our validation cohort confirmed that lesion MTV predicts local progression, with a 30-month cumulative incidence rate of 23% for lesions above 25 cc compared with 4% for lesions below 25 cc (P=.008). We found no evidence that radiation therapy dose was associated with local progression risk. TCP modeling yielded predicted 30-month local control rates of 98% for a 1-cc lesion, 94% for a 10-cc lesion, and 74% for a 50-cc lesion. Conclusion: Pretreatment FDG-PET identifies lesions at risk for progression after CRT

A test to evaluate the earthquake prediction algorithm, M8

USGS Publications Warehouse

Healy, John H.; Kossobokov, Vladimir G.; Dewey, James W.

1992-01-01

A test of the algorithm M8 is described. The test is constructed to meet four rules, which we propose to be applicable to the test of any method for earthquake prediction:  1. An earthquake prediction technique should be presented as a well documented, logical algorithm that can be used by  investigators without restrictions. 2. The algorithm should be coded in a common programming language and implementable on widely available computer systems. 3. A test of the earthquake prediction technique should involve future predictions with a black box version of the algorithm in which potentially adjustable parameters are fixed in advance. The source of the input data must be defined and ambiguities in these data must be resolved automatically by the algorithm. 4. At least one reasonable null hypothesis should be stated in advance of testing the earthquake prediction method, and it should be stated how this null hypothesis will be used to estimate the statistical significance of the earthquake predictions. The M8 algorithm has successfully predicted several destructive earthquakes, in the sense that the earthquakes occurred inside regions with linear dimensions from 384 to 854 km that the algorithm had identified as being in times of increased probability for strong earthquakes. In addition, M8 has successfully "post predicted" high percentages of strong earthquakes in regions to which it has been applied in retroactive studies. The statistical significance of previous predictions has not been established, however, and post-prediction studies in general are notoriously subject to success-enhancement through hindsight. Nor has it been determined how much more precise an M8 prediction might be than forecasts and probability-of-occurrence estimates made by other techniques. We view our test of M8 both as a means to better determine the effectiveness of M8 and as an experimental structure within which to make observations that might lead to improvements in the algorithm

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.; 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

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.

Drought Predictability and Prediction in a Changing Climate: Assessing Current Predictive Knowledge and Capabilities, User Requirements and Research Priorities

NASA Technical Reports Server (NTRS)

Schubert, Siegfried

2011-01-01

Drought is fundamentally the result of an extended period of reduced precipitation lasting anywhere from a few weeks to decades and even longer. As such, addressing drought predictability and prediction in a changing climate requires foremost that we make progress on the ability to predict precipitation anomalies on subseasonal and longer time scales. From the perspective of the users of drought forecasts and information, drought is however most directly viewed through its impacts (e.g., on soil moisture, streamflow, crop yields). As such, the question of the predictability of drought must extend to those quantities as well. In order to make progress on these issues, the WCRP drought information group (DIG), with the support of WCRP, the Catalan Institute of Climate Sciences, the La Caixa Foundation, the National Aeronautics and Space Administration, the National Oceanic and Atmospheric Administration, and the National Science Foundation, has organized a workshop to focus on: 1. User requirements for drought prediction information on sub-seasonal to centennial time scales 2. Current understanding of the mechanisms and predictability of drought on sub-seasonal to centennial time scales 3. Current drought prediction/projection capabilities on sub-seasonal to centennial time scales 4. Advancing regional drought prediction capabilities for variables and scales most relevant to user needs on sub-seasonal to centennial time scales. This introductory talk provides an overview of these goals, and outlines the occurrence and mechanisms of drought world-wide.

Regional Homogeneity Predicts Creative Insight: A Resting-State fMRI Study.

PubMed

Lin, Jiabao; Cui, Xuan; Dai, Xiaoying; Mo, Lei

2018-01-01

Creative insight plays an important role in our daily life. Previous studies have investigated the neural correlates of creative insight by functional magnetic resonance imaging (fMRI), however, the intrinsic resting-state brain activity associated with creative insight is still unclear. In the present study, we used regional homogeneity (ReHo) as an index in resting-state fMRI (rs-fMRI) to identify brain regions involved in individual differences in creative insight, which was compued by the response time (RT) of creative Chinese character chunk decomposition. The findings indicated that ReHo in the anterior cingulate cortex (ACC)/caudate nucleus (CN) and angular gyrus (AG)/superior temporal gyrus (STG)/inferior parietal lobe (IPL) negatively predicted creative insight. Furthermore, these findings suggested that spontaneous brain activity in multiple regions related to breaking and establishing mental sets, goal-directed solutions exploring, shifting attention, forming new associations and emotion experience contributes to creative insight. In conclusion, the present study provides new evidence to further understand the cognitive processing and neural correlates of creative insight.

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Singh, Vijay P.; Xia, Youlong

2018-03-01

Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large-scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high-quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

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.

Structure-Based Predictions of Activity Cliffs

PubMed Central

Husby, Jarmila; Bottegoni, Giovanni; Kufareva, Irina; Abagyan, Ruben; Cavalli, Andrea

2015-01-01

In drug discovery, it is generally accepted that neighboring molecules in a given descriptors' space display similar activities. However, even in regions that provide strong predictability, structurally similar molecules can occasionally display large differences in potency. In QSAR jargon, these discontinuities in the activity landscape are known as ‘activity cliffs’. In this study, we assessed the reliability of ligand docking and virtual ligand screening schemes in predicting activity cliffs. We performed our calculations on a diverse, independently collected database of cliff-forming co-crystals. Starting from ideal situations, which allowed us to establish our baseline, we progressively moved toward simulating more realistic scenarios. Ensemble- and template-docking achieved a significant level of accuracy, suggesting that, despite the well-known limitations of empirical scoring schemes, activity cliffs can be accurately predicted by advanced structure-based methods. PMID:25918827

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.

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

Derivation and External Validation of Prediction Models for Advanced Chronic Kidney Disease Following Acute Kidney Injury

PubMed Central

Pannu, Neesh; Hemmelgarn, Brenda R.; Austin, Peter C.; Tan, Zhi; McArthur, Eric; Manns, Braden J.; Tonelli, Marcello; Wald, Ron; Quinn, Robert R.; Ravani, Pietro; Garg, Amit X.

2017-01-01

Importance Some patients will develop chronic kidney disease after a hospitalization with acute kidney injury; however, no risk-prediction tools have been developed to identify high-risk patients requiring follow-up. Objective To derive and validate predictive models for progression of acute kidney injury to advanced chronic kidney disease. Design, Setting, and Participants Data from 2 population-based cohorts of patients with a prehospitalization estimated glomerular filtration rate (eGFR) of more than 45 mL/min/1.73 m2 and who had survived hospitalization with acute kidney injury (defined by a serum creatinine increase during hospitalization > 0.3 mg/dL or > 50% of their prehospitalization baseline), were used to derive and validate multivariable prediction models. The risk models were derived from 9973 patients hospitalized in Alberta, Canada (April 2004-March 2014, with follow-up to March 2015). The risk models were externally validated with data from a cohort of 2761 patients hospitalized in Ontario, Canada (June 2004-March 2012, with follow-up to March 2013). Exposures Demographic, laboratory, and comorbidity variables measured prior to discharge. Main Outcomes and Measures Advanced chronic kidney disease was defined by a sustained reduction in eGFR less than 30 mL/min/1.73 m2 for at least 3 months during the year after discharge. All participants were followed up for up to 1 year. Results The participants (mean [SD] age, 66 [15] years in the derivation and internal validation cohorts and 69 [11] years in the external validation cohort; 40%-43% women per cohort) had a mean (SD) baseline serum creatinine level of 1.0 (0.2) mg/dL and more than 20% had stage 2 or 3 acute kidney injury. Advanced chronic kidney disease developed in 408 (2.7%) of 9973 patients in the derivation cohort and 62 (2.2%) of 2761 patients in the external validation cohort. In the derivation cohort, 6 variables were independently associated with the outcome: older age, female sex, higher

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

Prediction of response to chemoradiation in rectal cancer by a gene polymorphism in the epidermal growth factor receptor promoter region

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

Spindler, Karen-Lise Garm; Nielsen, Jens Nederby; Lindebjerg, Jan

2006-10-01

Purpose: Epidermal growth factor receptor (EGFR) has been associated with radioresistance in solid tumors. Recently a polymorphism in the Sp1 recognition site of the EGFR promoter region was identified. The present study investigated the predictive value of this polymorphism for the outcome of chemoradiation in locally advanced rectal cancer. Methods and Materials: The study included 77 patients with locally advanced T3 rectal tumors. Treatment consisted of preoperative radiation therapy at a total tumor dose of 65 Gy and concomitant chemotherapy with Uftoral. Blood samples from 63 patients were evaluated for Sp1 -216 G/T polymorphism by polymerase chain reaction analysis. Forty-eightmore » primary tumor biopsies were available for EGFR immunostaining. Patients underwent surgery 8 weeks after treatment. Pathologic response evaluation was performed according to the tumor regression grade (TRG) system. Results: Forty-nine percent had major response (TRG1-2) and 51% moderate response (TRG 3-4) to chemoradiation. The rates of major response were 34% (10/29) in GG homozygote patients compared with 65% (22/34) in patients with T containing variants (p = 0.023). Fifty-eight percent of biopsies were positive for EGFR expression (28/48). The major response rates with regard to EGFR immunostaining were not significantly different. EGFR-positive tumors were found in 83% of the GG homozygote patients compared with 38% of patients with TT or GT variants (p = 0.008). Conclusions: There was a significant correlation between EGFR Sp1 -216 G/T polymorphism and treatment response to chemoradiation in locally advanced rectal cancer. Further investigations of a second set of patient and other treatment schedules are warranted.« less

A Unified Data Assimilation Strategy for Regional Coupled Atmosphere-Ocean Prediction Systems

NASA Astrophysics Data System (ADS)

Xie, Lian; Liu, Bin; Zhang, Fuqing; Weng, Yonghui

2014-05-01

Improving tropical cyclone (TC) forecasts is a top priority in weather forecasting. Assimilating various observational data to produce better initial conditions for numerical models using advanced data assimilation techniques has been shown to benefit TC intensity forecasts, whereas assimilating large-scale environmental circulation into regional models by spectral nudging or Scale-Selective Data Assimilation (SSDA) has been demonstrated to improve TC track forecasts. Meanwhile, taking into account various air-sea interaction processes by high-resolution coupled air-sea modelling systems has also been shown to improve TC intensity forecasts. Despite the advances in data assimilation and air-sea coupled models, large errors in TC intensity and track forecasting remain. For example, Hurricane Nate (2011) has brought considerable challenge for the TC operational forecasting community, with very large intensity forecast errors (27, 25, and 40 kts for 48, 72, and 96 h, respectively) for the official forecasts. Considering the slow-moving nature of Hurricane Nate, it is reasonable to hypothesize that air-sea interaction processes played a critical role in the intensity change of the storm, and accurate representation of the upper ocean dynamics and thermodynamics is necessary to quantitatively describe the air-sea interaction processes. Currently, data assimilation techniques are generally only applied to hurricane forecasting in stand-alone atmospheric or oceanic model. In fact, most of the regional hurricane forecasting models only included data assimilation techniques for improving the initial condition of the atmospheric model. In such a situation, the benefit of adjustments in one model (atmospheric or oceanic) by assimilating observational data can be compromised by errors from the other model. Thus, unified data assimilation techniques for coupled air-sea modelling systems, which not only simultaneously assimilate atmospheric and oceanic observations into the

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

USDA-ARS?s Scientific Manuscript database

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...

Finite element modeling predictions of region-specific cell-matrix mechanics in the meniscus.

PubMed

Upton, Maureen L; Guilak, Farshid; Laursen, Tod A; Setton, Lori A

2006-06-01

The knee meniscus exhibits significant spatial variations in biochemical composition and cell morphology that reflect distinct phenotypes of cells located in the radial inner and outer regions. Associated with these cell phenotypes is a spatially heterogeneous microstructure and mechanical environment with the innermost regions experiencing higher fluid pressures and lower tensile strains than the outer regions. It is presently unknown, however, how meniscus tissue mechanics correlate with the local micromechanical environment of cells. In this study, theoretical models were developed to study mechanics of inner and outer meniscus cells with varying geometries. The results for an applied biaxial strain predict significant regional differences in the cellular mechanical environment with evidence of tensile strains along the collagen fiber direction of approximately 0.07 for the rounded inner cells, as compared to levels of 0.02-0.04 for the elongated outer meniscus cells. The results demonstrate an important mechanical role of extracellular matrix anisotropy and cell morphology in regulating the region-specific micromechanics of meniscus cells, that may further play a role in modulating cellular responses to mechanical stimuli.

Regional calibration models for predicting loblolly pine tracheid properties using near-infrared spectroscopy

Treesearch

Mohamad Nabavi; Joseph Dahlen; Laurence Schimleck; Thomas L. Eberhardt; Cristian Montes

2018-01-01

This study developed regional calibration models for the prediction of loblolly pine (Pinus taeda) tracheid properties using near-infrared (NIR) spectroscopy. A total of 1842 pith-to-bark radial strips, aged 19–31 years, were acquired from 268 trees from 109 stands across the southeastern USA. Diffuse reflectance NIR spectra were collected at 10-mm...

An Injury Severity-, Time Sensitivity-, and Predictability-Based Advanced Automatic Crash Notification Algorithm Improves Motor Vehicle Crash Occupant Triage.

PubMed

Stitzel, Joel D; Weaver, Ashley A; Talton, Jennifer W; Barnard, Ryan T; Schoell, Samantha L; Doud, Andrea N; Martin, R Shayn; Meredith, J Wayne

2016-06-01

Advanced Automatic Crash Notification algorithms use vehicle telemetry measurements to predict risk of serious motor vehicle crash injury. The objective of the study was to develop an Advanced Automatic Crash Notification algorithm to reduce response time, increase triage efficiency, and improve patient outcomes by minimizing undertriage (<5%) and overtriage (<50%), as recommended by the American College of Surgeons. A list of injuries associated with a patient's need for Level I/II trauma center treatment known as the Target Injury List was determined using an approach based on 3 facets of injury: severity, time sensitivity, and predictability. Multivariable logistic regression was used to predict an occupant's risk of sustaining an injury on the Target Injury List based on crash severity and restraint factors for occupants in the National Automotive Sampling System - Crashworthiness Data System 2000-2011. The Advanced Automatic Crash Notification algorithm was optimized and evaluated to minimize triage rates, per American College of Surgeons recommendations. The following rates were achieved: <50% overtriage and <5% undertriage in side impacts and 6% to 16% undertriage in other crash modes. Nationwide implementation of our algorithm is estimated to improve triage decisions for 44% of undertriaged and 38% of overtriaged occupants. Annually, this translates to more appropriate care for >2,700 seriously injured occupants and reduces unnecessary use of trauma center resources for >162,000 minimally injured occupants. The algorithm could be incorporated into vehicles to inform emergency personnel of recommended motor vehicle crash triage decisions. Lower under- and overtriage was achieved, and nationwide implementation of the algorithm would yield improved triage decision making for an estimated 165,000 occupants annually. Copyright © 2016. Published by Elsevier Inc.

Advanced interatrial block predicts new-onset atrial fibrillation and ischemic stroke in patients with heart failure: The "Bayes' Syndrome-HF" study.

PubMed

Escobar-Robledo, Luis Alberto; Bayés-de-Luna, Antoni; Lupón, Josep; Baranchuk, Adrian; Moliner, Pedro; Martínez-Sellés, Manuel; Zamora, Elisabet; de Antonio, Marta; Domingo, Mar; Cediel, Germán; Núñez, Julio; Santiago-Vacas, Evelyn; Bayés-Genís, Antoni

2018-05-18

Advanced interatrial block (IAB) is characterized by a prolonged (≥120 ms) and bimodal P wave in the inferior leads. The association between advanced IAB and atrial fibrillation (AF) is known as "Bayes' Syndrome", and there is scarce information about it in heart failure (HF). We examined the prevalence of IAB and whether advanced IAB could predict new-onset AF and/or stroke in HF patients. The prospective observational "Bayes' Syndrome-HF" study included consecutive outpatients with chronic HF. The primary endpoints were new-onset AF, ischemic stroke, and the composite of both. A secondary endpoint included all-cause death alone or in combination with the primary endpoint. Comprehensive multivariable Cox regression analyses were performed. Among 1050 consecutive patients, 536 (51.0%) were in sinus rhythm, 464 with a measurable P wave are the focus of this study. Two-hundred and sixty patients (56.0%) had normal atrial conduction, 95 (20.5%) partial IAB, and 109 (23.5%) advanced IAB. During a mean follow-up of 4.5 ± 2.1 years, 235 patients experienced all-cause death, new-onset AF, or stroke. In multivariable comprehensive Cox regression analyses, advanced IAB was associated with new-onset AF (HR 2.71 [1.61-4.56], P < 0.001), ischemic stroke (HR 3.02 [1.07-8.53], P = 0.04), and the composite of both (HR 2.42 [1.41-4.15], P < 0.001). In patients with HF advanced IAB predicts new-onset AF and ischemic stroke. Future studies must assess whether anticoagulant treatment in Bayes' Syndrome leads to better outcomes in HF. Copyright © 2017 Elsevier B.V. All rights reserved.

Tumor-infiltrating lymphocytes predict response to chemotherapy in patients with advance non-small cell lung cancer.

PubMed

Liu, Hui; Zhang, Tiantuo; Ye, Jin; Li, Hongtao; Huang, Jing; Li, Xiaodong; Wu, Benquan; Huang, Xubing; Hou, Jinghui

2012-10-01

Accumulating preclinical evidence suggests that anticancer immune responses contribute to the success of chemotherapy. The predictive significance of tumor-infiltrating lymphocytes (TILs) for response to neoadjuvant chemotherapy in non-small cell lung cancer (NSCLC) remains unknown. The aim of this study was to investigate the prognostic and predictive value of TIL subtypes in patients with advanced NSCLC treated with platinum-based chemotherapy. In total, 159 patients with stage III and IV NSCLC were retrospectively enrolled. The prevalence of CD3(+), CD4(+), CD8(+) and Foxp3(+) TILs was assessed by immunohistochemistry in tumor tissue obtained before chemotherapy. The density of TILs subgroups was treated as dichotomous variables using the median values as cutoff. Survival curves were estimated by the Kaplan-Meier method, and differences in overall survival between groups were determined using the Log-rank test. Prognostic effects of TIL subsets density were evaluated by Cox regression analysis. The presence of CD3(+), CD4(+), CD8(+), and FOXP3(+) TILs was not correlated with any clinicopathological features. Neither the prevalence of TILs nor combined analysis displayed obvious prognostic performances for overall survival in Cox regression model. Instead, higher FOXP3(+)/CD8(+) ratio in tumor sites was an independent factor for poor response to platinum-based chemotherapy in overall cohort. These findings suggest that immunological CD8(+) and FOXP3(+)Tregs cell infiltrate within tumor environment is predictive of response to platinum-based neoadjuvant chemotherapy in advanced NSCLC patients. The understanding of the clinical relevance of the microenvironmental immunological milieu might provide an important clue for the design of novel strategies in cancer immunotherapy.

Prediction of advanced endovascular stent graft rotation and its associated morbidity and mortality.

PubMed

Crawford, Sean A; Sanford, Ryan M; Doyle, Matthew G; Wheatcroft, Mark; Amon, Cristina H; Forbes, Thomas L

2018-01-29

Advanced endovascular aneurysm repair (EVAR) with fenestrated and branched stent grafts is increasingly being used to repair complex aortic aneurysms; however, these devices can rotate unpredictably during deployment, leading to device misalignment. The objectives of this study were to quantify the short-term clinical outcomes in patients with intraoperative stent graft rotation and to identify quantitative anatomic markers of the arterial geometry that can predict stent graft rotation preoperatively. A prospective study evaluating all patients undergoing advanced EVAR was conducted at two university-affiliated hospitals between November 2015 and December 2016. Stent graft rotation (defined as ≥10 degrees) was measured on intraoperative fluoroscopic video of the deployment sequence. Standard preoperative computed tomography angiography imaging was used to calculate the geometric properties of the arterial anatomy. Any in-hospital and 30-day complications were prospectively documented, and a composite outcome of any end-organ ischemia or death was used as the primary end point. Thirty-nine patients undergoing advanced EVAR were enrolled in the study with a mean age of 75 years (interquartile range [IQR], 71-80 years) and a mean aneurysm diameter of 64 mm (IQR, 59-65 mm). The incidence of stent graft rotation was 37% (n = 14), with a mean rotation of 25 degrees (IQR, 21-28 degrees). A nominal logistic regression model identified iliac artery torsion, volume of iliac artery calcification, and stent graft length as the primary predictive factors. The total net torsion and the total volume of calcific plaque were higher in patients with stent graft rotation, 8.9 ± 0.8 mm -1 vs 4.1 ± 0.5 mm -1 (P < .0001) and 1054 ± 144 mm 3 vs 525 ± 83 mm 3 (P < .01), respectively. The length of the implanted stent grafts was also higher in patients with intraoperative rotation, 172 ± 9 mm vs 156 ± 8 mm (P < .01). The composite outcome of any end

Frequency of reporting and predictive factors for anxiety and depression in patients with advanced cancer.

PubMed

Salvo, N; Zeng, L; Zhang, L; Leung, M; Khan, L; Presutti, R; Nguyen, J; Holden, L; Culleton, S; Chow, E

2012-03-01

The prevalence of anxiety and depression in patients with advanced cancer has been reported to be on average 25% and to significantly affect patients' quality of life. Despite high prevalence rates, these disorders remain underdiagnosed and undertreated. The purpose of our study was to examine the self-report rates of anxiety and depression with the Edmonton Symptom Assessment System (ESAS) and to assess the predictive factors for these reports in cancer patients with metastatic disease. Consecutive patients who attended the Rapid Response Radiotherapy Program (RRRP) completed the ESAS as well as baseline demographic information. Ordinal logistic regression analysis was used to determine factors that significantly predicted anxiety and/or depression. Pearson χ(2) was used to test goodness-of-fit for categorical variables and established whether or not an observed frequency distribution differed from a predicted frequency distribution. A univariate analysis was conducted first and those variables with a P value<0.100 were included in a multivariate analysis. A score test was used to test the proportional odds assumption. In total, 1439 patients seen in the RRRP between January 1999 and October 2009 completed ESAS questionnaires. Fifty-five per cent of patients reported at least mild symptoms of depression and 65% reported at least mild anxiety. In the univariate analysis, patients who were female, who had a lower performance status score, or primary lung cancer were more likely to report depressed and anxious feelings. Primary prostate cancer patients were significantly less likely to report depression and anxiety. Patients referred for spinal cord compression were significantly less depressed. The multivariate models showed that younger patients were significantly more anxious than older patients and females reported more anxiety than males. Patients who reported higher feelings of nausea, tiredness, drowsiness, dyspnoea, and worse appetite and overall well

Winter Precipitation Forecast in the European and Mediterranean Regions Using Cluster Analysis

NASA Astrophysics Data System (ADS)

Totz, Sonja; Tziperman, Eli; Coumou, Dim; Pfeiffer, Karl; Cohen, Judah

2017-12-01

The European climate is changing under global warming, and especially the Mediterranean region has been identified as a hot spot for climate change with climate models projecting a reduction in winter rainfall and a very pronounced increase in summertime heat waves. These trends are already detectable over the historic period. Hence, it is beneficial to forecast seasonal droughts well in advance so that water managers and stakeholders can prepare to mitigate deleterious impacts. We developed a new cluster-based empirical forecast method to predict precipitation anomalies in winter. This algorithm considers not only the strength but also the pattern of the precursors. We compare our algorithm with dynamic forecast models and a canonical correlation analysis-based prediction method demonstrating that our prediction method performs better in terms of time and pattern correlation in the Mediterranean and European regions.

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.

Prediction of near-term breast cancer risk using local region-based bilateral asymmetry features in mammography

NASA Astrophysics Data System (ADS)

Li, Yane; Fan, Ming; Li, Lihua; Zheng, Bin

2017-03-01

This study proposed a near-term breast cancer risk assessment model based on local region bilateral asymmetry features in Mammography. The database includes 566 cases who underwent at least two sequential FFDM examinations. The `prior' examination in the two series all interpreted as negative (not recalled). In the "current" examination, 283 women were diagnosed cancers and 283 remained negative. Age of cancers and negative cases completely matched. These cases were divided into three subgroups according to age: 152 cases among the 37-49 age-bracket, 220 cases in the age-bracket 50- 60, and 194 cases with the 61-86 age-bracket. For each image, two local regions including strip-based regions and difference-of-Gaussian basic element regions were segmented. After that, structural variation features among pixel values and structural similarity features were computed for strip regions. Meanwhile, positional features were extracted for basic element regions. The absolute subtraction value was computed between each feature of the left and right local-regions. Next, a multi-layer perception classifier was implemented to assess performance of features for prediction. Features were then selected according stepwise regression analysis. The AUC achieved 0.72, 0.75 and 0.71 for these 3 age-based subgroups, respectively. The maximum adjustable odds ratios were 12.4, 20.56 and 4.91 for these three groups, respectively. This study demonstrate that the local region-based bilateral asymmetry features extracted from CC-view mammography could provide useful information to predict near-term breast cancer risk.

Antibody specific epitope prediction-emergence of a new paradigm.

PubMed

Sela-Culang, Inbal; Ofran, Yanay; Peters, Bjoern

2015-04-01

The development of accurate tools for predicting B-cell epitopes is important but difficult. Traditional methods have examined which regions in an antigen are likely binding sites of an antibody. However, it is becoming increasingly clear that most antigen surface residues will be able to bind one or more of the myriad of possible antibodies. In recent years, new approaches have emerged for predicting an epitope for a specific antibody, utilizing information encoded in antibody sequence or structure. Applying such antibody-specific predictions to groups of antibodies in combination with easily obtainable experimental data improves the performance of epitope predictions. We expect that further advances of such tools will be possible with the integration of immunoglobulin repertoire sequencing data. Copyright © 2015 Elsevier B.V. All rights reserved.

Predictability and prediction of the total number of winter extremely cold days over China

NASA Astrophysics Data System (ADS)

Luo, Xiao; Wang, Bin

2018-03-01

The current dynamical climate models have limited skills in predicting winter temperature in China. The present study uses physics-based empirical models (PEMs) to explore the sources and limits of the seasonal predictability in the total number of extremely cold days (NECD) over China. A combined cluster-rotated EOF analysis reveals two sub-regions of homogeneous variability among hundreds of stations, namely the Northeast China (NE) and Main China (MC). This reduces the large-number of predictands to only two indices, the NCED-NE and NCED-MC, which facilitates detection of the common sources of predictability for all stations. The circulation anomalies associated with the NECD-NE exhibit a zonally symmetric Arctic Oscillation-like pattern, whereas those associated with the NECD-MC feature a North-South dipolar pattern over Asia. The predictability of the NECD originates from SST and snow cover anomalies in the preceding September and October. However, the two regions have different SST predictors: The NE predictor is in the western Eurasian Arctic while the MC predictor is over the tropical-North Pacific. The October snow cover predictors also differ: The NE predictor primarily resides in the central Eurasia while the MC predictor is over the western and eastern Eurasia. The PEM prediction results suggest that about 60% (55%) of the total variance of winter NECD over the NE (Main) China are likely predictable 1 month in advance. The NECD at each station can also be predicted by using the four predictors that were detected for the two indices. The cross-validated temporal correlation skills exceed 0.70 at most stations. The physical mechanisms by which the autumn Arctic sea ice, snow cover, and tropical-North Pacific SST anomalies affect winter NECD over the NE and Main China are discussed.

Prediction of high incidence of dengue in the Philippines.

PubMed

Buczak, Anna L; Baugher, Benjamin; Babin, Steven M; Ramac-Thomas, Liane C; Guven, Erhan; Elbert, Yevgeniy; Koshute, Phillip T; Velasco, John Mark S; Roque, Vito G; Tayag, Enrique A; Yoon, In-Kyu; Lewis, Sheri H

2014-04-01

Accurate prediction of dengue incidence levels weeks in advance of an outbreak may reduce the morbidity and mortality associated with this neglected disease. Therefore, models were developed to predict high and low dengue incidence in order to provide timely forewarnings in the Philippines. Model inputs were chosen based on studies indicating variables that may impact dengue incidence. The method first uses Fuzzy Association Rule Mining techniques to extract association rules from these historical epidemiological, environmental, and socio-economic data, as well as climate data indicating future weather patterns. Selection criteria were used to choose a subset of these rules for a classifier, thereby generating a Prediction Model. The models predicted high or low incidence of dengue in a Philippines province four weeks in advance. The threshold between high and low was determined relative to historical incidence data. Model accuracy is described by Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity, and Specificity computed on test data not previously used to develop the model. Selecting a model using the F0.5 measure, which gives PPV more importance than Sensitivity, gave these results: PPV = 0.780, NPV = 0.938, Sensitivity = 0.547, Specificity = 0.978. Using the F3 measure, which gives Sensitivity more importance than PPV, the selected model had PPV = 0.778, NPV = 0.948, Sensitivity = 0.627, Specificity = 0.974. The decision as to which model has greater utility depends on how the predictions will be used in a particular situation. This method builds prediction models for future dengue incidence in the Philippines and is capable of being modified for use in different situations; for diseases other than dengue; and for regions beyond the Philippines. The Philippines dengue prediction models predicted high or low incidence of dengue four weeks in advance of an outbreak with high accuracy, as measured by PPV

Prediction of High Incidence of Dengue in the Philippines

PubMed Central

Buczak, Anna L.; Baugher, Benjamin; Babin, Steven M.; Ramac-Thomas, Liane C.; Guven, Erhan; Elbert, Yevgeniy; Koshute, Phillip T.; Velasco, John Mark S.; Roque, Vito G.; Tayag, Enrique A.; Yoon, In-Kyu; Lewis, Sheri H.

2014-01-01

Background Accurate prediction of dengue incidence levels weeks in advance of an outbreak may reduce the morbidity and mortality associated with this neglected disease. Therefore, models were developed to predict high and low dengue incidence in order to provide timely forewarnings in the Philippines. Methods Model inputs were chosen based on studies indicating variables that may impact dengue incidence. The method first uses Fuzzy Association Rule Mining techniques to extract association rules from these historical epidemiological, environmental, and socio-economic data, as well as climate data indicating future weather patterns. Selection criteria were used to choose a subset of these rules for a classifier, thereby generating a Prediction Model. The models predicted high or low incidence of dengue in a Philippines province four weeks in advance. The threshold between high and low was determined relative to historical incidence data. Principal Findings Model accuracy is described by Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity, and Specificity computed on test data not previously used to develop the model. Selecting a model using the F0.5 measure, which gives PPV more importance than Sensitivity, gave these results: PPV = 0.780, NPV = 0.938, Sensitivity = 0.547, Specificity = 0.978. Using the F3 measure, which gives Sensitivity more importance than PPV, the selected model had PPV = 0.778, NPV = 0.948, Sensitivity = 0.627, Specificity = 0.974. The decision as to which model has greater utility depends on how the predictions will be used in a particular situation. Conclusions This method builds prediction models for future dengue incidence in the Philippines and is capable of being modified for use in different situations; for diseases other than dengue; and for regions beyond the Philippines. The Philippines dengue prediction models predicted high or low incidence of dengue four weeks in advance of

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

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

Baumann, Brian C.; Guzzo, Thomas J.; He Jiwei

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 aftermore » 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

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

PubMed Central

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.

2016-01-01

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

Application of Multivariable Model Predictive Advanced Control for a 2×310T/H CFB Boiler Unit

NASA Astrophysics Data System (ADS)

Weijie, Zhao; Zongllao, Dai; Rong, Gou; Wengan, Gong

When a CFB boiler is in automatic control, there are strong interactions between various process variables and inverse response characteristics of bed temperature control target. Conventional Pill control strategy cannot deliver satisfactory control demand. Kalman wave filter technology is used to establish a non-linear combustion model, based on the CFB combustion characteristics of bed fuel inventory, heating values, bed lime inventory and consumption. CFB advanced combustion control utilizes multivariable model predictive control technology to optimize primary and secondary air flow, bed temperature, air flow, fuel flow and heat flux. In addition to providing advanced combustion control to 2×310t/h CFB+1×100MW extraction condensing turbine generator unit, the control also provides load allocation optimization and advanced control for main steam pressure, combustion and temperature. After the successful implementation, under 10% load change, main steam pressure varied less than ±0.07MPa, temperature less than ±1°C, bed temperature less than ±4°C, and air flow (O2) less than ±0.4%.

Hydrology of the North Cascades region, Washington: 2. A proposed hydrometeorological streamflow prediction method

USGS Publications Warehouse

Tangborn, Wendell V.; Rasmussen, Lowell A.

1976-01-01

On the basis of a linear relationship between winter (October-April) precipitation and annual runoff from a drainage basin (Rasmussen and Tangborn, 1976) a physically reasonable model for predicting summer (May-September) streamflow from drainages in the North Cascades region was developed. This hydrometeorological prediction method relates streamflow for a season beginning on the day of prediction to the storage (including snow, ice, soil moisture, and groundwater) on that day. The spring storage is inferred from an input-output relationship based on the principle of conservation of mass: spring storage equals winter precipitation on the basin less winter runoff from the basin and less winter evapotranspiration, which is presumed to be small. The method of prediction is based on data only from the years previous to the one for which the prediction is made, and the system is revised each year as data for the previous year become available. To improve the basin storage estimate made in late winter or early spring, a short-season runoff prediction is made. The errors resulting from this short-term prediction are used to revise the storage estimate and improve the later prediction. This considerably improves the accuracy of the later prediction, especially for periods early in the summer runoff season. The optimum length for the test period appears to be generally less than a month for east side basins and between 1 and 2 months for those on the west side of the Cascade Range. The time distribution of the total summer runoff can be predicted when this test season is used so that on May 1 monthly streamflow for the May-September season can be predicted. It was found that summer precipitation and the time of minimum storage are two error sources that were amenable to analysis. For streamflow predictions in seasons beginning in early spring the deviation of the subsequent summer precipitation from a long-period average will contribute up to 53% of the prediction error

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

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

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

MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins

PubMed Central

Disfani, Fatemeh Miri; Hsu, Wei-Lun; Mizianty, Marcin J.; Oldfield, Christopher J.; Xue, Bin; Dunker, A. Keith; Uversky, Vladimir N.; Kurgan, Lukasz

2012-01-01

Motivation: Molecular recognition features (MoRFs) are short binding regions located within longer intrinsically disordered regions that bind to protein partners via disorder-to-order transitions. MoRFs are implicated in important processes including signaling and regulation. However, only a limited number of experimentally validated MoRFs is known, which motivates development of computational methods that predict MoRFs from protein chains. Results: We introduce a new MoRF predictor, MoRFpred, which identifies all MoRF types (α, β, coil and complex). We develop a comprehensive dataset of annotated MoRFs to build and empirically compare our method. MoRFpred utilizes a novel design in which annotations generated by sequence alignment are fused with predictions generated by a Support Vector Machine (SVM), which uses a custom designed set of sequence-derived features. The features provide information about evolutionary profiles, selected physiochemical properties of amino acids, and predicted disorder, solvent accessibility and B-factors. Empirical evaluation on several datasets shows that MoRFpred outperforms related methods: α-MoRF-Pred that predicts α-MoRFs and ANCHOR which finds disordered regions that become ordered when bound to a globular partner. We show that our predicted (new) MoRF regions have non-random sequence similarity with native MoRFs. We use this observation along with the fact that predictions with higher probability are more accurate to identify putative MoRF regions. We also identify a few sequence-derived hallmarks of MoRFs. They are characterized by dips in the disorder predictions and higher hydrophobicity and stability when compared to adjacent (in the chain) residues. Availability: http://biomine.ece.ualberta.ca/MoRFpred/; http://biomine.ece.ualberta.ca/MoRFpred/Supplement.pdf Contact: lkurgan@ece.ualberta.ca Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22689782

Factors predicting non-alcoholic steatohepatitis (NASH) and advanced fibrosis in patients with non-alcoholic fatty liver disease (NAFLD).

PubMed

Tasneem, Abbas Ali; Luck, Nasir Hassan; Majid, Zain

2018-04-01

Introduction To determine the factors predicting non-alcoholic steatohepatitis (NASH) and advanced fibrosis in patients with non-alcoholic fatty liver disease (NAFLD). Methodology All patients aged >18 years and having a fatty liver on abdominal ultrasound (US), presenting from January 2011 to January 2017, were included. A liver biopsy was performed on all the patients. Results Of 96 patients undergoing liver biopsy for non-alcoholic fatty liver disease (NAFLD), 76 (79.2%) were men. On liver US, diffuse fatty liver (DFL) was noted in 68 (70.8%) patients. Liver biopsy showed non-alcoholic steatohepatitis (NASH) in 78 (81.3%) patients. Factors associated with NASH were male gender, body mass index (BMI) > 27 kg/m 2 , DFL and raised alanine aminotransferase (ALT). A GULAB score (based on gender, US liver findings, lipid (fasting) levels, ALT level and BMI) of ≥5 predicted NASH with 82.05% sensitivity. Factors associated with advanced fibrosis in NAFLD were age >40 years, diabetes mellitus, AST/ALT ratio > 1 and raised GGT. Conclusion NASH is common in patients with male gender, high BMI, DFL on liver US, raised ALT and GULAB score ≥5.

Brain Activity in Self- and Value-Related Regions in Response to Online Antismoking Messages Predicts Behavior Change

PubMed Central

Cooper, Nicole; Tompson, Steve; O’Donnell, Matthew Brook; Falk, Emily B.

2017-01-01

In this study, we combined approaches from media psychology and neuroscience to ask whether brain activity in response to online antismoking messages can predict smoking behavior change. In particular, we examined activity in subregions of the medial prefrontal cortex linked to self- and value-related processing, to test whether these neurocognitive processes play a role in message-consistent behavior change. We observed significant relationships between activity in both brain regions of interest and behavior change (such that higher activity predicted a larger reduction in smoking). Furthermore, activity in these brain regions predicted variance independent of traditional, theory-driven self-report metrics such as intention, self-efficacy, and risk perceptions. We propose that valuation is an additional cognitive process that should be investigated further as we search for a mechanistic explanation of the relationship between brain activity and media effects relevant to health behavior change. PMID:29057013

[Research on Kalman interpolation prediction model based on micro-region PM2.5 concentration].

PubMed

Wang, Wei; Zheng, Bin; Chen, Binlin; An, Yaoming; Jiang, Xiaoming; Li, Zhangyong

2018-02-01

In recent years, the pollution problem of particulate matter, especially PM2.5, is becoming more and more serious, which has attracted many people's attention from all over the world. In this paper, a Kalman prediction model combined with cubic spline interpolation is proposed, which is applied to predict the concentration of PM2.5 in the micro-regional environment of campus, and to realize interpolation simulation diagram of concentration of PM2.5 and simulate the spatial distribution of PM2.5. The experiment data are based on the environmental information monitoring system which has been set up by our laboratory. And the predicted and actual values of PM2.5 concentration data have been checked by the way of Wilcoxon signed-rank test. We find that the value of bilateral progressive significance probability was 0.527, which is much greater than the significant level α = 0.05. The mean absolute error (MEA) of Kalman prediction model was 1.8 μg/m 3 , the average relative error (MER) was 6%, and the correlation coefficient R was 0.87. Thus, the Kalman prediction model has a better effect on the prediction of concentration of PM2.5 than those of the back propagation (BP) prediction and support vector machine (SVM) prediction. In addition, with the combination of Kalman prediction model and the spline interpolation method, the spatial distribution and local pollution characteristics of PM2.5 can be simulated.

Prediction of household and commercial BMW generation according to socio-economic and other factors for the Dublin region.

PubMed

Purcell, M; Magette, W L

2009-04-01

Both planning and design of integrated municipal solid waste management systems require accurate prediction of waste generation. This research predicted the quantity and distribution of biodegradable municipal waste (BMW) generation within a diverse 'landscape' of residential areas, as well as from a variety of commercial establishments (restaurants, hotels, hospitals, etc.) in the Dublin (Ireland) region. Socio-economic variables, housing types, and the sizes and main activities of commercial establishments were hypothesized as the key determinants contributing to the spatial variability of BMW generation. A geographical information system (GIS) 'model' of BMW generation was created using ArcMap, a component of ArcGIS 9. Statistical data including socio-economic status and household size were mapped on an electoral district basis. Historical research and data from scientific literature were used to assign BMW generation rates to residential and commercial establishments. These predictions were combined to give overall BMW estimates for the region, which can aid waste planning and policy decisions. This technique will also aid the design of future waste management strategies, leading to policy and practice alterations as a function of demographic changes and development. The household prediction technique gave a more accurate overall estimate of household waste generation than did the social class technique. Both techniques produced estimates that differed from the reported local authority data; however, given that local authority reported figures for the region are below the national average, with some of the waste generated from apartment complexes being reported as commercial waste, predictions arising from this research are believed to be closer to actual waste generation than a comparison to reported data would suggest. By changing the input data, this estimation tool can be adapted for use in other locations. Although focusing on waste in the Dublin region

Predictive Models for Regional Hepatic Function Based on 99mTc-IDA SPECT and Local Radiation Dose for Physiologic Adaptive Radiation Therapy

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

Wang, Hesheng, E-mail: hesheng@umich.edu; Feng, Mary; Frey, Kirk A.

2013-08-01

Purpose: High-dose radiation therapy (RT) for intrahepatic cancer is limited by the development of liver injury. This study investigated whether regional hepatic function assessed before and during the course of RT using 99mTc-labeled iminodiacetic acid (IDA) single photon emission computed tomography (SPECT) could predict regional liver function reserve after RT. Methods and Materials: Fourteen patients treated with RT for intrahepatic cancers underwent dynamic 99mTc-IDA SPECT scans before RT, during, and 1 month after completion of RT. Indocyanine green (ICG) tests, a measure of overall liver function, were performed within 1 day of each scan. Three-dimensional volumetric hepatic extraction fraction (HEF)more » images of the liver were estimated by deconvolution analysis. After coregistration of the CT/SPECT and the treatment planning CT, HEF dose–response functions during and after RT were generated. The volumetric mean of the HEFs in the whole liver was correlated with ICG clearance time. Three models, dose, priori, and adaptive models, were developed using multivariate linear regression to assess whether the regional HEFs measured before and during RT helped predict regional hepatic function after RT. Results: The mean of the volumetric liver HEFs was significantly correlated with ICG clearance half-life time (r=−0.80, P<.0001), for all time points. Linear correlations between local doses and regional HEFs 1 month after RT were significant in 12 patients. In the priori model, regional HEF after RT was predicted by the planned dose and regional HEF assessed before RT (R=0.71, P<.0001). In the adaptive model, regional HEF after RT was predicted by regional HEF reassessed during RT and the remaining planned local dose (R=0.83, P<.0001). Conclusions: 99mTc-IDA SPECT obtained during RT could be used to assess regional hepatic function and helped predict post-RT regional liver function reserve. This could support individualized adaptive radiation treatment

Predictive models for regional hepatic function based on 99mTc-IDA SPECT and local radiation dose for physiologic adaptive radiation therapy.

PubMed

Wang, Hesheng; Feng, Mary; Frey, Kirk A; Ten Haken, Randall K; Lawrence, Theodore S; Cao, Yue

2013-08-01

High-dose radiation therapy (RT) for intrahepatic cancer is limited by the development of liver injury. This study investigated whether regional hepatic function assessed before and during the course of RT using 99mTc-labeled iminodiacetic acid (IDA) single photon emission computed tomography (SPECT) could predict regional liver function reserve after RT. Fourteen patients treated with RT for intrahepatic cancers underwent dynamic 99mTc-IDA SPECT scans before RT, during, and 1 month after completion of RT. Indocyanine green (ICG) tests, a measure of overall liver function, were performed within 1 day of each scan. Three-dimensional volumetric hepatic extraction fraction (HEF) images of the liver were estimated by deconvolution analysis. After coregistration of the CT/SPECT and the treatment planning CT, HEF dose-response functions during and after RT were generated. The volumetric mean of the HEFs in the whole liver was correlated with ICG clearance time. Three models, dose, priori, and adaptive models, were developed using multivariate linear regression to assess whether the regional HEFs measured before and during RT helped predict regional hepatic function after RT. The mean of the volumetric liver HEFs was significantly correlated with ICG clearance half-life time (r=-0.80, P<.0001), for all time points. Linear correlations between local doses and regional HEFs 1 month after RT were significant in 12 patients. In the priori model, regional HEF after RT was predicted by the planned dose and regional HEF assessed before RT (R=0.71, P<.0001). In the adaptive model, regional HEF after RT was predicted by regional HEF reassessed during RT and the remaining planned local dose (R=0.83, P<.0001). 99mTc-IDA SPECT obtained during RT could be used to assess regional hepatic function and helped predict post-RT regional liver function reserve. This could support individualized adaptive radiation treatment strategies to maximize tumor control and minimize the risk of liver

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.

Prediction of response to preoperative chemoradiotherapy and establishment of individualized therapy in advanced rectal cancer.

PubMed

Nakao, Toshihiro; Iwata, Takashi; Hotchi, Masanori; Yoshikawa, Kozo; Higashijima, Jun; Nishi, Masaaki; Takasu, Chie; Eto, Shohei; Teraoku, Hiroki; Shimada, Mitsuo

2015-10-01

Preoperative chemoradiotherapy (CRT) has become the standard treatment for patients with locally advanced rectal cancer. However, no specific biomarker has been identified to predict a response to preoperative CRT. The aim of the present study was to assess the gene expression patterns of patients with advanced rectal cancer to predict their responses to preoperative CRT. Fifty-nine rectal cancer patients were subjected to preoperative CRT. Patients were randomly assigned to receive CRT with tegafur/gimeracil/oteracil (S-1 group, n=30) or tegafur-uracil (UFT group, n=29). Gene expression changes were studied with cDNA and miRNA microarray. The association between gene expression and response to CRT was evaluated. cDNA microarray showed that 184 genes were significantly differentially expressed between the responders and the non‑responders in the S-1 group. Comparatively, 193 genes were significantly differentially expressed in the responders in the UFT group. TBX18 upregulation was common to both groups whereas BTNL8, LOC375010, ADH1B, HRASLS2, LOC284232, GCNT3 and ALDH1A2 were significantly differentially lower in both groups when compared with the non-responders. Using miRNA microarray, we found that 7 and 16 genes were significantly differentially expressed between the responders and non-responders in the S-1 and UFT groups, respectively. miR-223 was significantly higher in the responders in the S-1 group and tended to be higher in the responders in the UFT group. The present study identified several genes likely to be useful for establishing individualized therapies for patients with rectal cancer.

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

2015-05-27

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.

Predictability of the Ningaloo Niño/Niña

PubMed Central

Doi, Takeshi; Behera, Swadhin K.; Yamagata, Toshio

2013-01-01

The seasonal prediction of the coastal oceanic warm event off West Australia, recently named the Ningaloo Niño, is explored by use of a state-of-the-art ocean-atmosphere coupled general circulation model. The Ningaloo Niño/Niña, which generally matures in austral summer, is found to be predictable two seasons ahead. In particular, the unprecedented extreme warm event in February 2011 was successfully predicted 9 months in advance. The successful prediction of the Ningaloo Niño is mainly due to the high prediction skill of La Niña in the Pacific. However, the model deficiency to underestimate its early evolution and peak amplitude needs to be improved. Since the Ningaloo Niño/Niña has potential impacts on regional societies and industries through extreme events, the present success of its prediction may encourage development of its early warning system. PMID:24100593

Stochastic Earthquake Rupture Modeling Using Nonparametric Co-Regionalization

NASA Astrophysics Data System (ADS)

Lee, Kyungbook; Song, Seok Goo

2017-09-01

Accurate predictions of the intensity and variability of ground motions are essential in simulation-based seismic hazard assessment. Advanced simulation-based ground motion prediction methods have been proposed to complement the empirical approach, which suffers from the lack of observed ground motion data, especially in the near-source region for large events. It is important to quantify the variability of the earthquake rupture process for future events and to produce a number of rupture scenario models to capture the variability in simulation-based ground motion predictions. In this study, we improved the previously developed stochastic earthquake rupture modeling method by applying the nonparametric co-regionalization, which was proposed in geostatistics, to the correlation models estimated from dynamically derived earthquake rupture models. The nonparametric approach adopted in this study is computationally efficient and, therefore, enables us to simulate numerous rupture scenarios, including large events ( M > 7.0). It also gives us an opportunity to check the shape of true input correlation models in stochastic modeling after being deformed for permissibility. We expect that this type of modeling will improve our ability to simulate a wide range of rupture scenario models and thereby predict ground motions and perform seismic hazard assessment more accurately.

Decadal climate predictions improved by ocean ensemble dispersion filtering

NASA Astrophysics Data System (ADS)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-06-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.